Your AI Was Right. Did Anyone Do Anything About It?

Subrata (Subu) Biswas is the co-founder and CEO of Cimba, a company building proactive AI for business operations.

The conversation I keep having with operations leaders is not about whether the software was right. It’s about a recommendation somebody saw, agreed with and then didn’t act on. The reasons are rarely dramatic. Usually the person who would have had to make the change was three meetings deep in something else, or the number landed on a Thursday when the call had already been made on Tuesday. Sometimes someone got burned moving fast last quarter and wasn’t going to do it twice. What stays with me is how rarely any of that gets counted as a system failure. The model was right, so the system gets its credit and the quarter still ends the way it was going to end anyway.

What We Assumed Was The Bottleneck

For the past three years, the pitch on enterprise AI has overwhelmingly been about access. Give the team a forecast instead of a hunch. Give the ops manager a flag where there used to be nothing at all. The assumption underneath was that people were making poor calls because they lacked information, and that handing them better information would change the output.

McKinsey’s 2026 global survey shows how that bet is playing out. Forty-four percent of organizations now report scaling AI across the enterprise, up from 38% a year earlier, and 80% of respondents say it has made them personally more productive. But the share attributing any EBIT impact to AI sits at 37%, about where it was the year before, and only 6% clear McKinsey’s bar for high performers. So, deployment went up, but the earnings line didn’t move with it.

The Question Nobody Asks Afterward

Ask why the 94% who fall short of that bar have not seen it show up, and the answer is rarely that the model was wrong. The more useful question is one that almost no vendor evaluation asks: What happens after the system is right?