An elite machine learning model that nobody uses is just an expensive math experiment.
The focus must shift from technical excellence to business impact.
Leaders often mistake model sophistication for strategic readiness.
The real tension exists between the data scientist who wants accuracy and the person responsible for the P&L who needs to explain every decision to a customer.
I saw this when we built an AR prediction model that identified overdue invoices within 30 days with 88 percent accuracy (iteration 1).
Despite the high success rate, the business unit leaders simply did not believe the machine.
They distrusted the numbers because the math was a black box they could not explain to their clients.
Here is an action plan:
Prioritize explainability so your managers actually trust the output.
Measure success by business outcomes.
When was the last time an AI tool failed not because the math was wrong, but because your people refused to use it?
#AIStrategy #DigitalTransformation #Leadership #EnterpriseAI