Table of contents
Highlights
- AI-powered enterprise search connects employees to information across systems using reasoning, context, and generative AI.
- Traditional search is limited to retrieval; agentic AI introduces reasoning and action.
- Enterprise search tools now combine LLMs, vector search, and semantic understanding to deliver personalized, context-aware results.
- Moveworks stands apart as the only agentic AI platform that unifies search, action, and extensibility across all enterprise systems.
- Glean, Microsoft Copilot + Graph Search, Coveo, and Elastic Enterprise Search represent other key players in the evolving search landscape.
- The next evolution of search isn’t just finding information—it’s using it to get work done.
An employee wants to know whether they’re eligible for parental leave. The policy lives in Confluence, eligibility rules sit in an HR system, and the request itself is submitted through Workday.
Instead of jumping between tools and piecing things together, they should be able to ask one question and get a clear, relevant answer. This kind of friction is common.
Many employees spend nearly 20 percent of their workweek searching for information or tracking down colleagues who can help. Traditional enterprise search was built to retrieve documents, not to understand intent or guide next steps.
As generative AI becomes ubiquitous, expectations for enterprise search are rising. Employees no longer just want links: they expect accurate answers that reflect their context and help them move work forward.
Advances from leaders like OpenAI are accelerating this shift.
The next evolution goes further. With agentic AI (AI that can reason through requests and act across systems), enterprise search starts to support real work, not just information lookup.
In this guide, you’ll learn what defines AI-powered enterprise search today, how agentic AI is reshaping the category, and how leading platforms compare as organizations plan for 2026 and beyond.
At a glance: Top AI-powered enterprise search tools
Platform | Core focus | Primary strength | Key limitation |
Moveworks | Agentic AI search that turns enterprise questions into actions across systems | Reasoning-driven search with cross-system execution | Best suited for large, complex enterprises |
Glean | LLM-powered enterprise search focused on contextual knowledge discovery | Strong semantic search and personalization | Primarily retrieval-focused, limited action |
Microsoft Copilot + Graph Search | AI search embedded across Microsoft 365 workflows | Deep context within the Microsoft ecosystem | Limited reach beyond Microsoft tools |
Coveo | AI-driven search and recommendations optimized for relevance | Personalized results and content insights | Not designed for reasoning or orchestration |
Elastic Enterprise Search | Developer-first enterprise search built for scale and flexibility | High customization and scalability | Requires significant technical resources |
What exactly is AI-powered enterprise search?
AI-powered enterprise search helps employees find and understand information across the systems an organization uses by interpreting natural language questions and returning relevant, context-aware answers.
Instead of relying on keywords alone, it brings together large language models, semantic understanding, and modern search techniques so employees don’t need to know where information lives. A single question can surface the right policy, document, or record from tools like Confluence, SAP, or Workday and highlight what matters most.
To understand where modern enterprise search is headed, it helps to look at how the category has evolved over time.
How enterprise search has evolved
Search stage | How it works | What it enables | Key limitation |
Keyword search | Matches exact words in documents | Basic document retrieval | Requires knowing what to search for and where |
Semantic search | Interprets meaning behind queries | More relevant results across varied phrasing | Still returns lists of links |
Generative search | Uses LLMs to summarize information | Clear, natural-language answers | Often stops at summarization |
Agentic search | Reasons over context and can act across systems | Supports next steps and real work | Requires strong integrations and governance |
As these systems mature, search moves beyond answering questions. With agentic AI, enterprise search can reason through requests and support next steps. For example, instead of just finding a policy, a system can confirm what applies to a specific situation and help initiate the appropriate action.
As organizations move from keyword and semantic search toward generative and agentic capabilities, enterprise search shifts from finding information to resolving requests within the flow of work, increasing employee productivity.
Top AI-powered enterprise search platforms
Choosing the right enterprise search platform can make a real difference in how easily your teams find answers and get work done. The tools below show how different approaches to AI-powered search reduce friction and help work move faster across organizations.
1. Moveworks
Moveworks Enterprise Search represents the most advanced evolution of enterprise search, powered by agentic AI that goes beyond finding information to helping work get done.
Rather than returning a list of links, Moveworks’ Reasoning Engine can understand intent, reason across organizational context, and help complete common tasks directly from the search experience (e.g., resetting a password or updating a record).
At its core is a unified reasoning layer that not only identifies relevant sources but also connects data across IT, HR, finance, collaboration platforms, and knowledge systems, allowing the platform to validate answers against enterprise data and support next-step actions.
This experience is delivered through a conversational AI Assistant available in tools employees already use, such as Slack, Microsoft Teams, and web interfaces.
Key features
- Agentic reasoning that interprets intent, assesses context, and routes actions across systems
- Agentic RAG (Retrieval-Augmented Generation) that grounds responses in enterprise data with transparent sourcing
- Conversational AI Assistant embedded in collaboration tools and web UIs
- Cross-system connectivity across ITSM, HRIS, finance, collaboration, and knowledge platforms
- Extensible architecture that supports evolving workflows
2. Glean
Glean is an AI-powered enterprise search platform focused on discovering and understanding internal knowledge across documents, conversations, and files. It uses LLMs and a knowledge graph to improve relevance based on document relationships and user activity.
Glean works well for organizations looking to centralize access to content across cloud storage and collaboration tools.
Key features
- Semantic and generative search for documents and knowledge bases
- Knowledge graph that connects content, people, and activity
- Personalization based on user behavior and access permissions
- Integrations with common workplace tools and content repositories
3. Microsoft Copilot + Graph Search
Microsoft Copilot combined with Graph Search provides an AI-powered search experience embedded within the Microsoft 365 ecosystem. It helps employees find and summarize information across emails, documents, chats, and meetings by leveraging Microsoft Graph data.
This approach works best for organizations that are heavily standardized on Microsoft tools and want search capabilities built directly into familiar applications like Outlook, Teams, and SharePoint.
Key features
- Context-aware search across Microsoft 365 apps
- AI-generated summaries based on Microsoft Graph data
- Personalization informed by user role, activity, and permissions
- Native experience within Microsoft interfaces
4. Coveo
Coveo is an AI-driven search and relevance platform known for personalized search results and recommendation-based experiences. It uses machine learning models to tune relevance and improve content discovery, particularly in customer support and knowledge-heavy environments.
Coveo is often used where search relevance and recommendations are the primary goal, rather than end-to-end task execution or workflow automation.
Key features
- Relevance tuning and recommendation-driven search
- Personalization based on user behavior and engagement signals
- Generative AI capabilities for content summarization and insights
- Support for large content repositories
5. Elastic Enterprise Search
Elastic Enterprise Search is a developer-centric platform that provides flexible, API-first search capabilities for organizations that want full control over indexing, relevance, and customization. It is often used to build custom search experiences for internal portals or analytics applications.
Elastic is best suited for teams with strong technical resources that prefer to design and manage their own search layers rather than deploy an out-of-the-box AI search solution.
Key features
- Highly customizable, API-driven search architecture
- Scalable indexing for large and complex datasets
- Open and extensible platform for custom implementations
Key considerations for choosing an enterprise search solution
As enterprise search evolves, evaluation criteria shift beyond speed alone. It’s about how well a platform fits your environment, supports real work, and scales over time.
The considerations below can help you assess which approach makes sense for your needs today and where you want to go next.
Scalability and performance
Enterprise search needs to perform reliably across large volumes of data and frequent queries. Look for platforms that can handle distributed indexing, maintain low latency, and deliver consistent results as usage scales across teams and regions.
Integrations and extensibility
The value of enterprise search depends on how well it connects to the systems your employees already use. Platforms with broad connector coverage, flexible APIs, and clear governance make it easier to search across tools and adapt as your tech stack evolves.
AI capabilities
AI-powered search spans a maturity curve, from semantic retrieval to generative summarization and, more recently, agentic capabilities. Understanding where a platform sits on this curve helps clarify whether it simply surfaces information or can also interpret intent and support follow-on actions.
Generative summarization
Summarization can reduce information overload by turning long documents or threads into concise, relevant answers. The most effective solutions pair summarization with strong grounding in enterprise data to maintain accuracy and trust.
Security and compliance
Enterprise search must respect permissions and data boundaries. Key considerations include role-based access control, auditability, encryption, and the ability to enforce governance policies across sensitive systems and content.
Ease of implementation and adoption
Adoption often hinges on how naturally search fits into everyday workflows. Platforms that integrate into familiar tools and require minimal operational overhead are typically easier to roll out and maintain over time.
Learn how to get more out of enterprise search with Moveworks
Traditional enterprise search tools help employees find information, but they often stop there. Employees still have to verify answers, navigate multiple systems, and figure out what to do next.
Moveworks is built for what comes after search. By bringing search, reasoning, and action into a single agentic AI platform, it helps employees move from question to resolution with less friction.
Moveworks is designed to support common requests across the enterprise. It can interpret intent, retrieve relevant information from connected systems, and help initiate next steps, such as provisioning access, submitting a PTO request, or guiding an employee through a workflow.
At the core of this experience is agentic RAG, which grounds responses in trusted enterprise data while supporting transparency and traceability. A unified reasoning layer connects systems across IT, HR, finance, and more, enabling consistent, context-aware execution at scale.
Built-in governance, security, and compliance help protect sensitive data as usage grows. An extensible architecture allows organizations to expand capabilities across teams and workflows as needs evolve.
The result is an enterprise search experience designed to reduce time to resolution, improve accuracy, and support reliable outcomes as AI becomes foundational to how work gets done.
See how Moveworks helps transform enterprise search into fast, reliable resolution.
Frequently Asked Questions
AI-powered enterprise search uses artificial intelligence, including large language models and semantic search, to help employees find and understand information across company systems. Unlike traditional keyword search, it is able to interpret intent and surface more relevant, context-aware results.
Traditional enterprise search typically focuses on retrieving documents based on keywords and providing links. AI-powered enterprise search goes further by understanding natural language, summarizing information, and connecting data across tools, which reduces time spent searching and reviewing results.
Agentic AI refers to AI that can reason through requests and act across systems. When integrated with enterprise search, it can understand context, validate answers with enterprise data, and support next steps and actions, rather than stopping at information retrieval.
Common enterprise search platforms include Moveworks, Glean, Microsoft Copilot with Graph Search, Coveo, and Elastic Enterprise Search. These tools vary in capabilities, from retrieval-focused search to more advanced, agentic approaches that support reasoning and action.
Moveworks is designed as an agentic AI platform that unifies search, reasoning, and action. Instead of returning static results, it can understand context to interpret intent, retrieve information from multiple enterprise systems, and help initiate next steps through a conversational AI Assistant.
Yes. Most enterprise search platforms integrate with common workplace tools and content systems. The depth of integration varies by platform, with some tools focused on retrieval and others designed to connect search with workflows across IT, HR, and business applications.
Organizations often see reduced time spent searching for information, faster access to accurate answers, and less friction across everyday workflows. More advanced platforms can extend these benefits by helping employees move from questions to resolution more efficiently through an AI Assistant experience.
Governance becomes more critical as enterprise search moves from answering questions to taking action. In agentic search, systems don’t just surface information. They may also initiate requests, update records, or trigger workflows across multiple platforms while following organizational policies and permissions.
This requires stronger controls around identity, permissions, auditability, and decision transparency. Effective agentic search platforms are able to address role-based access, validate actions, and provide visibility into how decisions are made and executed.
As a result, governance in agentic search goes beyond content access to include oversight of automated actions and AI-driven workflows at scale.
