Stalled-Project Rescue is a short, senior-led review of a data platform, AI pilot, or MLOps project that has been running for two or more quarters and still has not landed. In 2 to 3 weeks we return one of three recommendations: kill it, fix it, or reset it. The output is forward-looking, not a post-mortem.
Six months in. Maybe nine. The spend is already north of half a million. Two internal sponsors have burnt political capital defending the timeline. The vendor is still confident. The board is not.
The cost of a stalled AI or data project is rarely just the invoice. It is the months your best engineers spent on it instead of something else. It is the second project that never started because the first one has not finished. It is the credibility of the person who signed the business case, which now needs protecting before the next conversation can happen.
We see this pattern across mid-market financial services, healthcare, government-adjacent agencies, and large family-owned manufacturers. The stalled AI project rescue work we do is almost never about blame. It is about a calm, outside set of eyes and a decision the organisation can defend.
Scoping call with the sponsor to agree the artefacts, the interview list, and the question the board actually wants answered.
Document review: business case, vendor SOW, architecture, code, data contracts, steering minutes, cost run-rate.
Interviews with the project lead, engineers, vendor, sponsor, and two to three intended users. No large workshops.
Diagnosis against six common failure modes. We name what is broken and why, not who broke it.
Draft recommendation in week two: kill, fix, or reset. Tested with the sponsor before it goes wider.
Final written recommendation with a 12 month forward economics view and, if relevant, a scoped first phase ready to execute.
A prioritised AI agenda anchored in business outcomes, with the data foundations assessment to make it real.
Learn moreA senior AI engineer embedded inside your organisation for a fixed engagement. They scope, build, and ship production-grade AI.
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Learn moreOne of kill, fix, or reset. Written. Defensible. Dated. Your board gets the answer. Your sponsor gets the language. Your project team gets to stop rehearsing the same status update.
The conversation moves from what has already been spent to what the next 12 months look like under each option. Forward economics replace defensive ones. The decision gets cheaper and cleaner to make.
The recommendation comes with a plan and the reasoning behind it. No scapegoating. The person who funded the original work keeps their credibility because the reset arrives with an answer, not an accusation.
If the project is salvageable, you leave with a scoped first phase ready for AI Development or a Forward Deployed Engineer to execute the following quarter. Not a wish list. A statement of work.
We have run this rescue enough times to know the shapes. Most stalled projects fit one of six. The diagnosis is usually visible inside the first week.
| Typical failure mode | What it looks like | What we do about it |
|---|---|---|
| The use case was wrong | The model works. The forecast is accurate. Nobody uses the output because it does not change a decision anyone was going to make. | Recommend kill. Redeploy the budget to a use case with a real decision attached. |
| Data foundations missing | A model or agent is built, but it cannot get clean, timely data in. Every pipeline is bespoke and half of them are red. | Recommend fix, paired with a focused foundation build (typically an AIS drop-in) before the use case is resumed. |
| Ownership gap | No real internal sponsor. The project lead is a project manager without a mandate. Decisions take weeks because nobody owns them. | Recommend pause until an accountable owner is named. No owner, no restart. |
| Vendor dependency | You are locked into a platform or model provider that cannot deliver what was sold. Roadmap slips are quarterly. | Recommend reset with a platform swap. We scope the swap and the migration path. |
| Scope sprawl | 40 use cases on the backlog. Zero shipped. Every steering committee adds two more. | Recommend reset around a single use case, shipped end to end, before anything else is touched. |
| Talent gap | The project team is all analysts and a project manager. No engineers. The code is running in a notebook on someone's laptop. | Recommend an embedded build team (Forward Deployed Engineers) as the fix path. Analysts stay for domain. Engineers ship. |
Blunt version: most stalled projects are not technology problems. They are scope, ownership, or capability problems dressed up as technology problems. The rescue names which one.
The engagement runs quietly. A senior consultant reads every artefact: the business case, the vendor SOW, the architecture, the code, the data contracts, the steering committee minutes. They speak with the project lead, the sponsor, the engineers, the vendor, and two or three of the intended users. No workshops with 30 people. No all-hands re-discovery.
By week two you have a draft recommendation in one of three shapes. Kill: the use case does not deserve more money, here is what to redeploy. Fix: the project is worth finishing, here is the shortest credible path. Reset: something structural is wrong, here is the narrower scope or the platform swap that makes it shippable. By week three the final document is signed off and the sponsor has the language they need for the board.
A funded AI plan in 6 to 10 weeks, not 6 to 10 months, for leaders who want direction before delivery.
6-10 weeks
board question answered with a funded plan
A senior AI engineer inside your business in weeks, shipping production-grade from day one.
10 weeks
from scope call to production
Senior AI leadership at the exec table, without committing to a $250k full-time hire you can't yet justify.
~$250k vs 1-2 days/fortnight
Full-time Head of AI loaded cost vs fractional engagement
30-minute call, no slides, no obligation. We'll tell you plainly whether this is the right fit for what you're trying to do.
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