Notes From Deploying AI With Customers in the GCC
A few things we picked up after shipping AI systems with real teams. What worked, what broke, and what we'd do differently next time.
We have now put AI systems in front of real users across a few sectors in the region. Some of it went well. Some of it did not. This is a short write-up of what actually mattered once the demo was over and people started using the thing every day.
The demo is the easy part
Getting a good demo takes a week. Getting something a team trusts takes months. The gap is almost never the model. It is the plumbing around it: access rules, data that is out of date, edge cases nobody mentioned in the first meeting.
We now spend the first two weeks of any project mapping where the data lives and who is allowed to see it. That is not glamorous work, but it is what decides whether the project ships.
What we changed
- We stopped promising accuracy numbers before we had seen the real data.
- We put a person in the loop for anything that touches money or safety.
- We logged every answer so we could show our work when someone asked.
None of this is exciting. All of it is why the systems are still running.
Where we are heading
The teams getting value are not the ones with the biggest models. They are the ones who picked a narrow, painful problem and stuck with it until it worked. We plan to keep doing exactly that.
