AI beyond proofs of concept

Enterprise AI that survives production.

I help senior teams move AI from promising demos to products people use, trust, and measure. The focus is practical: clearer decisions, accountable delivery, and outcomes that stand up in the boardroom.

Operating model From use case to governed, measured product system.
Live system
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The thesis

The hard part starts when the model works.

Most AI content stops at the exciting prototype. Enterprise value depends on everything that happens after: accountability, integration, monitoring, cost control, user adoption, measurement, risk management, and the ability to decide what not to automate.

Production AI is an operating system, not a model demo.

The portfolio is organised around the disciplines that make AI useful at scale: decisioning, experimentation, orchestration, governance, and business impact.

Strategy senior leaders can useFraming AI around operating model, investment decisions, measurable outcomes, and organisational readiness.
Hands-on systems understandingRecommendation systems, next-best-action logic, search relevance, agentic workflows, and marketing automation.
Measurement disciplineIncrementality, A/B testing, statistical significance, guardrail metrics, and continuous optimisation.
Enterprise AIRecommendation systemsDecisioningMLOpsFinOpsGovernanceIncrementalityA/B testingAgentic commerceRetail AI Enterprise AIRecommendation systemsDecisioningMLOpsFinOpsGovernanceIncrementalityA/B testingAgentic commerceRetail AI
Measured proof

Credibility should show up as shipped systems and measured impact.

217M+customers reached through global platforms.
66countries supported across product and data operations.
€ 675MGMV influenced by AI, CRM, and personalisation systems.
€ 110Mincremental sales measured through experimentation.
+14.4%ARPU uplift delivered through personalisation.
40+product, data, and engineering collaborators led across markets.
Portfolio tracks

Three proof tracks for production-minded AI leadership.

01

Enterprise AI in production

Governance, adoption, delivery models, cost control, monitoring, and accountable AI product ownership.

Open track
02

Recommendation and decisioning

Product recommenders, next-best action, search relevance, churn prevention, and personalisation loops.

Open track
03

Measurement and experimentation

Incrementality, A/B testing, statistical significance, guardrails, and continuous campaign optimisation.

Open track
Knowledge graph

A living research system behind the public portfolio.

Research infrastructure, not content theatre.

The AI wiki compounds learning from authoritative sources into an interlinked map of concepts, tools, people, and operating models. It is the source layer for sharper articles, better case studies, and more useful senior conversations.

Explore the graph Read the wiki
AI knowledge graph preview
Interactive research map Click to explore how the AI wiki connects topics, sources, and operating models. This is the knowledge layer behind the portfolio: useful for discovery, search authority, and sharper executive content.
Product leadership

For teams that need AI to connect strategy, systems, and outcomes.

I am interested in Staff Product Manager-equivalent roles, enterprise AI transformation, decisioning and personalisation strategy, AI operating model work, workshops, advisory, speaking, and collaborations with senior leaders building production AI.

Start a conversation → About me → Explore portfolio → GitHub evidence →