A convincing AI demonstration answers one question: can this work? A useful business capability has to answer several more. Can people rely on it? Does it improve the workflow? Who owns the result when something goes wrong?
Our view is that the work between a promising pilot and a dependable product deserves as much attention as the model itself. Before expanding an AI initiative, start with these five questions.
1. What outcome are we trying to change?
Begin with a specific workflow and a measurable problem. For an internal knowledge assistant, that might be the time people spend locating a reliable answer. For a document workflow, it might be the amount of manual review needed to complete a task.
Establish the baseline before introducing AI. Agree on the improvement that would justify continued investment, and include the cost of reviewing or correcting the output.
2. Can the system access the right information?
Useful answers depend on useful context. Identify which sources the system needs, who maintains them, and how their access permissions will carry into the AI experience.
A prototype built on a small, clean collection of documents may not reflect the inconsistencies in your everyday data. Test with representative information, including missing, outdated, or conflicting records.
3. How will we know when it is wrong?
Build an evaluation set from actual tasks. Include the straightforward examples, the ambiguous requests, and the situations where the right answer is to ask for help.
Define quality with the people who understand the work. Track more than whether an answer sounds plausible: consider accuracy, completeness, source support, and whether the system stayed within its permissions.
4. Where does human judgment belong?
Separate drafting from deciding. A system that proposes an action can have a different risk profile from one that executes it.
Decide which actions require approval, what information the reviewer needs, and how users can correct or reverse the result. Design that experience into the workflow rather than leaving it as a policy document.
5. Who operates it after launch?
Assign ownership for quality, access, cost, and incidents. Plan how changes to data, prompts, tools, or models will be evaluated before release.
Start with a controlled rollout. Keep the scope small enough to observe real usage and learn from it. Expand when the evidence supports the next step.
Progress is a delivery discipline
AI creates new possibilities, but useful adoption still depends on clear priorities, sound engineering, and people who understand the business. A strong pilot is a beginning. The opportunity is to turn it into a capability your team can depend on.