Every large enterprise now has an AI portfolio. Very few have AI in production that a CFO would sign off as delivering measurable value. The gap is rarely the model. It is almost always the path: the sequence of decisions, approvals and integration work that has to happen between a promising proof of concept and a system that staff use every day.
Across banking, refining, manufacturing and healthcare, pilots stall for the same five reasons. Naming them is the first step to designing them out.
The five frictions
- Stalled pilots. A proof of concept is approved without a definition of success, so there is never a moment where it clearly passes.
- Scattered data. The data needed sits in several systems with different owners, and nobody has agreed who grants access.
- No workflow integration. The model produces a score, but no one has redesigned the queue, screen or approval step where the score is used.
- Unclear ROI. Benefits are described in percentages without being tied to money the business recognises.
- Unmanaged risk. Security, model-risk and compliance teams are brought in late, and rightly stop the project.
None of these is a technology problem. Each is a decision that was not made early enough. That is why we structure every engagement as a series of gates, each with an explicit question that must be answered before more money is spent.
Gate 1 — Is this the right problem?
Value-first use-case selection means starting from the operating P&L, not the technology. We ask where a better decision, made faster or more consistently, changes a number the business already tracks: fraud losses, approval rates, cost per review, unplanned downtime, procurement spend. If we cannot write the value formula on one line, the use case is not ready.
Gate 2 — Is the data and security path real?
During a four-to-six-week discovery we confirm the data exists, that it can be accessed, and where it will be hosted. Hosting, access and residency are agreed with the CISO before any data moves. This is also when we capture the baseline: how the process performs today, measured the same way it will be measured in the pilot.
Gate 3 — Did the pilot beat the baseline?
A controlled pilot runs for eight to twelve weeks on one use case with one team. The acceptance criteria are written before it starts. The executive readout compares results against the baseline and ends in a go/no-go decision. Fixed fees for discovery and pilot mean there is no large capital approval before value is shown.
Gate 4 — Is it governed for production?
Before scaling, the human decision points are designed in. AI recommends; authorised staff approve, override and record every regulated decision. Every score carries readable reason codes. Monitoring for accuracy, drift, bias and false positives is configured, with named owners for each alert.
Gate 5 — Is the value still there?
Production is not the end. A quarterly value review re-runs the formula from Gate 1 with live numbers. Models are refreshed on demand when drift appears. If value erodes, the review is where it gets caught.
Why proven platforms matter
The fastest way through these gates is to avoid building from scratch. Platforms that already run in production at large banks and industrial groups arrive with integration patterns, monitoring and documentation that a bespoke build would take a year to produce. That is why we deliver through a vetted network of specialist platforms and act as the single point of accountability for scoping, governance, security and results.
The practical takeaway
If your organisation has more than three AI pilots that have not reached production, pick the one closest to a number your CFO tracks. Write its value formula. Agree its success criteria. Run it through the gates. One measured success will do more for your AI programme than ten promising experiments.