My research focuses on how generative AI is reshaping managerial decision-making in small and medium-sized enterprises (SMEs), particularly under conditions of uncertainty, ambiguity, and information overload.
Rather than treating AI as a standalone technological tool, I examine it as part of a broader socio-cognitive system in which human interpretation, organisational context, and algorithmic outputs interact continuously.
Core Research Question
How do SME managers use generative AI in strategic decision-making under uncertainty, and how do they calibrate trust in AI-generated outputs over time?
This question is driven by a broader concern: decision-making in SMEs is rarely linear or fully rational, yet most AI adoption frameworks implicitly assume structured and optimisable processes.
Research Problem
Despite rapid growth in AI adoption discourse, a key assumption persists in both academic and industry literature:
Better AI systems will naturally lead to better decisions.
My work challenges this assumption.
Instead, I argue that:
- AI does not remove uncertainty — it redistributes it
- AI outputs are not “answers” but interpretive inputs
- Managerial judgement remains central, not peripheral
- Organisational context shapes how AI is actually used
This creates a gap between technical capability and lived organisational decision-making.
Theoretical Foundations
My research is grounded in three interconnected theoretical perspectives:
1. Sensemaking and Organisational Cognition
Building on Karl Weick’s sensemaking theory within Organizational Theory, I examine how managers construct meaning in ambiguous environments.
Key idea:
Decision-making is not based on objective clarity, but on plausible interpretations of incomplete information.
2. Human–AI Trust Calibration
This stream examines how managers dynamically adjust their reliance on generative AI systems.
Trust is treated not as a binary variable, but as a shifting calibration process influenced by:
- task complexity
- perceived risk
- organisational norms
- prior experience with AI outputs
3. Strategic Decision-Making under Uncertainty
Drawing from strategic management and behavioural decision theory, this perspective focuses on how SMEs make decisions under constraints such as:
- limited resources
- time pressure
- imperfect information
- competitive uncertainty
Conceptual Contribution
My research contributes a process-oriented understanding of AI-enabled decision-making in SMEs.
It reframes AI use as a recursive interpretive loop:
AI Output → Manager Interpretation → Trust Calibration → Decision Action → Feedback → Updated Interpretation
This model highlights that AI value is not fixed in the system itself, but emerges through use.
Analytical Focus
The study focuses on three core analytical dimensions:
1. Interpretation
How managers make sense of AI-generated outputs in real decision contexts.
2. Trust Calibration
How reliance on AI changes depending on context, experience, and perceived reliability.
3. Contextual Constraint
How SME-specific conditions shape whether AI is integrated, ignored, or selectively applied.
Methodological Approach
The research adopts an interpretive and qualitative-first methodology designed to capture decision-making as it occurs in practice.
Methods include:
- SME case studies
- Semi-structured interviews with managers and founders
- Thematic and abductive analysis
- Cross-case comparison (where applicable)
The goal is explanatory depth rather than predictive modelling.
Expected Contributions
To Theory
- Extends sensemaking theory into AI-mediated environments
- Develops trust calibration as a dynamic organisational process
- Strengthens behavioural perspectives on AI-enabled decision-making
To Practice
- Helps SMEs understand risks of over-reliance or under-use of AI
- Supports more balanced AI integration strategies in managerial workflows
To Policy
- Contributes to more realistic frameworks for SME digital transformation and AI adoption
Current Research Status
This research is currently being developed as a PhD-level project focused on UK SMEs, with an emphasis on managerial cognition, organisational decision-making, and emerging digital technologies.
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