Five mistakes made when building an AI assistant
Why AI projects stall at the demo stage, and how each of the five most common causes is avoided.
Most AI projects work technically and never reach production. These five reasons explain a great deal of why.
1. Success was never defined
"Give better answers" isn't a target. Without a measurable threshold nobody can decide whether the system is good enough, and the project stays permanently in "let's improve it a bit more".
The fix: Before starting, agree on one number. "85% of incoming enquiries routed to the right team" works. That number is both the finish line and the criterion for walking away.
2. The test data isn't real
A system built on tidy hand-written examples falls apart when it meets real data. Real emails contain typos, off-topic sentences, and sometimes nothing at all.
The fix: Work with real data from day one. Fifty real examples teach you more than five hundred invented ones.
3. Uncertainty wasn't designed for
The model answers every question — including the ones it doesn't know. Unless it's built to signal uncertainty, it delivers wrong answers with the same confidence as right ones.
The fix: Leave the model a way to say "I don't know", and hand the work to a person when it does. An assistant giving a wrong answer costs far more than one giving no answer.
4. The source of the answer isn't shown
If users can't see where an answer came from, they can't verify it. A system they can't verify is one they stop using.
The fix: Show the source alongside every answer — which document, which section. This single change does more for trust than anything else.
5. Cost is measured in production
During the trial a few hundred requests make cost look negligible. In production, thousands of requests a day turn the bill into a surprise.
The fix: Work out cost per request up front, multiply by expected daily volume, and set a monthly ceiling. Model choice is part of this arithmetic: not every job needs the most capable model.
Bonus: the biggest mistake
The most expensive mistake isn't any of those five: using AI on a job that doesn't need it.
Classification that could be handled with fixed rules becomes both more expensive and less predictable when handed to a language model. Write down why the simple solution isn't enough first; if you can't, it probably is.
If you have a specific job in mind, we can assess whether it fits. AI integration.
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