Generative AI for Business: How to Run a Pilot That Actually Ships

Every company has an AI mandate; very few have an AI system in production. The gap is almost never the model — it's the process. Here's the pilot structure we use to get from demo to deployed.

Pick one workflow, not a platform

The best pilots target a single, high-frequency, well-bounded workflow: answering support tickets from a knowledge base, extracting fields from documents, drafting first-pass reports. If the workflow's inputs and "good output" can't be described in one paragraph, it's not pilot material yet.

Ground it in your data

Generic chatbots hallucinate; grounded systems cite. Retrieval-augmented generation (RAG) keeps your documents in your cloud, retrieves the relevant passages per query, and forces the model to answer from them — with citations users can check.

Measure before you believe

• Build an evaluation set of 50–100 real questions with known-good answers.
• Score every change against it — accuracy, groundedness, and refusal correctness.
• Track cost per query from day one. A pilot that can't afford production isn't a pilot.

Plan the guardrails early

Access control (who can ask what), audit logs, PII handling, and fallback-to-human paths are production requirements — retrofitting them after the pilot doubles the timeline. Build them into week one.

The 6-week shape

Weeks 1–2: data audit and eval set. Weeks 3–4: RAG build and iteration against evals. Weeks 5–6: guardrails, user testing, and a go/no-go decision backed by numbers. That's a pilot leadership can fund with confidence.