The Imperfectionists: Strategic Mindsets for Uncertain Times

by Robert McLean, Charles Conn

The world is changing faster and faster, with increasing uncertainty and threat of disruption in every business and nonprofit segment. Conventional approaches to strategy development and problem solving no longer work―there is no stable industry or market equilibrium structure that we will return to “when change abates.” Most company planning processes are fantasy; market conditions are changing too quickly for arm-chair strategizing to be useful. As a consequence, many management teams are stuck in a wait-and-see posture in response to extreme uncertainty in the post-Covid environment, while others are making panicky bets, including ‘leap before you look’ acquisitions. In this sequel to their Amazon-bestseller, Bulletproof Problem Solving , Conn and McLean introduce a novel approach to strategic problem solving. Based on a decade of research and 30 new case studies, The Imperfectionists posits a dynamic approach to developing organizational direction under uncertainty based on harnessing six reinforcing strategic mindsets, which they call curiosity, dragonfly eye, occurrent behaviour, collective wisdom, imperfectionism, and show and tell. Imperfectionists are curious, they look at problems from several perspectives, and gather new data and approaches, including from outside their current industry. They deliberately step into risk, proceeding through trial and error, utilizing nimble low consequence and reversible moves to deepen their understanding of the unfolding game being played, and to build capabilities. They accept ambiguity and some apparent failures in exchange for improved learning and market position. Imperfectionists succeed with dynamic, real time strategic problem solving, confidently moving forward while others wait for certainty, or make impetuous and foolish bets. These strategic mindsets for solving tough problems in uncertain times help you fight decision biases and give you the data to develop informed strategies to win. In the fast changing world we all find ourselves in, being an imperfectionist is a critical advantage for you and your organization.

Genres: Business, Leadership, Psychology, Nonfiction, Management, Personal Development

192 pages, Hardcover

Published April 18, 2023

Reviews

[Frank Calberg · Rating 5 out of 5]

Takeaways from reading the book.

Initiative # 1: Be curious.
- Pages 9 and 25: Ask why and how. That helps you go beyond conventional answers.
- Page 11: Curiosity reduces uncertainty as you seek to learn what you need to know to solve a problem.
- Page 22: Curiosity is the desire to close a gap between what you know and what you.want to know.
- Page 23: Babies are enormously curious. From ages 5 to 12 curiosity diminishes rapidly, as fewer everyday events bring surprises, and as the number of worked-out answers increases.
- Page 23: For Walt Disney, curiosity was the driving force behind his company's evolution.
- Page 26: Albert Einstein said, "I have no special talent. I am only passionately curious."
- Page 31: The development of Nespresso illustrates the benefits of having time and space to explore curiosity, the resources to experiment, and a charter to innovate and possibly cannibalize a core business. For example, Eric Favre went to Italy to learn about coffee. He always tested various ideas / solutions in his lab.
- Page 35: A psychologically safe environment in which to ask any questions is essential to promote curiosity.
- Page 37: Organizations that always value answers over questions fail to innovate - especially in uncertain times.
- Page 37: Use surprises to get curiosity flowing.
- Page 37: Block time in your calendar to do deep work without distractions.

Initiative # 2: Open your mind when you look at the world
- Page 11: By using different lenses, you can understand a problem in different ways.
- Page 42: Ford separated electric vehicle and combustion engine units - thereby helping the new electric vehicle unit see the electric vehicle innovation challenge from different perspectives.
- Page 56: Have you thought about your company as a technology company?

Initiative 3: Test hypotheses
- Page 13: Patagonia works with athletes, who test different versions of products - thereby helping the company learn and be able to develop products that better satisfy needs of people.
- Page 63: The city of Stockton in California tested a hypothesis that giving people money would encourage people to work less. Result of the test: More people, who received money, worked more a year later than people, who received no money.
- Page 70: Jean Liu, COO of Didi, a ride hailing service somewhat similar to UBER, drove around as driver and talked to Didi users to learn about what to improve. She also invited drivers to send feedback to her personally over Weibo. Following that Liu tested ideas relentlessly in several areas.
- Page 73: In Australia and the UK, a machine learning model, which can predict the probability of underground water pipe failure based on multiple attributes, is being tested by water utility partners. Already, the solutions have saved the equivalent of the amount of water in 2,000 olympic-sized swimming pools.
- Page 79: Princeton University economist Orley Ashenfelter tested how weather influences the quality and price of Bordeaux wine. In general, Ashenfelter said, high quality vintages for Bordeaux wines correspond to the years in which the previous winter has been wet, the growing season is warm, and the months August and September are dry. He formulated the results of his test in this equation: Wine quality = (12.145 / 0.00117 * Winter rainfall) + (0.0614 * Average growing season temperature) - (0.00386 * Harvest rainfall). With his method, Ashenfelter was able to explain 83% of the variation in the average prices of Bordeaux vintages.
- Page 81: If you do not test, articulate why. Examples: 1. We have a culture of answers. 2. We feel fear. 3. We do not know how to test inexpensively.

Initiative # 4: Crowdsource
- Page 86: In a study characterized by 17,302 images of melanoma and nevus, AI outperformed all 157 dermatologists. Similarly, analysis of echocardiogram test data shows that AI is 50% better than cardiologists at predicting future cardiac problems.
- Page 86: When problems display high uncertainty and deep complexity, it is a good idea to use collective intelligence, i.e. to look beyond individual experts. A community of people with diverse expertise will find more and/or better solutions than would be found by individuals working individually.
- Page 99: TikTok embraces the collective intelligence of its users with a highly sophisticated AI curation of user provided content.
- Page 99: On TikTok the AI works through liked videos, replayed videos, videos swiped rapidly and videos shared with others.
- Page 102: Collective intelligence can turn into collective illusions when people do not ask why. 2 examples: 1. Strong increases in the price of tulips in 1835. 2. Climate change in the USA.

Initiative # 5: Take small steps
- Page 15: Take a step. If that feels good, take another. If it does not feel good, take a step back.
- Page 108: Focus on learning.
- Page 109: Examine each decision after you know the outcome. Find out how the way you made decisions contributed to success.

Example: Drug innovation. Pages 121-124.
The road to successful drug innovation comprises several stages of discovery:
- The research stage of understanding diverse pathways, identifying mechanisms that affect those disease processes, finding candidate compounds to treat or cure the disease, and then designing and testing targeted therapeutics.
- The development stage is divided into four phases in the USA. Phase 1: Small safety studies. Phase 2: Small-scale efficacy and side-effect trials. Phase 3: Expensive large-scale studies of safety and efficacy. Phase 4: Regulatory approval and post-approval monitoring. The large challenge in drug innovation is successfully moving past phase 2. This is where proof of concept is tested in human subjects. There are many reasons why the development of a drug need to be stopped.

The superpower of pharmaceutical companies is knowing when to take on risk, and when to offload risk to others - including academic researchers and start-up biotech companies. Public and private academic researchers, funded by grants, have a higher tolerance for risk, as do funders of start-up biotechs, which often take early academic ideas and develop them via lab and animal research. In the case of Kymriah, Novartis outsourced discovery risk to the Perelman School of Medicine at the University of Pennsylvania. Under an agreement with the university, UPenn granted Novartis a worldwide license to the technologies it had developed over the previous 9 years for treating chronic lymphocyctic leukemia (CLL) as well as future CAR-based therapies. Both UPenn and the inventors receive royalty payments.

Initiative # 6: Share what you learn
- Page 17: Sharing what you learn creates transparency and invites other people to learn.
- Page 132: Use visualizations / pictures / images / graphics / diagrams. Example: A graphic / picture by the nurse Florence Nightingale captured people's attention and generated more impact than a million words.
- Page 136: Use demonstrations / experiments. Example: Putting a rubber ring into a glass of ice water, Nobel Laureate Richard Feynman demonstrated that when you put some pressure on it, it does not stretch back. In other words, there is no resilience in the material when it is at a temperature of 32 degrees. Through this experiment performed live on television, Feynman explained the accident that took the lives of 7 astronauts on the Challenger space shuttle.
- Page 143: There is enormous value in using surprise and novelty to change hearts and minds.

[Lloyd Downey · Rating 5 out of 5]

I read an earlier book by these authors about problem solving and must say that I was quite impressed. Their methodology, honed by many years as McKinsey consultants seems both practical and well founded. However, in the current book they draw attention to the fact that the world is changing faster than ever. One statistic jumped out at me: The average life span of companies in the S&P 500 was 61 years in 1958; today it is 18. And the question they are posing is how do companies (and individuals ) develop strategies for coping in this age of uncertainty. Have they nailed it? Actually, I don’t think so. They have a bunch of suggestions....which all sound reasonable. But if collective wisdom and thinking outside the box and being curious and brining in people with different expertise and perspectives doesn’t work. Then, it seems, things are left to the fates.
I did like the suggestion of looking to the Pharmaceutical companies for outsourcing risk.....especially with clinical trials. They seem to be able to do this with universities and other groups so they are not shouldering the entire risk in a low success rate industry. Will I actually use any of this in my daily life? Well certainly parts of it. I well remember looking for improvement suggestions in my office in Kuala Lumpur many years ago. The best suggestion came from one of the lowest paid staff: the filing clerk. He noted that he was filing several copies of correspondence (in the days before email) and one of the copies was never used. Bingo!...Why hadn’t anyone else noticed this? Anyway, it highlighted the importance to me of seeking a wide range of inputs.
What did I think of the book overall. I liked it. I learned from it. I will probably try some of the suggestions. So five stars from me.
The rate of production of new knowledge and communications is overwhelming....More new information has been created since 2010 than in all of previous human history. Stop and think about that. It is impossible for even the most talented people to stay abreast of this wave. Even the polymath Newtons, Bacons, and Rousseaus of our age cannot begin to keep up with the race of new knowledge and its impact on our organizations and lives.
Artificial intelligence, automation, programmable biology, robotics, and other technologies are transforming every industry. The rate of disruption is overturning market leaders more quickly than ever and installing new top competitors, often from entirely outside that industry. There is not a single company in the Dow Jones Index today that was there at the inception of the Index in 1885.
Most new careers didn't exist 10 years ago, and many won't exist 10 years from now. No one will do the same thing for a lifetime as our grandparents did. In this vastly more complicated world, how can we and the organizations we work for be competitive? How can we shape strategies.....
Curiosity is an essential orientation for problem solving in uncertain times. Managers must be able to suspend their natural pattern recognition impulses long enough to see evolving challenges in a fresh light, especially when uncertainty is not just about known patterns (it will rain, it will not rain), but instead is governed by entirely novel events. When great problem solvers seek to close the gap between what they know and what they need to know, curiosity reduces uncertainty.
With massive changes in technology, there is now a kaleidoscope of possibilities. In this new environment there is huge value in testing several different perspectives on each problem, rather than assuming it is business as usual.
We call this mindset the dragonfly eye..... Dragonflies have huge compound eyes, with hundreds of lenses that are sensitive to different wavelengths of light. We do not know exactly how their insect brains process all this visual information, but we do know that they gather much more data than our human eyes, perceiving colours and movement unseen by us. By analogy, when faced with new and uncertain information, great problem solvers try on several different lenses in an effort to understand the problem. They zoom in and then zoom out, or widen the aperture to make sure they are seeing the real structure in front of them, and are not imposing an old solution or addressing its surface.
When the world is changing quickly, a great problem solving approach is to slow down and notice the nature of the new environment. This starts with curiosity-wondering why, asking questions. It then moves to seeing things through multiple lenses or angles, trying on alternative potential framings. Next, it seeks novel data on the emerging world via fresh experimentation, rather than relying on experts or older structured information. It augments this new data when required by crowdsourcing potential solutions from outside the obvious fields or sources. This curiosity, structured perspective taking, and new data generation in turn leads to a pragmatic plan to edge into uncertainty, gathering more information through small moves, adding capabilities and assets, and learning from mistakes and successes-that is imperfectionism, not waiting for certainty. .....Finally, clever strategic problem solving rallies others to support your plans with visual storytelling, not just facts and logic.
These strategic mindsets for solving tough problems in the risky circumstances of high uncertainty help you fight the decision biases inherent in being human,
Occurrent behaviour is a term that describes what actually happens in the world rather than what was modelled or predicted....... Occurrent behaviour is relentless experimenting to reduce uncertainty, via deliberate trial and error of the kind carried out at the Federal Reserve.
Using this mindset, great problem solvers constantly test hypotheses. The results help decide their next steps-either to abandon a path, decide with confidence to proceed, or to collect more data to become comfortable making a decision.
The essence of the scientific method is reducing uncertainty by experimenting. It's an approach that was formalized in what has become known as Bayes' rule, after Thomas Bayes, an eighteenth-century statistician and clergyman....... Bayes' rule estimates the probability that a hypothesis is true given the evidence from the data. Consider, for example, car accidents. Who is most likely to cause them? From years of observation, we know that the risk is greater for new drivers. Bayes' rule informs car insurers that they should charge higher premiums for teenage drivers, rather than assign a single premium for everyone based on the broader average probability of accidents for all drivers. It's a dream solution for problem solvers...... Bayes' rule is the core intellectual scaffolding for the mindset of occurrent behaviour. Start with what we have observed, estimate the probability of an occurrence if a hypothesis is true, then update your beliefs as you follow the evidence. [Not every statistician is happy with Bayesian statistics and it also suffers from the problem that the past is not necessarily a good predictor of the future...especially if rare events (black swans) are involved.]
When we were management consultants, we discovered for ourselves that the best way to solve strategic problems is by testing a hypothesis against real experimental evidence. We've also seen the approach go wrong. Too often, junior or less curious senior problem solvers conflate the "hypothesis" they start with as the "solution" and immediately try to prove it. But a hypothesis is most definitely not the solution. It must be tested and challenged—or rejected —in the light of what the data actually demonstrate.
Experiments such as [the examples cited] have made us disciples of occurrent behavior. To pursue occurrent behavior we have to do three things.
• First, we look to create or use data in real time.
• Second, we explore whether there are new tools or novel ways to capture and observe existing data, in order to generate insight and reduce uncertainty.
• And third, we use natural experiments.
like so much else, things are moving more rapidly now. One hundred years ago there were 18.65 automobile-related deaths for every 100 million miles. Tesla estimates that there is one fatality for every 320 million miles covered by self-driving cars in autopilot mode—a massive improvement since the days of the Model T.
The likes of Waymo, Alibaba's AutoX, Baidu, and Cruise are developing machine learning algorithms that capture the properties of the surrounding environment and predict possible changes to those surroundings.
These tasks are mainly divided into four subtasks: regression algorithms, cluster algorithms, decision matrix algorithms, and pattern recognition.
But as yet there is no standardized metric that ensures that the machine learning algorithms used in AVs are safe. Self-driving cars need to learn and adapt to different situations so they can detect and interpret objects they come across.
There are currently no regulations sufficient for a whole autonomous driving system in any country. Even in the "simple" case of Australia, more than 50 federal and state laws would need to be amended-amounting to nothing less than a complete overhaul of the country's motoring insurance system and traffic network. That will only happen when AVs become the accepted norm.
When collective intelligence is mentioned in conversation, there is often much nodding that "yes, we know what that is—the wisdom of crowds is guessing how many beans there are in a jar." We think there is a lot more to it. We see collective intelligence playing a key role in how organizations compete. We believe that we should be respectful of experts, but skeptical of historically derived perspectives in settings where the rules are rapidly changing. You should embrace diversity of perspectives in your own teams, and you should look outside your own four walls for rich sources of self-disruption.
Our schema for how to tap collective intelligence involves three branches.
• The first involves crowdsourced expertise. This comes in two varieties, competitive and collaborative.
• The second branch is collective wisdom. This has several dimensions, including historical community wisdom. We illustrate ancestral wisdom, a subset of community wisdom, with reference to what is now called "right way fire" management in Northern Australia.
• Our third branch involves the combination of human problem solving and artificial intelligence.
Quibi was like a shooting star you might have missed. A short-form streaming platform launched with great fanfare in April 2020, the company was dissolved on December 1 of the same year. An A-list of investors included Alibaba, Disney, Google, Goldman Sachs, and 2ist Century Fox, invested $1.75 billion and lost over $1 billion in those short months.
Quibi's failure—as short-form video content competitor TikTok was rising had several dimensions. Most importantly, its model of curating content via experts was less attractive than the Al-enabled crowd curation model adopted by TikTok, which reported 315 million downloads in the first quarter of 2020. Indeed, Christian Stadler's post-mortem on Quibi argued that "it failed because executives refused to see TikTok as its biggest competition."" What was it about TikTok that made it such a formidable threat to Quibi? The answer is that TikTok embraced the collective intelligence of its users........ Quibi could not compete with a social media platform that could pivot to social trends instantaneously, nor sustain a viable revenue model from subscriptions in the face of a service that was free to users and supported by paid advertising. TikTok ultimately created additional revenue streams by transferring users to an online shop site inside the app.
Collective intelligence from crowds also has its limitations. Todd Rose
reminds us of collective illusions that occur when individuals subsume their private view to conform with what they think the group wants. An example is the tulip mania of 1835, when tulip prices exceeded their weight in gold.
What if organizations allocated 20% of their innovation funding to collective intelligence solutions such as the Kaggle platform competitions? Or followed the lead of pharma companies in partnering for much of their drug discovery with universities and risk-taking, creative biotech companies? We would most likely see a huge upsurge in innovation as collective intelligence from outside organizations is brought to bear on our most difficult problems. We would have myriad talented people toiling in our gardens, as Bill Joy proposes. Lego's invitation to its millions of users to design new bricks and kits would become a model for new product development. Canada's Goldcorp initiative inviting proposals on where to find gold, having made available their geologic datasets, could become another model for crowdsourced R&D.
Here are some steps to get started.
• Shine a light on major unsolved problems that, if solved, would impact performance or growth.
• Map the ecosystem to identify innovative approaches.
• Tackle the barriers to collective intelligence..... These barriers could be policies on intellectual property or licensing that need to be reviewed to reward innovative solutions, or a lack of resourcing for collective intelligence solutions.
• Address the social side of collective intelligence.
History shows that the returns on M&A, particularly transformational acquisitions outside core businesses, are often abysmal. By one analysis published in the Harvard Business Review, 70-90% of all mergers fail......In this chapter we discuss the third strategic possibility between risk aversion and betting the company, stepping into risk as an imperfectionist.
Imperfectionists don't wait for perfect conditions to act, but they also don't leap before they look.
Our friend Professor Dan Lovallo, together with his co-authors, coined the term risk aversion tax, or RAT, to explain the phenomenon of risk aversion embedded in corporate hierarchies. The RAT is the difference in value between the choices a manager should favour, based on the odds, and those that managers actually make..... In fact, in one real-life corporate case study, Lovallo found the actual RATor hidden tax to be 32% of the economic value of all of the investments the firm made that year. Risk aversion in companies and nonprofits is a fact, and it carries costs that are less obvious than strategic moves that go wrong, but are just as real.
Since competitive landscapes are ever-changing, smart firms step into risk, initially with modest moves that help the firm understand the parameters of uncertainty in adjacent mar-kets, and eventually with larger moves that build defensible asset and capability positions. They partner to share risk; they hedge risk and lay it off onto others when they can.

Development risk is stacked on top of the earlier discovery risk. But pharmaceutical companies are sophisticated about when to take on risk, and when to offload it to others, including academic researchers and start-up biotech companies. This is their Imperfectionism superpower.
Public and private academic researchers, funded by grants, have a higher tolerance for risk, as do the funders of start-up biotechs, which often take early academic ideas and develop them via in-vitro (lab) and in-vivo (animal) research........In the case of Kymriah, Novartis outsourced discovery risk to the Perelman School of Medicine at the University of Pennsylvania. Under an agreement with the university, UPenn granted Novartis a worldwide license to the technologies it had developed over the previous nine years for treating chronic lymphocytic leukaemia (CLL) as well as future CAR-based therapies. Both UPenn and the inventors receive royalty payments.

[Fernando Bragança · Rating 5 out of 5]

The Imperfectionists: Strategic Mindsets for Uncertain Times is a guide to navigating the complexities and uncertainties of the modern business world. The book introduces six strategic mindsets that are essential for thriving in today's rapidly changing environment:

1. Curiosity: Encourages a continuous quest for knowledge and understanding, fostering innovation and adaptability.
2. Dragonfly Eye: Promotes viewing problems from multiple perspectives to gain a comprehensive understanding.
3. Experimentalism: Emphasizes the importance of trial and error, learning from failures, and making incremental improvements.
4. Collective Intelligence: Highlights the power of collaboration and leveraging diverse insights and expertise.
5. Storytelling: Stresses the significance of effective communication and the ability to convey complex ideas through compelling narratives.
6. Imperfectionism: Advocates for embracing uncertainty and imperfection as opportunities for growth and learning.

The authors draw on decades of research and numerous case studies to illustrate how these mindsets can be applied in real-world scenarios. They argue that traditional approaches to strategy and problem-solving are no longer sufficient in a world where change is constant and unpredictable. Instead, adopting these strategic mindsets can help individuals and organizations navigate ambiguity, make informed decisions, and achieve sustainable success¹.

[Fed · Rating 4 out of 5]

This book is an easy read full of real life stories. It gives a sense of security on how to deal with uncertainty.

Some key takeaways from the book:
We can't build the future looking at the back mirror only

Curiosity is the desire to fill in the gap between what you know and what you want to know

The good leader, asks questions (building curiosity in your team)

See the issue from different lenses. And cover a 360 degree view to avoid having blind spots

Using multiple lenses make us approach and understand reality better, so we come up with better solutions.

experimenting and collecting data could provide the needed solutions/ answers

Outsourcing & diverse teams are the best to find solutions

step into risk after collecting all of the available information.

To overcome change resistance & skepticism when implementing the solutions:
show & tell, build storytelling skills, conspire with the CEO, execute with surprise & novelty

Take small steps, to measure the risk and feedback while changing. Or trail in a small part.

Under uncertainty: our knowledge will always be limited & incomplete

[Yanal · Rating 3 out of 5]

Blinks:
1. Curiosity will keep you abreast of changes in life, society, and the modern world
2. Dragonfly eye perspective alludes to having a multi-perspective approach to the problems one can face.
3. Experimental or occurrent behavior means to try, test, fail, and fix until you get the right answer.
4. Collective intelligence is better than single experts. Crowsource expertise, collective wisdom, and human AI collaboration.
5. Storytelling that has insights wrapped in engaging narratives will ultimately be understood best.
6. Imperfectionism let's us move forward even if we don't have all the information at hand. Call out the risks but don't let it paralyze from moving forward. Lean into risks with reversible moves.

[Bill Graca · Rating 5 out of 5]

This book introduces a set of six mindsets that can be applied to structuring and solving complex problems. It is not only extremely practical but, insightful for the strategist who is wrestling with ambiguity and uncertainty in today’s market environment. I found the myriad of case studies referenced in this book to be excellent examples of the principles highlighted by the authors put to practice. This was a great read and I’d highly recommend this for executives and strategy practitioners.

[Simona Žeimytė · Rating 4 out of 5]

Understand the problem and then look for a good a story and iterate.