Knowledge Management Strategy and Program Development FAQ

Whether you are launching a new KM initiative, building support for an existing program, or looking to mature your organization's approach, developing a successful KM strategy requires thoughtful planning, strong stakeholder engagement, and continuous adaptation. This FAQ addresses common questions about KM program development, governance, organizational alignment, and long-term success, along with considerations for integrating emerging technologies such as artificial intelligence (AI) into KM strategies and practices.

How Do I Design, Build and Manage a KM Program?

Where should the KM Function Report Within the Organization?

There is no single right answer when it comes to where KM should report. In large organizations, KM often reports to IT, strategy, operations, organizational learning, or HR. In small and mid-size organizations, it often reports directly to the c-suite. Ideally, KM should be in a position that secures high visibility and a strong executive champion. APQC’s research finds that KM programs that have a direct tie to senior leadership are more effective and have greater rates of participation.

Of course, many KM programs can’t pick where they sit—but, they do have a say in how they build relationships across the organization. APQC recommends that new KM programs find a group of mid- and high-level leaders who will act as a KM steering or advisory committee to provide guidance and cross-functional support for KM.

How Do I Create a Business Case for A KM Role or Department?

First, you need to assess the knowledge needs within your organization and develop a relevant scope. APQC recommends interviewing executives to understand the big-picture challenges, goals, and opportunities they’re focused on. Then, create a business case that articulates exactly how KM can support these priorities. This is the best way to gain leaders’ buy-in and help them see KM as a strategic discipline.

If My Organization Doesn’t Have a Formal KM Approach, Is There Anything I Can Do at The Grassroots Level?

Absolutely! In fact, some of the most impactful formal KM programs begin as grassroots efforts. One of the best ways to get started is to find a knowledge-related problem to solve. For example, if you have some new team members who keep asking the same questions over and over, you might start a wiki or create a community of practice for your discipline. If you have an expert who’s nearing retirement or is fielding the same questions repeatedly, you might take steps to capture their knowledge by interviewing them or having them create some learning resources. You can get started with these, and most other KM approaches, without buying any new tools.

It’s fine to start local, but don’t start alone. Work with peers and colleagues to define business norms and processes for any early KM effort you’re starting. Find out if other teams or departments are working on similar initiatives so you can collaborate and learn from one another. You need to define the how and why of your KM effort and set some measures in place so you can determine whether it is fulfilling its intended purpose. Once the effort gains momentum, you’ll have a success story for making the case for functional- or enterprise-level KM.

How Do You Build KM Into The Way People Work?

When you design tools and approaches for knowledge sharing, make sure they fit into how employees already work rather than asking them to completely change how they operate. APQC calls this “in the flow” and encourages you to make KM easy and intuitive by:

• Embed solutions in the places problems emerge: Put KM solutions into the systems and situations where employees need help. Embedding knowledge directly where work occurs reduces friction, increases adoption, and makes it more likely that employees will find and apply knowledge when they need it most.
• Build KM into the structures that define and guide work: Embed KM into project steps and key business processes so it’s just a step in the regular workflow. When KM activities are integrated into how work gets done, employees are more likely to participate consistently and view knowledge sharing as part of their responsibilities rather than an additional task.
• Ensure KM roles are present in the business: When you have KM people (whether they’re full-time or part-time staff, or simply KM champions and super users) in the business, it’s a lot easier to provide high-touch support to those who need it and secure on-the-ground feedback to continuously improve KM tools and approaches.

That said, there will always be some KM activities that require people to step out of their workflow to document, share, and validate knowledge. APQC calls these “above the flow” activities. There are many ways to incentivize participation in these activities—gamification, gifts, thankyous, and financial compensation, to name a few—but the best way is to integrate them into performance expectations and career paths. Organizations should also consider how emerging technologies such as AI-powered search, knowledge assistants, and expertise-location tools support strategic KM objectives.

What Should KM Focus on When an Organization Is Going Through a Merger or Acquisition (M&A)?

Prior to an M&A, KM can help source knowledge about the potential target, identify knowledge-related risks and opportunities associated with the deal, and build mitigation plans. During a restructuring, KM may be limited in terms of what it can do. But once collaboration is allowed, use this moment to drive immediate value for the newly formed organization.

In instances where both component organizations have established KM programs, the stronger KM program (or that of the larger component) should reach out to the other to start integrating KM efforts and bringing employees into the combined KM environment. Consider rebranding the merged KM program so that one side does not feel subsumed by the other.

If only one organization has a KM program, look for ways to assist newly merged employees without overwhelming them. For example, communities can be a safe space for employees to get to know each other. Knowledge maps can help everyone understand how work gets done and how content is structured. Even a simple glossary of terms and acronyms can be a huge benefit to employees in such disruptive and stressful moments.

For more on the KM skills and techniques that are most critical in this scenario, see How KM Programs Respond to Mergers & Acquisitions.

What Practices Contribute to Long-Term KM Success?

Successful KM programs tend to share several common characteristics:

1. Develop a clear KM strategy: KM programs are most successful when they have a strategy that aligns with business priorities and delivers measurable value. A well-defined strategy helps organizations focus their efforts, secure stakeholder support, prioritize investments, and establish a roadmap for achieving KM objectives.
2. Continuously evolve your approach: KM strategies should adapt as business needs, workforce expectations, and technologies change. Regularly reviewing priorities, gathering feedback, monitoring results, and incorporating lessons learned can help ensure that KM programs remain relevant and continue to meet organizational needs.
3. Set realistic goals and expectations: Successful KM programs balance ambition with available resources and recognize the time and effort required to capture, organize, and share knowledge. Establishing achievable milestones and communicating realistic timelines can help maintain momentum, demonstrate progress, and build long-term support for KM initiatives.
4. Balance technology, people, and process: Technology is an important enabler, but sustainable KM success depends equally on governance, culture, and well-designed work practices. Modern KM technology ecosystems increasingly incorporate AI capabilities such as semantic search, automated tagging, content summarization, expertise discovery, and conversational knowledge assistants.
    

How Does AI Affect KM Strategy and Program Development?

As organizations adopt generative AI and other AI-enabled technologies, KM strategies are evolving to support new ways of creating, accessing, sharing, and applying knowledge. Effective KM remains essential because AI systems depend on trusted, well-governed information and knowledge sources to deliver reliable results.

How Can Organizations Prepare Their Knowledge for AI?

Organizations can prepare for AI by improving content quality, establishing governance, standardizing metadata, reducing redundant content, and ensuring that critical knowledge is accessible and current. These practices improve both human knowledge reuse and AI performance.

How Should AI Influence a KM Strategy?

Organizations should evaluate how AI supports their KM goals, including knowledge capture, search, expertise discovery, learning, and reuse. Rather than creating separate KM and AI strategies, many organizations are integrating AI considerations into existing KM strategies and governance models.

What Role Does KM play in Successful AI initiatives?

AI systems depend on accessible, accurate, and well-governed knowledge. Organizations with mature KM practices are often better positioned to deploy AI effectively because they already have processes for managing content quality, metadata, governance, and knowledge sharing.

Should KM Teams Be Responsible for AI?

KM teams may not own enterprise AI programs, but they often play an important role in ensuring that knowledge assets are organized, governed, and accessible for AI-enabled tools.

How Should KM Governance Evolve for AI?

As organizations adopt AI-enabled tools, KM governance becomes even more important. Effective governance helps ensure that both employees and AI systems can access accurate, trustworthy, and relevant knowledge while reducing risks related to quality, security, and compliance.

• Content quality: AI systems can only provide reliable outputs when they are built on high-quality knowledge. Organizations should establish processes for maintaining accurate, current, complete, and relevant content.

• Validation: Knowledge used by AI should be reviewed and validated regularly. Organizations should define who is responsible for verifying content and determining whether AI-generated outputs require human review before being shared or acted upon.

• Ownership: Clear ownership helps ensure accountability for knowledge assets. Organizations should identify content owners, subject matter experts, and governance roles responsible for maintaining knowledge and addressing issues when they arise.

• Security: Not all knowledge should be accessible to every employee or AI system. Governance frameworks should include controls for protecting confidential, proprietary, and sensitive information while ensuring that employees can still access the knowledge they need.

• Responsible AI: Organizations should establish guidelines for the ethical and appropriate use of AI, including transparency, fairness, privacy, and compliance with organizational policies and regulatory requirements.

• Human oversight: AI can support knowledge discovery, creation, and reuse, but it should not replace human judgment. Organizations should define when human review, approval, or intervention is required, particularly for high-risk decisions or critical knowledge assets.

One of the hallmarks of successful KM programs is a commitment to continuous learning and improvement. By benchmarking with peers, exploring APQC research, and staying connected to the broader KM community, organizations can identify emerging opportunities, adapt to changing needs, and continuously strengthen their KM capabilities.

See More KM FAQs

• Knowledge Management FAQ
• Communities & Networks FAQ
• Knowledge Retention & Transfer FAQ
• KM Engagement & Culture FAQ
• KM Partnerships FAQ