Sharing Knowledge at Speed and Scale: The Convergence of KM and AI

WHY AI NEEDS A KNOWLEDGE ECOSYSTEM

A strong AI solution can’t stand on its own. It depends on a healthy knowledge ecosystem—the people, processes, and practices that shape how “know-how” and individual and collective knowledge flows and evolves. Some key components of a knowledge ecosystem are structure, people, governance, process, and culture.

One part of that is structure. Taxonomies, metadata, and content standards give information its spine. They make it possible to surface the right thing at the right time and to do it consistently.

But structure alone doesn’t keep a system healthy. People do. Human stewards play an often quiet but vital role in maintaining, curating, and refining knowledge assets over time. They notice what’s outdated, what’s missing, and what’s getting used in ways no one expected. The policies and checks that support trust are also essential.

When governance is working, it ensures transparency, accountability, and version control without grinding everything to a halt. A knowledge ecosystem also relies on established and enforced processes for identifying, capturing, storing, sharing, and utilizing knowledge.

And finally, there’s the cultural piece. Knowledge sharing does not happen organically in most environments. It’s a habit, a mindset, and sometimes a leap of faith. Without cultural buy-in and top management support, even the best tools and solutions will remain unused.

THE SYMBIOTIC RELATIONSHIP BETWEEN KM AND AI

The relationship between AI and KM is symbiotic. Each enhances the other in ways that go beyond simple additive value.

AI tools can improve how we do KM. They help us partially automate the boring stuff like indexing, tagging, content summarization, and analysis of transcripts and can even lend a hand in taxonomy development and maintenance. In turn, many of the AI systems we’re relying on today, both generative and agentic, simply can’t function without high-quality, context-rich knowledge assets. That’s where KM shines: AI helps KM scale. KM helps AI make sense.

KM Enables AI

AI can only be as good as its source material. KM ensures that knowledge is properly classified and tagged, so it can be found and used. It also makes sure that content is validated and version-controlled, so AI implementations are not relying on outdated or conflicting information. And it brings everything under a consistent set of organizational standards, so that what AI presents is not only technically accurate, it is also relevant and trustworthy.

AI Enhances KM

AI, in turn, can give KM a real boost. It can handle the tedious stuff, such as auto-tagging content with metadata or summarizing long documents that no one has time to read in full. It can help personalize how knowledge is delivered, tailoring information based on someone’s role, preferences, or behavior.

AI is also good at spotting patterns and gaps. For example, an organization can use AI to analyze customer support transcripts. It may surface that the most downloaded knowledge articles are being accessed repeatedly but customer service agents are not actually able to answer the related customer inquiries.

KM professionals can then update the articles accordingly. Just looking at transactional document usage data would not enable this and would instead make it seem like those documents were the most important in the knowledgebase.

AI capabilities don’t replace the work of knowledge professionals. However, they do make that work go faster and be more focused and provide new automation possibilities.

THE HUMAN ADVANTAGE: WHY KM STILL NEEDS PEOPLE

Even with all the advances in AI, humans still have the edge when it comes to sensemaking. We’re the ones who connect the dots between different ideas, who understand nuance, and who can spot when something doesn’t quite fit the business context.

KM professionals decide what’s relevant and what’s outdated, and they know how to tailor content to the needs of different audiences. They act as stewards of shared knowledge spaces, keeping things organized and usable over time. Perhaps most importantly, they build trust. They translate between the technical, the operational, and the strategic to make sure that what we know gets used in the right way, by the right people, at the right time.

End users or non-KM employees also help shape our KM and AI interventions by showing us, through their behaviors and attitude, where we should invest our time and money. Look to the humans as a guide to what should be done next and how. As the late Larry Prusak often reminded us, “If you have one dollar to invest in knowledge management, put 1 cent into information management and 99 cents into human interaction.” Even as the technology becomes more and more important to KM, the human factor remains central.

EMBRACING EXPERIMENTATION: A CALL TO KMERS

If you work in KM, this is not the time to shy away from AI, it is the perfect time to get curious. AI isn’t the enemy. It’s a new tool in our kit, and it’s also a huge opportunity. Knowledge and information professionals should lean into experimentation, test what works in their context, and bring their knowledge of people, process, and purpose into AI conversations.

This is the moment where the often quiet and unsung work of KM can step into the spotlight. AI can help raise the profile of KM and related disciplines, making them more visible and more integral to how organizations operate. This is already happening in many contexts. We don’t have to be data scientists to lead in this AI and KM convergence. We just need to show up, ask smart questions, and help shape the systems that are already shaping us. The work that provides the perfect foundation for these new technologies is something that library science and KM have been doing for decades already.

Now it is time to put its importance in the spotlight.

ENABLING, ENHANCING, CONVERGING

In the quest to scale knowledge faster and farther, it’s tempting to rely solely on AI. But without the structure, stewardship, and social dimensions that KM provides, efforts to operationalize AI risk becoming sophisticated but shallow.

The convergence of KM and AI offers a future that balances speed with structure and data with discernment. This integrated approach allows organizations to not just move faster, but to move smarter.

If AI is a hot new high-performance engine, KM is the steering mechanism that keeps it on course. Without applying some KM and IM principles behind the scenes, there are more crashes waiting to happen.

With AI and KM in harmony, organizations won’t just become faster. They’ll become wiser, which will help ensure that the impact of both AI and KM investments will be tangible, scalable, and sustainable.