Enterprise Search Trends: AI, Agents, and What's Next

Traditional search relied on keyword matching, text-only queries, poor data governance, and heavily manual processes. But now, that has changed, and the enterprise search trends are no longer the same.

RAG has made AI responses more accurate and grounded. Agentic AI has turned search into a workflow layer. Governance has moved from a compliance checkbox to the primary buying decision.

The result is that your employees can find the information they need faster, generate insights in seconds, and make better data-backed decisions with less effort.

In this article, we’ll cover the eight enterprise search trends to look out for and how to evaluate the best enterprise search platform for your organization.

What We'll Cover

What is Enterprise Search and How It Works

Enterprise search refers to the process of pulling up information from your organization's internal data sources, including documents, emails, CRM records, support tickets, wikis, code repositories, and SaaS applications, through a single search interface.

This is different from web search and basic intranet search:

Modern enterprise search includes:

• Connectors to aggregate information from different sources
• An index and a ranking model to ensure relevance
• Embedding models to keep output semantically relevant
• An LLM layer to add natural language understanding, summarisation, and query interpretation
• A permissions-aware access layer that restricts users to only the data they are allowed to view
• A front-end interface that feels like a single search box across the organization

Enterprise Search Market Size and Growth Outlook

According to Precedence Research, the enterprise search market size rose to $5.34 billion in 2025 and is expected to reach $12.71 billion by 2035.

Two things contribute to this:

• First, more companies are pushing to digitize data in order to prevent data isolation, which happened during the COVID-19 pandemic.
• Second, rapid advancements in AI have made internal search easier and a more preferable option compared to keyword-based search.

Compound Annual Growth Rate (CAGR) and market size reports from other credible groups also show similar patterns.

Projections from each report vary due to differences in the scope of definitions for unified search and knowledge management, as well as the study's duration. Some studies, such as Mordor Intelligence, have a shorter horizon of around 5 years compared to others and thus have a higher CAGR.

However, despite these differences, one thing is clear: enterprise search adoption will continue to grow through the next couple of years. Early integration into your system helps your organization gain an edge over competitors.

Top 8 Enterprise Search Trends to Watch Now

Advancements in tech and user demand have shifted enterprise search trends.

Let’s discuss some of them:

1. RAG Has Become the Default Architecture for Enterprise AI Search

RAG (retrieval-augmented generation) is a framework that ensures AI searches your actual documents first, finds the relevant page, and builds its answer from that.

Take Notion AI as an example. If you ask it what your Q3 revenue target is, it pulls the relevant slide from your last meetings, the budget doc your CFO uploaded, and the forecast sheet in Drive. Then it builds one clean answer from all three.

Platforms like Coveo and Copilot now ship RAG as a core feature and default architecture for AI-powered enterprise search because it minimizes inaccuracies and produces context-aware responses.

When choosing a vendor, prioritize indexing frequency. The more frequently a search interface re-indexes your data, the more likely your team will always work with current information.

2. Agentic AI Is Turning Search Into a Work Execution Tool

Agentic AI is an advanced feature that enables AI to autonomously achieve goals and take action on your behalf across multiple steps, without you having to manually direct each step.

For instance, let’s say your support lead asks for a summary of all unresolved tickets from a key account this week. An agentic-powered enterprise search system pulls the account history from your CRM. Then it cross-references the open tickets in your helpdesk, identifies patterns across them, prepares a response, and logs the activity, all in one flow.

Agentic AI can draft a document, send a message, update a record, create a task, or trigger a workflow based on what it retrieved. That’s in contrast to an RAG-only AI system, which can only provide a summary of your data or enable access, but can’t autonomously use that data to execute tasks.

When evaluating vendors, ask how multi-step retrieval is handled and whether the agent can call tools across your connected data sources or only search within a single index.

3. Multimodal Queries Are Replacing Text-Only Search

Multimodal search is the ability to query your internal systems using multiple formats beyond text, such as voice recordings, images, and videos. This is important because enterprise data includes multiple content formats for AI search, and text-only infrastructures leave a significant portion of your organization's knowledge unsearchable.

Take a field technician, for example. They can photograph a broken machine part on-site and upload the photo to the search system for repair guidelines. A multimodal search system uses optical recognition to interpret that image, matches it against your equipment database, and surfaces the relevant repair manual and the last three tickets filed for that component.

Note that multimodal capability comes with infrastructure trade-offs, since indexing images, audio, and video requires significantly more storage and processing power than indexing text. Factor that into your total cost of ownership before committing.

4. Vertical Platforms Are Replacing Generic Search Solutions

Vertical enterprise search solutions are built specifically for a single industry, unlike horizontal tools that work across multiple industries. They matter greatly in highly regulated niches, such as health, legal, and finance, which require distinct terminology, document formats, and compliance approaches.

For instance, a legal enterprise system from enterprise search companies like Relativity has been trained on domain-specific data. That means it understands jurisdictional language and privilege rules better than a generic platform like ElasticSearch. The result is more accurate retrieval, better data governance, and reduced inefficiencies.

The only caveat is that getting domain-specific enterprise search systems makes it difficult to scale if your company divests into other industries. So, consider your long-term outlook before deciding.

5. Data Governance Is Now a Key Decision Driver

Data governance in enterprise search involves a set of rules that control who retrieves what, how AI handles sensitive data, and the auditability of each AI response. This is important given the rising incidence of data breaches and potential misuse of data.

Some of the things that make data governance possible on modern enterprise search platforms include permissions-aware indexing and document-level Access Control List (ACL). The list controls the rules you’ve set, and permissions-aware indexing enforces them.

That ensures your sales rep cannot access or interact with employee salary data, and that HR cannot access internal data on the company’s financial outlook.

Before choosing a vendor, ensure the platform adheres to governance standards such as SOC 2, ISO 27001, HIPAA, and GDPR, depending on your region and niche.

6. Knowledge Graphs Are Replacing Keyword Indexes

Knowledge graphs are semantic networks that map the relationships among people, projects, products, and documents, so that the search system understands what your query actually means.

Say your sales rep searches for "renewal risk Q3." They get results that connect the account record, the last support ticket, the customer success manager’s notes, and the relevant contract because the knowledge graph knows they are related.

That’s in contrast to traditional keyword indexes, which only pull up documents that contain “renewal risk Q3” even if they are not contextually relevant to your needs. The benefit of this shift is improved disambiguation, smarter responses, and greater accuracy.

7. Personalized Search Results Are Now the Baseline Expectation

Contrary to traditional internal search, which focuses on what is being asked, enterprise search now weighs results based on who is asking.

For instance, a developer searching for "authentication error" gets API logs and code documentation. Whereas an account manager searching the same term gets relevant support tickets and customer history. The same query, yet two completely different answers.

Modern platforms, like Coveo and Glean, do this by tracking role, team, recent activity, and project context to rank results differently for each user.

When evaluating vendors, ask how the system handles observability and whether personalization decisions can be audited. That’s because you don’t want your system surfacing the wrong information to the wrong roles.

8. Open Source and Hybrid Architectures Are Gaining Ground

Open-source enterprise search infrastructure, built on tools such as Elasticsearch, OpenSearch, pgvector, and open embedding models, is gaining prominence. They allow you to own the architecture.

The benefit? Cost control, data sovereignty, and freedom from vendor lock-in.

Hybrid deployments are also gaining traction. What that means is you run your sensitive data on your own private servers while connecting to a commercial LLM, such as GPT-4 or Claude, for the AI generation layer. Your data stays inside your environment, but you still get the power of a frontier model without building one yourself.

The trade-off is that you need a strong internal engineering team to set it up and keep it running.

As you build enterprise search to give your employees quick access to data, simultaneously implement Enterprise SEO to keep your business visible to customers.

At MADX Digital, a search agency for SaaS companies, we scaled MoonPay's global SEO by expanding into 4+ markets, driving +120,000 in organic traffic, and boosting 3,000+ business-critical keywords to Google’s first page.

Get your SEO opportunity report and see how our team can help you close the visibility gap as we did for MoonPay.

Top Enterprise Search Use Cases Driving Adoption

Let's quickly explore some enterprise search use cases below:

In each use case, enterprise search surfaces the right information for the right person at the right time. This reduces the time spent locating data and enhances execution speed.

How to Evaluate an Enterprise Search Platform

Your enterprise search strategy fails if you choose the wrong enterprise search platform. That’s why we created a scored criteria table you can follow to avoid that:

Target at least 8 out of 11, including quality data retrieval history and good governance structure.

After evaluation, and before you commit, run a 30- to 60-day pilot on a single high-value use case, such as customer support or internal HR search.

Connect the platform to two or three of your real data sources, give it to a small group of actual users, and measure retrieval accuracy, latency, and adoption rate against your baseline.

What's Next for Your Enterprise Search Stack

Enterprise search now determines how fast, accurate, and safe your teams work, rather than remaining a background utility.

Before choosing a platform, compare each option against the evaluation framework above. Start with governance and retrieval quality. Then build everything else on those two.

As you scale your internal search, your visibility on the internet shouldn’t lag behind. Your customers need to find you the same way your employees need to find information.

And that’s where we come in. At MADX Digital, a search agency for SaaS brands like yours, we provide:

• GEO and AI services
• Traditional SEO
• Content writing
• Digital PR

If you want help building an enterprise SEO and GEO strategy that keeps your organization visible, let's talk.

Frequently Asked Questions (FAQs)

These are the questions buyers and practitioners ask most often when evaluating enterprise search platforms and planning their next 12 months:

What is the Difference Between Enterprise Search and Web Search?

Web search pulls information from publicly accessible pages.

Enterprise search queries internal, permissioned sources like your CRM, documents, and ticketing system, and only surfaces what the user is authorized to see.

What Are the Top Enterprise Search Companies to Know?

Leading enterprise search companies include Coveo, Glean, Elastic, Microsoft SharePoint, Sinequa, Kore.ai, and Relativity.

AI is rapidly changing platform capabilities, so use the evaluation framework above before making a decision.

Is Open Source Enterprise Search Viable for Large Companies?

Yes, when paired with strong internal engineering. Most setups combine Elasticsearch or OpenSearch, pgvector, and open embedding models.

Note that a lower license cost comes with higher operational overhead for setup and ongoing maintenance.

What Does Gartner Say About the Enterprise Search Market?

By 2028, Gartner predicts that enterprise AI search will be embedded in 60% of enterprise applications, resulting in further market growth.

Gartner also says cognitive search will become the retrieval backbone for agentic AI platforms across the organization.

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