Your AI is running.
Your P&L isn’t moving.
I’m Jitin Kapila. I find exactly where your AI investment stops returning value, and fix the decision logic causing it. I call it the Logic Leak.
30 minutes. No pitch. No deck. You’ll leave knowing whether you have a problem and roughly where it is.
For 15 years (before AI was a buzzword), I’ve been building the systems that run inside Fortune 500 companies.
Most AI pilots don’t fail because the technology is wrong. They fail because nobody mapped where it should connect to the P&L.
That gap, the specific point where data exists but never reaches the decision. What I find and fix is the value it should inform. I call it the Logic Leak.
$90M+ in AI portfolios. Not as a consultant who hands over decks. As the person who architects the logic, owns the P&L impact, and can explain it to the CFO and the engineering team in the same room.
The gearbox doesn’t fix itself. Neither does a Logic Leak.
The Problem Nobody Is Naming
Organisations run AI pilots that succeed in isolation but fail in production. The few that launch rarely move the P&L.
Post-mortems usually blame the vendor, the data, or user adoption. That is a comfortable excuse. The harder truth is that most organisations build AI before they understand what they are building it for.
Every operation has a specific point where data exists, but the intelligence never reaches the decision it should inform. Models run in isolation. Vendors deliver exactly what was contracted, but the wrong problem was specified. The structural connection between data and decision is missing.
That gap has a cost.
The inventory left on the wrong shelf, the defects reaching the field, the platform procured without a quantified use case, these are not technology failures.
They are architectural failures.
Finding these missing connection and building the architecture to close it, that is the real work.
“The gap between a pilot that works and one that moves the P&L has nothing to do with the AI model.”
The method
Three steps. In sequence. Every engagement runs on these.
Step 1 - Test the use case before building it
Most organisations skip straight to build. Before any commitment, three questions need answers:
- Is this the right problem to solve given the operational constraint?
- Does the data actually exist to support it?
- Is this the right priority given what else is in motion?
Most bad vendor contracts begin precisely here, in the gap between a good idea and a tested one.
Step 2 - Specify it in business terms
Every use case worth building can be written in four terms: what outcome is being optimised, what business logic connects input to decision, what operational constraints the solution must respect, and what data actually exists. If it cannot be written this way, it is yet to become a use case. Till then it is just a hypothesis.
Step 3 - Put a number on it before spending
A structured model that converts a use case into a P&L figure, before even a line of code is written. Not a range of assumptions. A specific, defensible number built from the actual constraints of the business, in language a CFO can interrogate without a data scientist in the room.
Where it’s been applied
CASE STUDY • Retail
Global FMCG - $35M inventory correction
Fortune 500. Beauty and personal care. SA-MENA region.
Four previous vendors had attempted to solve a forecast accuracy problem below 60%. The stated issue was model quality. The actual issue was signal contamination: the entire supply chain was reacting to delivery data, a lagging indicator polluted by 3–10 days of logistical noise rather than order intent.
The intervention reframed the problem from forecasting sales to modelling customer intent. Outcome: forecast accuracy from below 60% to 94%. Planning cycle from 7 days to 2. $36M in annual value recovered in form of $24M in inventory correction and $12M in margin expansion.
| Metric | Before | After |
|---|---|---|
| Forecast accuracy | < 60% | 94% |
| Data Signals | Only sales | Sales, Spends, Macro Factors, etc. |
| Planning cycle | 7 days | 2 days |
| Annual value recovered | - | $35M |
CASE STUDY • Automotive
Automotive OEM - 100% quality inspection
One of the world’s largest automotive plants. ~100,000 units/month i.e at 1.2M units/year
The plant was running statistical sampling on quality control catching defects reactively and because existing ML models took 3 minutes to produce a result on a production line with a 30-second cycle. The internal team was using computer vision logic on a signal processing problem.
The fix came from cardiology, not industrial AI. Engine vibration data behaves like a heartbeat. A defect is an arrhythmia. By applying biomedical signal processing instead of deep learning, inference dropped from 3 minutes to under 5 seconds. The plant moved from sampling to 100% digital inspection across 1.2 million units per year.
| Metric | Before | After |
|---|---|---|
| Inference time | 3 minutes | < 5 seconds |
| Inspection coverage | Statistical sampling | 100% - 1.2M units/year |
| Critical faults caught | Reactive / field claims | ~100 per year, prevented |
| Line stoppages | Frequent | Zero |
$35M
Annual value recovered, large FMCG
95%
Forecast accuracy (from below 60%)
2 days
Planning cycle reduction from 7 Days
1.2M
Units inspected annually, automotive
“Four vendors attempted to solve the same forecasting problem and treind with better models each time, yet failed. The fix was identifying that the entire system was trained on the wrong input variable.”
- Jitin Kapila, on the global FMCG engagement ($36M outcome)
What clients say
“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 Operations, Zespri
“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 Jain, Head of Tech Accelerator & AI Innovation, L’Oréal
Three ways to
work with me
Phase 1 - Find the problem worth solving
AI Profit OS, Executive AI Defense System
For VPs, COOs, CFOs, and senior leaders who need clarity before commitment. A 3-day sprint that produces a ranked use case list, an ROI formula anchored to your specific P&L, and a board-ready investment memo. The defense system that prevents bad vendor contracts.
From $1,500 per seat
Phase 2 - Roadmap the solution
AI Strategy Audit
For organisations that know the problem and need the architecture. A 2–3 week Red Team diagnostic that identifies where operations are failing and builds a 12-month implementation roadmap. Leadership gets the numbers. The technical team gets the blueprints.
From $2,500
Phase 3 - Architect what to build
Consulting & Fractional CTO
For organisations with active AI programmes that need an architect in the room and not another vendor. Monthly retainer or 6–12 month embedded engagement. Vendor review. Technical proposal oversight. Scope management. Board communication.
Custom engagement
Not sure which fits your situation? A 30-minute Clarity Call costs nothing and ends with a specific answer: whether you have a Logic Leak, and roughly where. No pitch. No deck. Book a Clarity Call →
The background
I’m Jitin Kapila. I trained as a mechanical engineer and have spent fifteen years working 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’ve built systems for. Not advised on strategy decks. Built for.
I still debug production systems. I read the code. I understand what the data science team is actually saying, and I can translate it into language the CFO can act on. That is not a common combination.
What I am not: a vendor. I don’t sell platforms, I don’t take referral fees, and I don’t recommend technology I haven’t evaluated against the specific constraints of your business.
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