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Gartner® Research: “What Impact Will Generative AI Have on Search?”
What Generative AI Means for Enterprise Search and What Leaders Should Do About It
When Gartner publishes a Quick Answer on a technology question, it signals one thing: enterprise decision-makers are asking it at scale. “What Impact Will Generative AI Have on Search?” — published in June 2023 by four of Gartner’s leading analysts covering data, analytics, and AI — addressed a question that every CIO, CDO, and technology leader was asking as generative AI moved from experiment to enterprise agenda.
This report is not available for download on this page. What follows is context for why Gartner’s framework matters, what the report concludes, and why those conclusions are directly relevant to enterprise search and AI platform decisions today.
What the Report Addresses
The central argument of the Gartner research is a distinction that matters enormously for enterprise platform strategy — and that many organizations get wrong when evaluating AI tools. Search is both an experience and a technology, and generative AI affects each differently.
As an experience, the Gartner analysts found that search will increasingly recede behind the user interface. Employees and knowledge workers will not “search” in the traditional sense — they will receive synthesized, contextually relevant answers through conversational interfaces, with the retrieval layer operating invisibly underneath. The shift is from reactive retrieval to proactive knowledge delivery.
As a technology, the Gartner finding is critical: generative AI does not replace search infrastructure — it depends on it. The report concludes that search technology will augment generative AI to power the synthesis of information, and that generative AI in isolation is not an alternative to or a replacement for current search technologies.
This finding directly addresses one of the most consequential misunderstandings in enterprise AI strategy: that deploying a large language model on top of enterprise data is equivalent to deploying an enterprise search capability. It is not. LLMs generate fluent, confident responses from statistical patterns in their training data. Without grounded retrieval from current, accurate, access-controlled enterprise content, those responses are disconnected from the organization’s actual knowledge base. Gartner’s framework makes explicit what enterprise leaders need to understand: the quality of the retrieval layer determines the quality of the AI answer.
The report also covers insight engines — the category of platforms that combine search with AI to deliver actionable insights from the full spectrum of enterprise content and data, sourced from within and external to the organization. Sinequa is a recognized player in the insight engine market.
Why This Research Remains Relevant today
Enterprise technology research published in 2023 carries an asterisk in a market that has moved as fast as generative AI. This report warrants an exception — for a specific reason.
The Gartner framework distinguishes between search as experience and search as technology. In the two and a half years since publication, the experience side has evolved dramatically: conversational AI interfaces are now mainstream, AI assistants are in production across industries, and the expectation of synthesized answers rather than ranked results lists is now widespread. But the technology conclusion — that search infrastructure remains essential, not optional, for trustworthy enterprise AI — has been confirmed repeatedly by enterprise deployments that attempted to bypass it.
Organizations that deployed LLMs directly against enterprise data without purpose-built retrieval infrastructure encountered hallucinations, access control failures, and answer quality that degraded at the precise moments that mattered most: complex queries, specialized domain knowledge, and regulated content environments. The enterprises that built AI on top of governed, high-precision retrieval architecture produced better answers, higher adoption, and auditable outputs. The Gartner framework predicted exactly this outcome.
What has evolved is the scope of the application: search infrastructure now enables not just AI assistants (RAG-grounded question answering) but agentic AI workflows — governed, multi-step autonomous actions taken on behalf of knowledge workers, powered by the same retrieval layer. The enterprise search infrastructure that Gartner identified as essential for generative AI in 2023 is the same infrastructure that enables agentic AI in 2025.
About the Report and Its Authors
Gartner® Quick Answer: “What Impact Will Generative AI Have on Search?” Published: June 14, 2023 Analysts: Stephen Emmott, Hao Yin, Arun Chandrasekaran, Mike Lowndes
This research was authored by four members of Gartner’s data and analytics practice covering enterprise search, insight engines, machine learning, and AI infrastructure. The Quick Answer format is Gartner’s structured response to a high-frequency practitioner question — designed to give technology leaders a clear, defensible framework for decision-making on a topic where market noise exceeds signal. For enterprise IT and data leaders, Gartner Quick Answers are frequently the starting point for internal strategy conversations and vendor shortlisting.
Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Frequently Asked Questions (FAQ)
This is a Gartner Quick Answer — a structured analyst research document addressing a high-frequency enterprise technology question. It is titled “What Impact Will Generative AI Have on Search?” and was published on June 14, 2023. It was authored by four Gartner analysts from the data, analytics, and AI practice: Stephen Emmott, Hao Yin, Arun Chandrasekaran, and Mike Lowndes. Gartner Quick Answers are designed to give technology leaders a clear framework for decision-making on a rapidly evolving topic, and are widely used by CIOs, CDOs, and data and analytics leaders as a reference point for internal strategy alignment and vendor evaluation.
Gartner’s central finding is that generative AI does not replace enterprise search infrastructure — it depends on it. The report distinguishes between search as an experience (which will increasingly shift to conversational, synthesized delivery) and search as a technology (which will underpin and augment generative AI capabilities). The explicit Gartner conclusion is that generative AI in isolation is neither an alternative to nor a replacement for current search technologies. For enterprise leaders evaluating AI platforms, this means the quality of the retrieval and search layer underneath the AI interface determines the quality, accuracy, and trustworthiness of the AI outputs employees receive.
Gartner defines insight engines as platforms that combine search with AI to deliver actionable insights derived from the full spectrum of content and data sourced within and external to the enterprise. Insight engines represent the category of platform that is specifically designed for the enterprise knowledge work use case — as distinct from web search tools, database query tools, or general-purpose LLM deployments. For organizations deploying generative AI for knowledge workers in complex, regulated, or data-intensive environments, the insight engine is the architectural category Gartner identifies as the appropriate foundation.
The report’s core framework — that search infrastructure is essential to trustworthy enterprise AI, not optional — has been validated by subsequent enterprise deployments. Organizations that bypassed purpose-built retrieval infrastructure in favor of direct LLM deployment encountered hallucinations, access control failures, and answer quality problems that degraded specifically in the complex, specialized, and regulated scenarios that matter most for enterprise knowledge work. The framework has held. What has evolved is the scope of the application: the retrieval infrastructure Gartner identified as essential for generative AI in 2023 now also enables agentic AI workflows — governed, multi-step AI actions — in 2025 and 2026. The strategic logic has expanded, not changed.
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