A decade ago, organizations rushed into digital transformation.
They bought tools, launched platforms, hired consultants, and declared success—long before work actually changed.
Today, the same pattern is playing out again. Only this time, it’s called AI enablement.
Leaders say they’ve learned from the past. In reality, they’re repeating it almost exactly.
AI initiatives start with technology instead of behavior. Tools are rolled out before workflows are redesigned. Training programs are launched without changing how decisions are made or how success is measured. And when adoption stalls, the blame quietly shifts to employees.
This is digital transformation déjà vu.
The original failure wasn’t technical—it was organizational. Systems changed, but power structures didn’t. Processes stayed the same. People were expected to adapt without being enabled to work differently.
AI enablement is running into the same wall.
AI doesn’t fail because models are weak. It fails because organizations try to layer intelligence onto outdated operating models. They expect faster decisions without removing approvals, better output without reducing cognitive load, and innovation without psychological safety.
Just like digital transformation, AI enablement cannot be delegated to tools, training, or task forces. It requires leaders to confront uncomfortable questions about control, trust, and accountability.
The companies that succeed won’t be the ones with the most advanced AI stacks.
They’ll be the ones that finally learned the real lesson of digital transformation:
Technology only scales when the organization is willing to change how work actually happens.
AI enablement isn’t new.
It’s the same transformation—just with less patience and much higher stakes.
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