What Is an AI Knowledge Base? Tools, Features, and Best Practices

How much time have you spent searching through databases or messages for that one file? According to Gartner, 47% of digital workers struggle to find the information they need to do their jobs.

Many knowledge bases are only marginally better than a physical user manual. Keyword searches don’t always bring up what you’re looking for, leading to an endless hunt for the right section, file, or piece of information.

AI knowledge bases can surface information faster than traditional ones with a superior user experience. Learn how to implement an AI knowledge base system with this guide.

What is an AI knowledge base?

An AI knowledge base is a structured repository of information that uses AI to organize, analyze, and retrieve information. AI knowledge bases also have the capacity for continuous learning and personalization.

Types of AI knowledge bases

AI knowledge bases have many uses including streamlining customer support, assisting customer service and sales teams, onboarding new employees, and enhancing collaboration. Here are the three most common types of AI knowledge bases.

1. Customer-facing

According to Salesforce research, 61% of customers would rather use self-service for simple issues. Instead of waiting for business hours to reach out to customer service and potentially wait in a queue, many customers would rather have the tools to resolve a problem on their own.

Businesses are listening: 75% of service organizations now offer a self-service solution, while 73% offer a service chatbot. As case volumes rise, AI knowledge bases can reduce tickets and ease operational costs for businesses.

2. Internal support

Customers aren’t the only ones with a lot of information to wade through. Professionals like customer service and sales reps and tech support professionals need a quick and efficient way to retrieve information to do their jobs.

For example, a popular accounting software company built an internal AI knowledge base to help customer service reps answer questions faster with higher satisfaction. The knowledge base uses a bot that answers reps’ questions using AI. Using machine learning (ML)to continually improve, the bot now answers 60% of reps’ questions and helped the company drop case resolution from two days to 1.2 days. The company also saw its NPS score go up by 12%.

AI knowledge bases also enable better peer collaboration. Imagine a space where employees across teams can share ideas, best practices, and documents throughout the company—with AI facilitating, structuring, and distributing the information.

3. Employee experience

In the same way, many employees struggle to find and sift through information like HR policies, benefits, or standard operating procedures (SOPs). A structured AI knowledge base can improve employee engagement and experience by making it easy to onboard and learn.

Some AI knowledge bases can even create personalized learning paths for employees, guiding them through an onboarding flow or upskilling journey to reach a goal.

Core features of AI knowledge bases

AI knowledge bases are much more than a supercharged search function. Here’s how they work and what makes them unique.

Natural language processing (NLP)

Natural language processing (NLP) is a machine-learning technology that allows computers to understand, interpret, and generate human language. In other words, it translates human queries into code and action.

For example, if you ask a question like “Where is my package?”, NLP enables AI to understand that you’re looking for order tracking even though you didn’t use those specific keywords. It can give instructions about how to track your order or even request more information to track your package.

Machine learning (ML) for continuous improvement

Unlike traditional knowledge bases, AI knowledge bases is they aren’t static. They continually learn and adapt from user interactions.

ML algorithms analyze patterns and trends in user queries and feedback to refine their search results and provide more accurate recommendations.

For instance, if an AI agent receives low user feedback for its response to a particular query, it can automatically adapt its response to give more relevant information. It can also surface trends for customer service or HR teams, alerting them to an uptick in queries on a certain topic.

Automated content organization and tagging

Organizing and maintaining content are challenging yet necessary parts of knowledge base management. AI knowledge bases can alleviate this problem through automatic content tagging and categorization.

Proficient AI systems can process both structured and unstructured content, such as emails, chat transcripts, or files. With a base architecture, AI can tag, categorize, and organize new content as it’s added.

For example, Slack’s enterprise search lets companies connect documents from apps like Google Docs, Asana, and GitHub into a single knowledge base. Slack analyzes and organizes each asset so it’s searchable in one hub.

Integration with chatbots and virtual assistants

With a static knowledge base, users must click through many layers of topics to find what they need or fine-tune their search queries until they use the exact keyword(s) for their request. Using NLP, chatbots and assistants can greatly improve the user experience by generating answers based on commonly asked questions.

Multichannel accessibility

Text-based chatbots aren’t the only mode of interaction with an AI knowledge base. Web-based interfaces, mobile apps, and voice assistants give users many ways to interact and receive the information they need no matter where they are or which device they’re using.

Personalization

One of the most powerful features of AI knowledge bases is the potential for personalized communication and service. By integrating your AI knowledge base with a CRM or other database and built-in tools like AI agents, you can get personalized answers and help with accomplishing tasks.

For instance, instead of asking how much PTO employees generally receive in a year, an employee could ask how much PTO they have remaining that year and get a personalized answer.

Custom AI systems can also save conversations, enabling personalization by providing the knowledge base with context about past service tickets or questions.

Benefits of using an AI knowledge base

A knowledge base may not seem like an urgent priority to some businesses—but its benefits are big. If you’re contemplating whether investing in one is worth it, consider the advantages it can provide.

Improved customer support with AI

According to 86% of service professionals, customer expectations are higher than they used to be. Most decision makers (76%) also anticipate higher case volumes in the next year. Companies face the challenge of meeting these ever-rising expectations without raising costs.

An AI knowledge base can help customers resolve issues faster and increase satisfaction. For example:

• AI assistants and chatbots can answer common questions and resolve minor issues quickly without involving human reps.
• Quick access to technical documentation and other information helps service teams provide better customer experiences.
• AI agents can perform many routine tasks for customers and service reps, such as managing subscriptions, opening a ticket, or placing an order.

After implementing Slack for internal collaboration and knowledge sharing, one ecommerce company saw its customer satisfaction score skyrocket from 58% to over 90%. Its resolution time also dropped from five days to 24 hours.

Streamlined internal knowledge sharing

Sharing and accessing information isn’t always easy, particularly at the enterprise level. Marketing may use different data and systems than sales, finance, HR, and so on. An AI knowledge base enables data sharing across apps, systems, and formats. This not only saves time but also increases access to information that can unlock business growth.

80% of marketers using Slack for internal communications say it improved their ability to share files, data, and key information with non-marketing teams, and 87% of people using Slack say it has improved communications.

Increased efficiency and cost-effectiveness

Besides streamlining knowledge sharing across organizations, AI knowledge bases can contribute to higher satisfaction, less customer churn, and fewer resources that must be dedicated to customer support. Companies using Slack for service saved $2.2 million in service costs over three years, with a 16% cost savings per ticket.

This transformation also translates to internal productivity and efficiency. Marketers using Slack’s AI capabilities found they saved 100 minutes a week and reduced their onboarding time by 50%.

Enhanced data security and compliance with knowledge access controls

Most of the time, AI knowledge bases solve the problem of not enough access to data. But what about when you need to restrict data and information? AI knowledge bases can control the flow and security of information with knowledge access controls so only those with permissions can access secure data.

Top AI knowledge base software solutions for 2025:

Choosing the right AI knowledge management solution for your needs will help you streamline processes and ensure your team stays connected. Below are four AI knowledge base platforms to consider in 2025.

The following list is curated from G2, which scores software based on user feedback, features available, and overall satisfaction. G2 uses a five-star system focused on usability, customer satisfaction, and market presence. Each of the tools listed here has a minimum rating of four stars, ensuring they are top contenders for streamlining your knowledge base, improving collaboration, and helping your team work smarter.

1. Slack

Slack is a comprehensive work OS that helps teams integrate knowledge, files, and workflows into a single place for enhanced collaboration and productivity.

Enterprise search, a feature of Slack’s conversational AI search experience, lets you connect all your third-party apps and drives to Slack, creating a central, searchable hub for all your company’s knowledge and data. Teams can find anything and everything they need instantly—from files and documents to conversational data and insights.

2. Zendesk

Zendesk’s AI-powered knowledge base enables instant customer self-service through autonomous AI agents. Agents are trained on vast interaction data, enabling them to deliver personalized solutions and automate routine inquiries. Users appreciate Zendesk’s user-friendly design and efficient knowledge management capacity.

3. Guru

Guru is a comprehensive knowledge management solution that centralizes company knowledge, making it easily accessible and searchable for employees. Guru ensures that all company information is organized and up to date, reducing the need for manual searches or reliance on individual expertise. It boasts a high customer satisfaction rate, with users praising its ability to streamline knowledge sharing and improve team collaboration.

4. Slite

Slite is an AI-powered knowledge management tool designed to document company knowledge effectively and facilitate real-time collaboration. Slite integrates with Slack and other apps to support existing workflows and create a single source of truth for documentation that’s easy to access and maintain.

Best practices for maintaining an AI knowledge base

AI knowledge bases can benefit both large and small businesses, but when poorly organized or maintained, they can introduce errors, make information hard to find, and even harm trust. Follow these best practices to build an effective and powerful AI knowledge base.

Start with a specific use case

Start by choosing a focus area or top priority. Consider which business challenge you want to solve first, such as increasing customer satisfaction or reducing ticket numbers.

Then choose a clear and measurable objective, such as “Implement a fully functional AI knowledge base capable of handling 30% of customer questions within six months.”

With a goal in mind, you can plan which information to train your database on and establish metrics to evaluate success. Depending on your goal, you may want to include the following documentation in your knowledge base:

• FAQs
• Product or service descriptions
• Customer and employee onboarding flows
• Troubleshooting and technical documentation

Organize and structure your data for AI

While many AI tools can understand unstructured data, it’s good to provide as much structure as possible when building an AI knowledge base. Establish a taxonomy for your content, outlining categories, tags, and metadata that you’ll use for a better search experience. Semantic annotations like categories, subcategories, and internal links help the system understand the relationship between topics.

Avoid technical jargon and consider writing documentation in the form of FAQs, which will help NLP to contextualize common user questions.

Train AI for better search and recommendations

Once you have your initial model, it’s important to test rigorously before deploying it. Have your team submit its 20 most commonly asked questions to the knowledge base, and assess the answers to see if it responds accurately. If not, adjust your knowledge base content or feed it new content to address any gaps or inaccuracies.

Once launched, regularly train AI models using updated datasets to improve their search capabilities and recommendation accuracy. Ask each user to rate their experience so you can incorporate user feedback into the training process to further refine performance.

Continually update and improve content

A Salesforce report found that out-of-date materials are a top challenge for customer self-service. To ensure your knowledge base stays relevant and useful, it’s important to keep all documentation up to date. Assign owners to each topical area so they can take ownership over documentation updates and feeding new data into the system.

While manual updates may be necessary in certain cases, many updates can be automated. For example, integrate your knowledge base with your sales software or KPI dashboard to pull real-time data and statistics into your knowledge base.

One software company found a way to harness its institutional knowledge and enable its 600 sales engineers around the world by building an AI knowledge base in Slack. Now, every time a sales engineer reaches out to their peers for help with a problem, the answer and documentation are saved in the knowledge base—expanding the database with useful information.

Use AI-powered knowledge management to spark action

Knowledge isn’t useful unless it’s accessible to everyone who needs it when they need it. Accessible knowledge helps teams be more productive and make better, faster decisions, while empowering customers to find the answers and help they need around the clock.

Don’t let creating an AI knowledge base be a footnote in your operations—when done well, it’s a system that powers fast action and growth.

Enable your team with enterprise search in Slack.

This article is for informational purposes only. This article features products from Slack, which we own. We have a financial interest in their success, but all recommendations are based on our genuine belief in their value.