Feature
Knowledge management is undergoing a profound transformation, driven by the rapid integration of artificial intelligence. This technological upheaval is reshaping how businesses capture, organize and leverage their collective data and content, turning traditional knowledge repositories into dynamic, intelligent ecosystems.
Knowledge Management With GenAI
The fusion of AI and knowledge management represents a critical opportunity for businesses to enhance decision-making, boost productivity and gain a competitive edge in an increasingly information-driven economy.
Organizations have already integrated AI-driven knowledge management systems into their digital workplaces, said Heather Richards, vice president of go-to-market strategy at Verint, including GenAI.
For instance, applications leveraging generative AI are highly effective for identifying sections of lengthy, complex documents, such as procedures or policies, and summarizing them into smaller, more searchable chunks. This streamlines the process for authors, making it quicker and easier to finalize and update information.
Retrieval-augmented generation (RAG) tools are especially useful, as they combine search technology with content sources to provide concise, accurate results.
To maximize the effectiveness of AI-curated content, the same best practices used for traditional knowledge bases apply, including:
- Taxonomies and metadata: Create useful structures to filter and segment results for specific groups or content sets.
- Usage reports: Identify areas with high traffic but low content quality to target for improvement.
- Validated content only: Ensure AI scenarios rely on trusted, organization-approved resources to avoid inaccuracies or hallucinations.
Ideally, reporting tools and dashboards should also evolve to track GenAI-driven activities, incorporating both statistical and personal feedback. This helps assess content usage and quality more comprehensively.
If an organization requires highly compliant, structured and validated information to address specific questions and issues, then GenAI should act as a complement to the core compliant objects, not a substitute, Richards said. This is often the case in domains such as healthcare, insurance and financial services.
Richards also advised businesses retain human oversight of any auto-generated content for accuracy, especially content that is provided for public self-service.
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From Static Repositories to Dynamic Ecosystems
AI-generated content is being integrated into knowledge management systems in other ways. Cobus Greyling, chief evangelist at Kore.ai, said some organizations are turning static knowledge systems into dynamic, intelligent ecosystems. In these cases, AI categorizes unstructured data in real time, using vector embeddings for contextual understanding and predictive responses. Integrated into enterprise platforms and unified via APIs, these systems ensure seamless access to relevant information and improved collaboration.
"Traditional knowledge management relied on manual data entry, static FAQs and siloed databases," Greyling said. "AI now bridges these gaps by employing natural language processing (NLP), machine learning (ML) and neural search algorithms.”
He warns, however, that ensuring the accuracy and reliability of AI-curated knowledge systems is no longer optional; It’s a strategic imperative. By combining cutting-edge techniques like knowledge graphs and human oversight, organizations can establish systems that evolve with precision and integrity.
The cornerstone of an accurate AI-curated knowledge base lies in its data architecture and continuous refinement cycles. One proven strategy is leveraging knowledge graphs. These graphs enable AI to map relationships between entities, ensuring the system understands not only the data but also its context and interconnections.
Some organizations are also implementing data versioning and automated content lifecycle management. This approach ensures that outdated or redundant information is pruned, while the latest updates are continuously ingested. For instance, a pharmaceutical company can employ automated workflows where drug research papers are tagged, reviewed and periodically validated to align with evolving regulations.
Ultimately, Greyling says, AI facilitates tacit knowledge transfer through context-aware recommendation systems. This means a sales team using AI might receive automated suggestions for case studies, competitive insights or objection-handling scripts based on their ongoing deal conversations.
Integration with collaborative platforms is key in this process. By embedding AI in tools like Microsoft Teams or Trello, organizations ensure that knowledge is not only archived but actively disseminated during live discussions. This contextual delivery of information eliminates time wasted in searching and fosters a culture of collaboration.
Related Article: 5 Key Challenges of Knowledge Management (and How to Conquer Them)
Introducing GenAI Into Knowledge Management
Taking the first step in this journey may feel intimidating. But for many organizations, the brand name solutions can provide an excellent entry point, said Chris Brown, president of Intelygenz.
Still, effective, off-the-shelf solutions may rely on external data processing, leading some industries to face privacy or intellectual property risks. Brown said it’s therefore crucial to evaluate the terms of use and data-sharing policies of any third-party provider before sharing data.
"For highly regulated industries, custom AI agents are the better choice. Building an AI solution within the organization’s own infrastructure ensures that all data remains secure and proprietary," Brown said. Custom AI agent models, for instance, can be trained to meet brand guidelines and given access to proprietary content libraries or industry knowledge.
To ensure success with either off-the-shelf or custom AI solutions, organizations should follow these best practices:
- Continuous training: Update AI models regularly to align with brand guidelines and organizational changes.
- Set guardrails: Define clear parameters to ensure AI outputs meet brand values, legal requirements and user expectations.
- Maintain human oversight: Use AI for optimization but ensure human review and approval, particularly in regulated sectors.
- Track metrics: Monitor engagement, accuracy and ROI to evaluate AI tool effectiveness.
"Generative AI is not about replacing experts; It’s about amplifying their impact," Brown said. "Whether you’re streamlining knowledge management or amplifying your brand’s reach, the key is to use the AI solution to meet your business goals and bring value to your organization."
Regardless of the approach, Elisa Montanari, head of organic growth at Wrike, stressed the importance of human oversight to monitor the content in your knowledge bases. No matter how trustworthy your AI tools are, they all have the potential for bias and inaccuracy because no system is built flawlessly, she said.
While AI can make it easier to build and scale knowledge bases without massive teams, you still need some people to steer the ship, monitor quality and ensure your knowledge base stays accurate and on-brand.
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It is also important to understand the company's objectives. Originality matters if users are building content to draw an audience, so AI might take more of a backseat, Montanari said. But for those building a knowledge base for customer support to reference, for instance, AI can help. Know your goals and determine where the value comes from; Sometimes, the human touch matters more than others.
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Knowledge Managers Play a Pivotal Role
Knowledge managers are playing an ever-evolving role in the age of generative AI, and their contributions are critical to harnessing AI's potential while aligning it with organizational goals and values.
GenAI excels in producing "amazing outputs from content collections that are accurate, well-structured,and worded impeccably,” said Richards, but the foundation of such performance is a well-curated content collection. Knowledge managers remain essential for ensuring the availability of high-quality resources and for auditing AI systems to maintain desirable outcomes, particularly as AI tools evolve rapidly.
Content creation and curation will continue to be vital, Richards said, while prompt engineering, summarization and human wisdom remain pivotal to aligning AI outputs with brand, audience and situational requirements.
Greyling says AI is transforming knowledge managers from operational gatekeepers to "strategic enablers." In this new role, KMs will drive data governance, design intelligent systems and ensure alignment with organizational objectives. They will leverage ontological frameworks to define taxonomies and relationships, enabling AI to deliver deeper, more nuanced insights, in a role that may be called to expand to include setting ethical and operational standards, conducting audits and ensuring compliance.
In sum, the role of knowledge managers is pivotal and evolving. They act as curators, strategists and ethical overseers, ensuring that AI systems not only optimize content but also align with organizational goals, values and user needs in an increasingly AI-driven ecosystem.