Technology
Enterprise AI: The Work Begins After the Demo
A convincing demonstration shows that a system can produce an answer. A useful service must also show when to act, when to stop, and how people can tell whether it is working.
Start with the work, not the model
Choose a bounded task with a clear user, a known starting point, and an observable outcome. Describe the current workflow before introducing automation: what information arrives, who makes the decision, what exceptions occur, and what a successful result looks like. This makes it possible to compare the AI-assisted process with the process it is meant to improve.
Define evidence of usefulness
Measure more than whether an output sounds plausible. A pilot can track task completion, error rates, time to resolve exceptions, user corrections, and the cost of review. The right measures depend on the task. For consequential decisions, quality and safety thresholds should be agreed before the system is used in production.
Design the operating model
Assign an owner for the workflow, an owner for the system, and a route for users to report problems. Set limits on the data and actions the system may use. Make human review available where the cost of an error is high, and define how to pause or roll back the feature. These choices are part of delivery, not cleanup after launch.
Learn in small steps
Begin with a narrow pilot, collect examples of success and failure, and use those observations to improve the process. Expand only when evidence supports the next step. The purpose of a demonstration is to create a testable hypothesis; the purpose of delivery is to prove that the complete workflow is useful and governable.
This is a general framework, not a report of a specific client project. It reflects a simple principle: the quality of an AI product includes the surrounding decisions, safeguards, and feedback loops.