What Is an AI Knowledge Base & Why You Need One?

Modern businesses run on knowledge. Most of the time, that means a knowledge base which allows employees to access information and data from across the company. However, knowledge bases can be difficult to access, parse, and maintain, driving up costs and inhibiting productivity.

AI is changing that.

AI knowledge bases can streamline both access and management of KBs, bringing substantial boosts in areas such as customer service and repair work. The more your operation relies on internal knowledge sharing, the more benefit you could potentially see.

In this article, we’ll look at current problems with knowledge base management and how AI is revolutionizing KBs, as well some of the challenges still facing this new technology.

Key Challenges In Knowledge Base Management

Companies attempting to manually manage their knowledge bases have historically faced several key challenges:

• Information fragmentation: Too often, critical knowledge becomes scattered across numerous databases. It may even be “siloed” so that each department has its own database, making it difficult for other departments to access that information.
• Poor data discovery: Employees such as call center agents or field repair techs may lack access to critical information necessary. Or, the search process may be convoluted, leading to inefficient work.
• Slow information updates: When a critical piece of knowledge such as an SOP is updated in one place, it may not automatically propagate across the larger information network. This can lead to agents in different areas following outdated procedures.
• Lack of version control: If a knowledge management system cannot properly track changes / versions of information, including the user and date changed, you can quickly end up with a muddle of contradictory or outdated information with no accountability.

Ultimately, all these issues lead to one core problem: inefficiency, and the wasted money which comes with it. Calls to CS take longer, with more calls necessary to reach resolution. On-site field techs require multiple visits to solve an issue. Key regulatory requirements are not met, leading to legal action.

In certain critical operations such as medicine, poor knowledge management could potentially even endanger people’s health.

AI-powered knowledge bases seek to remedy these issues.

What Goes Into An AI Knowledge Base?

There are several key aspects of modern AI tech which combine to make AI an excellent option for managing KBs.

• Machine Learning Models: MLMs are “deep learning” systems that take in vast amounts of information, indexing and cross-referencing it. This can then be used for data retrieval, analysis, and prediction.
• Natural-Language Processing: NLP systems allow AI to take in requests or queries using everyday English, and respond in kind. A user can simply talk or type to the AI like any other coworker.
• Gen-AI: Generation systems allow AI to go beyond simple information retrieval. A properly-trained AI can offer insights and analysis beyond even what the initial request specified.
• Contextual awareness: An AI can access large amounts of data across an organization to improve the accuracy of its recommendations. For example, taking a customer’s past call history into account when offering suggestions to a CS agent.
• Autonomous oversight: AIs can be tasked with oversight capabilities as well, such as monitoring changes to the KB and checking them against existing data to avoid inaccurate information. Potential issues can be automatically flagged and reported for human investigation.

When these features come together, they create an exceptional system for managing even huge knowledge bases while overcoming challenges which have hindered KB management in the past.

What Does An AI Knowledge Base Look Like In Action?

A software development company is spending too much of its budget on technical support. So, they invest in a new AI knowledge base. After training the AI on their existing KB and other technical materials, it puts together a robust data set ready for use.

A customer calls in. An AI picks up the call and, based on the user’s phone number, pulls up their records. It sees that they always call for technical problems, so it automatically routes them to tech support as their first stop. This avoids needless transfers.

After describing their problem, the CS agent inputs the query into the AI with a simple ChatGPT-style interface. The system automatically parses the user’s past calls, and sees that several common troubleshooting steps have already been done previously. It recommends the agent skip these steps, while offering suggestions on new possibilities. It can crawl through hundreds of pages of technical documents, even the program’s code, while searching for new insights.

This spares the agent and the user from extended waits, offering valid insights in a matter of moments, rather than minutes – or hours.

The problem is solved, and everyone goes away happy. The agent has the satisfaction of quickly resolving an issue – while lowering their call time. Meanwhile, the customer enjoys a fast, efficient, positive experience. Customer satisfaction scores go up, and the customer is more likely to recommend.

In addition, the customer’s call is available to the development team, advising them of the situation and encouraging updates.

What Challenges Currently Face AI Knowledge Bases?

No new technology is perfect, and there are still issues that potential buyers should be aware of:

• AI training times. Currently, training AIs on existing materials can be a costly time-consuming process, slowing implementation and ROI.
• Data accuracy issues. An AI is only as smart as the data it’s fed. Information in the existing KB must be scrubbed and vetted for accuracy before training to avoid inaccuracies or ‘hallucinations.’
• Maintaining security. Any plan to implement AI knowledge base technology requires robust planning to protect critical information from malicious access or tampering, especially data such as medical records which are legally protected.
• Cross-system integration. Be careful when selecting an AI database tool to ensure it’s capable of integrating with your existing software, such as Salesforce, Zendesk, Office360, or AWS.
• Future-proofing. AI is a fast-developing field. Your new investment should be ready for future modules or other expansions as new innovations reach the market.

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