Artificial Intelligence in Service of Society: Navigating Our Way Forward – Accessible Version

COUNCIL Paper No.173 April 2026

Since the arrival of ChatGPT in late 2022, artificial intelligence (AI), a field with decades of
development in specialist settings, has entered mainstream public and policy discourse. It is
often presented in starkly opposing terms: either as a panacea capable of solving entrenched
societal problems, or as a source of profound and even existential risk. These competing
narratives coexist in media, policy and public debate, reflecting both the rapid diffusion of AI
technologies into daily life and deep uncertainty about their longer-term implications.
This conflicted discourse is underpinned by the political economy of AI. The technology
is attracting unprecedented levels of investment and is increasingly framed as inevitable,
indispensable and a critical driver of competitiveness, productivity and strategic advantage.
At the same time, concerns persist about whether expanding financial and infrastructural
commitments, rising energy use and other significant environmental impacts are fully matched
by realised value, thus highlighting tensions between strategic momentum, sustainability and
long-term return. In parallel with the acceleration in AI technologies, recent years have seen
a proliferation of national strategies, international frameworks and ethical guidelines aimed
at steering the development and deployment of AI, signalling growing recognition that AI
governance is now a core public policy concern rather than a niche regulatory issue.
There is growing evidence that AI is already delivering tangible benefits in specific domains,
particularly those characterised by large volumes of structured data and complex information
processing. These include medicine, finance, education, agriculture, public administration and
software development. With a strong technology ecosystem, a highly skilled workforce, a vibrant
research base and a commitment to responsible AI through the National Digital & AI Strategy
2030, Ireland has the foundations to take advantage of AI’s transformative potential.
The ultimate trajectory of AI remains highly uncertain, particularly regarding when, where and for
whom AI will deliver the greatest value, and at what social and environmental cost. Although AI’s
potential is substantial, its ultimate impact depends on the decisions we make now about how it
is built, governed and deployed. The central challenge is to actively shape AI, rather than allow it
to shape us, to ensure that its development aligns with our values, priorities and aspirations for
the future.

The purpose of this report is to offer a series of reflections on how Ireland can best secure
its ambition to develop and deploy AI in ways that are safe, ethical and rights-respecting. It
considers how Ireland can harness AI to support economic prosperity and serve the public good,
align with emerging European and international norms, and build public trust in technologies
that are already reshaping work, education and everyday life. The report takes a broad, high-level view of the field rather than offering a deep dive into any single issue, providing a holistic foundation from which to consider Ireland’s overall direction in AI.
The report was informed by discussions with practice experts and policy stakeholders in the AI
field in Ireland.

Chapter 1 provides the context for this report and sets out the AI landscape. Chapter 2 traces
the evolution of AI, from early symbolic systems to modern generative and agentic models
and explores likely future directions. Chapter 3 examines the safe and ethical use of AI,
addressing risks such as bias, fairness, transparency, accountability, privacy, malicious use and
environmental impacts. Chapter 4 analyses how AI systems interact with wider social, cultural,
legal and economic contexts. Chapter 5 reviews emerging AI governance frameworks at
international, regional and national levels, with particular emphasis on anticipatory governance,
an approach especially suited to the high uncertainty that characterises today’s AI landscape.
Chapter 6 shifts from systems to people, highlighting AI literacy as an essential, lifelong
capability for engaging effectively with this technology. Drawing on the data and insights
developed across the previous six chapters, Chapter 7 offers five interconnected reflections
that provide a path for navigating the uncertainties of AI and translating Ireland’s ambition for
responsible and inclusive AI into a set of priority actions.

The Council adopts a socio-technical framing of artificial intelligence, recognising that AI
systems cannot be understood or governed as isolated technical artefacts. Their impacts
emerge from the interaction between algorithms and the social, organisational and institutional
contexts in which they are developed, deployed and used. On this basis, the NESC argues that
Ireland’s task is not simply to implement AI effectively, but to actively shape its role in society
so that AI adoption aligns with democratic values, supports competitiveness and ensures that
benefits are distributed broadly while foreseeable harms are anticipated and mitigated.
Applying this integrated perspective, the Council developed five interconnected reflections
that together provide a structured way of navigating the opportunities and uncertainties of
AI. From these reflections, a set of priority actions is derived to guide policy, governance and
implementation in pursuit of responsible and inclusive AI in Ireland.
First, responsible and strategic adoption begins with clearly defined societal or organisational
needs, ensuring that AI is used where it adds genuine value and supports environmental
sustainability and meaningful transformation of systems and processes, rather than being
limited to the automation of established practices.
Second, safe and ethical AI requires converting high-level principles into concrete operational
tools and depends on building an ethics capability across people and institutions.
Third, due to the fast-moving and uncertain technological landscape, governance must be
adaptive and capable of learning. The report argues that anticipatory governance complements
regulatory frameworks like the EU AI Act by integrating strategic foresight, horizon scanning
and scenario planning into policy cycles. This approach requires institutionalising continuous
monitoring and evaluation, ensuring that real-world evidence consistently informs decision-making and prevents technological or policy lock-in.
Fourth, AI literacy must be treated as national infrastructure, an ongoing societal capability that
equips leaders, workers and citizens to understand system limitations, interpret outputs and
participate meaningfully in decisions about deployment.
Finally, public deliberation and social licence are critical. The role of AI in society cannot be
determined by experts alone; it must reflect the values and priorities of the public. This requires
genuine, sustained engagement in which people can debate what values they want to protect,
what trade-offs they consider acceptable, and where the red lines should be drawn.
Through deliberate governance, targeted deployment and sustained investment in AI
literacy, Ireland can ensure that AI development aligns with public values and societal goals,
demonstrating how a small, open economy can shape, rather than merely absorb, global
technological change.

This chapter traces the evolution of artificial intelligence from its conceptual origins to its
current generative era, providing an accessible foundation for understanding what AI is, how it
works and where it is heading. It introduces the core definitions that shape the field and charts
the technological breakthroughs that underpin today’s AI systems. The chapter also highlights
current limitations and emerging future trajectories of AI.

Although no universally accepted definition currently exists, AI is broadly understood as the
science and engineering of creating machines capable of performing tasks that typically require
human intelligence. This includes learning, reasoning and decision-making, or problem-solving
(Russell and Norvig 2021).
One of the most widely accepted definitions of AI is the Organisation for Economic Co-operation and Development (OECD) definition, which states:
‘An AI system is a machine-based system that, for explicit or implicit objectives, infers, from
the input it receives, how to generate outputs such as predictions, content,
recommendations, or decisions that can influence physical or virtual environments. Different
AI systems vary in their levels of autonomy and adaptiveness after deployment.’ (OECD,
2023a)
The definition, contained in the OECD Recommendation of the Council on Artificial Intelligence,
was most recently revised in 2023 to take account of the emergence of generative AI. The
European Union (EU) definition of AI, as contained in Article 3(1) of the European Union AI Act,
was substantially informed by the OECD definition and defines AI systems as machine-based
systems that can influence physical or virtual environments through adaptive and autonomous
behaviour (European Union, 2024). The European Commission (EC) further distinguishes
between AI as software-based (e.g. chatbots) or embedded in hardware (e.g. autonomous
vehicles) (European Commission, 2018).
The ambiguity surrounding the definition of AI reflects the field’s breadth and rapid
development. Nonetheless, despite definitional differences, consensus exists that AI enables
machines to mimic or augment human-like capabilities.

AI systems can be categorised in various ways, most often based on their capabilities, which
considers how intelligent a system is relative to humans; by functionality, which looks at how
systems process information and interact with the world; and by learning method, which
describes how systems acquire knowledge and improve over time.

Humanity’s preoccupation with the idea of intelligent machines capable of thought and action
extends back to antiquity. Greek myth tells us stories of the god Hephaestus creating the giant
bronze automaton, Talos, to guard the island of Crete. Intelligent machines appear in other
cultures, such as intricate automata in ancient China, mechanical birds in Islamic engineering
and talking heads in medieval Europe. These ideas persisted into modern times and became a
staple of science fiction imaginings, from the humanoid creatures of Karel Čapek’s 1920 play
R.U.R (which gave us the term robot) to Isaac Asimov’s I, Robot stories which elaborated the
Three Laws of Robotics. However, it was not until the mid-20th century that AI moved from
myth and fiction into a serious scientific pursuit. In 1950, Alan Turing published the milestone
paper ‘Computing machinery and intelligence’ (Turing, 1950), which considered the fundamental
question ‘Can machines think?’ Turing acknowledged the difficulty of precisely defining the
philosophical concept of thinking and instead proposed a thought experiment, later known as
the Turing test or ‘Imitation Game’, in which a machine could be said to exhibit intelligence if
its responses were indistinguishable from a human’s. The term ‘Artificial Intelligence’ was first
coined in 1956 at the Dartmouth Conference (McCarthy, 1955) organised by John McCarthy,
Marvin Minskly, Nathaniel Rochester and Claude Shannon, and is commonly considered to mark
the birth of AI as an academic discipline.

The first wave of AI was symbolic AI, also referred to as rule-based AI. In this paradigm,
intelligence was represented explicitly through symbols and logical rules. Developers would
encode human knowledge as a structured set of ‘if–then’ statements or logic-based instructions
(Choi et al., 2020). The system would then manipulate these symbols according to formal rules
to reach conclusions. Such systems worked well in controlled domains with clear rules, but they
struggled when faced with uncertainty or incomplete information. By the 1970s, the limitations
of symbolic AI had become clear. Building and maintaining huge rule-sets was time-consuming
and systems broke down when faced with situations that were not explicitly programmed.
Moreover, computers lacked the speed and memory to support large-scale reasoning. Early
optimism in the field had created strong expectations, and when those expectations were not
met, funding and interest declined sharply, leading to the so called first ‘AI winter’.

In the 1980s and 1990s, AI research regained momentum through machine learning (ML), a
fundamentally different approach to symbolic AI. Instead of manually encoding every rule, ML
systems could learn patterns from data. By feeding the system examples, it could adjust its
internal parameters to make predictions or decisions without explicit rule-writing. A key tool in
ML was artificial neural networks (ANNs) inspired by the structure of the human brain, consisting
of layers of interconnected ‘neurons’ that process information collectively. Examples of artificial
neural networks include transformers or generative adversarial networks (GANs). By the late
1980s, enthusiasm for the field had waned again. While machine learning offered more flexibility
than symbolic AI, the algorithms of the time were still limited, data was scarce, and hardware
could not handle large-scale computation. Funding tightened once again, marking the second AI
winter.

The late 2000s mark a turning point in the field of AI. Three factors converged: the abundance
of big data from the internet, massive increases in computational power, and improved
algorithms for training multi-layer neural networks.¹ Together, these advances enabled deep
learning, a subset of machine learning that uses very large, multi-layer neural networks to
automatically learn complex patterns in data. Deep learning differs from earlier machine learning
approaches by largely eliminating the need for human feature extraction; the network learns
the relevant features directly from raw data. Deep learning has proved especially powerful in
fields like computer vision, speech recognition and natural language processing (NLP). Similar
architectures now power facial recognition systems, medical image analysis tools and real-time
translation apps.

In the last decade, AI has entered yet another transformative phase with the advent of
generative AI. These systems do not just analyse or classify data but can create new content.
In simple terms, generative AI can draw from its training data to create a new work that’s similar,
but not identical, to the original data, and which is often indistinguishable from human created
works. For example, ChatGPT can generate essays, code and dialogue; DALL·E and Midjourney
can produce realistic or artistic images from textual prompts (e.g. create a picture in the style of
Rembrandt). Generative AI typically involves deep learning and neural networks to learn patterns
and relationships in the training data, using unsupervised learning techniques. Large language
models (LLMs) – a category of foundation models trained on immense amounts of data, making
them capable of understanding and generating natural language – and multimodal models –
capable of processing and integrating information from multiple modalities or types of data –
are at the forefront of generative AI. OpenAI released the newest version, GPT-5.2, to worldwide
users on 11 December 2025.
According to Gartner’s 2025 Hype Cycle for AI, generative AI has moved into the ‘trough of
disillusionment’, meaning that many organisations are experiencing disappointment as initial
excitement gives way to challenges in respect of reliability, governance and quantifying return
on investment (Gartner, 2025a).
This stage, however, is a familiar phase in the life cycle of emerging technologies and often
precedes maturity. Narayanan and Kapoor (2005) argue that AI should be regarded as a ‘normal
technology’ likely to follow the trajectory of previous technological revolutions. It has already
evolved into a general-purpose technology, capable of generating text, images, audio, video and
code, and is likely to be transformative in terms of its societal and economic impacts (Mucci,
2024).

Agentic AI is an emerging form of generative AI that goes beyond producing outputs based
on prompts, to autonomously planning and executing complex tasks by interacting with
digital environments. Theoretically, agentic AI is capable of goal-directed behaviour, dynamic
adaptation and self-improvement. For example, an AI agent could manage travel arrangements
by comparing flights and booking tickets, or in a business context could autonomously
monitor markets and manage financial investments within set constraints. Agents have already
demonstrated the ability to design biomedical molecules with high success rates, outperform
experts on tightly scoped R&D tasks, and operate software environments with increasing
competence (Maslej et al., 2025). Salesforce currently employs autonomous AI agents to handle
intricate workflows, such as product launches and marketing strategies.
While there is much excitement around agentic AI, current systems still frequently fail and
remain dependent on human-in-the-loop oversight to ensure accuracy, compliance and ethical
governance. Gartner predicts that over 40% of agentic projects will be abandoned by 2027
due to high costs, unclear business value or inadequate risk controls (Gartner, 2025b). If fully
realised, agentic AI is likely to deliver substantial efficiency gains; however, achieving true
autonomy simultaneously introduces challenges for accountability and oversight (Pati, 2025).
This is aptly illustrated by research demonstrating emerging systemic vulnerabilities of agentic
AI. Research by Gu et al. (2024) reveal how a single compromised agent can propagate harmful
behaviour across an entire multi-agent ecosystem in so-called ‘infectious jailbreaks.

Most LLMs lack long-term memory, which impacts their capacity for continuous learning. They
cannot store, retrieve or build upon experience over time, which means that their knowledge
is fixed to the training cut-off date. This forces them to relearn context in each interaction
(Hendrycks et al., 2025). While some AI applications can retrieve real-time information,
the underlying models themselves do not automatically learn from new data. Their internal
knowledge remains fixed unless developers re-train or finetune them.
Moreover, LLMs struggle with consistent reasoning and abstract logic. They rely on recognising
statistical patterns and generating statistically probable outputs, which means they can produce
fluent text that sounds correct without genuinely grasping the underlying concepts or meaning.
As a result, the validity of the Turing Test has increasingly come under pressure; while most
current LLMs pass this conversational benchmark, it is becoming increasingly clear that this
does not necessarily equate to genuine comprehension or intentional reasoning. The reasoning
demonstrated by LLMs is often shallow, reflecting statistical mimicry rather than genuine
inference. The seeming ability of LLMs to generate step-by-step reasoning (chain-of-thought)
has been described as a ‘brittle mirage’ (Zhao et al., 2025) that breaks down when the problems
deviate slightly from the distribution of data used in training. While LLMs can demonstrate
excellent problem-solving skills, their underlying reasoning appears to be fundamentally fragile
and breaks down as task complexity increases, suggesting reliance on pattern matching over
formal logic (Shojaee et al., 2025, Dellibarda Varela et al., 2025).
That said, advances have been made recently in reasoning-oriented architectures using chain-of-thought prompting (asking the model to show intermediate explanations for how it is going about solving a particular problem). This enables AI models to explicitly generate and refine intermediate reasoning steps, thereby enhancing transparency and substantially improving performance in domains such as mathematics, programming and scientific problem-solving (Bengio et al., 2026).

Current LLMs lack robust grounding in real-world understanding. While they excel at generating
coherent text and simulating reasoning based on vast linguistic data, their knowledge is derived
almost entirely from static datasets rather than direct interaction with the physical or social
world. This absence of genuine world modelling constrains their reliability in complex or dynamic
environments. This limitation underscores Moravec’s paradox, which observes that tasks humans
find effortless, like perception and motor co-ordination, are disproportionately difficult for
machines (Moravec, 1988). Humans acquire intelligence through embodied interaction with the
world, by integrating sensory input, feedback and social learning. In contrast, LLMs largely exist
in static, text-based environments devoid of physical embodiment or lived experience, creating
an embodiment gap (Roy et al., 2021). This gap acts as a barrier to LLMs developing common
sense, emotional intelligence and experiential reasoning.

While LLMs such as ChatGPT, Claude and Gemini have dominated the public discourse on
AI, they represent only one branch of a rapidly diversifying ecosystem of AI architectures.
A growing set of alternatives, including specialised scientific models and small language
models (SLMs), offer complementary or domain-specific capabilities that address some of the
limitations of LLM’s inaccuracy, cost and interpretability. Some of the most significant advances
in AI have occurred outside the language domain. AlphaFold, developed by Google DeepMind,
exemplifies this trend. Using deep learning to predict three-dimensional protein structures from
amino-acid sequences, it revolutionised structural biology and its impact was recognised with
the 2024 Nobel Prize in chemistry being awarded to Demis Hassabis, John Jumper (for protein
structure prediction) and David Baker (for computational protein design). Unlike LLMs, AlphaFold
is trained on highly structured biochemical data rather than natural language, enabling precise,
verifiable outputs instead of probabilistic text predictions.
In contrast to general-purpose LLMs that demand enormous computational and energy
resources, SLMs are trained on smaller high-quality datasets (limiting their flexibility and
general knowledge compared to LLMs) and fine-tuned for specific tasks or contexts. Their key
advantages include lower cost, faster inference and reduced carbon footprint (Whiting, 2025).
They can also be easier to deploy and are also often more secure, since they run on devices
locally, meaning they do not need to send sensitive personal information across the internet.
This makes SLMs particularly attractive for sectors such as finance and healthcare, where strict
compliance and privacy regulations exist. A recent position paper from NVIDIA Research has
argued that SLMs are the future of agentic AI as most tasks in an agentic workflow are relatively
simple and repetitive (Belcak et al., 2025). Where higher-level strategic reasoning is required, a
hybrid architecture can be pursued, with an LLM coordinating the activities of the various SLMs.

Ageing populations, the growing burden of chronic diseases, the rising costs of healthcare and
a shortage of healthcare professionals are driving the need for innovation and transformation of
models of healthcare delivery. Forecasts estimate that AI in health could lead to savings of up to
10% in healthcare spending (Sahni et al., 2023).
Artificial intelligence is reshaping healthcare across operations, clinical care and research.
Operationally, AI-driven forecasting tools help hospitals anticipate admissions, optimise staffing
and manage supply chains more efficiently (European Commission 2025f), while digital scribes
using speech recognition reduce administrative burden, resulting in time savings for clinicians,
improved patient-clinical interactions and enhanced clinician satisfaction (Tierney et al., 2025).
Clinically, AI enhances radiology and medical imaging by rapidly analysing complex scans with
high accuracy, enabling earlier and more accurate diagnoses (Faiyazuddin, 2025) and supports
precision medicine by analysing genomic and clinical data to tailor treatments (Alowais, 2023).
Drug development has undergone a paradigm shift because of AI, which can substantially
reduce the time and cost involved in bringing new therapies to the market (Blanco-González,
2023).
Yet the integration of AI in medicine raises new challenges. Healthcare professionals require
training to interpret and oversee AI outputs safely, while patients’ trust depends on transparency
about how algorithms influence care decisions (Sagona, 2025). Concerns also persist that
automation may erode the doctor-patient relationship, reducing empathy and shared decision-making if time savings are channelled into throughput rather than connection (Council of Europe, 2024c). Liability questions, principally who is accountable when an AI-assisted decision leads to harm, remain unresolved. There is a consensus that AI will not replace doctors but rather will complement them. By empowering clinicians, AI can improve efficiency and outcomes, but human oversight remains critical to achieving safety and patient trust.

Leading AI systems now demonstrate remarkably high performance, passing professional
licensing exams in fields such as law and medicine, capable of generating software from
simple prompts, and answering PhD-level scientific questions at a level comparable to human
experts. At the same time, their capabilities remain highly uneven or ‘jagged’, with systems often
excelling at difficult, abstract tasks while failing at others that appear comparatively simple. An
AI system which can solve complex mathematical problems may still struggle with what humans
would consider easy tasks, such as counting objects in an image.
Despite this unevenness, recent years have seen rapid and measurable improvements in
overall system performance. The 2025 AI Index Report from the Stanford Institute for Human
Centered Artificial Intelligence (Maslej et al., 2025) chronicles a year of strong progress for AI
and documents major gains in model performance. Performance on some coding benchmarks
has jumped from 4.4% to 71.7% in a single year. In parallel, generative models are extending into
video and multimodal domains, and in some narrow tasks even surpass human performance.
Meanwhile the cost of using high-performing AI models has plummeted. The cost to query a
model with GPT 3.5-level performance has dropped over 280-fold in around 18 months, from
$20 per million tokens in late 2022 to just $0.70 by October 2024.

The medium to longer-term goal of many leading technology companies is the realisation of
Artificial General Intelligence (AGI) and, ultimately, superintelligence. AGI refers to an advanced
theoretical form of artificial intelligence capable of understanding, learning and applying
knowledge across a wide range of tasks at a human-like level of competence. Unlike narrow AI,
which is designed for specific functions such as language translation or image recognition, AGI
would demonstrate flexible reasoning, creativity and adaptive problem-solving across domains.
Superintelligence, a theoretical stage beyond AGI, denotes an intelligence that surpasses the
best human minds in virtually every field, including scientific reasoning, social understanding
and strategic planning. Tech companies such as OpenAI, Google DeepMind and Anthropic
have articulated ambitions toward these milestones, framing them as the next evolutionary
step in AI development. While predictions on timelines vary, there is consensus that the arrival
of AGI or superintelligence, if it occurs, will mark a transformative inflection point, posing
profound societal and ethical implications. Leading figures in AI and related fields signed a
statement calling for a global moratorium on superintelligence research, warning that continued
development without assured alignment and control could result in the loss of human oversight
and pose existential risks (Future of Life Institute, 2025a).

The evolution of AI has not been linear, rather it is characterised by cycles of hope and
pessimism. This is worth keeping in mind when trying to divine the future of the field. In 1970
Marvin Minsky, one of the fathers of AI, was quoted in Life magazine: ‘In from three to eight
years we will have a machine with the general intelligence of an average human being’ (Minsky,
1970, cited in Haenlein and Kaplan, 2019). This projection proved premature, and the timeline
for achieving AGI and superintelligence is subject to much debate, reflecting deep uncertainty
about both technological progress and theoretical feasibility.
The most near-term projections, often voiced by technology entrepreneurs and leaders of
frontier AI laboratories such as OpenAI, Google DeepMind and Anthropic, suggest that AGI
could emerge as early as 2026–2035, driven by rapid advances in computing power and model
capability. In contrast, a survey conducted in October 2023 of 2,778 AI researchers provided
an aggregate forecast of 50% chance of achieving ‘high-level machine intelligence’ (defined
as unaided machines which can accomplish every task better and more cheaply than human
workers) by 2047 (Grace et al., 2024). It is worth noting this estimate is 13 years earlier than a
similar survey of experts conducted in 2022, which underscores the uncertainty around this
issue. As for the development of superintelligence, there is debate and uncertainty regarding
if and when it will be realised. Geoffrey Hinton, often called the ‘godfather of AI’, anticipates
superintelligence in five to twenty years (Sproule, 2025). In August 2025, Mark Zuckerberg, CEO
of Meta, stated in a personally penned essay setting out his goals for personal superintelligence
that Artificial Super Intelligence (ASI) was ‘now in sight’ (Zuckerberg, 2025).
The difficulty in reaching any consensus about the likely emergence of AGI is at least in part
related to the fact that few people agree on exactly what AGI means, beyond the shorthand
that AGI will match human intelligence.² Similar issues arise in the context of superintelligence.
There is no agreement on what counts as smarter than humans, nor whether machines could
ever achieve human consciousness (Searle, 1980). This raises thorny questions of what exactly
constitutes human-level performance, and in relation to which tasks.³ Matters are further
complicated by the fact that human intelligence, the comparator for AGI, is complex and
multifaceted, and is difficult to define or quantify. This illustrates the difficulty of creating
objective benchmarks to measure progress toward AGI or determining when AGI has been
achieved. A recent framework for evaluating AGI, based on the Cattell-Horn-Carroll (CHC)
theory of human intelligence, defines AGI as an AI capable of matching the cognitive versatility
of a well-educated adult. The model measures 10 core abilities, including reasoning, memory,
language and processing speed, to produce a standardized ‘AGI Score’. Using this approach,
GPT-4 scores around 27% and GPT-5 about 57%, indicating notable progress towards AGI,
though the results also indicate that full realisation remains some distance away (Hendrycks et
al., 2025).

Despite the impressive capabilities of current LLMs, it has been argued that LLMs may be
reaching the limits of their scalability in their current form (Marcus, 2025). A March 2025 survey
of AI researchers, conducted by the Association for the Advancement of Artificial Intelligence,
found that a majority (76%) of researchers who participated in the survey believed that scaling
up current approaches was ‘unlikely’ or ‘very unlikely’ to achieve AGI (Association for the
Advancement of Artificial Intelligence, 2025). The prevailing paradigm, summarised in Sutton’s
(2019) ‘Bitter Lesson’ essay, posits that progress in artificial intelligence primarily arises from
scaling computation and data, which underpins the vast investments made by the largest AI
companies, which have adopted deep learning approaches based on scaling. Initially, scaling
laws appeared to predict near linear improvements as models expanded in parameters, compute
and data. However, more recent analyses indicate diminishing returns as systems approach the
upper limits of available high-quality, human-generated data (Villalobos et al., 2024). Almost all
useful publicly available internet text has been consumed for training, leading developers to rely
increasingly on synthetic data (artificially generated material produced by previous models).
This introduces systemic risk through Model Autophagia Disorder (MAD), a feedback loop where
models trained on their own outputs progressively degrade in diversity, precision and factual
reliability over time (Shumailov et al., 2023).
2.6.4 Future Directions of AI Technology
Given uncertainties around compute availability, algorithmic progress, investment, regulation
and societal acceptance, the future trajectory of artificial intelligence remains highly uncertain.
The OECD has identified four plausible development trajectories that differ in the pace and
impact of progress of AI:

• In a stalled scenario, technical or economic barriers halt major advances, with AI systems not
  moving beyond current narrow capabilities.
• A slowed scenario sees steady but incremental improvements, with AI mainly acting as a
  tool that supports human decision-making.
• Under continued progress, AI systems become capable of performing many complex tasks
  autonomously, driving broad productivity gains while remaining under human oversight.
• An accelerated scenario involves rapid breakthroughs leading to highly general systems with
  transformative societal and economic effects (Hobbs et al., 2026).
  As no single outcome can be reliably predicted, policymakers and institutions need to prepare
  for a wide range of possible futures. Despite this uncertainty, the focus of current research does
  provide some indication for the future of AI development.
  Over the coming decade, it is likely that AI will evolve from the current paradigm of single,
  large generative models toward hybrid and interacting systems that combine different types
  of intelligence, data and computational tools. This reflects a growing recognition that no single
  model architecture can reliably meet the demands of complex real-world environments. Instead,
  capability will increasingly emerge from co-ordination among diverse components, each
  contributing a specialised function within a wider system.
  One promising direction is the development of hybrid neuro-symbolic architectures, which
  blend the pattern-recognition strengths of neural networks with the rules-based reasoning used
  in traditional AI. These systems aim to overcome current weaknesses in consistency, reasoning
  and transparency (Lu et al., 2024). Another emerging area involves models capable of planning
  and acting. Unlike today’s systems, which mostly generate short, independent responses, future
  AI will need to manage extended sequences of decisions such as running workflows or coordinating autonomous agents. These models may be memory systems or world models to help
  them understand the consequences of their actions over time (Meng et al., 2025).
  The future will also rely heavily on small, efficient and more local models running directly on
  personal devices or local servers, which should support privacy, energy efficiency and resilience.
  In many applications including healthcare, public services and safety-critical domains, local
  processing will be essential for secure and trustworthy deployment (Zhou et al., 2024).
  16
  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  As AI moves into the physical world, embodied and robotic AI will likely play an increasingly
  important role. These models combine language, perception and movement, allowing them
  to interact with their surroundings and support applications in transport, manufacturing,
  environmental monitoring and assisted living (Ruaridh Mon-Williams et al., 2025).
  Another key challenge is developing AI systems that can learn safely over time. Unlike current
  static models, future AI may update its knowledge as environments change or new information
  becomes available (Meng et al., 2025). Finally, exploratory areas such as quantum-accelerated
  machine learning and world-model-driven agents are under active investigation, and could open
  up new pathways for efficiency and problem-solving.
  17
  National Economic & Social Council
  Chapter 3: Safe & Ethical AI
  3.1 Introduction
  This chapter examines the interconnected challenges of ensuring that artificial intelligence
  is both safe in its operation and ethical in its impact. This represents a dual imperative as
  AI systems increasingly shape decisions with profound implications for individuals and
  society, making the pursuit of both safe AI and ethical AI imperative. It outlines the technical
  vulnerabilities that threaten system reliability, the emerging risks of malicious use and
  disinformation, and the ethical concerns surrounding fairness, transparency and accountability.
  The discussion also considers the systemic and environmental implications, such as the
  widening AI Digital Divide and the technology’s growing resource demands. Together, these
  themes provide the foundation for understanding why adopting a socio-technical lens and a
  multi-layered approach is essential for governing AI responsibly.
  3.2 Why Safe & Ethical AI?
  Safe AI emphasises technical robustness, predictability and resilience to errors and misuse,
  ensuring that systems behave as expected in complex or unforeseen situations. According
  to the Future of Life Institute’s AI Safety Index: Winter 2025 Edition (2025b), the rapid
  advancement of frontier AI capabilities has not been matched by commensurate progress in
  safety practices. The report evaluates eight frontier-model companies on their safety practices
  and risk-management frameworks and finds that even the highest-scoring firms only earn
  C-range grades overall, with Anthropic and OpenAI both receiving C+, Google DeepMind a C,
  and the remaining companies (including xAI, Meta, DeepSeek and others) D or lower. Moreover,
  recent research has raised questions about whether the benchmarks used to evaluate AI safety
  in fact capture meaningful risk. A systematic review (pre-print) of over 440 AI safety and
  capability benchmarks found that many tests rely on vague or poorly specified constructs, lack
  adequate validation, and rest on weak statistical foundations, calling into question the reliability
  and interpretability of current safety scores (Bean et al., 2025).
  Despite those limitations, the pursuit of safe AI as a means to unlock the potential of AI has
  attracted international and government support. The Bletchley Declaration marks the first
  major international political agreement focused specifically on the risks posed by AI systems
  (UK Government, 2023). The declaration, signed by 30 countries, including Ireland, recognises
  that general-purpose AI could pose significant societal, economic and security risks if not
  properly governed. It committed signatories to deepen international co-operation, improve
  scientific understanding of frontier AI risks, and ensure that AI is developed and deployed in
  a safe, human-centred and trustworthy manner. The declaration set out concrete areas of
  collaboration including joint risk assessment, information sharing between governments and AI
  developers, development of safety testing and evaluation frameworks, and the establishment
  of interoperable governance mechanisms. On foot of the declaration, the UK established the
  AI Safety Institute, focused on model evaluation and safety testing, while the US and other
  signatories have since launched sister institutes to support co-ordinated research.
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  Ethical AI focuses on the moral principles and values that should guide the development and
  deployment of AI systems, ensuring respect for fundamental rights and societal norms. The two
  concepts of safe and ethical AI are deeply intertwined as trustworthy systems must not only
  function reliably without causing harm but should also align with human values and the principle
  of fairness. As the discourse on AI has grown, so too has the field of AI ethics, producing a
  wide range of frameworks and guidelines (Hagendorff, 2024). While the literature is broad, a
  set of critical ethical concerns has emerged, most notably fairness and equity, transparency,
  privacy and environmental sustainability. An ‘ethics by design’ approach, as advocated by the
  European Commission, emphasises embedding ethical principles into the development process
  from the outset, rather than treating them as afterthoughts or external constraints (European
  Commission, 2021). A key premise here is that design choices are not morally neutral but rather
  can have significant ethical consequences. In tandem with this, a principle-based approach has
  also been advanced; it sets out foundational principles that AI must adhere to, such as safety,
  privacy and non-discrimination. Ethical AI is concerned with mitigating potential harms but also
  maximising the potential of AI to enhance human capabilities and promote human flourishing,
  ensuring that technological innovation in the field aligns with human values.
  3.3 Reliability
  One important aspect of responsible AI development is ensuring that systems behave in
  ways that are accurate, trustworthy and consistent with intended outcomes. AI hallucinations,
  sometimes referred to as confabulations, occur when AI systems, particularly LLMs, generate
  false or misleading outputs that appear convincing. They may include fabricated facts, nonexistent citations or nonsensical text or images. These hallucinations occur because of the
  stochastic nature of LLMs; they are designed to predict the next most probable word rather
  than guarantee factual accuracy. Other causes include biases or limitations in the training data,
  and a model’s limitations in performing common-sense reasoning.
  There is currently no agreed framework for measuring hallucinations in AI models and reported
  incidence rates vary widely depending on the task, dataset and evaluation method. For
  example, Vectara’s Hallucination Leaderboard, which tests models on summarising real news
  articles, found that even top-performing systems introduce fabricated details with ‘nontrivial’ frequency, underscoring that hallucinations remain a persistent problem in practical use
  (Hughes and Bae, 2023).
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  National Economic & Social Council
  Figure 3.1: Grounded Hallucination Rates for Top 25 LLMs
  Source: Vectara’s Hallucination Leaderboard (as of 21 March 2026).
  OpenAI (2025) has claimed that the hallucination rate of the recently released ChatGPT5 is 26%
  lower than GPT-4o and has 44% fewer responses with ‘at least one major factual error’. Most
  recently, research from OpenAI (Kalai et al., 2025) provided a mathematical explanation showing
  that hallucinations are not just artifacts of imperfect training data but are inevitable given how
  language models generate text. OpenAI’s findings suggest that while mitigation strategies may
  reduce incidence in certain contexts, it is highly unlikely that hallucinations can ever be fully
  eliminated. The consequences of hallucinations can be serious and far-reaching. Unchecked
  reliance on AI outputs can cause harm (physical, psychological, reputational and financial) to
  individuals and organisations, as well as erode trust in AI systems themselves. This will ultimately
  reduce willingness to adopt AI (Bengio, 2025).
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  3.4 Malicious Use, Misuse and Harm
  The malicious use of AI is a rapidly evolving threat, with bad actors leveraging generative AI to
  cause harm through fraud, extortion and scams. These threats manifest as AI-generated fake
  content and deepfakes, which range from cloned voices and fake documents to deepfake
  images and videos. AI outputs, whether text, images or videos, are often indistinguishable from
  human-generated content and are extremely cheap to produce. This risk is further heightened
  by the emergence of advanced image and video generation AI tools such as SORA 2, which
  make the creation of highly realistic synthetic media effortless and widely accessible, thereby
  lowering the bar for malicious actors to produce convincing and scalable disinformation.
  Criminals can use AI to clone a person’s voice for a fraudulent phone call, tricking their targeted
  victims into authorising a financial transfer or sharing sensitive data. The technology can also
  facilitate blackmail and extortion by creating non-consensual intimate imagery and threatening
  its release for financial gain. Similarly, AI can produce fake content that depicts an individual in
  compromising situations to damage their reputation or career. The UNICEF Innocenti Guidance
  on AI and Children 3.0 explicitly recognises the risks posed by harmful AI-generated content,
  including deepfakes and AI-generated child sexual abuse material (CSAM), and treats these as
  real harms with implications for children’s safety, rights and wellbeing. The guidance calls for
  regulatory frameworks, oversight and safeguards that prevent the generation and dissemination
  of such material, protect children’s rights in algorithmic environments, and ensure accountability
  and compliance by governments and industry actors (UNICEF Innocenti – Global Office of
  Research and Foresight, 2025). Ireland’s Digital & AI Strategy 2030 identifies online safety,
  particularly for children and young people, as a central public policy priority. The strategy
  pledges supports for the implementation of the nation’s Online Safety Framework and commits
  to ensuring that children’s voices are reflected in the development of future digital safety
  measures.
  Anecdotal reports of harm from AI-generated fake content are common, but systematic
  collection of data remains limited. A 2019 report (Ajder et al., 2019) found that 96% of all
  deepfake videos online were pornographic, with almost all the content targeting women.
  Research by Ofcom (2024), the communications regulator in the UK, has shown that 43%
  of adults and 50% of children aged 8–15 report having seen at least one deepfake in the
  previous six months, with a significant share involving sexual or fraudulent content. The recent
  controversy surrounding Grok, the generative AI system integrated into X, exposed serious
  safety and value-alignment failures after the tool allowed users to create ‘nudified’ images
  and sexual deepfakes of real women and children, as well as CSAM. In an 11-day period, Grok
  generated an estimated three million sexualised and violent images, including approximately
  23,000 depicting children, at a rate of around 190 images per minute (Center for Countering
  Digital Hate, 2026). A significant proportion of the material remained publicly accessible even
  after posts were removed. The initial response from X was to restrict the feature to paid users
  and to implement geoblocking in certain jurisdictions; a move widely criticised as insufficient.
  Following continued pressure from Irish and European regulators as well as the public outcry, X
  introduced more substantive technical measures worldwide to prevent the AI model’s ability to
  ‘undress’ individuals.
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  On 26 January 2026, the European Commission expanded its Digital Services Act (DSA)
  enforcement action against X by opening a formal investigation into its deployment of the Grok
  AI tool. The investigation will assess whether X properly identified, assessed and mitigated the
  systemic risks associated with Grok’s generation and dissemination of manipulated sexually
  explicit images, including content that may amount to CSAM, as required under the DSA. In
  parallel, European Commission Vice-President Henna Virkkunen publicly signalled that the
  EU was considering categorising the creation of such harmful AI outputs as an ‘unacceptable
  risk’ under Article 5 of the EU AI Act, a move that aligns with recommendations from Ireland’s
  AI Advisory Council to explicitly ban AI-enabled non-consensual intimate imagery and child
  sexual abuse material generation at the EU level (AI Advisory Council, 2026). The episode starkly
  illustrates the need for oversight and platform accountability to ensure that generative AI
  systems are aligned with Irish and European safety standards and core values.
  Beyond cases of overtly harmful or illegal content generation, growing attention is also being
  paid to the risks that arise when general-purpose AI systems are used by young people
  in sensitive and high-stakes contexts, particularly where there is limited oversight, weak
  safeguarding, or misalignment between system design and child-centred needs. While health
  systems are cautiously evaluating AI tools for triage, monitoring and therapeutic applications,
  many adolescents are increasingly turning to general-purpose chatbots for emotional support,
  often without parental awareness or professional oversight. This creates risks as models may
  inadvertently reinforce harmful thought patterns, fail to de-escalate crises, or encourage
  unhealthy anthropomorphism. Several lawsuits have been filed against AI companies on foot
  of young people dying of suicide for alleged failures in crisis-appropriate responses (Bhuiyan,
  2025). In response to such incidents, major AI companies have introduced mitigation measures,
  including crisis-intervention guardrails, refusal to engage in self-harm content, improved safety
  classifiers, and redirection to human support services. While not strictly falling into the category
  of malicious use, this does highlight the potential for catastrophic outcomes when unsupervised
  AI systems are used as substitutes for professional mental health care, especially among
  younger users.
  The OECD’s AI Incidents Monitor (AIM) collects data by scanning global media and using AIdriven classification tags events as ‘AI incidents’ (actual harm) or ‘AI hazards” (potential harm).
  Between January 2021 and January 2026, there has been a 7-fold increase in the number of
  AI-related incidents captured by AIM. Among the incidents recorded, harms to human and
  fundamental rights are the most documented.
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  Figure 3.2: Evolution of Incidents and Hazards by Harm Type
  Source: OECD, AI Policy Portal.
  3.4.1 Cybersecurity
  Artificial Intelligence is re-shaping cybersecurity in ways that bring both significant benefits
  and serious risks. It can strengthen cyber resilience by automating threat detection, identifying
  anomalies in real time, and helping organisations to respond more quickly to attacks. However,
  the same tools can be weaponised to launch more sophisticated and automated cyberattacks.
  Artificial intelligence can reduce the technical knowledge and effort required to commit
  cybercrime, lowering the bar to entry for attackers of various skill levels. This creates an
  asymmetry of power where it is easier for bad actors to attack than defenders to protect. This is
  particularly true for smaller organisations or critical national infrastructure that might be slower
  to adopt AI-defence capability. AI-mediated cyber-attacks on energy grids, healthcare systems
  and transportation could cause widespread disruption, physical damage and even loss of life.
  In August 2025, the AI company Anthropic reported that cyber criminals were increasingly using
  generative AI to develop malware and ransomware (Moix, Lededev & Klein, 2025). The National
  Cybersecurity Centre is due to publish an updated Cyber Security Guidance for Public Service
  Use of AI in 2026 to support secure procurement and deployment in alignment with the EU
  AI Act and the EU Network and Information Security Directive (Department of the Taoiseach,
  2026).
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  3.4.2 Impact on Democracy
  Artificial intelligence has the potential to strengthen democratic processes by supporting
  access to information, improving citizen engagement and facilitating debate. For example,
  tools like Polis, which use algorithms to map opinions, assist in identifying common ground
  and support more collaborative and inclusive policy-making (OECD, 2025a). The Collective
  Intelligence Project in the UK has been piloting the use of LLMs to support AI-assisted citizen
  deliberation by summarising citizen input from large-scale public consultations and identifying
  areas of emerging consensus.
  However, the rise of AI-generated disinformation has raised concerns about its potential to
  undermine democracy. The evidence to date is mixed; while research studies show that AIgenerated political messages can be persuasive, the generalisability of these effects to realworld contexts is uncertain. Some scholars argue that the risks have been overstated (Bengio,
  2025). Disinformation campaigns by foreign actors in recent elections, such as those in
  Taiwan, Slovakia and Romania, have used AI to spread false narratives, thereby demonstrating
  its potential for political interference. In the 2025 Irish presidential election, a deepfake video
  purporting to show Catherine Connolly withdrawing from the race was viewed almost 30,000
  times on Facebook before being removed by Meta (Ryan, 2025). Social media algorithms,
  which prioritise engagement, can amplify this content, though it has been suggested that the
  primary bottleneck for widespread influence is not content creation but rather its large-scale
  distribution (Bengio, 2025). A further threat is ‘information pollution’, where the sheer volume
  of AI-generated content degrades the overall quality of information available online, posing an
  epistemic threat (Seger et al., 2020).
  In response to growing concerns about the use of AI to undermine democratic processes,
  the European Commissioner for Democracy, Justice, the Rule of Law and Consumer
  Protection, Michael McGrath, announced the publication of the European Democracy Shield
  in November 2025. It aims to protect the EU’s democratic systems from foreign and domestic
  threats (European Commission, 2025a). The Democracy Shield is built on three linked pillars:
  countering AI-driven disinformation and interference, strengthening electoral integrity through
  transparency and responsible use of AI, and boosting societal resiliencewith enhanced digital
  literacy and co-ordinated democratic preparedness.
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  Box 3.1: AI in Teaching and Learning
  The introduction of artificial intelligence (AI) into education is driven by persistent global
  challenges: teacher shortages, rising administrative workloads, and the need to equip
  learners with strong digital and AI literacy skills. AI holds much promise across teaching and
  learning, particularly in administration, assessment and feedback, and personalised learning. In
  administrative tasks such as scheduling, attendance tracking and resource allocation, AI can
  automate routine work, reducing the substantial proportion of teachers’ time spent on nonteaching duties and alleviating a major source of stress (WEF, 2024b; OECD, 2025a).
  In assessment and feedback, AI systems can streamline marking and provide students with
  rapid, targeted feedback, helping identify learning gaps earlier and allowing teachers to prioritise
  one-to-one engagement. AI-driven personalised learning tools can further adapt content, pace
  and instructional approaches to individual learner needs, supporting more flexible and inclusive
  learning pathways (Merino-Campos, 2025).
  Automating assessment and feedback to students, while timesaving, can mean that teachers
  lose valuable opportunities to develop an in-depth understanding of students’ competencies
  (Cardona, Rodríguez & Ishmael, 2023). The use of generative AI heightens challenges to
  academic integrity, prompting institutions to rethink assessment design and emphasise ethical
  technology use. Teachers themselves will require new skills to effectively oversee, interpret
  and integrate AI systems into their practice. Realising AI’s potential in education will therefore
  depend on careful governance, sustained teacher training, and pedagogical models that balance
  technological support with the central role of human educators.
  3.5 Fairness & Equity
  Ensuring fairness and equity is fundamental to the development of safe and ethical AI. Fairness
  is a complex concept; there is no single universally agreed definition of fairness, as its meaning
  can change across social, cultural and disciplinary contexts. For the purposes of this discussion,
  fairness in AI requires that AI systems and tools operate in a way which treats individuals and
  groups equally and avoids discrimination based on protected attributes such as gender, age
  or race. Fairness has been explicitly incorporated into the UK Government’s A pro-innovation
  approach to AI regulation white paper, which requires that AI systems comply with existing
  regulations and avoid discriminatory or unjust outcomes. Responsibility rests with sectoral
  regulators to interpret what fairness means within their domain and to ensure that organisations
  embed ethical safeguards so that AI-driven decisions, particularly in high-impact contexts, are
  transparent, justified and non-arbitrary (Department for Science, Technology and Innovation,
  2023). The Irish Guidelines for the Responsible Use of AI in the Public Service mandate that AI
  adoption be underpinned by principles of diversity, non-discrimination and fairness (Department
  of Public Expenditure, Infrastructure, Public Service Reform and Digitisation, 2025).
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  National Economic & Social Council
  Bias in AI systems is a critical issue as it has the potential to undermine the principle of fairness.
  AI systems, when biased, can lead to real-world harm, including discriminatory outcomes, and
  can perpetuate structural inequalities such as racism, sexism, ageism or ableism. AI bias is a
  pervasive and complex issue, stemming from inherent biases in human-generated data, the
  design choices made by developers, and the context in which AI systems are deployed.
  3.5.1 Bias
  Bias can occur when the data used to train the AI system is unrepresentative or incomplete, or
  reflects existing societal prejudices. Sources include skewed data collection (over-representing
  some populations while under-representing others), reliance on historically based records
  (e.g. policing or health data) and human bias introduced during labelling, as in the case of
  supervised learning. The dominance of English language and Western-centric datasets has
  created cultural and geographic biases in AI systems, which has limited their effectiveness for
  diverse populations. Under-representation of specific demographic groups, such as women,
  older people, racial and ethnic minorities, and people with disabilities, leads to AI systems which
  perform poorly for these populations. Healthcare datasets with limited demographic diversity
  have resulted in misdiagnosis and delayed treatment for under-represented populations. For
  example, AI systems developed to diagnose skin cancer run the risk of being less accurate for
  people with dark skin due to the under-representation of skin lesion images from darker-skinned
  populations (Wen et al., 2021).
  Even when training data is representative, AI systems can still produce biased outcomes. This
  is because many forms of bias are baked into the patterns of real-world data itself, especially
  in areas where minority or disadvantaged groups have historically been treated differently. In
  such cases, the problem is not that the dataset is incomplete or unbalanced, or that the system
  is intentionally prejudiced. Rather, AI models are designed to detect and replicate patterns,
  and if the underlying patterns reflect historical inequalities, the model will often reproduce and
  reinforce the status quo. A 2024 study demonstrated that mortgage application evaluations
  conducted by LLMs (including GPT-4 Turbo) demonstrated significant racial bias, with black
  applicants consistently less likely to be approved than white applicants. This stemmed from the
  training data used to develop the AI models which reflected historical patterns of discrimination
  in lending (Bowen III et al., 2025).
  Bias can also arise from the design choices made by AI developers. These decisions are
  influenced not only by technical considerations but also by the social and cultural perspectives
  of the development teams. In that context, it is worth noting that women currently make
  up about 30% of the global AI workforce. The disparity in representation becomes more
  pronounced at higher seniority levels; women hold less than 14% of senior executive roles in
  AI globally (Pal, Marino Lazzaroni & Mendoza, 2024). The OECD.AI policy observatory data
  indicates that in 2023, 53% of data scientists/machine learning experts were in the 25-34 yearold bracket (OECD, 2025b). This narrow pool of perspectives can result in the conscious or
  unconscious biases of AI developers being encoded into AI models.
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  3.5.2 Power Asymmetries
  The concentration of power in AI also raises concerns about fairness and equity. In 2025, the
  four large American AI technology companies, Microsoft, Alphabet, Meta and Amazon, were
  projected to spend €400bn on AI infrastructure (The Economist, 2025). This concentration of
  power can lead to disproportionate influence on shaping policy and the public discourse on AI.
  Control over essential resources such as proprietary datasets, powerful computing power and a
  highly skilled workforce is largely concentrated within a small number of technology companies.
  Thus, information about how an AI system works, its safety and its effectiveness in specific
  contexts is often proprietary. Attracting AI talent into public-sector development and regulatory
  roles is increasingly challenging, as government bodies struggle to compete with private sector
  salaries and conditions. This concentration of influence could enable private actors to shape the
  trajectory of AI in ways that could create an AI ecosystem in which risks are widely dispersed
  but benefits remain narrowly concentrated.
  There is already an AI research and development gap, with AI innovation largely focussed in
  Western countries and China. This has the potential to create technological dependence of
  middle- and low-income countries and limit their ability to compete in high-value sectors.
  Adoption of AI in the Global North remains roughly twice that in the Global South and continues
  to rise (Microsoft AI Economy Institute, 2026). In many low- and middle-income countries,
  adoption rates remain low. As AI development becomes increasingly concentrated within a small
  number of powerful corporations and institutions primarily in the Global North, low-income
  countries risk being positioned primarily as sources of raw material rather than beneficiaries
  of innovation. This growing global imbalance, in which the communities that provide the data,
  labour and resources underpinning AI systems are often the least able to benefit from them, is
  often referred to as AI colonialism (Santino, 2024).
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  AI User Share in the Global South and Global North
  13.1%
  14.1%
  22.9%
  0%
  5%
  24.7%
  10%
  15%
  20%
  25%
  30%
  H1 2025 H2 2025
  Global South Global North
  The gap
  continued
  to widen, rising
  from
  9.8 in H1 2025
  to
  10.6 in H2 2025
  Figure 3.3: AI User Share in the Global South and Global North Diffusion by Economy
  Source: Microsoft AI Economy Institute, 2026.
  Countries where low-resource languages dominate also tend to show lower levels of AI
  diffusion. AI presents both significant risks and major opportunities for these languages.
  While AI can expand access, improve services and support revitalisation efforts, it can also
  inadvertently marginalise smaller linguistic communities. Without deliberate intervention, lowresource languages risk ‘digital extinction’, becoming unusable in mainstream AI tools. This risk is
  already evident, as commercial LLMs frequently misinterpret the grammar, idioms and dialectal
  variation of low-resource languages, including Irish, producing inaccurate or misleading outputs
  that discourage use and push speakers toward dominant languages online (Fiontar et al., 2025).
  In response, a co-ordinated national effort is emerging to secure the digital future of Irish.
  Údarás na Gaeltachta is leading an initiative to develop bespoke speech-to-speech generative
  AI for Irish, including capabilities for real-time conversation, translation, proofreading and
  integration with Irish-language corpora (collections of texts in machine-readable form). Dublin
  City University and the ADAPT Centre are expanding foundational infrastructure through major
  investments in bilingual data repositories, digital folklore archives, dialect resources and national
  corpora. Minister Dara Calleary recently announced €5m of government funding to support
  Irish-language AI projects, signalling growing political recognition of the challenge of lowresource languages in the era of AI (Department of Rural and Community Development and the
  Gaeltacht, 2025).
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  When AI development is confined to a handful of technology companies, competitive pressure
  can create a race to the bottom, where products are rushed to market without adequate safety
  testing or safeguards (Bengio, 2025). Moreover, when critical sectors of society become reliant
  on a small number of AI systems or the underlying infrastructure provided by a few companies,
  these systems effectively become single points of failure, creating systemic vulnerabilities. To
  combat reliance on a small number of private actors, governments and organisations across
  Europe have started to invest heavily in their own AI infrastructure.
  EU AI sovereignty
  The issue of AI sovereignty has emerged as a strategic priority for the EU in response to
  converging economic, geopolitical and technological pressures. Europe currently imports
  more than 80% of its digital infrastructure and core technologies, while three US hyperscalers
  dominate nearly 70% of the European cloud market (Draghi, 2024). This dependence has
  deepened with the rise of AI, which intensifies reliance on large-scale compute and cloud
  platforms. Geopolitical instability and supply-chain disruptions have reinforced the view that
  technological dependence is a strategic vulnerability, much like energy or defence. This has
  driven a policy shift towards building European capacity to capture the economic and social
  value created by AI. It is important to note that the focus of the European policy discourse
  around sovereign AI revolves around building capacity to make independent, values-based
  choices about the technology, rather than envisaging technological isolation or complete
  self-sufficiency. This ‘EuroStack’ approach emphasises ‘strategic interdependence’, developing
  sufficient domestic capability across critical layers of the AI stack (chips, cloud, data and AI
  models) to avoid one-way dependencies, while continuing to participate in global innovation
  networks (Bria, Timmers & Gernone, 2025).
  While European firms continue to trail US frontier models in raw scale and capital intensity,
  Europe has several structural advantages that underpin its ability to compete in sovereign
  and industrial AI, including its manufacturing base, engineering expertise and access to
  proprietary industrial data. Europe also retains strategic footholds in critical technologies,
  including in advanced chipmaking equipment and supercomputing. The EU has committed
  to a €200bn investment agenda, including €20bn for AI factories and gigafactories, major
  support for supercomputing through EuroHPC, a €43bn European Chips Act, and large-scale
  funding vehicles such as InvestEU, the European Innovation Council Fund and the European
  Tech Champions Initiative (European Commission, 2025b). Public procurement is increasingly
  positioned as a demand-side lever, with proposals to allocate significant shares of public
  digital spending to European providers. The UK has announced a £1bn investment in national
  computing power (Reuters, 2025), while France and Germany have announced the creation
  of AI hubs as part of digital sovereign strategies (Business Outstanders, 2025). In January
  2026, France announced that public officials will phase out reliance on US videoconferencing
  platforms such as Zoom and Microsoft Teams in favour of a domestically developed platform
  called Visio, designed to strengthen digital sovereignty. This followed a November 2025
  announcement of a public-private partnership in which the French and German governments
  agreed to work with SAP, Germany’s largest enterprise software firm, and Mistral AI, a leading
  French AI developer, to build a sovereign, government-owned and -operated digital tool for use
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  across the two countries’ public administrations. The AI Advisory Council (2025b) has called for
  an urgent national discussion on AI and data sovereignty and considers it imperative that Ireland
  develop its own indigenous AI capability.
  3.5.3 Digital Divide
  The rapid deployment of AI technologies risks intensifying socioeconomic and demographic
  disparities, creating a new form of inequality known as the AI Digital Divide. UNESCO describes
  a growing ‘AI divide’ in which marginalised communities have fewer opportunities to understand
  and use AI, even as the technology increasingly shapes work, public services and daily life
  (Gonzales, 2024). This divide is not only about having devices or broadband, but is also about
  AI literacy, confidence, language, and the ability to influence how AI is designed and governed.
  The divide is an intersectional issue, compounding historical inequities across severable cohorts.
  Those most at risk include older adults, people on low incomes, individuals with disabilities,
  and those with lower educational attainment. If these groups encounter additional barriers to
  AI participation, it is likely that AI systems will not be designed or delivered with their needs in
  mind. This takes on particular relevance when public services are being delivered through AI, as
  it may limit the reach of essential supports.
  In Ireland, digital exclusion is most pronounced among older adults, especially women, lowincome households and rural communities, largely mirroring EU-wide trends. However,
  older adults, rural communities and low-income households in Ireland are more digitally
  excluded than the EU average (Eurofound, 2025). These groups risk being left behind unless
  targeted inclusion policies are strengthened. Without the means to develop AI skills, socioeconomically disadvantaged groups may find themselves marginalised in the job market. Further
  entrenchment of socio-economic divides will not only affect employment opportunities but
  may also affect social cohesion.
  Survey data indicates that the AI divide is especially pronounced among older people. Younger
  Irish adults (18–24 years) have been shown to be almost nine times more likely to use AI often
  or daily compared to the older cohort aged 55–64. A recent study from the London School of
  Economics and global consulting firm Protiviti challenges assumptions about a widening digital
  divide tied to age. It found no inherent generational barrier to AI adoption as older workers were
  not less capable of using AI once they had received appropriate training and support. Moreover,
  the study found that productivity gains increased across teams with more generational
  diversity. The report concludes that older workers bring valuable domain knowledge, context
  and judgement to AI-enabled work, and that excluding them, whether through assumptions
  or lack of support and training, risks deepening inequities and weakening overall organisational
  performance (Jolles & Lordan, 2025).
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  Figure 3.4: Individuals with Above Basic Digital Skills,
  by Educational Attainment, 2023 (%)
  Source: Eurostat [I_DSK2_AB] (as reproduced in Eurofound, 2025).
  Note: International Standard Classification of Education 0-2 refers to early childhood to lower secondary education; 3-4 refers to
  upper secondary to non-tertiary education; 5-8 refers to tertiary education.
  ISCED 0–2 ISCED 3–4 ISCED 5–8
  Netherlands
  Finland
  Czechia
  Malta
  Croatia
  Spain
  Portugal
  Denmark
  Ireland
  Hungary
  Austria
  France
  Sweden
  EU-27
  Belgium
  Italy
  Luxembourg
  Cyprus
  Poland
  Slovakia
  Estonia
  Lithuania
  Greece
  Germany
  Slovenia
  Romania
  Latvia
  Bulgaria
  0 01 02 03 04 05 06 07 80
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  National Economic & Social Council
  Digital for Good: Ireland’s Digital Inclusion Roadmap provides a strong national framework
  for digital inclusion, and a wide range of initiatives already support older people, low-income
  households and rural communities (Department of Public Expenditure, Infrastructure, Public
  Service Reform and Digitalisation, 2023). Programmes such as Hi Digital, Age Action’s Getting
  Started and Alone’s Digital Champions deliver essential skills training and one-to-one support
  for older adults, while Connect Age, SICAP and local digital community strategies extend
  connectivity and resources to rural and disadvantaged areas. However, these broad initiatives
  need to be complemented by specially tailored programmes that equip digitally excluded groups
  with AI-related skills, ensuring that emerging technologies enhance rather than widen existing
  inequalities. Initiatives such as the TU Dublin and ADAPT Centre Age Friendly AI programme are
  welcome initiatives in that context.
  3.6 Transparency & Accountability
  Transparency in AI refers to making systems understandable to stakeholders, including what
  data is used and how decisions are reached, as well as the limitations of the technology.
  Explainability, which is an extension of transparency, seeks to ensure that information can
  be communicated in clear terms to users. These principles are fundamental to building
  public trust as people must be able to understand, at least in broad terms, how AI systems
  influence decisions with important implications for their lives. Higher levels of transparency
  and explainability are likely to be required regarding public service decisions made with
  the assistance of AI in relation to social welfare, health, justice and education. In 2024, the
  UK introduced the use of Algorithmic Transparency Recording Standard (ATRS) across all
  government departments (Government Digital Service, 2023). This policy initiative is designed
  to enhance transparency in the use of algorithmic tools that significantly affect decisions with
  public implications or that directly engage with the public.
  Transparency and explainability are also the foundation of accountability, as they provide
  people with the means to trace outcomes back to responsible actors, and if necessary to
  contest decisions. Accountability itself is complicated by the problem of ‘many hands’ where
  multiple actors, including designers, engineers and operators, may all contribute to a given
  outcome, making it difficult to establish liability. For example, in autonomous driving accidents,
  responsibility could be laid at the door of the manufacturer, the software developer or the
  driver. The European Commission withdrew the proposed AI Liability Directive in February 2025,
  following the adoption of the 2024 Revised Product Liability Directive, which extended strict
  liability rules to include AI systems and software. This revised framework covers harm caused by
  defective AI products without requiring proof of fault, offering a harmonised EU-level approach.
  However, it does not address all forms of AI-related harm (e.g. emotional distress, reputational
  damage), leaving such cases to be governed by national tort law, which continues to operate in
  parallel.
  3.6.1 Black Box Phenomenon
  Achieving meaningful transparency remains deeply challenging, if not impossible given the
  increasing complex nature of generative AI systems. Deep neural networks have been described
  as ‘black boxes’ whose internal logic is too complex for even their developers to understand. This
  is because their decision-making relies on millions or even billions of interconnected parameters
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  and layers of computation, making it nigh on impossible to trace the exact reasoning behind a
  given decision. Proprietary and commercial concerns further complicate the picture. Companies
  often withhold details about algorithms, training data and methodology to protect intellectual
  property, which limits independent scrutiny. The combination of these factors makes it difficult
  for regulators and the public to monitor for biases, ensure compliance and enforce legal liability,
  creating a clear governance challenge. Without transparency, AI risks undermining trust in
  institutions and creating accountability gaps that can weaken democratic legitimacy.
  3.7 Privacy & Data Protection
  Artificial intelligence systems inherently rely on access to vast amounts of data for training and
  operational purposes. These datasets can include information collected from publicly available
  internet sources, which may contain personal or sensitive data. Such data is often gathered
  indirectly, and in some cases without individuals being explicitly aware of it or without their
  consent. This raises important concerns around privacy, data protection, consent and personal
  autonomy. Beyond collection, AI also poses risks of inadvertent data leakage, as seen when
  chatbots unintentionally reproduce fragments of training data, containing personal information
  such as phone numbers or medical data, in response to user queries. AI identification and
  tracking technologies used in public spaces without the explicit knowledge or consent of those
  being surveilled raise human rights and civil liberty concerns. In a 2022 Global Surveillance
  index, at least 79 out of 179 countries were actively using AI and big-data technology for
  public surveillance purposes (no distinction made between legitimate and illegitimate uses of
  AI surveillance techniques). Slightly more democratic governments than authoritarian regimes
  have known AI surveillance capabilities (Feldstein, 2022). Furthermore, AI can facilitate powerful
  prediction and profiling. Data obtained from healthcare wearable devices has been used to
  infer mental health conditions, while social media activity has been analysed to predict political
  preferences, often without user awareness.
  Addressing the extent of privacy violations is very difficult as harms may occur unintentionally
  and without the knowledge of the affected individual. Even where data leaks are documented,
  finding the source is problematic as data may have been handled across multiple devices.
  Erosion of privacy is an important concern, as privacy is linked to personal autonomy.
  Privacy allows us control over what others know about us and protects a space for personal
  development and relationships with others (Rössler, 2005).
  3.8 Environmental Impact
  The United Nations Environment Programme (UNEP) conceptualises the environmental impacts
  of AI across three categories: direct, indirect and higher-order effects. Direct impacts arise from
  the immediate resource use involved in training and operating AI models. A single large-model
  AI query typically consumes 0.3–2.9 Wh of electricity, compared to approximately 0.1–0.3 Wh
  for a standard internet search, implying that AI queries may use up to 10 times more energy,
  depending on model size, hardware and optimisation (de Vries, 2023).
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  Figure 3.5: Estimated Energy Consumption per Request for Various AI-powered Systems
  Compared to a Standard Google Search
  Source: de Vries, 2023.
  Globally, data centres, AI and cryptocurrencies consumed 1.5 per cent of the world’s energy
  in 2024. The International Energy Agency (IEA) (2025a) projects that this figure will double by
  2030, which is roughly equivalent to the entire electricity consumption of Japan.
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  Figure 3.6: Projected Electricity Demand Growth by End Use, 2023–2030
  Source: Ginelle Greene-Dewasmes and World Economic Forum, 2025.
  This increased energy consumption, often generated from fossil fuels, is contributing to
  greenhouse-gas (GHG) emissions. Data centres and data transmission are estimated to account
  for 1 per cent of global energy-related GHG emissions (IEA, 2025a). The percentage share of
  metered electricity consumption used by data centres in Ireland rose to 22 per cent in 2024
  from 5 per cent in 2015 (CSO, 2025a). Contracted demand is anticipated to reach ≥30 per cent
  of Ireland’s supply by 2030 (IEA, 2025b). The National Economic & Social Council (NESC) has
  similarly noted that the growth of AI workloads in Irish data centres is already exerting pressure
  on electricity demand and complicating decarbonisation planning (NESC, 2025). The IEA has
  evaluated Ireland’s energy security outlook to 2035 and presents an adapted transition pathway
  showing how climate, economic and social objectives converge on the electricity system.
  The analysis highlights the need for a unified, cross-sectoral energy strategy, supported by
  comprehensive security assessments, to guide this transition effectively. The IEA recommends
  that growth in data centre electricity demand be managed to support system adequacy,
  renewable integration and flexibility, including requiring large users such as data centres to
  contribute generation, storage or flexibility services as part of grid connection conditions and
  aligning their consumption with renewable supply (IEA 2025b).
  These recommendations align closely with requirements set out in the Large Energy-User
  Action Plan (LEAP) published in January 2026, which conditions data centre development on
  decarbonisation and active grid support. It introduces a plan-led framework that reorients the
  development of AI infrastructure and data centres around state-identified strategic locations
  and prioritises location of new facilities in regional areas and Strategic Green Energy Parks,
  where grid capacity and renewable resources are strongest. Projects are expected to be
  powered primarily by renewables and to actively support the electricity system through flexible
  demand and on-site dispatchable generation or storage (Department of Enterprise, Trade and
  Employment, 2026). The LEAP initiative sits alongside the Commission for Regulation of Utilities
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  (CRU) (2025a) Large Energy Users Connections Policy, published in December 2025. It requires
  new data centres seeking grid access to provide on-site or proximate generation or storage, and
  to meet at least 80 per cent of annual electricity demand with additional renewable generation
  within six years, while taking locational constraints and system security into account. Moreover,
  under the Public Review 6 grid investment plan, the Government has committed to investing
  up to 18.9bn in transmission and distribution infrastructure to strengthen the electricity grid,
  support long-term security of supply, and enable the accelerated connection of renewable
  generation and large energy users, including data centres (Commission for Regulation of
  Utilities, 2025b).
  It should be said that current projections for future AI and data-centre energy use rely
  heavily on estimates and extrapolations, and it is widely acknowledged that publicly available
  information about current patterns in AI energy use is incomplete. Mandatory reporting
  obligations under the Energy Efficiency Directive, requiring data centres to report on their
  energy performance, including renewables and water use, should progressively improve
  transparency and the evidence base for policymaking. The AI Advisory Council (2025b) has
  recommended that Ireland establish an ‘AI Energy Council’ to ‘ensure necessary measures are
  taken to rapidly develop clean energy capacity, while transitioning from fossil fuels and winning
  public trust’.
  Water consumption is another major direct impact. Global data-centre water use is expected to
  rise significantly as AI scales, owing to the cooling demands of advanced computation (UNEP,
  2024). Furthermore, AI hardware also relies on resource-intensive supply chains. Research
  on the life-cycle emissions of AI chips shows high embedded carbon costs and increasing
  quantities of rare earths and metals in successive generations of hardware (Schneider et al.,
  2025).
  Indirect impacts occur when AI-induced efficiencies lead to an overall increase in consumption.
  Optimisation (e.g. in transport or logistics) may create rebound effects if total system size
  expands faster than efficiency improves (UNEP, 2024). It also needs to be recognised that AI
  accelerates demand for cloud infrastructure, land, minerals and energy, with growth trajectories
  that risk outpacing renewable energy deployment. Shifting AI systems onto renewable
  electricity alone does not eliminate environmental pressures as renewable generation itself
  requires land, materials and water.
  Higher-order impacts reflect long-term, systemic consequences, such as lock-in to highconsumption technological infrastructures, intensified demand for critical minerals, and pressure
  on environmental governance.
  Mitigation strategies being adopted include the increased adoption of renewable and nuclear
  energy sources, with Microsoft and Google investing in small modular reactors and geothermal.
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  Figure 3.7: Green Power Share of Top 10 Data Centre Operators
  Source: Lyu and Tang, 2025.
  While clean electricity is forecast to meet all global demand growth through 2026 (IEA, 2024),
  scaling variable renewable energy faces challenges with grid integration, transmission capacity
  and waste, and will require substantial investment. Efforts are also focussed on making AI
  systems themselves more efficient through algorithmic and hardware innovations. Combining
  quantisation, model compression and reducing prompt length can cut AI energy demand by up
  to 90 per cent without significant performance loss (UCL, 2025). Small language models offer a
  promising way to mitigate the environmental costs of generative AI. These models are designed
  with smaller architecture and reducing training data, enabling them to run very efficiently in
  specific domains and on task-focussed applications, with far lower power demands than LLMs
  (UNESCO, 2023b). However, as previously mentioned efficiency gains risk being offset by
  ‘rebound effects’ from growing AI use.
  AI itself can play a positive role in the energy transition by forecasting renewable generation,
  optimising grid stability and detecting faults in energy networks to improve efficiency (Tuhin,
  2025). In fields such as climate modelling, biodiversity monitoring, freshwater management
  and urban sustainability, AI can enhance predictive accuracy and help integrate data that span
  multiple spatial and temporal scales. Beyond research, AI can strengthen decision-making by
  supporting scenario analysis, early-warning systems, and multi-criteria evaluation tools that help
  policymakers navigate complex trade-offs (Galaz et al., 2025). The concept of a ‘twin transition’,
  in which AI is deliberately integrated with clean-energy goals to help accelerate the global
  shift toward sustainable, low-carbon systems received attention at COP30 in Brazil. At that
  conference, COP30 countries launched the AI Climate Institute, a global initiative designed to
  equip governments, researchers and communities, especially in developing countries, with the
  skills and tools to build locally adapted, low-energy AI solutions for climate mitigation and
  adaptation. Alongside it, the Green Digital Action Hub was established to provide access to
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  data, expertise and technical support to help nations scale sustainable digital technologies, track
  emissions and e-waste, and implement low-carbon, socially inclusive digital infrastructure.
  3.9 Mitigation of AI Risks
  Promotion of safe and ethical AI requires a multifaceted approach that combines technical,
  regulatory, and organisational strategies, acknowledging that no single solution is a panacea.
  As with any technology, all risk cannot be eliminated, but well-designed mitigation measures
  can reduce both the likelihood and severity of harmful outcomes. On the fairness and equity
  front, strategies to minimise bias remain essential; these include correcting data imbalances,
  drawing from more diverse and representative data sources, and employing bias detection
  tools throughout the development lifecycle. However, modifying training datasets as a means
  to remove bias can be difficult in practice, particularly when biased historical data reflects
  systemic inequalities and may introduce new distortions. As a result, configuring or constraining
  the model itself to mitigate biased behaviour is, in many cases, a more practicable approach.
  Organisational interventions also matter; by challenging assumptions and incorporating different
  lived experiences, diverse development teams can design systems that better account for the
  needs of a global user base. Nonetheless, complete elimination of bias is not currently possible
  and may even be theoretically unachievable, underscoring the need for continuous monitoring
  and iterative refinement.
  Transparency and accountability can be strengthened through the use of explainable AI (XAI)
  techniques, such as local interpretable model-agnostic explanations (LIMEs), which perturb
  input data to illustrate how changes affect predictions. These tools can provide valuable
  insights into model behaviour, particularly for high-stakes decisions. However, current XAI
  techniques have significant limitations and cannot offer full visibility into complex deep-learning
  architectures. This limitation reinforces the importance of robust governance structures that do
  not rely solely on explainability tools.
  One such governance mechanism is independent auditing. External audits covering system
  design, training data, evaluation methods and real-world performance can help identify risks
  that internal teams may overlook and provide public assurance that systems meet safety
  and ethical expectations. Governments are increasingly supporting this approach through
  emerging regulatory frameworks. For example, the EU AI Act introduces mandatory conformity
  assessments and post-market monitoring for high-risk systems, while the United States and
  United Kingdom have issued guidance encouraging third-party evaluations, transparency
  reporting and risk assessments. These frameworks help establish common expectations and
  provide a structured basis for organisations to evaluate and mitigate risks.
  Protecting privacy and ensuring responsible data use is another essential component of
  mitigation. Technical safeguards such as differential privacy, which introduces statistical ‘noise’
  to obscure individual identities, and federated learning which allows models to be trained
  on decentralised devices without transferring raw data, can help to reduce the exposure of
  personal information. These tools can then be complemented by clear and enforceable data
  governance frameworks that outline requirements for consent, data retention, data sharing
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  and secondary use. Increasingly, governments are developing or updating privacy regulations
  to address AI-specific risks, including rules on automated decision-making and dataset
  documentation, and restrictions on sensitive data processing.
  Together, these technical measures, organisational practices and government-backed regulatory
  frameworks form a layered mitigation strategy that can meaningfully reduce the risks of AI
  systems while supporting innovation and public trust.
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  Chapter 4: AI through a Socio-Technical Lens
  4.1 Introduction
  This chapter explores the integration of artificial intelligence into society, balancing its
  transformative potential with the safeguards needed to ensure responsible and equitable
  deployment. It challenges the idea that complex social problems can be addressed through
  reliance on technology alone, highlighting the risks of AI solutionism and emphasising the
  importance of a socio-technical perspective that situates AI within the social, cultural and
  institutional contexts in which it operates. Through this lens, the chapter examines key
  dimensions of AI’s societal impact, including patterns of adoption, public attitudes and trust,
  workforce implications and economic gains.
  4.2 Techno-solutionism
  Artificial intelligence holds enormous potential to transform multiple dimensions of our lives,
  from medicine and education to agriculture, transportation, energy and beyond. Deployed
  with care, it can enhance efficiency, augment human decision-making and support largescale innovation in public services and private enterprise. However, AI will work better in some
  domains than in others and the specific conditions of success or failure are often deeply
  contextual. It should be kept in mind that AI systems are not deployed in a vacuum; their
  success will depend upon their interaction with existing systems and environments. As we
  integrate AI into more areas of society, we must thoughtfully consider where its use is most
  appropriate, and guard against the seductive but problematic logic of technological solutionism.
  Morozov (2013) has pointed to the folly of thinking that complex societal problems can be
  solved through technological fixes alone. Techno-solutionism treats technology as an easy
  button, reducing deep-seated societal issues into simplistic, quantifiable problems to be
  engineered away (Morozov, 2013). Within artificial intelligence, this manifests as AI solutionism;
  the assumption that AI systems are ideologically neutral tools capable of solving wide-ranging
  issues such as welfare provision, climate adaptation or public health management. While AI
  clearly has a role to play in all of these areas, a mindset of techno-solutionism can encourage
  oversimplification by concentrating on symptoms rather than root causes and privileging
  optimisation over understanding. This presents two distinct problems; at a macro-level, it
  prevents us from seizing the opportunity to re-image systems; at the micro-level, it impedes our
  ability to choose the right problem and the right tool for AI to solve.
  4.3 Socio-technical Thinking
  Adopting a socio-technical approach provides an antidote to AI solutionism. A socio-technical
  lens recognises that AI systems are built, deployed and used within complex social, cultural,
  legal, and political contexts (Sartori & Theodorou, 2022). It requires us to consider both the
  technical artefacts and the social practices that shape and are shaped by AI.
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  A socio-technical approach involves integrating expertise from several fields, such as ethics,
  sociology and user-experience research, throughout the AI life cycle, from design to testing to
  deployment and monitoring. Such an inter-disciplinary perspective can contribute to building
  AI systems that are responsible, fair and aligned with broader societal values. It recognises that
  technologies are not neutral, but rather embody the values, assumptions and power dynamics
  of those who build and implement them. As the International Organization for Standardization
  (2025, p11) has noted, ‘At their core, AI systems are socio-technical in nature: they do not
  operate in isolation, but interact continuously with people, institutions and processes, cultural
  norms, other technologies, and broader social, economic and political contexts.’ A sociotechnical approach can ensure that AI augments rather than substitutes essential social
  processes such as deliberation, professional judgement and community participation.
  Governments have traditionally emphasised innovation and competitiveness as key objectives
  of national AI strategies. While these are clearly important goals, a narrow focus on economic
  productivity can obscure the broader societal impacts of AI in areas such as privacy, fairness,
  accountability and democratic control. Ireland’s Wellbeing Framework and models such as
  doughnut economics reinforce the need to balance economic considerations with wider social
  and environmental outcomes, so that technological progress can remain within ecological limits
  and contribute to quality of life. A socio-technical framing can help recalibrate this balance.
  Crucially, this lens also enables a more holistic view of public benefit. As UNESCO affirms in its
  Recommendation on the Ethics of Artificial Intelligence (2021), the goal should be to align AI
  with the principles of human dignity, inclusion and environmental sustainability. This perspective
  does not reject competitiveness or innovation but embeds them within a richer matrix of public
  values.
  Through a socio-technical lens, AI can be seen as a powerful accelerator of the attention
  economy by maximising user engagement through highly personalised and algorithmically
  optimised content delivery. While effective in capturing attention, this dynamic raises concerns
  about potential impacts on user autonomy, privacy and, in broader terms, the quality of public
  discourse. As AI systems increasingly shape online behaviour, their design and deployment carry
  broader societal implications, highlighting the need for governance that ensures transparency,
  accountability and alignment with the public interest.
  4.4 Value Alignment
  Value alignment seeks to guarantee that AI systems operate in a way that is consistent with
  human interests and values (World Economic Forum, 2024a). Failure to achieve value alignment
  poses significant consequences for human rights, the rule of law and democratic governance.
  Recent research suggests that AI models can inherit the values and behaviours of the systems
  that train them, raising the prospect that if the ‘teacher’ model or training process is misaligned,
  the downstream ‘pupil’ models will be too, allowing undesirable values and behaviours to
  propagate unless alignment is addressed at every stage (Cloud et al., 2025). Frontier AI systems
  have their operational values and decision-making frameworks encoded by the companies
  that build them. For example, Anthropic has developed a constitution that explicitly specifies
  normative principles, ethical constraints and behavioural guidelines intended to shape how
  their model Claude reasons, responds and aligns with human values (Anthropic, 2026). While
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  the attention to values is welcome, the fact that they are being formulated by private actors
  rather than through broad societal deliberation makes it hard to assess how well they reflect the
  diverse public values of the populations who will rely on services being delivered through AI.
  When discussing value alignment in AI, a central challenge is deciding which values to privilege,
  since these may differ between individuals and cultures. For this reason, the determination
  of which values to uphold should be the subject of public deliberation. This can help
  establish ‘red lines’ representing non-negotiable ethical limits which AI systems should not
  cross. It is important to distinguish between public information, which focuses on one-way
  communication, and public deliberation, which requires listening to diverse voices, including
  those that are critical and challenging.
  Complexity is heightened by the fact that values can conflict with one another, requiring
  difficult trade-offs and careful balancing. Moreover, the salience of particular values often
  shifts depending on the context, meaning that value alignment is not a one-time task but
  requires ongoing reflection. Value alignment in AI means retaining human oversight, control and
  accountability, and mindfully and deliberately designing, deploying and maintaining oversight of
  these systems so that their societal and ethical impacts serve the public good rather than erode
  it. The Special Eurobarometer 566 report on The Digital Decade, commissioned by the European
  Commission and conducted between February and March 2025, reported that 93 per cent of
  Irish respondents (EU average 86%) considered it important for public authorities to shape the
  development of AI and other digital technologies to ensure they respect our rights and values
  (European Commission, 2025c).
  4.5 Public Attitudes
  Public attitudes towards AI play a critical role in determining the legitimacy, adoption and
  societal alignment of AI systems. Recent global studies show that, while the public recognises
  AI’s potential benefits, there is concern about its risks, especially where transparency, fairness
  and oversight are lacking. The IPSOS AI monitor (Carmichael, 2025) surveyed over 23,000 adults
  across 30 countries between March and April 2025. The study revealed that 52 per cent of
  global respondents felt optimistic about AI’s impact, while 53 per cent reported feeling worried.
  Irish respondents, however, expressed lower optimism (41%) and higher worry (64%), indicating
  a more cautious stance than the global average.
  This cautious attitude is echoed in the Data Protection Commissioner’s Public Attitudes Survey
  (2025) in which 61 per cent of those surveyed reported being quite/very concerned about the
  use of AI and how it is applied. Further research capturing the views of over 48,000 people in
  47 countries found that 42 per cent of people believe the benefits of AI outweigh the risks,
  compared to 32 per cent who believe the risks outweigh the benefits, and 26 per cent who
  believe the risk and benefits of the technology are balanced. Of the Irish participants in the
  study, 33 per cent were of the view that the benefits of AI outweigh the risks, with the top
  risk (67%) identified as a loss of human interaction and connection due to AI (Gillespie et al.,
  2025). In another global survey involving over 32,000 people in 40 countries, Irish respondents
  reported spread of false information, fear of jobs losses and personal data breaches as key
  concerns in relation to AI (Worldwide Independent Network of Market Research, 2025).
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  Despite this caution, 72 per cent of the Irish public believe that the use of AI will result in a wide
  range of benefits, the most cited being improved efficiency and a reduction in repetitive tasks.
  Importantly, 60 per cent of Irish people are personally experiencing or observing these benefits
  (Gillespie et al., 2025). In the same study, it was found that people who expect and experience
  or observe benefits from AI are more likely to trust and use AI. This highlights the importance
  of designing and deploying AI systems which can deliver a wide range of benefits across the
  population. Other key drivers of trust in AI included AI literacy, the presence of safeguards and
  confidence that AI would be used in the best interests of the public.
  Figure 4.1: A Model of the Key Drivers of Trust and Acceptance of AI Use in Society
  Source: Gillespie et al., 2025.
  Public trust, which depends upon AI trustworthiness, is essential; without confidence in the
  technology, the public will not adopt AI systems, thereby undermining their legitimacy, and
  by extension their ability to deliver meaningful public benefit. The public sector is subject to
  greater scrutiny and accountability than the private sector in relation to legitimacy, fairness
  and equality.⁴ Higher levels of transparency and explainability are likely to be required regarding
  public service decisions made with the assistance of AI in relation to social welfare, health,
  justice and education. Indeed, one of the three pillars of policy development in Ireland in the
  public sector is legitimacy, where buy-in, or at least acceptance, by the people who will be
  affected by the policy is considered essential (Department of the Taoiseach, 2025a).
  Trust in AI remains a critical challenge. Ireland does not compare favourably with other countries
  in respect of this metric (Gillespie et al., 2025; Worldwide Independent Network of Market
  Research, 2025; Carmichael, 2025). Only 38 per cent of Irish respondents, as compared to a
  47-country average of 46 per cent, are willing to trust AI systems (Gillespie et al., 2025). Trust is
  highest in universities, research and healthcare institutions, while just under half of Irish people
  asked have confidence in the Government to develop and use AI in the public’s best interest.
  Uncertainty: risks
  Knowledge: AI literacy
  Institutional: safeguards
  & confidence
  Motivational: benefits
  Trust in AI Systems AI Acceptance
  .11
  .23
  .43
  .01 .03
  -.08
  .62
  Emerging
  economy Education
  4 The Public Sector Equality and Human Rights Duty places a statutory obligation on public bodies to have regard to human rights and
  equality considerations in the performance of their functions.
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  Ireland is one of only three countries (along with Italy and Singapore) across 30 countries
  surveyed which trust people more than AI systems not to discriminate or show bias towards any
  group of people. Globally, younger people (18–34 years), high-income households, people with
  a university education and those with AI-related training were more accepting and trusting of AI
  (Gillespie et al., 2025).
  In summary, Irish public opinion reflects a measured and discerning view of AI, open to
  its benefits but more sceptical and privacy-conscious than global peers. These attitudes
  underscore the importance of building trustworthy, rights-respecting AI systems with strong
  human oversight, transparent design and meaningful public engagement at their core.
  4.6 Adoption of AI
  The adoption of AI does not hinge on technology alone but on a complex interplay of
  interdependent conditions. Effective integration requires robust digital infrastructure and well
  curated, interoperable data, yet these technical foundations must be matched by organisational
  capacity, workforce skills and positive attitudes toward innovation. Equally important are
  governance structures that ensure transparency, accountability and ethical use, as well as the
  broader economic and policy environment that shapes investment, incentives and readiness to
  change. Taken together, these factors form an ecosystem in which deficiencies in any one area
  can limit the overall impact of AI, underscoring the need for a holistic, system-wide approach to
  realising its potential.
  Adoption of AI is advancing, albeit unevenly, across geographies and sectors. Data from
  the Microsoft AI Economy Institute (2026) show that, in 2025, countries such as the UAE,
  Singapore, Norway, France and Ireland were among the fastest adopters of generative AI. In
  Ireland, the share of the working-age population using generative AI tools increased by 2.9 per
  cent, reaching 44.6 per cent by the end of the year. These rankings are based on estimates
  derived from observed AI usage data; while they provide a useful proxy for adoption, the authors
  note that they cannot capture all forms of AI use, particularly informal or enterprise-internal
  deployment.
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  Figure 4.2: AI Diffusion by Economy, Second Half 2025
  Source: Microsoft AI Economy Institute, 2026.
  A 2024 EU survey on use of AI technologies found that, within the EU, Denmark, Sweden and
  Belgium lead, with approximately a quarter of enterprises reporting AI adoption in 2024, as
  compared to an EU27 average of 13.5 per cent. In Ireland the percentage of enterprises using
  AI technologies increased from 8 per cent in 2023 to 14.9 per cent in 2024 (Eurostat, 2025).
  According to the CSO, 51.2 per cent of large enterprises in Ireland used AI technology in 2024,
  compared with 25.1 per cent of medium and 12 per cent of small enterprises (CSO, 2025b). This
  largely reflects international findings that larger, more productive firms are more likely to adopt
  AI (OECD, 2023b). Survey results from the OECD show generative AI usage of 33 per cent
  among Irish SMEs, placing Ireland third among the surveyed countries, just behind Germany
  (38.7%) and Austria (34.1%) (Expert Group on Future Skills Needs, 2025).
  Early adoption of AI is most evident in knowledge-intensive services such as finance and
  insurance, ICT, legal and consulting, while sectors such as hospitality, construction and
  transportation show low AI intensity (OECD/BCG/INSEAD, 2025).
  UAE
  64%
  60.9%
  Singapore
  AI User Share
  Insufficient Data
  10-19%
  <10%
  20-29%
  ≥40%
  30-39%
  44.6%
  France
  Ireland
  44%
  Norway
  46.4%
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  National Economic & Social Council
  Figure 4.3: Percentage of Enterprises Using AI technologies by Economic Activity,
  EU, 2025
  Source: Eurostat (online data code: isoc_eb_ain2).
  The OECD has found that AI adoption in government trails behind that of the private sector
  (OECD 2025a). In a survey of senior leaders in 250 organisations across Ireland, publicsector organisations reported an AI adoption rate of 50 per cent, compared to 63 per cent
  in multinational organisations (Kumar Jha & Danks, 2025). Despite the lower rate of adoption,
  approximately half of the reported AI use cases in G7 countries aimed to increase the efficiency
  of internal public-sector operations (OECD/UNESCO, 2024). In a mapping exercise conducted
  in 2020, the European Commission (Misuraca & van Noordt, 2020) found that a majority of EU
  member and associated states were already using AI across a variety of government functions.
  Applications ranged from automating administrative processes to delivering citizen-facing
  services and supporting complex policymaking. A more recent report by the OECD found that
  government use of AI is most common in public services, justice and civic participation, and less
  prevalent in policymaking and highly regulated areas such as tax. Of note is that applications aim
  to streamline services, with much less focus on creating new opportunities (OECD, 2025a).
  4.6.1 Barriers to AI Adoption
  The most commonly cited barriers to AI adoption include limited digital and data readiness,
  high implementation costs and uncertainty over both returns on investment and the practical
  application of AI to specific challenges. Organisations often struggle with integrating data
  across systems, developing new business models, and managing organisational change
  (Sternfels & Atsmon, 2025).
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  Successful AI adoption is not a standalone achievement but a capability that sits on top of deep
  digital foundations. Rushing to deploy AI tools without first securing high-speed connectivity
  and curated, governable data is a strategic error that risks failure. This can both undermine
  trust and be expensive, depending on the AI technology being deployed. The Government’s
  Harnessing Digital – The Digital Ireland Framework explicitly recognises this dependency,
  positioning ‘Digital Infrastructure’ (Dimension 2) as a prerequisite for advanced technology
  adoption (Department of the Taoiseach 2022). To prevent the ‘rush to AI’ from outpacing its
  rails, the framework mandates that the physical backbone be ready first, setting strict targets
  of Gigabit network coverage for all households and businesses by 2028 and 5G coverage for all
  populated areas by 2030.
  Furthermore, the National Digital and AI Strategy 2030 emphasises that digital infrastructure
  extends beyond connectivity and computational capacity to encompass high-quality,
  standardised and well-governed data, positioning data integrity and interoperability as
  foundational enablers of secure, trusted and responsible AI deployment. In that context, the
  fact that Ireland lags behind our European counterparts in terms of health system digitisation
  is concerning. While the Digital for Care: A Digital Health Framework for Ireland 2024–2030
  (Department of Health, 2024) sets out an ambitious roadmap, accelerated progress in this
  domain will be required if Ireland is to realise the full potential of AI to improve patients’
  outcomes, safety and efficiency. That said, Ireland is currently laying the groundwork for the
  adoption of AI at scale in healthcare. In 2024 the Health Service Executive (HSE) established
  an Artificial Intelligence and Automation Centre of Excellence to ensure that AI could be
  effectively integrated across the Irish health service. In March 2026, the Department of Health
  published the ‘AI for Care’ strategy, to guide the responsible adoption of AI across the health
  and social care system between 2026 and 2030 (Department of Health, 2026). The strategy
  aims to improve patient outcomes, support clinicians and healthcare staff, and increase system
  efficiency. It outlines the use of AI across clinical care, healthcare operations, research and
  innovation, and public health. In addition, the Health Information and Quality Authority (HIQA) is
  currently developing guidelines for the use of AI in health and social care.
  Although developments in AI technology have been remarkably rapid, it is important to make
  a distinction between technological breakthroughs and their practical application. A gap exists
  between innovation and widespread diffusion; adoption is proving much slower, particularly
  in safety-critical domains where the regulatory burden is high. Current data indicate that
  few organisations can yet be considered AI-mature; many are still building the necessary
  foundations to scale up from pilots to system-wide transformation. Organisational structures,
  professional practices and individual habits take time to adjust, and effective use of AI will
  require new skills, workflows and cultural acceptance. If AI does follow the trajectory of previous
  general-purpose technologies, adoption is likely to unfold over decades rather than years
  (Narayanan & Kapoor, 2025).
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  National Economic & Social Council
  4.6.2 Worker Sentiment
  Another key factor in AI adoption is securing workers’ trust and engagement. At EU level,
  workers already experience AI and related digital technologies as reshaping work, but with mixed
  social consequences. In a Special Eurobarometer on AI and the Future of Work, 66 per cent of
  EU27 respondents said that recent digital technologies, including AI, had a positive impact on
  their current job (European Commission, 2025d). The corresponding figure for Ireland is also 66
  per cent, but Irish respondents were somewhat less negative about these technologies’ impact
  on their job (16% negative in Ireland versus 21% in the EU overall). Interestingly, this positive
  orientation also held when asked about the impact of AI on the economy, quality of life and, to
  a lesser extent, society. In the workplace context, AI was viewed positively in terms of improving
  workers’ safety but viewed more negatively when it came to assessing workers’ performance. A
  majority of those surveyed also agreed that, due to robots and AI, more jobs will disappear than
  new ones will be created (66% EU27 vs 72% Ireland). Thus, workers already interpret AI through
  a risk/benefit frame; they simultaneously see efficiency gains and potential job losses.
  When asked if employers had informed workers about the use of digital technologies, including
  AI, to manage activities in the workplace, 20 per cent of Irish employees reported having
  received a detailed explanation (EU27 18%), while a further 23 per cent had been made aware
  of the use of these technologies but without further details (EU27 16%). There is support for
  clear rules on the use of digital technologies; for instance, protecting workers’ privacy (82%)
  and involving workers and their representatives in the design and adoption of new technologies
  (77%). Irish and EU workers alike emphasise the need for strong rules that protect rights
  and keep workers in the loop in respect of adoption of digital tools, including AI. Of the Irish
  respondents, 84 per cent rated protecting workers’ privacy as important in addressing risks and
  maximising the benefits of digital technologies, including AI, in the workplace, while 80 per cent
  said involving workers and their representatives in the design and adoption of new technologies
  was important.
  Engaging employees early on and on an ongoing basis is crucial, as it leverages their tacit
  knowledge to align algorithmic solutions with actual operational workflows. When workers are
  excluded, systems often fail to address the nuance of daily tasks, leading to resistance and
  implementation gaps. This dynamic is well-documented in the German automotive industry,
  where a failure to consult assembly-line workers initially resulted in systemic inefficiencies;
  however, once the manufacturer integrated worker feedback into the redesign, the company
  achieved smoother workflows and higher output (Cotton, 2024). Consequently, policy
  frameworks must prioritise early employee involvement to ensure that AI tools are not merely
  deployed but effectively assimilated to drive genuine productivity.
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  Figure 4.4: Rules around Digital Technologies in the Workplace
  Source: European Commission, 2025c.
  4.6.3 Shadow AI
  Shadow AI refers to employees using AI-powered tools or platforms without the awareness or
  approval of the organisation’s IT or security functions. Shadow AI represents a fundamentally
  socio-technical challenge: a confluence of workforce behaviour, rapid tool-adoption,
  organisational workflow pressure and lagging governance. Quantifying shadow AI precisely is
  challenging because many instances remain hidden and unreported. However, in a 2025 global
  study on attitudes and use of AI, 44 per cent of employees reported having used AI in ways
  which contravene policies and guidelines, indicating a significant prevalence of shadow AI in
  organisations (Gillespie et al., 2025). A shadow AI culture has also been identified in Ireland; 61
  per cent of managers in organisations which prohibit free AI tools reported knowing that their
  employees still used them (Kumar Jha & Danks, 2025).
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  National Economic & Social Council
  Figure 4.5: Inappropriate and Complacent Use of AI at Work (%)
  Source: Gillespie et al., 2025.
  The use of unsanctioned AI tools introduces multiple risks. As these tools may handle sensitive
  or proprietary data outside formal controls, organisations face heightened exposure to data
  leakage, intellectual property loss, flawed decision-making and regulatory non-compliance.
  In that context, organisations need to focus on practical governance by developing clear,
  accessible policies that define approved AI usage, data-sharing limits and escalation processes.
  Equally important is training and awareness as employees will need practical guidance on what
  constitutes safe use of AI, how to evaluate outputs, what data may be shared (and what must
  not), and why the governance matters. In May 2025, the Department of Public Expenditure,
  Infrastructure, Public Service Reform and Digitisation (2025) published Guidelines for the
  Responsible Use of Artificial Intelligence in the Public Service and a tool for use of AI in public
  services. The guidelines contain a range of resources designed to support the adoption of
  fair, inclusive, accessible and trustworthy AI. Online learning modules for the guidelines and an
  Introduction to AI have also been developed by the Institute of Public Administration.
  38
  % Never % Rarely % Sometimes to very often
  Presented AI-generated content as your own
  Non-transparent use
  Avoided revealing when you’ve used AI tools in your work
  Quality issues
  Put less eort into your work knowing you can rely on AI
  Overall
  Contravening policies
  Uploaded copyrighted material or IP to a Gen AI tool
  Uploaded company information into a public AI tool
  Used AI in ways that contravene policies or guidelines
  Ethically ambiguous
  Seen or heard of people using AI tools inappropriately
  Used AI tools at work without knowing whether it is allowed
  Used AI tools in ways that could be considered inappropriate
  ‘At your work, how often have you…’
  44
  51 15 34
  45 16 39
  39 19 42
  34 24 42
  28 21 51
  44 25 31
  53 16 31
  44 18 38
  37 20 43
  52 14 34
  56 13 31
  18
  50
  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  4.7 Labour Market Impacts
  Viewed through a socio-technical lens, the rise of AI represents not just a technological
  shift but a profound reconfiguration of work itself through reshaping labour markets and
  redistributing skills and responsibilities. The labour market effects of AI are complex, as the
  technology is capable of both substituting and complementing human work (Pizzinelli et al.,
  2023). Where complementarity dominates, workers stand to benefit by focussing on highervalue, creative or interpersonal activities, amplifying both job quality and output (Brynjolfsson
  et al., 2025a). Where substitution dominates, workers face the risk of displacement, deskilling
  and unemployment (Chen et al., 2025; Acemoglu & Restrepo, 2019:5; Filippucci et al., 2024).
  International evidence indicates that the principal near-term labour market impact of AI
  adoption will be to reallocate tasks within jobs, rather than to eliminate whole occupations.
  Task displacement is most likely in occupations where a large share of work consists of
  information processing, routine drafting, summarisation and standardised interaction – activities
  that are already executable by AI systems at acceptable quality and reliability (Lane & SaintMartin, 2021). The greatest exposure lies in clerical, telephony, sales support and administrative
  roles, where routine cognitive tasks are easily automated (Gathmann, Grimm & Winkler, 2024).
  Professional roles such as accountancy, legal services and software development contain a mix
  of automatable and non-automatable tasks, with outcomes depending on how organisations
  redesign work (Gmyrek et al., 2025). Where processes are redesigned to prioritise oversight,
  client engagement and cross-disciplinary collaboration, overall job levels may be maintained
  even as routine entry-level tasks decline. By contrast, if AI adoption leads firms to streamline
  staffing structures toward fewer but more senior roles, net employment losses are likely to
  materialise despite stable levels of output (Filippucci et al., 2024).
  Figure 4.6: Share (%) of High and Medium Exposure in All Tasks
  by Occupational Category
  Source: Pizzinelli et al., 2023.
  0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
  Clerical support workers
  Technicians and associate professionsal
  Professionals
  Service and sales workers
  Managers
  Skilled agricultural, forestry and fishery…
  Plant and machine operators and assemblers
  Elementary occupations
  Craft and related trades workers
  Medium High
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  National Economic & Social Council
  Evidence suggests that Ireland is marginally more exposed to AI-related labour displacement
  risks than the advanced economy average, with uneven adjustment costs likely across regions,
  sectors and demographic groups (DoF & DETE, 2024; Pizzinelli et al., 2023; Filippucci et al.,
  2024). A joint study by the Departments of Finance and of Enterprise, Trade and Employment
  estimates that 63 per cent of Irish employment lies in highly AI-exposed occupations, compared
  with an advanced-economy benchmark of approximately 60 per cent (Department of Finance
  & Department of Enterprise, Trade and Employment , 2024). Exposure is polarised, with a
  significant share of workers in high-exposure, low-complementarity roles such as administrative
  and support functions (facing greater displacement risks), while others in high-exposure, highcomplementarity roles have greater potential for augmentation. Women are disproportionately
  represented in the higher-risk cohort, reflecting a larger share of female workers in
  administrative roles.
  More recent analysis from the Department of Finance (DoF, 2026) suggests significantly weaker
  employment growth over the past two years in AI-exposed sectors as compared to sectors
  with lower relative exposure. This trend is more pronounced for younger workers. Employment
  among 15–29-year-olds in AI-exposed sectors fell between 2023 and 2025, despite overall
  growth in those sectors. In contrast, in lower AI-exposed sectors, youth employment continued
  to grow faster than among older cohorts (DoF, 2026). This impact on early-career workers
  is also seen internationally. Between late 2022 and mid-2025, employment among workers
  aged 22–25 in the most AI-exposed occupations declined by 13 per cent relative to peers in
  less exposed fields, even after controlling for firm-level shocks (Brynjolfsson et al., 2025a). By
  contrast, employment for more experienced workers in the same occupations has remained
  stable or continued to grow. As noted by NESC (2024), the future impacts of AI on the Irish
  labour market remain uncertain; continued monitoring and research will be required to assess
  how these dynamics evolve.
  The World Economic Forum (2026a) takes a scenario-based approach to examine how AI might
  reshape the labour market by 2030. Drawing on expert consultation and economic data, the
  study explores four distinct futures based on two key uncertainties: the pace of AI advancement
  and the level of workforce readiness. In Scenario 1: Supercharged Progress, exponential AI
  development combined with widespread skills training leads to major productivity gains and
  a reimagined workforce, where humans manage intelligent machines. Scenario 2: The Age of
  Displacement envisions a future where rapid AI outpaces readiness, resulting in job losses,
  erosion of consumer confidence, and societal instability. In Scenario 3: Co-Pilot Economy,
  incremental AI progress and strong workforce preparation foster human-AI collaboration (as
  distinct from automation), enabling gradual transformation of industries. Finally, Scenario
  4: Stalled Progress presents a world where both AI development and workforce skills lag,
  producing uneven productivity gains and a fragmented labour market, thus fuelling inequality.
  The report underscores that the trajectory of future jobs depends not only on technological
  breakthroughs but also on coordinated investment in human capital. A ‘no regret’ strategy
  of investing in human-AI collaboration and aligning technology with talent strategies is
  recommended as it would provide value, whichever scenario eventually unfolds.
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  Education and training, re-skilling initiatives and social safety nets will need to evolve and adapt
  to the potential disruptive effects of AI in the labour market. This should be underpinned by
  social dialogue, collective bargaining, updated social protection policies and pro-active state
  interventions to direct labour to where it is needed in areas of the economy that are less suitable
  for automation.
  Labour displacement on a significant scale has implications for the public finances. A decline
  in the labour share of income could erode payroll tax revenues, weakening the funding base
  for social protection systems that rely on stable employment. A key structural issue is whether
  AI-enabled, capital-intensive production and remote service delivery will erode the labour tax
  base over time. The International Monetary Fund (IMF) cautions that generative AI could shift
  the labour-capital income split and recommends modernising fiscal systems to account for
  such structural changes by strengthening social protection, adjusting capital taxation relative
  to labour, and avoiding narrow ‘AI taxes’ in favour of principle-based frameworks that preserve
  neutrality across technologies while mitigating concentration and inequality risks (Cazzaniga et
  al., 2024; Brollo et al., 2024).
  The OECD’s work on taxation and the future of work underscores how differences in tax
  treatment across different forms of employment creates arbitrage risks and threatens the
  integrity of labour-based revenues (Milanez & Bratta, 2019). In an AI-intensive economy
  characterised by more platform work, telemigration and cross-border services trade, tax policy
  will need to maintain horizontal equity across employment statuses and ensure contribution
  adequacy for social insurance.
  Considerations around the composition and sustainability of the tax base will increasingly
  intersect with AI-driven changes in the labour share, profit location and the form of work.
  This strengthens the case for medium-term fiscal planning that anticipates slower growth of
  labour-tax receipts relative to capital and corporate income in high-adoption scenarios, while
  safeguarding incentives for productive investment (Brollo et al., 2024).
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  National Economic & Social Council
  Box 4.1: AI in Agriculture
  Agriculture is facing mounting pressures from climate change, growing global food demand,
  rising input costs and declining natural resources. AI is emerging as a key tool to help farmers
  produce more with less by improving efficiency, sustainability and resilience. Precision farming is
  one of the most transformative applications (Aijaz et al., 2025). AI-powered sensors, drones and
  computer-vision systems monitor soil health, moisture and crop conditions in real time, enabling
  targeted fertilisation, irrigation and pest control (Dalal & Mittal, 2025). These technologies can
  reduce chemical use and environmental impact while improving yields (Anastasiou et al., 2023).
  AI-driven forecasting tools also help farmers plan planting and harvesting by analysing weather
  patterns, soil conditions and historical crop performance (Goel & Pandey, 2024).
  Labour shortages are accelerating interest in robotics, from autonomous tractors and robotic
  weeders to automated milking systems, an area where Ireland is an early leader (ESOFT, 2024).
  AI-enabled sorting and grading technologies, such as Ireland’s first AI-powered shellfish grader,
  enhance product quality and reduce waste (McCann, 2025).
  High upfront costs, particularly for small farms, fragmented agricultural data and limited rural
  broadband connectivity remain substantial barriers to adoption of AI in agriculture (Thomasson
  et al., 2025). Skills shortages and the risk of eroding traditional agricultural knowledge also pose
  challenges. As connectivity improves and AI literacy expands, AI has strong potential to support
  sustainable, high-productivity farming, but targeted investment and policy support will be
  essential to ensure benefits are shared across farms of all sizes.
  4.8 Skills
  Ireland enters the AI transition with a comparatively strong digital and ICT skills foundation.
  The share of ICT specialists in overall employment in Ireland was 6.3 per cent in 2024, the 5th
  highest in the EU and above the EU average of 5.0 per cent. Moreover, the percentage of
  people in Ireland with ‘basic or above’ digital skills stood at 73 per cent in 2023, compared with
  56 per cent for the EU, giving Ireland the 3rd-highest ranking in the EU (Expert Group on Future
  Skills Needs, 2025). Overall, Ireland appears well positioned to harness AI, combining a digitally
  literate population with a deep pool of ICT talent.
  Recent IMF analysis suggests that Ireland is among the countries best positioned to meet
  future skills needs, ranking highly on measures of skill readiness alongside Finland and Denmark.
  This reflects high levels of foreign direct investment (FDI) in the tech sector and sustained
  investment in tertiary education and lifelong learning, which have helped build a workforce with
  strong adaptability as technologies evolve. However, the same analysis cautions that Ireland’s
  relative strength on the supply side of skills may not be matched by sufficient demand from
  firms. To avoid under-use of this skills base, the authors of the paper (Jaumotte et al., 2026)
  recommend that policy focus on stimulating demand by supporting firms to absorb and deploy
  advanced skills, including through stronger innovation incentives, easier business formation,
  export promotion, and measures to ease financial constraints on growing companies.
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  Figure 4.7: Skills Readiness Index
  Source: Jaumotte et al., 2026.
  Note: The left axis displays the Skill Readiness Index; the right axis presents the skill imbalance index (relative weight of potential
  future new skill demand versus supply).
  Data from the higher education sector reinforce the relatively positive picture regarding AI skills.
  The Higher Education Authority (2025a) Key Facts and Figures report shows that ICT is now one
  of the largest fields of study, accounting for over 13 per cent of postgraduates and a particularly
  high share of international graduates, alongside strong enrolments in engineering and other
  STEM disciplines. This flow of graduates contributes to Ireland’s high ranking in the LinkedIn AI
  Talent Index which places Ireland fifth in the world for AI talent density (Expert Group on Future
  Skills Needs, 2025).
  Nevertheless, issues are emerging in the AI skills pipeline. Demand for AI-related skills is rising
  sharply, with AI-related job postings more than doubling since 2023, with approximately 63 per
  cent of jobs judged to be exposed to AI in some way (Expert Group on Future Skills Needs,
  2025). This implies that both specialist and AI-literate roles will need to expand significantly
  just to maintain current adoption trajectories. A shortage of skilled workers is among the main
  obstacles; surveys show that many firms have difficulties in recruiting staff with the right
  expertise, even in larger organisations with substantial resources (European Commission,
  2020b). Although ICT and STEM output in Ireland is substantial, it still represents a minority of
  total graduates, and there are concerns about stagnation or decline in domestic enrolments
  in some digital disciplines over time (Higher Education Authority, 2025a). The National AI
  Leadership Forum (2025) warns that critical research pipelines, such as the Centres for Research
  Training (CRTs), face discontinuity without renewed investment, risking erosion of advanced AI
  capability. A further structural challenge is Ireland’s reliance on internationally mobile AI talent.
  While FDI continues to bring expertise into the economy, this workforce is inherently mobile;
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  National Economic & Social Council
  this raises the question of strategic resilience in this area. Ireland ranks third globally in terms
  of net migration flows of LinkedIn members with AI skills (Expert Group on Future Skills Needs,
  2025).
  Figure 4.8: Net Migration Flows of LinkedIn Members with AI Skills (per 10,000)
  Source: Expert Group on Future Skills Needs, 2025.
  56
  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  4.8.1 Up/Reskilling
  Irish and international evidence shows that AI adoption is reshaping skill demand. Analyses
  indicate a growing need not only for technical expertise such as machine learning and data
  engineering, but also for capabilities in management, co-ordination, analytical thinking and
  communication. In highly AI-exposed occupations, vacancies increasingly emphasise emotional,
  cognitive and digital competencies. Thus, effective AI readiness requires a balanced mix of
  specialist expertise, workforce-wide AI literacy and human-centred skills such as leadership,
  creativity and complex problem-solving.
  Ireland and the European Union have established a wide range of supports to develop these
  skills. At EU level, the Digital Europe Programme funds specialised education in AI and other
  advanced digital domains, while the 2025 Union of Skills package expands advanced digital
  academies and improves cross-border recognition of digital skills. The National Digital & AI
  Strategy 2030 situates digital and AI skills development as a cross-cutting priority for economic
  and societal transformation, explicitly embedding mechanisms to support workforce upskilling
  and SME digital adoption. Research Ireland’s Centres for Research Training provide structured
  PhD-level training in AI, machine learning and data science, while CeADAR, the national
  centre for applied AI, offers industry-aligned training and graduate placement. Further and
  adult education provision is expanding through SOLAS micro-qualifications in AI and digital
  transformation, alongside enterprise-led training via Skillnet Ireland and flexible upskilling routes
  such as Springboard+ and the Human Capital Initiative.
  Despite the number of supports available, the IBEC Skills Survey 2025 finds that Irish enterprises
  display uneven strategic prioritisation of digital and AI capabilities; only 44 per cent of firms
  consider AI skills important while 75 per cent attach importance to digital skills, leaving a
  significant minority of employers underprepared for technological change. This undervaluation
  is particularly acute among SMEs, where resource constraints mean firms prioritise immediate
  operational and compliance needs over long-term upskilling. As a result, large enterprises are
  substantially more likely to recognise AI as a critical future skills challenge (69% versus 41% of
  small firms) and to invest accordingly, being nearly twice as likely to provide digital training (41%
  vs 21%) and AI training (30% vs 13%) compared to SMEs (Ibec, 2026).
  4.8.2 De-skilling
  An extensive body of research reveals a paradox at the heart of AI adoption. While AI tools offer
  gains in efficiency and productivity, their use can risk the erosion of human cognitive skills. This
  phenomenon, variously described as ‘cognitive offloading’, ‘deskilling’ or even ‘never-skilling’,
  manifests as a measurable decline in critical thinking, complex problem-solving, creativity
  and self-sufficiency due to over-reliance on AI tools. The ‘automation paradox’ describes the
  predicament where the introduction of an automation, intended to simplify and improve human
  performance, can paradoxically lead to a decline in human proficiency (Bainbridge, 1983). The
  central tension is that, as AI automates routine cognitive tasks, the neural pathways responsible
  for higher-order thinking may atrophy as a result of underuse, following a neurological principle
  of ‘use it or lose it’.
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  National Economic & Social Council
  A recent study (Budzyń, 2025) suggests that routine use of AI-assisted colonoscopy systems
  can lead to a 20 per cent drop in the ability of experienced endoscopists to detect adenomas
  (precancerous growths in the colon) when performing colonoscopies without AI assistance.
  These results are concerning but randomised crossover trials will be needed to make more
  robust claims regarding de-skilling because of the introduction of AI into clinical practice. The
  OECD highlights the risk of a ‘crutch effect’ in education, where students rely on generative AI
  to complete tasks rather than engage in the cognitive effort required for deep learning, creating
  a ‘mirage of false mastery’ (OECD, 2026 p51). Evidence from a randomised controlled trial in
  Türkiye involving nearly a thousand secondary school students, demonstrated that while those
  students using a generic GPT-4 interface achieved dramatically higher practice performance
  (up to 127% greater accuracy in some tasks and around 48% higher scores overall), their
  understanding proved fragile. Once access to the tool was removed, these students performed
  17 per cent worse on closed-book exams than peers who had never used generative AI. The
  findings show that, although generative AI can boost short-term task performance, it can also
  weaken metacognitive engagement and retention (Bastani et al., 2024).
  The decline in cognitive skills is not merely a passive consequence of disuse but is actively
  driven by a psychological tendency to over-rely on AI. Research on ‘algorithm appreciation’
  shows that people often prefer algorithmic judgment over human judgment (Logg, Minson
  & Moore, 2019). This preference can lead to a state of over-trust, where users follow AI
  recommendations without critical scrutiny.
  Mitigating the risks of cognitive decline requires a conscious and strategic approach from
  individuals, educators and policymakers. This includes redesigning educational frameworks
  to cultivate AI-specific critical thinking, implementing organisational policies that effectively
  promote hybrid human-AI intelligence, and fostering a culture of mindful technology use that
  leverages AI as a tool for empowerment rather than a cognitive crutch.
  4.9 Productivity and Economic Gains
  Artificial intelligence has the potential to deliver substantial productivity and economic
  benefits by automating routine tasks, augmenting complex ones, and accelerating research
  and development. These capabilities can lower costs, increase efficiency and stimulate
  innovation across a wide range of sectors. Knowledge-intensive industries such as finance, ICT
  and professional services are already reporting measurable productivity gains (OECD, 2025c;
  Filippucci et al., 2024), while in manufacturing AI is expected to support productivity growth
  primarily through improved process optimisation, data-driven decision-making and more
  efficient production systems (OECD, 2025c).
  4.9.1 Productivity
  Artificial intelligence offers the potential to reshape productivity at multiple levels of the
  economy. Experimental studies demonstrate substantial productivity gains, particularly in tasks
  that align with the ‘jagged technological frontier’ – i.e. those tasks that AI can perform reliably.
  Consultants using GPT-4 completed tasks 25 per cent faster, accomplished 12 per cent more
  work, and produced outputs of over 40 per cent higher quality, compared to a control group
  (Dell’Acqua et al., 2023). In customer service, generative AI increased issue resolution rates
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  by 14 per cent overall, and by 34 per cent for less experienced agents, highlighting a ‘skilllevelling’ effect (Noy & Zhang, 2023). In software engineering, AI copilots have been shown to
  increase task completion by over 50 per cent (Peng et al., 2023), while, in professional writing,
  performance improvements of up to 48 per cent have been documented (Noy & Zhang, 2023).
  According to the Randstad Workmonitor (2026), which surveyed over 27,000 workers and
  1,200 employers across 35 countries, 62 per cent of employees and 54 per cent of employers
  reported productivity gains from AI adoption.
  The available evidence suggests that productivity gains from generative AI are significant but
  highly context-dependent; evidence shows that such improvements require careful planning,
  structured training and effective implementation to be realised, otherwise they can reduce
  productivity in poorly prepared settings.
  A recent meta-analysis of 45 studies found an average productivity improvement of
  approximately 17 per cent when generative AI was used for specific work tasks (Coupé &
  Wu, 2025). Importantly, these gains were not uniform. The meta-analysis reports substantial
  variation across contexts and documents a non-trivial minority of cases where AI adoption
  reduced productivity. This highlights the risks associated with inefficient forms of automation,
  i.e. those that can led to increased costs or the need for additional work, sometimes referred to
  as ‘so-so automation’. Likewise, in a global study of 3,031 professionals, substantial productivity
  improvements were documented when AI tools were adopted effectively. Workers using AI
  reported saving an average of 7.5 hours per week, but this was contingent on structured, recent
  and inclusive training (Jolles & Lordan, 2025).
  An evaluation of Microsoft 365 (M365) Copilot conducted in the UK civil service from October
  2024 to March 2025 found that small time savings were observed across most use cases, but
  additional time was incurred for tasks such as generating images or scheduling (Department for
  Business & Trade, 2025). The pilot did not find any evidence that time savings led to increased
  productivity. Interestingly, where additional time was required to complete tasks using M365
  Copilot, this was due to the tool not being able to produce high-quality outputs or the task
  being additional workload assigned because M356 Copilot was in use.
  In Ireland, the Office of the Government Chief Information Officer co-ordinated three pilots
  (a customer service chatbot, a policy and strategic forecasting assistant, and a documentlibrary assistant) to test how Large Language Models could improve public service delivery,
  policy analysis and internal knowledge management in the public service. Run in partnership
  with departments and industry specialists, these proof-of-concept studies explored feasibility,
  usability, integration and cost. Several key lessons were captured which were common to all
  three pilots; success depends on starting with a well-defined, high-value use case, supported
  by strong planning around objectives, governance, risks and data quality. The pilots also showed
  that poor preparation is costly as LLM projects require significant resources, skilled teams
  and adaptable designs, and getting it wrong can quickly become expensive (Office of the
  Government Chief Information Officer, 2025).
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  Productivity benefits generally lag behind technological implementation; thus AI’s impact
  remains modest and difficult to detect in national productivity statistics. This lag is consistent
  with the Productivity J-Curve hypothesis, which posits that productivity improvements are
  initially low due to intangible investments in complementary assets such as data restructuring,
  worker training and workflow redesign (Brynjolfsson et al., 2021). The long-term impact of AI on
  productivity will depend in part on whether it primarily augments human labour or substitutes
  for it. If AI complements human labour and diffuses broadly aggregate productivity, gains could
  be substantial. If substitution dominates and displaced workers reallocate toward sectors with
  structurally low productivity growth, gains may be dampened through a ‘Baumol-type structural
  effect’. Historically, general-purpose technologies have produced productivity gains over
  time, often when embedded as complements to human labour, rather than pure replacement
  (Acemoglu, 2024). According to chief economists’ projections, Europe is expected to start
  reaping the productivity benefits of AI adoption and deployment within the next three years
  (World Economic Forum, 2026b).
  4.9.2 Higher-Value Work
  Artificial intelligence does not simply increase output; it also reshapes the composition of
  tasks within occupations. The prevailing assumption is that AI increases productivity by
  automating routine tasks, thereby freeing workers to focus on more complex, higher-value
  activities. Evidence suggests the impacts of AI adoption on productivity are more nuanced,
  and that effects can vary across different tasks and segments of the workforce. An analysis by
  Brynjolfsson, Li and Raymond (2025b) found that using an AI chatbot to support call-centre
  workers tended to substantially enhance the productivity of less experienced workers. By
  contract, such benefits were found to be more modest for more experienced workers and even
  led to a slight reduction in the quality of their work.
  Autor and Thompson (2025) argue that the labour market effects of AI and automation depend
  not only on which tasks are automated, but on how that automation reshapes the expertise
  required for remaining work. When AI removes low-expertise tasks, it raises occupational skill
  thresholds, concentrating labour demand on higher-value human capabilities such as judgment
  and problem-solving. Thus, AI can increase the value of workers’ skills by shifting effort toward
  tasks that require greater expertise, which can raise wages and change occupational roles.
  However, when automation removes expert tasks, the work may require less specialised skill,
  lowering wages while making the occupation easier to enter.
  An OECD analysis of vacancy data shows that, in jobs with high AI exposure, employers
  increasingly demand competencies such as management, administration, communication
  and complex problem-solving (Green, 2024). This supports the view that, with appropriate
  job design, AI can shift human effort toward higher-value activities that require interpretation,
  oversight and interpersonal skills.
  However, the phenomenon of ‘workslop’ serves as a critical caveat to optimistic narratives about
  AI driven productivity; this refers to the proliferation of low quality, AI generated content that
  appears legitimate but lacks substantive value, thereby shifting effort from value creation to
  human verification and oversight (Niederhoffer et al., 2025).
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  4.9.3 Macroeconomic Impact of AI
  Analysis by the IMF anticipates that AI adoption could lift the average annual growth rate of
  global GDP by between 0.1 per cent and 0.8 per cent per annum from 2025 to 2030 (IMF,
  2024: 76). Further analysis by the IMF examines a central scenario where AI adoption leads to
  additional growth in global GDP of 0.5 per cent per annum from 2025 to 2030 (IMF, 2025, pp:
  6-7). The OECD similarly argues that AI has the potential to increase productivity growth but
  warns that this depends on complementary investments in skills and innovation (OECD, 2024a).
  It should be noted that environmental externalities associated with AI systems are generally not
  sufficiently accounted for in standard economic and commercial metrics.
  Macroeconomic projections of AI’s impact on growth diverge sharply depending on modelling
  assumptions. Acemoglu (2024), using a tightly constrained task-based methodology to
  estimate how much work AI can realistically and profitably automate, concludes that only a
  small fraction of tasks will be affected over the next decade, suggesting a cumulative total
  factor productivity (TFP) gain of roughly 0.6–0.7 per cent. By contrast, Aghion and Bunel (2024)
  present higher projections using two alternative approaches. The first is a historical analogy
  that compares AI to past general-purpose technologies such as electrification or ICT, yielding
  potential productivity gains of 0.8–1.3 percentage points per year. The second is based on
  Acemoglu’s task-based model but crucially relaxes some of Acemoglu’s constraints on task
  exposure and rate of diffusion, producing a median estimate of 0.68 percentage points in
  additional TFP growth. The significant difference in estimates does not reflect disagreement
  about AI’s capabilities per se but rather rests on different assumptions about how quickly AI
  will diffuse across tasks, whether it will become a broad engine of discovery or be confined to
  automation, and whether historical technological revolutions provide a reliable guide for the
  trajectory of AI.
  In a similar vein, studies of AI adoption report differing findings on returns on investment (ROI).
  An MIT study found that 95 per cent of AI pilots yielded no measurable financial return, primarily
  due to organisational barriers such as inadequate integration and poor data infrastructure
  (Challapally et al., 2025). In contrast, research from the Wharton School at the University of
  Pennsylvania paints a more optimistic picture, with 70–75 per cent of firms reporting positive
  business outcomes, particularly where AI was embedded into core workflows (Korst, Puntoni
  & Tambe, 2025). This divergence is likely a reflection of different adoption stages and metrics.
  While the MIT study focuses on early pilots and narrow financial returns, Wharton captures laterstage deployments and uses broader measures, including cost savings and workflow efficiency.
  4.9.4 AI ‘Bubble’
  There is an animated debate taking place about whether the surge in AI investment reflects
  a sustainable technological revolution or a speculative bubble comparable to the dot-com
  bubble, marked by soaring equity valuations and capital spending. The Magnificent 7 technology
  stocks now account for over a third of the S&P 500’s value and it is estimated that companies’
  capital spending on AI will reach $527bn in 2026 (Goldman Sachs, 2025). Proponents of the
  ‘bubble’ thesis highlight stretched equity prices and the gap between investment and realised
  returns. The European Central Bank’s (ECB) Financial Stability Review, published in November
  2025, states that ‘stretched valuations and extreme market concentration, particularly in US
  technology and AI-related firms, heighten the risk of the sharp repricing’ (European Central
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  Bank, 2025). Chief economists surveyed by the WEF are divided over AI asset valuations in
  2026; 52 per cent expect a decrease or significant decrease in that asset class. Almost threequarters (74%) expect a significant decrease in the value of US AI assets to have widespread
  impacts on the global economy, while a quarter expect it to be more contained. More
  encouragingly, the majority (59%) expect any correction to have short-lived impacts on the
  global economy (World Economic Forum, 2026b). Ireland faces particular vulnerability given the
  concentration of US tech operations based in the country, which could affect employment and
  corporate tax receipts if there were to be a sharp correction.
  However, a crucial distinction from previous financial crises is the financing structure; unlike
  the debt-fuelled bubbles of 2008, the current boom is largely equity-financed. IMF chief
  economist Pierre-Olivier Gourinchas suggests that this would reduce the risk of systemic
  financial contagion if a correction occurs, potentially limiting fallout to equity holders rather
  than triggering broader instability in the financial system (Lawder, 2025). This has led some
  economists, including Nobel laureate Peter Howitt, to characterise the situation as a potentially
  ‘rational bubble’, driven by fundamental technological progress that, even if it leads to a crash,
  may be essential to fund long-term physical infrastructure and build a knowledge base through
  widespread innovation across the industry.
  This chimes with economist Carlota Perez’s framework on technological revolutions which
  argues that major technological revolutions follow a predictable pattern: the ‘Installation
  Phase’ characterised by speculative investment and irrational exuberance, followed by a crash
  that marks the transition to a ‘Deployment Period’ where previously loss-making investments
  become the productive foundation of the economy (Perez, 2002). Applied to AI, this suggests
  that, even if many AI-focussed startups fail, supporting physical infrastructure such as data
  centres, expanded power generation capacity and semiconductor manufacturing facilities will
  sustain productive capacity in the longer term. Such an outcome would mirror the historical
  experience whereby bankrupt telecom companies left behind the fibre-optic networks that
  ultimately enabled the modern internet. Some caution regarding this analogy is warranted on the
  grounds that a not insignificant amount of AI investment is directed toward rapidly depreciating
  hardware such as GPUs, and (as previously discussed in this report) the technology itself may
  be confronted with structural limitations.
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  Chapter 5: AI Governance
  5.1 Introduction
  This chapter maps the rapidly evolving landscape of AI governance, tracing the expansion of
  global regulatory activity and the diverse mechanisms emerging to guide the safe and ethical
  development of artificial intelligence. It introduces the major international frameworks that
  have laid the groundwork for collective oversight, examines national and regional approaches,
  including the EU’s landmark AI Act and Ireland’s evolving regulatory architecture, and highlights
  the shared principles that underpin contemporary governance models. The chapter also
  considers how fast-moving technological change is prompting governments to explore more
  adaptive, forward-looking approaches such as anticipatory governance, setting the stage for a
  richer discussion of how policy, regulation and standards can keep pace with AI’s accelerating
  impact.
  5.2 International Initiatives
  The OECD’s Principles on Artificial Intelligence (OECD, 2019) are one of the earliest and most
  influential standard-setting instruments in the field AI and have been endorsed by over forty
  countries. The United Nations Educational, Scientific and Cultural Organization (UNESCO)
  Recommendation on the Ethics of Artificial Intelligence (UNESCO, 2021) was the first global
  governance instrument on AI ethics. The Council of Europe (CoE) Framework Convention on
  Artificial Intelligence and human rights, democracy and the rule of law (Council of Europe,
  2024a) opened for signature in September 2024, and its reach extends beyond the 46 Council
  of Europe member states, with the US, Canada and Japan signing this legally binding treaty.
  Ireland is included as part of European Union’s signature on behalf of its 27 Member States.
  The African Union (2024) agreed and published in 2024 the Continental AI Strategy, which
  adopts a regional and development-focused approach to AI. On a more technical level, the
  joint International Organization for Standardization (ISO) and International Electrotechnical
  Commission (IEC) committee on AI have developed several international voluntary standards to
  facilitate the responsible adoption of AI technologies.⁵
  5.2.1 Transnational Governance
  In July 2025, China announced its Action Plan for Global Artificial Intelligence (AI) Governance,
  which promotes open-source and cross-border collaboration, risk management, and a
  recommendation for the establishment of a global AI co-operation organisation to foster
  international collaboration on AI development and regulation (People’s Republic of China, 2025).
  It should be noted that the Hiroshima Process International Guiding Principles were developed
  for a similar purpose by the G7 nations in 2023 (G7 Hiroshima Conference, 2023). Coherent
  global regulation is required as AI systems are developed, deployed and hosted across multiple
  jurisdictions, making it very challenging for any single regulator to ensure effective oversight.
  5 Seven AI standards have been published by the ISO/IEC which range from guidance on terminology to impact assessment to risk
  management: ISO – Artificial intelligence, accessed 20 August 2025. The National Standards Authority of Ireland is represented on
  AI sub-committee working groups.
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  Box 5.1: AI in Finance
  Artificial intelligence is increasingly being adopted across the financial sector, shaping how
  institutions deliver services, manage risk and organise operations (OECD, 2023c). Banks and
  financial services firms are using AI-powered virtual assistants and chatbots to personalise and
  expedite customer support. Financial firms are deploying AI to detect and prevent fraud and
  other financial crime, including anti-money-laundering monitoring and suspicious transaction
  analysis. Stripe’s machine-learning-based engine, Radar, analyses thousands of transaction
  attributes in real time to identify anomalous patterns and block fraudulent payments. In trading
  and investment management, algorithmic systems leverage machine learning to execute
  trades, interpret market signals, and optimise portfolios with speed and precision beyond
  human capability. Artificial intelligence can also support regulatory compliance and supervisory
  functions, automating reporting, monitoring risk exposures, and helping firms and regulators
  keep pace with evolving standards (Najem et al., 2025).
  Despite this promise, adoption remains cautious. Finance is a highly regulated sector, and
  many advanced AI models function as ‘black boxes’, complicating explainability, accountability
  and regulatory approval (OECD, 2024d). Key challenges include algorithmic bias and fairness
  risks, data privacy and governance constraints, model robustness issues such as GenAI
  ‘hallucinations’, and concerns about systemic risk arising from widespread reliance on similar
  models (Maple et al., 2023). In the Irish financial services context, three principal obstacles that
  need to be addressed to realise AI’s full potential have been identified: integrating AI agents
  with legacy data and systems; a shortage of advanced and generative AI skills; and building trust
  in AI through responsible practices and governance frameworks (Financial Services Ireland and
  IBEC, 2025).
  5.3 National Initiatives
  Building on international initiatives, individual countries have adopted diverse governance
  approaches, with some notable divergences in their scope, binding nature and implementation
  mechanisms. These differences likely reflect different national priorities such as innovation,
  economic competitiveness, human rights and fundamental freedoms, as well as legal traditions
  and geopolitical strategies.
  The UK has adopted a ‘pro-innovation’ and non-binding framework for AI regulation, favouring a
  sector-specific model, empowering existing regulators and emphasising voluntary measures and
  ethical guidelines rather than overarching AI legislation (HM Government, 2021). The Australian
  approach to governance of AI focuses on ethical frameworks and guidelines, with a Voluntary AI
  Safety Standard (Australian Government, 2024) published in August 2024, but there is ongoing
  debate about the need for more binding regulation.
  The United States does not have federal AI legislation, but instead relies on a mixture of existing
  laws, sector-specific regulations and voluntary guidelines. The Trump administration signalled a
  shift towards AI deregulation and industry-led innovation, revoking President Biden’s Executive
  Order 14110 ‘Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence’ in
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  January 2025 (Mackowski et al., 2025). Nonetheless, there is an extensive patchwork of federal
  agencies and state-level initiatives, each covering different aspects of AI. In 2024, 59 AI-related
  regulations were introduced – more than double the 25 recorded in 2023 (Maslej et al., 2025b).
  This is supplemented by soft law in the form of voluntary standards by the National Institute of
  Standards and Technology (NIST) that aim to advance ethical AI (NIST, 2024).
  Figure 5.1: Governance of AI at the US State Level
  Source: IAPP, 2025.
  In contrast, following the publication of Canada’s Voluntary Code of Conduct on the
  Responsible Development and Management of Advanced Generative AI Systems (Government
  of Canada, 2023a), the Canadian government opted to regulate AI at the federal level, through
  the proposed Artificial Intelligence and Data Act, which is currently under legislative review
  (Government of Canada, 2023b). China’s approach to AI governance and regulation is a hybrid
  one, sitting between the centralised, top-down approach of the EU and the decentralised, freemarket approach in the US. China does not have a single comprehensive law on AI governance
  but has implemented industry-specific binding regulations and technical standards which often
  target AI outputs as distinct from AI systems (Chun, Schroeder & Elkins, 2024). For example,
  in March 2025, the Cyberspace Administration of China (2025) introduced rules requiring
  internet service providers to clearly label AI-generated content, using both explicit and implicit
  methods. China’s AI governance principles emphasise human control, fairness and transparency
  and, interestingly, endorse the principle of open-source models. DeepSeek-R1, a Chinese
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  LLM optimised for reasoning, was launched in January 2025, while Deepseek V3.2, which
  incorporates a ‘sparse attention’ mechanism that reduces computational work to provide similarquality outputs, was introduced in December 2025.
  5.3.1 Ireland
  The EU AI Act (discussed in detail below) provides the overarching legal framework for AI in
  Ireland. In February 2026, the General Scheme of the Regulation of Artificial Intelligence Bill
  2026 was published by the Department of Enterprise, Tourism and Employment (2026b) to give
  effect to the Regulation.
  Ireland’s first National Artificial Intelligence Strategy, AI – Here for Good, was published in 2021
  (Department of Enterprise, Trade and Employment, 2021) and established an initial framework
  for the responsible development and adoption of AI. This was followed by a strategic refresh
  in 2024 (Department of Enterprise, Tourism and Employment, 2024) reflecting evolving
  technological, regulatory and economic contexts. The current National Digital and AI Strategy
  2030, Digital Ireland Connecting our People, Securing our Future, builds on these earlier
  initiatives, maintaining a consistent emphasis on trust, governance, skills development and
  enterprise adoption across all three policy iterations (Department of the Taoiseach, 2026).
  The 2026 strategy articulates an integrated vision for positioning Ireland as a digitally enabled,
  AI-ready society and economy, and is structured around five strategic ambitions, 20 high-level
  strategic objectives, and supported by 90 key deliverables, designed to guide co-ordinated
  public, private and societal action.
  Beyond these strategy-linked commitments, additional steps have been taken to enhance
  Ireland’s AI governance architecture. The AI Advisory Council, established in January 2024,
  provides expert advice to government and engages with the public to build confidence
  in trustworthy AI. While the National Digital and AI Strategy is silent on the future of the
  AI Advisory Council, it commits to pooling and institutionalising expertise through the
  establishment of an AI Advisory Unit to support public bodies in the effective and responsible
  adoption of AI. In addition, a National AI Fellowship Programme is to be established by Research
  Ireland to embed advanced research expertise within the public service and strengthen
  evidence-based and ethical AI adoption, while strengthening of knowledge-sharing and coordination of regulatory-related matters will be done through the Digital Regulators Group. In
  addition, the Oireachtas established a Joint Committee on Artificial Intelligence, chaired by
  Malcolm Byrne TD, in May 2025 to examine and make recommendations on AI’s development,
  deployment, regulation and ethical implications, ensuring that governance both supports
  innovation and safeguards societal interests. In December 2025, the committee published
  its first interim report in which it made 85 recommendations (Joint Committee on Artificial
  Intelligence, 2025).
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  5.4 AI Regulation in the EU
  The AI Continent Action Plan is the European Commission’s overarching policy, setting out
  ‘a set of bold actions’ to make the EU a leading AI continent, emphasising competitiveness,
  democratic values and cultural diversity. It highlights the need to invest in large-scale AI
  computing infrastructure, data, skills and innovation ecosystems, while ensuring human-centric
  and trustworthy AI (European Union, 2025). The ‘AI Continent’ ambition is operationalised
  through the AI Innovation Package launched on 24 January 2024. A central pillar of this is the
  GenAI4EU initiative, which aims to boost the uptake of generative AI in 14 strategic industrial
  ecosystems (e.g. robotics, health, manufacturing). The Apply AI Strategy, adopted in October
  2025, builds on that foundation, but shifts the focus from supporting AI creation to promoting
  AI adoption across strategic sectors of the European economy and public sector.
  5.4.1 EU AI Act
  The EU Artificial Intelligence Act (AI Act) (Regulation {EU} 2024/1689 of the European
  Parliament and of the Council of 13 June 2024 on artificial intelligence) is a landmark, legally
  binding regulatory framework that officially became law on 1 August 2024, with the Act being
  implemented on a staggered basis. The general-purpose rules of the Act came into force
  on 2 August 2025. The regulation lays down harmonised rules for the placing on the market,
  putting into service and use of AI systems, with the twin aim of fostering the uptake of safe,
  trustworthy AI and protecting health, safety and fundamental rights across the EU. The Act
  adopts a risk-based approach that categorises AI systems into different levels of risk; there are
  stricter obligations for higher-risk uses, with some AI practices prohibited (e.g. some biometric
  uses), with narrowly defined exceptions (see further detail under Section 5.5.1). The Commission
  has begun issuing non-binding guidance to support early application of the Act. In February
  2025, it published Guidelines on the definition of an artificial intelligence system established
  by Regulation (EU) 2024/1689 (AI Act), to help providers and other actors determine whether
  particular software falls under the legal definition of an AI system. The Commission has also
  issued Guidelines on prohibited artificial intelligence (AI) practices, explaining which AI practices
  are considered unacceptable and providing examples to support compliance. Further, the EU
  AI Act contains dedicated provisions on AI regulatory sandboxes (Articles 57–59), designed as
  controlled environments where competent authorities can support the development, testing
  and validation of innovative AI systems, including in real-world conditions. Each member state
  must ensure that their competent authorities establish at least one AI regulatory sandbox at
  national level.
  EU AI Office
  To implement and enforce the AI Act, the Commission has created a multi-level governance
  framework centred on the European AI Office, national competent authorities and EU-level
  advisory bodies. The AI Office, established within the Commission and operational since 2024,
  plays a key role in implementing the AI Act, especially for general-purpose AI models (European
  Commission, 2024). Its tasks include supporting coherent application of the Act across member
  states, developing tools and benchmarks for evaluating general-purpose AI, drafting codes of
  practice, preparing guidance and investigating possible infringements. It also advances policies
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  for trustworthy AI (including AI sandboxes and real-world testing), co-ordinates with the
  European Artificial Intelligence Board (AI Board), the AI Advisory Forum and a Scientific Panel,
  and promotes the EU’s approach internationally.
  Member state obligations
  Article 70 of the EU AI Act mandates that each member state designate national competent
  authorities and a single point of contact for the application and implementation of the Act.
  Article 28 requires each member state to designate at least one notifying authority responsible
  for assessing, designating and monitoring conformity-assessment bodies (notified bodies), and
  Article 74 requires the designation of market surveillance authorities. Article 77 requires that
  member states identify national public authorities which supervise or enforce the respect of
  obligations protecting fundamental rights.
  The Irish Government has opted for a distributed regulatory model to implement the Act and
  has designated 15 public bodies as national competent authorities within their respective
  sectors,⁶ and a further nine bodies as fundamental rights authorities for the Act.⁷ A distributed
  model was chosen as it allows for existing regulatory experience to be leveraged. A distributed
  model makes sense given the wide range of fields in which AI will be deployed, each with
  its own regulatory particularities; however, it also carries the risk of producing a fragmented
  approach if co-ordination is not carefully maintained.
  In that context, Ireland has signalled its intention to establish an AI Office of Ireland (AIOI) as
  the central, co-ordinating authority for implementing the EU Artificial Intelligence Act. The AIOI
  will serve as the ‘single point of contact’ to co-ordinate the activities of the sectoral competent
  authorities. Responsibility for its establishment lies with the Department of Enterprise, Tourism
  and Employment (DETE), perhaps reflecting the Government’s view that AI oversight should
  align with enterprise, innovation and economic policy. The AIOI’s core tasks will include coordinating the work of the designated competent authorities for consistent nationally coherent
  application of the EU AI Act; acting as Ireland’s single national contact point under the Act;
  providing centralised access to technical expertise for regulators; and hosting a national
  regulatory sandbox to support innovation and safe deployment of AI systems.
  Moreover, the National Standards Authority of Ireland (NSAI) acts as the State’s primary body
  for developing, co-ordinating, and contributing to technical standards that support compliance
  with the EU AI Act. Because the Act relies heavily on harmonised European standards, which are
  being developed through the European Committee for Standardization (CEN) and the European
  Committee for Electrotechnical Standardization (CENELEC), NSAI’s role is to represent Ireland
  in these committees, ensure Irish interests are reflected, and facilitate the adoption of these
  standards nationally.
  6 Competent authorities currently designated under the EU AI Act are: Central Bank of Ireland; Coimisiún na Meán; Commission for
  Communications Regulation; Commission for Railway Regulation; Commission for Regulation of Utilities; Competition and Consumer
  Protection Commission; Data Protection Commission; Health and Safety Authority; Health Products Regulatory Authority; Health
  Services Executive; Marine Survey Office of the Department of Transport; Minister for Enterprise, Tourism and Employment; Minister
  for Transport; National Transport Authority; Workplace Relations Commission.
  7 An Coimisiún Toghcháin; Coimisiún na Meán; Data Protection Commission; Environmental Protection Authority; Financial Services
  and Pensions Ombudsman; Irish Human Rights and Equality Commission; Ombudsman; Ombudsman for Children’s Office;
  Ombudsman for the Defence Forces.
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  Artificial Intelligence in Service of Society: Navigating Our Way Forward
  In addition, NSAI has a parallel international role as Ireland’s national member of ISO/IEC, where
  global AI standards are being developed – especially in ISO/IEC JTC 1/SC 42 on Artificial
  Intelligence. Through this channel, NSAI participates in drafting and refining international
  standards on AI terminology, governance, risk management, bias mitigation, quality, robustness
  and lifecycle processes. These ISO standards often feed into or are aligned with the European
  standardisation process. It is worth noting that Ireland holds the Convenorship and Secretariat
  of the ISO Working Group 3 on AI Trustworthiness, positioning the country to play a leading
  role in shaping international standards for safe, ethical and reliable AI and to influence how core
  principles of trustworthiness are operationalised globally.
  Digital Omnibus
  Europe was a ‘first mover’ in the context of AI regulation, which both offers opportunities and
  poses challenges. There has been some discussion as to whether the EU AI Act will generate a
  ‘Brussels Effect’, the phenomenon whereby EU regulation becomes a de facto global standard
  as firms and other jurisdictions adapt to EU rules. However, observers are sceptical that the
  Act will be emulated in the same way as the GDPR, noting that AI is not a single, uniform
  policy problem but a diverse set of technologies and domain-specific risks, making wholesale
  regulatory convergence far less likely (Ebers, 2024). The recently proposed Digital Omnibus
  on AI (European Commission, 2025e), which seeks to amend and fine-tune the EU Artificial
  Intelligence Act, is a critical inflection point, potentially reshaping how and when the regulatory
  ambitions contained in the Act will crystallise, casting further doubt on whether the ‘Brussels
  Effect’ for AI will materialise.
  The Digital Omnibus proposals introduce a significant recalibration of the EU AI Act by
  modifying the timelines for compliance and shifting several obligations to a more conditional,
  standards-based schedule. Instead of fixed dates (the original requirement that most high-risk
  AI obligations apply by August 2026 or, at the latest, August 2027), the Omnibus package links
  the entry into application of many provisions to the availability of harmonised standards or
  common specifications, with ‘long-stop’ deadlines that may extend into late 2027 or even 2028.
  The Digital Omnibus proposals are currently the subject of a public consultation process running
  until March 2026, after which the proposals will enter the EU’s trilogue process involving the
  European Parliament, the Council and the Commission before any measures can be adopted.
  The European Commission argues that these adjustments are necessary to ensure legal
  certainty, reduce administrative burdens, and allow businesses and regulators to prepare
  effectively, given that the required technical standards and EU-level support tools are still
  under development. Indeed, many member states have found it challenging to meet the original
  timelines laid down in the Act, raising concerns that the race to transpose and operationalise
  complex requirements could result in rushed national legislation and uneven implementation,
  each carrying risks of inconsistency, legal uncertainty and diminished regulatory effectiveness.
  However, the proposals have also sparked critical commentary (European Civic Forum, 2025).
  Several observers have noted that major technology firms lobbied intensively for these delays,
  framing compliance as impracticable without extended timelines. This raises concerns about the
  potential influence of powerful industry actors on the EU’s regulatory trajectory and whether
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  such revisions could dilute the original political commitment to strong, timely safeguards for
  fundamental rights and societal oversight in the deployment of advanced AI systems.
  The National Digital and AI Strategy commits Ireland to working with EU partner states
  to advance an ambitious digital simplification agenda and has prioritised this issue for
  Ireland’s 2026 EU Presidency (Department of the Taoiseach, 2026, p.56). At national level,
  this commitment is reflected in a streamlined regulatory approach focused on reducing
  administrative burden through single reporting mechanisms and enhanced co-ordination via the
  Digital Regulators Group.
  Governance v innovation
  The Digital Omnibus initiative is at least in part motivated by the narrative that the EU’s extensive
  regulatory approach to digital technologies, including AI, is causing Europe to fall behind in
  the ‘AI race’. Proponents of this view argue that regulation raises costs, diverts resources and
  slows innovation. This is particularly relevant for SMEs that may be forced out of the market or
  discouraged from entering by the regulatory burden imposed.
  Others dispute this trade-off logic, arguing that regulation is essential for consumer trust,
  provides predictable legal frameworks that reduce uncertainty, thereby promoting investment,
  and can even stimulate innovation by pushing firms toward more efficient, socially beneficial
  technological solutions (Porter, 1991). It has also been argued that Europe’s innovation deficit
  is driven less by regulation and more by structural factors such as fragmented capital markets,
  lower risk-tolerant investment, weaker scaling ecosystems, and under-investment in digital
  infrastructure (Bradford, 2024). Allen (2025) argues that policymakers may be overestimating
  the competitiveness gains from reducing the regulatory burden, while underestimating the
  unintended harms of such action. Rather than seeing regulation as a constraint, in the European
  context it could be seen as a positive differentiator enabling trust, adoption and scale in
  sensitive, high-value use cases (Tournesac et al., 2025).
  5.5 Common Threads in AI Governance
  While approaches vary across jurisdictions, several common themes in relation to AI
  governance have emerged, including the adoption of a risk-based approach and an emphasis on
  trustworthiness and ethical principles, as well as the necessity for human agency and oversight.
  5.5.1 Risk
  The adoption of a risk-based approach involves classifying AI systems into categories with
  varying regulatory burdens associated with each. The EU AI Act classifies AI systems into
  four categories: unacceptable risk systems which are strictly prohibited – e.g. social scoring,
  manipulative subliminal techniques or real-time biometric identification (with limited law
  enforcement exceptions); high risk systems – e.g. critical infrastructure, healthcare, justice,
  which must undergo rigorous risk impact assessment; limited-risk systems such as chatbots
  which carry transparency requirements so that users know they are interacting with AI; and
  minimal or no systems such as translation tools which are largely unregulated but encourage
  adherence to voluntary codes of conduct.
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  Figure 5.2: EU AI Risk-based Approach
  Source: European Parliament, 2021.
  While this framework provides legal clarity and aims at proportionality, a clear, detailed
  methodology for assessing AI risks in concrete situations is lacking (Novelli et al., 2024). The
  notion of risk within the Act is often vaguely articulated, leaving key definitions and thresholds
  open to interpretation (e.g. what constitutes ‘high risk’?). Another challenge with the risk-based
  framing is that classical conceptions of risk typically rely on quantifiable probabilities and
  measurable harms, but AI often introduces deep uncertainty and ‘known unknowns’ (Ebers,
  2025). As previously discussed, frontier AI systems can demonstrate unpredictable emergent
  behaviours, complex interactions with social systems, and harms that may not be foreseeable
  at design time. As a result, a purely probabilistic, quantification-based regulatory lens may
  systematically underestimate or even miss serious but non-quantifiable harms.
  The Act does not call for a risk–benefit analysis, even though ethical evaluation typically requires
  weighing potential harms against potential societal gains rather than considering risks in
  isolation. Instead, the Act focuses almost exclusively on mitigating risks, with little consideration
  of the potential social, economic or scientific benefits of AI deployment. As pointed out by
  Ebers (2025), the lack of such a risk-benefit analysis may lead to opportunity costs as there is
  no balanced appraisal of what might be lost.
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  Many of the harms associated with AI – especially those affecting EU fundamental human
  rights, as protected under the Charter of Fundamental Rights of the EU and explicitly referenced
  throughout the EU AI Act – are poorly suited to a standard risk-based framing. These rights are
  not marginal trade-offs but, in many cases, represent non-negotiable guarantees for individuals.
  Applying tools such as quantification or acceptable risk thresholds runs the risk of obscuring or
  normalising rights violations. International bodies have increasingly embedded human rights at
  the centre of AI governance. The OECD AI Principles (2019) explicitly anchors this approach in
  Principle 1, which calls for AI systems to ‘respect the rule of law, human rights, democratic values
  and diversity’. UNESCO’s Recommendation on the Ethics of Artificial Intelligence (2021) similarly
  grounds AI governance in dignity, fairness and human rights protections.
  Most prominently, the Council of Europe (CoE), consistent with its foundational pillars of
  human rights, democracy and the rule of law, has adopted a rights-first regulatory approach,
  which places the safeguarding of fundamental rights at the core of all stages of AI design,
  development and deployment. The Commissioner for Human Rights in the CoE highlights that
  AI technologies are not only sources of risk but also hold significant potential to promote and
  strengthen human rights – e.g. AI could help identify where individuals are entitled to public
  benefits. This would require AI to be approached through a holistic, human-rights-centred
  lens, rather than one focused narrowly on productivity gains or securitisation (Commissioner
  for Human Rights, 2025). The Ombudsman for Children (2025) has recommended adopting a
  rights-based approach to AI, to ensure that AI systems are designed and governed in ways that
  safeguard the best interests, privacy, dignity and developmental needs of children.
  5.5.2 Trustworthy AI
  Ethical principles play a foundational role in the governance of artificial intelligence, providing
  a normative framework to guide its design, deployment and oversight. By incorporating ethical
  principles into the fabric of AI governance, the ambition is to achieve technologically advanced
  systems that are aligned with democratic values, fundamental rights and the public good. Within
  the EU, seven principles (human agency and oversight, technical robustness and safety, privacy
  and data governance, transparency and explainability, diversity/non-discrimination, fairness,
  and societal and environmental wellbeing) have been formally consolidated into a foundational
  concept of trustworthy AI, which serves as the premise for the EU AI Act. Trustworthy AI is
  conceived as AI that is lawful (complies with existing laws), ethical (upholds fundamental values
  and rights) and robust (secure and reliable in practice). This framing has become a reference
  point for global AI governance.
  The OECD’s Framework for Trustworthy AI in Government provides a structure for how
  governments can ensure their use of AI is trustworthy by focusing on three essential
  pillars: Enablers, Guardrails and Engagement (OECD, 2025a). Key enablers include strong
  data foundations, digital infrastructure, skills, governance and purposeful investment and
  procurement. In relation to guardrails, the OECD stresses the importance of promoting
  transparency and explainability, as well as empowering oversight bodies and having the
  appropriate policy levers in place. Engagement is crucial, with both citizens and social partners
  and users being involved in AI development. There is also an emphasis on collaborating across
  borders.
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  However, trustworthy AI remains a difficult ideal to achieve and there is little concrete guidance
  for how to go about that goal (Laux, Wachter & Mittelstadt, 2023). As pointed out by Ballot
  Jones, Thornton and De Silva (2025), there is a danger that trustworthy AI becomes, in effect,
  a ‘regulatory visibility tactic’, a symbolic label rather than a guarantee of substantive safety,
  fairness and accountability.
  5.5.3 Human Oversight
  Human agency and oversight are consistently emphasised as core principles in global AI
  governance instruments, reflecting the desire that AI should augment rather than replace
  human decision-making. The regulatory frameworks developed by the OECD, UNESCO and
  EU all stress the importance of mechanisms such as human-in-the-loop or human in command
  to ensure that people retain meaningful control over AI systems, particularly in high-stake or
  sensitive contexts.
  Human oversight is a key requirement of the EU AI Act, which mandates that high-risk AI
  systems must be designed and developed so they can be effectively overseen by humans
  during their operation (Article 14). This obligation is grounded in the Act’s overarching goals of
  safeguarding health and safety, ensuring system reliability, and protecting fundamental rights.
  Yet the rationale for human oversight extends well beyond these regulatory imperatives. Across
  domains, oversight serves indispensable governance functions by introducing moral judgment,
  contextual sensitivity and empathy into decision-making processes which would otherwise
  be governed by opaque outputs. It helps to some extent to counteract algorithmic bias and
  anchor accountability in identifiable human or institutional actors, and provides a mechanism for
  aligning AI behaviour with societal values and ethical norms.
  However, achieving meaningful oversight presents substantial challenges, many of which stem
  from the very characteristics that make AI powerful. The opacity and scale of complex machinelearning models can make real-time monitoring or comprehension impracticable in many
  situations. At the same time, human cognitive limitations – including automation bias, vigilance
  decline (difficulty of maintaining attention over time) and reduced moral agency (tendency
  of humans to relinquish their sense of responsibility when interacting with technology) – all
  undermine the assumption that a human ;’in the loop’ will necessarily detect or correct errors
  (Holzinger, Zatloukal & Müller, 2024). Moreover, organisational constraints, such as insufficient
  training and inadequate time for review, can further erode operators’ ability or willingness to
  intervene. Meaningful human oversight is neither automatic nor guaranteed and, therefore, must
  be deliberately designed and institutionally supported to function as an effective governance
  mechanism.
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  5.6 Governance in Practice
  Despite a convergence around trustworthy AI and the ethical principles which underpin it, it is
  less clear how it can be operationalised in practice. Realising abstract values such as fairness,
  accountability and transparency into measurable, verifiable criteria that can withstand regulatory
  and public scrutiny is very challenging. This ‘principle to practice gap’ is a major area of focus as
  adoption of AI tools increases.
  A range of modalities are beginning to emerge to assure governance across the AI life-cycle.
  Regulatory frameworks adopting a risk-based framework require impact assessments depending
  on the potential harms of an AI system, while conformity assessment procedures – e.g. the
  University of Oxford capAI protocol (Floridi et al., 2022) and independent-based ethics auditing
  – provide structured means of validating compliance and ethical alignment with principles. The
  Council of Europe’s HUDERIA methodology (Council of Europe, 2024b) offers a structured
  framework for assessing how AI systems may affect human rights and democracy and offers
  practical tools to identify and address harms.
  Regulatory sandboxes can allow authorities to engage firms to test AI tools that challenge
  existing legal frameworks in a supervised setting (OECD, 2023d). Private and public-sector
  organisations are also employing internal controls tools such as datasheets and scorecards to
  improve accountability and transparency. Ethics committees, the creation of AI accountability
  roles and staff training further reinforce responsible practices. Thus, a multi-layered strategy is
  being adopted, but the effectiveness of such measures will depend on continuous monitoring
  and adaption.
  What is clear is that practical, accessible tools are essential to help practitioners bridge the
  ‘principle to practice’ gap. The UK has developed a series of eight practice-based workbooks
  offering end-to-end guidance on applying ethical principles in public-sector AI projects,
  covering issues from problem formulation and data use to safety, accountability and
  deployment (Alan Turing Institute, 2023). These kinds of grounded, operational tools are critical
  for enabling organisations to move beyond well-intentioned principles and embed ethical and
  safe AI practices in everyday decision-making.
  5.7 New Forms of Governance
  Innovation is difficult to govern because it creates novelty and surprise. The implementation of
  technology into society is a complex and unpredictable endeavour. By the time the full extent
  of risks and unintended consequences of a given innovation is fully appreciated, it has usually
  become embedded in social infrastructures, and at that stage it can be exceptionally difficult to
  change course (O’Sullivan, 2020).
  The development of social media provides an illustrative case in point. Early policy assumptions
  framed social media platforms as neutral intermediaries rather than as powerful socio-technical
  systems capable of reshaping behaviours, markets, information ecosystems and democratic
  processes in systemic ways. Arguably, meaningful regulation arrived only after mass adoption,
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  which meant that governance became reactive and path-dependent. Regulators were forced to
  manage an entrenched status quo shaped by dominant business models, technical architectures
  and user lock-in, rather than to shape the role of platforms in society ex ante.
  The rapid evolution of AI technology presents a significant challenge for effective governance,
  as legal and regulatory frameworks often struggle to keep pace with technological innovation.
  This ‘law lag’ creates a gap in which AI systems may be deployed before adequate safeguards
  are in place, which increases the risk of unintended and/or unexpected consequences at
  both individual and societal levels. In response, academics and policymakers have called for
  new forms of governance, including anticipatory innovation governance and experimental
  governance as a future-oriented approach to navigating uncertainty.
  Experimental governance is an adaptive approach which emphasises iteration, evidence
  gathering and participation to address complex and uncertain challenges (Sabel & Zeitlin, 2012).
  Rather than relying on fixed rules, experimental governance is open to revision and responsive
  to emerging data and stakeholder feedback. In the AI context, one could argue that regulatory
  sandboxes and algorithmic impact assessments are a form of experimental governance, as these
  tools allow governments to trial regulatory approaches, generate evidence on risk and impacts,
  and adjust frameworks as the technology evolves.
  Building on the logic of experimental governance, anticipatory governance is an even more
  developed approach in the AI context, a framing most notably advanced by the OECD. It
  emphasises the need for public institutions to proactively explore emerging futures, identify
  potential risks and opportunities, and adapt policy frameworks before problems fully materialise.
  5.7.1 Governance in Situations of High Uncertainty
  Anticipatory governance (AG) is specifically designed for high-uncertainty environments where
  the timeline, pathways and ultimate societal impacts are difficult to predict. By combining
  foresight, flexible policy design and iterative learning, AG provides institutions with the capacity
  to prepare for, rather than merely react to, rapidly evolving socio-technical landscapes.
  Anticipatory governance addresses uncertainty by stressing the need for a mix of problemsolving and problem-finding approaches, which involves an active and systematic search for
  potential future problems that the technology may raise.
  Anticipatory governance provides a flexible scaffolding for navigating the unknowns inherent
  in AI. By integrating foresight, broad engagement, and continuous learning, it can help
  policymakers prepare for diverse and evolving futures, rather than being constrained by narrow
  predictions or reactive responses. This adaptability makes AG especially well-suited to AI where
  technological trajectories are open-ended and their societal consequences not yet fully visible.
  5.7.2 Anticipatory Governance for AI
  The recent Steering AI’s Future report from the OECD focuses on five interdependent elements
  of the OECD Framework for Anticipatory Governance of Emerging Technologies: guiding values,
  strategic intelligence, stakeholder engagement, agile regulation and international co-operation.
  The five elements function collectively, with each reinforcing the others.
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  Figure 5.3: Five Elements of Anticipatory Governance
  Source: OECD, 2024b.
  Values
  A robust anticipatory governance strategy begins with a shared set of guiding values that
  intentionally shape AI development and deployment. As discussed in earlier sections, there
  is growing international convergence around values frameworks, with alignment across the
  OECD AI Principles, the EU’s AI governance instruments (including the Seven Requirements
  for Trustworthy AI), the Council of Europe’s Framework Convention on AI, and UNESCO’s
  Recommendation on the Ethics of AI. This convergence provides a crucial foundation for global
  interoperability, reducing fragmentation and enabling coherent cross-border governance.
  While the OECD framework operates at a broad policy level and the EU requirements focus on
  operational, implementation-level guidance, both share a commitment to fairness, transparency,
  accountability and human-centred design. An interesting divergence is that the OECD explicitly
  incorporates sustainability, emphasising environmental, social and economic well-being as
  a core objective, whereas the EU principles do not feature sustainability as a standalone
  requirement, instead addressing it only indirectly through risk and impact considerations.
  Several types of tools and processes are being developed in an effort to embed guiding
  values throughout the AI system lifecycle. The OECD.AI Catalogue of Tools & Metrics enables
  practitioners to identify and compare techniques to operationalise fairness, explainability,
  robustness and other principles, while deliberative processes, including public dialogues and
  multi-stakeholder roundtables, can help to elucidate societal values, identify red lines and
  surface concerns about emerging AI capabilities.
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  Strategic intelligence
  Strategic intelligence provides the ‘early warning system’ necessary for anticipatory governance.
  Because AI evolves rapidly and in unpredictable ways, governments require mechanisms that
  capture weak signals, synthesise expert insight and illuminate plausible medium- and long-term
  trajectories of the technology.
  Foresight methodologies such as scenario building, horizon scanning, Delphi surveys
  and backcasting⁸ enable policymakers to explore divergent futures, challenge prevailing
  assumptions, and prepare for high-impact uncertainties. The OECD.AI Expert Group on AI
  Futures has used foresight methods to map possible AI trajectories, identifying numerous
  benefits, risks and policy options relevant to governments. Akin to the public health approach
  to infectious disease, sentinel and real-time monitoring of AI can identify weak signals such
  as patterns of misuse, failure modes and systemic vulnerabilities, which may indicate future
  governance challenges.
  Stakeholder engagement
  Stakeholder engagement is indispensable for anticipatory governance because AI systems
  affect diverse communities, rely on public trust, and raise normative questions that cannot be
  resolved by experts alone. Engagement processes broaden understanding, surface blindspots
  and can promote legitimacy. A comprehensive approach to engagement involves civil society
  organisation, industry and technical experts, academia, public-sector actors and general publics,
  whose perspectives are essential for shaping values and expectations.
  Several forms of engagement are being used in AG, including informative engagement –
  for example, explainers and transparent communication of risks and system behaviours.
  Consultative engagement includes surveys, targeted interviews and public consultations,
  which can be useful in collecting views on proposed regulation. Collaborative engagement, the
  most demanding and potentially most rewarding, is where stakeholders co-design governance
  tools, participate in deliberative assemblies or citizens’ juries, and contribute to community red
  teaming⁹ or participatory audits. As previously discussed, AI itself can be leveraged to enhance
  engagement by enabling citizen participation in policymaking and processing consultation data.
  Participation-washing, where the appearance of engagement can mask predetermined agendas
  and sideline community interests, poses a risk in public discussions on AI. An analysis of national
  AI strategies reveals a persistent gap between governments’ rhetoric of public involvement
  and the absence of concrete mechanisms to secure meaningful input (Wilson, 2021). As
  Wilson (2021) argues, private-sector values like efficiency and competitiveness often eclipse
  democratic commitments to equity, deliberation and accountability. Governance frameworks
  should embed genuine, inclusive participation and ensure that AI policy development is
  grounded in public interest values rather than performative consultation.
  8 Backcasting is a strategic foresight method which starts with a desired future outcome and works backward to identify the steps,
  decisions and interventions needed to reach that future from the present.
  9 Red teaming is a structured, adversarial testing exercise designed to identify vulnerabilities, potential harms and failure modes in an
  AI system before it is widely deployed.
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  Agile governance
  Given AI’s rapid evolution, governance systems must remain adaptable, iterative and capable of
  learning through experimentation. Agile governance complements anticipatory governance by
  enabling policy innovation alongside technological innovation. Agile governance also requires
  integrating good practice ‘by design’, such as safety-by-design, privacy-by-design and ethicsby-design. Standards and shared risk-management frameworks provide predictable structures
  that promote interoperability while supporting rapid adaptation.
  Table 5.1: Anticipatory Innovation in Policymaking
  Source: OECD, 2024c.
  Regulatory sandboxes allow developers and regulators to test innovations in controlled
  environments. They provide temporary adjustments or exemptions from certain rules, enabling
  regulators to observe real-world risks and gather evidence for longer-term policymaking.
  Norway’s Regulatory Sandbox for Responsible Artificial Intelligence and privacy has enabled
  firms to experiment with privacy-preserving machine learning systems while regulators observe
  risks and identify areas requiring legal clarification or policy reform. The sandbox has produced
  actionable insights on data minimisation, transparency practices and novel approaches to
  safeguarding rights.
  The Digital & AI Strategy 2030 positions Ireland as a trusted, agile and forward-looking digital
  regulatory hub, and has committed to the establishment of a national AI regulatory sandbox by
  the AI Office in 2026 (Department of the Taoiseach, 2026).
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  Table 5.2: Benefits and Challenges of Regulatory Sandboxes
  Source: OECD, 2025d.
  International co-operation
  International co-operation is fundamental to anticipatory governance, enabling interoperability,
  pooling of expertise and co-ordinated responses to shared risks. The transboundary nature of
  AI means that no single country can govern it effectively alone. While Ireland operates within
  a wider European regulatory framework, it is also essential to remain cognisant of and work
  collaboratively with other countries and their distinct regulatory systems.
  International co-operation avoids regulatory fragmentation, as without alignment developers
  or deployers of the technology could engage in ‘ethics shopping’, by choosing the least
  restrictive jurisdiction in which to operate. It also recognises that issues such as cyber-security
  vulnerabilities or harms arising from global deployment require co-ordinated solutions. Moreover,
  by involving countries with diverse capacities, it prevents governance architectures from being
  shaped solely by technologically dominant actors.
  Effective co-operation requires a multilayered approach, and Ireland is well positioned in this
  regard thanks to its expert and engaged participation in working groups, standard-setting
  processes and wider AI initiatives across the European Commission, Council of Europe, OECD
  and ISO, while also ensuring it continues to build capacity in strategic intelligence and related
  capabilities.
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  5.7.3 Importance of Monitoring and Evaluation
  Monitoring and evaluation must be embedded throughout the entire AI lifecycle rather than
  be treated as activities that begin and end at the point of deployment. Because AI systems are
  dynamic, context-dependent and capable of behaving unpredictably in real-world environments,
  ongoing assessment is essential to ensure safety, effectiveness and alignment with societal
  values.
  Traditional evaluation models are insufficient for fast-moving technologies whose impacts
  unfold over time. Developmental and real-time evaluation support iterative learning and allow
  policymakers to revisit assumptions, adjust strategies and refine interventions as conditions
  change. Rather than relying on retrospective, end-stage assessments, anticipatory governance
  requires continuous feedback loops across development, testing, deployment and operation.
  Such feedback loops ensure that real-world evidence informs the evolution of policies, system
  design and implementation strategies. Multidimensional evaluation spanning social (e.g. access
  to services and inclusion, impacts on employment), environmental (e.g. energy consumption,
  water and land use) and economic impacts (e.g. productivity gains, impacts on regional and
  sectoral development) ensures that governance systems capture the full range of outcomes
  rather than relying solely on technical metrics such as accuracy, speed and cost-efficiency. By
  integrating these dimensions into monitoring and evaluation frameworks, public bodies can
  better understand how AI systems affect society as a whole, not just how well they function
  technically.
  As previously described, AI systems frequently behave differently in controlled testing
  environments compared with real-world settings, where data quality, user behaviour, operational
  pressures and contextual variation introduce complexities that cannot be fully simulated
  in advance. This makes ongoing monitoring essential to detect performance degradation,
  biases, emergent risks and unintended consequences. The Epic Sepsis Prediction tool serves
  to illustrates this point (Patient-Safety-Learning, 2024). Although it demonstrated strong
  performance and high accuracy during internal testing, real-world deployment revealed a
  significant gap; the tool failed to identify two-thirds of sepsis cases when first implemented in
  a hospital setting. A recently published randomised study found that, although LLMs performed
  very well when tested on complete clinical cases (correctly identifying relevant conditions in
  ~95 per cent of cases), lay users interacting with the same models identified relevant conditions
  in fewer than 35 per cent of cases (Bean et al., 2026). People using LLMs were no better than
  those relying on standard internet searches at identifying important conditions or judging
  how urgently care was needed. The performance drop was largely driven by communication
  failures, as users often provided incomplete information, misunderstood or ignored advice
  from the LLM, or struggled to interpret mixed or inconsistent suggestions from the LLM. This
  mismatch between laboratory performance and ‘in the wild’ behaviour, sometimes referred to
  as the ‘evaluation gap’, highlights the critical need for continuous monitoring, post-deployment
  evaluation and system recalibration to ensure clinical safety and reliability. Similar patterns have
  emerged in sectors such as education, where optimistic performance claims of AI systems
  have not yet translated into consistent improvements in student learning outcomes at scale
  (Fengchun et al., 2021; Bauer, 2025). This reinforces why early-stage and ongoing evaluation
  should be considered foundational to responsible anticipatory AI governance.
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  Chapter 6: AI Literacy
  6.1 Introduction
  This chapter examines the growing importance of AI literacy as a foundational competency for
  participating in an increasingly AI-mediated society. It explores how AI literacy has evolved from
  a niche technical skill to a civic, educational and organisational necessity. It outlines the key
  components of AI literacy, traces its development across established conceptual frameworks,
  and surveys how governments, educational institutions, businesses and the wider public are
  cultivating the knowledge, skills and critical capacities needed to engage with AI responsibly and
  effectively.
  6.2 The Imperative for AI Literacy
  The increasing integration of artificial intelligence across economic, social and civic domains
  has rendered AI literacy an increasingly indispensable competency for meaningful participation
  in contemporary society. As AI systems increasingly mediate decisions in healthcare, finance,
  education and the public sector, AI literacy has become a civic, strategic and economic
  necessity. The capacity of individuals and institutions to understand, use and evaluate AI has
  become key to realising the opportunities the technology offers.
  Figure 6.1: Key Benefits of AI Literacy
  Source: Gartner, 2025c.
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  Ireland’s National Digital and AI Strategy (2026) positions artificial intelligence as a
  central enabler of digital transformation, public service reform and sustainable economic
  competitiveness within an integrated national digital policy framework. Yet as AI technologies
  evolve at remarkable speed, the gap in understanding among professionals and the public risks
  widening, threatening both engagement and responsible adoption. This concern is echoed
  at the European level through the EU AI Act (European Union, 2024), which makes explicit
  in Article 4 of the Regulation the requirement for a ‘sufficient level of AI literacy’ among all
  staff involved in providing or deploying AI systems. The Act recognises that ethical and safe
  implementation of AI cannot occur without the human capacity to interpret, challenge and
  govern these systems responsibly.
  Global economic and workforce trends also underscore the urgency of fostering AI literacy. The
  World Economic Forum’s Future of Jobs report (2025a) anticipates that 44 per cent of workers’
  core skills will be disrupted by technological change by 2030, with AI playing a leading role. In
  this context, AI literacy is not ‘a nice-to-have’ but rather should be considered a foundational
  skill to navigate the digital world, access opportunity and participate in the shaping of the future
  of AI.
  6.3 What is AI Literacy?
  AI literacy refers to the foundational knowledge, skills and dispositions required to understand,
  interact with, evaluate and use AI systems responsibly and effectively. The concept builds on
  earlier literacies, particularly data literacy and digital literacy, yet extends beyond them in both
  scope and purpose. While related literacies form the foundation for AI literacy, data literacy
  fosters the ability to interpret and reason with data. It enables individuals to use computational
  devices, but AI literacy emphasises a functional and critical understanding of AI’s mechanisms
  and implications (Chiu, 2025). This involves knowing how AI works, what it can and cannot do,
  and how to use it responsibly.
  Kandlhofer and colleagues (2016), were among the first to formalise the term, defining AI
  literacy as a set of competencies that enable individuals to know, understand and use AI
  technologies. Long and Magerko (2020) later expanded this definition, framing AI literacy as ‘a
  set of competencies that enables individuals to evaluate AI technologies critically; communicate
  and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace’.
  Long and Magerko’s (2020) framework remains one of the most comprehensive early
  conceptualisations of AI literacy. The authors identify 17 specific competencies necessary for
  AI literacy by reference to five guiding questions: What is AI? What can AI do? How does AI
  work? How should AI be used? How do people perceive AI? The questions serve as a thematic
  framework for exploring what individuals need to know, be able to do, and critically reflect
  upon to participate meaningfully in an AI-driven world. The 17 competencies span technical,
  conceptual, social and ethical dimensions, from recognising and understanding AI systems
  to appreciating their social implications. The study positions AI literacy as a multidimensional
  construct that integrates technical understanding with ethical reasoning and social awareness.
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  Ng, Leung, Chu and Shen (2021) expand on this conceptual groundwork by proposing a
  structured framework for AI literacy that links cognitive development with ethical understanding.
  They identify four dimensions: know and understand, use and apply, evaluate and create, and
  ethical issues. The authors explicitly align these with Bloom’s Taxonomy (Bloom et al., 1956,
  pp.1103–1133), a cognitive model of learning that describes progression from foundational to
  higher order thinking.
  Figure 6.2: Bloom’s Taxonomy and AI Literacy
  Source: Ng et al., 2021.
  Their framework illustrates how AI literacy involves not only the acquisition of knowledge but
  also the capacity to analyse, evaluate and act responsibly in relation to AI technologies. The
  authors further proposed three inter-related components – conceptual, practical and ethical –
  that provide a basis for curriculum design and policy development.
  Extending the focus beyond formal education, Chee, Ahn and Lee (2024) frame AI literacy as
  a lifelong and cross-sectoral capability. They argue that AI literacy must be understood as a
  competence relevant to all groups in society, each requiring different levels of engagement
  and cognitive complexity. Education may focus on awareness and responsible use, while
  professional and policy domains require more advanced analytical, evaluative and ethical
  capacities.
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  Figure 6.3: Pathway for Educating Competencies for AI Literacy
  Source: Chee, Ahn and Lee, 2024.
  These frameworks collectively reflect a growing global consensus. AI literacy is more than
  technical fluency; it is a structured, developmental capability that moves from knowledge to
  application to critique and creative engagement.
  6.4 AI Literacy Across the Life Course
  AI literacy is not a monolithic competency but a differentiated set of capabilities that spans a
  continuum from foundational awareness to advanced technical proficiency, calibrated to the
  specific requirements of diverse audiences and contexts. It is also a lifelong learning activity,
  requiring continuous opportunities for people to develop and update their understanding so
  that they can engage constructively and ethically with AI as the technology evolves. The Digital
  & AI Strategy 2030 frames AI literacy as a form of critical, ethical and interpretive competence
  for citizens, learners and businesses, and contains actions to support AI literacy through
  targeted awareness campaigns for SMEs, curriculum and teacher guidance in education, and
  national initiatives to strengthen basic digital, media and AI literacy across the life course
  (Department of the Taoiseach, 2026).
  6.4.1 Primary, Secondary & Tertiary Education
  Children and adolescents are growing up immersed in AI-mediated environments, often
  interacting with recommendation algorithms, chat bots or generative AI systems long before
  they understand how these tools work. The OECD (2026) estimates that student use of
  generative AI ranges from about 8 per cent in primary education to 70–90 per cent in upper
  secondary and over 86 per cent in higher education, while around 36 per cent of lower
  secondary teachers on average report using AI tools, mainly for lesson planning, assessment
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  support and resource design. Embedding AI literacy into primary and secondary education is
  therefore vital, not only to equip students for future work but also to enable them to become
  informed, ethical digital citizens. Higher education institutions play a dual role in AI literacy,
  preparing students for AI-driven careers and equipping them to think critically about AI societal
  impacts. Students are preparing for a rapidly evolving labour market shaped by automation,
  algorithmic decision making and digital transformation.
  The OECD Digital Education Outlook 2026 highlights that generative AI offers substantial
  benefits for personalised learning, teaching productivity and system efficiency, but it also warns
  that poorly implemented systems can amplify inequities, weaken pedagogy and undermine
  professional judgment (OECD, 2026). An important finding is that learning gains from
  generative AI are not evenly distributed; large-scale trials show stronger effects for students
  with higher prior attainment and higher socio-economic status, indicating that without careful
  design and targeted support, generative AI risks widening rather than narrowing existing
  educational gaps. The report indicates that many students are using chatbots to generate
  complete answers, which can shortcut cognitive effort and reduce deep learning, increasing
  the likelihood of surface-level engagement rather than conceptual understanding. In contrast,
  the clear educational advantage of fine-tuned, purpose-built systems co-created with teachers
  and students – which can be aligned to curricula, restrict direct answer-giving and embed
  scaffolding and socratic questioning – is highlighted. On that basis, the OECD recommends
  a shift away from general-purpose chatbots toward rigorously governed, pedagogy-first
  generative AI tools, strengthened AI literacy for teachers and learners, and robust public
  oversight. This closely aligns with the Irish Children’s Rights Alliance’s (2025) call for Government
  to systematically review and monitor EdTech applications for compliance with children’s safety,
  learning and wellbeing across all educational environments.
  International frameworks
  UNESCO’s Guidance for generative AI in education and research (UNESCO, 2023a) sets out a
  policy framework for the ethical and responsible integration of AI technologies into teaching,
  learning and academic inquiry. It emphasises that generative AI should enhance human
  creativity and critical thinking rather than replace them, and it calls on governments to develop
  national regulations, teacher training programmes and institutional policies to ensure safe and
  equitable use of AI. Notably, UNESCO recommends a minimum age threshold of 13 years for
  the independent use of generative AI tools by students, aligning with international standards
  for digital consent and data protection. This safeguard, the organisation argues, is essential to
  protect learners’ rights, privacy and cognitive development in the face of rapidly evolving AI
  systems.
  In 2024, UNESCO introduced the AI Competency Framework for Students (UNESCO, 2024a), a
  global initiative designed to equip learners to be both responsible users and active co-creators
  of AI. The framework provides a human-centred, ethics-first roadmap structured around four
  competency aspects (Table 6.1). Together these competency blocks outline a comprehensive
  model for cultivating not only technical proficiency, but also for ethical critical and reflective
  capacities needed to shape AI for the public good.
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  Table 6.1: AI Competency Framework for Students
  Source: UNESCO, 2024a.
  Complementing the student framework, UNESCO’s AI Competency Framework for Teachers
  (UNESCO, 2024b) outlines the knowledge, pedagogical strategies and ethical principles
  teachers require to integrate AI safety and meaningfully into classrooms. The framework
  emphasises three core dimensions: fostering teachers’ AI literacy and critical understanding
  of generative tools, equipping them to guide students’ responsible engagement with AI, and
  enabling them to use AI to enhance inclusion, assessment and creativity in teaching practice.
  In May 2025 the OECD (2025e) in conjunction with the European Commission published a
  draft AI Literacy Framework for Primary and Secondary Education (AILit Framework) for public
  consultation. The finalised framework will be published in 2026. It emphasises that AI literacy
  is not solely technical but civic, ethical and creative. It recommends that AI literacy become
  a foundational competence in primary and secondary curricula, calls for the development
  of teacher training and professional learning pathways in AI pedagogy, and encourages
  and investment in high-quality, age-appropriate resources and open learning materials. The
  framework further recommends national co-ordination mechanisms to ensure coherence
  between education, technology and data governance policies, and calls for the involvement
  of students, teachers and wider communities in co-designing AI learning experiences that are
  inclusive, equitable and relevant.
  Building on earlier international efforts, including the UNECSO work, the AILit Framework
  identifies four interrelated domains (engaging with AI, creating with AI, managing AI, and
  designing AI) that describe the diverse ways learners engage with AI, encompassing 22
  competencies in total. It recognises that learners may develop proficiency across these domains
  to varying degrees without necessarily achieving full mastery in any single one. Within the
  framework, knowledge, skills, and attitudes operate as the core building blocks that structure
  each competence. They ensure that learning addresses conceptual understanding, practical
  capability and ethical awareness in equal measure. Together, these elements enable learners
  to engage with AI confidently and responsibly as technologies and contexts evolve, as they
  invariably will.
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  Figure 6.4: Dimensions of AI Literacy
  Source: OECD, 2025e.
  The EU is advancing a comprehensive agenda on AI and education, with a strong emphasis
  on AI literacy as a cornerstone of digital readiness. The Digital Education Action Plan 2021–
  2027 highlights the need for both learners and educators to develop critical digital and AI
  competences, supported by investments in infrastructure, teacher training and research
  (European Commission , 2020a). The EU-funded Artificial Intelligence for and by Teachers
  (AI4T) project aims to strengthen AI literacy among secondary school teachers by helping
  them understand core AI concepts, ethical considerations and practical classroom applications.
  Central to the project is a ‘Massive Open Online Course’ and an open textbook that provide
  accessible training on both teaching about AI and teaching with AI (Ai4t.eu, 2023). The project
  also includes school-based experimentation and evaluation to understand how teachers
  engage with and apply AI tools in practice. Ireland was one of the five participating countries,
  contributing to the project’s cross-national piloting and insights on effective teacher learning.
  The Commission’s 2030 Roadmap on the Future of Digital Education and Skills, expected in
  2026, is set to strengthen efforts to ensure equal access to AI-enhanced learning and to embed
  AI literacy across education systems.
  Knowledge
  Skills
  Attitudes
  The knowledge statements in the framework focus
  on conceptual knowledge, outlining the technical and
  societal understandings that learners need to apply
  and engage with AI systems. These concepts include
  how AI processes data, how AI differs from human
  thinking, and how bias can emerge in AI systems.
  The skills demonstrate how fundamental
  abilities, such as critical thinking, creativity, and
  computational thinking, apply in an AI context. They
  guide learners in using AI effectively and ethically,
  their lives.
  prepare learners to engage with AI, not only with
  technical skills, but also with an awareness of AI’s
  impact on themselves and others. These include
  a sense of curiosity and adaptability in using AI
  systems, as well as a readiness to question outputs
  and a commitment to using AI responsibly.
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  National initiatives
  At the national level, many governments are taking steps to incorporate AI literacy into formal
  education frameworks. UNESCO’s 2022 global survey on K-12 AI curricula (UNESCO, 2022)
  found that only 11 countries had developed and officially endorsed AI programmes for primary
  and secondary education, with a further four in development. The report concluded that, while
  global awareness of the importance of AI literacy was growing, formal curriculum integration
  remained limited and uneven. More recent research (Yeter, Yang & Sturgess, 2024; Edwards,
  2025) shows that the integration of AI literacy into primary and secondary education is gaining
  momentum but remains uneven across countries. China and the United Arab Emirates have
  made AI a mandatory component of their national computing curricula from early grades
  onward, while Portugal, Singapore and New Zealand have integrated computational thinking,
  robotics and AI fundamentals across primary and secondary education. South Korea has
  introduced basic AI principles and ethics into primary school curricula and elective courses at
  second level.
  Universities are developing programmes to support faculty, staff and students, often with
  an interdisciplinary focus. Many institutions are experimenting with AI-across-the-curriculum
  approaches, where students in non-technical disciplines learn to use AI tools critically for
  analysis, drafting and design, while technical students are exposed to ethical, legal and social
  implications. A 2024 study of university students in the US, UK and Germany identified
  three distinct groups based on their AI-related cognitive and behavioural traits. These were
  AI advocates (exhibiting a high level of AI literacy, interest and positive attitudes to the
  technology), cautious critics (low levels of AI literacy coupled with negative attitudes towards
  AI, and pragmatic observers (representing an intermediate group with moderate AI literacy
  and agnostic views towards the technology (Bewersdorff et al., 2024). This suggests that
  educational strategies need to go beyond teaching technical concepts and need to foster AI
  literacy and interest to build students’ confidence. This is especially important from a labour
  market perspective as AI literacy needs to be understood as part of a broader digital skills
  portfolio, thereby future-proofing graduate careers.
  National approaches vary but share an emphasis on making complex AI concepts accessible
  through hands-on, engaging, and ethical learning experiences. Teachers have been identified
  as the key agents of change in developing AI literacy across educational systems. Thus,
  engendering understanding, confidence and pedagogical capacity to integrate AI meaningfully
  are pivotal to ensuring equitable and ethical student engagement with AI (UNESCO, 2024b;
  OECD, 2025e). In that context it is worth noting that the speed of adoption of AI in the
  education sector has outpaced the upskilling of educators, many of whom report low AI literacy
  and uncertainty about how to apply these tools ethically and effectively (UNESCO, 2023a).
  A persistent weakness in many initiatives is the lack of rigorous assessment frameworks. Few
  programmes systematically measure what students learn about AI, making it difficult to evaluate
  the depth of understanding or the long-term impact of AI literacy interventions (Lorena Casal
  Otero et al., 2023).
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  Ireland
  In Ireland, substantial work is underway to shape policy and practice around the integration of
  AI in education. Efforts span national strategy, sectoral guidance and institutional initiatives.
  The publication of the Department of Education and Youth’s (2025) Guidance on Artificial
  Intelligence in Schools is an important step within this international landscape. The guidance
  emphasises safe, ethical and appropriate AI use that supports rather than replaces teachers,
  prioritising student wellbeing and learning outcomes. Importantly, it situates AI literacy not as
  a standalone subject but as a transversal competency to be embedded across curricula. The
  Irish AI Advisory Council statement on education reinforces this approach, characterising AI
  literacy as a civic skill like reading or critical thinking rather than purely technical competency (AI
  Advisory Council, 2025a).
  This framing situates AI literacy within broader educational objectives of fostering informed,
  engaged citizenship. The pilot phase of the ADAPT Centre’s AI Literacy in the Classroom
  initiative, supported by Google and launched in 2024, involved over 340 teachers. Evaluation
  data from ADAPT itself shows that 96 per cent of teachers reported improved ability to explain
  AI concepts, while 92 per cent felt more confident discussing AI with students. Building on
  this, the programme plans to expand and aims to train a further 500 teachers, with targeted
  pilots in DEIS schools (Irish Tech News, 2025). This effort is supported by a wider ecosystem
  of resources, included those curated by Oide, the support service for teachers funded by the
  Department of Education and Youth.
  The Higher Education Authority (HEA) has developed a sector-wide resource portal titled
  Artificial Intelligence in Irish Higher Education, which offers institutional guidelines, open
  educational resources and policy materials to help staff and students build foundational AI
  literacy, critically covering both ethical/critical awareness and practical engagement with AI
  tools. The National Forum for the Enhancement of Teaching and Learning in Higher Education
  has issued Ten Considerations for Generative Artificial Intelligence Adoption in Irish Higher
  Education, offering practical and ethical guidance for institutions (Higher Education Authority,
  2025b). Research commissioned by the HEA stresses that strengthening AI literacy across the
  sector is essential for a coherent and ethical response to AI. It recommends equipping both
  students and staff with not only practical skills for using AI tools, but also the critical capacity
  to understand their limits, risks and implications for academic integrity and learning. The report
  highlights the need for professional development, updated assessment practices and sectorwide co-ordination to ensure that AI literacy becomes a foundational competence within Irish
  higher education (O’Sullivan et al., 2025). Irish higher education institutions (HEIs) have also
  developed academic supports as well as modules designed to integrate AI literacy across
  diverse disciplines.
  In February 2026, a new suite of Further Education and Training (FET) micro-qualifications in AI,
  developed with Microsoft, were launched to help upskill citizens and businesses in emerging
  AI technologies, covering topics such as machine learning basics, ethical AI and data analysis.
  These accredited short courses will be delivered through the network of 16 Education & Training
  Boards nationwide to help address critical AI skills gaps and strengthen digital capability across
  the workforce.
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  While these initiatives are welcome and valuable, they tend to focus on specific skills and
  use cases, rather than adopting a holistic AI literacy framework that recognises the need for
  a differentiated range of capabilities, spanning a continuum from foundational awareness to
  critical understanding and engagement.
  6.4.2 Employees & Organisations
  In the corporate sphere, AI literacy has transitioned from a niche technical skill to a core
  business competency. This shift is being driven by a desire to leverage AI for a competitive
  advantage as well as the legal obligation to ensure its responsible deployment. AI literacy is
  increasingly being recognised as a requirement across all levels of an organisation to adopt
  workflows, use AI tools effectively, interpret AI outputs and maintain critical oversight. Roles
  such as AI champions, AI governance and AI risk functions are becoming more common in
  organisations in order to lead adoption, tailor training and ensure compliance.
  EU AI Act
  As previously, mentioned, Article 4 of the EU AI Act mandates that providers and deployers of
  AI systems ensure their staff have a ‘sufficient level of AI literacy’. Under Article 4, this obligation
  falls on providers and developers of AI systems, while in the proposed Digital Omnibus on AI, it
  is the responsibility of member states to ‘encourage’ providers and deployers of AI systems to
  provide AI literacy (European Commission, 2025e).
  The specific level and nature of literacy required are not prescribed, leaving flexibility for
  organisations to tailor their approach based on staff knowledge and on the specific application
  of the AI system in question. To support organisations in meeting their obligations under Article
  4, the EU AI office has established a living repository of best practices in AI literacy. It is notable
  that most examples are drawn from larger organisations and relatively few examples from smallto-medium enterprises, potentially reflecting the lower levels of adoption of AI in this sector.
  Training
  Internationally, a diverse market of AI training has emerged to meet the demands of businesses
  and public bodies. Offerings range from compliance focused e-learning modules to strategic,
  non-technical diplomas for business leaders and specialised workshops for senior public
  servants, ensuring that the current workforce can navigate AI’s operational, ethical and legal
  dimensions.
  In Ireland, a diverse ecosystem of corporate training has also emerged. This includes CeADAR’s
  free AI for You: An introduction to AI and the EU AI Act course, developed in partnership with
  the Department of Enterprise, Trade and Employment to demystify the regulation for Irish SMEs.
  The UCD Professional Academy offers a Diploma in AI and Business to prepare organisations
  to integrate AI technology, taking account of people and processes. For the public sector, the
  Institution of Public Administration offers a one-day AI masterclass for senior leaders and a
  practical workshop on implementing the AI guidelines for operational staff.
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  Senior leaders
  Recent literature characterises AI literacy as an increasingly important competence for senior
  organisational leaders. Studies note that, as AI systems become embedded in core business
  processes, senior executives are more frequently required to engage with decisions that involve
  algorithmic outputs, data-driven insights and automated processes. As AI-related decisions
  can influence areas such as operational performance, compliance and reputational resilience,
  senior leaders are often expected to understand the strategic implications of AI, its potential
  contributions to growth and efficiency, and the limitations that may affect its reliability or
  suitability for specific applications. While detailed technical expertise is not necessarily required,
  insufficient executive understanding can contribute to fragmented AI initiatives, misaligned
  investments and governance gaps. Crucially, senior leaders also play an important role in shaping
  cultural norms, setting expectations around responsible AI use, and communicating strategic
  priorities. Their engagement is associated with clearer decision-making processes, improved
  alignment across business units, and more consistent application of safeguards during AI
  deployment.
  The OECD stresses the necessity for leadership knowledge about data inputs, model behaviour
  and system reliability (OECD, 2025a). The governance domain incorporates executive
  responsibility for risk management, regulatory compliance, ethical standards and accountability.
  Likewise, the European Commission guidance emphasises the need for leaders to ensure
  systems are transparent, traceable and deployed in accordance with legal and organisational
  requirements (European Commission, 2019).
  Many governments and organisations have implemented structured initiatives to strengthen
  executive AI literacy. Singapore mandates AI literacy training for all civil servants, with dedicated
  executive-level modules developed by the Smart Nation and Digital Government Group.
  These modules focus on strategic, governance and assurance aspects of AI, reflecting the
  country’s public-sector governance framework (Smart Nation Singapore, 2020). Telefónica
  has introduced a Responsible AI Culture Plan that incorporates role-specific AI governance
  training for board members and establishes a Responsible AI Champions Network to promote
  governance consistency and strategic alignment across leadership levels (UNESCO, 2024c).
  6.4.3 Public
  Artificial intelligence is transforming public life. Algorithmic systems now mediate access to
  credit, welfare, information and even justice. For this reason, AI literacy is now a civic necessity,
  empowering individuals to understand and question the technologies that shape their lives.
  In October 2025, OpenAI CEO Sam Altman announced that ChatGPT had 800 million weekly
  active users, although only a small fraction (around 5%) are paid subscribers. Interestingly,
  most AI interactions today are personal rather than professional; about 70 per cent of ChatGPT
  use focuses on non-work activities such as advice-seeking, entertainment and self-reflection.
  Across studies, six broad use categories have emerged: content creation and editing; technical
  assistance; personal and professional support; learning and education; creativity and recreation;
  and research and decision-making. Notably, therapeutic, companionship and life-organisation
  uses are rapidly becoming prominent, indicating a shift from productivity-oriented applications
  toward emotional and existential support (Chatterji et al., 2025). Evidence on the psychological
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  effects of companion chatbots is mixed; however, some early research suggests they may
  contribute to loneliness and reduced social interaction for frequent users (Bengio et al., 2026).
  Despite the extraordinary number of people using AI tools, recent surveys reveal substantial
  gaps in public AI understanding, alongside complex, sometimes contradictory attitudes.
  According to the IPSOS AI Monitor 2025 survey, 65 per cent of Irish participants said they had
  a good understanding of what AI is (slightly below the 30-country average of 67%). However,
  when asked whether they knew which products and services use AI, only 43 per cent of Irish
  respondents said yes, compared with a 30-country average of 52 per cent (Carmichael, 2025).
  Similar findings emerge in the Attitudes and Use of Artificial Intelligence: A Global Study 2025
  (Gillespie, et al., 2025). The survey reports that, while 52 per cent of Irish respondents feel
  confident using AI tools effectively, a much smaller share (38%) believe they have the skills
  and knowledge to use AI appropriately, highlighting a gap between perceived ease of use and
  deeper understanding. This gap is reinforced by the fact that only 32 per cent have received any
  form of AI-related training, whether formal or informal. The report concludes that, globally, high
  levels of adoption are coupled with low levels of AI training and literacy, and that while people
  may find AI intuitive to use, this does not necessarily translate into knowledge about where and
  how AI systems are being deployed.
  Survey data also reveal additional dimensions of public understanding and acceptance, including
  notable gender divides in AI attitudes and usage patterns. Research demonstrates that males
  report higher AI usage, more positive attitudes and less concern about AI chat bots compared
  to females, who have more concerns regarding transparency and fairness in relation to the
  technology; these disparities have implications for inclusive AI literacy programme design.
  A recent study involving Irish young people aged 13–17 examined their understanding, use
  and confidence in engaging with AI, with the aim of informing education and policy on AI
  literacy (Ombudsman for Children Office, 2025). Young people reported using AI regularly for
  schoolwork, fact-checking, creative projects, entertainment and, in some cases, advice on
  health and wellbeing. Although they expressed confidence in using AI, they also highlighted
  risks, including misinformation, bias, over-reliance and inadequate safeguards for younger users.
  The recommendations made by the Ombudsman Office emphasise the need for structured AI
  and digital-health literacy education in schools, clearer guidance for safe and age-appropriate
  use, better support for parents and educators, and stronger transparency and safety measures
  when AI provides health-related information.
  The rationale for public AI literacy operates at multiple levels. At the individual level, AI literacy
  enables informed decision-making on AI mediated services, products and interactions. At the
  civic level, AI literacy facilitates meaningful participation in policy debates, regulatory processes
  and value alignment discussions about AI development and deployment. At the societal level,
  widespread AI literacy represents a precondition for democratic governance of AI technologies.
  Simply raising AI literacy does not necessarily guarantee higher adoption or receptivity to
  the technology. A recent multi-study investigation challenges the common assumption that
  increasing AI literacy will naturally enhance public receptivity to AI technologies (Tully, Chiara
  Longoni & Appel, 2025). The authors found that individuals with lower AI literacy consistently
  report higher openness, usage and positive attitudes towards AI. The research suggests that
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  lower-literacy users may rely on ‘magical’ or overly optimistic perceptions of AI, whereas higherliteracy individuals tend to hold more calibrated, and sometimes more cautious, views of AI’s
  capabilities and limitations. These findings indicate that, while AI literacy remains essential
  for informed engagement, it should not be treated as a straightforward lever for increasing
  adoption. Effective public AI literacy must address knowledge gaps but also perceptions,
  expectations and trust in AI-enabled systems.
  Within the EU, the Digital Competence Framework for Citizens (DigComp 3.0), while not strictly
  an AI-literacy programme, provides foundational digital and data literacy competencies that
  underpin citizens’ ability to critically engage with AI-enabled technologies by systematic and
  transversal integration of AI across the framework (Cosgrove & Cachia, 2025) The University
  of Helsinki’s Elements of AI programme represents a pioneering initiative in mass public AI
  education. Launched in Finland, the programme has been translated into over thirty languages
  and has reached around 1 per cent of European Union citizens through a free, accessible online
  course designed for non-technical audiences. The programme’s success demonstrates the
  feasibility of large-scale public education and provides a model for similar initiatives. Australia’s
  AI for All government initiative explicitly targets the general public, recognising that AI literacy
  should not remain confined to professional or educational contexts. The programme emphasises
  accessible, practical understanding tailored to everyday AI encounters in consumer products,
  public services and media.
  While Ireland has developed a rich ecosystem of AI literacy initiatives aimed at organisations
  and the workforce, a comparable set of programmes specifically designed to build AI literacy
  among the general public is notably lacking. The National Digital and AI Strategy contains a
  commitment ‘to ensuring that all learners acquire the basic digital skills, digital literacy skills,
  and media literacy skills needed to thrive in an AI-driven world’ (Department of the Taoiseach,
  2026, p.69). The AI Advisory Council has called for a co-ordinated national approach to public
  AI literacy, positioning it as essential to democratic debate and ethical innovation (AI Advisory
  Council, 2025b).
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  Box 6.1: AI in Transport and Logistics
  Modern transportation and logistics systems are under immense pressure from rapid
  urbanisation, population growth and increasing motorisation. Ireland illustrates this strain; in
  2025 Dublin was ranked the 11th most congested city globally, with drivers losing approx. 95
  hours annually to delays (INRIX, 2025). Artificial intelligence holds the promise of providing datadriven solutions to persistent challenges of congestion, safety, inefficiency and sustainability
  (World Economic Forum, 2025b).
  In real-time traffic management, AI can combine CCTV, roadside sensors and connected
  vehicle and navigation data to detect incidents earlier and optimise network response. On
  Ireland’s motorway network, Transport Infrastructure Ireland’s use of intelligent transportations
  systems using AI reported incident detection up to 25 minutes earlier on the M1 and 35 minutes
  earlier on the M6, supporting faster intervention and reduced secondary disruption (Valerann,
  2025). In public transport, AI supports demand forecasting, dynamic scheduling and predictive
  maintenance (e.g. using sensor data to anticipate failures and minimise service disruption)
  (Son et al., 2025). In logistics, AI improves route planning, fleet maintenance and warehouse
  operations, reducing empty miles and emissions. Autonomous vehicles (AVs) go further,
  using AI for perception, prediction and planning, but raise more acute concerns around safety
  assurance, cybersecurity (evasion/poisoning attacks) and transparency. Under the EU AI Act,
  AI used as a vehicle ‘safety component’ is classified as high-risk, triggering requirements for risk
  management, robustness, and human oversight (Fernández Llorca et al., 2025).
  Widespread adoption will depend on high-quality interoperable data, resilient connectivity
  (IoT/5G), rigorous assurance and cybersecurity engineering, clear operational accountability,
  and harmonised regulation that enables innovation while safeguarding public trust.
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  Chapter 7: Strategic Reflections & Priority
  Actions for Navigating AI
  The debate around AI is often framed in extremes, as a revolutionary cure-all or as an existential
  threat to humanity. Neither of these framings is likely to be true and conceptualising the debate
  in such terms can be unhelpful. It diminishes the role of human agency and risks crowding out
  the more important discussion about the need to intentionally shape AI in line with our goals
  and values, and what that requires of policymakers, institutions and society. The impacts of AI
  will vary across domains and unfold over time in ways that are difficult to predict.
  The diffusion and embedding of AI into everyday life, workplaces and the broader economy will
  take time, creating a critical window in which Ireland can act deliberately rather than reactively.
  This period should be used to clarify where AI can generate meaningful value, identify the
  tasks to which it is best suited, and establish agile risk-informed and proportionate governance
  frameworks that can guide its responsible development and deployment. It also provides time to
  design mitigation strategies that address emerging risks and unintended consequences, expand
  AI literacy and technical skills, and support the adaptation of labour markets as roles evolve.
  NESC seeks to broaden the debate on AI by emphasising that it should not be seen merely
  as another tool in the digital toolbox. Instead, AI should be understood as a socio-technical
  system whose design, application and impacts are shaped by human decisions, institutional and
  economic incentives, and social norms. The effects of AI are therefore neither automatic nor
  inevitable; they reflect the priorities embedded within systems, the quality of governance, and
  the contexts in which AI is applied. Approaching AI in this way brings questions of responsibility,
  power, equity and accountability to the forefront, and highlights the need for intentional
  stewardship to ensure that technological advancement aligns with societal values. This framing
  provides an important foundation for considering how Ireland can guide the development and
  use of AI in a manner that is both strategic, safe, rights-respecting and aligned with the public
  interest.
  The Council offers five interconnected reflections that can help Ireland pursue a responsible,
  rights-respecting and inclusive approach to developing and using AI, one which supports
  productivity, economic prosperity, better public services and wider societal benefits. These
  reflections establish a strategic framework from which a set of priority actions is identified.
  While not exhaustive, the actions highlighted here focus on areas where deliberate and timely
  intervention is likely to be most impactful, building on the imperative for proactive stewardship
  outlined above. Taken together, the reflections and associated priorities are intended to help the
  Irish AI ecosystem translate broad ambition into co-ordinated, practical progress.
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  Reflections are centred on five main themes: Responsible and Strategic Adoption of AI;
  Safe, Ethical and Trustworthy AI; Anticipatory Governance and Institutional Readiness; AI
  Literacy as National Infrastructure; Public Deliberation, Legitimacy and Social Licence. Ireland
  already has expertise in a number of these areas, providing a good foundation for future policy
  and implementation efforts. The planned AI Advisory Unit will also play an important role in
  this regard. These capacities can be strategically leveraged to support the delivery of priority
  actions, strengthen institutional co-ordination, and accelerate progress towards inclusive,
  trustworthy and sustainable AI adoption.
  Reflection 1: Responsible and Strategic Adoption of AI
  A first reflection concerns the need for responsible, strategic and problem-led adoption of
  AI. Ireland’s ambition cannot be realised through a technology-first mindset or by pursuing AI
  adoption for its own sake. Too often, enthusiasm outpaces organisational readiness, leading to
  fragmented pilots, wasted investment and erosion of public trust. A sustainable path requires
  beginning with clearly defined problems and societal needs, and then determining whether AI
  provides a safe, effective and rights-respecting solution. This approach helps avoid technosolutionism, opportunity costs and the risk of introducing AI into domains where the conditions
  for success – including high-quality, curated data and a supportive socio-technical environment
  – do not exist.
  Strategic adoption also requires attention to the type of AI model deployed. Responsible
  practice involves matching model complexity to problem complexity, selecting energy-efficient
  tools, and ensuring transparency around environmental impacts. Equally, strategic adoption
  means focusing on transformation rather than incremental automation. Ireland should be
  ambitious in its use of AI and think beyond simply streamlining existing processes. We need to
  rethink how public services and organisational systems could be reorganised to enhance value,
  inclusion and efficiency using AI tools.
  Ultimately, a responsible adoption strategy demands socio-technical integration: investment in
  data governance, digital infrastructure, strengthened workforce capability, participatory design,
  and early engagement with employees and affected communities. Without these foundations,
  AI is unlikely to deliver sustained productivity or public benefit, and risks deepening distrust or
  embedding inequities into decision-making systems.
  Priority Actions

1. Establish a Problem-First Adoption Framework
   Work with public and private sector stakeholders to develop a national decision framework
   enabling organisations, particularly in the public sector, to clearly define the problem to be
   solved before pursuing AI solutions. This should include structured needs assessments,
   options analysis (including non-AI alternatives) and explicit tests of public value in the
   case of public-sector adoption. Embedding a ‘problem first’ approach can reduce
   fragmented experimentation and direct investment toward high-impact use cases.
   Reflection 1: Responsible and Strategic Adoption of AI
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   Artificial Intelligence in Service of Society: Navigating Our Way Forward
2. Implement ‘Right-Sized’ Model Protocols
   Create a Model Selection Matrix that guides organisations to match model complexity to
   the scale and sensitivity of the task at hand. This should encourage reflection on
   environmental impacts and sustainability considerations.
3. Incentivise Transformational Rather Than Only Incremental Uses
   Design public funding mechanisms and innovation programmes that reward projects
   capable of redesigning services or organisational processes, rather than only automating
   existing workflows. Encouraging system-level redesign can unlock greater long-term value
   and help Ireland avoid sub-optimal productivity gains.
   Reflection 2: Safe, Ethical and Trustworthy AI
   A second reflection centres on the imperative for safe, ethical and trustworthy AI. It is important
   to avoid undertones of techno-solutionism when we speak of ethical AI, as if AI itself had some
   inherent capability to be ethical. Rather, we need to focus on the integration of human ethical
   deliberation into AI policy discussions, as well as adoption and oversight of AI systems.
   Ensuring trustworthiness requires moving beyond high-level principles toward practical
   mechanisms such as algorithmic audits, impact assessments, structured documentation
   and transparent reporting in relation to firms’ safety testing procedures and results, and the
   training data used in model development (e.g. public registry of AI systems). These mechanisms
   make fairness, accountability and transparency meaningful in practice. They also support
   the detection of bias – not only algorithmic bias, but also the systemic biases embedded in
   historical data and human decision-making. Importantly, the goal is not to eliminate bias entirely,
   which is neither realistic nor a standard met by human systems, but to minimise harm, enhance
   scrutiny and ensure proportionate and equitable outcomes.
   To bridge the principle-to-practice gap, Ireland needs concrete guidance such as, for example,
   sector-specific playbooks that translate high-level ethical principles into actionable steps for
   real-world settings. However, such tools alone will not be sufficient. To ensure that ethical
   AI is not merely performative, organisations must also invest in building ethical capability,
   both among individual practitioners and within institutions, so that trustworthy AI becomes
   embedded in everyday decision-making rather than remaining an aspirational ideal.
   Trustworthy AI also depends on clear human oversight. Yet oversight cannot be assumed; it
   requires ensuring that systems are explainable enough for humans to interrogate, and that
   organisational conditions do not incentivise blind acceptance of AI outputs. There is evidence
   that uncritical reliance on AI can erode human proficiency, diminish skill over time and weaken
   epistemic capability. Addressing this requires careful design, training and culture-building, and
   it demands clarity on who is accountable when AI is used in decision-making. A key distinction
   must be maintained between trust in AI and trustworthy AI. The policy goal is not to persuade
   the public to trust AI systems, but to ensure that systems, and the institutions deploying them,
   are genuinely worthy of trust through verifiable, transparent and responsible practices.
   Reflection 2: Safe, Ethical and Trustworthy AI
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   Priority Actions
4. Build Ethics Capability through Sector-Specific Guidance and Institutional Capacity
   Work with public and private stakeholders to translate high-level ethical principles into
   sector-specific playbooks that provide practical, context-sensitive guidance for real-world
   AI use. Playbooks could include concrete decision tools, escalation pathways, and minimum
   documentation standards, in line with the EU AI Act, to support consistent and defensible
   practice. To ensure that ethical AI is not merely procedural or performative, organisations
   must also invest in ethical capability. This could include developing multidisciplinary ethics
   governance structures, embedding responsible-AI roles within teams, and providing
   professional training that equips practitioners and leaders to identify trade-offs, interrogate
   system behaviour and exercise informed judgment.
5. Embed Human Oversight and Accountability in AI-Assisted Decision-Making
   In line with EU AI Act requirements, establish clear lines of responsibility so that
   accountability remains traceable and with identifiable human decision-makers. This should
   include explicit guidance on when human review is mandatory, who holds final decision
   authority, and how affected individuals can challenge or seek redress for AI-influenced
   outcomes. In addition, minimum standards for explainability and interpretability in high-
   stakes applications should be set in line with EU AI Act requirements, so that human
   reviewers can meaningfully scrutinise and question system outputs rather than simply
   endorse them. Oversight frameworks should be supported by appropriate training and
   workflow design to mitigate automation bias and preserve human judgment and expertise.
6. Integrate Safe and Ethical AI into Procurement and Funding Criteria
   Leverage public procurement to promote the development and adoption of trustworthy AI
   by embedding expectations for safety, transparency and ethical governance within
   purchasing frameworks. This could be facilitated through a central procurement
   arrangement, as posited in the National Digital & AI Strategy 2030.
   Reflection 3: Anticipatory Governance and Institutional Readiness
   A third reflection concerns the need for anticipatory and adaptive governance. The rapid
   pace, unpredictability and heterogeneous nature of AI technologies mean that governance
   must be capable of learning, adjusting and responding to emerging risks and opportunities.
   Ireland is already embedded within the regulatory structure of the EU AI Act, which provides
   a strong baseline for trustworthy AI. The purpose of anticipatory governance is not to ‘goldplate’ this regulatory effort, but to complement it with a broader, future-oriented perspective
   that strengthens institutional resilience and prepares the State for uncertain technological
   trajectories. Anticipatory governance processes can assess the costs of delayed adoption
   alongside potential harms and allow precaution to be proportionally balanced with strategic
   ambition, ensuring Ireland can leverage beneficial innovation opportunities.
   Reflection 3: Anticipatory Governance and Institutional Readiness
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   Artificial Intelligence in Service of Society: Navigating Our Way Forward
   Anticipatory governance involves integrating strategic foresight, horizon scanning and scenario
   planning into policy cycles, enabling policymakers to identify weak signals of change and
   respond proactively rather than reactively. It also requires institutionalising monitoring and
   evaluation so that real-world evidence continuously informs decision-making. While AI systems
   may demonstrate impressive performance under controlled conditions, their behaviour can
   degrade in dynamic, real-world contexts where variables cannot be easily constrained. For
   this reason, rigorous piloting, careful evaluation and continuous monitoring are essential to
   understand how systems operate over time and across diverse populations. Such monitoring
   cannot be episodic but should be embedded throughout the entire lifecycle of AI systems. This
   supports early detection of harm, helps scale successful innovations and prevents policy or
   technological lock-in. It is important that the metrics chosen for evaluation are suitably broad,
   capturing social and economic impacts as well as technical performance. Ongoing oversight
   should be matched by systematic and regular sharing of information across organisations
   and sectors, enabling the development of best practices and helping to operationalise core
   principles of transparency and accountability.
   Governance must also be a whole-of-government endeavour, with clear lines of responsibility
   and strong co-ordination across departments, regulators and public bodies. While it is
   understandable and appropriate that much attention has focused on the risks of AI, this
   should not blind us to the opportunity costs of inaction. Anticipatory governance offers a way
   to stay agile, avoid technological and policy lock-in, and take advantage, where appropriate,
   of innovative new AI tools or novel applications of existing tools across different domains.
   Regulatory sandboxes and testbeds, as provided for in the EU AI Act, can support trustworthy
   AI and regulatory innovation while maintaining safeguards, while modular, adaptive governance
   frameworks can reduce the risk of rigidity in the face of rapid technological evolution.
   Crucially, anticipatory governance expands the view beyond risk mitigation alone. It is concerned
   with steering AI development toward public benefit, enabling re-imagining of systems and
   ensuring Ireland can respond effectively to multiple possible futures.
   Priority Actions
7. Institute Strategic Foresight into National AI Governance
   Establish a dedicated and coherent national AI foresight function with responsibility for
   horizon scanning, scenario development and long-range analysis of technological, societal
   and economic impacts. This capability could be integrated into decisions on AI policy and
   investment through scenario testing, stress-testing against plausible technological
   trajectories, and explicit assessment of opportunity costs as well as risks. Embedding
   foresight into routine decision-making would shift AI governance from reactive responses to
   anticipatory, strategically informed action at the cabinet level.
8. Institutionalise Life-cycle Monitoring of AI Systems
   Move beyond point-in-time approval models by requiring continuous, proportionate
   evaluation of AI systems once deployed. Monitoring frameworks should track not only
   technical performance but also social outcomes, distributional effects and unintended
   consequences. This supports early harm detection, enables timely recalibration and reduces
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   the risk of technological or policy lock-in. Obligations under the EU AI Act relating to post-
   market monitoring systems can be leveraged to support and institutionalise these
   continuous evaluation practices.
9. Establish a National AI Evaluation and Learning Framework
   Develop and publish a cross-sector national framework for evaluating AI deployments
   that defines shared metrics and methodologies for assessing public value, equity, safety,
   environmental impacts and economic effects, alongside technical performance. This
   framework should be supported by systematic knowledge-sharing mechanisms that
   enable regular exchange of evaluation results, operational lessons and incident reports
   across departments, regulators and sectors.
   Reflection 4: AI Literacy as National Infrastructure
   A fourth reflection highlights AI literacy as a form of national digital infrastructure, essential for
   responsible innovation, democratic engagement and organisational readiness. Seen through this
   lens, AI literacy initiatives should be grounded in a clear public service mandate, designed by
   independent expertise, adapted to local needs, and subject to strong public accountability. AI
   literacy is not simply knowledge of tools or technical concepts; it is a socio-technical capability
   that enables individuals to interpret outputs critically, understand system limitations, identify
   opportunities, recognise ethical implications and participate effectively in decisions about AI
   procurement and deployment.
   Ireland has a growing ecosystem of AI literacy initiatives across education, enterprise and civil
   society, but they remain fragmented. A co-ordinated national approach is needed to embed AI
   literacy across all levels of education, professional training and public engagement in line with
   the European Union’s Digital Decade framework, under which member states have committed
   to ensuring that at least 80 per cent of adults possess basic digital skills by 2030. A national
   approach should include age-appropriate curricula in schools; accredited programmes and
   continuing professional development for educators; expanded AI-related training across
   disciplines such as law, health, humanities and public administration; and, critically, sustained AI
   literacy initiatives for the general public.
   Leadership literacy is particularly important. Executives and senior public-sector leaders shape
   organisational culture and determine how AI is procured, governed and used. Without AIliterate leadership, organisations risk adopting systems they cannot adequately assess, oversee
   or evaluate. Embedding AI literacy within risk management, audit processes and governance
   frameworks is therefore integral to ensuring responsible deployment.
   A national commitment to AI literacy would empower citizens to critically evaluate AI, to be
   appropriately trusting or distrustful where warranted, and to take an active role in shaping
   Ireland’s AI future rather than being passive recipients of technological change.
   Reflection 4: AI Literacy as National Infrastructure
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   Priority Actions
10. Implement a Comprehensive National AI Literacy Strategy
    Adopt a whole-of-society national AI literacy strategy that defines core competencies, sets
    measurable objectives and aligns efforts across education systems, workforce development,
    public services and civic engagement. The strategy should be delivered through sustained
    public AI literacy initiatives that provide accessible learning resources, community-based
    programmes and trusted information campaigns aimed at enabling informed, critical
    engagement with AI. To ensure coherence and quality, establish a national AI literacy hub
    to leverage existing initiatives in the first instance, to curate high-quality materials, share
    best practices and co-ordinate initiatives across government, business, academia and civil
    society. Treat AI literacy as long-term national infrastructure by introducing periodic
    assessments to track literacy levels, identify demographic and regional gaps, and guide
    targeted interventions. Throughout, prioritise inclusion to prevent a new digital divide,
    ensuring that AI understanding and capability are equitably distributed across age groups,
    regions and socio-economic backgrounds.
11. Embed AI Literacy as a Core Expectation for Senior Leadership and Governance
    Foster AI literacy as a standard component of effective leadership and governance for
    senior public-sector leaders, board members of state bodies and executives in regulated
    sectors. This should be reflected in leadership development pathways and board education,
    with a focus on strategic judgment, procurement scrutiny, opportunity and risk evaluation,
    and governance, rather than only on the technical capabilities of AI systems. Organisations
    should incorporate AI literacy into routine governance and risk practices, including audit
    committees, risk frameworks and assurance processes, so that senior decision-makers are
    equipped to interrogate AI-enabled systems, avoid uncritical adoption or vendor over-
    reliance, and exercise informed oversight and accountability.
    Reflection 5: Public Deliberation, Legitimacy and Social Licence
    The final reflection concerns public deliberation and the broader question of social licence.
    Artificial intelligence has the potential to reshape society in ways that are distributed unevenly,
    creating different opportunities and risks across communities. What counts as AI for the public
    good cannot be determined solely by experts, industry or government; it must be shaped
    through sustained, inclusive engagement with the public. In this regard, the Council welcomes
    the commitment in the National Digital & AI Strategy 2030 to launch a National Conversation on
    AI to ensure societal values and concerns can directly inform the adoption of AI technologies.
    Public deliberation must be more than awareness-raising or consultation. It requires meaningful
    two-way dialogue that recognises diverse values, lived experiences and perspectives, and, in the
    context of national policymaking, may take different forms and employ different methodologies
    depending on the issue, scale and level of public impact involved. Citizens should have an
    informed role in determining where AI should or should not be used, what boundaries should
    Reflection 5: Public Deliberation, Legitimacy and Social Licence
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    be set, and what trade-offs – be they ethical, social or economic – are acceptable. Without
    this engagement, AI systems risk rejection, resistance or loss of legitimacy, regardless of their
    technical performance.
    Deliberation is also essential for navigating contested issues such as the balance between
    innovation and rights protection, concerns about surveillance or misinformation, the impact on
    labour markets, and questions of environmental sustainability. By embedding public deliberation
    into governance cycles, Ireland can ensure that AI development is aligned with democratic
    values, strengthens institutional trust and gives citizens agency in shaping technological futures.
    Priority Actions
12. Integrate Inclusive Public Deliberation in AI Governance
    Integrate structured public deliberation into AI policy, regulatory and high-risk public-sector
    deployment cycles, positioning engagement upstream at defined stages of decision-making
    rather than after choices have been made. In a sociotechnical framing of AI, where impacts
    emerge from the interaction between technology, institutions and society, ongoing public
    deliberation is a necessary condition for legitimate and effective governance. Engagement
    processes should prioritise inclusion and representativeness, and be treated as a continuous
    democratic practice, with sustained channels for dialogue that evolve alongside AI systems
    and reinforce public trust over time.
13. Engage Workers and Communities Affected by AI in Deliberative Dialogue
    Prioritise early and ongoing dialogue with workers and communities likely to experience the
    direct impacts of AI deployment, particularly in sectors and local settings where changes to
    roles, services and decision-making will be most tangible. Supporting deliberation at
    workplace, sectoral and community levels can highlight lived experience, practical concerns
    and context-specific opportunities and risks that are often missed by national processes.
    Building trust and mutual understanding through sustained discussion is critical to securing
    co-operation and ensuring that AI adoption is socially legitimate and operationally effective.
    Concluding Remarks
    The path forward is not about allowing AI to determine our future but about defining our future
    with AI. The progress of this technology is non-linear, and history should make us cautious about
    predicting what it will or will not achieve. Rather than treating the complexity of AI as a source
    of apprehension, we should recognise it as a marker of opportunity. The question is not whether
    AI will match or surpass human intelligence, especially when such comparisons often rely on
    opaque or narrow benchmarks, but rather how we understand the different forms of intelligence
    involved. It is important that we do not conflate intelligence (either human or machine) with
    wisdom, which remains a uniquely human trait. By focusing on how human and AI capabilities
    can be harnessed together in safe, ethical and purposeful ways, we can ensure that AI becomes
    a tool for human flourishing – advancing social wellbeing, economic prosperity and democratic
    values.
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