From Damir
Why I Do Not Call an AI Project Successful Until We Measure the Value
Damir Grubisa explains why AI success requires a baseline, adoption, accountable ownership and measurable business value.
A working model is only the beginning
I have seen how quickly an AI demonstration can create excitement. But a model producing a good answer is not the same as a business process producing a better result. I want the operating baseline, the intended outcome and the person who will own the change.
I define the measure before the pilot
Before implementation, I ask what should move if the project works: cycle time, capacity, error rate, response time, cost, revenue support or risk. If the team cannot name the measure, it will be difficult to judge the investment honestly.
Adoption belongs in the value equation
A technically capable system creates little value when people do not use it or when they continue running the old process beside it. I treat adoption and eligible transaction volume as core business-case assumptions.
Time saved needs a destination
Saved time is not automatically cash. Leadership must decide whether that capacity will support more customers, faster work, improved service, avoided hiring or higher-value activity.
The forecast must become an actual
After launch, I replace assumptions with real implementation cost, usage, recurring expense and business results. That is when AI moves from a promising project to an accountable investment.