4 key benefits of adding AI to your knowledge management program

A knowledge management system helps people in an organization share, access, and update business knowledge and information. And it delivers many crucial benefits: it boosts productivity, minimizes risk, creates competitive advantage, and helps businesses make better, faster decisions.

And while that’s all great in theory, in practice, knowledge management is imperfect.

At most companies, effective knowledge management requires a significant amount of time, attention, and energy. But it doesn’t have to. Adding artificial intelligence to knowledge management drastically reduces the amount of oversight required to manage the program.

So how can artificial intelligence help knowledge management? Let’s take a look at four key benefits.

1. AI simplifies knowledge discovery

An effective knowledge management program provides employees with the tools they need to engage in four key knowledge management practices: capturing, sharing, discovering, and maintaining knowledge.

In the last 20 years, many tools were developed that simplified the first two practices: capturing and sharing knowledge. From intranets and wikis to cloud drives and collaboration tools, technological advances provided plenty of ways for knowledge workers to get information out of their heads and into a shareable and searchable source.

But the processes of capturing and sharing knowledge created a new problem: as the amount of knowledge captured and shared grew, so did the difficulty of discovering knowledge.

Companies implemented a number of ways to combat the discovery problem:

  • They created guidelines for capturing and storing information in the knowledge base.
  • They organized captured knowledge into detailed and complex hierarchies.
  • They adopted tools with tagging and categorizing features that simplified searching.

Each of these practices helped, but none really provided a perfect, permanent solution. And as knowledge became harder and harder to discover, employees lost sight of the value of knowledge management, stopped capturing and sharing knowledge, and abandoned knowledge management tools.

AI solves this problem by using modern technologies to simplify knowledge discovery.

AI-powered knowledge bases use new technologies—semantic search, natural language processing, and machine learning—to make it easier for employees to find the knowledge they’re looking for quickly and easily.

  1. Semantic search and natural language processing eliminate the need for Boolean searches, complex hierarchies, and granular tagging and categorization. Instead, AI lets employees search the knowledge base using natural language. Then, it makes inferences and delivers results based on search terms, synonyms, and implied contexts.
  2. Machine learning monitors both the terms that are used and user behaviors over time to predict what employees are looking for. Machine learning algorithms look at what employees search for, then they predict what information they’re looking for based on what information satisfied other employees who had similar queries in the past.

Essentially, AI delivers intuitive search capabilities to your knowledge base, making it easier than ever for employees to discover the knowledge they need at the moment they need it, and helping them rediscover the value of your knowledge management program.

2. AI connects data from disparate sources

Another major knowledge management hurdle is that employees in different departments don’t always capture and share information in the same way:

  • Support teams capture and share knowledge in a ticketing system.
  • HR uses a secured intranet portal.
  • Sales reps manage their knowledge in a CRM tool.
  • Product teams use project management tools.

And so on. This creates another knowledge discovery problem: employees don’t know where to look to find the knowledge they need. In many cases, they may not even have access to the tools where the knowledge is stored.

AI-powered tools help you connect and combine knowledge across multiple systems, giving all employees access to knowledge regardless of where it lives.

For example, atSpoke supports native integrations with many other popular workplace tools, which lets employees:

  • add knowledge in text format
  • link to a webpage, intranet site, or Google Drive file
  • upload an attachment
  • import Confluence pages
consolidating knowledge in atSpoke

When answering a coworker’s question in atSpoke, support teams can add knowledge from other sources by uploading a file, linking to another webpage, or linking to a Google Drive file.

This consolidates fragmented knowledge, making all documented knowledge accessible to everyone in your company from a central source.

The combination of AI’s ability to quickly search through massive libraries—and its capability of predicting what searchers are looking for—make it an extremely powerful tool that solves some of the biggest knowledge discovery problems businesses have faced in the past.

3. AI helps you keep your knowledge base content up-to-date

We’ve discussed how AI helps with knowledge discovery, but it’s also useful in another problematic knowledge management process: knowledge maintenance.

Here’s how it usually works: there’s a big push to document knowledge and upload it to a new company knowledge base. Eventually, there’s a tremendous amount of information stored in your knowledge base—which would be great if it weren’t for the fact that no one ever bothered to go back and update outdated knowledge.

Keeping outdated knowledge in your knowledge base is detrimental to your knowledge management program. When employees find outdated information in your knowledge base and make errors because of it, they lose trust in the system. Eventually, they stop using it altogether.

AI solves this problem by reminding employees to update saved knowledge regularly.

Here are a couple of examples of how atSpoke’s AI helps companies maintain their knowledge base content and keep all knowledge up to date:

  • atSpoke asks people if the answer provided was correct when it serves information to employees from the knowledge base. If the employee replies that that answer wasn’t correct or was insufficient, atSpoke routes the question and answer back to the person/team who originally answered it to update the saved information.
keeping knowledge up to date in atSpoke

If a coworker responds that the information atSpoke provided isn’t correct, atSpoke sends the request to the correct team to updated the outdated or incorrect knowledge base entry.

  • Employees can set reminders to update knowledge base entries directly in atSpoke, reminding them to review the entry before a specific expiration date. For example, if someone in HR uploads instructions for how to enroll for benefits in 2018, they can set a reminder in atSpoke to update that entry before open enrollment begins for 2019.
set review date reminders in atSpoke

When adding a knowledge base entry, just use the “Review date” dropdown to set a date when atSpoke will automatically remind you to review and update the information.

AI that utilizes machine learning technology also helps employees find more up-to-date information. When it notices that a specific result is performing poorly, it may stop sending people that information and begin serving something that seems to better satisfy user intent.

4. AI tools provide important knowledge management metrics

How can you tell or—more importantly—prove that your knowledge management program is providing its promised benefits? For most businesses, it’s very difficult—if not impossible.

You could survey employees to see how often your team’s wiki allowed them to self-service and find the information they needed, but that approach doesn’t provide a definitive answer. After all, people often recall things incorrectly while feeling confident that they’re remembering clearly.

An AI-powered knowledge management tool like atSpoke lets you track metrics that show the exact, definitive impact that your knowledge management program has on productivity and operational costs.

It tells you exactly how many requests atSpoke resolved automatically.

atSpoke metrics

With atSpoke’s metrics, you can see exactly how many incoming requests were resolved instantly and automatically by atSpoke’s AI

In the screenshot above, you can see that 12 of this team’s requests were auto-resolved by atSpoke’s AI Additionally, when a person is responsible for resolving the request, each request takes about three weeks to resolve.

If you calculate the average support employee’s salary for three weeks of work and multiply that the number of auto-resolved requests—you get a good measure of how much money the system has saved your company.

Additionally, if 12 of your 25 requests were auto-resolved, you can see that you’ve reduced nearly 50% of your team’s incoming work. That number easily translates into a figure for increased productivity that’s the direct outcome of your knowledge management efforts.

Using cognitive computing and AI for knowledge management

Artificial intelligence and machine learning are more than just buzzwords used to make new knowledge management tools sound exciting and unique. Even in their infancy, these technologies are making a huge impact on the field of knowledge management.

Modern AI is helping teams discover and maintain company knowledge more effectively. Additionally, it lets you produce more accurate reports on the cost and productivity benefits of your company’s knowledge management program.

And those are benefits all companies should drive toward.

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