What is enterprise search?

Enterprise search is the retrieval of relevant information from disparate data sources throughout an organization.

Enterprise search is powered by enterprise search solutions, which gather information from internal data sources such as document management systems, customer relationship management (CRM) systems, and knowledge bases. Then, they organize that data to create searchable indexes—data structures that enable query processing.

Through this comprehensive approach to internal information retrieval, enterprise search helps organizations optimize knowledge management and drive improvements in data-driven decision-making, productivity, collaboration, compliance and artificial intelligence (AI) initiatives.

Modern enterprise search platforms incorporate AI technologies, including generative AI, retrieval augmented generation (RAG) and agentic AI. These AI-powered search platforms can help tailor information retrieval to deliver more precise, context-aware results.

“Data is the new oil” has become the standard metaphor for describing how access to the right information can drive transformational business outcomes—similar to how crude oil has powered the world since the Industrial Revolution. Companies can use data for analytics and artificial intelligence solutions to forecast trends, uncover new opportunities and seize competitive advantages.

But if data is oil, then enterprise data is the oil organizations can tap in their own backyards—and there’s a lot of it: One 2024 global study of organizations found that nearly two-thirds of respondents said they managed at least one petabyte of data.¹

Leveraging enterprise data, however, takes more than just sweeping collection and voluminous storage. Enterprises, and more specifically, enterprise users, must be able to retrieve the right data, at the right time.

However, achieving this level of knowledge sharing and access can be a significant challenge. Information is often stored across fragmented data landscapes, and enterprise users must navigate multiple systems and sprawling intranet document repositories.

In fact, according to one 2025 survey of senior and executive managers, 74% said they had to use different platforms to find the information they needed.² An earlier survey found that nearly half of digital workers struggled to find the information necessary to do their jobs.³

The right enterprise search tool can offer a more integrated, faster search experience, empowering users to query their organization’s data assets from a single window or search bar—and obtain relevant results.

There are several foundational differences between enterprise and web search, including:

In summary, enterprise searches require more specificity and security than web searches—all while taking place in environments that, although smaller than the internet itself, are diverse and complex nonetheless.

The ability to successfully conduct search queries in enterprise environments can yield a host of key benefits to organizations and users alike:

• Elevated business intelligence: Effective enterprise search can help users find more relevant enterprise data to drive smarter decision-making.

• Greater efficiency and productivity: Efficient enterprise search can help enterprise users find the information they need more quickly, speeding their productivity and giving them more time to pursue high-value work.

• Better collaboration: When more people in an organization can successfully find and access the same data, it leaves less room for misunderstanding and allows for better cross-functional collaboration.

• AI-powered innovation: Many AI projects stall because of fragmented enterprise data. Effective enterprise search gives access to the data that organizations need to achieve scalability for AI initiatives.

• Improved employee onboarding: New hires or employees taking on unfamiliar roles can use enterprise search to obtain internal information necessary for their new workflows.

• Support for compliance: Laws such as the European Union’s General Data Protection Regulation (GDPR) impose strict requirements on the management of citizens’ personal data. Enterprise search can help organizations locate and identify such data to ensure compliant use and storage.

In an enterprise search system, internal data is organized to enable successful queries. There are several components integral to the functionality of the system.

The system discovers and accesses information from structured and unstructured data sources across the organization. It uses crawlers and connectors to regularly scan for new information or updates, while application programming interfaces (APIs) can provide real-time or near-real-time changes as they occur.

Text and metadata are extracted and analyzed from the collected content through processes such as tokenization (decomposing text into smaller units) and stemming (reducing a word to its root form). The data is organized into logical groupings, creating a searchable data structure—an index—to enable retrieval.

The system interprets user queries and retrieves information. Common retrieval techniques include:

• Keyword search: Keyword search is a traditional method which surfaces documents with keywords that match the search term.

• Vector search: Vector search identifies similar data points represented as vectors through algorithms such as k-nearest neighbors (KNN) and approximate nearest neighbors (ANN).

• Semantic search: In semantic search, natural language processing, or NLP, enables the retrieval of relevant results based on the context and meaning of search terms.

The term “federated search” is sometimes used interchangeably with “enterprise search,” but the two concepts are distinct.

Federated search refers to submitting a query to multiple systems (known as federated systems) simultaneously through a single search interface. The federated systems each deploy their own search engines or other retrieval mechanisms to access relevant information, and then the search application combines and delivers the results.

The ability to query multiple systems simultaneously without centralizing data makes federated search a common choice for organizations with diverse and distributed data ecosystems.

However, federated search is not the only type of enterprise search. For instance, a distributed search approach entails indexing and replicating data across multiple nodes. This process results in what proponents describe as an efficient, reliable and unified search process.⁴

Modern enterprise search platforms increasingly rely on AI-powered search capabilities. Key technologies include:

Large language models (LLMs) are a category of deep learning models trained on immense amounts of data. They can understand and generate natural language and other types of content to perform a wide range of tasks. In search applications, their reasoning capabilities can generate higher-quality answers than traditional search engines.⁵

However, LLMs have a well-known pitfall: they hallucinate, conjuring responses that sound convincing but have no basis in fact. In the context of enterprise search, when search results can influence operations—whether it be an ecommerce firm’s customer support capabilities or a pharmaceutical company’s inventory management decisions—the consequences of hallucinations can be disastrous. Fortunately, retrieval augmented generation can mitigate hallucinations.

Retrieval augmented generation is an AI framework that improves the quality of LLM responses by grounding them in external sources of knowledge. Those sources supplement what the model learned during its initial training.

In the case of enterprise search, this means that RAG-powered LLMs can access specific sources of data within an enterprise, such as a Salesforce CRM system or a Slack communications channel, and use that information to surface precise results that empower confident decision-making.

Agentic AI refers to AI systems that can accomplish specific goals with limited human supervision. These systems consist of AI agents, which are machine learning models that can make decisions, form plans and problem solve in real time. In a multiagent system, each agent performs a specific subtask required to reach the goal, and their efforts are coordinated through AI orchestration.

Agentic AI can make the LLM and RAG workflows within enterprise search platforms more adaptive and effective. For example, an AI-powered search platform can dynamically select the best retrieval approach, such as keyword or vector search, for a query to efficiently deliver relevant, accurate results.

Modern enterprise search solutions can unlock value from enterprise data and deliver desired outcomes across myriad applications and industries.

• IT service delivery: An IT company improved user experiences with document search and conversational Q&A capabilities that yielded semantically-matched results to queries such as “How do I set up a VPN?”

• Fraud investigations: A global investigation solutions provider used generative AI to change how fraud investigators interact with its tools, enabling self-service for complex data searches.

• Sales: AI-powered enterprise search technology enabled IBM’s global sales organization to improve the time to resolve sales inquiries by 82.5%.

While enterprise search can be a powerful tool, it is still subject to several challenges. 

One of the major challenges stems from user expectations shaped by modern web search engines. Users often assume internal enterprise search experiences will mirror the speed, intuitiveness and relevance of consumer-grade search—a phenomenon researchers refer to as “Google Habitus.”⁶

This expectation gap has contributed to a notable decline in performance and user satisfaction, with one survey finding that over half of enterprise search app users can’t find the information they need in an “acceptable” amount of time.⁷

Even with the introduction of AI-driven capabilities in modern enterprise search platforms, organizations often need to invest in training so users can take full advantage of emerging enterprise search tools.

Enterprise search tools are supposed to be able to access data across an organization—including data trapped in silos across on-premises, cloud and hybrid environments—but not every tool achieves this successfully. Choosing enterprise search solutions that can be deployed in multiple environments can support efforts to dismantle silos.

Organizations seeking to establish successful enterprise search systems must balance accessibility with security policies and data privacy requirements. Access controls and permissions can govern which data assets are available to specific users and applications, helping to prevent data leakage.

Proprietary enterprise search products are often of the “black box” variety that make it challenging for organizations to develop solutions for specific use cases and key features. “There are certain out-of-the-box enterprise search products that you can buy and install and set up in your companies,” Carter Rabasa, Lead of Open Agentic Platform Developer Relations at IBM, explained at a recent summit. “But there might be a limited degree of customization or tailoring.”

More customization is possible through open source solutions, which allow companies to avoid licensing restrictions and vendor lock-in. An open source solution such as OpenSearch can offer a more flexible alternative to enterprises and developers, Rabasa said. “You’re going to be able to dive in there and tailor things to make sure that whatever application or use case you’re trying to solve for, you’re going to be able to do.”