AI Experimentation & Innovation Program
The AI Experimentation & Innovation Program is a monthly retainer engagement that gives your company a standing pipeline for testing AI ideas. We maintain a prioritized backlog of use cases, run time-boxed pilots each cycle, evaluate them against agreed criteria, and promote winners to production. For teams that want AI progress without betting big on guesses.
The problem this solves
Companies get stuck in one of two failure modes with AI: paralysis, where ideas pile up in slide decks while competitors ship, or scattergun, where a dozen pilots run at once, none get evaluated properly, and nothing reaches production. Both waste the same thing: the window in which an AI capability is an advantage rather than table stakes. What is missing is not ideas. It is a disciplined process for testing them cheaply and killing or scaling them on evidence.
How we work
We run that process for you. The backlog is the anchor: use cases collected from your team plus patterns we see across engagements, scored on expected value, effort, and risk. Each monthly cycle picks the top candidates and runs them as time-boxed pilots with success criteria agreed before work starts, so evaluation is a comparison against a bar, not a feelings discussion afterwards.
Pilots are built lean: enough to test the core assumption, not production-grade. A prospect research agent, an internal knowledge assistant, an automated QA pass on outbound emails. Whatever the use case, the pilot answers one question: does this earn a production build?
Winners get a handover: a scoping note for the production version, delivered as a separate project or by your own team. Losers get a written post-mortem so the learning is kept. Either way, the backlog gets smarter every cycle.
Deliverables
- Prioritized AI use case backlog, rescored monthly
- Time-boxed pilots each cycle with predefined success criteria
- Working pilot prototypes your team can try hands-on
- Evaluation reports with scale, iterate, or kill decisions
- Production scoping notes for validated use cases
What buyers ask before scoping.
When a pilot succeeds, do you build the production version?
The production build is scoped as a separate project, deliberately. Folding builds into the retainer would stall the experimentation cadence every time something wins. We can deliver the build ourselves or hand a scoping note to your team; the pilot code and evaluation data transfer either way.
How many AI ideas do we need before starting?
None. The first cycle includes a discovery workshop where we mine your workflows for candidates, and our own backlog of patterns from other engagements seeds the rest. In practice the backlog rarely stays under a dozen items after the first month; prioritization, not idea generation, is the real work.
How do you decide a pilot has failed?
Against criteria agreed before the pilot starts: a quality bar, a time-saved threshold, or an adoption signal, depending on the use case. Miss the bar and it gets a documented kill with reasons, not a quiet extension. No zombie pilots is half the value of the program.
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Sounds like your situation?
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