“Managers don’t solve simple, isolated problems; they manage messes.” – Russell L. Ackoff
As an electronic engineer by training and passion, I often approach challenges within this field with established mathematical principles and known solutions. Complex issues can typically be broken down into simpler components, a method known as reductive thinking.
However, wouldn’t it be great if our leadership and technology challenges were so straightforward? In reality, they tend to be intricate and ever-changing, influenced by numerous interdependencies and contradictory or incomplete information. This complexity is why reductive thinking fails to address multifaceted issues like climate change or social inequalities. This is mirrored in many corporate strategies, which are often emergent, evolving as companies learn more about their environments and needing to adapt to shifts caused by their own actions.
So, why do we consistently apply reductive thinking to complex business problems? I believe it’s due to the seductive simplicity of arriving at an answer. I have seen this many times, such as the notion that a multinational corporation should implement a strategy globally just once, rather than repeatedly. This approach neglects to consider critical factors like agility, regional differences, political capital, or vendor capabilities—and it fails to account for the changes that the proposed strategy may impose on the environment it aims to influence. Similar arguments arise regarding complete abstraction from specific cloud services at significant costs to mitigate perceived risks, a notion effectively challenged in this insightful blog post by Thomas Blood.
As technology and human interaction become increasingly intertwined, the risk of program failures grows. How do we tackle this challenge? Here are four effective strategies I’ve observed.
Distinguish Between Complicated and Messy Issues
Investing time to thoroughly understand a problem or opportunity is rarely futile. Yet, in our fast-paced world, we often skim the surface. In our eagerness to find solutions, we confuse messy issues with complicated ones, the latter of which can be addressed by breaking them down into simpler problems. Two of my favorite tools for navigating this are:
- Systems Mapping: This technique aids in identifying the various stakeholders and variables involved in a problem and their interconnections. Collaborating with a professional facilitator and a diverse group within your organization during this exercise reveals new assumptions and interdependencies.
- De Bono’s “Six Thinking Hats”: This method encourages participants to view the same challenge from different angles.
Both approaches help align teams with a shared understanding of the issue, enabling them to develop suitable interventions.
Be Curious
Stay inquisitive. The allure of buzzwords—digital transformation, data-driven, AI-enabled, and agile—often conceals our lack of understanding of their true implications, leading to ineffective actions. The best leaders I know ask profound questions on topics where others might shy away from revealing their ignorance. The ability to inquire and learn should be a key consideration when selecting leaders; focusing solely on past experience can be a poor predictor of future success.
Curiosity necessitates humility, a trait that Mark Schwartz recently emphasized. The Amazon leadership principle “Leaders are right a lot” doesn’t promote omniscience; instead, it urges leaders to “seek diverse perspectives and work to disconfirm their beliefs.” How refreshing to encourage leaders to learn and embrace the possibility of being wrong, ultimately improving the accuracy of their judgments!
Frame Hypotheses, Not Requirements
When confronting complex problems, express your beliefs about potential solutions as hypotheses to be tested rather than rigid requirements to be fulfilled. By presenting a requirement, we assert knowledge of the solution; when it fails, debates ensue over whether it was specified or executed correctly. In contrast, a hypothesis acknowledges that while we believe we understand what is required, it must be verified through experimentation with a mindset geared toward learning. This challenge is central to many companies’ struggles in becoming data-driven. People are often inclined to seek validation for their positions rather than pursue truths that may contradict their beliefs. In the realm of machine learning, it’s vital to start from a (null) hypothesis that you can comfortably disprove, rather than solely attempting to prove why a new idea is correct.
Prioritize Future Options Over Immediate Solutions
Our predictive abilities regarding the future are notoriously unreliable. In an environment that often equates leadership with decisiveness, there is pressure to provide quick solutions. Historical events show that this mindset can limit options that might be more suitable as we gain further insight into the problem and can lead to confirmation bias.
Adopting strategies from financial, natural resources, and military sectors, such as scenario planning, enables us to pursue multiple future avenues rather than committing to a single path. This approach is especially crucial in fast-evolving areas like machine learning, where technology’s rapid evolution can have profound implications for society, businesses, and individuals. While this platform-based approach may not be suitable for every situation, it allows for the exploration of future possibilities that have yet to be considered.
All of this is facilitated by the cloud and contemporary organizational philosophies, such as agile methodologies. Together, they empower organizations to experiment, learn, and adapt swiftly to environmental changes while exploring diverse technology options cost-effectively, postponing decisions until they are genuinely needed and informed. This may be an uncomfortable approach for leaders conditioned to seek swift answers, but it is a practical one that fosters open-mindedness and agility.
So, contrary to what you might tell your children, embrace your messiness! For further insights on this topic, check out this engaging blog post. Additionally, for authoritative perspectives, visit this resource. And for practical tips, this is an excellent guide.
Jordan Smith
Email: jordansmith@amazon.com
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