Independent research
and analysis.
A ten-article series drawn from MBA dissertation research with 18 UK SMEs, examining how leaders are actually navigating Generative AI adoption — and why some organisations progress while others stall.
Leading Generative AI Adoption in UK SMEs.
Ten articles. One conclusion: GenAI adoption is not primarily a technology challenge. It's a leadership challenge — about helping people make sense of a confusing, fast-moving technology. The organisations that understood this — and acted accordingly — made the most progress.
GenAI Readiness Is Not a Technology Problem. It's a Leadership One.
Across 18 UK SMEs, the organisations that progressed with AI were not those with the best tools. They were those whose leaders took responsibility for meaning under uncertainty.
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Leading When You Don't Know: Sensegiving Under GenAI Uncertainty.
The leaders who made the most progress with AI were not those who had the most answers. They were those who made uncertainty discussable.
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Why GenAI Feels Threatening: Identity, Emotion, and Silent Resistance.
The most common barrier to AI adoption in the SMEs I studied was not technical. It was emotional — and almost entirely invisible to the leaders involved.
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From Sensegiving to Readiness: How GenAI Adoption Actually Unfolds. The Model
The Leadership Sensegiving–Readiness Heuristic — the central interpretive model from the research. Now lives as a dedicated page rather than a single article.
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What High- and Low-Success GenAI SMEs Do Differently.
The difference between organisations that made progress with AI and those that stalled was not ambition, funding, or technology. It was leadership practice.
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Governance Without Paralysis: Risk, Trust, and Provisional Boundaries.
In the SMEs I studied, governance introduced too early stifled experimentation. Introduced too late, it created anxiety. The right balance was a legitimacy framework, not a control mechanism.
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Six Leadership Moves That Build GenAI Readiness.
Readiness was not created through formal AI strategies or large-scale programmes. It was built through repeated leadership actions that signalled intent, legitimacy, and direction under uncertainty.
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Why 90-Day Cadence Beats Long-Term GenAI Strategy.
The instinct to write a three-year AI strategy is understandable. In a technology landscape moving as fast as this one, it is also counterproductive.
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Why GenAI Momentum Stalls: What Actually Undermined Progress.
GenAI initiatives rarely failed outright. They stalled. Understanding why momentum weakens — and what to do about it — is the practical conclusion of the series.
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Introducing Requisite Intelligence.
I set out to answer an academic question, not to start a business. Twenty interviews later, the gap I kept finding convinced me otherwise — and Requisite Intelligence is my answer to it.
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The work behind the series.
This series is grounded in original research conducted as part of an MBA dissertation at Warwick Business School. The study involved 30-minute semi-structured interviews with 20 senior decision-makers — founders, CEOs, MDs, COOs, and IT leads — across 18 UK SMEs of up to 500 employees.
The research focused on five interrelated dimensions: leadership framing and behaviour, organisational culture and trust, capability building, governance and risk, and the practical constraints organisations encountered. A self-reported 1–7 success rating was used as an interpretive aid to compare patterns across higher- and lower-progress organisations.
The central output was the Leadership Sensegiving–Readiness Heuristic (LSRH) — a model that frames GenAI adoption as a recursive interpretive process rather than a linear implementation programme. It is the foundation of how Requisite Intelligence approaches client work today.
Ready to apply this thinking to your organisation?
If these articles have prompted questions worth exploring, we are happy to talk.