We put AI on it and things are breaking, but we don’t know where or why
AI amplifies whatever it touches. When things start breaking after AI goes in, the cause is rarely where the errors show up. Finding it means tracing what the AI touched, upstream and downstream, before deciding what to keep, redesign or remove.
Which of these happened?
- Errors started after AI went in, and no one can trace them
- Problems show up downstream, far from the AI itself
- Our AI is confidently wrong in front of customers
- The system drifted from what it was supposed to do
- We automated something that should have stayed human
Why AI goes wrong
AI is a force multiplier. It does not care if it is multiplying your output or your chaos.
If you layer automation over a broken foundation, you are not scaling. You are accelerating the failure rate.
AI is not Frank’s Red Hot. We don’t put that on everything.
Some problems require deterministic solutions. Some benefit from probabilistic ones. Getting that wrong, in either direction, is one of the most expensive mistakes an organization can make right now.
What I do
Trace where it actually broke (the strategy, the process underneath it, the build, or the rollout), then decide whether to keep it, refactor it, rebuild it or replace it.
FAQ
Can you fix AI someone else built?
Yes. The first step is finding where it broke. What happens next depends on that, not on how much was already spent.
Does fixing it always mean rebuilding it?
No. Sometimes the build is fine and the process underneath it needs to change. Sometimes a configuration change is enough. Sometimes it needs to be replaced.
What if a vendor built it?
That changes your options, not the diagnosis. We identify what’s wrong first, then what leverage you have.