Originally published on lavkesh.com
Most companies today will tell you they are doing something with artificial intelligence, that it is woven into their operations, and that it is changing everything. They talk about agents, about models, about the way data flows. This is the common wisdom, the thing you hear at every conference. But fewer than four in ten of these same organizations can point to any actual profit or cost savings from their AI efforts. The Stanford AI Index for 2026 put the number at 39 percent, which means a large majority are spending money on AI without seeing it reflected on the balance sheet.
This disconnection feels familiar, like watching a team celebrate the deployment of a new service to production, calling that the win. The real win, of course, is what that service *does* for the business, how it solves a customer problem, or how it reduces a specific operational cost. The same pattern played out with cloud migrations, where companies moved everything to a new data center provider and declared victory, only to find their costs increased and their reliability stayed flat.
The enthusiasm for new technology often overshadows the hard work of defining clear, measurable outcomes. When I was in energy management, we had a system that could predict equipment failures. The initial excitement was around the prediction accuracy, how many false positives versus true positives. But the real value came when we could show that acting on those predictions reduced unplanned downtime by a specific percentage, saving millions in lost production and repair costs. That required integrating the AI output into the maintenance schedule, training technicians, and tracking the financial impact of each avoided failure.
