AI Enterprise Search: Top Features and Tools in 2026

If your teams struggle to find the information they need to do their jobs effectively, help is at hand. Enterprise search — a centralized tool that can search all your integrated platforms at once — can be enhanced with artificial intelligence.

AI enterprise search brings insights based on contextual intelligence directly into workflows, making knowledge immediately accessible and actionable. It can remove frustration, unify knowledge, and fuel your teams’ productivity.

What is AI enterprise search?

AI enterprise search uses artificial intelligence to help teams find relevant information across systems and data sources. It’s a significant improvement over traditional search tools that tend to match specific words and phrases or require Boolean operators to interpret query context and refine the results.

AI-powered platforms can be programmed and trained to logically deduce what you’re looking for, even if you don’t use the exact terms in the searched data. With generative AI capabilities, search tools can also create more precise answers by combining information from multiple sources.

How does AI enterprise search work?

AI enterprise search systems use advanced machine learning models to understand your organization’s content and the intent behind a query. Instead of depending on exact keywords, these systems gather meaning from the way you naturally write or speak — allowing you to ask questions as you would to a co-worker.

AI-powered platforms go beyond matching up search terms. Natural language processing (NLP) and deep learning models help AI understand how to respond to nuanced communication, like context, synonyms, and generalized concepts. The system tries to find what you mean, not just what you typed.

When connected to apps and drives, AI enterprise search creates a unified hub of your organizational knowledge so you can surface files, documents, decisions, and conversations from one search bar.

Eight top AI enterprise search software tools and solutions in 2026

Finding the right enterprise search solutions is challenging when knowledge is scattered across different systems. Understanding how these tools connect to your existing work environment makes the selection process easier.

The following list is curated from G2, which scores software based on user feedback, features available, and overall satisfaction. G2’s five-star system focuses on usability, customer satisfaction, and market presence. Each tool listed here has a minimum rating of 4 stars, making them top contenders for streamlining knowledge access, improving collaboration, and helping teams work smarter.

1. Enterprise search in Slack

Ideal for: Teams seeking natural-language queries and AI-driven answers for easier search within their current workflow

Slack connects your organization’s applications, databases, and business systems within its work operating system. Enterprise search in Slack lets you search your structured and unstructured data to instantly find company knowledge, when and where you need it. Currently, Slack supports Google Drive and GitHub, with more connectors, including SharePoint, OneDrive, and Jira, coming soon.

How it helps teams:

• Integrated search. Search in real time across all connected apps and data sources, with access that aligns with each system’s permissions.
• AI-powered results. Retrieval-augmented generation (RAG) technology trained on your company’s data delivers highly relevant, personalized results.
• App integration. Find what you need to stay productive without switching between apps.
• Accessible, unified data hub. A single source of truth for accurate, comprehensive data helps you make better decisions faster.

2. Coveo

Ideal for: Large enterprises that want personalized search and recommendations for customer-facing touchpoints

Coveo offers an AI-powered enterprise search platform that unifies content from multiple data sources. It works across both customer-facing and internal knowledge bases while providing a consistent search experience.

How it helps teams:

• Unified indexing. Combines content from more than 55 sources into a single search index, making scattered information accessible.
• Personalization capabilities. Tailors search results to each user based on their content, profile, and past interactions.
• Analytics dashboard. Provides insights into search patterns and identifies content gaps to optimize experiences.
• Security-aware results. Respects existing permission models to ensure users only see what they’re authorized to access.

3. Algolia

Ideal for: Organizations looking to build custom experiences where speed and developer-friendly search matter

Algolia provides enterprise search capabilities with customization in mind. Developers can craft tailored search and discovery experiences using flexible APIs and bespoke interfaces to unite information in apps, databases, and knowledge repositories. It offers granular control over filters and semantics that can produce fast, consistent search results.

How it helps teams:

• Intent comprehension. Understands intent even with misspellings, ensuring relevant results.
• Advanced filtering. Lets you refine search results using multiple parameters.
• NLP. Understands conversational queries and context for more intuitive searching.
• Customizable ranking. Fine-tunes results relevance based on business rules to optimize outcomes.

4. Elasticsearch

Ideal for: Technical teams that want open, scalable, highly customizable search and analytics

Elasticsearch is an open-source search and analytics engine designed to analyze large amounts of data by running on several machines at once. It’s a good fit for organizations with heavy data loads or complex technical needs.

How it helps teams:

• Distributed architecture. Spreads work over several servers, so it can handle large data volumes.
• Full-text search. Finds information in unstructured data by matching word variations and ranks results by relevance.
• Real-time analytics. Tracks and analyzes data as it is created so teams can quickly spot trends and changes.
• Open-source foundation. Built on a transparent, community-driven ecosystem to allow creative customization.

5. IBM Watson Discovery

Ideal for: Enterprises in regulated or specialized industries that handle complex, domain-specific text and require strict governance and compliance

IBM Watson Discovery uses NLP to analyze both structured and unstructured data. It excels in document enrichment and industry-specific applications.

How it helps teams:

• Document enrichment. Extracts metadata, entities, and insights from documents with out-of-the-box NLP enrichments.
• Industry-specific models. Includes pre-trained models that understand your industry’s language.
• Smart document understanding. Learns document structure by identifying elements like headers and tables.
• Natural language query. Goes beyond keyword search to understand complex questions in everyday language.

6. Glean

Ideal for: Organizations that need to unify across multiple SaaS apps and data sources

Glean is an enterprise work AI platform built around three core capabilities: search, assistant, and agents. It offers a unified space across documents, apps, messages, and people for users to locate and act on organizational information, which can help with tool sprawl.

 How it helps teams:

• Personal AI copilot. Powered by NLP, it helps users generate content, analyze data, and take actions directly within their workflow tools.
• Smart workflows. Lets users build intelligent, autonomous workflows for multistep business processes and execute actions at scale.
• Hybrid search. Merges semantic and keyword-based techniques to provide relevant and personalized results from the full range of company data.

7. Guru

Ideal for: Teams that want a single source of truth for internal knowledge

Guru is an AI knowledge platform that unifies apps, docs, and chats into a single knowledge layer. It automates accuracy by verifying content and flagging outdated information, so answers stay correct even as knowledge evolves.

How it helps teams:

• Permissions-aware security. Knowledge remains appropriately restricted across browsers, chats, and other AI tools with access controls.
• Built-in verification. Automates content review and assigns subject-matter experts to review and update content, ensuring answers are always accurate, compliant, and secure.
• Deep integrations. Eases workflow friction by providing unified search and research across Slack, Teams, browser extensions, and other AI platforms.
• Enhanced collaboration. Users can share and comment on information, supporting a stronger collective understanding.

8. Microsoft Azure AI Search

Ideal for: Enterprise teams using the Azure cloud platform seeking a highly scalable, developer-friendly search solution.

Azure AI Search is a scalable search infrastructure that supports both traditional workloads and modern retrieval-augmented generation. It organizes multiple types of content that can be retrieved via APIs, applications, and AI agents.

 How it helps teams:

• Accurate, relevant results. Extensive content processing ensures the system delivers precise, meaningful results across all data.
• Powerful query syntax. Provides advanced filters and detailed rules so users can combine and refine search results.
• Smart results. Generates helpful search results, including instant answers, recommended insights, and AI-powered summaries.
• Agentic search. Goes beyond simple search by interpreting intent and performing multistep tasks and workflows for users.

Key features of an effective AI enterprise search platform

The best AI enterprise search platforms combine several essential features to make your organization’s knowledge instantly useful. These include:

• Retrieval augmented generation. Get answers based on your organization’s data rather than generic information from public sources.
• Vector and semantic search. Find relevant information — even if it doesn’t contain your exact search terms — by understanding meaning and intent.
• Enterprise-grade security and compliance. Search results should respect user permissions and adhere to industry-specific data governance standards, especially in highly regulated organizations.
• Real-time data freshness. Connected systems let you access the most current information, whether it’s on your work operating system or an external app, ensuring you never work with outdated information.
• Integration across apps. Find everything you need where you already work, talk, and collaborate — no switching between applications.
• Conversation and file indexing. Save company knowledge by making team discussions, decisions, and shared documents searchable — and keep valuable insights from being lost.
• Scalability. Scalable search means your results grow with your organization — supporting fast, reliable search across large volumes of data, many teams, and hundreds of integrated applications without sacrificing performance.

Benefits of AI in enterprise search

According to Slack’s State of Work 2023 report, 90 percent of knowledge workers who use AI report higher productivity than those who do not. That comes primarily from eliminating time-consuming information searches and using AI-driven productivity to work faster. AI-powered enterprise search delivers key benefits that directly boost productivity and improve business outcomes, such as:

• Improved accuracy. Thanks to machine learning, your system learns to identify which results are most helpful for different types of queries, allowing it to get smarter the more you use it.
• Elimination of information silos. Connecting previously separate systems helps users find information they need, no matter where it lives — in documents, chat conversations, or other business applications.
• Faster, comprehensive results. Find complete information, pulled from multiple sources, to make better decisions faster.
• Enhanced customer support. Help your customer-facing teams instantly access relevant product details, past interactions, and solution documentation to provide faster, more accurate responses to customers.
• Streamlined workflows. Build automated processes based on information needs, like creating reports or updating records when specific information is accessed.
• Enhanced security and governance. Unified, real-time permissions across every connected app and detailed audit trails support organizations in strengthening oversight and reducing compliance risk.
• Improved productivity. Users gain direct access to the knowledge base they need within their workflows, reducing time-consuming context switching and increasing accuracy.
• Personalization. Deliver results to individual users by factoring in user roles, permissions, past activity, and preferred tools — improving accuracy, reducing noise, and speeding up searches.

How to measure success with AI enterprise search

By continually refining your enterprise search solution, you’ll maximize its ROI over time. To know if your tool is delivering value, track a few key metrics, including:

• Search success rates. Compare how often users find what they need with enterprise search versus traditional methods. This can identify areas where search functionality could be improved.
• User proficiency development. Track how search behavior evolves. Look for increases in the number and complexity of user questions, which may indicate growing comfort and familiarity with AI-powered search.
• Source diversity in answers. Monitor how often AI search pulls information from multiple connected systems to answer a single query. The higher this rate, the more value you’re getting from cross-system integration.
• Time saved per search interaction. Measure how quickly users find answers compared to manually searching across multiple systems to quantify productivity gains.
• Action rates after searches. Track how often searches lead directly to meaningful actions or decisions to reveal how effectively your search solution supports real work outcomes.

How to choose the best AI enterprise search software for your team

To find the right AI enterprise search solution for your business, start by identifying your needs. For example, where is your current search tool falling short? Follow these steps to determine your requirements:

1. Map your existing technology ecosystem

Start by identifying which systems and apps you need to connect to create a comprehensive search tool. Ask yourself:

• Which apps contain my team’s most critical knowledge?
• What file types do I need to search most frequently?
• Where do my team members currently spend the most time looking for information?

The best tool will fit naturally into your team’s existing workflows and integrate your data.

2. Prioritize data privacy and security

Enterprise search must have strong permissions models to ensure users only see information they’re authorized to access. That becomes especially crucial when handling:

• Sensitive customer data
• Intellectual property
• Regulated information
• Confidential business documents

Make sure your solution respects existing permissions without introducing new security gaps.

3. Evaluate the user experience

User experience determines adoption. Look for solutions with:

• Easy-to-use interfaces
• Fast, relevant results
• Minimal training needs
• Natural language capabilities

The best search tools feel natural to use and don’t require your team to change how — or where — they work.

4. Check integration capabilities

Your solution should connect not just with content databases, but also with:

• Collaboration platforms
• Business applications
• Communication tools
• Project management systems

That creates a unified experience where search becomes part of your work operating system rather than a separate activity.

5. Assess AI capabilities

Consider how the AI helps your team:

• Does it understand natural language questions?
• Can it provide direct answers, not just links?
• Does it learn from user behavior?
• Can it handle your company’s specific terminology?

If you’re already using Slack, enterprise search lets you search across your entire work operating system — from your team conversations to external systems — without bouncing between apps. That way, your team can stay in their workflow and find what they need faster, without wasting time asking a co-worker or scanning lengthy email threads.

Tap into organizational knowledge with AI enterprise search

Slack’s AI enterprise search turns your organization’s collective knowledge into an instantly accessible resource hub, bringing conversations, files, and third-party apps together in a single, integrated, AI-powered search bar.

AI capabilities enhance your search, going beyond just finding information to recommending actions based on context. With AI enterprise search in Slack, employees spend less time hunting for answers and more time acting on them — speeding decisions, reducing friction, and keeping work moving forward.

Find out more about a smarter, more connected way to work with Slack enterprise search.

AI enterprise search FAQs