How I think
The Diagnostic First stance:
Most transformation effort is wasted. Not because the people are wrong, or the ideas are bad, or the ambition is misplaced — but because the effort lands on the wrong layer. A founder perfects their sales deck while their ICP remains undefined. A corporate innovation team iterates on pilot design while the governance pathway from pilot to procurement doesn't exist. An executive restructures reporting lines while the operating model underneath them makes the new structure irrelevant.
The pattern is consistent: intelligent, motivated people doing good work at the wrong altitude. I start every engagement with the dependency structure. What has to be true before the thing you're working on can succeed? The answer is usually one or two layers below where the effort is currently concentrated.
This is the diagnostic. It comes before any advice, any framework, any plan. It often redirects the engagement entirely. A founder who came to me about GTM strategy leaves our first session working on value proposition clarity. A corporate programme that asked for a workshop on innovation methodology discovers that its actual bottleneck is governance alignment. This is what diagnostic-first means in practice: the willingness to abandon the presenting problem when the structural problem sits elsewhere.
What I reject
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Beginning with a methodology or platform and reverse-engineering the problem to fit it. If I can't diagnose a specific structural dependency, I have nothing useful to offer, and I'll say so.
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The assertion that a technology (AI, blockchain, automation) will transform an organisation, without first establishing whether the organisation's actual bottleneck is technological. Most of the time, it isn't. The bottleneck is structural: governance, ICP clarity, operating model coherence, or leadership bandwidth.
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Initiatives tdesigned to signal commitment to innovation without creating the structural conditions for adoption. Open innovation calls without procurement pathways. Accelerator cohorts without strategic alignment to business unit needs. Demo days that produce applause but no contracts.
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Confusing what AI can do (classification) with what an organisation needs (diagnosis). An organisation that hasn't resolved its data governance at Layer 0 will not be transformed by an AI tool at Layer 3, regardless of the tool's capability.
Evidence over intuition
This stance doesn't come from theory. It comes from years building and running data and analytics practices where the work was proving commercial impact through measurement. When you've spent a career demonstrating that intuition-based marketing spend can be reduced by 20% through evidence, you develop a particular allergy to strategy-by-assertion.
The diagnostic method I use now — Critical Path Layers — applies the same logic to transformation that analytics applies to commercial decisions: trace the dependency, find the constraint, redirect the effort. The framework exists because I kept seeing the same structural patterns — in startups, in corporate innovation, in PE portfolio companies — and the same misallocation of effort to the wrong layer.