The Model Was Never the Problem.
Most AI initiatives that go nowhere fail for operational reasons. The process around the tool was never redesigned, the data feeding it was not fit to use, or nobody owned the outcome once the pilot ended. We diagnose which of those stopped yours - and fix it.
An operations consultancy, not an AI vendor - we take no margin on any tooling we might recommend.
It Demoed Well. Then Nothing Changed.
The pattern repeats across companies of every size. Something is trialled, it performs well in the pilot, everyone is encouraged - and six months later the team has quietly gone back to how they worked before. The tool is still paid for. Nobody uses it.
The pilot ran on clean, hand-prepared inputs. Real operational data turned out to be nothing like that.
The tool was dropped into an existing process instead of the process being redesigned around it.
Nobody owned the outcome once the pilot team moved on to the next thing.
The people expected to use it were never given a reason it made their week easier.
It produced output nobody could check, so nobody trusted it enough to act on.
Leadership were told the initiative succeeded, because the pilot did.
What the Audit Covers.
Three honest separations: what was operational, what was data, and what was genuinely the technology.
What Actually Happened
We reconstruct the initiative from the people who ran it and the people who were meant to use it - which are usually two very different accounts of the same project.
Process Fit
Whether the workflow around the tool was ever redesigned, or whether the tool was inserted into a process that already did not work. This is the single most common cause.
Data Readiness
What the tool was actually fed, where that data came from, and whether it was ever good enough for the job. Pilot data is curated. Operational data rarely is.
Ownership and Incentives
Who was accountable after go-live, whether anyone's week got measurably better, and whether the people expected to change their habits had any reason to.
An Honest Verdict
Including the possibility that the initiative was solving a problem better process design would have solved more cheaply. We would rather tell you that than sell you a rebuild.
Prioritised Fixes
What to fix, in what order, and what each is worth. Some of it will be process work. Some will be data. Occasionally some of it is the technology - and we will say so when it is.
When This Fits.
A pilot that went quiet
Something was trialled twelve to eighteen months ago, it looked promising, and nothing came of it. Nobody has established why, and the subscription is probably still running.
Live but unused
It shipped, it is paid for, and the team has quietly reverted to the old way. Usage numbers are technically non-zero.
Before committing again
You are about to invest properly and want an honest read on whether the operation can actually absorb it this time.
Board or investor pressure
You are being asked what the AI strategy is and would rather answer with a diagnosis of what happened than with another pilot.
How the Audit Runs.
Short, specific, and pointed at a decision rather than a report.
Diagnostic Call
A free 45 minutes to establish what was attempted, what happened, and whether an audit is even the right next step. You leave with an initial read either way.
Reconstruct
We talk to the people who ran the initiative and the people who were meant to use it, and map the workflow as it existed before, during and after.
Examine the Inputs
Where the data came from, what condition it was in, and whether it was ever fit for what was being asked of it - the failure mode almost nobody checks for.
Separate the Causes
Operational, data, or technology. Most of what gets blamed on the technology turns out not to be, and knowing the difference is what stops the next attempt failing the same way.
Prioritised Recommendations
What to fix, in what order, with our honest view on what is worth doing at all. Implementation is a separate decision you are free to take elsewhere.