How Generative AI Is Revolutionizing Knowledge Management

As generative AI becomes more accepted in the mainstream, the auto-generated writing is on the auto-generated wall:

Small, growing teams must learn to leverage the new and emerging technology — or risk falling far behind their competition in pretty much all operational areas. While this can arguably be said with regard to most technological breakthroughs, the versatility of generative AI makes adoption non-negotiable.

Incidentally, one of the main business processes that stands to benefit the most from generative AI tech is knowledge management. Here, we’ll discuss everything you need to know to supercharge your team’s KM efforts with this continually-developing technology.

What is Generative Artificial Intelligence (AI)?

Generative AI is a type of artificial intelligence that is capable of generating new content, ranging from text and images to music and code, based on the data it has been trained on.

Though not truly autonomous and “intelligent” in human terms, generative AI comes as close as we’ve ever seen to replicating human-created text, art, and similar content.

Here’s a (very) basic explanation of how it works:

1. You feed the tool the data and content you want it to learn and learn from
2. You define the rules and parameters for the tool to follow
3. The AI tool creates new content based on the existing data and parameters provided

(Note: We’ll get a bit more into this — without getting too technical — a bit later on.)

Adoption of generative AI in various fields — from marketing and advertising to healthcare and education — continues to grow as the technology becomes more sophisticated.

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And, if current predictions hold fast, the generative AI industry is really only just getting started:

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To be sure, the potential applications of this emerging technology for your business are innumerable. Here, we’re going to focus specifically on how generative AI can be used to supercharge your knowledge management efforts.

The Impact of Generative AI on Knowledge Management

Even if you’ve only dipped your toes in the waters of generative AI, you can probably see how your team might be able to use it for knowledge management purposes.

Forget about all that “probably” and “might be” stuff, though.

The fact is, generative AI is slated to completely change the way businesses approach managing their organizational knowledge altogether.

Here’s how.

Automated Knowledge Content Generation & Updates

Perhaps most notably, generative AI can be used to automatically create new knowledge content — and update existing content as needed.

(And, yes, this process can be almost fully automated when paired with other AI-powered tools.)

For example, you can use generative AI to automatically update or create new content…

• When internal or third-party source data changes
• When spikes in support tickets are found in specific areas
• When customer feedback reflects a prioritized need

This automatically-generated content will emulate the appropriate tone and style of your human-created content for both internal knowledge bases and as well as customer-facing KBs. The more data you feed it, the more accurate this computer-generated content will be.

Real-Time Adaptation for Personalization & Engagement

Generative AI is also able to adapt to different situations to enhance experiences and improve outcomes.

Individually, AI-powered knowledge bases, chatbots, and other such tools to:

• Deliver relevant and contextual content recommendations to users
• Summarize or translate existing content into more practical terms
• Expand upon existing content directly while engaging with the user

(Generative AI can also translate existing knowledge content into any language it’s been trained on, in 100% real-time.)

In a broader sense, generative AI can be used to enhance employee development by developing laser-focused learning pathways and training curricula for individual team members. Additionally, AI can automatically deliver relevant knowledge content to trainees at critical moments along their developmental journey.

Enhanced Knowledge Content Organization

Keeping your collective knowledge content organized is a critical part of knowledge management.

Luckily, generative AI technology makes doing so super manageable — categorizing content based on topic, relevance, and reader preference with or without your guidance. As new content is added (by human or computer), the tool can quickly categorize it based on these existing parameters.

Note, however, that generative AI is used here to simply provide suggestions as to how to organize and tag your knowledge content — and to then identify which categories and tags apply to individual pieces of content. Still, this automated, intelligent approach to organizing knowledge content is much faster and more accurate than doing it all by hand.

This automated content analysis is also effective for identifying sensitive knowledge content that requires closer scrutiny before being published — or that should not be published at all. 

Intelligent Search Capabilities

Generative AI enhances search capabilities and functionality for both the provider and the end-user.

In fact, this ties in much of what we’ve discussed thus far.

Again, generative AI uses context to either provide immediate, tailored assistance — or to point the user to the absolute best resource possible for the given situation. This means the user will always get the answer they need right away, whether they’re using your chatbot or your knowledge base’s search feature.

For even more immediacy, generative AI can be used to provide search suggestions based on user input:

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Procedural Improvement

Finally, generative AI can actually help you improve your team’s strategic approach to knowledge management, overall.

For example, it can help with identifying…

• Gaps in knowledge content and in data sources
• Bottlenecks and potential problems within your knowledge management processes
• Ways to further optimize and automate your KM workflows

From there, you can use generative AI to develop plans for making the appropriate changes — and ensuring team members are equipped to make this change with ease.

Key Benefits of Using Generative AI to Empower KM

Though we’ve already made some of the key benefits of generative AI pretty clear, let’s dig a bit deeper into why it’s an essential tool for knowledge management purposes today.

Generative AI Improves Efficiency

With generative AI at the helm, you’ll be able to create knowledge content in a fraction of the time it takes you to do it manually.

(If you’ve seen GPT in action, you know how fast we’re talking, here.)

But, it’s not just the writing that’s now taking mere seconds to complete.

It’s also the research; the brainstorming; the outlining; the formatting.

It all happens right before your eyes as you enter a prompt for a new knowledge article.

Though you’ll likely need to make some tweaks to this auto-generated content, the time it takes to go from ideation to publication will absolutely plummet after introducing AI to the mix.

Generative AI Increases Knowledge Accuracy

Generative AI tools are created to be as accurate as possible based on the information they’re trained on.

It’s not going to intentionally mislead, lie, or make things up.

(It should be noted though that it can “hallucinate”…more on this in a bit.)

But, in the vast majority of cases, the content created by generative AI will not only be factually correct, but will also be relevant to the user’s current purposes and needs.

As for your knowledge base, as long as your source data is accurate, you can be nearly certain that the content generated by your AI tool will be as well. While this is always important, it’s absolutely crucial for teams in healthcare and other sensitive industries.

Generative AI Enhances the User Experience

As we said earlier, generative AI allows you to:

• Enhance your users’ search capabilities
• Deliver ultra-relevant content suggestions
• Offer personalized guidance to the individual user

…all in a streamlined and user-friendly manner.

Essentially, it allows you to optimize every user-facing aspect of your knowledge management practices.

(Which, when it comes down to it, is the reason you go through all the backend parts of the process…right?)

In all seriousness, generative AI ensures that delivery of your knowledge content is as strong as the foundation on which it stands.

Generative AI Provides Superhuman Analytics

It’s impossible to overstate the impressiveness of generative AI’s data analysis capabilities.

In terms of data quantity, it can analyze more data points in seconds than any human team could in hours (or days, weeks, months…). And, again, the chances of it making a mistake during this analysis is much lower than the chances of human error.

Generative AI can also complete highly-complex analytical tasks, such as:

• Identifying patterns in user behavior
• Comparing seemingly unrelated data points
• Recognizing blindspots in your KM workflows

As we said earlier, you can then use this and other contextual information to generate suggestions for improving your approach to knowledge management in practical and impactful ways.

Generative AI Aids in Scaling

With all of the above in place, you’ll be able to ramp up your knowledge management efforts across the board with relative ease.

On the customer-facing side, you’ll be providing:

• More personalized and practical self-service options
• More comprehensive informational content
• A more organized and cohesive user experience

With so much of all this being completely automated, your team will have more time and resources to a) ensure it all goes according to plan, and b) look for additional ways to scale up your KM efforts in ways that still require a human touch.

Risks and Challenges of Using Generative AI for KM

While generative AI is without question a benefit to your knowledge management processes, using it does come with its fair share of risks.

Mistakes and Limitations

Firstly, generative AI tools are not a replacement for human knowledge management teams.

Yes, they’re generally much more accurate and correct than their human counterparts — but they can still make mistakes. Left unchecked, a single error in data input, analysis, or interpretation could lead your AI to take your team or your customers completely off course.

Also, generative AI is currently only capable of acting upon the information it’s been fed. While it can analyze and synthesize this information in unique ways, it can’t come up with completely new ideas or applications of this info in the way that humans can.

(For that, we have to wait for AGI to become a reality…)

Data Privacy & Security

With the deluge of generative AI tools currently flooding the market, the importance of maintaining data security should be a top priority for small businesses.

Unfortunately, it’s…kind of not.

McKinsey reports that, of teams that have adopted generative or other AI technology, a mere 21% have established policies governing employees’ use of generative AI technologies, while only 38% focus on mitigating related cybersecurity risks.

This is pretty surprising, considering generative AI operates entirely on data provided by the company. Even if they haven’t encountered problems just yet, those taking a lackadaisical approach here are likely in for a rude awakening at some point in the near future. 

A Dependence on Technology

Once more, generative AI is not meant to replace your human knowledge management (or KM-adjacent) personnel.

To clarify, here are some things you shouldn’t be doing with generative AI:

• Publishing automatically-generated content without a QA process
• Pushing all support requests to your chatbot
• Acting on suggestions from your generative AI tool without question

Like any other tool, generative AI makes it easier to accomplish certain tasks — but it’s always necessary to have a human representative close at hand every step of the way. And, as we’ll discuss, your KM team should never take a “set it and forget it” approach to using your generative AI tool of choice.

Steps to Introduce Generative AI to Your KM Processes

A strategic approach is key to successfully introducing generative AI into your knowledge management workflows.

Here’s what this entails.

1. Assess Your Current Knowledge Management Situation

The first step is to take an objective and comprehensive look at your current knowledge management operations to see how you can get the most out of generative AI technology.

As we’ve discussed, some vital areas to focus on include:

• Your team’s ability to consistently create and maintain knowledge content
• Your ability to keep this content organized as your knowledge base grows
• Your user’s ability to find knowledge content whenever necessary

Once you’ve assessed your strengths and weaknesses in these areas, you’ll have a better idea of where you should first begin implementing generative AI.

That said, you’ll eventually want to use it for as much as you possibly can. For the time being, though, just focus on a single area of knowledge management.

2. Define & Prioritize Your Goals

With a single knowledge management process in focus, your next step will be to narrow down exactly what you hope to accomplish by introducing generative AI into the mix.

The SMART framework is reliable as usual, here.

An example:

• Specific: Create more high-quality knowledge base content for our end-users
• Measurable: Increase the number of new knowledge base articles by 25%
• Achievable: With generative AI assisting our knowledge team, we can improve both the quality and quantity of knowledge content created
• Relevant: Improved content quality and quantity will likely lead to higher user satisfaction and engagement
• Time-Bound: We plan to achieve this 25% within the next six months

Depending on your specific goal, you might need to create a rubric or scoring sheet of some kind to ensure objectivity. For the above example, you’d want to clearly define the qualities of “high-quality knowledge base content” according to your team.

3. Choose the Right Tools

While most generative AI tools on the market are pretty versatile in terms of knowledge management use cases, you’ll still want to narrow your search based on your immediate goals.

• For text-based content, GPT4, Bard, and Scribe are the go-to solutions
• For interactive content, check out Synesthia and Claude
• Duet AI aids in organizing and visualizing business data

Of course, you want to check each tool for compatibility with your current and future needs.

Some factors to consider here include:

• Ease of use, especially if your staff isn’t trained in backend generative AI development
• Integration capabilities, with both your current KM software and other tools in your tech stack
• Scalability to ensure continued use as your business grows and the tool evolves
• Support and training, again considering your team’s technical capabilities
• Cost: Initial, ongoing, and scaling

Again, since generative AI tools are still in their nascency, it’s important to consider how your tool of choice will continue to provide value to you in the future.

4. Train Your Team

You’ll then need to train your team on how to use your new generative AI tool — and in your new knowledge management processes, as appropriate.

Your first order of business should be governance training. You need to be sure your employees know how the tool is and is not to be used, and that you’ve fully enabled them to use it as intended.

Then, train your team on the tool’s functionality — along with its expected weaknesses. For example, you’ll want to be sure your team can easily identify hallucinations, false information, and other anomalies that aren’t exactly uncommon with today’s technology.

Finally, shift specifically toward how the tool will be used for knowledge management purposes. Throughout this part of the training, you’ll need to:

• Define how your current KM processes will change
• Explain how these changes will benefit the team and the individual
• Provide instruction and support as the change is implemented

(Need more help with employee training? Don’t forget to invest in the right tools.)

5. Phased Implementation

Now, it’s time to begin phasing your generative AI tool into your actual knowledge management processes.

As tempting as it will be to go all-in from the start:

Don’t.

The last thing you want is to fill your knowledge base with questionable content, have your chatbot lie to your customers, or lead your team astray with faulty data.

Whichever AI-powered tool you’re using, roll it out slowly. Give your team time to understand how it works (and to identify with clarity when it’s not). Monitor how they’re using it, and guide them as to how they could get more use out of it.

And, importantly:

Get your team to give you feedback.

Give them the opportunity to tell you what’s working, what’s not — and what looks like it may be working but is actually only improving your knowledge management efforts on the surface.

From there…

6. Evaluate & Improve

As with all business processes, you’ll want to continually improve your use of generative AI for knowledge management purposes as time goes on.

Luckily, this shouldn’t be too difficult:

As the technology continues to evolve, it’s only going to become easier and easier to integrate it into your current operations and derive more value from it.

Still, you need to keep close tabs on how your use of generative AI is impacting both your team’s ability to manage your organizational knowledge — and how this is impacting your customers.

Not only will this give you insight into how to better utilize generative AI…but it will also tell you how to improve your knowledge management operations, overall.