How Enterprise Search Can Help Your Business

Enterprise search can be a powerful tool to help your organization become more efficient, due to its nature of indexing, searching, and displaying different types of data from multiple sources in one convenient solution.

Modern enterprise search often includes semantic search, vector embeddings, AI agents / generative search, and support for unstructured data (audio, video, transcripts). These raise its potential significantly beyond just keyword matching.

An enterprise search does not necessarily fit every organization. While everyone needs a search on their website, it may be overkill with an advanced solution if your data is small, simple, and all in one place.

However, if your organization has large amounts of data stored in several sources, dispersed systems, or mixed media types, enterprise search is probably the right choice.

Here are some up-to-date benefits you can expect:

The Importance of Enterprise Search

Today there is more data than ever, thanks to automation, business intelligence, and other capturing methods. The enormous amounts of information pose many challenges, and one of them is the timely retrieval of relevant data for employees and decision makers.

Recent studies highlight the high cost of finding information:

• 53% of workers’ time is spent on “work about work” like communicating and searching for information, leaving just 47% for skilled work. (Asana State of Work Innovation 2024)
• 61% of SMB leaders say their organizations spend too much time searching for files and information. (Microsoft Work Trend Index (SMB) 2024)
• Employees report spending ~2 hours per day (≈25% of the workweek) looking for documents or people with the information they need. (Glean/Harris Poll 2022)

The needs for an enterprise search should be clear.

See also: Proud of the Cloud - What Enonic's Planning »

Business Advantages of Enterprise Search

Boosting Team Productivity

When your digital team spends a large chunk of their workday looking for information, a lot of productive activity is lost. This consequently impacts potential business results and bottom line.

If this kind of wasted search time is cut in half (via better enterprise search), the aggregated number of man-hours saved each year would correspond to hiring new staff without increasing headcount significantly.

Increasing Profitability

Scale the number from the previous example up to an entire year, and it is evident that a strong enterprise search solution will save your organization money in a relatively cost-efficient manner.

Another important factor is an improved environment for informed decision making. When negotiating large contracts, handling regulatory compliance, or reacting to market shifts, speed and accuracy of information retrieval matters more than ever.

Improving the Customer Journey

Customer expectations have risen. Many organisations now embed enterprise search or AI-powered discovery features in customer portals, support systems, and self-service tools to help prospects and clients find what they need quickly. This can reduce support costs, increase satisfaction, and strengthen brand loyalty.

An enterprise search solution can also power knowledge bases / chatbots / generative assistants that help surface answers to commonly asked questions more efficiently, reducing friction and freeing employees for higher-value tasks.

Lowering Cost by Centralizing Information

Having one central access point to information (rather than many separate search tools across intranets, email systems, collaboration tools, cloud storage, etc.) reduces costs in licensing, support, maintenance, and duplicate infrastructure.

Centralization also helps with governance, compliance, and data security. Hybrid or cloud-native search solutions additionally allow scaling up or down without requiring equivalent on-prem hardware investment.

Unraveling the Hidden Value of Existing Tech

Legacy systems often contain significant value: old documents, archived data, historical records, un-structured media, etc.

A modern enterprise search platform can surface that content via semantic indexing or vector embeddings, making old data useful again in new contexts (e.g. analytics, trend detection, compliance). This increases ROI on past investments.

Handpicking Sources

You can combine your own internal sources with external or public data (open data, legal registers, geospatial data, etc.), plus newer modalities (images, audio, etc.).

Modern enterprise search solutions often allow you to weigh or filter by domain, date, source credibility, or relevance using natural language. They also support conversational / generative-assist interfaces (e.g. “show me latest contracts with X”) which rely on retrieval-augmented generation (RAG) and metadata filters (Mordor Intelligence – Trends in Vector & RAG Adoption).

Consider this in relation to enterprise search: Why Go Headless with a Hybrid CMS?

Examples of Enterprise Search

Norway Post remains an excellent example of combining editorial content, postal codes, address data, etc., into one search experience. But you might also find newer examples leveraging AI summarization, conversational agents, and embedding-based relevance ranking (for example in technical support portals, research libraries, or internal knowledge bases).

The Norwegian Directorate of Health example is still relevant; adding semantic search, reusable content components, or AI agents could make their system even more effective.

Other use cases for enterprise search include generating product recommendations, similarity-based retrieval (e.g. comparing patients, chemicals, research), expert finding, compliance monitoring, and internal AI agents for knowledge discovery. Modern cases often include conversational query interfaces or AI-powered assistants.

Enterprise Search Vendors

There are many vendors offering enterprise search services. As of 2025, the major players include both long-standing open source tools and newer AI-centred platforms. Below are examples across the spectrum:

• Elastic (Elasticsearch / Elastic Cloud) (strong in vector search and offering AI / embedding & RAG-type capabilities)
• Apache Solr
• Xapian
• Lucene / Lucene-based stacks
• Glean Technologies  (one of the newer cloud / generative-search / AI search tools focused on knowledge discovery)
• Contextual AI (providers building platforms for RAG agents and grounded language models)
• Other traditional vendors: Sinequa, Coveo, Lucidworks, Microsoft (Azure Cognitive Search), Google Cloud Search, IBM, etc.

First published 2 September 2020, updated 1 October 2025.