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Your AI is running.Your P&L isn't moving.

There is a point after which the data exists but it never reaches the decision. Even with AI, the leak continues. That point and gap is defined asLogic Leak and I'll help you find and fix the logic behind it.

30 min · no pitch · no deck

15+ years in AI · Nagarro Account AI-CTO experience · Managed a $90M+ AI portfolio

Systems built for leaders at

  • Mercedes-Benz
  • L'Oréal
  • Maruti Suzuki
  • British Telecom
  • Harman
  • Zespri
  • Betsson
  • Abdul Latif Jameel
  • Zeta
  • Dish
  • Mercedes-Benz
  • L'Oréal
  • Maruti Suzuki
  • British Telecom
  • Harman
  • Zespri
  • Betsson
  • Abdul Latif Jameel
  • Zeta
  • Dish

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 models, they fail here.

The method

A.R.T.: three stages before the build.

Test the use case, check the operating conditions, and put a number on the value: before anyone builds.

Applicability
Name the decision to improve and test whether AI beats a process change, a rule, or existing analytics.
Readiness
Check the data that exists, the infrastructure that must run it, and who owns the decision.
Transformation
Turn the use case into a defensible P&L figure a CFO can interrogate, before a line of code is written.

Four checks beneath the framework

  1. AI necessity

    Is AI needed, or would a process fix, a simple rule, or existing analytics do?

  2. Data sufficiency

    Coverage, quality, timing, and whether the relevant examples exist.

  3. Method fit

    Rules, prediction, optimization, or GenAI: chosen for the decision and its constraints.

  4. Infrastructure fit

    Latency, deployment, residency, and how the result reaches the decision-maker.

  • £11MAnnual cost recovery using proactive predictions · Telecom
  • 94%Forecast accuracy improved from <60% · High-volume consumables client
  • <5sInference reduced from minutes per unit · Automotive
  • +15%Gross Margin expansion within a year · 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.”
VP OperationsZespri
“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.”
Nikhil JainHead of Tech Accelerator & AI Innovation, L’Oréal

Recent work

What I am working on now.

  • In daily use

    Agents for steel trading

    I designed and built voice-led lead capture and plate-mark/certificate validation. Owner approvals govern lead changes, and people review flagged plate exceptions.

  • Demo built · development in progress

    Industrial IoT and digital twin

    Equipment-health consulting and a blast-furnace digital-twin demo combine descriptive, predictive and prescriptive views. The demo uses synthetic data.

  • Current offer active · five-day update coming soon

    AI Profit OS for managers

    A programme for managers making AI investment and implementation decisions. The current offer remains available while the revised five-day curriculum is prepared.

    See the programme
Explore GenAI and agent cases

Writing as proof

All writing
AI in automotive quality control: ECG-style anomaly detection flags faulty engines on the assembly line by measuring how far each engine deviates from a normal engine baseline.

Strategy

AI in Automotive Quality Control: How ECG-Style Anomaly Detection found Faulty Engines in <5 Secs

A case of AI in automotive quality control where ECG-style anomaly detection caught 20 faulty engines hidden in 1.3M units, in under 5 seconds each, by defining normal instead of chasing rare faults.

A planning session in a modern office, framing who inside a business should own AI tool exploration by temperament, not title

Business

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.

The journey back to clarity: leaving the AI-tool-tourism cycle behind, in favor of a workflow that already works

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.

The background

I'm Jitin Kapila. A Mechanical engineer turned AI CTO, and have spent 15+ years at the intersection of Business strategy, AI-driven outcomes 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 help 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.

Jitin Kapila

Ways to work

Where are you in the journey?

See how to work together

The Weekly AI Decision Brief

One operating question a week — the question your AI budget should be answering.

Email only · the weekly operator brief. Prefer the community feed? Read on Substack.

Common questions

What is the A.R.T. Assessment?

Applicability, Readiness, Transformation: assess whether AI is needed, whether the data is sufficient, whether the method fits the problem, and whether the infrastructure can support it. Define the operating decision and the business result before committing to a build.

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.