When we talk about artificial intelligence in IT service management, the conversation often gravitates toward chatbots handling service desk tickets or automated incident classification. But there’s a quieter revolution happening in the background — one that’s fundamentally changing how we capture, organize and leverage organizational knowledge within the ITIL framework. This transformation is occurring in an area that many organizations struggle with: the effective management of tacit knowledge and the continuous improvement of service management practices.

The Knowledge Paradox in Modern ITSM

Every IT service management team faces the same challenge: their most valuable insights often exist in the minds of experienced practitioners, scattered across email threads, buried in meeting notes or trapped in legacy documentation that becomes outdated the moment it’s written. While ITIL 4’s current Knowledge Management practice guide explicitly recognizes generative AI as a supporting technology and emphasizes AI-based capabilities for knowledge management, many organizations still struggle with translating this recognition into effective implementation within ITIL 4’s “Obtain/Build” and “Engage” value chain activities.

The challenge isn’t technological capability — it’s organizational readiness. Senior engineers rarely have time to structure their knowledge in ways that AI systems can effectively leverage. When organizations do implement AI-enhanced knowledge management, they often focus on basic search and retrieval functions rather than the more sophisticated applications that can truly transform service delivery. More critically, much of what makes an expert valuable isn’t their explicit knowledge — it’s their ability to recognize patterns, make connections between seemingly unrelated incidents and apply contextual judgment that requires careful consideration when integrating with AI capabilities.

Where generative AI enters the picture

Generative AI tools are beginning to address this knowledge management paradox in fascinating ways that align closely with ITIL 4’s emphasis on value co-creation and continuous improvement. Rather than replacing human expertise, these technologies are becoming sophisticated amplifiers of institutional knowledge, working within the “Improve” value chain activity to enhance how organizations learn from their service management experiences.

The applications extend far beyond incident management. Consider the breadth of AI-enhanced knowledge management capabilities now emerging across service management practices:
Intelligent knowledge discovery and curation: AI tools can automatically search knowledge bases using natural language queries, making expert insights more accessible to service desk analysts handling diverse request types. This supports both service request fulfillment and problem management by connecting current issues with historical solutions.

Automated knowledge lifecycle management: Rather than relying on manual processes, AI systems can detect obsolete knowledge articles by analyzing usage patterns, success rates and alignment with current service configurations. This addresses the persistent challenge of knowledge decay in rapidly evolving technical environments.

Policy and standards alignment: AI tools can automatically validate knowledge articles by checking alignment with approved corporate policies and regulatory requirements, ensuring that service management practices remain compliant across different service domains.

Knowledge clarity and accessibility: AI can automatically rephrase technical knowledge articles for different audiences — creating simplified versions for end users while maintaining detailed technical versions for specialist teams. This supports ITIL 4’s emphasis on value co-creation by making knowledge accessible to all stakeholders.

Proactive Knowledge Creation: Perhaps most significantly, AI can automatically draft knowledge articles based on existing incident, problem or service request records, identifying patterns in successful resolutions and translating them into reusable guidance.

Knowledge gap analysis: AI-assisted analysis can highlight incidents, service requests or events that could have been processed more efficiently if relevant knowledge articles were available and up to date, providing valuable input for service improvement planning.

These capabilities transform knowledge management from a reactive documentation exercise into a proactive intelligence function that supports all ITIL 4 value chain activities.

The double-edged nature of AI-generated knowledge

However, this technological capability brings both opportunities and risks that service management leaders must carefully consider. On the positive side, generative AI can democratize access to expert-level analysis. Junior team members can leverage AI tools to generate sophisticated troubleshooting guides, drawing from the collective experience embedded in organizational data. This supports ITIL 4’s emphasis on collaboration and shared accountability across the service value system.

The technology also excels at maintaining knowledge currency. Unlike traditional documentation that requires manual updates, AI-generated knowledge bases can continuously evolve as new incidents are resolved and patterns emerge. This addresses one of the most persistent challenges in service management: keeping procedural knowledge aligned with rapidly changing technical environments.

But these benefits come with significant caveats. Generative AI tools can create compelling documentation about processes or solutions that are incorrect, outdated, or contextually inappropriate. Unlike human experts who understand the limitations of their knowledge, AI systems can present flawed information with the same confidence as accurate insights. This creates a potential risk to service quality if teams begin relying on AI-generated knowledge without proper validation mechanisms.

There’s also the question of knowledge ownership and accountability. When an AI tool generates a service procedure or troubleshooting guide, who takes responsibility for its accuracy? How do organizations ensure that AI-enhanced knowledge management doesn’t create a false sense of expertise or diminish the value placed on human judgment and experience?

Balancing human expertise with AI amplification

The most promising approaches to integrating generative AI into ITSM knowledge management recognize that technology should enhance rather than replace human expertise. This means designing workflows where AI tools generate initial analyses or documentation drafts that experienced practitioners then review, refine and validate.

Within ITIL 4’s “Design and Transition” value chain activity, this might mean using AI to create initial service documentation based on technical specifications and historical incident patterns, while requiring human experts to review and approve the content before it becomes operational knowledge. For the “Deliver and Support” activity, AI could generate potential solution paths for complex incidents, but human analysts would evaluate these suggestions against their understanding of business context and risk tolerance.

This collaborative approach addresses several concerns simultaneously. It leverages AI’s ability to process vast amounts of information while preserving human judgment about what matters most in specific contexts. It also creates opportunities for knowledge transfer, as junior staff can learn from AI-generated analyses while working alongside experienced mentors who provide contextual guidance.

The Governance Challenge

Perhaps the most underexplored aspect of generative AI in service management is how it changes governance requirements within ITIL 4’s guiding principles. The principle of “focus on value” becomes more complex when AI tools can generate seemingly valuable insights that may or may not align with actual business needs. Organizations need new frameworks for evaluating AI-generated knowledge and ensuring it supports rather than distracts from value creation.

The principle of “progress iteratively with feedback” takes on new meaning when dealing with AI systems that can generate solutions faster than human teams can properly evaluate them. Service management leaders must develop feedback loops that capture not just whether AI-generated knowledge works, but whether it enhances or diminishes overall service quality and team capability.

Similarly, “collaborate and promote visibility” becomes more challenging when AI tools can create knowledge that appears comprehensive but may lack the contextual understanding that comes from human collaboration. Organizations must balance the efficiency of AI-generated documentation with the relationship-building and shared understanding that comes from human knowledge-sharing processes.

Looking forward: questions worth considering

As generative AI becomes more prevalent in service management tooling, several questions deserve careful consideration. How do we maintain the human connections and trust relationships that underpin effective service delivery while leveraging AI’s analytical capabilities? What new skills do service management practitioners need to effectively collaborate with AI tools while maintaining critical thinking about AI-generated outputs?

Perhaps most importantly, how do we ensure that AI-enhanced knowledge management supports rather than undermines the continuous learning culture that ITIL 4 emphasizes? The goal isn’t to create organizations that depend on AI for knowledge, but rather teams that use AI tools to become more reflective, more analytical, and more effective at learning from their experiences.

The integration of generative AI into service management knowledge practices represents both an opportunity and a responsibility. Done thoughtfully, it can help organizations capture and leverage their collective expertise in ways that weren’t previously possible. Done carelessly, it risks creating a false sense of knowledge completeness while undermining the human judgment and contextual understanding that truly effective service management requires.

The organizations that navigate this transition most successfully will likely be those that view generative AI not as a replacement for human expertise, but as a sophisticated tool for amplifying human intelligence within the collaborative, value-focused framework that ITIL provides.

For more insights and expert guidance, explore the ITIL AI Governance White Paper.