The local utility’s AI dispatch system pinpointed the county with the highest forecast wind speed as the utility’s greatest exposure. The model recommended stationing crews at a regional depot there to be ready. A 24-year veteran line supervisor disagreed.
He focused on a service area two counties away, an easily overlooked narrow stretch with old feeder lines beneath mature trees, a substation near a flood-prone creek, a major hospital, and a recently closed bridge on the major access route—a fact not reflected in the outage-management data. So, he overrode the recommendation. His more measured plan restored power to the critical circuit hours ahead of the algorithm’s plan.
This scenario illustrates the most pressing challenge now for AI: Without a deep, nuanced understanding of how things work in the real world, even the best AI model will be functionally hamstrung. Which is why so many AI pilots and proofs of concept don’t graduate to the real world. In one global study, the percentage of companies abandoning most of their AI initiatives before they reach production surged from 17 percent to 42 percent year over year, with organizations on average reporting that 46 percent of projects are scrapped between proof of concept and broad adoption.
Boards are looking for returns on their investments and coming up short. This is happening even though the technology is getting better, faster, cheaper, and more widely available. Today, a company can test powerful models without building its own from scratch.
Yet getting from a promising demo to a fully functioning solution that people can use, trust, and rely on remains elusive. That’s because an AI system needs more than just data. It needs to understand the work—not what the work is, but how, in the reality of day-to-day operations, it actually gets done.
The system needs an understanding of the semantics of the business and how work is prioritized, how work gets done and how failures are managed. For example, an AI system needs to know how a bank settles a payment when a routine instruction turns out to have a sanctions implication or why a claims professional treats one discrepancy as a clerical issue and another as a warning sign. It needs to know which maintenance alert can wait until morning, and which one signals a safety risk.
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