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KM as the Connector: Enabling Cross-Functional Business Transformation

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Organizations are investing heavily in transformation—adopting new technologies, redesigning processes, and preparing for a rapidly changing workforce. Yet many of these efforts are sputtering out because organizations struggle to translate what they learn into practices that can be shared, applied, and scaled. Even as teams solve problems and learn how to work with new technologies such as generative and agentic AI, that knowledge is not consistently captured, shared, or applied to improve how work gets done.

The stakes for getting transformation right are only increasing. Among other challenges, organizations today are navigating rapid technological change, dealing with the growing pressure for operational resilience amid disruption, and experiencing rising expectations for data-driven decision making. These priorities cut across functions, but organizations will struggle to deliver on them without consistent ways to capture, share, and apply knowledge.

This is where KM leaders have a clear opportunity to play a more strategic role. At its core, KM brings people, processes, content, and technology together to operationalize knowledge across the business.

To make this work in practice, organizations need to do the following:

♦ Enable knowledge to move across functions and silos.
♦ Embed knowledge into daily work so it can be applied in the moment of need.
♦ Establish approaches to sustain critical knowledge across time.

These capabilities are what allow organizations to put their knowledge to work in the service of transformation. The sections that follow show how KM delivers this transformation.

Enterprise Transformation Requirements

Enterprise transformation rarely happens within the boundaries of a single function. The priorities that matter most to organizations, such as workforce development, supply chain resilience, and operational efficiency, cut across multiple parts of the business and depend on coordination among them.

For example, developing workforce capability is not just an HR initiative. It depends on coordination between HR, learning teams, and operational leaders to map existing knowledge, define skills, develop training, and apply those capabilities in daily work.

As organizations execute their transformation priorities, they generate knowl- edge at every step: new and more effective ways to carry out work, lessons learned about what worked or what failed, and the context behind important decisions. This knowledge is created within specific functions, projects, and teams, but its value increases when it can be applied beyond them.

This is precisely where many organizations struggle. They invest heavily in transformation initiatives, but the knowledge created through that work remains confined to the teams that produced it.

What Gets in the Way of Knowledge Flow

APQC research shows that several persistent barriers, including these four, prevent knowledge from flowing effectively across functions:

♦ Weak mechanisms for capturing and sharing lessons learned
♦ Fragmented data and systems
♦ Inconsistent terminology and process definitions
♦ Limited visibility into expertise across departments

These challenges are widespread. For example, almost all respondents (92%) to APQC’s Great Retirement survey (apqc.org/resource-library/resource-listing/great-retirement-knowledge-loss-ai-and-workforce-shift-survey) report that they do not consistently capture knowledge. This means that much of what teams learn never becomes accessible beyond the moment it was created. Even when organizations do capture knowledge, they often rely on manual approaches that are time-intensive and difficult to scale.

At the same time, 50% of organizations report that their digital systems are only partially integrated across functions, leaving gaps that make it difficult to connect knowledge across teams and support end-to-end workflows. Challenges such as these limit an organization’s ability to move knowledge across an enterprise.

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