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  • Generative AI Is Changing Strategic Decision-Making, Not Because It Thinks Like Humans, but Because Humans Think Differently With It

    Generative AI Is Changing Strategic Decision-Making, Not Because It Thinks Like Humans, but Because Humans Think Differently With It

    Abstract

    Generative artificial intelligence (AI) is increasingly being integrated into organisational decision-making. However, much of the current discussion continues to frame AI as either a replacement for human cognition or as a productivity-enhancing tool. This article argues that these perspectives overlook a more fundamental transformation. The strategic importance of generative AI lies not in its capacity to make decisions independently but in its ability to reshape how managers construct, evaluate, and revise their own judgements. Drawing on organisational sensemaking, bounded rationality, and trust calibration, this article proposes that generative AI functions as a cognitive partner that alters managerial reasoning rather than replacing it. This perspective offers a richer explanation of AI-enabled decision-making in SMEs and identifies new directions for future research.

    Introduction

    Artificial intelligence is often discussed in terms of automation, efficiency, and productivity. Headlines frequently ask whether AI will replace managers, automate strategic planning, or outperform human decision-makers. Such questions, while important, may be asking the wrong thing.

    Managers rarely make strategic decisions in environments characterised by complete information and objective certainty. Instead, they operate within contexts shaped by ambiguity, incomplete knowledge, competing priorities, and organisational constraints. Under these conditions, strategic decision-making depends not only on information availability but also on interpretation.

    Generative AI changes this interpretive environment.

    Unlike previous generations of analytical technologies, large language models generate explanations, alternatives, summaries, scenarios, and recommendations that resemble human reasoning. These outputs influence not simply what managers know, but how they think about organisational problems.

    This article argues that the real contribution of generative AI is cognitive rather than computational. It changes managerial thinking by expanding, redirecting, and sometimes constraining the interpretive processes through which strategic decisions emerge.

    From Decision Support to Cognitive Partnership

    Traditional decision support systems were designed primarily to improve access to information.

    Generative AI performs a fundamentally different role.

    Rather than simply retrieving information, it actively generates possible interpretations, frames organisational problems in new ways, proposes strategic alternatives, and constructs persuasive narratives.

    Consequently, managers increasingly interact with AI through dialogue rather than command.

    This shift transforms AI from an information tool into what can be described as a cognitive partner.

    A cognitive partner does not make organisational decisions independently.

    Instead, it participates in the reasoning process by introducing new cues, challenging assumptions, generating counterfactual scenarios, and expanding the space of possible interpretations.

    The quality of managerial judgement therefore depends on the interaction between human cognition and AI-generated reasoning rather than either component operating in isolation.

    Theoretical Foundations

    Three complementary theoretical perspectives help explain this transformation.

    Bounded Rationality

    Managers cannot process unlimited information or evaluate every possible strategic alternative.

    Generative AI partially reduces these cognitive constraints by accelerating information synthesis and option generation.

    However, reducing information-processing costs does not eliminate bounded rationality.

    Instead, the boundaries themselves shift.

    Managers increasingly face new limitations concerning evaluation, verification, and interpretation rather than information access.

    Organisational Sensemaking

    Sensemaking explains how organisational actors construct meaning under conditions of ambiguity.

    Generative AI contributes new organisational cues that managers must interpret before action becomes possible.

    Importantly, AI-generated outputs possess no inherent organisational meaning.

    Meaning emerges only through interaction between the manager, the technology, and the organisational context.

    This makes sensemaking an essential mechanism connecting AI outputs to strategic action.

    Trust Calibration

    Effective AI-supported decision-making requires neither unconditional trust nor persistent scepticism.

    Instead, managers must continuously calibrate reliance according to task complexity, uncertainty, organisational risk, and AI capability.

    Trust calibration therefore functions as the regulatory mechanism governing productive human–AI collaboration.

    A New Conceptual Proposition

    Building on these perspectives, I propose the following proposition:

    The strategic value of generative AI is determined less by algorithmic capability than by the quality of managerial interpretation that transforms AI-generated outputs into organisational judgement.

    This proposition challenges assumptions that technological sophistication alone explains organisational performance.

    Instead, it suggests that identical AI systems may produce substantially different organisational outcomes because managers interpret and use their outputs differently.

    Implications for SMEs

    This argument is particularly relevant for SMEs.

    Unlike large organisations, SMEs often operate with:

    • concentrated decision authority
    • limited analytical resources
    • informal decision structures
    • rapid strategic adaptation

    Under these conditions, managerial cognition becomes especially important.

    Generative AI can significantly expand analytical capacity.

    However, if managers become overly dependent on AI-generated reasoning or fail to critically evaluate recommendations, improvements in productivity may not translate into improvements in strategic decision quality.

    The organisational challenge therefore becomes one of developing interpretive capability rather than merely technological capability.

    Towards a Human–AI Decision Ecology

    Current research frequently compares human decisions with AI decisions.

    A more productive perspective is to examine how decisions emerge through interaction.

    Rather than asking whether humans or AI make better decisions, future research should investigate the conditions under which collaborative human–AI reasoning produces superior organisational outcomes.

    This requires moving beyond binary debates concerning replacement versus augmentation.

    Instead, organisations should be viewed as decision ecologies in which humans and intelligent systems jointly contribute to interpretation, evaluation, and action.

    Research Agenda

    Future studies should examine several questions:

    • How do managers integrate AI-generated reasoning into strategic deliberation?
    • What organisational routines improve critical evaluation of AI outputs?
    • Under what conditions does AI enhance managerial creativity rather than constrain it?
    • How does trust calibration evolve through repeated interaction with generative AI?
    • How do SMEs differ from larger organisations in their development of AI-enabled decision routines?

    Addressing these questions would advance understanding of AI as a socio-cognitive phenomenon rather than merely a technological innovation.

    Conclusion

    Generative AI represents more than another digital technology entering organisations.

    Its distinctive contribution lies in its ability to participate in managerial reasoning.

    The future of AI-enabled strategic decision-making will therefore depend not only on increasingly capable algorithms but also on managers’ ability to interpret, challenge, and integrate AI-generated insights responsibly.

    The organisations most likely to benefit from generative AI will not necessarily be those possessing the most advanced technology.

    They will be those developing the strongest capabilities for human judgement, organisational learning, and critical sensemaking.

    Author Note

    Saba Shahzadi is an emerging researcher whose work examines generative AI, organisational sensemaking, managerial decision-making, and SME digital transformation. Her research focuses on how managers construct trust and meaning when using generative AI in strategic decision-making under uncertainty.

    How do you think generative AI should be used in strategic decision-making?

    • As an intelligent assistant?
    • As a strategic adviser?
    • As a brainstorming partner?
    • Or primarily as a tool that should always be verified before influencing important business decisions?

    I would be interested to hear your perspective.

  • Beyond Adoption: A Trust Calibration Framework for Generative AI in SME Strategic Decision-Making

    Beyond Adoption: A Trust Calibration Framework for Generative AI in SME Strategic Decision-Making

    Introduction

    Research and practitioner discussions surrounding generative AI have largely focused on adoption. Questions concerning whether firms use generative AI, how frequently they use it, and what benefits they obtain have dominated both academic and managerial conversations. While these questions are important, they leave a significant gap in our understanding of how generative AI influences organisational decision-making.

    Adoption alone tells us very little about the quality of managerial judgement.

    Two organisations may use the same generative AI system and achieve very different outcomes. One organisation may use AI as a valuable source of insight that enhances strategic thinking. Another may become overly dependent on AI-generated recommendations, reducing critical reflection and increasing the risk of poor decisions. A third may underutilise the technology entirely, failing to benefit from its analytical capabilities.

    The difference is not necessarily explained by the technology itself. Rather, it may be explained by how managers determine when, why, and to what extent they rely on AI-generated outputs.

    This article argues that future research should move beyond adoption-focused explanations and place greater emphasis on trust calibration. Specifically, I propose a conceptual framework that explains how SME managers calibrate trust in generative AI during strategic decision-making under uncertainty.

    The central argument is that decision quality depends not simply on AI capability but on the relationship between perceived AI competence, managerial expertise, contextual uncertainty, and organisational verification practices.

    The limitations of adoption-based explanations

    The literature on technological innovation has traditionally treated adoption as a key indicator of success. Models such as the Technology Acceptance Model (TAM), Diffusion of Innovations Theory, and the Unified Theory of Acceptance and Use of Technology (UTAUT) have generated valuable insights into why individuals and organisations adopt new technologies.

    However, generative AI presents a challenge to these approaches.

    Knowing that a manager uses ChatGPT, Claude, Gemini, or another generative AI system does not reveal how that manager uses the technology during strategic decision-making. Adoption measures cannot explain:

    • whether AI recommendations are critically evaluated
    • whether managers challenge AI-generated assumptions
    • whether AI is treated as an advisor or an authority
    • whether AI outputs influence final decisions
    • whether managers recognise AI limitations

    As generative AI becomes embedded in managerial work, these questions become increasingly important.

    Future research therefore requires a shift from understanding technology acceptance to understanding technology reliance.

    Why trust calibration matters

    Trust calibration refers to the alignment between actual system capability and user reliance.

    A manager demonstrates effective trust calibration when reliance on AI corresponds appropriately to the quality of the AI output and the level of risk associated with the task.

    Conversely, trust miscalibration occurs when reliance becomes disproportionate.

    Two forms of miscalibration are particularly important:

    Over-reliance

    Managers place excessive confidence in AI outputs despite uncertainty regarding their accuracy or contextual relevance.

    Under-reliance

    Managers reject potentially useful AI-generated insights because of excessive scepticism or lack of confidence in the technology.

    Both forms of miscalibration can reduce decision quality.

    The objective should therefore not be maximum trust but appropriately calibrated trust.

    A Trust Calibration Framework for SME Decision-Making

    I propose that trust calibration in AI-enabled strategic decision-making is influenced by four interacting dimensions.

    Dimension 1: Perceived AI Competence

    Managers continuously form judgements regarding AI capability.

    These judgements may be based on:

    • previous experiences
    • perceived accuracy
    • output quality
    • system transparency
    • task performance

    When perceived competence increases, managerial reliance often increases.

    However, perceived competence does not always reflect actual competence.

    Generative AI systems may produce highly persuasive outputs even when underlying reasoning is weak or incomplete.

    This creates a potential gap between perceived and actual capability.

    Dimension 2: Managerial Expertise

    The second dimension concerns the manager’s own expertise.

    Experienced managers often possess:

    • industry knowledge
    • contextual understanding
    • strategic intuition
    • pattern recognition capabilities

    Such expertise allows them to critically evaluate AI outputs.

    Less experienced managers may struggle to distinguish between genuinely valuable insights and superficially plausible recommendations.

    Consequently, managerial expertise acts as a moderating factor in trust calibration.

    Higher expertise may improve calibration by strengthening critical evaluation.

    Dimension 3: Environmental Uncertainty

    Strategic decisions rarely occur under conditions of complete certainty.

    Managers frequently face:

    • volatile markets
    • technological disruption
    • regulatory change
    • shifting customer preferences
    • competitive unpredictability

    Under uncertainty, confidence in human judgement may decline.

    This can increase reliance on AI-generated recommendations.

    Paradoxically, uncertainty often increases both the attractiveness of AI and the difficulty of evaluating its outputs.

    The result is a heightened risk of trust miscalibration.

    Dimension 4: Verification Mechanisms

    The final dimension concerns organisational safeguards.

    Verification mechanisms include:

    • evidence checking
    • peer review
    • scenario comparison
    • managerial discussion
    • challenge routines

    These mechanisms help managers evaluate AI-generated outputs before acting upon them.

    Where verification mechanisms are weak or absent, reliance on AI may become increasingly dependent on subjective impressions rather than systematic evaluation.

    Verification therefore serves as an important organisational buffer against both over-reliance and under-reliance.

    The proposed process model

    The framework can be summarised as a dynamic process:

    AI Output → Managerial Interpretation → Trust Assessment → Verification → Strategic Action

    At each stage, managerial judgement remains central.

    The process is not linear or automatic.

    Managers continually reassess AI recommendations in light of new information, organisational constraints, and contextual realities.

    This suggests that strategic decision-making remains fundamentally human even when supported by advanced AI systems.

    Implications for SMEs

    The framework is particularly relevant for SMEs.

    Compared with large organisations, SMEs often possess:

    • fewer specialist analysts
    • less formal governance
    • limited decision-support infrastructure
    • concentrated decision authority

    These characteristics increase both the potential value and the potential risks of generative AI.

    On one hand, AI can compensate for resource limitations by providing rapid access to information and analytical support.

    On the other hand, concentrated decision authority means that trust miscalibration may have direct strategic consequences.

    For SMEs, understanding trust calibration may therefore be more important than understanding adoption alone.

    Implications for Future Research

    This framework generates several research questions.

    Research Question 1

    How does managerial expertise influence trust calibration during AI-supported strategic decision-making?

    Research Question 2

    How does environmental uncertainty affect managerial reliance on AI-generated recommendations?

    Research Question 3

    What organisational practices improve trust calibration in SMEs?

    Research Question 4

    How do verification mechanisms moderate the relationship between trust and decision quality?

    Research Question 5

    Under what conditions does AI-supported decision-making outperform traditional decision processes?

    These questions provide promising directions for future empirical investigation.

    Conclusion

    The growing presence of generative AI in organisations requires a shift in research focus.

    The central challenge is no longer simply understanding why firms adopt AI.

    Instead, the critical question concerns how managers determine when and how to rely on AI-generated outputs during strategic decision-making.

    This article proposed a trust calibration framework consisting of four key dimensions: perceived AI competence, managerial expertise, environmental uncertainty, and verification mechanisms.

    Together, these dimensions help explain why identical technologies may produce very different organisational outcomes.

    Ultimately, the future of AI-enabled decision-making will depend not only on technological capability but also on the ability of managers to calibrate trust appropriately under conditions of uncertainty.

    For SMEs in particular, trust calibration may prove to be one of the most important managerial capabilities of the AI era.

    Author Note

    Saba Shahzadi is an emerging researcher whose work focuses on generative AI, managerial decision-making, organisational sensemaking, and SME digital transformation. Her research explores how managers interpret, trust, and integrate AI-generated outputs when making strategic decisions under uncertainty.

  • Sensemaking, Not Automation: How Managers Interpret AI Outputs in Strategic Contexts

    Sensemaking, Not Automation: How Managers Interpret AI Outputs in Strategic Contexts

    Introduction

    Much of the contemporary discussion surrounding generative AI assumes that the primary organisational challenge is technological adoption. The central question often appears to be whether firms will use artificial intelligence and how quickly adoption will occur. While adoption is undoubtedly important, this perspective risks overlooking a more fundamental issue. The strategic value of generative AI depends not only on what the technology produces but also on how managers interpret, evaluate, and act upon its outputs.

    Generative AI systems can summarise information, generate scenarios, identify patterns, and produce recommendations with remarkable speed. However, these outputs do not automatically become organisational decisions. They must first pass through processes of interpretation and judgement. Managers must decide what the output means, whether it is credible, how it relates to organisational goals, and whether it warrants action.

    For this reason, the growing integration of generative AI into managerial work should not be understood purely as a process of automation. Rather, it should be understood as a process of organisational sensemaking.

    This article argues that the strategic impact of generative AI in small and medium-sized enterprises (SMEs) is best understood through the lens of sensemaking. AI-generated outputs do not replace managerial judgement; they become inputs into ongoing processes through which managers construct meaning under conditions of ambiguity and uncertainty. Consequently, the future of AI-enabled decision-making depends not only on technological capability but also on how organisational actors make sense of what AI is telling them.

    The limits of the automation narrative

    The dominant narrative surrounding artificial intelligence often presents AI as a tool for replacing or automating human cognitive tasks. Within this view, technological progress is measured primarily by improvements in speed, efficiency, and predictive capability.

    While these developments are significant, they provide only a partial understanding of what occurs inside organisations.

    Managers rarely encounter decisions that are purely technical. Strategic issues are often characterised by uncertainty, competing objectives, incomplete information, and conflicting interpretations. Questions concerning market expansion, product development, investment priorities, workforce planning, or organisational change cannot be resolved solely through data processing.

    Even when AI systems provide recommendations, managers must still determine:

    • What problem is actually being addressed?
    • Which assumptions underpin the recommendation?
    • How does the recommendation fit organisational realities?
    • What risks might the system have overlooked?
    • How should competing interpretations be evaluated?

    These questions cannot be automated away. They require judgement, interpretation, and contextual understanding.

    As a result, the most important organisational consequence of generative AI may not be the automation of decision-making but the transformation of how managers construct meaning around strategic issues.

    Understanding sensemaking

    The concept of sensemaking is most closely associated with the work of Karl Weick, whose research emphasised how organisational actors create meaning in ambiguous situations.

    Sensemaking refers to the process through which individuals interpret events, extract cues from their environment, and construct plausible explanations that enable action. Rather than assuming that managers simply discover objective reality, sensemaking suggests that managers actively participate in creating meaningful interpretations of that reality.

    This perspective is especially valuable when examining AI-enabled decision-making because generative AI frequently produces outputs that are open to multiple interpretations.

    An AI-generated market analysis, for example, does not arrive with a universally agreed meaning. Different managers may interpret the same output differently depending on their experiences, expertise, priorities, and assumptions.

    One manager may see opportunity.

    Another may see risk.

    A third may question the validity of the underlying analysis altogether.

    The output remains the same, but the interpretation varies.

    This observation highlights a critical point: AI does not eliminate sensemaking. In many cases, it increases the need for it.

    Why AI outputs require interpretation

    Generative AI systems produce outputs that are often coherent, persuasive, and professionally written. This fluency creates an important organisational challenge.

    Managers may mistakenly assume that a well-presented output is necessarily a reliable one.

    Yet generative AI does not truly understand organisational context. It does not possess tacit knowledge, political awareness, historical memory, or lived experience within a firm. Instead, it generates responses based on statistical relationships learned during training.

    Consequently, AI outputs often require substantial interpretation.

    Consider a scenario in which an SME manager asks a generative AI system whether the firm should enter a new market.

    The system may provide:

    • market opportunities
    • competitor analysis
    • potential risks
    • customer segments
    • strategic recommendations

    At first glance, this appears highly useful.

    However, managers must still determine:

    • whether the analysis reflects current market conditions
    • whether the assumptions are realistic
    • whether the recommendations fit organisational capabilities
    • whether critical information has been omitted
    • whether the suggested strategy aligns with long-term objectives

    These evaluations are fundamentally interpretive activities.

    The strategic value of the output therefore depends not solely on the AI system but on the quality of managerial sensemaking surrounding that output.

    AI as a source of cues

    A useful way to conceptualise generative AI is to view it as a producer of organisational cues.

    Within sensemaking theory, cues are pieces of information that individuals notice and use to construct understanding. Managers continuously extract cues from reports, conversations, market developments, customer behaviour, and organisational events.

    Generative AI introduces a new source of cues into this process.

    The technology can highlight trends, generate alternative explanations, identify possible scenarios, and surface considerations that managers may not have previously recognised.

    This capability can be valuable because it expands the range of information available for interpretation.

    However, cue generation is not the same as meaning generation.

    AI can provide cues.

    Managers must determine what those cues mean.

    This distinction is crucial because organisational action is based not on information alone but on interpretations of information.

    Plausibility versus accuracy

    One of the most influential ideas within sensemaking theory is that organisational actors often operate according to plausibility rather than certainty.

    Managers frequently make decisions before complete information is available. Under uncertainty, waiting for perfect knowledge is rarely feasible. Instead, decision-makers construct explanations that are sufficiently plausible to support action.

    This insight is particularly relevant in the context of generative AI.

    Generative AI systems are exceptionally effective at producing plausible outputs. Responses are often coherent, convincing, and internally consistent. Yet plausibility should not be confused with accuracy.

    A recommendation can appear highly credible while containing flawed assumptions, incomplete evidence, or incorrect conclusions.

    The challenge for managers is therefore not simply to evaluate whether an AI output sounds convincing but to determine whether it provides a sufficiently robust basis for action.

    This requires critical reflection rather than passive acceptance.

    In practice, effective managerial use of AI may depend less on identifying perfect answers and more on developing the capacity to distinguish between plausible narratives and genuinely useful strategic insights.

    Sensemaking under uncertainty

    The importance of sensemaking becomes particularly visible under conditions of uncertainty.

    SMEs frequently operate in environments characterised by:

    • changing customer preferences
    • economic volatility
    • resource constraints
    • technological disruption
    • competitive pressure

    These conditions create ambiguity regarding both problems and solutions.

    Generative AI may help managers process information more efficiently, but it cannot eliminate uncertainty itself.

    Indeed, AI sometimes introduces new forms of uncertainty.

    Managers may question:

    • where information originated
    • whether assumptions are valid
    • how recommendations were generated
    • whether important contextual factors are missing

    As a result, AI adoption often shifts uncertainty rather than removing it.

    Managers move from asking:

    “What should we do?”

    to asking:

    “How should we interpret what the AI is suggesting?”

    This shift highlights why sensemaking remains central to strategic decision-making despite advances in AI capability.

    The danger of outsourced thinking

    One of the most significant risks associated with generative AI is not technological failure but cognitive dependency.

    As AI systems become increasingly capable, managers may gradually transfer interpretive responsibilities to the technology itself.

    Instead of using AI as a tool for exploration, they may begin treating it as an authority.

    This creates several risks:

    Premature closure

    Managers may stop searching for alternative explanations because the AI has already produced a plausible answer.

    Reduced critical reflection

    The persuasive nature of AI-generated content may discourage deeper evaluation.

    Narrow framing

    Managers may become anchored to the problem definition implied by the AI response.

    Overconfidence

    Confidence in AI outputs may exceed their actual reliability.

    Collectively, these tendencies can weaken rather than strengthen organisational judgement.

    The challenge is therefore not simply to increase AI use but to ensure that AI use supports rather than replaces managerial thinking.

    What effective sensemaking with AI might look like

    If generative AI is to enhance strategic decision-making, managers need practices that encourage active interpretation rather than passive acceptance.

    Several principles appear particularly important.

    1. Treat AI outputs as provisional

    Recommendations should be viewed as starting points for inquiry rather than final answers.

    2. Seek alternative interpretations

    Managers should explore multiple explanations, scenarios, and viewpoints before committing to action.

    3. Challenge assumptions

    Every recommendation contains assumptions. These assumptions should be identified and examined explicitly.

    4. Compare AI insights with organisational knowledge

    Outputs should be evaluated against internal experience, industry expertise, and contextual understanding.

    5. Maintain human accountability

    Responsibility for strategic decisions must remain with organisational actors rather than technological systems.

    These practices help ensure that AI contributes to organisational learning rather than encouraging uncritical reliance.

    Implications for future research

    Current research on generative AI in organisations remains heavily focused on adoption, performance outcomes, and technical capability.

    While these topics are important, greater attention should be devoted to the interpretive processes that connect AI outputs to organisational action.

    Several questions deserve further investigation:

    • How do managers interpret conflicting AI recommendations?
    • How does organisational experience influence AI-related sensemaking?
    • What factors shape the credibility assigned to AI outputs?
    • How do SMEs develop routines for evaluating AI-generated information?
    • Under what conditions does AI enhance or undermine managerial judgement?

    Addressing these questions would provide a richer understanding of how AI becomes embedded within organisational decision-making processes.

    Conclusion

    The future of generative AI in organisations should not be understood solely as a story of automation.

    The most significant organisational challenge is not whether AI can generate recommendations but how managers interpret those recommendations under conditions of uncertainty.

    Generative AI does not eliminate the need for sensemaking. Instead, it introduces new cues, new ambiguities, and new interpretive demands into managerial work.

    For SMEs, where strategic decisions often depend heavily on managerial judgement, this distinction is particularly important.

    The organisations that benefit most from generative AI are unlikely to be those that automate thinking entirely. Rather, they will be those that combine technological capability with disciplined processes of interpretation, reflection, and critical evaluation.

    Ultimately, the future of AI-enabled decision-making may depend less on the intelligence of machines and more on the quality of human sensemaking surrounding them.

    Author Note

    Saba Shahzadi is an emerging researcher whose work focuses on generative AI, organisational sensemaking, managerial decision-making, and SME digital transformation. Her research explores how managers interpret and use AI-generated outputs under conditions of uncertainty, with particular attention to trust calibration and human–AI collaboration.

    Suggested References

    Weick, K. E. (1995). Sensemaking in Organizations. Sage Publications.

    Weick, K. E., Sutcliffe, K. M., & Obstfeld, D. (2005). Organizing and the Process of Sensemaking. Organization Science, 16(4), 409–421.

    OECD. (2025). Generative AI and the SME Workforce: New Survey Evidence. OECD Publishing.

    Maitlis, S., & Christianson, M. (2014). Sensemaking in Organizations: Taking Stock and Moving Forward. Academy of Management Annals, 8(1), 57–125.