How should we structure a forward-deployed AI pilot so we do not pay for a demo that never reaches daily use?

Question

Tyler Smith · Accepted Answer

Structure the pilot around an operational acceptance decision, not a presentation date. Define which workflow will change, who will use the tool, and what evidence will justify continued investment before development starts.

Forward Deployed AI Engineering by Nasser Ghanemzadeh connects technical deployment with the commercial challenge of serving non-technical buyers. Applied here, the buying decision should stay connected to deployment evidence rather than the apparent sophistication of the AI.

Use staged commitments:
- Discovery: confirm the workflow, data access, baseline, and accountable business owner.
- Working slice: complete one bounded task using representative inputs and the required permissions.
- Supervised use: let intended users perform actual work with a clear fallback.
- Acceptance: review business results, exceptions, support needs, and the cost of continuing.

Specify what happens when the AI is wrong or unavailable. A pilot is incomplete if success depends on the engineer silently correcting every failure.

Lovable's documented frontend, backend, database, authentication, and integration capabilities can accelerate a working slice. Treat that speed as a way to test assumptions earlier, not as proof that the application is ready for unrestricted production use.

In an EOS-run business, review the chosen weekly measure through the Scorecard when it belongs there. Track unresolved blockers through the Issues List. My recommendation is to release further budget only after intended users can complete the agreed workflow and the operating owner accepts the support model.