AI starts with
the right foundations.

Before your next AI pilot, prepare the business around it

Start with the workflow, not the demo.

An impressive demonstration is a beginning. The harder question is whether the same system can help your team on an ordinary working day, when information is incomplete, customers ask unexpected questions and someone must take responsibility for the result.

Start with one workflow. Choose a repeated task with a clear owner, a measurable baseline and a manageable consequence if the system is wrong. Preparing an internal knowledge answer for review may be a better first step than allowing an agent to make an irreversible business decision.

Next, inspect the information that workflow needs. Identify where it lives, who owns it and who is allowed to use it. Remove obsolete guidance. Resolve contradictory documents. Decide what the system should do when it cannot find an answer.

Then design the human handover. A reviewer should see enough context to judge a suggestion, reject it and correct the underlying information. Approval should happen before sensitive external actions, and the team should know how to pause the workflow when something behaves unexpectedly.

Define success before building. Useful measures might include time spent per task, the share of answers accepted without changes, review effort and the types of errors that reach users. Test realistic examples and awkward edge cases.

Our suggested first workshop ends with five concrete decisions: one workflow, one accountable owner, approved information sources, a review path and a small evaluation set.

The aim is a repeatable service the business can operate, improve and trust. Start small enough to learn, and connect those lessons to a clear decision about the next release.