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A prospective client forwarded us a "WordPress audit" last year. It was a one-page PDF from a security plugin: a green checkmark next to "no known malware," a list of out-of-date plugins, and a recommendation to upgrade to the vendor's premium tier. The site, the report concluded, was healthy.

Every services firm runs on the same hidden equation, whether or not anyone has written it down: revenue is a function of headcount. To grow, you hire. To take on more work, you staff up. To protect margin, you push utilization. The whole apparatus of an agency, a consultancy, or a professional-services firm is a machine for converting human time into invoices, and the better you run it, the more cleanly that conversion happens.

We talk to a lot of leaders who have already made up their minds. AI is a fad. AI is garbage. They tried it, it underwhelmed, and they filed it next to every other overpromised technology a vendor once swore would change their business. It is a reasonable conclusion, and most of the time it is an honest report of a real experience.

It is also, almost always, a report on the wrong experiment.

There is a quiet assumption buried in most enterprise AI adoption: that the path to better output is a better single prompt. Refine the wording, add more context, raise the stakes in the instruction, and the model will eventually produce the answer you wanted. That assumption is responsible for a lot of plateaued AI programs. It treats a probabilistic system as if it had one best answer waiting to be unlocked by the right key.