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.
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