As AI continues to reshape industries at an unprecedented pace, what does the future hold for knowledge managers? Will our roles be transformed by technology, or will our expertise remain essential?
Automation has been a part of our workflows for years. However, the advent of generative AI (Gen AI) has sparked a renewed push for organizations to adopt AI tools that drive efficiency. Over the past year, I’ve explored how Gen AI could revolutionize knowledge management (KM). From completing specialized courses to developing AI-driven tools that enhance KM processes, I’ve witnessed firsthand the potential of AI to summarize, classify, and analyze knowledge quickly and accurately. By processing vast amounts of information, AI enables organizations to transform their KM efforts into strategic assets. With routine tasks automated, knowledge professionals can focus on higher-level work—curating insights, fostering collaboration, and ensuring that knowledge serves a purpose beyond mere storage.

But does this mean that AI will replace knowledge management roles? I don’t think so. While AI tools are powerful, they still require human oversight, judgment, and context to function effectively. The future of KM professionals will not be about competing with AI but about mastering these AI-driven tools to push the boundaries of how knowledge is captured, shared, and applied. Traditional KM priorities—like capturing tacit knowledge, harvesting insights, and managing Communities of Practice (CoPs)—will continue to be invaluable. However, AI and other emerging technologies will streamline these processes, allowing us to concentrate on strategic tasks that add greater value.
One of the primary use cases for Gen AI is in Knowledge Management. Many major players have launched AI-powered tools designed to improve KM processes. Tools like Microsoft’s Co-Pilot, Power Virtual Agents, Salesforce’s Einstein, and ServiceNow’s knowledge solutions all focus on enhancing our roles in KM. For instance, using Google’s LM Notebook to create a podcast in seconds demonstrates how content dissemination will evolve dramatically in the near future.
So, where can AI play a role in KM? Here are some prominent areas where we can leverage Gen AI: summarization, content creation, automated tagging and classification, chatbots for retrieving stored knowledge, semantic search to improve search efficiency, personalized content based on user interests, content gap analysis, and building a Subject Matter Expert (SME) database from shared knowledge. Imagine a tool that captures the essence of a rich CoP conversation, auto-tags it, identifies the SMEs, and creates a knowledge artifact ready for submission. With advanced agents or AGI, we could even push for content cleansing and contribute automatically. Fascinating, isn’t it?
As a passionate knowledge manager and Gen AI enthusiast, I’ve begun leveraging these technologies to increase efficiency. One challenge I faced was the time required to draft case studies, newsletter blurbs, and knowledge summaries. To address this, I developed an internal GPT model that drafts this content in seconds. What used to take 15–30 minutes now takes just 2 minutes—an impressive efficiency gain. I also created a Power Apps solution to simplify and automate the submission and review process for success stories, ensuring that knowledge capture is both efficient and accurate. Recently, I’ve been working on a conversational bot that captures tacit knowledge by asking a series of questions and summarizing the conversation. This bot will contribute to our KM repository by integrating explicit and tacit knowledge assets from our projects.
Another emerging technology poised to transform knowledge management is Retrieval-Augmented Generation (RAG). RAG is among the most effective methods for embedding organizational knowledge into large language models (LLMs). I’ve spent some time delving deeper into this technology and do think we might have a role to play in this space. As KM professionals, we can enhance the knowledge embedding process by curating and tagging content more effectively. I envision a pre-processing stage where we strategically curate our content, making it easier to create filters based on classifications. It allows for better organization and classification of content, making it easier for RAG systems to retrieve and utilize information effectively. I believe this step can significantly improve the accuracy and relevance of AI outputs. While I’m not yet an expert in RAG, my exploration has underscored the importance of ensuring that the knowledge we embed is well-structured and meaningful. I’ve posed several questions to experts in the field and look forward to sharing my insights on this topic soon.
In conclusion, I believe the future of the knowledge manager role lies in embracing AI-driven tools and combining them with our expertise in KM. As long as we stay informed about AI capabilities and continually update our skills, our roles will remain relevant. We’ll shift from being custodians of knowledge to strategic advisors who use AI to unlock new value from information. Understanding the capabilities of different tools, including concepts like Prompting, RAG, and Fine-tuning, will be invaluable. There are community calls available online that provide excellent insights into the new capabilities being added to existing tools. I invest a lot of time in learning about these tools, and I’m confident these calls would benefit you too. Additionally, participating in knowledge forums can be extremely valuable. Nothing beats the knowledge of our KM experts who have been driving KM across organizations for years.
I’m excited to continue exploring new technologies like knowledge graphs, RAG, and Microsoft’s AI solutions, and I encourage other KM professionals to do the same. Feel free to connect with me if you’d like to discuss the intersection of AI and KM—I’m passionate about both fields and would love to engage in a conversation. Let’s continue driving the future of knowledge management together!
About the Author:
Angshumala Sarmah is a seasoned Knowledge Management professional with over 18 years of experience in Knowledge Management. She specializes in fostering knowledge-sharing cultures, utilizing data-driven metrics, and leading cross-functional teams to achieve impactful results. Her expertise includes content strategy, data governance, and process automation, with a focus on M365 tools, Power Automate, and Generative AI. Recently, she developed a Gen AI-powered app that streamlines case study and credential drafting, successfully rolled out across the firm she works with.

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