Founder · Advisor
I work with leadership teams when those decisions start to collide — and the cost of getting them wrong becomes material.
Over two decades in quantitative risk and data systems — across global financial institutions and technology companies, including Barclays, Deutsche Bank, BNP Paribas, and Citigroup — I saw how organisations build lasting advantage through better infrastructure and cleaner data. More importantly, I saw how poor decisions compound when those foundations are weak.
That experience now informs how I work with founders, executives, and boards — particularly where data exists but cannot be fully trusted, and where technology decisions are beginning to shape commercial outcomes.
Background
The work is typically triggered by a specific kind of situation:
These are not purely technical problems. They are decision problems that sit across business, data, and technology.
Quantitative risk taught me something important: how to make high-stakes decisions from imperfect, incomplete data. You build models not because the data is clean, but precisely because it is not. The discipline is knowing what the model can and cannot tell you — and making decisions with that constraint in mind.
That thinking transfers directly to what businesses face today. Many organisations are sitting on years of transaction records, customer data, and operational history — most of it unstructured, much of it underused. The question is not whether the data is perfect. The question is what decisions can be made reliably on what already exists.
This is where most AI efforts break down. Not at the level of tools, but at the level of foundations and decision clarity.
Much of this work is often associated with emerging markets, where structure is not always given. In practice, the same conditions appear in growing companies globally — where systems lag behind ambition, data is fragmented, and decision-making depends more on individuals than infrastructure.
I work with boards and leadership teams on decisions where product, technology, data, and business model intersect — particularly where digital infrastructure is becoming strategically important.
The focus is on improving how decisions are framed and evaluated: clarifying the real decision, making trade-offs explicit, and ensuring that technology direction aligns with commercial reality.
Senior product and technology leadership without the commitment of a full-time executive hire. Useful when the business needs judgement, structure, and decision support close to the work.
Talks and panels on AI adoption, the operational foundations businesses need before AI becomes useful, and what two decades of quantitative risk work reveal about building on imperfect data.
Implementation and delivery work runs through Ahjayee Consulting — where these ideas are applied in practice across businesses, from foundational systems to automation and intelligent workflows.
The advisory work here is grounded in that exposure. It reflects what is actually being built, where it breaks, and what consistently works across different environments.
Published thinking
Board roles, advisory, speaking, or fractional CxO work. Send a brief outline and I will tell you directly whether I am the right fit and what the most sensible starting point would be.