Measuring and Maximizing the ROI of Enterprise Search: How Modern AI Transforms Knowledge Discovery

When your finance analyst needs to find an updated expense policy, she might spend 20 minutes hunting for it — checking SharePoint, scrolling through Slack, searching the intranet. If she doesn't find it, she starts reaching out to coworkers, then management. By the time she gets an answer, 45 minutes have passed.

That's a pretty terrible employee experience, not to mention a major productivity blocker. Multiply that across 5,000 employees, each losing just 20 minutes a day searching for information, and it's over 1,600 hours of lost productivity every single day. 

It's not theoretical. According to Gartner, 47% of digital workers struggle just to find the data and knowledge they need to do their everyday jobs effectively.

When enterprise search is powered by artificial intelligence, it can serve as a core workflow accelerator, reducing time-to-answer, improving self-service, and lowering support demand by understanding employee intent and surfacing relevant, permission-aware information across enterprise systems. With the right setup and solution, enterprises are often better positioned to generate measurable productivity, cost savings, and employee satisfaction.

Of course, most business leaders know enterprise search creates value, but they struggle to measure and prove that value in business terms and outcomes. Tighter budgets and rising expectations for measurable impact mean "better search" alone isn't enough anymore. You need concrete numbers.

Discover how modern AI-powered enterprise search can improve knowledge discovery and enable measurable productivity gains across the enterprise.

Why ROI is hard to measure in enterprise search

Enterprise search has the potential to deliver significant return on investment. It's just that traditional measurement approaches often struggle to capture it accurately and clearly because search spans multiple systems and supports knowledge discovery across many teams, rather than operating as a single, isolated tool.

Search impacts many teams, not one owner

Search can support work across IT, HR, operations, and business teams. An engineer can use it to find API documentation, while an HR specialist can use it to answer benefits questions. A sales rep can also use it to pull templates for a new contract.

That cross-functional value makes ownership and attribution unclear. When everyone benefits, no single team owns the outcome and improvements in search primarily reduce time spent locating information rather than producing a single, directly attributable transaction.

Traditional search metrics measure activity, not outcomes

Search volume, queries per user, and click-through rates tell you what employees are doing. But they don't necessarily indicate whether those employees resolved their questions or issues.

On its own, activity doesn't indicate success or business value. You need outcome-based measurement to understand what's actually working (or not), including whether employees were able to find relevant information quickly and avoid additional manual searching or escalation.

Knowledge lives across disconnected systems

Enterprise knowledge is typically scattered across tools like SharePoint, Confluence, Google Drive, ticketing systems, and Slack or Microsoft Teams. Each tool stores overlapping or inconsistent information (leaving employees wondering which one is actually "correct"), and tracking performance across all of them is nearly impossible.

This fragmentation ultimately prevents reliable baselines and makes it harder to attribute improvements to search performance because employees often search across multiple systems independently, making it difficult to measure how unified search improves efficiency and discovery.

What's missing from most ROI discussions

When organizations evaluate how well their search solution is performance, they often overlook some key indicators that tell a much bigger story than usage metrics alone:

• Time-to-answer: How long does it take employees to find information?
• First-answer resolution: Do employees get the right answer on the first try?
• Escalation paths: When search fails, do employees manually create IT or HR support tickets instead?

These missing signals can show you where search breaks down and where improvements may create the most value by showing how improved search reduces time spent searching and lowers reliance on manual support channels.

The hidden cost of poor search experiences in the enterprise

Enterprise knowledge ecosystems are complex, and they only become more complex as you scale. Information ends up living in everything from SharePoint to Slack, making overlapping or inconsistent answers the norm.

When search fails, the business impact is immediate: wasted time, support overload via massive ticket queues, inconsistent answers, and slower decision-making as employees spend more time navigating systems and verifying information manually.

Productivity losses

Even small delays in search can add up fast. An employee who loses 10 minutes a day searching loses 40 hours a year. Multiply that across thousands of employees, and the impact becomes pretty eye-opening.

Time-to-answer is an essential baseline metric for quantifying productivity impact, and later, measuring ROI improvements. It tells you just how long it takes employees to get an accurate answer after asking their questions and reflects how efficiently enterprise knowledge can be discovered and accessed.

Ticket volume increases

When employees are unable to find answers through search, they often turn to human support teams instead. That can ultimately contribute to ticket backlogs, increase cost per request, and slow down resolution times.

Better search can help reduce the number of questions that make their way to IT and HR, freeing those teams for higher-value work by enabling employees to find answers independently through faster, more reliable knowledge discovery.

Fragmented knowledge and duplicated work

Maintaining multiple knowledge bases can lead to outdated, duplicated, or conflicting information. Employees may end up conducting repeated searches, duplicating work, and getting inconsistent outcomes that can ultimately impact business decisions.

This disconnect can also make measuring ROI harder, which is exactly why a single, unified search solution often delivers more long-term value than orgs realize by providing a centralized, permission-aware way to discover information across systems.

How to measure the ROI of enterprise search

You'll need a repeatable, standardized framework for measuring enterprise search ROI, using outcome-based metrics tied to real (and specific) business value such as time saved, improved knowledge discovery efficiency, and reduced reliance on manual support channels.

Use the right measurement framework

At a high level, the basic framework follows this pattern: 

• Baseline → Metrics → Impact → Business Value

Perfect data isn't mandatory. Directional consistency is often enough to demonstrate ROI — or at least that things are headed in the right direction. 

One of the biggest mistakes businesses make with enterprise search is treating it as a usage problem instead of an outcome problem. Many leaders assume high search volume or better relevance means success. However, those metrics don't show whether employees' questions were actually resolved, which is the whole point.

However, those metrics don't show whether employees were able to find the right information quickly and complete their work more efficiently.

Ignoring what happens after a failed search (like follow-up questions or IT/HR tickets) is also a mistake. That's where you can find much of the real cost and value.

Measure enterprise search performance by discovery efficiency, answer relevance, and downstream impact on support demand, not activity alone.

Establish a baseline for key metrics

Before implementing a new search solution, document current-state performance to determine your baseline.

The primary starting metrics to track include:

• Time-to-answer: Average time it takes employees to find information
• Search success rate: Percentage of searches that result in employees locating relevant information
• Ticket volume: How many IT/HR tickets are created for questions search should handle
• First-answer resolution: Percentage of queries where the first result or answer provides the needed information
• Repeated or abandoned queries: How often employees search for the same thing multiple times

Usage metrics like searches per employee provide context, but they're not primary ROI indicators. The more specific and targeted you can get, the better.

Quantify productivity gains

Translate minutes saved per query into annual business value:

• Minutes saved × Number of searches per employee × Fully loaded salary rate = Annual productivity gain

For example, if enterprise search reduces average search time by 5 minutes and each employee conducts 10 searches per week, that's 50 minutes saved weekly. For 5,000 employees, that's over 20,000 hours saved annually.

This model can provide directional estimates. The goal is to show measurable impact that continues to improve over time, not predict outcomes to the penny.

Other productivity indicators might include:

• Reduction in average search time per task (for example, 30% savings)
• Decrease in time to complete standard projects or workflows that depend on faster access to knowledge and documentation
• Lower time to productivity for new hires (thanks to faster onboarding) 

Monitor ticket deflection and self-service impact

When search improves, you may start to see ticket volumes drop for questions like, "Where is the PTO policy?" or "How do I reset my password?" That's a strong signal that search is driving deflection.

Deflection through self-service creates measurable cost savings and faster response times, leading to fewer routine tickets sent to human-driven service teams.

This reduction reflects improved knowledge accessibility, allowing employees to find answers independently instead of relying on support teams.

Measure answer quality and confidence

Accurate, context-aware answers also help reduce rework and follow-up questions. When employees get the right answer the first time, they can move forward instead of repeating searches.

First-answer resolution is a modern search KPI that can show whether your search solution is truly effective. Higher-quality search results means fewer repeat queries and more time spent actually doing work.

First-answer relevance and accuracy indicate whether search results successfully surface the right information quickly.

Employee usage and satisfaction

Track adoption and engagement as well:

• Search adoption (daily and monthly active users, queries per user)
• Success rate of queries — the percentage of searches that satisfy the user's question
• Employee satisfaction or frustration with the search experience and result quality

This mix of quantitative and subjective signals can indicate whether employees trust your search system and how well it's delivering on its intended value.

High adoption and trust are strong indicators that enterprise search is effectively improving knowledge discovery and productivity.

How AI drives better search and ROI

Forward-looking orgs are already using AI-powered enterprise search to make ROI more achievable and measurable by improving accuracy, context-awareness, and visibility into outcomes.

Beyond just faster answers, they're benefitting from more accurate, consistent results that are easier to track over time. In some cases, these solutions can even support next-step actions, further reducing follow-ups and escalation to strengthen measurable impact by helping employees find the right information faster and reducing the need for manual searching across multiple systems.

AI improves accuracy and intent understanding

Natural language understanding (NLU) and semantic reasoning can help reduce irrelevant or incomplete results. Instead of matching specific keywords, AI can understand the context and intent behind what employees are asking for and surface what they actually need.

When AI is capable of recognizing intent, employees can spend less time refining their searches, which may lead to:

• Higher search success rates
• Increased first-answer resolution
• Fewer follow-up questions or escalations for manual intervention
• Higher first-answer relevance and fewer repeated searches

Accuracy is also required for reliable ROI measurement. If search results are inconsistent, tracking improvement becomes nearly impossible.

Unified retrieval increases visibility across systems

AI-driven search should work across all of your knowledge bases and enterprise systems, pulling information from SharePoint, Confluence, Slack, Google Drive, and anywhere else it lives into a single interface.

With unified retrieval, you can get cleaner baselines, more consistent tracking, and a clearer line from search improvements to business outcomes.

When search outcomes are trackable across your entire knowledge ecosystem, you can better gain a complete picture of where search succeeds (and where it fails).

Modern enterprise search platforms achieve this through indexing, metadata enrichment, and permission-aware retrieval, ensuring employees see only the information they are authorized to access while improving discoverability.

Built-in analytics clarify impact over time

AI-powered platforms should offer actionable insights into search success and failure patterns, repeated or abandoned queries, and escalation paths from search to IT or HR tickets. Those insights can point to what might need your attention the most.

Analytics also support continuous improvement and ongoing ROI tracking. Visibility into both search outcomes and follow-up actions can help leaders understand what was answered, what was resolved, and what still required support from human staff.

This visibility allows organizations to measure improvements in search efficiency, knowledge accessibility, and support demand over time.

See the top five AI-powered, enterprise-grade search solutions.

How to maximize enterprise search ROI

You don't need your enterprise search solution to work perfectly everywhere to drive ROI. You need the right search in the right places.

Focus on high-pain point workflows, frequent questions, and areas where failed searches are the most expensive. That's where improvements often deliver the fastest returns because reducing time spent searching in high-frequency knowledge areas produces the greatest productivity gains.

Prioritize high-impact workflows first

Start with workflows where employees search for information frequently and outcomes matter. That means high-impact processes like IT support and access issues, HR policy and benefits questions, and onboarding and role transitions.

From there, you may want to prioritize into tiers based on:

• Frequency: How often employees search for this information
• Complexity: How hard answers are to find or interpret
• Effort: How much time or escalation is required today

Weight the three factors according to your business goals, and don't be afraid to start small. Proving ROI in a narrow scope can help you build momentum and executive support for expansion initiatives.

Maximize content and connector coverage

Search can only drive ROI when it actually finds what employees need. That means connections to the tools they use daily, like Slack, Teams, Confluence, Google Drive, SharePoint, ticketing systems, internal apps, and HRIS platforms.

Focus first on high-impact sources like support knowledge bases, runbooks, and policies. Then keep refining your connectors based on real search patterns and employee feedback.

These connectors allow enterprise search platforms to index and continuously sync content, enabling unified, permission-aware discovery across systems

Improve content quality to support better outcomes

Even the smartest AI search can't fix bad content. If your knowledge base is cluttered with outdated or duplicate information, search results will be too. 

Start with these best practices to get your resources ready for search:

• Remove any duplicates.
• Archive outdated content.
• Ensure your most-searched resources are accurate, clear, and up to date. 
• Assign clear ownership for content management.
• Set regular update cycles so that "source of truth" information stays current.

Knowledge management needs to be an ongoing discipline to keep search working well across multiple use cases.

Clean, well-structured content improves indexing accuracy, ranking quality, and overall search relevance.

Reinforce adoption and trust over time

Deloitte found that trust is dropping in employer-provided AI solutions, and businesses have to actively combat that trend to keep AI adoption high. When employees trust search to deliver accurate answers, they're more likely to adopt it, use it regularly, and recommend it to others, compounding productivity gains over time.

That said, consistency and accuracy need to be the initial focus. You don't want to force usage, especially if the underlying datasets or knowledge sources are invalid. 

Employees will only adopt tools that make their work easier. As search performance improves and employees rely on it more, then ROI can continue to grow naturally.

Higher adoption leads to more consistent knowledge discovery, which strengthens measurable productivity gains and long-term ROI.

How Moveworks helps make enterprise search ROI measurable

The ROI framework outlined here can only work when your search platform actually delivers the intended outcomes and makes them measurable.

Moveworks AI Assistant serves as the front door, letting employees find information across enterprise systems. Through conversational search in tools like Slack, Microsoft Teams, or the web, it interprets intent, retrieves relevant, permission-aware knowledge, and helps employees resolve common workplace issues such as access requests, policy questions, and routine IT or HR tasks. 

By reducing time spent searching and minimizing reliance on support teams, the AI Assistant makes it easier to improve productivity and measure the impact of faster knowledge access.

Moveworks Enterprise Search builds on the same platform by providing a dedicated, search-optimized discovery experience in the web interface. Employees can search across enterprise systems like SharePoint, Confluence, Slack, and Workday from a single place, using filters, previews, and AI-ranked results to quickly locate the information they need.

Behind the scenes, Moveworks connects to enterprise systems to continuously index and sync content while enforcing permission-aware access controls. Built-in analytics track key metrics like time-to-answer, first-answer relevance, and repeated searches, giving organizations clear visibility into search performance and opportunities to improve knowledge quality.

Together, Moveworks enables faster knowledge discovery, increases self-service, and provides the insights organizations need to measure and maximize the ROI of enterprise search.

See how Moveworks Enterprise Search supports a better search experience that leaders can finally measure.