Bloomfire unveils guide to enterprise intelligence systems

Bloomfire has released the 2026 Guide to Enterprise Intelligence Systems, which assesses 12 enterprise platforms across knowledge management, enterprise search and business intelligence.

Conducted by Dr Anthony J. Rhem, the guide is intended to give technology and business leaders a framework for examining the systems that underpin their AI strategies. It evaluates five knowledge management platforms, four enterprise search products and three business intelligence platforms.

Bloomfire argues that many enterprise AI problems stem less from the language models themselves than from the quality, structure and governance of the information those systems rely on. The guide focuses on the links between software categories that many organisations have bought and managed separately.

Those categories have typically served different purposes: knowledge management tools capture what staff know, enterprise search tools help them find information, and business intelligence systems analyse numerical data. The assessment examines how well platforms perform across 12 criteria, including decision-centric capability, AI behaviour and responsible augmentation, governance and knowledge quality, and cross-category integration.

Dr Rhem said fragmentation across these systems has created a gap that many companies have not properly examined.

"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, PhD, Bloomfire. "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."

Bloomfire also cited several figures that it said illustrate the problem: more than half of enterprise workers had bypassed company AI tools in the previous 30 days and completed work manually instead; AI budgets had risen 38% year on year, while 40% of that spending was underperforming; and three in four executives viewed their AI strategy as more show than substance.

The company used its own software to carry out what it described as a knowledge health analysis of its internal content library. According to Bloomfire, the exercise identified missing definitions and assumptions in documentation despite a high overall score.

"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 Philip Brittan, Chief Executive Officer, Bloomfire. "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."

Assessment framework

The guide presents what Bloomfire describes as an independent evaluation and scoring methodology for enterprise intelligence. The criteria cover not only product functions but also governance questions, including who owns information and how knowledge is maintained over time.

That emphasis reflects a broader market debate over whether AI investment problems are rooted in software choice or organisational design. Companies have spent heavily on tools intended to improve access to information, yet many still struggle to connect data, documents and institutional knowledge in ways that produce reliable outputs.

For large organisations, this can become a practical rather than technical issue. When systems are owned by separate teams and judged against different goals, staff can end up with incomplete search results, outdated documents or AI responses built on patchy information. In that setting, governance becomes central to whether AI tools are trusted and used.

Dr Rhem said organisations seeing stronger results have taken a more deliberate approach to how these systems fit together.

"The organizations getting AI right built the stack intentionally," said 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."