A design studio spent a year rebuilding its entire process around AI across four real products with real clients and real deadlines. Sprint length did not change. Five days before, five days after. The speed pitch is wrong, and the author knows it firsthand because he sold that pitch internally and his COO of 20 years told him directly it would not work. He was right. What changed was not velocity but output quality, and with it came a new category of failure modes nobody is documenting honestly.
The most important operational finding here is not about AI tools. It is about who you put in the room. The author kept his loudest skeptic closest, gave him the validation role, and watched doubt become quality control. That move maps to documented research: worker trust in company-provided AI fell as the tools grew more capable, and the leaders who treated internal resistance as an obstacle were the ones whose transformations stalled. The Jurassic Park framing is blunt and accurate. The system can generate a screen in ninety seconds. It cannot tell you whether that screen should exist.
Read the full piece for the subscription media product case study, where the author walks through five distinct user states, logged out through multiple paid tiers, and explains what AI actually handled well versus where human judgment was non-negotiable. The debt he mentions in the subhead, the kind nobody is talking about, gets named specifically in the sections that follow this summary. That is the part worth your time.
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