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Short abstract

We expect AI to solve our problems. More often, it makes existing ones visible faster. Drawing on twenty years inside technology organizations I will show why automation does not remove coordination problems but scales them. If we save time thanks to AI, what happens to that time?

Long abstract

For twenty years I have worked inside technology organizations: an award-winning agency, live gaming entering four regulated markets, a scaling SaaS startup, and 5G/6G R&D at Ericsson. I map where delivery breaks down and why.

Across all of them I saw the same pattern. Organizations adopt technology expecting it to solve a problem. What it usually does is make an existing problem visible faster.

One example. A product supported in 27 languages. Design defines how many characters the interface can hold. Translators fit within that limit or flag an exception. Frontend handles both extremes. Testers try to break it. If one of these four roles does not know about the other three, the interface falls apart. No one is at fault: everyone did their part correctly. Automation does not remove this coordination problem. It scales it.

I want to talk about what happens when AI enters organizations that never made their own decision-making visible. Who owns a decision when an algorithm proposes it? What happens to people whose competence quietly compensated for structural weakness?

Recent research found that students who outsourced homework to AI scored 18 to 24 percent worse on exams. Not because the tool was bad, but because the time saved was removed from learning instead of reinvested.

Every organization automating work faces the same question: what happens to the time we save?