Unlocking the True Value of Generative AI with Knowledge Management

How Knowledge Management Systems Support Effective Generative AI Adoption

In recent years, many companies have started integrating generative AI into their business operations. According to a survey conducted by McKinsey in July 2024, 71% of the responding companies reported using generative AI in at least one function. Usage has been particularly widespread in core business areas such as marketing and sales, product and service development, service operations, and software engineering. These domains are not only central to many organizations but also the areas where generative AI holds the greatest potential for value creation. To drive results in core business areas, generative AI needs to be implemented thoughtfully—with support from a robust knowledge management (KM) system.

Digitization Alone Is Not Enough for Generative AI

When implementing generative AI, some may question whether a KM system is truly necessary. It’s true that AI can process digitized information, even if the information isn’t fully structured. However, assuming that digitization alone is enough for AI to generate meaningful output is a mistake. Scattered files, missing version histories, and absent context make it harder for AI to interpret information accurately or learn from it effectively. In fact, having more data can sometimes create more noise—raising the risk of incorrect or misleading results.

What Kind of Knowledge Works Best for Generative AI?

For AI to generate accurate responses and meaningful insights, it needs more than just access to information. The knowledge must be reliable and well-structured. It should also include context—such as the creator, reviewer, and date. FAQs, operation manuals, and best practice documents that include this metadata serve as effective “training material” for AI. With well-organized knowledge, AI can better understand and leverage an organization’s unique operational context.

The Role of Knowledge Management Systems in AI Utilization

A KM system does much more than simply store digitalized documents. It provides the infrastructure to organize, maintain, and give access to reliable, usable knowledge assets. One of its key roles is preventing valuable information from being scattered across individual computers or departmental folders. By managing content centrally, a KM system enhances both search-ability and reusability.

It also supports version control and history tracking, as well as helping to keep knowledge up to date and trustworthy. Features such as tagging, categorization, and tracking of authors and reviewers make it possible to manage knowledge along with the contextual metadata that gives the information meaningful. Approval workflows and review processes help teams verify the accuracy and quality of the knowledge they share.

With more reliable and well-contextualized knowledge, generative AI can better interpret and apply the information. In this way, a KM system helps create an environment where AI can learn more effectively and produce more accurate, relevant output.

Conclusion: Build a Strong Knowledge Foundation to Maximize Generative AI

To make the most of AI in streamlining business operations, it is critical to consider what you are enabling AI to learn. A KM system helps build the foundational knowledge base that AI depends on. By systematically organizing and maintaining trustworthy knowledge, AI can better interpret company-specific processes and terminology and apply that understanding effectively.

If your goal is to develop a generative AI system that truly reflects your organization’s expertise and way of working, the first step is to provide it with high-quality knowledge.

As generative AI increasingly becomes a core element of business, now is the time to invest in your knowledge management infrastructure and prepare your company for the AI-driven future.