1. Vague scope

"Use AI to improve customer support" is a wish, not a scope. A workable scope names the user, the task, the expected output and how success will be measured. Narrow scope is not a lack of ambition. It is what lets a team ship something real and learn from it.

2. Skipping the boring data work

Most AI features depend on the quality of the information behind them. If documents are outdated, scattered or contradictory, the output will be too. Budget time for cleaning, organising and owning the data. It is unglamorous, and it is usually where the results come from.

3. No feedback loop

Software improves through feedback, and AI even more so. Build an easy way for users to flag a wrong answer, and review those flags regularly. Short sprints with a demo at the end keep everyone looking at real behaviour, not slide decks.

4. Ignoring cost and speed

A prototype that works on ten requests may be slow or expensive at ten thousand. Measure response time and cost per request early, and choose the simplest approach that meets the need. A smaller, cheaper setup that is good enough often beats the most powerful option.

5. Forgetting the people

Any tool changes how people work. Explain what the tool does, what it does not do and who is accountable for the final result. Teams adopt tools they understand and resist tools that are imposed on them.

The pattern

Clear scope, good data, fast feedback, sensible cost and real involvement from users: none of this is new. It is ordinary delivery discipline, applied to a new kind of component. That discipline is what turns an AI idea into something people rely on.

Jaimin Mehta is an AI and technology consultant and the founder of Bolo Media. More at bolo-media.com.