“Don’t write code. Let AI do the work.”
The CEO wants an AI transformation. The company has invested heavily, and the expectation is that things should start moving much faster.
But the money can leave the account much faster than the company can change.
The tools arrived. The old problems stayed.
Take a company where a feature needs three approvals. Two teams disagree on the requirements. The data is unreliable, and nobody wants to own the cleanup.
Now everyone has an AI assistant.
Which of those problems has gone away?
An engineer might finish the code in an afternoon instead of three days. It can still sit for two weeks waiting for the same approval it needed before.
The customer is still waiting.
A working demo gets expectations moving
A demo makes transformation look close. Someone builds a convincing prototype, and suddenly people are asking why the rest of the business can’t work like that.
Then the prototype needs access to real systems. The data is incomplete. Permissions need approval. Someone has to decide what happens when the output is wrong.
Those details were easy to leave out of the demonstration. They are much harder to leave out of production.
Employees end up caught between the speed leadership has seen and the work still needed to make it reliable.
“Why is this taking so long? We’ve already invested in AI.”
You still have to fix the company
AI can remove hours of repetitive work.
But the problems that slowed a company down before AI deserve attention too. Unclear priorities, poor data, slow decisions, and teams working against each other will keep getting in the way.
If people couldn’t agree on what to build before AI, generating the code faster won’t settle the argument.
Comments