During a presentation to 160 IT vice presidents in Las Vegas, tech entrepreneur Azeem Azhar conducted an informal poll regarding enterprise artificial intelligence adoption. Approximately two-thirds of the executives present indicated that their organizations have achieved measurable results from AI implementations. However, when asked if those results were significant enough to justify interrupting a chief executive officer’s summer vacation, only eight audience members raised their hands to affirm that their gains reached that threshold.
Measuring AI Return on Investment
The poll highlights a central tension in current enterprise technology planning: while artificial intelligence tools are delivering identifiable utility, high-impact breakthroughs remain rare. Over the past two years, tech vendors and corporate IT departments have directed billions of dollars toward generative AI infrastructure, software licenses, and integration. The modest scale of major successes feeds into broader industry debates over whether current corporate AI spending constitutes a market bubble. While incremental productivity gains are relatively straightforward to document, transformative business outcomes that fundamentally alter profitability or strategy have proven far harder to realize.
What Remains Unclear
The informal show of hands leaves several technical and operational specifics unspecified. The report did not disclose which AI platforms, software vendors, or hardware systems the surveyed companies were deploying, nor did it define the specific metrics used by the IT vice presidents to quantify their results. Furthermore, the exact date of the Las Vegas conference, the specific business sectors represented by the 160 executives, and the methodology of the audience query were not detailed in the original reporting by The Decoder.
As corporate boards review future technology budgets, enterprise technology leaders will face heightened scrutiny over whether modest operational improvements can ultimately justify the high ongoing cost of AI deployments.
Lead image: Yan Krukau / Pexels
