Solving Organizational Intelligence

You are either building a learning organization or losing to someone who is… -Andrew Clay Shafer

We exist in a complex technology landscape where adaptability isn’t just a competitive advantage, but perhaps the key to survival. The difference between successfully surfing the evolution of Agile, DevOps, Cloud, and now GenAI, or feeling battered by the waves, depends on the individual and organizational capacity to respond.

In 1967 Harold L. Wilensky coined the term ‘Organizational Intelligence’ with a book examining structures and processes that create an organizational tendency to become victims to the dominant beliefs, resisting and silencing warning voices that challenge assumptions. The term now refers to an organization’s ability to make sense of complex situations and act effectively; to discover, interpret and act upon relevant signals in the environment.

I have tried to help people adopt new technologies and methodologies, Agile, DevOps, Cloud, and Kubernetes for many years. Many organizations—staffed by incredibly smart and capable people—struggle to receive new information and change responsively. I grew frustrated watching organizations that appeared the same get dramatically different results after similar exposure to and training on the new methods and tools. The difference in results was a function of individual and organizational capability to react to the stimulus with a change in behaviors.

Over ten years ago, a chance encounter with a dissertation (on the 7 Dimensions of a Learning Organization in Chinese Factories, of all things) put me into the research on learning organizations (and organizational learning, which exists as a similar body of research in a world that only makes sense to academics trying to distinguish between the indistinguishable). At the time, many enterprise organizations complained that they didn’t have enough talent to accomplish their digital initiatives, and tech companies were competing in a ‘war for talent’. Some of my thoughts at the time were recorded for posterity.

Now we’re in a new transition as organizations and individuals adjust to the rapid evolution of generative AI. What has become clear now is that a lot will change. What is not clear, yet, is who will benefit, but there will be winners and losers. I contend the individuals and organizations’ who successfully synthesize new information into revisiting beliefs and behaviors will have a better chance.

The Cost of Not Learning

No one is guaranteed the success of their mission or business model. Value migrates. Companies that once dominated—think of Blockbuster or Kodak—failed to adapt to changing landscapes. This isn’t just true for large organizations. Those are just the companies big enough that we know, where failure stands in contrast to prior success. Startups shut down every year for similar reasons, the world changed, and they didn’t. Or what worked when there was a small team didn’t scale to meet the larger opportunity. The recurring theme: What worked yesterday might not work tomorrow.

What is an investment in learning? What signals are worth tracking? Internal and external signals? Can there be too much? What triggers action instead of just collecting information?

Answering these specific questions without context is a fool’s errand, but here are principles that I believe apply across all the flavors of research on organizational learning and performance:

• Promote Psychological Safety: Without psychological safety, learning becomes nearly impossible. In high-performing environments, failure is seen as an investment in improving the system. The most pathological organizations actively avoid language that suggests experimentation or even any lack of certainty.
• Embrace a Growth Mindset: If people believe they cannot develop new skills to improve, then that becomes a self-fulfilling prophecy. When the challenge of developing new skills and ideas represents growth opportunities, that growth becomes possible.
• Emphasize Holistic Systems: Local optimizations can be globally sub-optimal, especially without good feedback or aligned incentives. Making the organization aware of interconnected dependencies, teams from different functions can better align incentives and outcomes.
• Respond to Internal and External Signals: Optimizing the internal performance without reacting to external changes in customers, economies, and technologies will not optimize the outcome. Often, the most significant opportunities come from changes in the ecosystem.
• Make Learning A Positive Outcome: The work should improve by doing the work. Recognizing issues are learning opportunities. Create a forum to share issues and praise the messenger who provides information and insights into the system.
• Take Action: Information does not always produce action, but action always produces information. Collecting information for information’s sake can be insightful, but informed action is the magic sauce.
• Celebrate Incremental Progress: Developing ‘Organizational Intelligence’ is not a project or event, but an accumulation of improvements. Celebrate and reward small wins to build momentum and keep everyone motivated.
• Play the Long Game: Real change takes time, and there is tension between getting something done today and making that thing better for tomorrow. Many organizations are hesitant to sacrifice short-term productivity for long-term adaptability until an acute crisis threatens their existence.

Conclusion: Learning is the Only Sustainable Advantage

As technology continues to evolve, organizations that fail to adapt will find themselves left behind, trying to compete in a game that might no longer exist. By addressing structural barriers to learning, organizational responsiveness transforms from merely an aspiration to a vital competitive differentiator in the game of survival. Not every scenario is a crisis, and not every crisis is existential, but then how do we know? The best time to become a learning organization was years ago. The second best time is now. Let’s go.