Many AI experiments fail because they repeat the same mistakes from the early digital transformation era: lots of hype, many pilots, little that scales.
Check out “Beware the AI Experimentation Trap” where the authors warn that 95% of generative AI investments produce no measurable returns.
They argue that much of current experimentation is diffuse, disconnected from what customers really need, and overly focused on flashy or peripheral tests instead of core capabilities.
Key lesson: anchor AI experiments in solving real customer problems.
“The takeaway is not that AI experimentation is broken, but that it must be disciplined — focused on solving core customer problems; chosen with frameworks like intensity, frequency, and density; run at low cost to enable iteration; and designed with scaling in mind through empowered ‘ninja’ teams.”
For product designers & PMs, that means: before building or approving yet another pilot, ask:
• What customer-pain does this address, and how often?
• How intense is the need vs how visible is the opportunity?
• Can we test cheaply, learn fast, and scale if successful?
It’s tempting to chase novelty with AI. But without customer-centric discipline, we risk repeating the digital transformation cycles where many firms experimented a lot—and few really delivered.
If you want strategies to escape this trap, this article is well worth your time.
🔗 https://lnkd.in/e4BFZNGG
#productdesign #AI #PM #experimentation #customersuccess
Thanks Dimitri Samutin for sharing this article initially.