Information retrieval is quietly running your digital business.

From intranet searches to support queries, every digital interaction hinges on how quickly and accurately users can find what they need. But most enterprise search engines aren’t built for modern information retrieval—fragmented data, disconnected platforms, and high user expectations create costly friction.

For CIOs and digital leaders, that’s more than a UX issue—it’s a business risk.

The good news? Advances in AI and relevance tuning are transforming information retrieval from a reactive tool into a proactive driver of efficiency, self-service, and satisfaction.

In this article, we’ll unpack the key challenges in enterprise information retrieval—and explore smarter, scalable ways to solve them.

Common Challenges in Enterprise Information Retrieval

Information retrieval helps organizations retrieve information from both structured and unstructured data stores to answer a search query. Yet, despite its importance, effective information retrieval is difficult to implement at the enterprise level for a number of reasons. 

Let’s review the most common obstacles organizations face, as well as unpack solutions.

Retrieval Accuracy Among Silos and Fragmentation

Data silos are more than an inconvenience—they’re a systemic barrier to operational efficiency and decision-making. They form naturally as teams adopt specialized tools and workflows. Yet over time, they fragment your enterprise knowledge and slow everything from employee onboarding to customer support.

According to our 2025 Employee Experience (EX) Relevance Report, nearly half of respondents cited dispersed information across multiple applications as their top workplace frustration. Many reported needing to search across four to six different repositories just to find what they need—numbers that climb even higher in technical roles and industries.

For CIOs, this fragmentation drives up costs and risks: reduced productivity, inconsistent service experiences, and missed opportunities due to hidden content gaps. Worse still, customers can be left sifting through irrelevant results—or worse, no results at all—when systems rely solely on lexical or vector search.

Modern information retrieval platforms must bridge these gaps without triggering massive data migrations. Solutions like Coveo’s AI-Relevance Platform offer out-of-the-box connectors for platforms like Salesforce, ServiceNow, SAP, and Sitecore, allowing you to unify content access without overhauling your tech stack.

Relevant reading: Why Search Engines Outperforms Vector Database for RAG

Scalability With Large Datasets

In most enterprises, data lives in dozens of places and formats—from structured records in CRMs to unstructured PDFs, slide decks, and emails. This diversity poses a challenge: how do you enable fast, accurate retrieval without creating silos or performance bottlenecks?

For CIOs, the answer lies in choosing an information retrieval system that’s purpose-built to handle the complexity of enterprise content ecosystems. A solution like the Coveo platform can index a wide range of formats—more than 100 file types across 15 document categories—and scale to support millions of content items. This ensures your systems grow with the business, not against it.

By contrast, federated or point solutions often hit performance ceilings as content scales—leading to slow retrieval times, duplicate systems, and rising operational overhead. The right IR system isn’t just about findability—it’s about future-proofing how your enterprise works.

Relevance in Retrieval Process

Relevance is at the heart of effective information retrieval. When search results miss the mark—whether for an employee or a customer—the cost is more than frustration. It’s lost time, stalled workflows, and in competitive markets, lost revenue.

CIOs need to look closely at how their systems interpret intent. Federated search solutions or those that rely on a single technique—whether keyword matching or semantic search—often struggle to deliver consistent, high-quality results. They tend to over-rely on either retrieving exactly what was asked for, or everything potentially related to the query — missing the nuance of user context and intent.

A more resilient approach combines multiple methodologies. Hybrid search, supported by machine learning models, enables IR systems to balance precision and recall—surfacing not just the right answer, but also adjacent content that supports better decision-making. When combined with unified content access, this approach significantly improves both customer experience and employee efficiency.

Relevant reading: Top Information Retrieval Techniques and Algorithms

User Experience that Simplifies Search

A core function of any information retrieval system is to make complex information easy to find. But in practice, delivering that simplicity—especially across vast and varied enterprise content—is anything but straightforward. Users expect fast, intuitive experiences. They don’t want to hunt across multiple systems or adapt to different interfaces.

For CIOs, the challenge is clear: reduce friction, consolidate tools, and enable seamless access to knowledge—wherever users work.

Relevant reading: Essential Search UX Strategies to Drive Business Outcomes

This is where embeddability and UI flexibility matter. Effective information retrieval systems go beyond indexing content; they integrate natively with enterprise systems and embed directly into the applications your teams and customers already use.

For example, Coveo offers embeddable components like its In-Product Experience—a lightweight, chatbot-style search interface that fits directly within SaaS apps and websites. Coveo also offers hosted templates and drag-and-drop tools to stand up unified search pages in minutes. And for enterprises needing deep customization, its Atomic library and Headless framework let developers build performant, composable UIs that align with modern web standards and your brand identity.

Relevant viewing: UX Best Practices that drive Customer Satisfaction

Information Retrieval System Security and Compliance

Security isn’t optional—it’s foundational. When dealing with corporate information, especially at scale, search systems must be built to enforce data access policies, protect sensitive content, and comply with global privacy standards like GDPR, HIPAA, and CCPA.

For CIOs, the balance is delicate: ensure strict security and governance while maintaining seamless access for authorized users across the enterprise.

Leading platforms like Coveo are secure by design, not by add-on. With early-binding access control enforcement, encryption at rest and in transit, and six customizable user roles, platforms like these align with your enterprise’s security architecture. 

Certifications such as SOC 2 Type II, HIPAA, and Cloud Security Alliance standards provide further assurance. Paired with high-availability cloud infrastructure and a 99.9% uptime SLA, these capabilities reduce both risk exposure and operational overhead.

Cost Efficiency

When it comes to enterprise search, the “build vs. buy” debate isn’t new—but it’s never been more urgent. As generative AI becomes embedded in digital strategy, information retrieval isn’t just a backend function—it’s the engine behind every personalized, context-aware experience.

Yet building a custom solution comes with steep costs. From talent shortages to prolonged timelines, even getting from proof of concept to production can be a major hurdle. Gartner forecasts nearly a third of GenAI projects will be scrapped for exactly this reason. And 62% of leaders who are unhappy with their AI progress point to internal skill gaps as the core blocker.

Off-the-shelf solutions like Coveo offer an alternative: fast deployment, embedded AI capabilities, and native integrations with enterprise platforms—without the burden of ongoing maintenance or infrastructure. You don’t just save on upfront costs—you avoid the hidden costs of complexity, technical debt, and missed opportunities.

Relevant reading:Build vs Buy Search? Choosing the Best Path for Your Enterprise 

Real-World Lessons from Enterprise-Grade Information Retrieval

Successfully implementing information retrieval at scale is less about the tool—and more about aligning technology to real business challenges. These organizations provide a useful blueprint for CIOs navigating similar complexities.

Tyler Technologies faced a common issue: customer data scattered across four CRMs and nine additional platforms. This fragmentation made it difficult for support agents to quickly retrieve accurate information, reducing overall service effectiveness. By unifying content access with an AI-powered retrieval platform, Tyler Tech increased agent efficiency and eliminated the guesswork behind answering customer inquiries. 

LCBO, a major retailer and wholesaler, needed to differentiate its digital experience in a highly competitive market. Rigid manual search rules and the absence of machine learning created friction for customers. By automating relevance and personalization through AI-driven search, LCBO improved the experience dramatically—resulting in a 58% increase in click-through rates. 

Motorola Solutions focused its IR investment on increasing website conversion and content discoverability. With smart features like query suggestions and natural language processing, the company saw a 23% increase in search success, and 97% of visits ended in relevant information being found. 

SAP Concur faced growing inefficiencies in self-service support due to aging knowledge assets and disjointed content systems. After rigorous governance and security reviews, SAP implemented Coveo’s Relevance Generative Answering — which uses advanced retrieval augmented generation — to unify and simplify the experience. The results were transformative: a 30% drop in case submissions, a 91% decrease in searches per session, and substantial cost savings that allowed SAP to redirect resources to strategic initiatives. 

Each of these examples highlights a broader truth: when information retrieval aligns with your digital strategy, the results cascade across operational performance, customer experience, and business impact.

Conclusion

Enterprises that lead in digital experience don’t just manage search—they modernize it. By investing in advanced information retrieval systems that unify content and apply AI for relevance and personalization, organizations can resolve today’s biggest search challenges: data fragmentation, poor findability, and scaling complexity.

The payoff? Faster, more secure, and scalable experiences that meet the expectations of modern users—whether employees navigating internal systems or customers seeking fast answers.

Ready to modernize your search strategy? Explore how Coveo can help you build enterprise-grade IR that delivers real business outcomes.