AI knowledge management: smarter ways to capture, organize, and use Information

It is no surprise that as humans, we forget a lot of things, and there are multiple reasons for that, including the limited capacity of working memory, the active pruning of unused memories, and the interference of new information with older ones. 

I recently read this really nice article on “Why humans forget” and being an overthinker, I connected the dots with why everyone in the tech world is suddenly talking about memory.

 It is not just about OpenAI’s memory in ChatGPT announcement, but also about how memory to machines is about resources and something that can be solved. 

To me, this is where I see AI actually making a difference in our lives and helping us be smarter. 

I am somebody who forgets terminologies a lot; I would remember the entire concept, but when it comes to that one word, my brain will not be able to remember it. 

Now imagine how easy it would be if I could ask an AI tool, “Hi, what was the term that I was referencing a week before about managing traffic?” and then it references me to load balancing and the resources I read about it.

In this article, we will talk about how AI’s memory features can speed up workflows and also help in knowledge management.

What is AI in knowledge management?

Before we move into AI knowledge management systems, let’s take a step back and think about how we got here. 

Knowledge management has always been a bit of a necessary evil in tech. It started with static wikis, shared drives, and long email threads. 

Then came the golden era of Confluence and Notion – where we all collectively decided, “Let’s make docs more beautiful and collaborative.” 

And honestly, they did help. 

But let’s be real: these tools still expected humans to remember where things were, update them regularly, and know exactly what to search for (and this was not very long ago).

Now with AI moving really fast, it is not only helping us autocomplete, but being a real assistant — it’s watching how we work, learning what’s important, and surfacing the right thing at the right time. That’s a huge shift.

We’re moving from “write everything down and hope someone finds it” to “the tool already knows what you’re looking for, because it saw you debug that same thing last month.” This, to me, is what actual knowledge management looks like.

What makes up AI knowledge management?

Before AI can remember things for you, it needs the right setup under the hood. Most AI-powered knowledge systems rely on three main components:

• Memory layer: This is where context gets stored. Think of it as the AI’s brain that tracks what you’ve worked on, read, or asked before. 

Some tools support long-term memory that builds over sessions.

• Semantic layer: Instead of relying on exact keywords, this lets the AI understand what you mean, it’s powered by vector databases and LLMs that support natural language understanding.

• Integration layer: This connects the AI to your tools (like Slack, GitHub, VS Code) so it can pull and surface info where you actually work.

Together, these form the foundation of any AI knowledge assistant, and the real magic happens when all three work in sync.

How does AI memory work

AI memory works by mimicking how humans store and retrieve information. 

Just like how we start by recognizing simple patterns (like shapes and colors as kids), AI starts with basic recognition and gradually builds up to more complex understanding, like we do as adults. 

What’s cool is that AI memory is also split into types – semantic memory helps it remember facts (like pulling data from manuals), episodic memory lets it recall past interactions (super useful for chatbots and personalized support), and procedural memory helps it remember how to do things step by step (like troubleshooting or running a machine). 

These aren’t just fancy terms; they’re actually trained in different ways – semantic through structured data, episodic through past conversations, and procedural through repetition and imitation.

Why use AI memory?

Let’s be honest, traditional knowledge management tools were great when teams were small, documentation didn’t change every other day, and AI didn’t exist (because now every company has its shipping speed at least doubled). 

But once your engineering team starts scaling, things get messy. Docs get outdated, search feels broken, and tribal knowledge lives in Slack threads or someone’s brain. 

I’ve seen teams struggle to onboard new devs because no one knows where to find the “latest” answer. 

This is where AI memory is truly helpful. 

Gartner in its article on knowledge, covers how GenAI can convert normal conversations into structured knowledge sets. 

AI in knowledge management is not just about storing info or summarising, it is also contextually aware and can help you with your tasks where you need to reference conversations/documentation/messages very often. 

Imagine asking, “What did we decide about rate limiting last sprint?” and getting an actual answer based on meeting notes, Slack chats, and PR discussions. 

Companies like McKinsey are already doing this – Lilli, their internal chatbot, pulls insights from a century of knowledge. 

Let alone big corporations, it is also super helpful for personal workflows. I recently took a 10-day vacation and had the classic “I don’t remember what I do anymore” holiday withdrawal. 

This is where using a tool with a long-term memory feature helps. In the screenshot below, I show you how I use AI to give me a refresher on what I was working on (which is writing this article), and it not only summarizes what I worked on but also gives suggestions on what more I would like to know or focus on in the article. 

This is a classic example of AI-enabled knowledge management.

Traditional tools vs AI-driven memory tools

In the example above, you saw how useful an AI-driven memory tool can be (it not only remembers, but also assists and does some of the mundane parts of the job for you). 

And here’s where the real shift starts to happen – because using AI memory isn’t just a cool productivity hack, it’s actually solving a much deeper problem that’s been haunting engineering teams for years: knowledge management.

Here’s a tweet where Arnav mentioned how working in big corporations means you have to deal with a lot of legacy architecture, code and alignment.

This is a classic example of where AI-driven memory tools can help. 

We've all been in those situations where a critical piece of info is buried in a Notion doc from three quarters ago, or lost in an outdated Confluence page that nobody updated because the person who wrote it left the team. 

Traditional tools are great until your team scales or changes rapidly, and that’s when things start falling apart. 

That’s why it’s worth comparing how the old-school tools stack up against the new wave of AI-driven memory tools that are not just storing information, but actively working to surface the right thing at the right time. 

AI in knowledge management, or what to be excited about and what to worry about

Like any technology/tool, using AI in knowledge management has both benefits and disadvantages. 

That being said, 30% of code at Microsoft is generated by AI, 30% of code at Google is generated by AI, and Cursor generates over 1 billion lines of code every day. 

AI is a natural part of our development process now, and it will soon become a part of other workflows, including knowledge management.

And honestly, there’s a lot to love here. 

AI-powered tools help you find the right answer faster, reduce the time wasted digging through outdated docs, and even summarize things you’ve completely forgotten about. 

Tools like Pieces don’t just store information; they understand it and resurface it when it actually matters. 

But with all that power comes a fair bit of chaos, too. 

According to IBM, over 80% of organizations say they struggle with data governance when implementing AI.

Add privacy issues (hence maybe worth checking on AI governance and SLMs), hallucinations, and biased results to the mix, and it’s easy to see why companies are hesitant to go all-in.

If your AI pulls the wrong “truth” from a noisy dataset or exposes a piece of internal context that shouldn’t have been surfaced, that’s not just a glitch; that’s a liability. 

So yes, the benefits are real, but this space needs clear ethical standards and active human oversight to make sure we’re not just replacing old problems with shinier ones.

So, how do you even adopt AI in knowledge management?

Start small, but start smart. You don’t need to rebuild your entire system overnight. B

egin by identifying where your team wastes the most time – maybe it’s searching Slack for a link someone shared last week, or trying to remember how a bug was fixed in the last sprint. 

From the examples above, you have seen how tools like Pieces are great because they don’t force you to change your workflow; they quietly plug into it. Y

ou can save snippets, links, and even entire coding sessions, and it’ll surface them later when you need them most. 

The trick is to treat AI as a collaborator, not a replacement. 

Ending notes

If you’ve made it this far, here’s my take: AI memory isn’t just a productivity flex, it’s a shift in how we handle complexity at scale. 

It won’t replace your brain (or your messy Notion workspace), but it can seriously reduce the cognitive load. 

Start by playing around with AI tools that support knowledge management like Pieces, Cursor, or experiment with ChatGPT’s memory to get a feel for what a smarter workflow looks like.

Here are some resources that you can checkout:

• Blog by Salesforce on “What is Knowledge Management AI?”

• Research paper on “Towards General Text Embeddings with Multi-stage Contrastive Learning”

• Article on “Artificial intelligence and knowledge management: A partnership between human and AI”