How to choose a useful first AI project, ground it in reliable data and move to production without creating an expensive science experiment.
Begin with workflow friction, not a model
A strong AI use case starts with a repeated business problem: staff searching across scattered knowledge, support teams rewriting similar answers, analysts reconciling documents or developers spending time on routine review. Define the current cost, the user affected and the decision or output that should improve.
Avoid starting with a broad ambition to 'add AI everywhere'. McKinsey has described a gap between widespread generative-AI use and bottom-line impact, often caused by fragmented experiments that are not tied to redesigned workflows. A narrow use case with a measurable baseline is easier to evaluate and safer to deploy.
Design the evidence and guardrails
For a knowledge assistant, identify authoritative sources, access rules and how freshness will be maintained. For an agent, define each permitted action, spending or data limits, and the points where a person must approve. Create a representative evaluation set before building so quality can be tested consistently rather than judged by an impressive demo.
The production design should record source references, model and prompt versions, latency, cost and user feedback. Sensitive information needs explicit handling rules. These controls make it possible to improve the system and explain what happened when an answer or action is wrong.
Pilot, measure, then expand
Release first to a small group with a fallback process. Measure task completion, correction rate, time saved and whether users actually return. A system that creates plausible output but adds verification work may shift cost rather than remove it.
Expand only when the evidence supports it. The next investment may be a better retrieval pipeline, a simpler interface or an integration—not necessarily a larger model. Durable AI products are software products: they need ownership, testing, monitoring, security and continuous learning from real use.
Key takeaways
- Choose one costly, repeated workflow with a measurable baseline.
- Build evaluation data and human escalation into the first version.
- Track quality, adoption, latency and cost—not demo appeal.
- Scale the workflow only after a controlled pilot produces evidence.