CHANGE TALK

Shifting Focus: Organization Design to Generate Expertise

Organization design has long been recognized as critical to driving business impact. This trend has prompted many companies to establish internal functions, from Organizational Design teams to expanding C-suite roles, such as Chief Transformation Officer. Additionally, reorganizations are on the rise. McKinsey data shows that nearly 60% of executives have experienced organizational redesigns within the past two years, and 25% more companies are redesigning three or more times as often as they did just a few years ago. These developments evidence the growing number of businesses embracing organizational design to enhance adaptability and ensure business continuity. While this is a promising trend, the challenge now lies in moving beyond approaches that might necessitate frequent redesigns, toward creating systems that naturally regenerate and evolve dynamically.

Static vs. Adaptive Organization Design: A Tale of Two Approaches

 

Organizational design can broadly be categorized into static, traditional approaches and adaptive, generative approaches. Each has its strengths and limitations, and understanding these differences is crucial for organizations aiming to thrive in today’s complex environment. The choice of the right organizational design approach does not only depend on an organization’s size and business objectives but also on how in-house expertise is perceived.

 

 

Figure 1: Static Organization Designs

Figure 1 shows how static organizational design approaches prioritize stability but lack the flexibility to adapt naturally. This often leads to frequent top-down reorganizations as organizations struggle to respond to changing realities and emergent needs. However, when designed well, a static organization design can provide excellent support systems and people operations.

 

Static, Traditional Organization Design Approaches: In traditional organization design, the expertise required to support targeted business outcomes is often viewed as a static asset or "human resource." This resource is mapped onto structured systems, such as roles, workflows, cultures, and organizational connections, to ensure optimal conditions for maintaining current expertise and attracting similar talent.

This approach emphasizes stability and predictability, aligning current skills and knowledge with predefined goals. Design principles under this model are typically based on assumptions about the expertise needed today and in the immediate future, building systems that preserve current expertise while ideally serving as magnets for future talent.

 

Static organizational design approaches have led to significant improvements in many companies. However, in fast-changing landscapes with emergent problem-solving demands, static designs often fall short. By prioritizing the maintenance of expertise over fostering new capabilities, these systems may inadvertently hinder development, particularly in units tasked with responding to emergent challenges or innovation.

The latest Boeing incidents may serve as an example. As discussed in the MIT Sloan article, "How Not to Organize In-House Experts,"—reliance on static systems may have left the company vulnerable to blind spots ultimately failing to address glaring issues decisively.

 

 

 

Figure 2. Adaptive Organization Design & Generative Learning.

Figure 2 illustrates how expertise-centric focus clusters can self-organize within an adaptive system, generating both continuous learning and their own evolution.

 

Adaptive and Generative Organization Design Approaches: Adaptive and generative organization designs take a fundamentally different approach. Rather than treating expertise as a static resource, they create systems that allow expertise to emerge, evolve, and redefine itself over time.

 

These designs prioritize flexibility and learning, fostering immersive environments where individuals and teams can adapt to shifting demands. By enabling natural feedback, self-organized collaboration, and goal-oriented experimentation, adaptive designs ensure expertise evolves to meet emerging challenges and opportunities.

 

Instead of relying on predefined roles and structures, adaptive designs drive innovation and resilience by:

  • Enabling self-organization and autonomy.
  • Creating spaces for co-creation and collaborative problem-solving.
  • Embedding generative learning principles to sustain continuous growth.

This represents a fundamentally different way of structuring. However, it’s not the only pivotal difference. Interestingly, the way expertise is viewed in adaptive systems changes significantly, too.

 

Recent research has shown that in adaptive systems, expertise is not only highly valued for success but also consistently generated, naturally assessed, and dynamically sidelined—or even made obsolete—through dynamic processes arising from the interplay of organizational practices, work environments, and human dynamics.

The natural creation of new expertise emerges organically as individuals tackle specialized tasks, experiment, and learn from experience. Notably, these dynamics are driven more by the nature of the work than by intentional organizational efforts. As a natural and regular process, expertise is dynamically assessed through everyday demands, evolving as contributions are observed and categorized. Meanwhile, some forms of expertise may fade or become undervalued when they no longer align with visible priorities or celebrated outcomes—a subtle shift driven by changing project demands rather than deliberate exclusion. This constant generation, evolution, and de-prioritization of expertise is what makes emergent crisis responses more effective.

 

 

 

What’s the Secret Sauce?

 

Adopting an adaptive organization design holds great promise for fostering emergent problem resolution, radical innovation, and generative expertise. This often fuels the belief that the right structure alone holds the key, frequently leading to the search for a specific formula that will supposedly make everything fall into place. However, organizations are far too complex to be improved by a one-size-fits-all approach. Without tailored, collaborative design, frameworks risk losing their intended purpose. For example, in many companies AGILE has devolved into a rigid system, prioritizing rules and procedures over fostering true agility and a mindset of continuous improvement. To truly succeed, adaptive designs must go beyond structural adjustments and address the individual needs of the company and its specific teams. Additionally, adaptive structures must be enriched with generative learning principles.

 

Embedding Emergent Learning in Adaptive Design

This underscores a key insight: Adaptive organization designs are only as effective as the learning systems they evoke. By aligning adaptive design with emergent learning principles, companies can cultivate self-regulated environments or sub-environments where expertise evolves organically, self-organization thrives, and employees are empowered to naturally co-create solutions. Below are foundational principles to guide this integration:

 

 
1.  Align Organization Design with Desired Outcomes
  • Enable Autonomy and Self-Organization in Cluster Groups: Enable teams to self-organize around goals as well as emergent challenges.
  • Connect Cluster Groups to the Umbrella Organization: Work on supportive connections between the cluster groups and the traditional umbrella organization to ensure the right level of support, alignment, and collaboration.
  • Set Ambitious Micro and Macro Goals: Establish ambitious micro and macro goals to drive both immediate progress and long-term strategic impact.
  • Spark Creative Tension: Balance competing priorities to promote innovation and emergent problem-solving, emphasizing generative solutions over static alignment.
2.  Instill Generative Learning
  • Decentralize Knowledge Creation and Assessment: Create circumstances under which sharing expertise and assessing contributions naturally occurs through emergent and self-regulated processes.
  • Promote Informal Networks and Peer Learning: Foster generative knowledge sharing through informal connections and collaborative relationships.
  •  Foster Experimentation and Iteration: Embed a 'fail fast' mindset and processes that empower self-organized groups to embrace rapid learning through experimentation.
  • Enable Contextual Learning: Create learning opportunities embedded directly in the work environment, allowing real-time challenges to drive emergent understanding.
  • Allow for Continuous Natural Adaptation: Ensure that the cluster groups naturally self-evaluate and adjust strategies based on self-organized insights and changing circumstances, keeping systems generative and responsive.
3. Leverage Technology and Data as Enablers
  • Use data and technology to support self-regulated collaboration, inclusivity, and communication.
4. Embrace Experimentation and Feedback Loops
  • Build a culture that values trial-and-error, allowing iterative cycles to dynamically refine solutions and reinforce generative learning.
5. Measure and Evolve Through Emergent Outcomes
  • Focus on outcomes that arise organically rather than rigidly predefined goals, assessing success based on how well self-regulated systems adapt and learn.
  • Use real-time feedback to drive ongoing evolution, ensuring systems remain responsive, emergent, and generative in addressing future challenges.

By enriching tailored adaptive designs with these principles, organizations can ensure that the systems stay as dynamic as possible.

 

The Best of Two Worlds?

 

Of course, not every function within an organization can be fully adaptive. This raises the question: Is it possible to have the best of both worlds? The answer lies in carefully designing how highly expertise-driven response groups, which thrive in adaptive systems, interconnect with a more traditional, fully supportive umbrella organization. By aligning the flexibility of adaptive teams with the stability of a more traditional structure, organizations can create a dynamic interplay that leverages the strengths of both approaches.

 

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