Too many businesses today are drowning in institutional knowledge. All that data is needed for various functions, but sorting through it can be extremely difficult. Knowledge bases (KBs) continue to grow in size, while becoming harder to search or verify the knowledge within.
Manually managing and vetting a KB becomes a time-consuming process, one which may not even be able to keep up with fast-changing information. Or, knowledge may become fragmented across different offices and departments, leading to conflicting information or SOPs that disrupt business processes.
New approaches to Knowledge Management (KM) are needed – and Generative AI is stepping up.
Key Takeaways
- Generative AI allows for robust self-managing knowledge systems.
- AI can take over routine KB maintenance tasks, error checking, even new article creation.
- Workers and customers alike will benefit from easier access to accurate information.
- An AI-powered KMS is future-proofed and ready to scale alongside your growing knowledge base.
What Is Knowledge Management Automation?
Simply put, knowledge management automation is a new approach to KM that focuses on using Generative AI to make KM faster, easier, and more reliable.
Instead of relying on humans to manually scan the KB and other knowledge sources, the AI can continuously scan for outdated or conflicting information. Depending on how much autonomy you give it, the AI could potentially attempt to fix these issues on its own, or else flag the problem for human review.
Generative AI also has the ability to create new articles. For example, a technical process might be best-documented in a series of archived emails between engineers. The AI can take those emails and rewrite them into a new KB article which makes the information easier for other workers to utilize. Once the article is approved, this institutional knowledge becomes part of the core KB.
In some cases, AI can even spot knowledge gaps – articles that should exist in the KB, but are missing.
Numerous processes associated with knowledge management can be handed off to AIs, which takes a big burden off of your human managers. They can focus on the ‘big picture’ aspect of KM, while leaving AI to handle the smaller details.
What Challenges Can Automated Knowledge Management Solve?
Traditional manual knowledge management has several major problems:
- Out-of-date information: When a full survey of a KB could take days – or even weeks – to complete, it becomes easy for outdated information to hang around without being noticed.
- Slow article additions: New articles take time to draft, be reviewed, and get approved for inclusion in the KB. This can significantly extend the time it takes to disseminate important new information throughout an organization.
- Missing articles: If there’s a knowledge gap in the KB, it can be very difficult for human managers to notice it unless they get complaints.
- Difficult searching: Traditional KBs typically rely on keyword-based searches, which slow down workers seeking specific information if they don’t know the right keywords to search. Either they can’t find appropriate articles, or they get flooded with too many hits.
- Poor scalability: The larger a manually-managed KB becomes, the worse all these issues become. Modern KBs can easily hit a point where manual management is simply impractical.
AI-powered knowledge management tools can address all these issues, either eliminating them entirely, or at least reducing the human labor burden.
AI knowledge management can:
- Continuously scan for outdated or inaccurate articles.
- Suggest new articles when appropriate.
- Compose new articles, pending human review.
- Streamline searching with common-sense natural language queries, rather than using keywords and esoteric search parameters.
- Utilize MCP connections to seek additional information off-site.
- Offer improved customer self-serve access.
- Scale effortlessly alongside your growing KB.
Knowledge base automation basically becomes your trusted helper, handling the detail work which is too time-consuming for human managers.
What Capabilities are Enabled by Knowledge Management Automation?
Automated knowledge management can have positive impact across virtually your entire organization. Here are just a few capabilities and real-world examples of how AI KM is improving operations.
1 – Customer Service
Few areas of business are improved by AI KM automation than your customer service contact centers. The AI directly supports your agents by making it easier for them to find information, as well as utilizing contextual data to customize the knowledge presentation to fit the agent and their caller. Handle times go down, while customer satisfaction goes up.
2 – Self-Serve Apps
Of course, handle times go down even further if the customer doesn’t have to call in. AI can handle highly robust self-serve portals for customers to ask questions or do minor account maintenance like updating contact details.
3 – Standardizing Regulatory Compliance
Are you certain that all your workers are familiar with the relevant regulations on their work, and are adhering properly to SOPs? AI knowledge management systems can ensure regulatory information is always standardized and kept up-to-date, even in fast-changing legal situations. This, along with alerts for potential violations, make it much easier to avoid costly legal issues.
4 – Capturing Institutional Knowledge
Hard-learned information isn’t always in the knowledge base. It can also end up in email chains, chatlogs, Slack channels, and other places which are traditionally hard to search. AI can scan all available institutional knowledge, evaluate its utility, and even create new articles making that knowledge much easier to find.
5 – Simplifying Difficult Knowledge
In technical fields, there could be a wide knowledge gap between the engineers and other worker such as the sales or support staff. Critical information may be captured in ‘high level’ tech documents that would be difficult for everyday workers to understand. The AI can take these documents and create versions more suited for people without PhDs.
6 – Improved Oversight
AI systems can capture a huge amount of meta-data on how the KB is being used. How many searches users require to find data. Which articles are most-accessed, and which may not have useful information. This provides human knowledge managers with a wealth of data for improving KM operations further, always seeking to make key business knowledge as easy to locate as possible.
How Do You Build an Automation-First KM Strategy?
Exact strategies will depend on each individual business and your current knowledge situation. Experts can help with that! However, as a general set of guidelines:
- Commit to the project, and make automation a priority in all strategic decisions.
- Define clear goals and objectives, such as improving KPIs relating to knowledge retrieval or agent call handling times.
- Research the market. Look at multiple KMS vendors. Try their demos.
- Vet and structure your existing knowledge base as much as possible. AI will be more reliable if it’s working off of a KB that’s already vetted for accuracy and with enough meta-tagging to be easily indexed.
- Get workforce buy-in. “Sell” the project to the people who’ll be using it every day, so they’re excited by the possibilities.
- Create a feedback loop of humans who are overseeing the AI deployment and operation, watching for errors and opportunities to improve performance.
- Focus on continuous improvement. Your AI KM system should only get smarter and more reliable as time passes.
Frequently Asked Questions
AI can assist with numerous tasks including:
– Scanning for incorrect or outdated information.
– Monitoring KB usage and reporting usage data.
– Creation of simple articles based on existing knowledge.
– Identifying key knowledge gaps.
– Streamlining searches to improve knowledge discovery.
It mostly differs in scope and capability. Rules-based automation systems must be coded by hand, with rules put in place for each specific situation. There’s no room for edge cases or ‘fuzzy’ inputs.
AI has much greater ability to understand natural language prompts, along with making informed guesses about the user’s intention. It can go beyond simple scripts to make intelligent recommendations, or even proactively fix issues in the database, if you choose to give it that level of autonomy.
You should think carefully about how much automation is deployed. Some of the major concerns include:
– Maintaining security, so your AI can’t be a vector for attack.
– Putting guardrails on the AI’s behavior in place, so it cannot potentially damage critical databases.
– Utilizing enough human oversight to prevent hallucinations from creeping into KB articles.
– Encouraging independent thought among your workforce; they shouldn’t become over-reliant on AI to do their thinking for them.
– Providing enough view into the AI’s ‘thought processes’ to understand its decision-making without it becoming a black box.
These are all surmountable issues, but should be considered before committing to an AI knowledge base.
A hybrid approach is best here: Give the AI enough information sources that it can find reliable information and check that against existing data. However, you should still have humans in the loop, overseeing the AI and monitoring – or explicitly approving – changes it makes to the KB.
Absolutely! Automated knowledge management is entirely scalable, small to large. In some cases, such as startups in a highly technical or regulated field, starting off with an AI-powered database will ensure you have a KB that’s fully future-proofed.
In Conclusion: Automated Knowledge Management Tools Streamline Your KM
If you’re starting to feel like your current knowledge management strategies have become a time-consuming burden, it’s time to look for better options. AI knowledge management can result in a faster, easier-to-use knowledge base which requires far less human intervention to remain reliable. Your staff and your customers will also benefit from faster access to highly-accurate information.
KMS Lighthouse leads the way with cutting-edge AI-powered knowledge management systems already in use by major operations around the world. We can streamline your existing KM systems, providing an AI partner who can handle your knowledge needs for years or decades to come. Contact us to learn more, or book a free demonstration.
