Preparing experience
A practical way to prioritize AI use cases, prepare data, define guardrails and move from prototype to production.
Practical guidance for teams planning software, AI adoption or delivery capacity.
Most "AI roadmap" conversations start in the wrong place—picking a model or a flashy demo use case before asking which operational bottleneck is actually worth automating. A roadmap built on Claude and OpenAI's current capabilities, applied to a well-scoped internal process, will beat an ambitious customer-facing AI feature that never gets past the demo stage.
Rank candidate use cases by two factors: how accessible and clean the relevant data already is, and how costly a wrong answer would be. Start with high-data-readiness, low-blast-radius use cases—internal knowledge search, draft generation with human review—before attempting anything customer-facing and autonomous.
Retrieval-augmented generation is only as good as what it retrieves. Cleaning up document structure, tagging, and access permissions on the source data usually matters more to output quality than which model you choose.
Before rolling an AI feature out beyond a pilot group, define what a wrong or harmful output looks like for your specific use case, build an evaluation set of realistic queries, and decide where a human needs to stay in the loop. Retrofitting guardrails after a public incident is a much worse position to be in.
A working demo and a production system differ in observability, cost controls, failure handling and access management—not in the core model call. Budget for this gap explicitly rather than assuming a convincing prototype is 80% of the way to a shippable feature.
Share your context and WLC can recommend team shape, timeline and the first milestone worth shipping.
