AI Knowledge Base: What You Need To Know in 2024

Artificial Intelligence (AI) makes knowledge bases faster, smarter, and more helpful to customers. If your knowledge base isn’t AI-powered already, competitors that have taken advantage of the technology might offer better B2B technical customer support and leave you behind.

Learn what an AI knowledge base is, how it works, and why you should implement it in your business.

AI Knowledge Base Explained

An AI knowledge base is a centralized repository for storing, managing, and retrieving customer facing documentation such as articles, technical guides, FAQs, and tutorials. It uses artificial intelligence to keep everything organized and easy to find.

When customers have questions or need information about your products, an AI-powered knowledge base system can quickly pull up the specific details they’re looking for at any time. This enhances customer support and experience.

How Do AI-Powered Knowledge Bases Work?

AI knowledge bases work just like their traditional counterparts but with advanced capabilities. Creating, updating, organizing, and retrieving content in a conventional knowledge base is manual. When you throw AI into the mix, you automate these processes using cutting-edge technologies.

Key Technologies Behind AI Knowledge Bases

Here are powerful capabilities that take customer service knowledge management to the next level:

• Natural Language Processing (NLP): This technology enables AI knowledge base software to understand human language (text or speech). When your product users ask technical questions, the AI can determine the meaning of the queries and quickly determine the best answers from your knowledge base.
• Natural Language Understanding (NLU): NLU is a subset of NLP. It enables AI tools to understand the grammatical structure (syntax) of a sentence and the right context or intended meaning (semantics) of each word in the sentence. This allows AI knowledge bases to correctly comprehend search terms or questions, even if the wording is casual, inaccurate, or has multiple interpretations. So when your B2B customers search your knowledge base, they don’t have to use the exact keywords to get relevant results.
• Machine Learning (ML): ML algorithms can analyze a large amount of information, such as customer queries, feedback, and previous interactions, to identify patterns. The insights can help you personalize customer support and add more content to your knowledge base to address emerging issues.
• Retrieval-Augmented Generation (RAG):This is a clever technique for making AI in knowledge bases more accurate and reliable when retrieving information. Through powerful search algorithms, RAG enables AI to fetch relevant information in real time from your knowledge base. As a result, users always get up-to-date, accurate, and contextually relevant responses when they interact with your customer service AI.

Types of AI Knowledge Base Content

Today’s knowledge bases are more versatile than ever. They house all three types of content: structured, unstructured, and automated. They also contain images and other rich media.

Structured content

Structured content includes anything you can organize or format in a way that makes it easy to find. For example, blog posts, FAQs, technical guides, and manuals can be categorized based on topics and optimized by keywords. That way, it’s easy to retrieve them in the knowledge base.

Unstructured content

Unstructured content doesn’t have a predefined format or specific structure. This includes forum discussions, customer emails, images, social media interactions, videos, and audio recordings.

The lack of a specific structure makes this type of content challenging to organize and analyze. However, NLP and ML algorithms in an AI-based knowledge base can extract insights from unstructured data efficiently and effectively. This makes the content accessible and useful in your knowledge base.

Automated content

Automated content refers to AI-generated information. Artificial intelligence produces, refines, and updates this content over time based on insights from data analysis. For example, AI can identify common customer questions in your organization and automatically generate relevant, accurate responses or articles you can add to your knowledge base.

Benefits of Implementing an AI Knowledge Base

Why should you set up an AI knowledge base in your business?

24/7 Availability

With an AI knowledge base, your support is available around the clock. Customers can get instant answers anytime through AI chatbots, technical guides, tutorials, FAQs, or any other relevant resource.

Cost Efficiency

An AI knowledge base enables customers to solve issues independently by utilizing your provided content. This reduces the number of customers contacting your support agents for help, minimizing the need for extensive B2B technical support or service desk teams. And as a result, you cut down operational costs.

Personalized B2B Technical Support

Using NLP technology, an AI knowledge base system can understand the context behind technical questions. It then uses that insight to create personalized responses or suggest content that will help users solve their problems. AI can also recommend content based on user preferences and past interactions.

Data-Driven Insights

Using powerful ML algorithms, AI can dig through customer data, spot trends, and identify common issues. For example, if many customers ask for help generating a specific report over live chat, the AI can flag this and suggest your team write some knowledge base articles or blog posts to cover it.

AI Knowledge Bases vs. Traditional Knowledge Bases

Traditional and AI knowledge bases are great for keeping helpful content in one spot so customers can easily find what they need when they need it. The big difference? AI-powered ones have smart features that make them more powerful than the old-school versions.

Here is a side-by-side comparison of the two:

Feature	AI Knowledge Base	Traditional Knowledge Base
Search Capability	Advanced search function powered by NLP, NLU, and ML for quick, accurate results even without keyword matching	Simple, keyword-based searches that typically require specific terms to find content
Ability to Learn and Improve	Gets smarter over time by learning from data patterns and trends	Stays the same unless someone manually upgrades the system
Automation	Can handle things like recommending knowledge base updates or answering customer questions without human help	Manual content updates and no auto-responses

When choosing the right AI knowledge base, ensure it meets the following requirements:

• Integrates with tools your team uses daily,such as Zendesk, Slack, and Microsoft Teams
• Contains the must-have capabilities, including NLP, ML, advanced search
• Complies with relevant data security and privacy regulations

The Future of AI Knowledge Bases: What’s Coming Next

With AI, the future of knowledge management systems is all about smarter, faster customer support. The AI-powered solutions will learn what customers need, enabling them to offer instant, more personalized answers and recommendations.

According to Harvard Business Review, customers increasingly expect companies to provide AI-enhanced experiences. For this reason, organizations that won’t infuse AI into their knowledge bases by 2025 will risk falling behind and may struggle to meet customer demands.

Unlock the Full Potential of AI for B2B Technical Support

Looking to implement an AI-driven knowledge system in your business?

AptEdge is a robust, AI-powered platform. Equipped with automation workflows, search functionality, and generative AI, you can set up knowledge systems for your B2B technical support, customer care, and service desk teams.

Learn more about AptEdge to see how it helps you resolve support tickets faster and boost customer satisfaction in your business.