Your AI is running. Your P&L isn't moving.
I find the one point where data exists but never reaches the decision — the Logic Leak — and fix the logic behind it.
30 min · no pitch · no deck
$90M+ in AI portfolios · 15 yrs before AI was a buzzword
Systems built for leaders at
What is a Logic Leak?
A Logic Leak is the specific point where data exists and decisions are made — but intelligence never flows between them. Most AI does not fail on the model. It fails here.
The method
Three steps. In sequence.
- 01
Test the use case before building it
Before any commitment: is this the right problem given the operational constraint, does the data actually exist to support it, and is it the right priority? Most bad vendor contracts begin in the gap between a good idea and a tested one.
- 02
Specify it in business terms
Every use case worth building names four things: the outcome being optimised, the logic connecting input to decision, the operational constraints, and the data that actually exists. If it cannot be written this way, it is still a hypothesis.
- 03
Put a number on it before spending
A structured model that converts the use case into a P&L figure before a line of code is written — a specific, defensible number a CFO can interrogate without a data scientist in the room.
- £11M Annual cost recovery · telecom
- 94% Forecast accuracy, from <60% · FMCG
- 3min → 5s Inference at the line · automotive
- +15% Margin expansion · perishable supply chain
Where it's been applied
“15% profit margin growth in six months. We'd spent 18 months on dashboards that told us what happened. Jitin shifted us to systems that change what happens next.”
“He doesn't deliver a deck and disappear. He stays until the logic is in the system and the team can run it. That's rare.”
Writing as proof
All writingBusiness
Who Should Explore AI Tools Inside a Business?
A practical operating rule for assigning AI tool exploration by temperament, not title, so curiosity creates operating memory instead of distraction.
Business
AI Tool Tourism
Why chasing every new AI tool creates cognitive load, workflow breakage, and fragmented productivity — and the three reasons that actually justify a switch.
Framework
Before You Buy an AI Tool, Ask These 6 Questions
A practical AI buying scorecard for leaders who need to separate useful systems from impressive demos: data, output, governance, ROI, upgrade path, and ownership.
The background
I'm Jitin Kapila. I trained as a mechanical engineer and have spent fifteen years at the intersection of AI strategy and operational delivery — across manufacturing, FMCG, logistics, telecom, and automotive.
The companies whose logos appear above are ones I have built systems for. Not advised on with decks. Built for.
I still debug production systems and read the code, and I can translate what the data-science team is actually saying into language a CFO can act on. What I am not: a vendor. I do not sell platforms or take referral fees.
Ways to work
Where are you in the journey?
AI Profit OS Logic Leak Audit Fractional Chief AI Officer
See how to work togetherCommon questions
What is a Logic Leak?
A Logic Leak is the specific point in an organisation's operations where data exists and decisions are being made, but intelligence is not flowing between them. The data that could improve a decision is not reaching it — due to structural, architectural, or process gaps. Identifying and closing the Logic Leak is the central diagnostic task in most AI strategy engagements.
What does an AI strategy consultant do?
An AI strategy consultant identifies which operational problems are worth solving with AI, defines those problems in terms a technical team can build from, quantifies the expected return before investment, and provides independent guidance that is not tied to any vendor or platform.
How do I know if I need an AI strategy consultant?
Three signals: (1) your AI pilots produced results in isolation but have not moved the P&L; (2) you are being asked to make an AI investment decision without a framework for evaluating it; or (3) an active AI programme is underway but results are not tracking to the original business case.
How do you calculate ROI before the model is even built?
ROI is calculated by mapping your current operational baseline (defect rates, inventory holding costs, manual processing time) against the known capabilities of standard AI architectures. We don't guess what the model will do; we calculate what the business process must achieve to justify the investment, setting a strict performance target for the build.
Do you build the AI models or just advise on them?
I architect the solution, quantify the expected return, and oversee the build as an advisory lead or Fractional CTO. I do not write the production code or sell proprietary platforms. My value is in independent specification and vendor management — ensuring the technical team builds exactly what the P&L requires, without scope creep or vendor lock-in.