Top 7 enterprise search use cases leaders must know

‍Here’s how AI enterprise search is becoming the knowledge layer of work.

Enterprise search is undergoing its biggest evolution in decades. What was once merely a feature to retrieve information buried inside document repositories now sits at the centre of how organizations access knowledge and get work done. 

The rise of Agentic AI has transformed enterprise search from a tactical tool into the foundational platform for AI agents. For AI agents to work, they must be able to think, reason, and act based on enterprise data. But that’s only possible if they have a trusted, unified way to find and understand knowledge across the organization.

Enterprise search is emerging as that knowledge layer. It connects unstructured and structured data across systems, breaks down silos, and provides the structured access needed for AI agents and employees to work intelligently.

AI-powered enterprise search has become a cognitive engine for the enterprise, capable of understanding natural language, pulling context from multiple sources and systems, and delivering actionable responses in real-time. As Gartner puts it, “Search is shifting from being the experience that intermittently informs to powering the experience that continuously informs.”

In this guide, we explore the most impactful enterprise search use cases, with examples of how leading organizations are using it to boost productivity and improve customer experience.

What enterprise leaders need to know (The TL;DR):

Before you dive deeper, here are the key takeaways:

• Enterprise search is the knowledge layer of the enterprise. It connects people, systems, and data so employees and AI agents can work from the same source of truth. 
• The impact is across all departments. From HR and customer service to R&D and leadership, every department gains speed and accuracy when knowledge becomes instantly searchable.
• The cost of poor search is real. IDC estimates that ineffective search costs companies over $5 million per 1,000 employees each year, through lost time and slow decisions.
• Agentic AI is the new multiplier. Enterprise search now powers AI agents that not only find information but also reason, plan, and act on it.

What is enterprise search?

Enterprise search enables users to find information across all of an organization’s applications, documents, conversations, and data sources through a single, unified search experience. It connects knowledge scattered across an organization’s structured data (such as databases) and unstructured data (including emails, CRMs, PDFs, chat threads, and policy documents), so employees and AI agents can instantly retrieve what they need. 

Unlike traditional search, which dumps a long list of links, modern enterprise AI search understands natural language and context, making it possible to ask questions like “Show me the latest roadmap deck” or “What did we agree in the last meeting with Client X?” and get accurate, actionable answers.

What are the top use cases of enterprise search?

From a very high level, enterprise search cases can be divided into:

• Employee experience (EX) - These use cases focus on general-purpose knowledge access as part of digital workplace suites and access to employee-facing operational functions.

• Operational experience (OX)- These use cases are geared toward optimizing back-office processes and decision-making for business operations.

• Customer experience (CX) -  These use cases enhance how external stakeholders, including prospects and customers, access and engage with information.

Here are the 7 most impactful enterprise search use cases shaping modern organizations. 👇

1. Knowledge management

Every enterprise sits on a mountain of knowledge, such as policy documents, product notes, research reports, training resources, and customer insights, but much of it is buried in silos across dozens of systems and tools. Employees know the knowledge exists somewhere, but not where or how to find it. In fact, according to the Gartner Digital Worker Survey, 36% of employees still struggle to find relevant information. Enterprise AI search solves this by unifying access to expertise across repositories and making it discoverable through natural language queries such as: 

• “Show me the latest onboarding process for new engineers.” 
• “What are the security guidelines for third-party vendors?” 
• “Do we already have market research for the BFSI sector?” 

With semantic understanding and contextual retrieval, enterprise search cuts through duplication and outdated content, surfacing the right version of information. 

2. Research and advisory insights

In many firms, vast bodies of proprietary research, client communications, and market-intelligence documents sit scattered in disconnected systems. Advisors know the insights exist, but finding them in the moment of need can feel like digging through a digital maze. That’s where AI-powered enterprise search shines. 

Morgan Stanley, for instance, addressed this by deploying a unified search-and-reason layer for its wealth advisors. When advisors ask questions like “What’s our research view on Alphabet stock?” or “Which report covers bear-case trends in cloud infrastructure?”, they receive citation-backed answers drawn from over 100,000 internal documents, data feeds, and client-interaction logs. As a result, advisors spend less time on document retrieval and more time on high-value client engagement.

3. Customer service and support

Customer service teams work under constant pressure. With customers waiting for answers in real-time, every second of delay impacts satisfaction and loyalty. Yet support agents often spend more time searching for answers than actually solving problems.  In fact, research shows that in a typical 6-minute service call, research shows agents spend up to 75% of their time searching for information, not solving problems. 

AI-powered enterprise search flips that ratio. Instead of switching between five different tools, Agents can ask, “How do I fix error code E203?” or “What’s our latest refund policy for premium users?” and instantly get the precise solution pulled from tickets, product manuals, and policy documents. This dramatically reduces average handle time (AHT), improves first contact resolution (FCR), and boosts customer satisfaction (CSAT). 

Beyond assisting agents, enterprise search also powers customer self-service portals, allowing customers to resolve common issues themselves with search-derived answers.

4. Engineering and IT teams

For IT and engineering teams, speed and precision are table stakes. Every minute spent hunting for the right configuration file or debugging history is a minute not spent building or fixing.

Enterprise search eliminates this waste. It connects systems like Jira, Confluence, GitHub, and ServiceNow to give engineers a single lens to recall any incident, log, or design document.

For instance, the GIF below demonstrates how enterprise search works for the query “How to troubleshoot router remotely.”

(The GIF demonstrates how enterprise search works.)

For IT and Engineering, AI-powered enterprise search works like an intelligent co-pilot. It enables knowledge reuse, so teams don’t waste time reinventing what another developer has already solved.

5. Product development and R&D

Product and R&D teams thrive on knowledge such as user feedback, market data, technical research, design iterations, and testing reports. Yet these critical insights are often scattered across countless tools, such as Jira, Notion, Slack, survey platforms, and shared drives. 

The result? Valuable lessons and discoveries end up buried, slowing innovation and leading teams to reinvent the wheel. AI-powered enterprise search unifies research data, feedback loops, and documentation, allowing teams to instantly surface insights and past work that inform smarter decisions. 

Product managers can simply ask questions like “What feedback did we receive about the onboarding flow last quarter?” or “Which patents or designs overlap with our new product line?” In seconds, the system retrieves consolidated, contextual insights from customer surveys, tickets, roadmap decks, and engineering notes, all grounded in verified enterprise data. 

For R&D teams, this means spending less time rediscovering what’s already been tested and more time building what’s next.

6. Sales and business development 

For sales and business development teams, speed and context win deals. Every conversation, proposal, and negotiation depends on how quickly teams can access the right insights, from pricing sheets and case studies to competitor updates and client histories.

AI-powered enterprise search acts as a single command centre where sales teams can instantly surface everything they need, such as the latest product one-pagers, deal histories, competitive comparisons, and customer interactions, all in one place.

Imagine a rep preparing for a client pitch and simply asking: 

• “Show me the latest case study for fintech clients in EMEA.” 
• “What pricing tiers have we offered to similar customers before?” 
• “Pull key objections from previous calls with this prospect.” 

In seconds, the system retrieves verified, up-to-date answers drawn from CRM notes, sales decks, emails, and support interactions, saving hours of manual searching and giving reps sharper, data-backed responses mid-conversation.

7. Management and leadership team

For senior leaders, time is the ultimate constraint, and decisions are only as good as the information they’re based on.. Whether it’s preparing a weekly business review, a monthly performance update, or a board presentation, executives spend hours pulling data from multiple systems.

AI-powered enterprise search changes that dynamic completely. It allows executives to focus on strategy and decision-making rather than data wrangling. Leaders can now ask questions like: 

• “Summarize regional sales performance for the past quarter.” 
• “What were our top-performing campaigns and their ROI?”
• “Combine customer feedback trends with support escalations for the last month.”

Rather than waiting days coordinating with analysts to cobble together reports from CRM systems, ERP platforms, and marketing dashboards, enterprise search gathers, synthesizes, and presents insights from across systems, complete with citations and visual summaries. It acts as an intelligent business analyst that connects dots across functions, surfaces key takeaways, and prepares leadership-ready summaries in minutes. 

What is the cost of ineffective enterprise search?

Now that we’ve explored how enterprise search drives value across departments, let’s look at the hidden cost of when it doesn’t. 

Ineffective search is one of the most expensive inefficiencies hiding inside modern enterprises. According to IDC, a company with just 1,000 knowledge workers can lose over $5 million annually in wasted salary costs because of time lost searching for or recreating existing information.

But the true impact, however, goes far beyond payroll. When information is scattered, outdated, or impossible to find: 

• Customer experience suffers. Service agents spend 70% of their time looking for answers instead of helping customers, leading to longer wait times and higher churn. 
• Employee productivity drops. Knowledge workers lose nearly a full workday each week searching for information, time that should be spent on innovation or customer value. 
• Decisions slow down. Leaders rely on incomplete data or outdated reports, resulting in missed opportunities and reactive decision-making. 
• Duplicate work proliferates. Teams recreate content that already exists but can’t be found, wasting resources and eroding operational efficiency. 

And these are only the measurable costs. The unmeasured costs, such as disengaged employees, delayed innovation, inconsistent customer experiences, and loss of competitive edge, compound quietly in the background.

👉 Learn more on why enterprises need to unify disconnected tools.

How to select the right enterprise search platform?

An enterprise search platform is an investment by an organization to deliver frictionless search experiences to employees. However, a wrong choice can increase costs and derail knowledge management initiatives. Here are some of the capabilities that organizations should consider while evaluating enterprise search solutions:

1. Supports conversational and multilingual search

Your search solution should understand natural language queries and support multiple languages to serve global teams.

• Conversational Search: With the continuous rise in the usage of chat and voice-enabled assistants, it’s important to have an enterprise search solution that can understand natural language search (conversational search) queries and return the most relevant search results through a virtual assistant or traditional search bar.

• Multilingual Search: The enterprise search solution should be able to deliver search results in multiple languages, like Japanese, Korean, Italian, German, French, etc., which would allow enterprises to cater to employees across the world.

2. Covers all your data

Your search platform should meet your enterprise where it already works. That means connecting seamlessly to CRMs, ERPs, cloud drives, ticketing systems, and chat platforms, without forcing you to migrate data and manually tag everything. Look for platforms with deep, pre-built connectors and the ability to unify structured and unstructured data under one search layer. This ensures employees don’t waste time guessing which system holds the answer. 

Kore.ai, for instance, supports over 100 enterprise connectors, including Microsoft 365, Salesforce, ServiceNow, SAP, and Slack, making it easy to search across your entire workplace.

👉 Read more on how you must evaluate enterprise AI vendors.

3. Intelligent search experience

Keyword search alone is no longer enough. Modern enterprise search should interpret intent and adapt to the user’s role and context. Look for features like semantic search, contextual snippets, and personalized recommendations. 

Kore.ai’s agentic RAG architecture enables multi-step reasoning, role-aware responses, and conversational search, making it feel less like a search bar and more like a smart colleague.

4. Insightful search analytics dashboards

Enterprise search should include more than just delivering search results. It should provide out-of-the-box search analytics and dashboards to gain actionable insights into users' search behavior with metrics like total searches, popular searches, and searches with clicks or no clicks. These insights further help organizations to understand user adoption and make necessary changes to improve the search experience and increase user engagement.

(The GIF shows an extensive dashboard to improve the search experience.)

5. Experience that employees actually use 

Adoption of a solution hinges on simplicity. Even the most powerful platform won’t deliver results if no one uses it. Employees need intuitive interfaces that feel familiar, such as simple search bars, smart filters, and conversational guidance. 

Look for platforms that embed search into your existing workflows, like IT support or HR self-service, so your organization can see value from day one.

6. Industry validation and reliability 

Your vendor should not just be innovative but also highly reliable. One way to gauge reliability is by looking at how long the vendor has been active in enterprise AI and the scale of their deployments. A practical shortcut is to look for platforms that are recognized by independent analysts, like Gartner, Forrester, IDC, and G2. These firms apply rigorous evaluation criteria, so if your vendor is on their list, it signals both credibility and capability. 

Kore.ai has been named a Leader in the Forrester Wave™: Cognitive Search 2025 report for visionary strategy, strong R&D investments, and powerful conversational search capabilities. According to Forrester, “Kore.ai is a great fit for companies that want to build conversation-first search experiences with a deep understanding of the user journey.”

Get your copy of the Forrester Wave: Cognitive Search 2025 report.

Future of enterprise search

We’re entering a new era of work where information doesn’t just sit in systems; it actively drives action. 

According to Microsoft, by 2028, there will be 1.3 billion AI agents operating across enterprises. But for them to perform effectively, they’ll need the same trusted access to knowledge that human employees do. 

That’s where enterprise search comes in. As Forrester puts it, “Cognitive search will become the brains of accurate agentic AI.” This means that enterprise search is becoming the knowledge layer that feeds both humans and AI agents with contextually rich, secure, and relevant information. It ensures that every digital agent, whether it’s handling IT tickets or drafting client updates, can reason and act based on enterprise truth. 

What used to be a background utility is now fast becoming the productivity operating system for the modern enterprise that ties people, knowledge, and AI together, helping everyone move faster and make smarter decisions. In fact, Gartner predicts that by 2028, enterprise AI search will be embedded into 60% of enterprise applications.

Forward-looking leaders are already treating enterprise search as a foundation upon which AI co-pilots, agents, and automation will operate. It’s what enables the enterprise to think, learn, and act as one connected system.

Conclusion: Enterprise search with Kore.ai

Enterprise search isn’t just a backend feature anymore; it’s becoming the intelligence layer that drives the modern enterprise. When people and AI systems can tap into the same trusted knowledge, the organization starts to operate as one connected brain.

Kore.ai is shaping that future. Built on Agentic AI, our enterprise search platform blends reasoning, orchestration, and context awareness to give you a ChatGPT-like experience. It connects to 250+ enterprise systems, understands natural language, and uses Agentic RAG to deliver precise, contextual, and verifiable answers. It’s the foundation that powers co-pilots, AI agents, and enterprise automation, turning search into action and knowledge into outcomes. 

Because in the era of Agentic AI, the enterprises that search best, think fastest, and act intelligently will define the future of work.

Want to see how Kore.ai’s enterprise search can help your enterprise work smarter? Schedule a custom demo today. Not ready yet? Head over to our resources to learn more. 

FAQs

Q1. Which departments benefit most from enterprise search? 

Almost every team in an enterprise benefits from search. Customer service teams use it to resolve issues faster. HR and recruitment teams use it to find the right talent. Sales and business development rely on it for pricing, proposals, and client insights. Engineering teams use it to locate technical documentation and past fixes. And leadership teams depend on it to make decisions with real-time, cross-functional data.

Q2. How does enterprise search improve compliance and legal workflows? 

In regulated industries, access to the right document at the right time can make or break an audit. Enterprise search ensures legal and compliance teams can instantly retrieve policies, contracts, and audit logs from anywhere in the organization. It helps maintain consistency across versions of regulated documents and reduces the risk of acting on outdated data. 

Q3. What are the enterprise knowledge search benefits for hr teams?

Enterprise search helps HR teams manage internal mobility and employee self-service. Employees can quickly find policies, benefits, and training materials, while HR can identify talent ready to grow into new roles.

Q4. How does enterprise search support AI agents and automation? 

As AI agents and co-pilots become integral to how work gets done, enterprise search is becoming their knowledge backbone. For an agent to reason or act, whether drafting a report, analyzing customer data, or updating a workflow, it first needs trusted information. Enterprise search provides that layer, ensuring agents work with verified, up-to-date knowledge. Think of it as the neural link between data, humans, and AI systems. 

Q5. How should enterprises decide which use case to start with enterprise search? 

Start where the pain is most visible, usually in high-volume, high-search areas like customer support or HR. Track metrics like time-to-answer, duplicated work, or employee search frustration. Once you prove success in one department, scaling across others becomes easier because enterprise search naturally compounds its value across shared data. 

Q6. How are enterprise search use cases evolving with Agentic AI? 

Agentic AI is redefining what enterprise search can do. Instead of just finding answers, search is starting to reason, summarize, and act. An executive might ask, “How did Q2 performance compare to last year?”, and the system will generate a contextual summary and draft the follow-up actions. This evolution marks enterprise search as the cognitive engine of the modern workplace, driving both human and AI productivity.