Enterprise search best practices: how members of large teams save 3+ hours daily

Imagine you’re heads-down on an important project, where the deadline is tight and the stakes are high. You know that just last month, you created the executive one-pager you need to reference. But it’s nowhere to be found. 

We’ve all been there, and we know that this is when panic-searching starts across all the platforms you use—and maybe even some you don’t. You search your own Google Drive. You search public drives. You open folder after folder, jumping between internal wikis, old knowledge bases, old email threads, and Slack channels and messages, hitting one dead end after another. 

Sometimes you find what you’re looking for (you think). Other times, you start from scratch, combining pieces you remember with pieces you found in the frantic search. What should have taken seconds stretches into hours, fracturing concentration, momentum, and productivity. And, truthfully, pride and excitement about your moment to shine.

This scenario isn’t rare. It plays out every day across organizations worldwide. Employees use dozens of work apps daily, and knowledge fragmented across all of them—cloud systems, direct messages, and more—turns the simple act of finding what you need to do your work into a massive productivity tax. Our analysis found that employees lose more than three hours each day searching for information, recreating lost work, or waiting on colleagues for answers.

The good news is that it doesn’t have to be this way.

This guide walks through the Notion five-pillar enterprise-search framework, which has helped companies we’ve onboarded reduce search time by up to 73 percent, boost organizational clarity, and help teams instantly find the knowledge they need—no matter where it lives.

You’ll walk away with practical guidance on governance, information architecture, adoption, measurement, and optimization and proven strategies for implementing enterprise search using Notion’s unified platform.

Most organizations underestimate how much poor search drains productivity and morale. The impacts are subtle at first: small delays, repetitive questions, occasional duplicated work. But at scale, the cost becomes enormous. 

Knowledge workers spend between three and 3.6 hours per day searching for information, toggling between apps, or trying to reconstruct context they can’t find. Across a thousand-person company, this adds up to more than fifteen thousand lost hours every week.

Complicating things further, we found that the average enterprise uses 112+ SaaS tools—each with its own search experience, permissions, and structures. The result is information fragmentation so pervasive that employees often give up and simply ask someone else, which repeats the cycle and fragments knowledge even further.

On the flip side, let’s quantify the ROI of an improved internal-search experience.

• Average knowledge-worker salary (global blended): $110,000

• Daily hours wasted: 3.1 hours

• Productivity cost per employee per year:
  3.1 hours × 260 workdays × hourly rate (~$55/hr) ≈ $44,330

For a five-hundred-person team that is $22 million in productivity lost annually.

Even modest improvements generate massive ROI. A 50% improvement in search effectiveness saves $11 million per year.

A 73% improvement (what some Notion enterprise teams achieve) saves $16 million per year.

Standalone search tools often require expensive connectors, custom indexing, and separate admin layers. It becomes yet another thing to manage, for sometimes meager results. But unified platforms like Notion’s Enterprise Search centralize:

• Content creation

• Knowledge management

• Collaboration

• Security and permissions

• Search

This eliminates friction and reduces the number of systems employees must search across.

Before you redesign your architecture, index content, or evaluate tools, you need governance. Without it, enterprise search fails—not because of technology, but because of chaos.

Strong governance ensures your search system respects permissions, safeguards sensitive data, and reflects how your organization actually works.

Search touches every function, so clarity of ownership is essential. First, create a Search Governance RACI. The responsible party executes the work. The accountable party oversees the execution and ensures it’s in line with the strategy. Consulted parties help guide the search-governance process to become truly cross-functional, helping others see themselves in the work and feel involved in its success. The informed category includes everyone who will benefit from the new search-governance process.

• Responsible: Knowledge management team, IT

• Accountable: Head of Ops or CIO

• Consulted: Team leads, security, compliance

• Informed: All employees

This governance body, led by the responsible and accountable parties, enforces standards and makes decisions around metadata, structure, and knowledge content lifecycle.

Metadata is the backbone of findability. Create universal standards for:

• Document titles

• Project names

• Acronyms

• Tags and labels

• Mandatory metadata fields (owner, audience, last updated, status)

Without consistency, search relevance degrades over time.

A great search index depends on fresh content. Stale pages clutter search results and confuse users. To help keep content fresh, define the following content lifecycle policies:

• Review cycles (quarterly, semiannual)

• Archive rules and triggers

• Expiration policies

• Orphaned content procedures

• Automated reminders for owners

This committee ensures alignment across teams and oversees:

• Architecture changes

• Permissions policies

• Department-specific needs

• Issue triage

• Governance compliance

• Executive reporting

Security isn’t optional—it’s foundational. An enterprise search system must:

• Respect existing permission structures

• Enforce least-privilege access

• Support encryption in transit and at rest

• Offer audit logs for compliance

• Enable role-based access and SSO

• Provide admin visibility into search usage

Enterprise search must respect existing security protocols and safeguard data at every stage. The search-governance model must embed these controls from day one, because retrofitting security of even internal-facing documentation is almost impossible.

A secure system is a trustworthy system. And trust drives widespread adoption.

Information architecture (IA) determines how content is organized, labeled, interlinked, and surfaced. Tools and the knowledge content itself matter, but IA determines whether people can find the content they need across those tools. In other words, “if you build it, they will come” needs an edit: If you build it and make it easy to get there, they will come.

Your IA should mirror how employees think and work, not your organizational chart. Avoid deep folder structures with multiple click-downs. Instead, create clear, intuitive hubs for major functions:

• Sales

• Marketing

• Engineering

• HR

• Leadership

• Policies

• Projects

Within each hub, keep depth shallow and consistent. A massive, cascading file structure would be a red flag, however comprehensive it might seem.

Metadata enables relevance. Define schemas with mandatory fields:

• Owner (team + person)

• Audience (internal, leadership, new hires, customers)

• Type (policy, playbook, meeting notes, project doc)

• Last reviewed

• Status (draft, active, archived)

This transforms your content library into a structured, machine-readable knowledge graph, which is especially important to AI search.

A centrally governed system still needs local flexibility. Think:

• Central hubs for company-wide resources

• Local spaces for teams

• Guardrails around metadata, naming, and permissions

• Shared templates for consistency

• Local owners with accountable responsibilities

This hybrid structure gives teams freedom while ensuring global findability.

Any piece of critical knowledge should be discoverable within three clicks:

• Click 1: Navigate to the right hub

• Click 2: Select the relevant category

• Click 3: Open the specific item

If content requires more than three steps, users default to manual searches—or worse, Slack DMs. A shallow hierarchy makes browsing an equally viable discovery path.

Modern AI search systems, like those built into Notion, assist IA through:

• Automatic tagging

• Entity detection (projects, people, systems)

• Relationship mapping

• Suggested metadata

• Intelligent de-duplication

• Semantic clustering

The benefit: your IA stays healthy even as your content library grows exponentially.

AI fills the gaps humans forget—ensuring your search system stays accurate, current, and consistent.

Choosing enterprise-search tech isn’t only about features, it’s about how search integrates into everyday work.

Unified search brings content into one system (e.g., Notion as a central workspace).
Federated search indexes content across many systems and returns consolidated results. Each has advantages, outlined below.

Notion's Enterprise Search is unified, with rich integrations enabling federated searches across connected data sources, giving you the best of both worlds.

High-value integrations include the following, for their widespread use across an organization:

• Slack

• Google Drive

• Jira

• GitHub

• HRIS systems

• CRM tools

When evaluating your medium- to lower-priority integrations, prioritize them based on:

• Expected search volume—how frequently will that integration be used across teams and orgs?

• Importance of content—how frequently will people need to access this information? How many people or teams?

• Governance requirements

• Security posture

AI search systems require strong uptime and low latency. Some systems rely on traditional indexing, which can lag or break as APIs change. Real-time search pipelines provide greater stability and fewer sync issues.

For Notion Enterprise, uptime and indexing consistency directly influence:

• Query accuracy

• Search freshness

• Permission correctness

• Confidence from end-users

So don’t overlook performance and uptime. A finicky search system people don’t trust becomes a system people don’t use.

When evaluating whether to build an enterprise search tool internally, consider the following:

Build if:

• You have an in-house search- engineering team

• You need highly specialized internal tooling

• You require custom scoring models

• Your content systems are proprietary

Buy if:

• You want faster time to value

• You want guaranteed uptime and maintenance

• You lack search- engineering expertise

• You want integrated workflows, not just search

• You need scalable permissions and compliance

Most companies choose “buy” because search requires:

• Indexing infrastructure

• Ranking algorithms

• Vector databases

• Permission sync

• Ongoing maintenance

• Monitoring and analytics

Regardless of whether you build or buy, enterprise search is never a “set it and forget it” initiative following the guidelines outlined above.

Leadership and others holding the budgetary purse strings must consider:

• Engineering maintenance

• Security overhead

• Compliance costs

• Admin and governance time

• Integration upkeep

• Training and change management

• Infrastructure resources

Unified platforms like Notion reduce TCO because collaboration, content creation, knowledge management, and search coexist in one system, which minimizes operational load via a streamlined tech stack.

Even the best enterprise search system fails without adoption. Efficient search behavior is learned, and old habits, like DMing someone for a link to their presentation, are persistent. Here are some ways to help streamline change management and drive adoption of your enterprise search solution.

Champions accelerate adoption. Recruit one or two per team, whose roles include:

• Modeling the desired behaviors

• Hosting office hours

• Providing real-time feedback to the governance body at regular intervals

• Reinforce governance in the moment

• Share small wins publicly (such as on Slack or in all-hands meetings)

Give champions early access to new features and empower them to shape the evolving standards.

Don’t stop at inviting employees to adopt the new processes and search capabilities. Embed search behavior into key workflows, regardless of the team or function. For example:

• Start every meeting by searching for prior notes

• Replace onboarding lists with search-first instructions

• Encourage teams to “search before you create”

• Add search steps to project- kickoff templates

Small, everyday moments like these compound into sound organizational habits.

Long trainings do not inspire the kind of buy-in and long-term investment required for healthier habits. Instead:

• Deliver two- to four-minute how-to videos to demystify search right off the bat

• Provide weekly tips in a highly visible and socialized Slack channel

• Offer side-by-side examples (bad vs. good metadata)

• Embed tooltips directly inside Notion

• Use lightweight quizzes or checklists

Micro-learning moments like these help support the absorption and retention of these behaviors. For many, regardless of role, tenure, or level, these are new muscles to build.

Everyone loves a chance to use the party or applause emoji. As you’re culling enterprise search performance metrics for leaders, don’t forget to highlight some wins for the whole company. After all, everyone contributes to this success with their enthusiasm and adoption. Try highlighting these in a Slack message or a meeting slide:

• Search-time reductions

• Teams with cleanest metadata

• Most improved groups

• Successful clean-ups

• Notable search-discovery stories

Recognition across the board accelerates the cultural change needed for these processes to succeed.

Change is difficult, but here are some ways to work through objections to the new-and-improved process:

1. “Search doesn’t work.”
Solution: Fix metadata, permissions, and stale content. Run a cleanup sprint and reach out to those teams and individuals with an update.

2. “I can never find what I need.”
Solution: Train employees on filters, operators, and naming conventions. This could be one of the videos mentioned above.

3. “This is extra work.”
Solution: Automate metadata and embed governance into templates.

4. “My team’s content is too unique.”
Solution: Provide local flexibility within global standards. The search governance body can evaluate on a team-by-team basis.

5. “We already have folder structures.”
Solution: Folders and cascading file structures do not equate to findability. Tout the three-click rule and how AI-assisted categorization relies on a simpler taxonomy.

Search systems improve through measurement, not guesswork. You need a clear KPI framework to evaluate where the rollout is successful or lagging. 

These metrics show not just how users search, but also where your content library is failing them.

Use:

• Zero-result rate goal: <10%

• Success rate: >80%

• Stale content: <15%

• First-click time: <6 seconds

These benchmarks give teams a clear sense of progress and how far they have to go to meet their goals.

Adopt a quarterly cycle:

1. Review metrics

2. Identify patterns

3. Update governance

4. Optimize metadata

5. Clean up stale content

6. Train teams on new norms

Remember, search is not a one-time project—it’s an ecosystem that requires tending.

A strong dashboard includes:

• Real-time search logs

• User segmentation (team, role, location)

• Content performance

• AI-search accuracy

• Permission errors

• Heatmaps of top queries

• Queries with no good answers

• Changes over time

Notion’s analytics, combined with your identity provider and usage logs, give you full visibility into how search drives productivity.

Most enterprise-search initiatives fail for predictable reasons.

1. Over-engineering the solution. Complex taxonomies collapse under real-world usage. Keep structures simple and intuitive.

2. Ignoring user feedback. Search quality is subjective and depends heavily on individual semantics. If teams say something isn’t working, listen. Their mental models matter more than the architecture or naming conventions on paper.

3. Insufficient change management. Even the best-designed system won’t succeed if people don’t know how to use it. Avoid an explosive launch in favor of a gradual rollout. Your goal isn’t to be one board for one big moment and then never again—your goal is to build good habits through many everyday moments.

4. Poor data quality. Bad metadata = bad search. Invest early in cleanup and automation.

5. Lack of ongoing optimization. Search ecosystems degrade unless continuously maintained. Quarterly IA reviews are mandatory to keep the system tidy.

Enterprise search is transforming from enabling users to find the file they need to truly understanding the organization’s knowledge graph and performing tasks using it as a guide. In other words, modern systems do more than retrieve documents—they interpret them for maximum impact. Generative AI within enterprise search enables:

• Summaries from multiple sources

• Contextual answers

• Drafted reports

• Risk detection

• Automated action suggestions

• Cross-document insights

As we speak, search is evolving so that engines can generate knowledge, not just return it.

Expect to see:

• Conversational search replacing keyword queries

• AI agents that find, summarize, and act

• Knowledge graphs powering relevance

• Predictive search anticipating user needs

• Deeper integrations across apps

• Enterprise-grade retrieval-augmented generation (RAG) pipelines, which connect the large language model (LLM) interpreting enterprise knowledge to all the data needed to provide contextually relevant, accurate responses

Build durability into your search ecosystem by:

• Standardizing metadata

• Automating governance

• Consolidating knowledge into fewer systems

• Reducing fragmentation

• Adopting AI-native platforms

• Monitoring search health consistently

In the next few years, enterprise search will become:

• Proactive rather than reactive

• Personalized to each user

• Context-aware

• Fully integrated into collaboration

• Zero-latency and real-time

• AI-curated

Organizations that invest early will operate with clearer visibility, faster decision-making, and dramatically higher productivity.

Check out this demo for how a good implementation of Enterprise Search should support your day