Top Six Use Cases For GenAI In Knowledge Workflows

After completing The Forrester Wave™: Knowledge Management Solutions, Q4 2024, I wanted to understand where the market was with the adoption of generative AI in knowledge management (KM) workflows. Vendors use genAI to streamline and accelerate different parts of the agile KM process. While there have been some doubts from reference customers about the early results, many vendors are pouring money into genAI to tackle the repetitive tasks that often bog down KM practices. That said, IT leaders have a bit of work ahead of them to address challenges related to knowledge quality and difficulties in navigating the necessary cultural shifts if they want to capitalize on AI’s full potential in agile KM.

The State Of Your Captured Knowledge Matters

The success of genAI in KM is contingent on the quality of the captured knowledge. ​High-quality, structured organizational knowledge is essential for accurate and reliable AI outputs. IT leaders must emphasize the need for a supportive culture, well-defined roles and responsibilities, and consistent KM practices to capture the necessary information to help improve decision-making, drive innovation, and enhance workforce agility. Known problems with knowledge include:

  • Quality — inaccurate or deliberately false information
  • Findability — scattered across different platforms, databases, or departments
  • Relevance — no longer up to date
  • ​​​Retention — loss of key staff and poor KM practices
  • Sharing — silos and permissions deny access

Six Ways That GenAI Increases Agility In Knowledge Workflows

GenAI can significantly enhance agile KM practices by automating and improving various aspects of knowledge workflows.​ In my recently published report, Move Beyond Agile Knowledge Management With Generative AI, I separated the most common use cases for genAI into three categories: what is generally available today, what is still in pilot, and what is in development. These are the top six generally available use cases for genAI in agile KM workflows:

  1. Knowledge gaps and sentiment analysis
  2. Enterprise retrieval-augmented generation (RAG) search
  3. Improving knowledge with generated summaries and suggested improvements
  4. Applying a style guide and some data scrubbing
  5. Cocreation of knowledge with subject-matter expert (SME) via a generated first draft
  6. Understanding the context of questions from end users with conversational AI

 

While new features may soon be available in your KM platform, reference customers should head into this new way of working with a bit of caution. Be ready to support the financial decisions with a solid ROI that illustrates improved productivity, and work closely with your vendor or service provider to get the system ready to support a production environment. Some vendors will help you clean up your knowledge stores before you implement a new solution to help generate a higher level of accuracy.

A Change Of Perspective Is Required

​IT leaders need to address the cultural and managerial changes that are needed. To capitalize on genAI advancements, changes are required in our traditional working methods — meaning that our knowledge workers must embrace these changes as a new way of doing business:

  • From static to dynamic. AI is revolutionizing knowledge capacity building by automating and enhancing knowledge practices. IT leaders must integrate knowledge creation, improvement, and sharing into core business processes. Capturing and updating knowledge needs to be everyone’s responsibility.
  • From a single source of truth to a moment of truth. Instead of building a single source of truth, IT leaders should focus on making decisions at a moment of truth, accessing comprehensive information from multiple sources in real time.
  • From capture to cocreation. IT leaders should shift from capturing knowledge from SMEs to cocreating knowledge with technology. ​AI acts as a cognitive partner, speeding up analysis and generating new insights.
  • From finding to discovery. IT leaders should shift from finding information to discovering new ideas and innovations, enabling a creative state where AI suggests novel solutions and optimizes processes.
  • From closed silos to open to everyone. IT leaders should mentor staff to move from closed knowledge-sharing silos to open knowledge accessible to everyone, improving valuable knowledge-sharing across teams.
  • From the search for answers to the power of the next question. IT leaders must realize that knowledge is not just about searching for answers; knowledge empowers employees to ask the next question, develop critical thinking skills, and uncover more profound insights.

 

By embracing these shifts, organizations can leverage genAI to create a more dynamic, innovative, and collaborative KM ecosystem, ultimately enhancing decision-making, productivity, and business performance.

Let’s Connect

Have questions? That’s fantastic. Let’s connect and continue the conversation! Please reach out to me through social media or request a guidance session. Follow my blogs and research at Forrester.com.

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