For most companies today, employees are drowning in information but starving for knowledge. Documents are everywhere, all around them, hidden away in folders and chats. 

But when it comes time to use that information, most employees can’t find the actual knowledge they need in the moment. 

So they interrupt a coworker, pull a sales engineer away from mission-critical work, or waste time digging through pages of documentation.

What’s the solution? 

(Hint: It’s not AI)

Well, not AI alone. Solving this problem requires a mix of mindset and technology. The answer lies in what we’re calling AI-driven knowledge sharing. 

Here, we explore the reasons behind knowledge gaps and how AI is reshaping the way organizations share information. We also look at what business leaders need to consider when connecting information to Large Language Models or similar systems.

What Is Knowledge Sharing? 

Knowledge sharing (or management, if you still want to call it that) is the practice of making all of your company’s internal information readily available to your team members. All those years of expertise, insights, and wisdom should be usable by employees to do their jobs.

Before the AI knowledge era, we had a more traditional form of knowledge management. This focused heavily on collecting, categorizing, and storing information in centralized locations. 

Historically, companies relied on systems like wikis, intranets, and knowledge platforms like Guru, Seismic, or Hyperlink. 

These tools made their names by gathering tribal knowledge, training materials, and product documentation and storing it in one place. This was a push-based model. Teams created content and published it, all the while hoping that the right people would find and use it.

But publishing never guaranteed adoption, or even discoverability. And that’s the main flaw of this model.

Employees usually struggled to locate relevant information. Or, worse, they didn’t trust that the information was up to date. Traditional knowledge management allowed for far too many gaps. And those gaps slowed productivity way down.

Why the Push-Based Approach to Knowledge Failed

Traditional push-based systems have two key characteristics: 

1. Companies generate enormous volumes of content. 
2. Employees waste time looking for even the most basic information.

While working in a push-based knowledge environment, you’ll often hear questions like:

• “Where’s the latest guide on configuring XYZ?”
• “What did that training manual say again? The one about onboarding customers?”
• “How do I respond to this support ticket?”

When the answers aren’t blatantly obvious, your team members default to the fastest solution: they ask a teammate.  

Now, your Slack, Teams, or Google Chat channels are flooded. These pings disrupt the daily workflow and create conversations that are redundant and inefficient.

Teams are stuck in this endless cycle. 

The information is documented somewhere. But no one can find it when it matters most, right now. 

Push-based models are one-directional. 

Your content creators amass vast amounts of knowledge in the hopes that their messaging reaches the right audience. But there’s no guarantee. Anytime your employees can’t pull what they need effortlessly (literally), they’re gonna fall back on human workarounds.

In other words, push systems generate more noise than clarity. 

True knowledge sharing requires a pull-based model. This is one in which your team members can request, retrieve, and apply information instantly and effortlessly.

How AI Reshaped Knowledge Management

Artificial intelligence is the key to the shift from push to pull. In particular, we’re talking about large language models (LLMs). AI serves as your universal interface for knowledge. That way, your employees can query systems in ways that are both natural and intuitive to them.

The modern approach to AI knowledge management emphasizes accessibility, usability, and contextual delivery of information. And that access is instantaneous.

The difference is clear: no more hoping your staff will be proactive about finding information. Now, they can proactively browse documentation. And AI will meet them at the moment of need with precise, validated answers.

• Knowledge is centralized. Instead of being scattered across intranets, email attachments, and wikis, AI will unify all of your content into a single index.
• Search is conversational. Your team members don’t have to parse their language.  They can type a phrase or ask a question in natural language, just like they would when they interrupt their coworkers. The AI can interpret their intent and retrieve the most relevant results.
• Answers are contextual. Instead of returning a lengthy doc no one’s gonna read, AI can summarize or extract the relevant section. And if you’re still skeptical, it can give you a link back to the original source.
• Delivery is embedded. No, you don’t need another platform to toggle to. Your knowledge flows into the tools employees already use, like Slack, Teams, or a browser extension. So you won’t be forcing a new standalone portal on your team.

The Impact of LLMs and Knowledge Automation

Large Language Models have taken knowledge sharing way beyond just static documents and outdated message threads. Connecting an LLM to company data transforms that knowledge into a dynamic resource that’s always available. AI removes the friction running rampant across so many workflows by delivering the right information at the right time. 

These are some of the most powerful benefits adopting automated knowledge management. 

1. Self-Service Training

AI makes it possible for employees to learn at their own pace. No more sitting through hours of onboarding sessions or searching static PDFs. Now, they can ask the system questions directly:

• A new sales rep can pull up product details before a client call.
• A support agent can query troubleshooting steps in real time.
• A manager can refresh themselves on HR policy instantly.

This self-service training cuts down on training costs, improves retention, and supports just-in-time learning. Here’s what this looks like in Slack, for example:

2. On-Demand Content Delivery

Your docs are usually scattered across multiple systems. With AI, the inevitable hunt is eliminated:

• Engineers can instantly access the right API documentation without digging through Confluence.
• Operations managers can grab updated compliance checklists directly from Slack.
• Marketers can confirm brand guidelines without emailing another department.

This on-demand model means no more dependency on teammates. Your experts are now free from those endless interruptions, and your employees can now work more independently.

3. Multilingual Knowledge

For global organizations, language barriers pose major problems. Your clients speak one language, and your materials are in another. Your employee speaks the language you need, but they can’t sell or counter effectively in that language. 

AI solves these problems with real-time translation and content conversion.

• A team in Japan can access technical documentation in Japanese that was originally written in English.
• Customer-facing teams in Latin America can translate knowledge articles instantly into Spanish or Portuguese.
• Cross-border collaboration becomes smoother without the delays or expenses of human translation.

AI helps you make sure that your knowledge is both accessible and inclusive across geographies.

How to Organize Company Information for AI

The effectiveness of AI-powered knowledge sharing, of course, depends entirely on the quality and structure of your content. To get the best results, you’ll have to rethink how you create and maintain your knowledge.

Optimize for Text-Based Content

LLMs work their best when they have structured, textual input. Formats like Word documents, PDFs, Excel sheets, or well-formatted webpages produce the most rewarding outcomes.

In contrast, content hidden in image-heavy slide decks, unstructured videos, or interactive JavaScript-heavy pages is harder for AI to parse. So make sure you prioritize content formats that are machine-readable.

Keep Knowledge Fresh

AI systems are only as good as their sources. If your docs are outdated, the AI will obviously come back with incorrect or incomplete answers. To avoid this, make sure you:

• Assign a dedicated “knowledge engineer” to monitor updates and ensure accuracy.
• Integrate AI knowledge systems with existing repositories like Google Drive, SharePoint, or Dropbox.
• Establish workflows where every product update or policy change is reflected in the knowledge base.

Freshness isn’t optional. It’s the foundation of AI knowledge sharing that you and your staff can trust.

Integrate with Company Tools

The hard, cold truth is that your employees won’t adopt a system if they have to switch platforms. They just won’t. What do you do? Make sure you choose an AI knowledge base that connects with where your people already work:

• Slack and Teams integrations for conversational Q&A.
• Browser extensions for quick answers without tab switching.
• CRM or ticketing system plugins so frontline workers can resolve cases in real time.

Your goal here is frictionless access to all internal systems. You want knowledge delivered at the right place and time.

What About Human Oversight?

Even the best AI systems are imperfect. To keep improving on their accuracy, they need human feedback loops.

For this reason, all of your team members must be empowered to:

• Upvote or downvote AI answers.
• Correct the content directly when the AI misinterprets.
• Provide feedback that the system can use to improve ranking and retrieval.

Without these feedback mechanisms, the AI has no idea whether it’s performing well for your team or not. You gotta let it know.

Platforms like 1up demonstrate the power of real-time corrections. When users edit or validate an answer, the system learns and becomes more reliable. Over time, you’ll get a self-reinforcing cycle of improvement.

How to Keep Business Data Safe

As AI expands our access to knowledge, we have to be able to make sure that our hyper-productivity doesn’t come at the expense of our security or compliance.

To keep your internal company knowledge safe, follow these best practices:

• Data Classification: Identify and sanitize sensitive information. This includes personally identifiable information (PII), financial data, or credentials. Do this before adding it to the system.
• PII Protections: Look for modern systems, like 1up, which automatically redact sensitive data to prevent leakage.
• Disable Model Training: Don’t feed proprietary company data back into foundational AI models. Instead, restrict training to in-house or controlled environments.
• File Hosting via Integrations: Sync your knowledge bases with your existing tools like Confluence, Google Drive, or Notion instead of trying to upload all of your files manually. This can help you make sure your updates remain secure and consistent.
• Access Controls: Implement single sign-on (SSO) and role-based permissions. That way, only the right employees can view or edit your content.

Taken together, these measures will keep your AI knowledge systems providing value without exposing you to unnecessary risk.

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