Most Enterprise AI Is Built on a Foundation No One Has Measured

The 2026 Guide to Enterprise Intelligence Systems Gives Enterprise Leaders the Framework to Examine the Foundation Their AI Depends On

AUSTIN, Texas, May 7, 2026 — Most enterprise AI is underperforming, and the cause is rarely the underlying language models. Organizations are turning to countless software tools to break through and achieve the return on their AI investments, but are often left wanting. To help enterprise leaders navigate these choices, Bloomfire has released the 2026 Guide to Enterprise Intelligence Systems—the first independent evaluation of twelve enterprise platforms across knowledge management, enterprise search, and business intelligence. Conducted by Dr. Anthony J. Rhem, PhD, the guide provides technology and business leaders with a framework to examine what lies beneath their AI strategy.

The finding that should concern every enterprise AI leader

“In the build-up to enterprise AI, companies have invested in three largely separate technology categories — knowledge management platforms to capture what people know, enterprise search to help them find it, and business intelligence systems to make sense of the numbers,” said Dr. Rhem. “Each was built to solve a different problem, governed by a different team, and measured against a different set of outcomes. The assumption that they’d converge into something coherent on their own was never realistic. That is the gap the 2026 Guide to Enterprise Intelligence Systems was built to close.”

The data reflects it. More than half of enterprise workers bypassed their company’s AI tools in the past 30 days, completing work manually instead. Despite a 38% YoY increase in AI budgets, 40% of that spend is underperforming. Three in four executives admit their AI strategy is more for show than substance. What those numbers share is a cause most organizations have never had the tools to see—the space between their systems where knowledge falls through.

Bloomfire CEO Philip Brittan used his company’s solution to produce a knowledge health analysis for his own organization to see exactly how wide those gaps can be.

“Our content library scored 91 out of 100, but even at that level, we found 31 concepts our documents assume every employee understands, with zero documents that actually define them—including our own core AI product. Ten documents referenced it, but none explained it,” said Brittan. “We’re the company that built the accountability layer, and we didn’t have a clean knowledge foundation. So the question isn’t whether your enterprise has this problem, it’s how bad it is.”

What the evaluation found

The 2026 guide evaluates five knowledge management platforms alongside four enterprise search and three business intelligence platforms. Each was scored across twelve criteria, including decision-centric capability, AI behavior and responsible augmentation, governance and knowledge quality, and cross-category integration.

“The organizations getting AI right built the stack intentionally,” said Dr. Rhem. “They didn’t just buy platforms. They decided how those platforms would talk to each other, who would own the knowledge inside them, and how that knowledge would stay current. That is not a technology decision. It is a governance decision, and most enterprises have not made it. The 2026 Guide to Enterprise Intelligence Systems gives them the framework to start.”

Click here to download the 2026 Guide to Enterprise Intelligence Systems. It includes platform evaluations, the Enterprise Intelligence scoring methodology, and Dr. Rhem’s recommendations for enterprise leaders making AI infrastructure decisions.

Read the full version of the press release from PRNewswire.