The secret isn’t glamorous. It’s not an app, a hack, or a shiny new framework.
It’s the part everyone pushes down the priority list as they break a problem into its component parts. The hardest bit. Break the problem into its pieces, then go straight at the hardest part first.
AI now helps us do the problem analysis faster. It can model options, run simulations, and point out blind spots. However, it cannot focus your attention on the hardest bit first, that requires you.
Failure is the toll on this road. Edison’s “I now know what doesn’t work” wasn’t optimism, it was realism. Most attempts will miss. Data won’t rescue you when you’re in uncharted territory. Only cycles of trial, error, and learning will.
And here’s where humans stumble. We hate failure, and often failure has consequences in corporate life, so we become risk averse. We look for shortcuts, silver bullets, or easy wins. AI makes the shortcuts more tempting because it gives us mountains of plausible-sounding answers in seconds. But plausible isn’t proven.
The real advantage belongs to people who can keep their “discovery tempo” steady, using AI as an accelerant while still accepting that most paths will be cul-de-sacs.
AI has changed the speed and nature of problem-solving. What hasn’t changed is the rule: robust innovation comes from persistence through failure. The cycle is now faster, but the psychology hasn’t shifted.
So, the winners will be those who combine two rare qualities: the resiliance and patience to face repeated failure, and the discipline to use AI not as a crutch, but as a lever to attack the hardest part of the problem first.
