AI project management.
AI-powered delivery: translating between engineering, product, and executive layers. Shipping, not experimenting. Generative models and automation tools abstracted into tangible ROI.
Vendor evaluation. Integration oversight. Change management.
Vendor evaluation. Picking the tool that ships the outcome, not the tool that wins the demo. Cutting through the vendor pitch layer — proof-of-concepts against real workloads, not curated benchmarks.
Integration oversight. Wiring AI into the existing stack without breaking the stack. Prompt and model governance, data-boundary enforcement, human-in-the-loop checkpoints where the risk is real.
Change management. Translating between the engineering layer, the product layer, and the executive layer — so the thing that ships is the thing the board approved. No experiment theatre; each pilot exits with a go/no-go decision and a handover note.
How I intervene.
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Forensic diagnosis
Identifying the true root cause, not the symptoms reported. Stakeholder audits across all operational tiers; mapping actual workflows against the theoretical.
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Structural architecture
Designing an AI-enabled framework resistant to the failures observed. Toolstack rationalisation, clear matrix of responsibility, build & delivery plan calibrated to real capacity.
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Dictated execution
Overseeing implementation with zero tolerance for scope creep. Vendor and agency management, team onboarding, transition enforcement.
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Handover on stabilisation
Transfer to internal leadership once the system holds. Staying longer than needed is a failure mode, not a business model.
Proof points
by AI Human judgment, AI leverage
Talk to me.
I read every message myself. Response within 2 business days. Bring the scope; I bring the plan.
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