Behavioural Sciences in the Age of AI
Unpublished Draft Paper Satish Pradhan, October, 2025)
Abstract
In an era where artificial intelligence (AI) is transforming work, organisations must blend insights from
behavioural science with technological innovation to thrive. This paper explores “Behavioural Sciences in the
Age of AI” by integrating foundational theories of human behaviour with emerging themes in AI, human–
machine teaming, organisational transformation, and dynamic capability development. We provide a historical
context for the evolution of behavioural science and its intersection with technology, from early socio-technical
systems thinking to modern cognitive science and behavioural economics. Key theoretical contributions are
discussed – including Herbert Simon’s bounded rationality, Daniel Kahneman and Amos Tversky’s heuristics
and biases, Gerd Gigerenser’s ecological rationality, Fred Emery and Eric Trist’s socio-technical systems, David
Teece’s dynamic capabilities, Edgar Schein’s organisational culture, Erik Brynjolfsson and Andrew McAfee’s
“race with the machine” paradigm, Gary Klein’s naturalistic decision-making, and Luciano Floridi’s digital
ethics – highlighting their relevance in designing human–AI collaboration. We build upon an internal strategic
framework – the composite capability paradigm and 2025+ capability stack – which posits that future-proof
organisations must orchestrate human intelligence, machine cognition, and agile interfaces within a purpose-
driven, values-grounded architecture. By situating this paradigm in the broader academic literature, we
demonstrate how purpose and trust, ethical AI, digital fluency, human agency, adaptive decision-making, and
robust governance become critical enablers of competitive advantage in the AI age. Real-world examples from
health, public services, business, and government illustrate how behavioural insights combined with AI are
enhancing decision quality, innovation, and organisational resilience. The paper argues for a rigorous yet
human-centric approach to AI integration – one that leverages behavioural science to ensure technology serves
human needs and organisational values. We conclude that the synthesis of behavioural science and AI offers a
strategic path to reclaiming human agency and purpose in a world of rapid technological change, enabling
organisations to adapt ethically and effectively in the age of AI.
Introduction
The rise of advanced AI has catalysed an inflection point in how organisations operate, decide, and evolve.
Today’s business environment has “transitioned from a predictable game of checkers to a complex, live-action
role-play of 4D chess” – an apt metaphor for the unprecedented complexity and dynamism that leaders face. In
this new game, even the “rulebook” changes continuously, rendering many traditional strategies and
organisational models obsolete. The convergence of rapid technological change with other disruptive forces
(such as globalisation, climate risks, and shifting workforce expectations) creates interconnected pressures that
demand integrated responses. As a result, organisations must fundamentally rethink their capabilities and
frameworks for decision-making. This paper contends that behavioural science, with its rich understanding of
human cognition, emotion, and social dynamics, offers essential principles for guiding this reinvention in the
age of AI.
The Imperative for Integration
Artificial intelligence, once a futuristic concept, is now embedded in core business processes across industries.
AI systems not only execute tasks or analyse data; increasingly, they function as “social-technological” actors
that form symbiotic relationships with humans. This blurring of the line between human and machine roles
raises fundamental questions about how we design work and make decisions: How do we ensure that AI
augment – rather than override – human judgment? In what ways must human cognitive biases, limitations, and
strengths be considered when deploying AI tools? How can organisations foster trust in AI systems while
preserving human agency and accountability? These questions sit at the intersection of behavioural science
(which examines how humans actually behave and decide) and technology management.
Historically, advances in technology have forced parallel evolutions in management and organisational
psychology. For instance, the introduction of electric motors in factories in the early 20th century did not yield
productivity gains until workflows and management practices were fundamentally redesigned decades later.
Today, we may be in a similar transitional period with AI: simply overlaying intelligent algorithms onto old
organisational structures is inadequate. Instead, as Erik Brynjolfsson observes, thriving in the “new machine
age” requires reshaping systems and roles to “race with the machine” rather than against it. This is a behavioural
and organisational challenge as much as a technical one. Leaders must guide their teams through “radical
unlearning” of outdated assumptions and foster a culture of continuous learning and adaptation. Edgar Schein
noted that effective transformation often demands addressing “learning anxiety” – people’s fear of new methods
– by cultivating sufficient “survival anxiety” – the realisation that failing to change is even riskier. In the context
of AI, this means creating a sense of urgency and purpose around AI adoption, while also building
psychological safety so that employees are willing to experiment with and trust new tools.
Behavioural Science and AI: A Convergence
Behavioural science spans psychology, cognitive science, behavioural economics, and sociology – disciplines
that have illuminated how humans perceive, decide, and act. AI, on the other hand, often operates on algorithms
aimed at optimal or rational outcomes. This creates a potential tension: AI might make recommendations that
are theoretically optimal, but humans might not accept or follow them due to cognitive biases, trust issues, or
misaligned values. Integrating behavioural science means acknowledging and designing for the reality of human
behaviour in all its richness and boundedness. For example, AI systems in hiring or criminal justice need to
account for issues of fairness and implicit bias – areas where social psychology provides insight into human
prejudice and decision bias. In consumer-facing AI (like recommendation engines or digital assistants),
understanding heuristics in user behaviour (from research by Daniel Kahneman, Amos Tversky, and others) can
improve design to be user-friendly and nudge positive actions. In high-stakes environments like healthcare or
aviation, the field of human factors and cognitive engineering (informed by behavioural science) has long
emphasised fitting the tool to the human, not vice versa.
Crucially, behavioural science also guides organisational behaviour and change management. As companies
implement AI, there are cultural and structural changes that determine success. Who “owns” decisions when an
algorithm is involved? How do teams collaborate with AI agents as teammates? What training and incentives
drive employees to effectively use AI tools rather than resist them? These questions invoke principles from
organisational psychology (motivation, learning, team dynamics) and from socio-technical systems theory. The
latter, pioneered by Emery and Trist in the mid-20th century, argued that you must jointly optimise the social
and technical systems in an organisation. That insight is strikingly applicable today: an AI solution will fail if it
is imposed without regard for the social system (people’s roles, skills, norms), and conversely, human
performance can be amplified by technology when designed as an integrated system.
This paper aims to bridge the past and present – anchoring cutting-edge discussions of AI and dynamic
capabilities in the timeless truths of behavioural science. We will review key theoretical foundations that inform
our understanding of human behaviour in complex, technology-mediated contexts. We will then propose a
synthesised framework (building on the composite capability paradigm and capability stack developed in an
internal strategy paper) for conceptualising how human and AI capabilities can be orchestrated. Finally, we
translate these ideas into practice: how can organisations practically build trust in AI, nurture human–machine
collaboration, uphold ethics and inclusion, and develop the dynamic capability to continuously adapt? By
examining illustrative examples from domains such as healthcare, public policy, business, and government, we
demonstrate that integrating behavioural science with AI is not a theoretical nicety but a strategic necessity. The
outcome of this integration is a new kind of enterprise – one that is technologically empowered and human-
centric, capable of “reclaiming human agency and purpose” even as algorithms become ubiquitous.
Literature Review: Foundations of Behavioural Science and Technology Interaction
Cognitive Limits and Decision Biases
Modern behavioural science began as a challenge to the notion of humans as perfectly rational actors. Herbert
A. Simon, a polymath who straddled economics, psychology, and early computer science, was pivotal in this
shift. Simon introduced the concept of bounded rationality, arguing that human decision-makers operate under
cognitive and information constraints and thus seek solutions that are “good enough” rather than optimal. He
famously coined the term “satisficing” to describe how people settle on a satisfactory option instead of
exhaustively finding the best. Simon’s insight – that our minds, like any information-processing system, have
limited capacity – has direct parallels in AI. In fact, Simon was an AI pioneer who, in the 1950s, built some of
the first software to mimic human problem-solving. The bounded rationality concept laid the groundwork for
behavioural economics and decision science, highlighting that if AI tools are to support human decisions, they
must account for our finite attention and memory. For example, too much information or too many choices can
overwhelm (a phenomenon later popularised as cognitive overload or the paradox of choice), so AI systems
need to be sensitive to how recommendations or data are presented to users – an idea reinforced by the
heuristics-and-biases research tradition.
Daniel Kahneman and Amos Tversky carried the torch forward by cataloguing the systematic heuristics (mental
shortcuts) and biases that affect human judgment. Their work demonstrated that humans deviate from classical
rationality in predictable ways – we rely on intuitive System 1 thinking (fast, automatic) which can be prone to
errors, as opposed to the more deliberate System 2 thinking. They identified biases like availability
(overestimating the likelihood of events that come readily to mind), confirmation bias (seeking information that
confirms prior beliefs), loss aversion (weighing losses more heavily than equivalent gains), and numerous
others. Kahneman’s influential book Thinking, Fast and Slow (2011) synthesised these ideas for a broad
audience, cementing his reputation as “the father of behavioural science”. The implication for AI and human-
machine teaming is profound: AI can either mitigate some human biases or amplify them, depending on design.
For instance, algorithmic decision aids can counteract certain biases by providing data-driven forecasts (helping
humans overcome intuition that might be flawed), but if not carefully implemented, they might also lull humans
into automation bias (over-reliance on the AI, assuming it is always correct) or confirmation bias (the AI might
learn from human decisions that are biased and reinforce them). An understanding of cognitive biases has thus
become vital in AI ethics and design – e.g. ensuring that an AI’s explanations don’t trigger biased reasoning or
that its user interface nudges appropriate attention.
Not all scholars agreed that human deviation from economic rationality was truly irrational. Gerd Gigerenzer, a
prominent psychologist, offered a counterpoint with the concept of ecological rationality. Gigerenzer argues that
heuristics are not just “biases” or flaws; rather, they are often adaptive responses to real-world environments. In
his view, the success of a decision strategy depends on the context – a heuristic that ignores certain information
can actually outperform complex models in low-information or high-uncertainty situations. He demonstrated,
for example, that simple rules like the “recognition heuristic” (preferring options one recognises over those one
doesn’t) can yield surprisingly accurate decisions in certain domains. Gigerenzer has been a strong critic of
Kahneman and Tversky’s emphasis on biases, cautioning that labeling human thinking as “irrational” in lab
experiments misses how humans have adapted to their environments. He suggests that rationality should be seen
as an adaptive tool, not strictly bound by formal logic or probability theory. This perspective is highly relevant
when considering AI-human interaction: rather than always trying to “debias” humans into perfect logicians,
sometimes the better approach is to design technology that complements our natural heuristics. For example,
decision dashboards might highlight key information in ways that align with how experts naturally scan for cues
(leveraging heuristics developed through experience), or AI might handle aspects of a task that humans are
known to do poorly at (like very large-scale calculations) while leaving intuitive pattern recognition to the
human. Gigerenzer’s work reminds us that context matters – a theme also echoed in machine learning through
the “no free lunch” theorem (no one model is best for all problems). In practice, it means organisations should
strive for human-AI systems where each does what it is comparatively best at – as one Gigerenzer quote puts it,
“intelligent decision making entails knowing what tool to use for what problem”.
Gary Klein, another figure in the decision sciences, provides additional nuance with his studies of naturalistic
decision-making (NDM). While Kahneman often highlighted errors in human judgment using tricky puzzles or
hypothetical bets, Klein studied experts (firefighters, pilots, doctors) making high-stakes decisions under time
pressure. He found that these experts rarely compare options or calculate probabilities in the moment; instead,
they draw on experience to recognise patterns and likely solutions – a process he described in the Recognition-
Primed Decision model. Klein and Kahneman once famously debated, but eventually co-authored a paper (“A
Failure to Disagree”) noting that their perspectives actually apply to different contexts: in high-validity
environments with opportunities to learn (e.g. firefighting, where feedback is clear and experience builds
genuine skill), intuition can be remarkably effective; in other cases, intuition can mislead. Klein’s emphasis on
tacit knowledge and skilled intuition has implications for AI: organisations should be cautious about completely
displacing human experts with algorithms, especially in domains where human expertise encodes nuances that
are hard to formalise. Instead, AI can be used to support expert intuition by handling sub-tasks or offering a
“second opinion.” For example, in medical diagnosis, an experienced radiologist might quickly intuit a
condition from an X-ray; an AI can provide a confirmatory analysis or flag something the radiologist might have
overlooked, with the combination often proving more accurate than either alone. Indeed, a 2023 study in
European Urology Open Science showed that a radiologist + AI hybrid approach achieved higher sensitivity and
specificity in detecting prostate cancer from MRI scans than either the radiologist or AI alone, demonstrating
how “a combination of AI and evaluation by a radiologist has the best performance”. This is a concrete example
of human intuition and AI analysis working in tandem – aligning with Klein’s insights that experienced human
judgment has unique strengths that, rather than being replaced, should be augmented by AI.
Socio-Technical Systems and Organisational Adaptation
As early as the 1950s, researchers like Fred Emery, Eric Trist, and others at the Tavistock Institute in London
began examining organisations as socio-technical systems (STS) – meaning any workplace has both a social
subsystem (people, culture, relationships) and a technical subsystem (tools, processes, technologies), and these
must be designed together. Trist and colleagues, working with British coal miners, noted that introducing new
machinery without altering work group norms and job designs led to suboptimal outcomes, whereas redesigning
work to give teams autonomy and better align with the new tech yielded significant productivity and satisfaction
gains. They coined “socio-technical” to emphasise this joint optimisation. Another famous work by Emery &
Trist (1965) introduced the idea of different environmental turbulences that organisations face, from placid to
turbulent fields, and argued that in more turbulent (fast-changing, unpredictable) environments, organisations
need more adaptive, open strategies. This foreshadowed today’s VUCA (volatility, uncertainty, complexity,
ambiguity) world. The lesson is that successful adoption of any advanced technology (like AI) isn’t just about
the tech itself, but about how work and human roles are reconfigured around it. Emery and Trist would likely
view AI integration as a prime example of STS in action: the firms that excel will be those that thoughtfully
redesign job roles, team structures, and communication patterns in light of AI capabilities – rather than those
who treat AI implementation as a purely technical upgrade. Indeed, current discussions about human-centric AI
and AI ergonomics are essentially socio-technical perspectives, emphasising user experience, change
management, and organisational context in deploying AI.
Parallel to STS theory, the field of organisational development (OD) and culture was greatly influenced by
Edgar Schein. Schein’s model of organisational culture delineated culture as existing on three levels: artifacts
(visible structures and processes), espoused values (strategies, goals, philosophies), and basic underlying
assumptions (unconscious, taken-for-granted beliefs). According to Schein, transforming an organisation – say,
to become more data-driven or AI-friendly – isn’t simply a matter of issuing a new policy or training people on
a new tool. It often calls for surfacing and shifting underlying assumptions about “how we do things.” For
example, a company might have an implicit assumption that good decisions are made only by seasoned
managers, which could lead to resistance against algorithmic recommendations. Changing that might require
leaders to model openness to data-driven insights, thereby altering assumptions about authority and expertise.
Schein also introduced the concept of learning culture and noted that leaders must often increase “survival
anxiety” (the realisation that not adopting, say, digital tools could threaten the organisation’s success or the
individual’s job relevance) while reducing “learning anxiety” (the fear of being embarrassed or losing
competence when trying something new). In the AI era, this is highly salient: employees may fear that AI will
render their skills obsolete or that they won’t be able to learn the new tools (learning anxiety), even as the
organisation’s competitive survival may depend on embracing AI (survival anxiety). Effective leaders use clear
communication of purpose – why adopting AI is critical – and create supportive environments for upskilling to
resolve this tension. We see enlightened companies investing heavily in digital fluency programs, peer learning,
and even redesigning performance metrics to encourage use of new systems rather than punish initial drops in
efficiency as people climb the learning curve. These practices reflect Schein’s principles of culture change.
Another relevant Schein insight is about ethical and cultural alignment. He argued that organisations should
have cultures that reinforce desired behaviours, and that when you introduce a foreign element (be it a new CEO
or a new technology), if it clashes with entrenched culture, the culture usually wins unless actively managed.
Thus, if a company values high-touch customer service as part of its identity, introducing AI chatbots needs to
be done in a way that augments that value (e.g., bots handle simple queries quickly, freeing up human reps to
provide thoughtful service on complex issues) rather than contradicting it (replacing all human contact).
Ensuring AI deployment aligns with organisational purpose and values – an idea from our internal capability
stack framework – is essentially a cultural alignment problem. If done right, AI can even reinforce a culture of
innovation or analytical decision-making; done poorly, it can create dissonance and distrust.
Dynamic adaptation at the organisational level has been formalised by David Teece in his Dynamic Capabilities
framework. Teece defines dynamic capability as a firm’s ability to “integrate, build, and reconfigure internal
and external competences to address rapidly changing environments”. This theory, originating in strategic
management, is particularly apt for the AI age, where technologies and markets change fast. Teece describes
dynamic capabilities in terms of three sets of activities: sensing (identifying opportunities and threats in the
environment), seizing (mobilising resources to capture opportunities through new products, processes, etc.), and
transforming (continuously renewing the organisation, shedding outdated assets and aligning activities). In the
context of AI, an example of sensing would be recognising early on how AI could change customer behaviour
or operations (for instance, a bank sensing that AI-enabled fintech apps are shifting consumer expectations).
4Seizing would involve investing in AI development or acquisitions, piloting new AI-driven services, and scaling
the ones that work. Transforming would mean changing structures – perhaps creating a data science division,
retraining staff, redesigning workflows – to fully embrace AI across the enterprise. Teece’s core message is that
adaptive capacity itself is a strategic asset. We can relate this to behavioural science by noting that an
organisation’s capacity to change is rooted in human factors: learning mechanisms, leadership mindset, and
organisational culture (again Schein’s domain). For example, dynamic capabilities require an organisational
culture that encourages experimentation and tolerates failures as learning – essentially a growth mindset
organisation. Behavioural science research on learning organisations (e.g., work by Peter Senge or Amy
Edmondson on psychological safety) complements Teece’s macro-level view by explaining what human
behaviours and norms enable sensing, seizing, and transforming. Edmondson’s research on psychological safety
– the shared belief that it’s safe to take interpersonal risks – is crucial if employees are to speak up about new
tech opportunities or flag problems in implementations. Without it, an organisation may fail to sense changes
(because employees are silent) or to learn from mistakes (because failures are hidden), thus undermining
dynamic capability. Therefore, we see that frameworks like Teece’s implicitly depend on behavioural and
cultural underpinnings.
Technology, Work, and Society: Human–AI Collaboration and Ethics
No discussion of behavioural science in the age of AI would be complete without addressing the broader socio-
economic and ethical context. Erik Brynjolfsson and Andrew McAfee, in works like The Second Machine Age
(2014), examined how digital technologies including AI are reshaping economies, productivity, and
employment. They observed a troubling trend: productivity had grown without commensurate job or wage
growth, hinting at technology contributing to inequality or job polarisation. However, they argue that the
solution is not to halt technology but to reinvent our organisations and skill sets – essentially to race with the
machines. Brynjolfsson’s famous TED talk recounted how the best chess player in the world today is not a
grandmaster nor a supercomputer alone, but rather a team of human plus computer – in freestyle chess, a
middling human player with a good machine and a strong process to collaborate can beat even top computers.
He concludes, “racing with the machine beats racing against the machine.” This vivid example underscores a
powerful concept: complementarity. Humans and AI have different strengths – humans excel at context,
common sense, ethical judgment, and novel situations; AI excels at brute-force computation, pattern recognition
in large data, and consistency. The best outcomes arise when each side of this partnership does what it does best
and they iterate together. This theme appears in many domains now. For instance, in medicine, some diagnostic
AI systems initially aimed to replace radiologists, but a more effective approach has been to let AI highlight
suspected anomalies and have radiologists make the final call, significantly improving accuracy and speed. In
customer service, AI chatbots handle routine FAQs, while human agents tackle complex or emotionally
sensitive cases, yielding better customer satisfaction. These human–AI team models are fundamentally about
organising work in ways that fit human behavioural strengths and limitations (as identified by behavioural
science) with machine strengths. Implementing such models requires careful attention to workflow design, user
experience, and trust. If the AI is too assertive or not transparent, the human may distrust it or disengage (there’s
evidence that some professionals will ignore algorithmic advice if they don’t understand or agree with it – a
phenomenon known as algorithm aversion). Conversely, if humans over-trust AI, they may become complacent
and skill atrophy can occur. Thus, a balance of trust – sometimes called calibrated trust – must be achieved,
which is an active research area in human factors and HCI (Human–Computer Interaction). Lee and See (2004)
suggested that trust in automation should be calibrated to the automation’s true capabilities; to do this, systems
might need to provide feedback on their confidence level, explanations, or have mechanisms for humans to
oversee and intervene.
Trust and ethics are tightly intertwined. Luciano Floridi, a leading philosopher in digital ethics, has argued that
we must develop a “Good AI Society” where AI is aligned with human values and the principles of beneficence,
non-maleficence, autonomy, justice, and explicability. Floridi’s work with the AI4People initiative synthesised
numerous AI ethics guidelines into a unified framework. Two principles stand out for behavioural science
integration: autonomy (respecting human agency) and explicability (the ability to understand AI decisions).
From a behavioural perspective, respecting autonomy means AI should be a tool that empowers users, not an
opaque mandate that constrains them. Users are more likely to adopt and appropriately use AI if they feel in
control – for example, a decision support system that suggests options and allows a human to override with
justification tends to be better received than an automated system with no human input. Explicability is critical
for trust and for human learning; if an AI system can explain why it made a recommendation, a human can
decide whether that reasoning is sound and also learn from it (or catch errors). Floridi and colleagues even
propose “AI ethics by design”, meaning ethical considerations (like transparency, fairness, accountability)
should be built into the development process of AI, not slapped on later. For practitioners, this could involve
5interdisciplinary teams (with ethicists or social scientists working alongside engineers), bias audits of
algorithms, and participatory design involving stakeholders who represent those affected by the AI’s decisions.
Another facet of ethics is inclusion and fairness. Behavioural sciences remind us how prevalent biases
(conscious and unconscious) are in human decisions; ironically, AI trained on historical human data can embed
and even amplify those biases if we’re not careful. There have been real cases: hiring algorithms that
discriminated against women (because they were trained on past hiring data skewed toward men), or criminal
risk scoring algorithms that were biased against minorities. Addressing this isn’t just a technical fix of the
algorithm; it requires understanding the social context (why the data is biased) and often a human judgement of
what fairness means in context (an ethical decision). Various definitions of fairness (e.g., demographic parity vs.
equalised odds) have to be weighed, which is as much a policy question as a math question. Here, governance
comes into play – organisations need governance mechanisms to oversee AI decision-making, much like human
decision processes are subject to oversight and compliance. Floridi’s emphasis on governance aligns with
emerging regulations (like the EU AI Act) that push for transparency, accountability, and human oversight of
AI. Behavioural science contributes to this conversation by highlighting factors such as: how do individuals
react to algorithmic decisions? what organisational incentives might cause people to deploy AI in harmful ways
(for example, a manager might be tempted to use an AI system to surveil employees in ways that hurt trust)? and
how can we create cultures of responsible AI use? Organisational behaviour research on ethical climates, tone
from the top, and decision biases (like the tendency to conform to perceived pressure) are all relevant when
instituting AI governance. A practical example is the creation of AI ethics committees or review boards within
organisations, which often include people with diverse backgrounds (legal, technical, HR, etc.) to review
sensitive AI deployments (e.g., systems affecting hiring or customer rights). These committees work best when
they consider not just compliance with regulations but also the psychological impact on those subject to the AI
decisions and on employees using the AI.
Finally, a macro societal perspective: behavioural sciences and AI are jointly shaping what it means to work and
live. Issues of human agency loom large. There is a risk that if we delegate too much decision-making to
algorithms, humans could experience a loss of agency or a “de-skilling” effect. On the flip side, AI can also
enhance human agency by providing people with better information and more options (for example, citizens
using AI-powered tools to understand their energy usage can make more informed choices, or disabled
individuals using AI assistants gain independence). This dual potential – to diminish or amplify agency – again
depends on design and context. A theme across behavioural literature is the importance of purpose and
meaningfulness for motivation. As AI takes over more routine work, what remains for humans should ideally be
the more purpose-rich tasks (creative, interpersonal, strategic). This calls for organizational vision: leaders need
to articulate how AI will free employees to focus on more meaningful aspects of their jobs rather than simply
framing it as a cost-cutting or efficiency drive. The theme of purpose is central to sustaining trust and morale.
Studies have shown that employees are more likely to embrace change (including tech adoption) when they
believe it aligns with a worthy mission or values, rather than just boosting the bottom line. Thus, infusing AI
strategy with a sense of higher purpose (e.g., “we are using AI to better serve our customers or to make
employees’ work lives better or to solve societal challenges”) is not just a PR move but a psychologically
important factor.
In summary, the literature suggests that an effective interplay of behavioural science and AI requires
recognising humans’ cognitive biases and strengths, designing socio-technical systems that leverage
complementarity, fostering organisational cultures that learn and adapt, and instituting ethical guardrails that
maintain trust, fairness, and human agency. With these foundations laid, we now turn to a conceptual framework
that synthesises these insights: the Composite Capability Paradigm and its accompanying capability stack for the
AI-era organisation.
Theoretical Framework: The Composite Capability Paradigm and Capability Stack
To navigate the age of AI, we propose an integrative framework termed the Composite Capability Paradigm,
rooted in the idea that organisational capabilities now arise from an orchestrated combination of human and
machine elements. This framework, developed internally as the 2025+ Capability Stack, posits that there are
distinct layers to building a resilient, adaptive, and ethical AI-era enterprise. By examining these layers in light
of broader academic perspectives, we illuminate how they resonate with and expand upon existing theory.
Orchestrating Human, Machine, and Interface Intelligence
At the heart of the composite capability paradigm is the recognition that capabilities are no longer confined to
“tidy boxes” of human-versus-technical functions. Instead, capability is seen as a dynamic interplay – “a
combined—and occasionally chaotic—dance of human intelligence, technical expertise, machine cognition, and
agile interfaces”. In other words, whenever an organisation delivers value (be it a product innovation, a
customer service interaction, or a strategic decision), it is increasingly the outcome of this fusion of
contributions: what humans know and decide, what machines calculate and recommend, and how the two
connect through interfaces. The paradigm likens this to a “jam session” in music, where different instruments
improvise together in real-time. Just as a jazz ensemble’s brilliance comes from the interaction among players
rather than any one instrument in isolation, an organization’s performance now hinges on synergy – how
effectively people and AI tools can complement each other’s riffs and how flexibly they can adapt to change in
unison.
Let’s break down the components of this dance:
Human Intelligence: This encompasses the uniquely human attributes that AI currently cannot replicate or that
we choose not to delegate. These include empathy, ethical judgment, creativity, strategic insight, and contextual
understanding. For instance, humans can understand subtleties of interpersonal dynamics, exercise moral
discretion, and apply common sense in novel situations. In the capability stack model, human intelligence is
essential for providing purpose and a “moral compass” to technological endeavours. It aligns with what
behavioural scientists would call System 2 thinking (deliberative, reflective thought) as well as emotional and
social intelligence. Gary Klein’s experienced firefighter exercising gut intuition, or a manager sensing the
morale of their team, are examples of human intelligence in action. In AI integration, human intelligence sets
the goals and defines what “success” means – reflecting our values and objectives. This is why the Foundational
Layer of the capability stack is Purpose, Values, and Ethical Leadership, ensuring that the enterprise’s direction
is guided by human insight and integrity. A key insight from behavioural science is that people are not cogs;
they seek meaning in work and will support change if it resonates with their values. Therefore, having a clear
purpose (for example, “improve patient health” in a hospital setting or “connect the world” in a tech firm) and
ethical guidelines at the base of your AI strategy engages the workforce and garners trust. It also provides the
lens through which any AI initiative is evaluated (Does this AI use align with our values? Does it help our
stakeholders in a way we can be proud of?).
Technical Expertise: Traditionally, this meant the specialised knowledge of how to operate machinery,
engineering know-how, domain-specific analytical skills (e.g., financial modeling). In the modern paradigm,
technical expertise is evolving under the influence of AI. Experts must now collaborate with AI and
continuously update their knowledge as AI tools change their fields. For example, a supply chain expert still
needs logistics knowledge, but they also need to understand how to interpret outputs from an AI demand
forecasting system, and perhaps even how to improve it. The capability stack envisions that technical expertise
“harmonises with predictive models”, meaning human experts and AI models work in tandem. This resonates
with socio-technical theory: rather than AI replacing experts, the nature of expertise shifts. A doctor with AI
diagnostics is still a doctor – but one augmented with new data patterns (e.g., AI image analysis) and thus able
to make more informed decisions. A data-savvy culture is part of technical expertise too: widespread digital
fluency (not just a few data scientists sequestered in IT) is needed so that throughout the organisation people
understand AI’s capabilities and limits. This democratisation of technical competence is facilitated by trends
like low-code or no-code AI tools, which allow non-programmers to leverage AI – effectively broadening who
can contribute technical know-how. In sum, technical expertise in the composite capability paradigm is about
humans mastering their domain plus mastering how AI applies to that domain.
Machine Cognition: This refers to the AI systems themselves – the algorithms, models, and computational
power that constitute the machine’s “intelligence.” From a capability standpoint, machine cognition brings
speed, precision, and scale to problem-solving. It includes everything from simple process automation bots to
sophisticated machine learning models and generative AI (like GPT-4). Machine cognition can detect patterns
invisible to humans (e.g., subtle correlations in big data), work tirelessly 24/7, and execute decisions or
calculations in milliseconds. However, machine cognition has its own limitations: lack of genuine
understanding, potential for errors or biases based on training data, and inability to account for values or context
unless explicitly programmed. This is why the paradigm stresses the interplay – machine cognition is powerful,
but it requires the other elements (human oversight, proper interface) to be truly effective and safe. In the
capability stack, machine cognition sits in the core layer as part of the fusion, not at the top or bottom,
symbolising that AI is integrated into the fabric of how work is done, guided by purpose from above and
controlled/governed by structures around it. The behavioural science angle on machine cognition is mainly
about human interpretation: how do humans perceive and react to AI outputs? Research on decision support
systems finds that factors like the AI’s explainability, transparency of confidence levels, and consistency affect
whether humans will accept its advice. Thus, a machine might be extremely “intelligent” in a narrow sense, but
if humans don’t trust or understand it, its capability doesn’t translate into organisational performance. In
designing composite capabilities, organisations are learning to invest not just in algorithms, but in features that
make those algorithms usable and reliable in human workflows (for example, an AI-generated insight might be
accompanied by a natural-language explanation or a visualisation for the human decision-maker).
Agile Interfaces: Perhaps the most novel element is the idea of agile interfaces as the “conductor” of the
human-machine symphony. Interfaces include the user experience design of software, the dashboards, the
collaboration tools, or even the organisational processes that mediate human-AI interaction. The paradigm notes
that “agile interfaces are the critical conduits for effective human-AI interaction”, enabling translation of AI’s
raw power into forms humans can act on. Examples range from a well-designed alert system in a cockpit that
draws a pilot’s attention at the right time, to a chatbot interface that a customer finds intuitive and helpful, to an
augmented reality tool that guides a factory worker in performing a task with AI assistance. These interfaces
need to be agile in the sense of flexible, user-centered, and evolving. We now recognise new skills like prompt
engineering (formulating questions or commands to get the best results from AI models) and data storytelling
(translating data analysis into compelling narratives) as part of this interface layer. If human intelligence sets
goals and machine cognition generates options, the interface is what makes sure the two can “talk” to each other
effectively. From a behavioural perspective, interface design draws on cognitive psychology (how to present
information in ways that align with human attention and memory limits), on social psychology (how to
engender trust – for instance, by giving the AI a relatable persona in a customer service chatbot), and on
behavioural economics nudges (how the choice architecture can influence safer or more productive behaviours).
A trivial example: a decision portal might default to a recommended option but allow override, thus nudging
users toward the statistically superior choice while preserving agency – this is an interface-level nudge that can
lead to better outcomes without coercion.
The Composite Capability Core (Layer 2 of the stack) is essentially the synergy of these human, machine, and
interface components. It is where, to quote the internal framework, “pattern fusion” occurs – “the seamless
integration of human sense, domain depth, machine precision, and systemic perspective”. Pattern fusion implies
that when humans and AI work together, they can solve problems neither could alone, by combining strengths:
human sense (intuition, ethics, meaning) + deep domain expertise + AI’s precision + a systemic or holistic view
of context. Notably, the inclusion of systemic perspective reflects the need to consider the whole environment –
a nod to systems thinking (as per Emery & Trist’s focus on interdependencies). In practice, pattern fusion might
manifest as follows: imagine an urban planning scenario where deciding traffic policy needs data on vehicles
(AI can optimise flows), understanding of human behaviour (people’s commuting habits, which a behavioural
expert can provide), political acceptability (requiring empathy and negotiation by leaders), and tools to simulate
scenarios (an interface for experiments). A fused approach could create a solution that optimises traffic without,
say, causing public backlash – something a purely AI optimisation might miss or a purely human intuition might
get wrong. The framework argues that such fusion leads to “wiser, kinder, better decisions” – interestingly
attributing not just smartness (wiser) but kindness (reflecting values) to the outcome, and also calls out
interpretability as a benefit (humans and AI together can make the solution more explainable).
Layers of the 2025+ Capability Stack
Surrounding this fusion core are two other layers in the stack model: the Foundational Layer and the Finishing
Layer. These roughly correspond to inputs that set the stage (foundation) and oversight/outcomes that ensure
sustainability (finishing).
Foundational Layer: Purpose, Values, and Ethical Leadership. This bottom layer is the base upon which
everything rests. It includes the organisation’s purpose (mission), its core values, and the tone set by leaders in
terms of ethics and vision. In essence, it is about why the organisation exists and what it stands for. Grounding
an AI-enabled enterprise in a strong foundation of purpose and values serves several roles. First, it guides
strategy: AI investments and projects should align with the mission (for example, a healthcare company whose
purpose is patient care should evaluate AI not just on cost savings but on whether it improves patient outcomes,
consistent with their purpose). Second, it provides a moral and ethical compass: decisions about AI usage (such
as how to use patient data, or whether to deploy facial recognition in a product) can be filtered through the lens
of values like integrity, transparency, and respect for individuals. This is effectively what Floridi et al. advocate
– embedding principles so that ethical considerations are front and center. Third, a clear purpose and ethical
stance help in trust-building with both employees and external stakeholders. Employees are more likely to trust
and engage with AI systems if they see that leadership is mindful of ethical implications and that the systems
uphold the company’s values (for instance, an AI decision tool that is demonstrably fair and used in a values-
consistent way will face less internal resistance). Externally, customers and partners today scrutinise how
companies use AI – a strong foundational layer means the company can articulate why its use of AI is
responsible and beneficial. Behavioural science here intersects with leadership studies: transformational
leadership research shows that leaders who inspire with purpose and act with integrity foster more innovation
and buy-in from their teams. Therefore, having Ethical AI governance as a leadership imperative is part of this
layer – boards and executives must champion and monitor the ethical deployment of AI, making it a core part of
corporate governance (indeed, the internal report suggests boards treat AI governance as a “fiduciary duty”). In
practice, this could mean regular board-level reviews of AI projects, training leaders about AI ethics, and
including ethical impact in project KPIs.
Composite Capability (Fusion) Core: Human–AI Fusion and Interfaces. We discussed this above – it’s the
middle layer where the action happens. It is dynamic and process-oriented, concerned with how work gets done
through human-AI teaming. In the stack model, this is depicted as the engine of innovation and decision-
making. It includes elements like the use of multimodal AI (combining text, image, voice data) and ensuring
Explainable AI (XAI) for transparency, as well as emerging methodologies like Human-in-the-Loop (HITL)
which keeps a human role in critical AI processes. All these features align with the idea of making the human-
machine collaboration effective and trustworthy.
Finishing Layer: Wellbeing, Inclusion, and Governance. The top layer of the capability stack is termed the
“Finishing Layer (The Frosting)”, emphasising the need for a stable and positive environment in which the other
capabilities function. It includes employee wellbeing, inclusion and diversity, and robust governance structures
(particularly AI governance around data, privacy, and ethics). While called “finishing,” it is not an afterthought
– it’s what ensures the whole cake holds together and is palatable. Wellbeing is crucial because a highly capable
organisation could still fail if its people are burned out, disengaged, or fearful. Behavioural science highlights
that change (like digital transformation) can be stressful, and prolonged stress undermines performance,
creativity, and retention. Thus, efforts to maintain reasonable workloads, provide support for employees
adapting to new roles alongside AI, and focus on ergonomic job design (so that AI doesn’t, say, force people
into hyper-monitoring or repetitive check work that hurts satisfaction) are part of sustaining capabilities.
Inclusion in this context has multiple facets: ensuring a diverse workforce (so that the people working with and
designing AI have varied perspectives, which can reduce blind spots and biases), and ensuring that AI systems
themselves are inclusive (accessible to people with different abilities, not biased against any group of users). A
practical example is providing training opportunities to all levels of employees so that digital literacy is
widespread, preventing a digital divide within the company where only an elite handle AI and others are
marginalised. Inclusion also refers to bringing employees into the conversation about AI deployment
(participatory change management), which increases acceptance – people support what they help create, as
classic OD teaches. Robust Governance ties to ethical AI and regulatory compliance. It’s about structures and
policies that maintain oversight of AI. For instance, data privacy committees to vet use of personal data
(anticipating regulations like GDPR or the new AI laws mentioned in the internal report), or AI model
validation processes to ensure models are fair and robust before they are put into production. Essentially, the
finishing layer provides checks and balances and ensures sustainability. It resonates with concepts like corporate
social responsibility and stakeholder theory: the organisation monitors the impact of its capabilities on all
stakeholders (employees, customers, society) and corrects course when needed. In behavioural terms, having
strong governance and an inclusive, healthy environment feeds back into trust – employees who see that
leadership cares about these issues will be more engaged and proactive in using AI responsibly themselves.
Conversely, if this layer is weak, one might get initial performance gains from AI but then face issues like
ethical scandals (which can destroy trust and brand value) or employee pushback and turnover.
In sum, the Composite Capability Paradigm anchored by the 2025+ Capability Stack is a strategic schema that
marries behavioural and technical elements. It mirrors many principles found in academic literature: it has the
human-centric values focus (aligning with Schein’s cultural emphasis and Floridi’s ethics), it leverages human-
machine complementarities (echoing Brynjolfsson’s augmentation strategy and socio-technical systems theory),
it invests in learning and adaptation (reflecting Teece’s dynamic capabilities and Argyris’s organisational
learning concepts), and it institutionalises trust and wellbeing (drawing on behavioural insights about motivation
and ethical conduct). By framing these as layers, it provides leaders a mental model: Start with purpose and
values, build the human+AI engine on that foundation, and secure it with governance and care for people.
One can see how this addresses the challenges noted earlier in our literature review. For example, consider trust.
The foundation of ethical leadership sets a tone of responsible AI use; the fusion core includes explainability
and human oversight, which directly fosters trust; the finishing layer’s governance monitors and enforces
trustworthy practices. Or consider adaptive decision-making. The fusion core is all about agility – humans and
AI adjusting in real time (the “jam session”), and the dynamic capabilities thinking is baked into the need for
orchestration and continuous upskilling mentioned in the paradigm. The finishing layer’s focus on learning (e.g.,
psychological safety as part of wellbeing, inclusion of diverse voices) enables adaptation too. Human agency is
reinforced by the foundation (purpose gives meaningful direction; ethical leadership ensures humans remain in
charge of values) and by design choices in the core (HITL, interfaces that allow human override). Digital
fluency is specifically called out as something to be fostered (“universal AI fluency”), meaning training and
comfort with AI at all levels – that’s both a skill and a cultural aspect.
To illustrate how this framework plays out, here are some real-world vignettes:
• In Customer Service, customer empathy is augmented by AI doing sentiment analysis in real time,
allowing human agents to tailor their responses – a perfect example of composite capability (machine
gauges tone, human shows empathy, interface feeds the insight live).
• In Operations, Lean principles are turbocharged by AI that predicts machine failures from sensor data
and video, improving efficiency.
• In Product Design, AI can suggest creative variations (say, generating design mockups) which
designers then refine – AI amplifying human creativity.
• In Strategic Foresight, AI (like GPT-based scenario simulators) helps leaders envision various future
scenarios (e.g., climate futures) so they can better plan, combining data-driven simulation with human
judgment and values to choose a path.
All these examples follow the pattern of human + AI synergy aligned to purpose. The composite capability
paradigm thus serves as a bridge between theory and practice: it gives a language and structure to ensure that
when we implement AI, we do so in a way that is holistic – considering technology, people, and process
together – and principled – guided by purpose and ethics.
Next, we move from concept to concrete practice: what should leaders and organisations actually do to realise
these ideas? In the following section, we discuss how to integrate behavioural science insights with AI
initiatives on the ground, through targeted strategies around purpose, trust, skills, decision processes, and
governance.
From Theory to Practice: Integrating Behavioural Science and AI in Organisations
Implementing the vision of human-centric, behaviourally informed AI integration requires action on multiple
fronts. In this section, we outline practical approaches and examples across key themes – purpose and culture,
trust and human–AI teaming, digital fluency and skills, adaptive decision-making, and governance and ethics –
highlighting how organisations in various sectors are putting principle into practice.
Cultivating Purpose-Driven, Human-Centered Culture in the AI Era
A clear sense of purpose and strong organisational culture are not “soft” niceties; they are strategic assets in
times of technological upheaval. As discussed, purpose forms the foundation that guides AI adoption.
Practically, this means organisations should start AI initiatives by asking: How does this technology help us
fulfill our mission and serve our stakeholders? By framing projects in these terms, leaders can more easily
secure buy-in. For example, a public sector agency implementing AI to speed up service delivery might
articulate the purpose as improving citizen experience and fairness in accessing public services, resonating with
the agency’s public service mission. This was effectively demonstrated by the UK Behavioural Insights Team
(BIT), which applied behavioural science to public policy: they would define the purpose of interventions (e.g.,
increasing tax compliance to fund public goods) and design nudges accordingly. Their success – like
simplifying tax reminder letters to encourage on-time payments – came from aligning interventions with a clear
public purpose and an understanding of human behaviour. Organisations can analogously use AI as a tool to
advance purposeful goals (such as targeting healthcare resources to the neediest populations, or customising
education to each learner’s needs), and communicate that clearly to employees.
Communication is a vital part of culture. Change management research emphasizes over-communicating the
“why” in transformations. Leaders should consistently connect AI projects to core values. For instance, if
innovation is a value, an AI project might be touted as enabling employees to experiment and create new
solutions faster. If customer centricity is a value, management can stress how AI will help staff respond to
customer needs more promptly or personalise services – thus framing AI not as a threat, but as a means to better
live out the company’s values. Satya Nadella of Microsoft provides a real-world example: under his leadership,
Microsoft’s culture shifted to a “learn-it-all” (growth mindset) culture, encouraging experimentation. When
incorporating AI (like Azure AI services or GitHub’s Copilot), Nadella consistently frames it as empowering
developers and organisations – aligning with Microsoft’s mission “to empower every person and every
organisation on the planet to achieve more.” This kind of narrative helps employees see AI as supportive of a
shared purpose, not a top-down imposition of technology for its own sake.
In practical terms, organisations can embed purpose and human-centric principles into AI project charters and
evaluation criteria. Some companies have introduced an “ethical impact assessment” or purpose-impact
assessment at the start of AI projects. This involves multidisciplinary teams (including HR, legal, user
representatives) reviewing proposals by asking questions: Does this AI use align with our values? Who could be
adversely affected and how do we mitigate that? Will this improve the employee or customer experience
meaningfully? By institutionalising such reflection, the project is shaped from the outset to be human-centric.
This practice aligns with CIPD’s call for HR to ensure interventions “are in sync with how people are ‘wired’
and don’t inadvertently encourage undesirable behaviour” – essentially a reminder to align any new tools with
positive behaviours and outcomes.
Another concrete practice is storytelling and exemplars: sharing stories internally where AI helped a person do
something better or live the company values. For example, an insurance company might circulate a story of how
an AI risk model helped a risk officer identify a struggling customer and proactively offer help – highlighting
empathy enabled by tech. These stories reinforce a culture where AI is seen as enabling employees to achieve
the organization’s human-centered goals.
Building Trust and Effective Human–AI Teams
Trust is the cornerstone of any successful human–AI partnership. Without trust, employees may resist using AI
systems or use them improperly, and customers may reject AI-mediated services. Building trust requires both
technical measures (like reliability and transparency of AI) and social measures (like training and change
management to build confidence and understanding).
On the technical side, organisations should prioritise Explainable AI (XAI) in applications where users need to
understand or validate AI decisions. For instance, a fintech company deploying an AI credit scoring tool might
implement an interface that not only gives a score but also highlights key factors contributing to that score (debt
ratio high, short credit history, etc.) in plain language. This allows loan officers to trust the system and explain
decisions to customers, aligning with the principle of explicability. Many high-performing firms now treat
explainability as a requirement, not a luxury, for any AI that interacts with human decision-makers. This stems
from a behavioural understanding: people trust what they understand.
In addition to transparency, performance consistency of AI fosters trust. Users need to see that the AI is right
most of the time (or adds value) in order to rely on it. To that end, phased rollouts where AI recommendations
are first provided in parallel with human decisions (allowing humans to compare and give feedback) can
calibrate trust. A hospital, for example, might introduce an AI diagnostic tool by initially running it “silently” –
doctors see its suggestion but still make decisions independently; over time, as they see that the AI often catches
things they might miss or confirms their hunches, their trust grows. This staged approach was recommended by
some naturalistic decision-making experts to avoid abrupt shifts that could trigger algorithm aversion.
Training is critical: digital literacy and AI fluency training doesn’t only teach how to use the tool, but also
covers the tool’s limitations and the importance of human judgement. For instance, pilots train on autopilot
systems extensively to know when to rely on them and when to disengage – by analogy, a financial analyst
might be trained on an AI forecasting tool to know scenarios where it’s likely to err (perhaps during market
disruptions) so they can be extra vigilant. This idea of appropriate reliance comes straight from behavioural
research on automation (Parasuraman et al., 1997) which showed that people often either under-trust (ignore
useful automation) or over-trust (get complacent). The goal is calibrated trust.
From a social perspective, involving end-users in the design and testing of AI solutions fosters trust. If a new AI
tool is coming to an employee’s workflow, having some of those employees participate in its pilot, give
feedback, and witness improvements based on their input can turn them into change champions who trust the
end product. This participatory approach also surfaces usability issues that, if left unaddressed, could erode trust
later. It mirrors the behavioural principle that people fear what they don’t understand; involvement demystifies
the AI.
Organisational roles may also need to evolve to optimise human–AI teaming. Some companies are creating
roles like “AI liaison” or “human-AI team facilitator” – individuals who understand both the tech and the work
domain and can mediate between data science teams and frontline staff. These facilitators might observe how
employees interact with AI tools, gather suggestions, and continuously improve the human-AI interface. This is
analogous to having a user experience (UX) expert, but specifically focusing on the collaboration between
human and AI. For example, in a call center that introduced an AI that listens to calls and suggests responses (a
real technology in use), a facilitator monitored calls to see if the suggestions were helpful or if they annoyed the
agents, then tweaked the system or trained the agents accordingly (maybe the AI needed to wait a few seconds
more before popping up suggestions, to not interrupt the agent’s own thought process). Such adjustments make
the partnership smoother and bolster trust in the AI as a helpful colleague rather than an intrusive overseer.
Team norms can also be established for human–AI interaction. If decisions are being made with AI input, teams
can adopt norms like: Always double-check critical decisions with another human or source if the AI gives low
confidence, or Use the AI’s recommendation as a starting point but consider at least one alternative before
finalising (to avoid lock-in). These are akin to pilot checklists or medical second-opinion norms, and they
acknowledge that while AI is a team member, human members are ultimately accountable. By formalising such
practices, organisations signal that AI is a tool, not a replacement for human responsibility. This can alleviate
anxiety (employees know they’re not expected to blindly follow AI) and encourage learning (comparing AI and
human conclusions can be instructive).
A case in point for trust and teaming comes from the military domain, where “centaur” teams (a term borrowed
from chess human–AI teams) are being explored. Fighter pilots work with AI assistants that might drone-fly
wingman UAVs or manage defensive systems. The military has found that trust is built through rigorous testing
in exercises and the ability of pilots to easily take control from the AI when needed – reflecting the principle of
keeping humans in the loop for lethal decisions. In business, the stakes are usually lower, but the same concept
of giving humans an “eject button” or override and making that as easy as pressing a button fosters a safety net
that ironically makes users more open to letting the AI handle things up to that point. It’s analogous to having
brakes when using cruise control.
Finally, an often overlooked element: celebrating successes of human–AI collaboration. When an AI-assisted
effort leads to a win (say, an AI+human sales team exceeds their targets or an AI-driven quality control catches
a defect that human inspectors missed, avoiding a costly recall), leaders should acknowledge both the human
and the AI contribution. This sends a message that using the AI is praiseworthy teamwork, not something that
diminishes human credit. If employees fear that AI will steal the credit or make their role invisible, they’ll resist
it. Recognising augmented achievements in performance reviews or team meetings helps normalise AI as part of
the team.
Developing Digital Fluency and Adaptive Skills
One of the most tangible ways to integrate behavioural science with AI strategy is through learning and
development (L&D) initiatives. The half-life of skills is shrinking; dynamic capability at the organisational level
rests on continually upskilling and reskilling the workforce (sensing and seizing opportunities, in Teece’s
terms). Behavioural science-informed L&D focuses not just on knowledge transmission, but on motivation,
reinforcement, and practical application.
A key capability for 2025 and beyond is digital fluency – the ability for employees to comfortably understand,
interact with, and leverage AI and data in their roles. Companies leading in AI adoption often launch company-
wide digital academies or AI training programs. For example, AT&T and Amazon have large-scale reskilling
programs to train workers in data analysis and machine learning basics, offering internal certifications. The
behavioural insight here is to reduce learning anxiety: make learning resources abundant, accessible (online,
self-paced), and rewarding (through badges, recognition, or linking to career advancement). By building a
culture where continuous learning is expected and supported (and not punitive if one is initially unskilled),
employees are more likely to engage rather than fear the new technology. This also ties to Carol Dweck’s
growth mindset concept – praising effort and learning rather than static ability – which many organisations now
incorporate into their competency models.
Another tactic is experiential learning through pilot projects or innovation labs. Instead of classroom training
alone, employees learn by doing in sandbox environments. For instance, a bank might set up a “bot lab” where
any employee can come for a day to automate a simple task with a robotic process automation (RPA) tool, with
coaches on hand to assist. This hands-on experience demystifies AI (or automation) and builds confidence.
Behaviourally, adults learn best when solving real problems that matter to them (a principle from adult learning
theory). So if an employee can automate a tedious part of their job through an AI tool, they directly see the
benefit and are likely to be more enthusiastic about AI adoption.
Mentoring and peer learning also accelerate digital fluency. Some firms have implemented a “reverse
mentoring” system where younger employees or tech-savvy staff mentor senior managers on digital topics
(while in turn learning domain knowledge from those seniors). This not only transfers skills but breaks down
hierarchical barriers to learning – a major cultural shift in some traditional organisations. It leverages social
learning: people often emulate colleagues they respect, so having influential figures vocally learning and using
AI can create a bandwagon effect.
A concept gaining traction is the creation of fusion teams (also called citizen developer teams), which pair
subject-matter experts with data scientists or IT developers to co-create AI solutions. For example, in a
manufacturing firm, a veteran production manager teams up with a data scientist to develop a machine learning
model for predictive maintenance. The production manager learns some data science basics in the process
(digital upskilling) and the data scientist learns the operational context (domain upskilling). This cross-
pollination means the resulting solution is more likely to be adopted (since it fits the work context) and the
participants become champions and trainers for others. It’s an application of Vygotsky’s zone of proximal
development in a way – each learns from someone a bit ahead of them in another dimension, scaffolded by
collaboration.
Adaptive decision-making skills are also crucial. Employees need training not just in using specific tools, but in
higher-level skills like interpreting data, running experiments, and making decisions under uncertainty –
essentially, decision science literacy. Some organisations train their staff in basic statistics and hypothesis
testing so they can better design A/B tests or understand AI output (which often comes with probabilities or
confidence intervals). This is informed by the behavioural notion that people are prone to misinterpreting
probabilistic data (e.g., confusion between correlation and causation, or biases like overconfidence). By
educating the workforce on these pitfalls (perhaps using engaging examples, like common fallacies), companies
improve the collective ability to make sound decisions with AI.
Continuous feedback loops are another practice: dynamic capabilities demand quick learning cycles. Companies
can implement frequent retrospectives or after-action reviews when AI is used in projects. For instance, after a
marketing campaign guided by an AI analytics tool, the team can review what the AI suggested, what they did,
and what the outcome was, extracting lessons (did we trust it too much, did we under-utilise it, did we encounter
surprising customer reactions?). These insights then feed into refining either the AI model or the human
strategies. Such reflective practices are advocated in agile methodologies and are rooted in Kolb’s experiential
learning cycle (concrete experience → reflective observation → abstract conceptualisation → active
experimentation). Over time, they build an organisational habit of learning from both success and failure, key to
adaptation.
It’s also worth noting the leadership skill shifts needed. Leadership development programs are incorporating
training on leading hybrid human–AI teams, asking the right questions about AI (since leaders might not be the
technical experts, they need the fluency to challenge and query AI outputs – e.g., “What data was this model
trained on? How confident should we be in this prediction?”). Leading by example, if managers regularly use
data and AI insights in their decisions and explain how they balanced that with experience, employees pick up
on that decision-making approach.
A concrete example of adaptive skill-building can be drawn from healthcare: during the COVID-19 pandemic,
hospitals had to adapt quickly to new data (like predictive models of patient influx). Some hospitals created ad
hoc data teams and trained clinicians to read epidemiological models – a crash course in data literacy under
pressure. Those who managed to integrate the predictions with frontline insights navigated capacity issues
better. This underscores that when the environment changes rapidly (turbulent environment in Emery & Trist’s
term), organizations benefit from having invested in general adaptability skills beforehand.
Enhancing Decision-Making and Innovation through Human–AI Collaboration
Organisations can leverage AI and behavioural insights together to drive better decisions and innovation on an
ongoing basis. One method is establishing a culture and processes of evidence-based decision-making. The idea,
championed by movements like evidence-based management and supported by CIPD research, is to encourage
decisions based on data, experiments, and scientific findings rather than just intuition or tradition. AI naturally
provides more data and analytical power, but behavioural science reminds us that simply having data doesn’t
ensure it’s used wisely – cognitive biases or political factors can still lead to suboptimal choices.
To address this, some organisations have set up “decision hubs” or analytics centers of excellence that both
churn out insights and coach decision-makers on how to interpret them. A bank, for instance, might require that
any proposal for a new product comes with an A/B test plan and data analysis – essentially building a decision
process that forces a more scientific approach. Product teams at tech companies routinely do this: before rolling
out a feature, they run experiments and the go/no-go is based on statistically significant results, not just the
HIPPO (highest paid person’s opinion). This discipline is part technical (knowing how to run tests) and part
behavioural (committing to act on what the data says, which can be hard if it contradicts one’s intuition).
Leaders play a role here by reinforcing that changing course in light of data is a strength, not a weakness. Jeff
Bezos called this “being stubborn on vision, flexible on details” – hold onto your core purpose but be willing to
change tactics when evidence suggests a better way.
Adaptive governance structures, like rapid steering committees or innovation task forces, can empower faster
decision loops. For example, during a crisis or fast-moving market change, a company might assemble a cross-
functional team that meets daily to review AI-generated forecasts and frontline reports, then make quick
decisions (similar to a military OODA loop: observe, orient, decide, act). This was observed in some
companies’ COVID responses – they effectively set up a nerve center mixing data (sometimes AI models
predicting scenarios) with human judgment to navigate uncertainty. The behavioural key is that these teams had
the mandate to act and adjust, avoiding the paralysis that can come from either fear of uncertainty or
bureaucratic slowness. They embraced adaptive decision-making, making small reversible decisions quickly
rather than waiting for perfect information.
In terms of innovation, AI can generate ideas (like design suggestions or optimisations) but human creativity
and insight are needed to choose and implement the best ideas. Companies are thus exploring human–AI co-
creation processes. One practical approach is ideation sessions with AI: for instance, marketers might use GPT-4
to produce 50 variations of an ad copy, then use their creative judgment to refine the best ones. In engineering,
generative design algorithms propose thousands of component designs, and engineers use their expertise to pick
one that best balances performance and feasibility. This speeds up the trial-and-error phase of innovation
dramatically, allowing humans to consider far more possibilities than they could alone. But it also requires a
mindset shift: designers and experts must be open to letting AI contribute and not feel that it diminishes their
role. To facilitate this, some organisations frame AI as a creative partner or brainstorming assistant. They
encourage teams to treat AI suggestions not as final answers, but as provocations or starting points. This reduces
the psychological defensiveness (“a robot is doing my job”) and instead fosters curiosity (“let’s see what it
comes up with, maybe it will spark something”). Pixar, for example, has experimented with AI for generating
plot ideas or character visuals – not to replace writers or artists, but to help break through creative blocks or
explore alternatives. They report that artists actually enjoyed riffing off AI outputs once they felt it was their
choice what to use or discard.
Bias mitigation in decisions is another area where behavioural science and AI together can help. AI can be used
to debias human decisions – for instance, in hiring, structured algorithmic screening can counteract individual
manager biases (though one must also ensure the AI itself is fair). Meanwhile, behavioural tactics like blinding
certain info or using checklists can be applied to AI outputs; e.g., if an AI produces a recommendation, a
checklist for managers might ask “What assumptions is this recommendation based on? Have we considered an
opposite scenario?” which forces a consideration of potential bias or error. The combination ensures neither
human nor AI biases dominate unchecked. The “premortem” technique by Gary Klein (imagine a future failure
and ask why it happened) can be used on AI-driven plans to uncover hidden issues. Some AI development teams
now do bias impact assessments as part of model development (a practice encouraged by IBM, Google etc.),
essentially bringing a social science lens into the tech development.
Strengthening Governance, Ethics, and Trustworthy AI Practices
Governance provides the scaffolding that holds all the above initiatives accountable and aligned. It’s the
embodiment of the “robust governance” and “ethical AI” focus in the capability stack’s top layer. Several
concrete governance measures are emerging as best practices:
AI Ethics Boards or Committees: Many organisations (Google, Facebook, Microsoft, to name tech giants, but
also banks, healthcare systems, universities, and governments) have convened advisory boards or internal
committees to review AI projects. The composition is typically cross-functional – legal, compliance, technical,
HR, and often external independent experts or stakeholder representatives. Their role is to examine proposed
high-impact AI uses for ethical risks, alignment with values, and compliance with regulations. For example, a
global bank’s AI ethics committee might review a new algorithmic lending platform to ensure it doesn’t
discriminate and that it has an appeal process for customers – effectively implementing principles of fairness
and accountability. These boards are a direct response to both ethical imperatives and looming regulations (like
the EU AI Act’s requirements for high-risk AI systems). They institutionalise the “slow thinking” System 2
oversight to balance the fast-moving deployment of AI. Behavioural science supports this by recognising that
individual developers or product owners may have conflicts of interest or cognitive blind spots – a formal
review by a diverse group brings more perspectives (avoiding groupthink and the bias of tunnel vision) and
creates a checkpoint for reflection (mitigating the rush that can lead to ethical lapses).
Policies and Principles: Organisations often publish AI principles (e.g., “Our AI will be fair, accountable,
transparent, and explainable” – similar to Floridi’s five principles) and then derive concrete policies from them.
A policy might dictate, for example, that sensitive decisions (hiring, firing, credit denial, medical diagnosis) will
not be made solely by AI – there must be human review (human-in-the-loop), which echoes one of the EU draft
AI regulations as well. Another might require that any customer-facing AI makes clear to the user that it is an AI
(so people aren’t duped into thinking a chatbot is a human, respecting autonomy). These policies are essentially
commitment devices at the organisational level – they set default behaviours that align with ethical intentions,
making it easier for employees to do the right thing and harder to do the wrong thing. They also serve to build
public trust, since companies can be held to their promises.
Transparency and Communication: Internally, transparency means informing employees about how AI is
affecting decisions about them (like performance evaluations or promotions, if algorithms play a role) and
decisions they make (providing insight into the tools they use). Externally, it means being honest with customers
about when AI is used and what data is collected. Some banks, for instance, let customers know that an
automated system did the initial credit assessment and give a route to request human reassessment – this kind of
candour can actually improve trust, as customers feel they are respected and have recourse. It also pressures the
AI to perform well since its suggestions might be scrutinised. Interestingly, behavioural research shows people
appreciate procedural fairness: even if they get a negative outcome, if they believe the process was fair and
transparent, they react less negatively. So transparency is not just an ethical duty, but also a strategy to maintain
trust even when AI systems must deliver unwelcome news.
Monitoring and Auditing: The governance framework should include continuous monitoring of AI
performance and impacts, not just one-time reviews. AI models can drift (their accuracy degrades if data
patterns change), and their use can evolve in unintended ways. Companies are starting to implement AI
monitoring dashboards, analogous to financial controls, tracking key metrics like bias indicators, error rates, and
usage statistics. For example, if an AI recruiting tool suddenly starts filtering out a higher percentage of female
candidates than before, that flag can trigger an investigation. This is similar to the way credit scoring models are
monitored for bias in lending. Some jurisdictions are likely to mandate such audits (the proposed EU AI Act
would require logging and oversight for high-risk AI). Incorporating this proactively is wise. It again brings in
behavioural science at the organisational level: what gets measured gets managed. By measuring ethical and
human-impact metrics, not just performance, an organisation signals its priorities and catches issues early. There
is also a behavioural aspect in that knowing one is monitored can deter negligent behaviour – if teams know
their AI deployment will be audited for fairness, they’re more likely to design it carefully from the start.
Responsive Governance: Governance shouldn’t just be rigid control; it must also be adaptive. If an audit or a
whistleblower or an external event reveals a problem (say, an AI is implicated in a privacy breach or bias
incident), an agile governance process can pause that AI’s deployment and convene a response team to fix it.
This happened in some tech companies, for example, when a facial recognition product was found to have racial
bias, the company voluntarily halted sales to law enforcement and invested in improvements. The ability to
respond quickly to ethical issues – essentially an organisational form of course correction – will define
companies that can retain public trust. It is analogous to product recalls in manufacturing: how you handle a
flaw can make or break your reputation.
A specific domain example: Public services and government are increasingly using AI (for welfare eligibility,
policing, etc.), and they have set up governance like independent oversight panels and algorithm transparency
portals where the code or at least a description is published for public scrutiny. The Netherlands, after a scandal
where a biased algorithm falsely flagged welfare fraud (the SyRI system), established stricter oversight and even
legal bans on such algorithms until proper safeguards are in place. The lesson taken was that not tempering
technical possibility with behavioural and ethical oversight can lead to serious harms, which then require
rebuilding trust from scratch. Now they emphasise citizen privacy, feedback from social scientists, and smaller
pilot programs to evaluate impacts before scaling.
Within organisations, employee involvement in governance is an interesting trend. For instance, some
companies have ethics champions or ambassadors in each department who ensure local context is considered in
AI use and act as liaisons to the central AI ethics committee. This decentralises ethical mindfulness – a bit like
having safety officers throughout a factory, not just at HQ. It leverages the behavioural principle of ownership:
people on the ground often see problems early, and if they feel responsible for ethics, they’re more likely to
speak up rather than assume “someone else up high will take care of it.” Creating safe channels for such voices
(whistleblower protections, open-door policies on AI concerns) is vital, reflecting Edmondson’s psychological
safety concept again, but in the ethics domain.
Finally, regulatory engagement is part of governance now. Organisations should keep abreast of and even help
shape emerging AI regulations and industry standards (like IEEE’s work on AI ethics standards). This proactive
approach means they’re not caught off guard by compliance requirements and can even gain a competitive edge
by being early adopters of high standards (much like companies that embraced environmental sustainability
early reaped reputational rewards). It also ensures that their internal governance aligns with external
expectations, making the whole ecosystem more coherent.
In conclusion, the practical integration of behavioural science and AI requires concerted effort in culture, trust-
building, skill development, decision processes, and governance. The themes we’ve discussed are deeply
interrelated: a purpose-driven culture facilitates trust; trust and skills enable adaptive decision-making; good
decisions and experiences reinforce trust and culture; and governance sustains it all by ensuring accountability
and alignment with values. Organisations that weave these elements together are effectively operationalising the
composite capability paradigm – they are designing themselves to be both high-tech and deeply human,
dynamic yet principled.
Conclusion
Behavioural Sciences in the Age of AI is not just an academic topic; it is a lived strategic journey for
organisations today. In this paper, we have traversed the historical and theoretical landscape that underpins this
journey – from Simon’s realisation that human rationality is bounded, to Brynjolfsson’s insight that humans and
machines, working as partners, can achieve more than either alone, to Floridi’s urging that AI be guided by
human-centric principles for a flourishing society. These insights form a tapestry of wisdom: they tell us that
effective use of AI requires understanding human cognition and behaviour at individual, group, and societal
levels.
We anchored our discussion in a practical framework – the composite capability paradigm – which captures
how human intelligence, machine cognition, and agile interfaces must seamlessly interact for organisations to
thrive. We situated this paradigm within broader literature, showing it resonates with socio-technical theory’s
call for joint optimisation, dynamic capabilities’ emphasis on agility and reconfiguration, and ethical
frameworks’ insistence on purpose and values. In doing so, we positioned the user’s internal frameworks as part
of a continuum of scholarly and practical evolution, rather than isolated ideas. This enriched perspective reveals
that the challenges of the AI era – building trust, preserving human agency, ensuring ethical outcomes, and
maintaining adaptability – are new in form but not in essence. They echo age-old themes of organisational life:
trust, purpose, learning, and justice, now cast in new light by technology.
Through real-world examples across health, public services, business, and government, we illustrated both the
opportunities and the cautionary tales. We saw how a hybrid of radiologists and AI improves diagnostic
accuracy, and how a poorly overseen algorithm can cause public harm and outrage (as in the welfare case).
These examples reinforce a key takeaway: human–AI collaboration works best when it is designed and
governed with a deep appreciation of human behaviour – our strengths (creativity, empathy, judgment) and our
weaknesses (bias, fear of change, fatigue). In healthcare, education, finance, and beyond, those deployments of
AI that succeed tend to be those that augment human decision-making and are accepted by humans; those that
fail often neglected the human factor, whether by ignoring user experience, eroding trust, or conflicting with
values.
Several cross-cutting themes emerged in our analysis: purpose, trust, digital fluency, human agency, adaptive
decision-making, and governance. It is worth synthesising how they interplay to inform a vision for
organisations moving forward. Purpose and values form the north star – they ensure AI is used in service of
meaningful goals and set ethical boundaries. Trust is the currency that allows humans to embrace AI and vice
versa; it is earned through transparency, reliability, and shared understanding. Digital fluency and skills are the
enablers, equipping people to work alongside AI confidently and competently. Human agency is the lens of
dignity – maintaining it means AI remains a tool for human intentions, not a black box authority; it means
employees at all levels feel they can influence and question AI, thereby avoiding a dystopia of uncritical
automation. Adaptive decision-making is the modus operandi for a complex world – using data and
experimentation (often powered by AI) but guided by human insight to navigate uncertainty in an iterative,
learning-focused way. And governance and ethics are the safety rails – without them, short-term wins with AI
can lead to long-term crashes, whether through regulatory penalties or loss of stakeholder trust.
Looking ahead, the Age of AI will continue to evolve with new advancements: more multimodal AI, more
autonomous systems, more integration into daily life. Behavioural science, too, will evolve as we learn more
about how people interact with increasingly intelligent machines. Concepts like algorithmic nudges (AI shaping
human behaviour subtly) or extended cognition (humans thinking with AI aids) will grow in importance. But the
core insight of this paper is likely to endure: that the human in the loop is not a weakness to be engineered away,
but the very source of direction, purpose, and ethical judgment that technology alone cannot provide. As the
internal strategy document eloquently put it, we are witnessing “a philosophical shift: reclaiming human agency
and purpose by ensuring capabilities reflect organisational values and aspirations in a world of rapid change.” In
practical terms, this means organisations must consciously design their AI deployments to amplify human
potential and align with human values, not suppress them.
For strategists and leaders, then, the task is clear. It is to become, in a sense, behavioural engineers of
organisations – crafting structures, cultures, and systems where humans and AI together can excel. It is to
champion ethical innovation, proving that we can harness powerful technologies while keeping humanity at the
center. And it is to invest in learning and adaptation as primary capabilities, so that as new research and new
technologies emerge, the organisation can incorporate them responsibly and effectively. The organisations that
succeed in the coming years will be those that manage this integration deftly – who neither fall into the trap of
techno-centrism (trusting technology blindly, neglecting the people) nor the trap of techno-skepticism (fearing
technology and falling behind), but find a harmonious path of augmentation, where technology elevates people
and people steer technology.
In conclusion, behavioural science offers both a caution and a promise in the age of AI. The caution is that
ignoring human factors can lead even the most advanced AI solutions to fail or cause harm. The promise is that
by embracing a human-centered approach, we can unlock the full potential of AI to create organisations that are
not only more innovative and efficient, but also more resilient, ethical, and responsive to those they serve. By
learning from the past and grounding ourselves in foundational principles of human behaviour, we equip
ourselves to shape a future where AI amplifies human wisdom and creativity rather than undermining them. In
doing so, we ensure that the Age of AI remains, fundamentally, an age of human progress and empowerment,
aligned with the values and behaviours that define our humanity.
Sources:
Investopedia – Herbert A. Simon: Bounded Rationality and AI Theoristinvestopedia.cominvestopedia.com
The Decision Lab – Daniel Kahneman profilethedecisionlab.com
The Decision Lab – Gerd Gigerenzer profilethedecisionlab.com
The Decision Lab – Gerd Gigerenzer (quote)thedecisionlab.com
CIPD – Our Minds at Work: Developing the Behavioural Science of HR
Medium (Link Daniel) – Structured thinking and human-machine success (chess example)
TED Talk (Brynjolfsson) – Race with the machine (freestyle chess)blog.ted.comblog.ted.com
Workplace Change Collab. – Radiologist + AI outperforms either alonewpchange.org
New Capabilities (Internal doc) – Composite capability paradigm excerpt
New Capabilities (Internal doc) – Reclaiming human agency and purpose
New Capabilities (Internal doc) – 4D chess analogy for modern environment
New Capabilities (Internal doc) – Human-machine “dance” metaphor
AI4People (Floridi et al.) – AI ethics principles (human dignity, autonomy,
etc.)pmc.ncbi.nlm.nih.govpmc.ncbi.nlm.nih.gov
Workplace Change Collab. – Radiologist-AI hybrid sensitivity/specificitywpchange.orgwpchange.org
David J. Teece – Dynamic capabilities definitiondavidjteece.com
David J. Teece – Sensing, seizing, reconfiguring (agility)davidjteece.com
Wikipedia – Sociotechnical systems theory (Trist et al.)en.wikipedia.orgen.wikipedia.org
P. Millerd (Blog) – Schein on learning anxiety vs survival anxietypmillerd.compmillerd.com
Workplace Change Collab. – Ethical AI, inclusion, governance as “finishing layer”
New Capabilities (Internal doc) – AI as social-technological actor symbiosis
New Capabilities (Internal doc) – Universal AI fluency and continuous upskilling
