Gartner Names Enterprise AI Search a Real Category

TL;DR: In September 2025, Gartner published its first-ever Market Guide for the enterprise AI search category, naming Sinequa by ChapsVision among the Representative Vendors (Gartner / Sinequa, 2025). The Gartner Market Guide enterprise AI search category, with Sinequa cited, signals that AI-powered search has moved from a bullet point inside other products to a named market with its own definition, buying criteria, and infrastructure expectations. For enterprises, that reframes the question from “should we add AI search?” to “which platform anchors our knowledge layer?”

For years, “AI search” was a feature you found buried in a release note. A vector index here, a chatbot bolted onto a wiki there. There was no shared definition, so every vendor described it differently and every buyer compared apples to toasters. That ambiguity ended when Gartner gave the enterprise AI search category a formal definition and a reference document. This post explains what changed, what the definition actually says, and why a named Gartner category is a buying signal rather than marketing noise.

What did Gartner actually define?

Gartner defines enterprise AI search as AI-powered platforms that let employees find, surface, and act on information distributed across an organization’s content systems, combining semantic search, natural language processing, generative AI, and retrieval-augmented generation to deliver contextual, answer-first experiences instead of ranked document lists (Gartner / Sinequa, 2025).

The guide draws a hard line between this and legacy enterprise search across three dimensions:

• Understanding intent, not keywords. The platform uses large language models and semantic understanding to interpret what a person is trying to accomplish, then surfaces information that answers the underlying need even when the wording does not match.
• Generating grounded answers. Through RAG, the system retrieves content from verified internal sources and writes a direct answer with citations and source attribution. The answer is grounded in the organization’s own knowledge, not an LLM’s training data.
• Acting across systems. The most advanced platforms connect to agentic AI so search can trigger workflows, populate forms, escalate issues, or kick off downstream processes based on what it finds.

That third point is the quiet shift. Search stops being a place you go and becomes a capability that does something.

Why does a named Gartner category matter?

A category name is not a trophy. It is a coordination mechanism. Before a market is defined, buyers and vendors talk past each other. After it is defined, there is a shared vocabulary, a list of capabilities to evaluate, and a shortlist of vendors worth a meeting.

A Gartner Market Guide is the report Gartner publishes to map an emerging market before it reaches Magic Quadrant maturity. It names Representative Vendors, those Gartner considers most relevant, without ranking them on a grid (Gartner / Sinequa, 2025). For an inaugural guide, inclusion means a vendor was identified as actively shaping what the category should be at the moment it was formally recognized. Sinequa by ChapsVision was named in that first guide, published September 15, 2025 by analysts Tim Nelms, Stephen Emmott, Jed Cawthorne, and Darin Stewart (Gartner / Sinequa, 2025).

For a buyer, this is a practical change. A named category gives a procurement team a reference document to justify budget, a definition to write into an RFP, and criteria to score vendors against. The technology graduates from a feature request into a line item with a strategy behind it.

What problem is the category actually solving?

The category exists because the underlying problem is expensive and measurable. Knowledge workers spend a large share of the week hunting for information that already exists somewhere in the company.

APQC’s research found the average knowledge worker spends 8.2 hours each week looking for, recreating, and duplicating information and expertise, roughly 20% of the workweek (APQC, 2022). A 2024 study of 716 employees across government offices reported that about a third of respondents spend between half a workday and a full workday every week just searching for information they need to do their jobs (Nakash & Bouhnik, 2024). The numbers vary by method, but the pattern is consistent: a meaningful slice of paid time goes to finding things rather than using them.

Traditional enterprise search did not fix this. It returned ten blue links into systems people had already given up on. Enterprise AI search targets the actual failure: a person has a question, the answer exists in three different tools, and no single query reaches across all three. The category’s whole premise is connection and synthesis, not another search box.

What capabilities define a serious platform?

The guide identifies the capability areas that separate enterprise-grade platforms from general-purpose AI tools. The vendor profile for Sinequa is built around five of them, and they double as a useful checklist for any buyer (Gartner / Sinequa, 2025):

1. Universal connectivity. Reaching across the full breadth of enterprise content rather than one migrated system. Sinequa cites connectors to over 200 sources such as SharePoint, Salesforce, Workday, SAP, Confluence, and ServiceNow.
2. Hybrid retrieval. Combining neural semantic search, keyword search, and structured data retrieval in one pipeline, since pure-vector or pure-keyword approaches each miss cases.
3. RAG-grounded answers. Every generated answer tied to verified content, with source citations and an audit trail.
4. Agentic capability. Multi-step workflows that act on findings, from automated research to compliance monitoring.
5. Security and governance by design. Enforcing each connected system’s existing access controls at query time, so an AI answer never surfaces content the requesting user is not allowed to see.

That last item is the line between a demo and a deployment. In regulated industries, an AI assistant that ignores permissions is not a productivity tool. It is a data-leak incident waiting to happen.

A concrete example

Picture Vantage Health, a mid-size biopharma company. A regulatory affairs associate named Priya is preparing a submission and needs the exact wording of a safety commitment the company made to a European agency two years ago. The commitment lives in a PDF in a shared drive, the follow-up discussion sits in an email thread, and the final approved language is in a different team’s document management system.

With legacy search, Priya pings two colleagues, waits a day, and reconstructs the answer from memory and guesswork. With an enterprise AI search platform sitting over those systems, she asks in plain language: “What did we commit to on adverse-event reporting timelines in the 2024 EU submission?” The platform retrieves the approved language, cites the source document, respects the access controls so she only sees what she is cleared for, and offers to draft the relevant section.

This is the layer SemanticOS is built to be: a knowledge graph plus AI search that connects fragmented tools into one operational brain, so a person, or an AI agent acting on their behalf, can find and reason over institutional knowledge instead of re-excavating it. The Gartner category gives that idea a name and a set of expectations to be measured against.

Key takeaways

• Gartner published the first Market Guide defining enterprise AI search as a category in September 2025, naming Sinequa by ChapsVision among the Representative Vendors.
• The definition separates AI search from legacy search on three axes: interpreting intent, generating grounded answers via RAG, and acting across systems with agentic AI.
• A named Gartner category turns scattered features into a defined market with shared vocabulary, buying criteria, and a vendor shortlist.
• The category answers a measurable cost: knowledge workers lose roughly a fifth of the week to searching, recreating, and duplicating information.
• Serious platforms are judged on connectivity, hybrid retrieval, RAG grounding, agentic action, and access-control enforcement at query time.