The Real Reason Companies Cannot Adopt AI Is Not Security
A PM who is closer to a mini CEO
He works as a PM at a public tech company in New York, though PM undersells it: at this company the role is closer to a mini CEO, owning the P&L and running a small business unit end to end, from revenue and marketing to technical support and sales.
The documentation culture is extreme. A product requirements document runs eighteen pages as a baseline, everyone reads every word, and mistakes get attacked. In that environment you can use AI tools, but simply delegating and shipping is impossible. From early planning to the final check, you do the careful work yourself. AI is a second brain, not a brain you hand over.
The interesting part was how he handles data. The company dashboards are hard to read and hard to search, so he feeds entire tables into one AI instance, teaches it the structure, and answers every data question through it.
That is my data engineer.
Still, he is a minority at his company. Very few PMs use AI aggressively, and managers above him often do not even know where to start.
The real reason is time
We asked why AI adoption is slow at large US companies. Security? Technical literacy? The answer was almost too simple.
There is no time.
The pile of immediate work never shrinks, quarterly targets have to be hit, and layoffs in the air mean people are working more than before, not less. One company attacked the problem head-on by stopping the entire organization for a full week so every employee could learn nothing but AI. Most companies cannot afford that week; results are due and customers need answers. In small organizations, forcing a fixed day of the week works. Forcing does work. Coordinating that across thousands of people is a different problem.
No longer wanting to be better at the job
His words, as he said them:
After work, who wants to sit around thinking about how to make more money? It is exhausting.
It is a realistic answer. When every day is already a chase, there is little appetite for building work skills at night and on weekends. The more telling part is the direction of the pressure: not the push to get better at the job to beat AI, but the amplified anxiety of being nothing now.
So attention shifts. Not toward doing the job better, but toward the question of what their own business would someday be. This PM is exploring exactly that.
Job titles are dissolving
The conversation naturally turned to the future of the PM role. Positions combining Chief Product Officer and Chief Technology Officer are increasing, and the reason is simple: with AI, a technical leader can run product and a product leader can reach into technology. The boundary between roles is collapsing. Marketers run search analytics inside coding tools, PMs touch code, and non-developers run agents. The gap between people who use AI well and people who do not has simply become the skill gap.
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This post is based on a conversation with a PM at a public tech company in New York.
