If you've spent any time in IT circles over the past year, you've probably heard the number: 95% of enterprise AI pilots fail to deliver meaningful business impact. It's been cited in presentations, board meetings, and media coverage so often it's become accepted truth.
Tom Zehren isn't buying it.
Zehren, CEO of Info-Tech Research Group, opened Info-Tech LIVE 2026 in Las Vegas by taking direct aim at the statistic — and the research behind it. "The challenge when media picks up research is truncated information," he told a packed Grand Ballroom. "The shorter you make information, the older you make the information, the easier it travels."
His assessment of the source? "It's an eight-page marketing memo. And it's terrible research."
The specific study Zehren was referring to — widely cited as MIT research — surveyed a relatively small sample and was conducted roughly a year ago, when most enterprise AI deployments were early-stage ChatGPT experiments, not production agentic systems. "They talked about Gen AI one year ago," Zehren said. "What they were really talking about is chat." He also noted a fundamental timing problem: many pilots counted as failures simply hadn't completed yet.
His larger point wasn't to defend AI hype. It was to argue that bad data leads to bad decisions — and that IT leaders need to dig into the details before accepting a narrative.
What Info-Tech's Data Actually Shows
To back his argument, Zehren presented results from two large-scale surveys Info-Tech conducted across roughly 550 IT executives, with a second survey focused specifically on the software development lifecycle.
The findings tell a different story than the 95% headline. Forty-two percent of organizations have already deployed AI solutions across multiple departments — not just pilots — and have seen measurable impact. Another third have deployed customer-facing AI solutions. And perhaps most importantly, the top drivers for AI adoption aren't cost savings. They're growth and removing operational constraints that have held IT back for years.
"Cost reduction is at the bottom," Zehren said. "People are primarily trying to drive growth."
On the development side, 84% of software developers are now using AI across the entire software development lifecycle — not just for code generation, but for testing, documentation, and design. Sixty percent say AI-generated code requires more oversight, but Zehren sees that number differently than most. "If you flip that around, you get to a third of people saying it requires the same level or less. And leading software companies have already moved past the phase of fixing bugs from AI-generated code. The vast majority of code that is prompt engineered by developers at those companies is ready for production."
Five Hypes Worth Examining
Zehren structured his keynote around five AI narratives he believes deserve more scrutiny.
The Klarna story — often cited as proof that AI saves millions — is more nuanced than the headlines suggest. The company reduced its contact center headcount from 3,000 to 2,300 agents and saved approximately $40 million. But customer satisfaction tanked when emotional, complex cases like fraud disputes had no clear path to a human agent. The mistake, Zehren explained, was routing all calls in a specific region to AI agents only — eliminating the human escalation option entirely. Compare that to AWS, which used AI agents to migrate thousands of legacy Java applications to Java 17 — a use case with clear parameters, measurable output, and no customer-facing complexity. "You are not AWS," Zehren noted. "You don't have 30,000 applications running around."
On job displacement, Zehren aligned with World Economic Forum projections: roughly 190 million jobs created globally by 2030 against 92 million displaced — a net gain. The bigger challenge isn't job loss. It's the skills gap. "AI is not going to take your job," he said. "Someone who knows how to use AI is going to take your job."
On SaaS, he sees pressure building but not collapse. Seat compression, autonomous workflows, and the shifting economics of build versus buy are all real forces — but the major SaaS players are evolving their models rather than being displaced overnight.
The Bottleneck Nobody Talks About
In a media roundtable following the keynote, Zehren got more specific about where organizations actually get stuck. The problem isn't model quality or infrastructure. It's organizational.
"The biggest bottleneck, besides data, is the gap between business intelligence and understanding of workflows, and how you attach AI to get value out of them," he said. "That thinking and that knowledge sits in two very different heads. How do you bring those heads together in a structured way?"
It's a deceptively simple observation — and one that explains why Info-Tech's value proposition has sharpened in the AI era. The firm has grown more than 350% recently, even as AI has put pressure on traditional research and advisory models. The reason, according to Zehren, is the combination of the "what" and the "how." "If you have a fancy stat and you don't know how to exploit that — that's the problem. We can combine those two things."
As one participant in the roundtable put it plainly: "AI democratized access to research. That's no longer the valuable piece. The value now is the advisory piece — here's what you do, here's what it means, here's the step."
Zehren's closing argument was direct: this is IT's moment, not IT's threat. "We believe this is actually IT's moment to step up and move from the 'please give me this app' mode into the push mode — where IT understands the technology best and steps up as an organizational leader."
The organizations that figure that out first, he suggested, won't be asking whether AI delivers value. They'll already know.
