AI in Knowledge Management: How AI Is Changing the Way Enterprises Use Knowledge

Introduction

AI tools are quickly becoming the front door to enterprise knowledge. Instead of spending time hunting through many systems for a policy or a set of meeting notes, employees and customers can simply ask a question and get an answer.

But AI in knowledge management is only as good as what it draws from. It can improve search, support content creation, and boost operational efficiency across business functions, as long as the knowledge behind it is accurate, up to date, and well governed.

What Is AI in Knowledge Management?

AI in knowledge management refers to the use of artificial intelligence to help capture, organize, manage, locate, share, and apply an organization's knowledge.

Traditional knowledge management systems already help businesses collect information, support knowledge sharing, and make content available to employees, customers, partners, and other audiences. AI adds another layer by helping those systems understand content, automate repetitive tasks, and respond more intelligently to users' goals.

Depending on the use case, that might involve technologies such as:

• Generative AI to summarize, draft, or transform knowledge content.
• Natural language processing (NLP) to understand questions written the way people actually speak.
• Machine learning to recognize patterns and improve recommendations.
• Semantic search to find information based on meaning and context rather than exact keyword matches.
• Retrieval-augmented generation (RAG) to ground AI-generated answers in approved enterprise information.

The goal isn't simply to add an AI chatbot on top of a knowledge base. It's to make organizational knowledge easier to manage and more useful wherever people need it, whether that's in a self-service portal, an internal tool, or a conversation with a support team.

Why Is AI Becoming Important for Knowledge Management?

The larger an organization becomes, the harder it can be to manage its knowledge.

Content may be scattered across intranets, shared drives, customer portals, ticketing systems, wikis, blogs, document repositories, and departmental tools. Even when the right information exists, employees and customers might not know where to look for it.

AI can help address several common challenges:

• Growing volumes of content. More information means more content to organize, review, update, and retire.
• Disconnected knowledge sources. Important information may be spread across systems that weren't designed to work together.
• Difficulty finding answers. Users often know what they need to accomplish without knowing the exact terminology or location of the relevant document.
• Lost institutional expertise. Valuable knowledge can disappear when experienced employees leave or change roles.
• Manual content management. Categorizing, tagging, reviewing, and maintaining thousands of knowledge assets can become difficult to scale.

For knowledge and content teams, that creates a balancing act. The organization needs to make more information available without complicating the experience for people trying to use it or driving up operational costs.

AI can help close that gap. Knowledge management teams clearly see the opportunity. In a 2026 survey by APQC, 49% of KM teams named incorporating AI and other smart technology as a top priority, more than any other area of focus.

How Can AI Improve Knowledge Management?

AI can support nearly every stage of the knowledge lifecycle. The most useful applications tend to shorten the distance between someone having a question and finding accurate information that answers it.

Make Enterprise Knowledge Easier to Find

Traditional enterprise search often depends heavily on users entering the right keywords. That's a problem when someone knows the question but doesn't know how the organization categorizes the answer.

AI-powered search can better interpret natural-language questions, intent, and context. Instead of forcing someone to try several variations of a search term, the system can help surface the most relevant article, document, or answer.

This is especially useful when employees need information quickly, when support agents are working through customer issues in real time, or when customers are trying to solve a problem on their own.

Help Create and Maintain Knowledge Content

Creating a knowledge base is one thing. Keeping hundreds or thousands of articles useful over time is another.

Generative AI can assist content teams with tasks such as:

• Drafting knowledge articles from existing material.
• Summarizing long documents.
• Rewriting technical information for different audiences.
• Creating shorter versions of existing content.
• Suggesting titles, descriptions, or classifications.
• Supporting updates to existing knowledge.

That doesn't mean handing content management over to AI. Subject matter experts still need to verify important details, and organizations still need workflows that control what gets published.

Used appropriately, AI can reduce some of the repetitive work around content, so teams can focus on ensuring the knowledge itself is accurate, up to date, and valuable.

Organize and Classify Knowledge at Scale

AI can help analyze content and assist with categorization, tagging, and identifying relationships between information. This can make large knowledge repositories easier to manage without requiring teams to manually classify every asset from scratch.

It can also help uncover connections that aren't immediately obvious. A support document, a product guide, and an internal troubleshooting article, for example, may all address different aspects of the same problem.

A clearer content structure helps both people and AI systems understand what information is available and when it's relevant.

Deliver More Relevant Knowledge

Not every user needs the same information presented in the same way. An employee may need internal troubleshooting steps. A customer may need a simplified help article. Developers may need technical documentation, while a partner may need product- or service-specific details.

AI can work alongside personalization to help surface knowledge based on factors such as:

• User role.
• Account or organization.
• Audience segment.
• Previous behavior.
• Current task or context.

The result is a knowledge experience that feels less like searching through an archive and more like getting information that applies to the situation at hand.

Expand Employee and Customer Self-Service

One of the most immediate applications of AI in knowledge management is making self-service easier. Employees might use an internal portal to find HR policies, IT instructions, training materials, or operational procedures. Customers may need product documentation, troubleshooting guidance, or answers to account questions.

When that knowledge is easy to search and navigate, users can get instant answers to routine questions instead of having to rely on a support team for every one.

There's already evidence of the value of strong self-service, even before AI enters the picture. In one case study, Airbus used a Liferay-powered self-service portal for 100,000 users and reduced Service Desk incidents by 30%. AI-powered knowledge discovery can build on that same principle by making the path from question to answer even more direct.

Preserve and Reuse Institutional Knowledge

Some of an organization's most valuable knowledge may never make it into a formal document. It can live in the experience of long-tenured employees, discussions between teams, chat history, old project files, or answers that support staff provide again and again. Much of that knowledge is at risk. According to APQC, only 8% of organizations consistently capture knowledge from departing retirees, while 16% don't attempt to capture it at all.

AI can help teams turn more of that information into reusable knowledge by summarizing source material, structuring unorganized content, and making existing information easier to rediscover.

That can be especially useful for onboarding, succession planning, cross-team collaboration, and reducing dependence on a small number of subject matter experts.

What Are the Benefits of AI-Powered Knowledge Management?

The real value of AI isn't the technology itself. It's what happens when people can make better use of the knowledge an organization already has.

Potential benefits include:

• Faster access to information. Employees spend less time searching across systems for the right document or answer.
• Greater productivity. Teams can reduce repetitive work involved in finding, organizing, summarizing, and maintaining knowledge.
• Better self-service. Customers and employees can resolve more questions without waiting for direct support, which can also improve customer satisfaction.
• More consistent experiences. Different channels and audiences can draw on the same governed knowledge source, creating a single source of truth.
• Improved knowledge retention. Institutional expertise becomes easier to capture, preserve, and reuse.
• More relevant information. Personalization and intelligent search can reduce the amount of unrelated content users have to sift through.

The impact isn't limited to customer interactions. Materion, for instance, connected 2,600 employees with policies, reference guides, and other critical documentation through a redesigned knowledge experience. The company saw page views increase 47% and sessions increase 18%.

Why AI Still Needs a Strong Knowledge Foundation

It's tempting to think AI can solve a messy knowledge environment on its own. In reality, it can make the weaknesses of that environment more visible.

If source content is outdated, duplicated, poorly structured, or inaccurate, AI doesn't suddenly make it trustworthy. A system may simply find or generate an answer based on the wrong information more efficiently.

That's why enterprise knowledge management still needs fundamentals such as:

• Clear content ownership.
• Consistent templates and structure.
• Taxonomies and metadata.
• Review and approval workflows.
• Defined permissions.
• Processes for updating and retiring content.
• Connections between relevant knowledge sources.

AI and knowledge management work best as a two-way relationship. Better knowledge gives AI more reliable information to work with, while AI can make that knowledge easier to maintain and use.

For teams responsible for enterprise AI initiatives, knowledge quality is as much a technology concern as a content concern. AI knowledge experiences are only as reliable as the content underneath them.

What Are the Risks of Using AI in Knowledge Management?

AI can remove friction in knowledge management, but it also introduces new questions, and it's important to acknowledge them early.

Before expanding its use, organizations should account for several risks.

Inaccurate AI-Generated Answers

Generative AI can produce responses that sound convincing even when they're inaccurate. Grounding answers in approved enterprise knowledge can reduce that risk, but critical information should still have appropriate human oversight to protect accuracy.

Outdated or Low-Quality Content

An AI system can't reliably distinguish good organizational knowledge from bad content unless the right processes and controls are in place.

Old policies, duplicate articles, and conflicting instructions can all undermine the quality of the resulting experience.

Security and Permissions

Finding the right answer isn't useful if the system exposes information to the wrong person.

AI-enabled knowledge experiences should follow the same access controls as the underlying content, particularly when organizations manage confidential employee, client, financial, or proprietary information.

Privacy and Compliance

Different industries and regions may place specific requirements on how data can be stored, processed, accessed, and used with AI.

Knowledge management and IT teams should understand what information an AI system can access, establish governance appropriate to their regulatory requirements, and keep the ability to audit how that information is used.

Too Much Reliance on Automation

AI can assist with knowledge work, but employees still provide judgment, expertise, and accountability.

As AI takes on more repetitive tasks, the human role shifts toward reviewing, governing, improving, and deciding how organizational knowledge should be used.

How to Prepare Your Knowledge Management Strategy for AI

Organizations don't need to apply AI to every piece of enterprise knowledge at once. Leading organizations often take a more practical approach: start with a clear problem and expand from there.

1. Start With the Knowledge Problem

Identify what users are struggling with today.

Are employees unable to find policies? Are customers opening support tickets for questions that are already documented? Are content teams overwhelmed by updates? Is critical expertise difficult to preserve?

A specific problem makes it easier to choose an AI use case and develop a solution that provides measurable value.

2. Identify Your Knowledge Sources

Map where important information currently lives.

That could include knowledge bases, intranets, wikis, ticketing systems, shared drives, product documentation, customer portals, and other enterprise applications.

You don't necessarily need to move everything into one repository. You do need to understand what information exists, who should have access to it, and how systems will connect.

3. Improve Content Quality and Structure

AI is a good reason to address any existing knowledge debt.

Review outdated information, remove unnecessary duplicates, establish consistent structures, and clarify who owns important content.

These improvements help people today while creating a stronger foundation for AI in the future.

4. Establish Governance and Permissions

Determine who can create, approve, update, access, and retire different types of knowledge.

The same governance should extend to AI. Teams need to understand which sources AI can use, how generated information is reviewed, and what content different users are permitted to see.

Watch how Liferay can help organizations use AI to make enterprise knowledge easier to find and use.

Watch the video →

5. Start With High-Value AI Use Cases

Look for areas where AI can remove meaningful friction without requiring the entire knowledge environment to change at once.

Good starting points might include:

• AI-powered enterprise search.
• Knowledge article summarization.
• AI-assisted authoring.
• Content recommendations.
• Employee self-service.
• Customer self-service.

Once teams can show value in one area, they can expand AI into additional knowledge workflows, leaving room for innovation without taking on too much at once.

6. Measure What Improves

AI adoption isn't the goal by itself.

Track whether the experience actually improves through measures such as search success, self-service resolution, support ticket volume, employee adoption, content engagement, and recurring knowledge gaps. Together, these measures show the real effectiveness of your AI solutions.

These insights can also reveal where the underlying knowledge strategy needs more work.

Bring AI and Enterprise Knowledge Together With Liferay DXP

AI can make knowledge easier to find and use, but enterprises still need a way to create, govern, connect, and deliver that knowledge across different experiences.

Liferay DXP brings these capabilities together on a single platform, enabling organizations to manage knowledge alongside the portals, intranets, and digital experiences where employees, customers, and partners already work.

Liferay DXP can support AI-enabled knowledge management through capabilities including:

• AI-powered knowledge discovery. Liferay AI Hub can work with knowledge content to answer questions and surface relevant information for users.
• AI-assisted content management. Content teams can use AI assistance when writing and updating knowledge articles.
• Governed, permission-aware search. Search results follow existing roles and permissions, helping ensure users only see information they're authorized to access.
• Structured content and workflows. Templates and approval workflows help keep knowledge consistent and ensure it is reviewed before publication.
• Enterprise integrations. Native connectors, APIs, and Liferay's integration framework can bring knowledge in from systems such as ticketing platforms, wikis, and shared drives.
• Personalized knowledge experiences. Content can adapt based on factors such as role, account, segment, or behavior.
• Multiple experiences from one foundation. The same platform can support employee intranets, customer self-service portals, partner knowledge hubs, and internal support tools.

AI changes what's possible with enterprise knowledge, but the goal remains familiar: give people reliable information when and where they need it.

Explore Liferay DXP's knowledge management capabilities to see how you can turn scattered enterprise information into connected, AI-ready knowledge experiences.

Frequently Asked Questions

How Is AI Used in Knowledge Management?

AI can support knowledge management by improving search, generating and summarizing content, organizing information, recommending relevant resources, supporting self-service, and helping teams maintain large knowledge repositories more efficiently.

What Is an AI-Powered Knowledge Management System?

An AI-powered knowledge management system combines traditional capabilities for capturing, organizing, governing, and sharing organizational knowledge with AI capabilities such as natural-language search, content generation, intelligent recommendations, and automation.

Can AI Replace Traditional Knowledge Management?

No. AI can automate and improve many knowledge management tasks, but organizations still need accurate content, governance, permissions, workflows, and human expertise. AI is most effective when it builds on a strong knowledge management foundation.

What Are Examples of AI in Knowledge Management?

Examples include AI-powered enterprise search, conversational self-service, automatic document summarization, content classification, article recommendations, AI-assisted authoring, and tools that help identify or update outdated knowledge.

How Does Generative AI Improve Knowledge Management?

Generative AI can help employees create, summarize, rewrite, and adapt knowledge content. It can also generate answers to user questions when connected to trusted enterprise knowledge sources.

What Are the Risks of Using AI for Knowledge Management?

Common risks include inaccurate answers, outdated source material, privacy concerns, unauthorized access to restricted information, regulatory issues, and excessive reliance on automated output without human review.

Can AI Improve Enterprise Search?

Yes. AI can help enterprise search understand natural-language questions, intent, and context rather than relying entirely on exact keyword matches. When connected to well-structured, well-governed knowledge, this can help users find relevant information more quickly.