Almost every strategy workshop I sit in ends the same way. Someone writes the problem on the whiteboard, the room talks for an hour, and the answer at the bottom says "use generative AI".
That is not an answer. That is a tool looking for a job.
We have done this before
In 2012 every business needed an app. In 2016 it was blockchain. In 2019 it was IoT sensors on everything. Each time, the question in the room was "what can we do with this?" instead of "what is actually costing us money?"
AI is the same pattern with a better demo. A language model can write a convincing email, summarise a 40-page contract, or answer questions about your product catalogue. It is genuinely useful. It is also just software. It reads text, it predicts text, and it is wrong some of the time.
Treat it like that and you will make good decisions. Treat it like magic and you will buy a prototype and call it a product.
The prototype trap
Here is how it usually goes. A team spends two weeks building a chatbot on top of the company's documents. The demo is great. Everyone is excited.
Six months later, nobody uses it. Not because the AI is bad. Because nobody asked who the user was, what they were trying to do, or why they were not doing it already. The answers were in the documents all along. The real problem was that the documents were out of date and nobody owned them.
A faster way to search stale information is still stale information.
The boring work is the real work
The projects that actually pay off rarely look impressive. They look like this:
- A quoting process that took two days now takes two hours, because we connected the CRM to the pricing sheet and let a model draft the first version.
- A job-costing report that someone rebuilt by hand every Friday now builds itself.
- An invoice-matching task that ate one person's whole Monday now flags the three exceptions that need a human.
