The Data Magician’s Rule for AI Adoption
I’ve spent over a decade working in analytics across healthcare, pharma, and global operations. In that time, I’ve watched a lot of AI initiatives launch with enormous fanfare and land with a quiet thud. And I’ve watched a handful of initiatives that nobody talked about generate real, compounding value that outlasted every reorganization.
The pattern is almost embarrassingly consistent. The ones that fail start with ambition. The ones that succeed start with specificity.
The Rule
Find the worst process in your building. Not the most complex, not the most strategic — the most “tolerated.” The one where someone has been doing the same manual workaround for three years because fixing it was never quite urgent enough to prioritize. The one that lives in someone’s inbox and dies when they go on vacation.
That’s your AI pilot.
Not because it’s glamorous. Because it’s contained. The scope is clear, the baseline is measurable, the stakeholders are motivated, and nobody’s identity is wrapped up in defending the status quo.
Success there builds credibility for the harder problem next quarter.
What I’ve Seen Work in Practice
In large-scale manufacturing analytics, the biggest wins I’ve been part of didn’t involve machine learning models or predictive algorithms at the outset.
They involved automating the movement of data between systems that had never been integrated, and giving operational teams real-time visibility they’d been building manually for years. The AI layer came after trust was established — not before.
The organizations that reverse that sequence — leading with the sophisticated model before the data infrastructure is clean, before the team trusts the output — struggle to get adoption no matter how technically impressive the solution is.
The Magician’s Framework
Strategy and product thinking. Advanced analytics. Real-world operational impact. When all three connect, the results look like magic to the people who didn’t see the work. That’s the goal. But the trick never starts on the big stage. It starts in the back room, with the simplest version of the problem, until the mechanics are bulletproof.
Start boring. Scale bold. The return is in the back office.