11 Ways to Use AI in Knowledge Management

Knowledge management (KM) plays a pivotal role in today’s organizations by ensuring that information is harnessed, disseminated, and leveraged for better decision-making. However, with the explosion in data, people across all roles are navigating a complex maze of knowledge management platforms, making content discovery increasingly more time-consuming and inefficient. 

The sheer volume of data is overwhelming, making curation and management seem like insurmountable tasks. Information often resides in isolated silos, diminishing its holistic value and leading to redundancies and contradictions that erode trust in knowledge bases.  

Keeping knowledge bases current in this ever-evolving environment is an uphill battle. Moreover, the difficulties in retrieving pertinent information, ensuring its relevance, safeguarding sensitive data, and integrating disparate systems compound the intricacy of their role. Add to this the tasks of ensuring the capture and transfer of tacit knowledge, customizing content for diverse users, and maintaining user engagement, and the scope of the challenge becomes evident. However, as technology evolves, especially AI, the strategies and tools behind KM are transforming too.  

Harnessing the Power of AI

Revolutionizing Knowledge Management for the Future

Explore how AI transforms knowledge use in organizations, using real cases to delve into techniques like NLP, ML, and knowledge graphs for innovation and efficiency.

Here are 11 ways AI has been used to address some of the intricate challenges faced by anyone using knowledge management tools: 

  1. Advanced Analysis: AI can sift through vast amounts of data, highlighting patterns and trends and turning raw data into actionable insights. To do this, AI employs statistical models and machine learning techniques to process data. By examining relationships between variables, AI can identify patterns and trends humans might overlook. This is not just number crunching — it is structured data interpretation. In KM, advanced analysis is used extensively to help discover related content through pattern recognition and natural entity extraction. 
  2. Smart Chatbots: Chatbots utilize Natural Language Processing (NLP) to understand user queries. These chatbots facilitate instant access to information, providing the necessary information on demand. Evalueserve is working with a Big 4 consulting firm that uses our proprietary Research Bot to answer questions related to sector trends and competitor moves.
  3. Content Creation: AI can mine datasets, generate summaries and reports, and ensure that knowledge bases are constantly enriched and updated. It can also utilize NLP to ensure the content’s language is optimized for its intended audience. Using this functionality, strategy teams can auto-generate summaries of 50+ page documents or a group of documents for presentation. Sales teams can utilize the same functionality to generate battle cards for key competitors or account profiles for mining existing clients. 
  4. Collaboration Tools: Predictive analytics can anticipate user needs, suggesting relevant documents or meeting schedules based on behavior, thus improving the effectiveness of an individual. AI collaboration tools support real-time communication, document sharing, and group problem-solving. Teams can receive proactive suggestions for document sharing or meeting scheduling based on past activities.
  5. Content Tagging and Categorization: AI can automatically tag and categorize new data entries, ensuring consistency, reducing duplication, and removing the manual labor of data categorization. The AI is trained on pre-labeled data through supervised learning. Unsurprisingly, this capability has been widely adopted by KM platforms as it significantly reduces the effort required for curating and classifying content.
  6. Expert Systems: Expert systems are rule-based systems where AI makes decisions based on a predefined set of rules. These rules are derived from human-in-the-loop, enabling the system to emulate human expertise in specialized areas, ensuring quality knowledge transfer. When used correctly, AI-based expert systems can mirror human decisions (to a large degree) and convert tacit know-how into organizational knowledge, which is a cornerstone of effective KM.
  7. Intelligent Search: AI integrates semantic understanding with traditional search algorithms. It can interpret context from user queries, ensuring that search results align with user intent rather than just literal keyword matching. Now, employees can retrieve precise, contextually relevant data, even if they search for vague or overused terms.
  8. Proactive Knowledge Discovery: AI can actively seek out new and relevant knowledge, ensuring knowledge bases are always current. AI employs unsupervised learning techniques, like clustering and association, to find patterns in unstructured datasets. This goes beyond mere data retrieval to uncover novel insights. An interesting example of this use case is the finance department of a Fortune 500 company leveraging AI to unearth unconventional investment opportunities by analyzing diverse economic indicators.
  9. Knowledge Transmission and Sharing: AI can analyze user interactions to push relevant recommended information to them. For instance, the IT-KM function could use this capability to auto-suggest a newly released IT (Information Technology) training module for employees whose past interactions indicate the need for a refresher.
  10. Recommendations: By understanding individual user behaviors, AI can suggest relevant content or courses, enhancing customization. For example, a corporate learning platform can suggest courses based on an employee’s past completions and the choices of peers with similar roles.
  11. Virtual Assistants: Virtual assistants leverage NLP to understand user commands, combined with task automation algorithms to execute diverse tasks. These AI-driven tools can parse content, set reminders, and even summarize lengthy documents, increasing user engagement and making KM tools more user-friendly.  

In essence, these KM use cases empower companies to tap into their reservoir of knowledge efficiently, making data-driven decisions that pave the way for growth, innovation, and unmatched customer experiences. As AI continues its forward march, the capabilities of KM are bound to expand, setting new paradigms for enterprises worldwide. 

Evalueserve is at the forefront of innovation and recognizes the transformative power of AI in KM. By integrating these AI-driven KM use cases, businesses are not only managing knowledge but amplifying it, setting new standards for growth and efficiency. It is more than a trend; it is the future of optimized enterprise knowledge management. 

Abhishek Khanna
Vice President and Head of Products Posts

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