Why most AI projects fail (and the ones that don't)
They rarely fail because the technology could not do the job. They fail for a small set of predictable reasons, almost all decided before a line of code is written. Here is that list, and what the successful ones do instead.
The six ways they fail
Solved a problem not worth solving
Impressive and pointless at once. If the problem cost nothing, no execution rescues it.
Started from the technology
"We should do something with AI" produces a solution looking for a problem.
Mistook the demo for the product
Ran out of budget in the hard, invisible phase where edge cases and reliability live.
Ignored the data
Assumed the data was ready when it was messy, scattered, or missing.
Tried to automate the judgment
Removed people from cases that needed them, producing results no one trusts.
No one could own it
Depended on constant specialist attention, and died when that attention moved on.
What the ones that succeed do
The successful projects are not more sophisticated. They are more disciplined, and they share a short list of habits:
- They start from a real, expensive problem, not from the technology.
- They decide it is worth building before building it, and are willing to say no.
- They keep the scope narrow, solving the part that matters and leaving the rest.
- They keep a person in the loop for the cases that need judgment, and automate only the pattern.
- They treat production as its own phase, budgeting for edge cases, reliability, and running costs.
- They make sure someone can own it, so it survives after launch.
None of these is about the model. All of them are about the decision. The technology is rarely the constraint now. Judgment is.