Glean, Gartner & the GenAI Knowledge Management Market

TL;DR: Gartner named Glean an Emerging Leader in its 2025 Innovation Guide for Generative AI Knowledge Management Apps (Glean, 2025). The bigger story is the category itself: when Gartner defines and ranks “GenAI knowledge management apps,” it confirms that the enterprise operational brain is now a real, comparable market with named leaders, not a science project.

When an analyst firm draws a quadrant around a market, something has shifted. The vendors stop being curiosities and start being line items in a budget. That is what happened in November 2025, when Gartner published an Emerging Market Quadrant for generative AI knowledge management apps and placed Glean among the emerging leaders (Business Wire, 2025). This post unpacks what the category is, why the recognition matters beyond one vendor, and what it tells enterprise buyers about how to evaluate an operational brain.

What did Gartner actually recognize?

Glean announced on November 17, 2025 that it was named an Emerging Leader in the 2025 Gartner Emerging Market Quadrant for Generative AI Knowledge Management Apps and General Productivity (Glean, 2025). The underlying Gartner research, authored by Marko Sillanpaa, Justin Tung, Stephen Emmott, and Darin Stewart, was published on November 13, 2025 (Gartner, 2025).

Two terms are worth defining plainly.

An Emerging Market Quadrant is a Gartner research format for early-stage, fast-moving markets. It maps vendors before a category is settled enough for a full Magic Quadrant, so technology leaders can survey the field while it is still forming. Gartner describes the GenAI applications market as a volatile, fast-growing segment where these quadrants help organizations get their bearings (Gartner, 2025).

An Emerging Leader, in Gartner’s words, is a vendor that “typically respond[s] to a wide market audience by supporting broad market requirements” (Business Wire, 2025). The placement reflects product strength and a forward-looking roadmap, not current market share alone.

What is a “GenAI knowledge management app”?

This is the part that matters most, because the definition tells you what the whole category is for. Gartner defines the submarket as “technologies that enable companies to better retrieve and contextualize information and insight from their knowledge bases, including enterprise AI search, conversational AI platforms, and productivity tools for communications and content development” (Gartner Peer Insights; also cited in Business Wire, 2025).

Read that again with an eye on the verbs: retrieve and contextualize. The category is not about generating more content. It is about pulling existing institutional knowledge out of scattered systems and putting it in context so it can be used. That is the exact problem an operational brain exists to solve.

A useful way to frame it: a GenAI knowledge management app is software that sits across an organization’s existing tools and turns the knowledge already inside them into answers and actions. The knowledge was always there. The new layer makes it findable and usable by both people and AI agents.

Why does a named category matter more than the ranking?

Rankings change every year. A defined category changes how buyers think.

The underlying problem is not new. McKinsey’s research on knowledge work found that interaction workers spend close to a fifth of the workweek looking for internal information or tracking down the colleague who has it (McKinsey Global Institute, 2012). What changed in 2025 is that the fix finally has an analyst-defined category around it.

Before a market has a name, every vendor pitch sounds bespoke and every evaluation starts from scratch. Once Gartner draws the quadrant, three things happen at once:

• Comparability. Buyers can line up vendors against a shared definition instead of comparing apples to slide decks.
• Budget legitimacy. A category with named leaders is easier to fund. It moves from an innovation experiment to a recognized software purchase.
• A shared bar. The market converges on what “good” means. In this case, Glean frames the bar as reliability, scale, business context, and measurable outcomes, and argues that experimentation is no longer the benchmark (Glean, 2025).

That last point is the real signal. The conversation has moved from “can AI do something interesting with our data?” to “can this system reliably understand our business, respect our permissions, and produce outcomes we can measure?” Those are operational questions, and they favor platforms built on connected context rather than standalone chatbots.

What separates an operational brain from a chatbot?

Glean’s own framing is instructive here, and it generalizes across the category. The company describes an Enterprise Graph that combines a company-wide knowledge graph with each person’s personal graph, connecting projects, experts, and company knowledge in a permission-aware way (Glean, 2025). It connects to more than 100 enterprise applications so a single question can span systems (Business Wire, 2025).

Strip away the brand names and you get the architecture of an operational brain:

• A knowledge graph that models the relationships between people, documents, tools, and projects, rather than a flat pile of indexed text.
• Permission-aware retrieval, so an answer never crosses a boundary the user could not cross themselves.
• Broad connectivity, because knowledge trapped in one tool is the original problem.
• Agents that act, instead of summaries that only read information back to you.

A plain chatbot bolted onto a single document store can do none of this reliably. As Glean co-founder and CEO Arvind Jain put it, “Enterprises don’t need more disconnected tools - they need a platform that understands their people, their knowledge, and their workflows” (Business Wire, 2025). This is the design thesis behind SemanticOS as well: a unified semantic layer that connects fragmented tools so both people and AI agents can reason over institutional knowledge.

A concrete example

Picture Vantage Health, a regional health insurer with about 1,800 employees. Its knowledge lives in the usual places: a claims platform, a policy wiki, a contract repository, Slack, and a few shared drives that predate anyone currently employed there.

A provider-relations specialist named Priya gets a call about an unusual reimbursement exception granted to a hospital network last year. The decision exists. It was made, documented, and discussed. But the rationale is split across an email thread, a contract amendment, and a Slack channel that has since gone quiet. Without a connective layer, Priya spends two hours pinging three teams and still pieces together a partial answer.

With a GenAI knowledge management platform sitting across those systems, Priya asks one question in plain language. The platform traverses the knowledge graph, respects her access permissions, and returns the amendment, the email that explains the reasoning, and the people who approved it, with citations. The afternoon of detective work becomes a single query. Now extend that to an agent that drafts the renewal note for review, and you can see why Gartner’s verbs were retrieve and contextualize, then act.

That is the difference a named category is starting to standardize: not flashier demos, but the boring, durable ability to find what the organization already knows.

Key takeaways

• Gartner named Glean an Emerging Leader in its 2025 Innovation Guide for Generative AI Knowledge Management Apps, published November 13, 2025 (Gartner, 2025).
• The headline news is the category itself: Gartner defines GenAI knowledge management as retrieving and contextualizing existing enterprise knowledge (Business Wire, 2025).
• A named, ranked category makes the operational-brain market comparable, fundable, and held to a shared bar of reliability and outcomes.
• The architecture that wins is a permission-aware knowledge graph with broad connectivity and agents that act, not a standalone chatbot.
• SemanticOS builds in this category: a semantic layer that unifies fragmented tools so people and AI agents can find and reason over institutional knowledge.