Biggest Enterprise Search Implementation Challenges and How to Overcome Them

Work grinds to a halt when people can’t find answers. Files vanish into cloud drives, conversations disappear into chat apps, and policies rot in forgotten wikis. Before long, employees can end up chasing information for hours on end.

Data shows that 70% of leaders say their employees spend over an hour searching for just one piece of information. Even worse, 23% say the search can drag on for over five hours. That’s not just an inconvenience—it directly affects productivity, decision-making, and employee morale.

Employees don’t need another search box. They need AI-powered enterprise search, implemented right.

Yet search implementations take work to achieve success, and must overcome poor application connectivity, varied content formats, inconsistent metadata, security blockers, and cautious user adoption too. 

We'll share how when implemented right, successful enterprise search is able to connects the entire organizational ecosystem, levarage AI breakthroughs like Retrieval Augmented Generation (RAG) and Agentic RAG to personalizes results for a better user experience, and optimizes for user productivity to make enterprise search powerfully effective at scale.

Enterprise search: What it is and why it’s so hard to get right

Enterprise search software empowers employees to search and retrieve information from across an organization's systems via a unified search interface. Instead of digging through different tools, they can use one search bar to access:

• Documents
• Tickets
• Applications
• Knowledge base articles
• Communications
• Databases

Enterprise search delivers relevant results based on user intent, permissions, and context. Pulling from multiple platforms at once, enterprise search technology aggregates data from tools like Microsoft SharePoint, Confluence, ServiceNow, Google Drive, and Slack, delivering the most relevant search results instantly. 

The new era of AI-powered enterprise search

AI-powered enterprise search doesn’t just surface documents — it uses natural language processing (NLP) to understand intent, context, and nuance in every query.

Behind the scenes, machine learning models map employee questions to the right knowledge, while deep integrations enable responses capable of respecting permissions across systems.

For instance, an employee searching “VPN troubleshooting,” could see steps from an IT knowledge base, a related Slack thread, and a ServiceNow ticket—all in one place. This improves speed and efficiency while helping employees cut through the silos created when each system stores information separately.

So if AI-powered enterprise search is so great, why doesn’t everyone have it? 

Because finding information at work is challenging. Employees get stuck in the volume, variety, and velocity of data and it quickly becomes overwhelming – and anything but seamless.

This is typically the result of:

• Disconnected tools: Information is scattered across apps, databases, and knowledge bases, making unified search difficult.
• Varied content formats: Documents, tickets, spreadsheets, and chat messages require different handling, which keyword searches can’t always manage well.
• Inconsistent metadata: Poor tagging or outdated categorization makes it hard for search engines to rank the right results.

This inefficiency adds up fast. When your team spends more time hunting for information than acting on it, productivity drops, projects stall, and employees get frustrated.

What successful enterprise search looks like

When done right, enterprise search becomes a powerful tool that helps employees quickly find information and make faster decisions without switching between apps or waiting for help. 

AI makes this possible by understanding natural language, interpreting intent, and surfacing precise answers instead of overwhelming employees with irrelevant results.

In its ideal state, enterprise search fades into the background. Employees ask questions in plain language and instantly get trusted, tailored answers without switching tools or filing tickets. 

This is driven by AI models that map queries to context, permissions, and past behavior—so the answers feel intuitive and reliable.

Policies, approvals, and workflows are all accessible in one place, so decisions happen faster, collaboration flows naturally, and support teams stay focused on higher-value work. In short, enterprise search becomes not just a search box, but the connective tissue of the digital workplace.

Here’s what success looks like and how to achieve it.

Connect to the entire enterprise ecosystem

Enterprise search has the power to connect your entire ecosystem of tools, from HR systems to ticketing platforms to file storage. By aggregating information across these silos, it democratizes knowledge—putting the right answers in everyone’s hands, instantly. 

When all your systems are connected, workflows become smoother, teams can collaborate more effectively, and employees spend less time hunting for answers. Instead of bouncing between tools, figuring out which system holds the answer, or waiting for someone to respond, AI makes the interface feel conversational and intelligent, helping them to find what they need immediately.  .

Add personalization for enhanced employee experience

Agentic reasoning allows advanced systems like Moveworks to personalize search results based on an employee’s goals, role, location, and language. So each employee gets the results that are most relevant to them, not a generic roundup.

This personalization comes from AI’s ability to analyze patterns, user profiles, and contextual signals to tailor every response.

A sales rep in Tokyo searching for travel policy details will see guidance tailored to their region, role, and upcoming trips, rather than a general global policy. This context-aware approach saves time, reduces confusion, and helps people act with confidence. 

Optimize for user productivity 

Once you’ve deployed your enterprise search solution, monitoring performance is essential and employee efficiency gains are essential. Key metrics to track include:

• Search abandonment: How often users give up before finding what they need
• Resolution rates: The percentage of searches that successfully answer the user’s query
• Query success or failure rates: Whether searches return meaningful results or none at all
• Frequency of repeated searches: How often users repeat queries, indicating initial results weren’t helpful

Additionally, other search-influenced employee productivity metrics could include average search response time, employee satisfaction with search experience, time-to-resolution for common requests, and frequency of successful self-service actions. 

Tracking these metrics continuously highlights areas for improvement, helping you optimize search relevance, usability, and adoption.

Why enterprise search is so hard to implement

Even the best enterprise search tools can run into roadblocks if they’re not set up carefully from the start. Understanding the common challenges can help you avoid pitfalls and get maximum value from your investment.

Siloed systems and fragmented data sources

When information spreads across multiple apps, it becomes tough to find what you need. Without a unified search system, your teams waste time looking through different platforms and repositories across the enterprise. But integrating these tools adds another layer of complexity. 

Each system comes with its own architecture, access rules, and ways of organizing datasets. Without careful planning, search results can be incomplete, inconsistent, or confusing. The more fragmented your data, the harder it becomes to deliver a smooth, reliable search experience that employees trust and actually use.

Security and access control

Aggregating personal and shared data demands strict permission management to protect sensitive information and maintain trust.

Most enterprise search solutions use indexing, which copies information from your source systems into a central repository. Indexing is popular because it makes search fast, but it introduces a limitation: data replication and custody. 

Permissions in your source systems change all the time—employees leave or get promoted, files move from public to private, and access rules get updates. Index snapshots only show permissions at one point in time, so even a short delay can expose sensitive information.

User-level permissions help enforce security, ensuring employees only see what they're allowed to see. But managing permissions across dozens of connected tools is complicated. Every system has its own rules, and syncing them in real time is a major challenge. 

Fortunately, these issues are easier to avoid with live, API-based search capabilities, since access is checked directly against the source system.

Poor search relevance

When enterprise search returns old, irrelevant, or hard-to-filter results, employees quickly lose trust in the system. They may spend extra time digging for answers or give up entirely and ask a colleague, which slows down productivity and creates frustration.

Ensuring search results are truly relevant to each query can be tough. Data is often scattered across multiple systems, stored in different formats, and tagged inconsistently. Simple keyword searches may return too many results, miss important updates, or fail to show the most helpful content first. 

User experience and adoption

Poor user experience or irrelevant results can quickly erode confidence. Think about how you use Google: If you can’t find what you’re looking for after two or three tries, you probably give up or ask someone directly for the link. 

It’s the same with enterprise search. When employees can’t easily find answers to common questions like “how do I request a new laptop,” they often end up flooding IT or HR with tickets. This low adoption leads to more manual work, duplicated efforts, and slower decisions, putting extra pressure on your support teams.

The AI breakthroughs that make enterprise search usable

AI offers a new way forward. By being able to understand intent, adapt to context, and connect directly to source systems, AI tackles the largest obstacles that have made traditional search unreliable.  It doesn’t just search faster—it can also understand the question, navigate the mess, and deliver the answer.

What makes AI different is its ability to handle complexity and context. It can interpret natural language, recognize patterns in unstructured data, and reason across multiple sources at once. Instead of forcing employees to match the system’s rules, AI adapts to the way people actually ask questions, and it keeps improving as it learns.

RAG: bringing context to enterprise search 

RAG is an AI approach that grounds large language models (LLMs) in your company’s own data. It works by pulling information from enterprise systems and then generating an answer based on that live context—so employees get responses that are accurate, current, and relevant.

In practice, that shift is huge. Instead of scrolling through a list of links, employees receive direct answers backed by the company’s most up-to-date knowledge, which improves trust and saves time. For example, if someone asks, “What’s our remote work policy?” RAG searches your systems for the right documents and then uses an LLM to craft a clear, concise response.

Here’s how it works:

• Retrieval: Searches your organization’s data (documents, databases, tickets) to find relevant information.
• Reranking: Prioritizes the most relevant results before generating a response.
• Generation: Uses an LLM powered by natural language processing (NLP) to summarize or rephrase the results into a concise, natural answer.

RAG isn’t perfect, especially when its used in an enterprise setting. It can struggle with real-time updates or queries that require reasoning across multiple systems. For instance, if an employee asks, “Can I expense my home office setup?” RAG might surface relevant policy documents but miss role-specific rules or the latest updates—leaving employees with incomplete answers.

Go beyond RAG with agentic RAG

Agentic RAG combines Agentic AI with RAG. It brings reasoning to search to deliver enhanced quality and accuracy. For every query, an agentic RAG system performs a few key steps: understanding the user's objective, query enrichment and planning, intelligent retrieval and ranking, and direct & reflected summaries. 

It transforms enterprise search with a goal-driven, autonomous decision-making approach to knowledge retrieval. The added layers of intelligence are what differentiates Agentic RAG from basic RAG, giving it the capabilities to determine the right systems and user signals to bring back results that are much better quality. Here’s how:

• Personalized and can predict what the user wants based on analyzing their prompt as well as attributes like their role, identity, location, etc.
• Directed and can prioritize systems and content based on the user’s prompt and type of query.
• Uses context to understand the relative importance and reliability of data sources, such as content recency, system, activity signals like most viewed or highly rated, and more.

Delivering on this new way forward with agentic RAG unlocks significant upside for employees. They get answers that are more accurate, trustworthy, and personalized at scale. These are critical enhancements necessary to drive meaningful adoption of enterprise search.

 A platform built for enterprise search challenges

Modern organizations face a unique challenge: data that’s spread across dozens of tools, apps, and knowledge repositories. Finding the correct information quickly can feel like searching for a needle in a haystack. That’s why investing in a powerful enterprise search solution is so important—it helps you navigate information complexity while reducing friction for employees.

Moveworks is able to address these challenges with a platform built for today’s dynamic workplaces, empowering enterprises with:

• Agentic RAG: Leverage an agentic reasoning engine that understands employee goals, builds intelligent plans, and searches across systems to deliver the most relevant results.
• Relevance and ranking: Improve the quality of search results with AI-powered relevance and ranking, ensuring employees find what they need faster.
• Security and permissions: Enforce strict access controls so employees only see the information they are authorized to view, protecting sensitive data.
• Integrations: Connect all your systems and data using 100+ prebuilt connectors and easily create gateways to custom content sources.
• Search and take action: Go beyond search by automating tasks and submitting requests directly from the search interface, streamlining workflows.

Make search actionable

Search shouldn’t stop at delivering information. Moveworks AI Assistant can take actions on the employee’s behalf directly from the search interface. Whether it’s submitting a request to HR or IT, triggering a workflow, or automating a routine task, a search system that supports end-to-end workflows turns a simple query into productive work.

• For example, an employee searching for info on how to request a new laptop would be able to find the relevant policy and submit the request directly from the search results without opening a separate system. Similarly, someone troubleshooting Wi-Fi could access instructions and automatically log a ticket if the issue persisted—all from the same platform.
• This seamless integration of search and action reduces friction, cuts down on manual steps, and helps employees complete tasks faster, improving overall team productivity.

With the right platform, enterprise search becomes more than a tool for finding information. It creates a hub for action, insight, and productivity across your organization.

Discover how Moveworks can make enterprise search faster, smarter, and easier for your teams.