Two-thirds of IT leaders report AI results but few are significant

By Splitfeed EditorialLast updated Sep 26, 2026, 18:54 UTC

At a Las Vegas gathering on September 26, 2025, tech entrepreneur Azeem Azhar posed an informal two-part poll to a room of 160 IT vice presidents. When he asked how many had measurable AI results, roughly two-thirds raised their hands, suggesting broad deployment across enterprises. When he then asked how many had results significant enough to interrupt their CEO's summer vacation, only eight hands went up. That is eight out of around 107 who had already claimed measurable results, and eight out of 160 overall.

The gap those numbers reveal is the crux of a wider argument about whether current enterprise AI spending is delivering proportionate value. Two-thirds reporting any measurable result sounds like a success story for adoption, but eight out of 160 reporting genuinely significant impact suggests that most of those results sit at the margin, useful perhaps, but not transformative enough to change business priorities. This matters because corporations and their investors have committed enormous sums to AI infrastructure and tooling, and the return on that spending is increasingly under scrutiny. The anecdote does not settle the question, but it gives a concrete, if informal, data point to a debate that has mostly traded in projections and case studies.

None of the three camps had published reactions to this specific story at the time of reporting, so what follows reflects what each would typically argue. The pro-AI camp would likely frame the two-thirds figure as evidence of genuine, accelerating enterprise adoption and would argue that transformative returns take time to compound after initial deployment. The anti-AI camp would seize on the eight-out-of-160 figure as confirmation that AI investment is inflated relative to real-world payoff, pointing to it as a data point in the AI bubble argument. The middle-ground camp would probably hold both numbers simultaneously, acknowledging real but uneven progress and calling for more disciplined evaluation of where AI spending is and is not producing returns.

The informal nature of Azhar's poll means it carries no statistical weight on its own, but it points toward a more consequential question: whether formal earnings disclosures, productivity studies, or industry benchmarks released over the next year will show a closing or a widening gap between AI deployment rates and significant business impact.