Why is your AI so stuck?
Your company spent millions on AI. Nobody's using it. Three-quarters of companies are stuck. And they’re solving the wrong problem.
BCG surveyed 1,000 CxOs across 20 sectors. Only 26% got past proof-of-concept to generate actual value. BCG found that companies throw IT at operational problems. They throw change management at technical gaps. They blame "culture" when the workflow design is broken.
It's like trying to fix a car that won't start by repainting it, then blaming the driver for not turning the key hard enough.
For the past three years, I've been seeing the same repeating failure patterns. If something’s holding your team back, it’s probably some gross cocktail of these:
—No Duh Technical Problems—
Your model is outdated (AI leapfrogged your solution 18 months ago). Your data is too messy or locked behind security walls. Your AI’s performance just isn’t enough—too slow, too expensive, too unreliable. IT and your vendors own these fixes.
The fix? Upgrade, integrate, replace the tech.
—Secret Killer Operational Problems—
The AI does exactly what you asked for, but that's not what you actually needed. Or it handles step three beautifully but creates manual chaos at steps two and four. Or worst—it's optimizing perfectly for metrics that don't matter. Process owners and product managers own these.
The fix? Redesigning workflows and closing automation gaps.
—Messy Human Problems—
What nobody budgets for. Zero adoption despite good technology. Active resistance from people who fundamentally don't trust it. Leadership has to own this.
The fix? Change management, new champions, and realigned incentives.
The companies that actually scale AI aren't the ones with the best models or the biggest budgets. They follow a 10-20-70 rule: 10% of effort on algorithms, 20% on tech and data, 70% on people and processes.
But! You have to know which of these nine problems you actually have before you can apply that formula. Misdiagnose the problem, and you waste another quarter solving the wrong thing.
This is exactly what I help innovation and ops leaders do—correctly diagnose which problem is actually blocking your AI before you spend time and budget on the wrong fix.
Where's your AI stuck?