It is tempting to start an AI initiative with a shared platform, a comprehensive architecture, and a backlog of use cases. A platform designed before a workflow is understood often standardizes the wrong abstractions—and delays the first piece of evidence that anything works.
Find a problem with an owner
Choose a recurring task someone already has to complete. Understand the inputs, decisions, exceptions, and what a good result looks like. Identify who will use the improvement and who can decide whether it is useful.
A vague desire to “use AI” is not a scope. Preparing a weekly release summary from an agreed set of repositories is much closer to one.
Make the first boundary explicit
Write down what the implementation will handle, what remains manual, and which integrations are included. Agree on acceptance criteria before building. A short engagement works only when it has a real boundary.
Choose the smallest implementation that lets the team evaluate the outcome in context—an internal interface, an existing-tool integration, or a reviewed batch process.
Ship the handover with the implementation
Documentation, operational ownership, and a way to inspect failures belong in scope. A tool that only its original author can operate has not finished the handover.
Leave behind configuration, evaluation examples, known limitations, and instructions for the person who will maintain the system.
Let repetition earn the abstraction
After a useful workflow is running, look for genuinely repeated needs: permission checks, provider access, evaluation, audit events, or cost visibility. Those are better candidates for shared infrastructure than assumptions from kickoff slides.
Build the platform around what the work taught you. Each next investment should have more evidence behind it than the last.