Many organisations have a drawer full of impressive AI demos that never made it into daily use. The models usually aren’t the problem. Pilots stall at the messy boundary between a prototype and the real business. That boundary is exactly where forward deployed engineers work.
1. The demo used clean data; production data is messy
Prototypes are often built on curated samples. Real documents are scanned, incomplete, inconsistent and spread across systems. FDE fix: work with real data from week one, invest early in extraction and data quality, and measure performance on representative cases.
2. Nobody can prove it works
Without systematic evaluation, every stakeholder judges the AI on a handful of anecdotes — and one bad answer can kill the project. FDE fix: build an evaluation set with the business, agree acceptance thresholds and publish results with every release.
3. It isn’t connected to anything
A standalone chatbot that requires users to copy and paste between systems rarely gets adopted. FDE fix: integrate the AI into the tools people already use — the CRM, the case system, Teams — and let it read and write where appropriate.
4. Security and compliance were an afterthought
Pilots built outside normal controls hit a wall at security review. FDE fix: involve security from the start, use existing identity and network controls, log everything and document data flows.
5. No one owns it
Innovation teams build pilots; operational teams are expected to adopt them; nobody owns the outcome. FDE fix: agree a business owner and success metric before building, and make the owner part of daily iteration.
6. Users don’t trust it
If people don’t understand when the AI is reliable, they either ignore it or over-trust it. FDE fix: design with users, show sources and confidence, keep humans in the loop for high-stakes steps and train teams on how to use it well.
A rescue plan for a stalled pilot
- Re-baseline: what does the pilot do today, measured on real cases?
- Fix the biggest blocker first — usually data or integration.
- Build the evaluation set and agree “good enough”.
- Integrate into one real workflow with one pilot team.
- Harden, release gradually and hand over.
Have a stalled AI project? Our forward deployed engineers specialise in getting pilots into production. See also our illustrative scenario on rescuing a stalled GenAI claims pilot.
Frequently asked questions
Why do so many AI pilots fail to reach production?
Common causes are unrepresentative data, no systematic evaluation, lack of integration with real workflows, late security reviews, unclear ownership and low user trust — rarely the model itself.
How can a stalled AI pilot be rescued?
Re-baseline it on real cases, fix the biggest blocker (usually data or integration), build an evaluation set, integrate it into one real workflow and release gradually with a clear owner.
How do forward deployed engineers help AI projects?
They embed with the business, work on real data and workflows, build evaluation and integrations, and take responsibility for getting the solution into production.