Key takeaways
- Semantic search uses embeddings to match meaning rather than exact words — so “comfortable running shoes” finds the right results even when product descriptions never use those exact terms.
- Enterprise search has historically lagged the web — indexing fragmented systems, permissions-aware retrieval, and poor query understanding all limit it.
- AI is rebuilding enterprise search around three pillars: hybrid retrieval (keyword + semantic), retrieval-augmented generation for direct answers, and conversational interfaces.
- Deployments like Glean, Perplexity Enterprise, Google Agentspace, and Microsoft Copilot for Microsoft 365 Search are remaking how employees find information.
- The hardest problems remain permissions, data freshness, and integrating dozens of disparate enterprise systems.
Why enterprise search needed reinventing
Most employees spend hours a week searching for documents, policies, past decisions, and subject-matter experts. Classic enterprise search was notoriously poor — fragmented indexes (one for SharePoint, one for Drive, one for Jira, one for Confluence), keyword-only matching, and no ranking signals equivalent to what Google uses on the web. The experience was search-and-hope.
Two shifts changed this. Semantic search, powered by embeddings, freed queries from exact-match brittleness. Retrieval-augmented generation (RAG) let search return direct answers rather than lists of documents. Together, they are the biggest enterprise-search revamp in a decade. For the technology foundations, see our rag primer.
Semantic search in plain terms
Embeddings (see our embeddings primer) represent text as dense vectors where semantically similar texts are geometrically close. A semantic search index stores document embeddings in a vector database (see our vector databases explainer). Query-time, the system embeds the query and returns documents whose embeddings are nearest neighbours.
This captures meaning that keyword matching misses. “How do I request time off?” finds a document titled “PTO Request Procedure” even though the words differ. “Customer canceled our contract” finds “churn mitigation” playbook content. Semantic retrieval shines for conversational queries and low-keyword-overlap matches — exactly the queries that keyword search fails on.
Hybrid retrieval
Pure semantic search is not always best. Exact-match keyword search (BM25) wins on queries like “Q3 2024 earnings call transcript” where the user wants a specific document with a specific name. Modern enterprise search combines keyword and semantic retrieval, reranking candidates with a cross-encoder model that considers both query and document together.
Getting the blend right is a tuning problem. Major vendors (Elastic, Vespa, Pinecone, OpenSearch, Weaviate) all support hybrid retrieval with configurable weighting.
Retrieval-augmented answers
Instead of just ranking documents, AI search can generate direct answers grounded in retrieved content. Type “what’s our PTO policy?” and get a paragraph summary with citations back to the source documents. This is RAG applied to enterprise search.
The UX improvement is substantial for certain query types — answerable-from-documents questions where a user wants the answer, not the document. For queries where the user wants to actually read or work with a document, traditional results lists remain preferable. Good AI search surfaces both.
Conversational search
Follow-up questions. Context retention across turns. Ability to refine results through dialogue rather than rewriting queries. “Show me all expenses over $500 last month” → “just the ones from travel” → “sorted by date”. Conversational search is the interface shift coming from LLMs applied to enterprise data.
This works only when the system can correctly interpret follow-ups in the context of prior turns and has access to the right structured data. Without structured access, conversational search becomes conversational document retrieval — useful but less transformative.
The enterprise search landscape
Generalized AI search products
Glean is the most prominent pure-play enterprise AI search platform, connecting to dozens of SaaS apps, respecting permissions, and providing unified search and AI answers. Similar products from Elastic, Hebbia, AlphaSense, Coveo, Lucidworks, and specialized vertical platforms compete.
Microsoft Copilot for Microsoft 365
Embedded in Word, Excel, Outlook, Teams, and SharePoint. Leverages the Microsoft Graph for permissions-aware access across an organization’s data. Widely deployed in Microsoft-heavy enterprises.
Google Agentspace and Workspace search
Google’s equivalent for organizations on Google Workspace. Integrates with Drive, Gmail, Calendar, and third-party connectors.
Developer-focused tools
Sourcegraph for code search. GitHub Copilot’s chat feature for code questions. Linear, Notion, and Atlassian each build their own search AI within their products.
Knowledge-management vendors
Confluence Intelligence, Notion AI, and other wiki-style products embed AI search into their own data. These handle their native data well but can’t span the whole enterprise.
The permissions problem
Enterprise search is fundamentally different from web search because access control matters. An employee’s search should only return documents they’re entitled to see. Get this wrong and you leak confidential data.
Building permissions-aware retrieval is an engineering challenge — access checks across dozens of source systems, caching permission decisions, handling permission changes in near-real-time. Major vendors spend significant engineering effort here; it’s often what differentiates serious enterprise products from generic RAG demos.
Data freshness
A search system that’s indexed yesterday’s data is useless for today’s work. Modern enterprise search emphasizes incremental updates — as documents change in source systems, indexes update within minutes. Traditional batch-indexing approaches are falling behind.
The integration challenge
Enterprises run on dozens of systems — Slack, Google Drive, Microsoft 365, Salesforce, Confluence, Notion, Jira, Zendesk, GitHub, Workday, dozens of smaller tools. Each has its own API, schema, and permissions model. Integrating them all into a unified search index is ongoing engineering work. Platforms like Glean differentiate partly on how many connectors they ship and how well they handle each one.
What’s next
Agents are the natural extension of AI search — not just retrieving documents but taking actions based on them. “Find all expired NDAs and renew them” or “summarize this week’s customer feedback and draft a Slack post for the team”. Current AI search products are adding agent capabilities incrementally. Full autonomous enterprise agents are still limited by reliability and permissions concerns, but the direction is clear.
Frequently asked questions
Can I use ChatGPT as enterprise search?
Not directly on your internal data — ChatGPT doesn’t have access to your Drive, Slack, or email. Products that wrap ChatGPT (or Claude/Gemini) around RAG over your enterprise data do exist. Typical approach: a connector ingests company data into a permissions-aware vector index, an LLM answers questions grounded in that index. This is what Glean, Microsoft Copilot, and similar products provide.
Is AI search better than Google?
For enterprise questions, generally yes — because Google can’t see your internal data. For general web questions, Google remains dominant, though Perplexity and OpenAI’s SearchGPT are competitive for specific use cases. The AI-search shift is more disruptive for enterprise search (a long-underserved market) than for web search (where Google’s infrastructure and data advantages are enormous).
How much data does an AI search system need?
The embedding quality matters more than raw volume. A 50-person company with a well-structured knowledge base can get excellent AI search quality. A 50,000-person enterprise with messy, inconsistent documentation may struggle even with large-scale tools. The most common quality issue in enterprise AI search is not the AI — it’s that the underlying documents are contradictory, outdated, or missing.
