Enterprises are facing a quiet but growing risk: the loss of institutional memory.

Today’s enterprises are incredibly interconnected, with vast webs of dependencies in both the digital and physical worlds. As systems become more complex and are carried on the hands of a limited set of subject-matter experts (SMEs), the risk of losing institutional memory is magnified.

Generative AI can help reduce that risk by making critical knowledge easier to capture, retrieve, and share across the organization.

The SME: Where institutional knowledge resides 

In many organizations, there are go-to people who understand the nuances, shortcuts, and edge cases needed to command the digital and physical systems that run the enterprise. These subject matter experts (SMEs) become incredibly valuable to organizations and are needed to keep the complex systems running. 

When SMEs leave, years of institutional memory and knowledge may be lost even with processes in place to transition that knowledge.

The effect can ripple across systems and teams, especially if the SMEs are the connective tissue across disparate parts of the business.

There is no substitute for human insight. But generative AI (GenAI) helps provide a method for knowledge capture and surfacing of knowledge that makes expertise more accessible, allowing newer employees to learn faster, become productive sooner, and develop into the next generation of SMEs.

Why documentation may fail to preserve institutional knowledge

Documentation and the system that supports it are nothing new. They’re the foundation for any well-functioning enterprise.

But documentation doesn’t capture the human insights and edge cases that make an SME invaluable.

SMEs are experts because they’ve built insights over years. They know where to look when something breaks. They recognize patterns. They know the details and the reasons behind decisions.

It’s tough to document that wisdom, but responsible use of generative AI can help capture knowledge and make it retrievable at the right time and place. Rather than simply storing information, AI can help employees understand the context, reasoning, and decision-making patterns that experienced SMEs have developed over time.

How generative AI is changing knowledge retrieval and access

This is where generative AI becomes an accelerator – large language models (LLMs) combined with information retrieval mechanisms such as retrieval-augmented generation (RAG), knowledge graphs, and knowledge surfacing via tooling actions (e.g., via model context protocol or MCP) help store and surface information in the right context at the right time.

Traditional documentation systems rely on users knowing the right keywords, the right sections to look in, and the right query to surface the knowledge most relevant in the moment.

LLMs change that dynamic because they are good at parsing natural language, gauging intent, and surfacing information from large corpuses of knowledge.

An LLM combined with a knowledge retrieval system allows you to type in a prompt, have that prompt parsed for intent, and then hydrated with the most relevant information across a large corpus of knowledge. The response from the LLM is then grounded in your enterprise knowledge, helping reduce hallucinations and allowing you to verify the source.

Knowledge graphs specifically add another important layer: connecting people, systems, processes, documents, and business concepts across the enterprise. This helps the LLM establish relationships among that information, providing richer context and making it easier to understand how pieces of institutional knowledge fit together. 

Combined with AI, these knowledge systems help employees discover not only the right answer but also the reasoning, dependencies, and historical context behind it.

Imagine a new employee working on a project. Instead of typing isolated keywords, they include the system they’re modifying, the context of the question, any constraints they’re facing, and what they’re trying to accomplish.

The AI can retrieve relevant documents, leverage knowledge graph relationships to provide additional context, parse those documents with knowledge of what the employee is trying to accomplish, and surface that insight directly back to the employee.

Importantly, this does not mean AI becomes the SME. Deep expertise still comes from experience, judgment, and human interaction. Instead, AI can augment that expertise by making hard-earned institutional knowledge more accessible, shortening the learning curve for employees, and helping future SMEs develop faster. At the same time, it can soften the knowledge loss that can follow when an experienced SME leaves.

From knowledge capture to proactive intelligence

Many organizations already have the building blocks to get started.

Most major cloud providers offer LLM plus RAG, LLM + MCP, and knowledge graph capabilities out of the box. You can start small, for instance, by loading FAQs and internal documentation into a system and piloting it for a specific use case.

A common example is internal support tickets: Instead of a Level 1 support team manually answering every request, a grounded AI chatbot can respond to common questions. The answers to these questions may already exist, and the chatbot makes them easy to find.

The impact can be measured by looking at whether response times are shorter, if there are fewer support tickets or fewer follow-ups, if the answers are more consistent (evaluations are important), and if general satisfaction of users has increased.

For enterprises already implementing AI, the above examples may seem trivial. But it gets more complex when you account for SME knowledge that isn’t easily captured in your enterprise FAQ. This requires a new process to understand questions an SME might answer that aren’t in your FAQ. This is the most difficult part.

I’ve seen this in practice with an SME who wanted to work with AI to capture their nearly 30 years of experience. They wanted their legacy imprinted in the organization, helping new employees build up the same knowledge they had, in perpetuity. Through the above techniques, their knowledge was cemented, and their colleagues thanked them. 

The risks of relying on AI for institutional knowledge

Like any tool, AI has limits. LLMs are not infallible, and even with grounding, hallucinations are not solved. You still need verification layers, including human review, standardized evaluations, and rigorous documentation practices.

That’s a broader workforce strategy question and, indeed, a conversation we should continue to have. Institutional memory fades, systems evolve, and people move on. GenAI doesn’t magically solve that, but it can parse, contextualize, and surface data in a way that softens the loss of institutional knowledge while helping employees acquire expertise faster and continue building the institutional knowledge of the future.

Get started

If you’re new to using generative AI to capture institutional memory, it’s important to understand exactly what you’re trying to solve.

I recommend starting with existing processes that are structured, repeatable, and backed by the latest data. Technical specifications, standard operating procedures, and system-level documentation are good candidates.

Solve processes where finding information is time-consuming and frustrating for your employees. In parallel, begin thinking about what constitutes SME knowledge and how it might be captured. 

That’s when the real unlock happens. The opportunity is not just to preserve knowledge, but to make that knowledge more discoverable so the next generation of SMEs can develop more quickly and effectively.