We often use the words "complicated" and "complex" interchangeably, treating them as synonyms for anything we find tricky. However, this linguistic imprecision may be at the root of widespread innovation struggles. A global McKinsey study revealed that while 84% of decision-makers considered innovation critical for growth, only 6% were satisfied with their innovation outcomes. 

Such findings are commonplace and have led to expressions like “innovation theatre” and “innovation fatigue”. The most revealing aspect of the McKinsey study was that most executives couldn't pinpoint the underlying problem, leaving them unable to address it effectively. Buried in the report, though, a crucial insight came to light: Many were confusing correlation with causality, comparable to relying on a broken clock that's right twice a day. 

This confusion may be the core issue decision-makers face when innovating in today's ever-changing and converging world. The machine-management model, inherited from the industrial age, still dominates business practices and education. While effective for complicated problems in stable environments, it falls short in our rapidly changing landscape.

Consider how we still rely on categorisation models where frameworks precede data, such as 2x2 matrices. When conducting a SWOT analysis, we obey the rules and force data points into predefined quadrants and make decisions accordingly. We make sure the data fits in. It’s fast but it comes with a danger. We miss important and often subtle nuances and don’t realise them until it’s too late. As models seek to represent reality, they become less useful during periods of significant change.

This highlights one of the big dynamic shifts between the last century and the 21st-century model regarding predictability versus uncertainty. In the previous model, stability was the norm, which allowed organisations to build for efficiency and predictability. However, that stable environment has largely vanished. Today, we must rethink how to operate effectively in an uncertain landscape. 

Dave Snowden, founder of the Cynefin Company, has developed an award-winning framework that revolutionises how leaders approach decision-making.

Published in the Harvard Business Review, "A Leader's Framework for Decision Making" offers a powerful tool for understanding challenges and making contextually appropriate decisions. We want to make sense of the world so we can act in it. Thus, a wider perspective is needed to understand the context and consequence of any decision before focusing on how to make it. Hence, data precedes framework. 

As opposed to models, frameworks are a way of looking at reality, where the starting point is to distinguish whether we’re dealing with an ordered (with predictable outcomes) or unordered system (with unpredictable outcomes). This distinction is crucial for leaders navigating today's complex business landscape.  

Thereafter, we’re figuring out the context, or domain, where the problem resides. We must recognise that our decisions need to match the system (reality) we find ourselves in. The Cynefin Framework consists of five domains: (1) Clear and (2) Complicated in the ordered system, and (3) Complex and (4) Chaotic in the unordered. The fifth domain is called (5) Confused (or Aporetic), where we cannot define the problem without rethinking it. 

Sense-making helps us to cultivate an awareness of what is (really) complex — and what is not — and respond accordingly. We do not want to waste energy in overthinking the routine applicable for ordered systems — where complicated problems reside — nor make the complex ones fit into standard solutions. If you’ve tried to implement “best practices” and not experienced the promised results, you know what I’m talking about. 

Imitations without understanding will not work — period. Effective learning and application require more than just imitation; they demand a deeper understanding of the principles and context involved. This reasoning holds true for business, education, and technology alike. For instance, implementing a software solution without understanding your architecture can lead to integration issues or security vulnerabilities. 

Instead, we must recognise that there is more learning in failure than in success. Experiences form stronger memories; thus, valuable lessons are embedded in failure. Our brains pay more attention to them:

“All human cultures have developed forms that allow stories of failure to spread without attribution of blame. Avoidance of failure has greater evolutionary advantage than imitation of success. It follows that attempting to impose best practice systems is flying in the face of over a hundred thousand years of evolution that says it’s a bad thing.” 

The Greek origin of the word complex is entangled — constantly shifting and changing. Contrary to complicated systems, complex systems lack predetermined cause-and-effect relationships, resulting in unpredictable outputs.

However, in hindsight the causality might seem obvious. Just like your “Aha!” reaction after getting the answer to a riddle. However, the results emerge from networks of numerous interacting causes, and these cannot be individually distinguished or predicted up-front. 

The information is entangled in the network dynamics. With this knowledge, we immediately see how misguided it’s to apply a case-based approach (copy/paste) from others' success. As a decision-maker in a complex system you must acknowledge that you cannot control it. In a dance, you must respond to the present.  

For some, the unfortunate truth is that people, organisations, and financial markets are all complex systems. It’s the real world. Here, small inputs may result in disproportionate outcomes (i.e. “a small lump can overturn a big load”) and any intervention merges into a new situation. When everything is entangled with everything else, the only thing you know with certainty is that there will be unintended consequences.

In highly uncertain conditions, specifying a fixed destination is counterproductive. Instead, the focus should be on understanding the current situation and identifying possible next steps. As aptly put in Frozen II, "You must go on and do the next right thing.”  When navigating complex systems, look for the adjacent possible and base your actions on present realities rather than a desired future state. This approach allows for surprises through experimentation, unlike setting rigid goals, based on predictions, which narrow your focus. 

The decision model is probe — sense — respond. Take a proactive role, rather than hiring experts for the purpose of analysis. Based on your situational awareness search for, and test out, several ideas — small-scale safe-fail experiments (probes). We, as humans, have difficulty assessing situations objectively because we seek confirmation for decisions we've already made. Therefore, actively slow things down and stay alert because systems shift more easily on the edges than the core. 

Failure is an essential part of learning, so we need to move away from a fail-safe approach that focuses on getting everything right. Instead, we should adopt a safe-fail mindset, which involves taking small bets, with pragmatic and low risks that align with the complexities of the system and our desired direction. Our objective is to gently nudge the system toward new possibilities.  

We should prioritise designing various fast approaches to problems rather than fixating on finding the "best" solution. Through experimentation, we can discover what is truly possible. It's also important to collaborate with employees who can quickly identify emerging trends and surprises because every action will create a reaction.