From fragmented to foundational: enterprise AI search as the key to information discovery

This isn’t just frustrating — it’s expensive. When employees can’t quickly locate the right policy document, customer history, onboarding asset, or product specification, decisions are delayed, meetings get rescheduled, and work is duplicated. The potential of AI-powered productivity hits a wall when the underlying search infrastructure can’t deliver accurate, relevant results.

That’s why Gartner’s recent Market Guide for Enterprise AI Search is so valuable. The research firm has established a key position, naming enterprise AI search as the foundational layer that powers effective AI assistants and agents. 

Simpplr was named as a Representative Vendor in the report’s in-application search category — which we consider to be validation that our approach to intranet-centered information discovery solves real problems for organizations worldwide. 

Here’s what Gartner’s framework means for IT leaders, where the market is headed, and how to think about building a workable enterprise AI search foundation.

The information crisis in today’s workplace

The statistics are straightforward: 34% of employees struggle to find information at work, according to Gartner. Organizations responded by deploying AI tools — 49% of workers now use platforms like Microsoft 365 Copilot and Google Gemini primarily to find data. Yet the 2024 Gartner Digital Worker Survey reveals the uncomfortable truth: 36% of these AI tool users still can’t access the information they need.

This creates a paradox at the heart of AI adoption: Companies invest in cutting-edge technology expecting it to solve information discovery problems, only to watch employees continue struggling with the same findability gaps.

Why current search solutions don’t work

Enterprise employees juggle information spread across multiple applications. Customer data lives in Salesforce, product documentation sits in Confluence, HR policies hide in SharePoint, and tribal knowledge gets buried in Slack threads. Each system comes with its own search function, its own permissions model, and its own quirks.

Context switching between these systems doesn’t just waste time — it kills productivity. It delays decisions, duplicates work, and frustrates employees.

Here’s what should worry IT leaders: AI assistants based on current retrieval-augmented generation (RAG) often underperform when scaled across diverse enterprise information. Gartner identifies the culprits as data source quality issues and weak retrieval relevancy mechanisms.

Your AI assistant is only as good as the quality of information it can find and how relevant the retrieved content is to the user.

Traditional search was built for a different era. Most systems treat search as a feature rather than infrastructure. They offer basic keyword matching without understanding user intent, user-document relationships, or organizational context.

These systems return hundreds of results with unclear relevance ranking. They surface documents users can’t actually access. They mix outdated content with current information with no way to distinguish between them. They were designed for surfacing information based on narrow criteria, often without prioritizing relevance.

Enterprise AI search is the foundation for effective AI assistants

Gartner makes a clear strategic recommendation: Reposition enterprise search as the foundational platform that powers AI assistants and agents. Search quality directly determines AI quality.

The reasoning is straightforward. AI assistants retrieve information before they synthesize answers. If retrieval fails — pulling the wrong documents, missing context, or ignoring permissions — everything downstream will be flawed.

How RAG connects search to AI answers

RAG combines search with large language models to generate grounded responses. The search component retrieves relevant documents, then passes that context to the AI to generate an answer. This keeps AI responses tethered to actual enterprise content rather than hallucinating information.

But RAG only works if retrieval works. The search system needs semantic understanding — the ability to grasp meaning, not just match words. It needs to combine traditional lexical search with vector search that leverages the power of LLMs. And critically, it must enforce permissions — users should only see results they’re authorized to view, and AI should only synthesize answers from content each user can legitimately access.

Gartner’s Market Guide details the specific capabilities and governance frameworks required to make this work at enterprise scale, including how to systematically apply metadata enrichment to ensure information remains accurate, pertinent, and trusted.

Understanding the enterprise AI search marketplace

Gartner organizes the market into three distinct categories, each addressing different organizational needs. Understanding these categories helps IT leaders map solutions to their specific use cases — and most organizations will deploy multiple approaches. In fact, Gartner predicts that by 2028, 60% of organizations will have more than six enterprise AI search platforms deployed across the business.

Search platforms: comprehensive and configurable

Search platforms like AWS Kendra, Elastic, Glean, Google Vertex AI Search, and Microsoft Azure AI Search provide broad connectivity across diverse data sources with maximum flexibility and customization. These solutions work well for complex needs that span many different systems and content types, though they require significant implementation effort and ongoing tuning.

In-application search: embedded and context-aware

In-application search brings capabilities directly into the applications where work happens. This is where Simpplr sits, alongside vendors like Atlassian Rovo, Microsoft 365 Search, Salesforce Agentforce, and SAP Joule.

The key advantage is context. These solutions understand the specific content, their types, user workflows, and relevance signals within their domain. 

For intranet-focused solutions like Simpplr, that means a deep understanding of corporate communications, knowledge bases, and digital workplace content patterns. The system knows not just what content exists but also how employees actually use it, which makes results more relevant from day one.

Deployment tends to be faster because core integrations and relevance models are prebuilt for the primary application environment. Rather than starting from scratch with a blank search platform, organizations get solutions tuned for their specific use case.

Gartner also identifies a third category of solutions that provide AI-powered access across multiple existing systems, including tools like Anthropic Claude and OpenAI ChatGPT Enterprise.

The Market Guide provides detailed evaluation criteria for each category, including specific capabilities to assess and implementation considerations based on organizational needs. For organizations evaluating intranet search solutions, understanding where specific vendors fit within this category — and what distinguishes their approach — becomes the next critical question.

Simpplr’s position in the enterprise AI search market

Being named in Gartner’s Market Guide for Enterprise AI Search as a Representative Vendor within the in-application search category puts Simpplr alongside companies like Atlassian, Microsoft, Salesforce, and SAP. For organizations evaluating intranet solutions, that recognition signals enterprise-grade capabilities and market relevance.

More importantly, it validates the approach: Intranet search works best when it’s purpose-built for intranet content, not retrofitted from general-purpose search technology.

Why intranet search needs specialized context

Corporate intranets have unique characteristics that generic search engines miss. There’s the mix of structured and unstructured content, e.g., policies, announcements, knowledge articles, event pages, employee profiles. There’s the relationship between content and organizational structure, e.g., departments, locations, roles. And there’s the temporal dimension — some content is evergreen, some is time-sensitive, and some becomes obsolete quickly.

Simpplr’s in-application search understands these patterns because it’s built for them. The system knows that a policy document should rank higher than a three-year-old blog post when someone searches for guidelines. It understands that announcements from someone’s own department are more relevant than companywide updates. It recognizes that content engagement patterns — what people actually read and find useful — are strong signals for relevance.

Governance and permissions built in, not bolted on

One of Gartner’s key recommendations in the Market Guide is establishing robust governance with clear policies for managing enterprise information. For intranet search, this means respecting the permissions and access controls already established in the platform.

Simpplr’s approach treats permissions as foundational, not optional. When an employee searches, they only see results they’re authorized to view based on their role, department, and group memberships. When AI assistants generate answers, they only draw from content that specific users can access. 

The Market Guide details the importance of metadata enrichment and content quality for effective AI assistants. Simpplr gives organizations the tools to maintain accurate, pertinent, and trusted content through governance workflows, content lifecycle management, and analytics that show what’s being used and what’s going stale.

The future of enterprise information discovery

Gartner’s predictions for 2028 paint a picture of distributed, specialized search rather than one-size-fits-all solutions. By then, 60% of organizations will have more than six enterprise AI search platforms deployed across the business, and enterprise AI search capabilities will be embedded into 60% of enterprise applications, up from 20% today.

This proliferation makes sense. Different types of content and different use cases benefit from different search approaches. Customer service teams need search optimized for support knowledge bases. Sales teams need search that understands CRM data and customer history. And employees need intranet search that makes sense of corporate communications and workplace resources.

The challenge for IT leaders is ensuring these multiple search platforms work together rather than creating new silos. 

Gartner’s Market Guide provides frameworks for evaluating interoperability, governance across platforms, and total cost of ownership as search capabilities multiply.

What this means for your intranet strategy

For organizations that have treated intranet search as an afterthought — good enough if employees can eventually find what they need — the stakes are rising. 

As AI assistants become standard tools for information discovery, the quality of underlying search determines whether those assistants deliver value or frustration.

Gartner recommends positioning enterprise search as foundational infrastructure that powers AI assistants and agents. For intranets, this means investing in search that understands your specific content, respects your governance model, and improves as employees use it.

Organizations that get this right will see measurable improvements in search results, decision speed, and employee satisfaction. Those that don’t will watch their investments in AI assistants underperform while employees continue hunting for information the old way.

Ready to evaluate your enterprise search strategy?

Gartner’s Market Guide for Enterprise AI Search provides the detailed framework IT leaders need to assess current capabilities, identify gaps, and build a roadmap for improvement. The report includes vendor evaluation criteria, implementation considerations, and governance recommendations based on research across hundreds of organizations.

Simpplr can help you apply these insights to your intranet strategy. We’ll show you how in-application search designed for intranets delivers faster deployment, better relevance, and stronger governance than generic search platforms. And we’ll walk you through what a pilot implementation looks like, from connecting your content sources to measuring improvements in findability.

Find out how Simpplr’s AI-powered enterprise search can change information discovery in your organization. Request a demo today.