Tag Archives: Decision Making

3 Reasons Why Bad Business Thinking Exists

GUEST POST from Greg Satell

“The single most important message in this book is very simple,” reads the first line in John Kotter’s highly regarded The Heart of Change. “People change what they do less because they are given analysis that shifts their thinking than because they are shown a truth that influences their feelings.

Really? That’s the important message? That emotive arguments are more powerful than factual arguments? What about other reasons why people change their behavior, such as social proof, conformity, incentives or coercion? By setting up a binary and artificial choice between two communication alternatives, he eliminates important strategic and tactical options.

It’s not just Kotter either, who is a well respected professor at Harvard Business School. The truth is that a lot of management thinking is surprisingly shoddy, with arbitrary notions and cognitive biases dressed up as scholarly work. We need to be more skeptical about “research” that comes out of business schools and consultancies. Here are three things to look for:

1. WYSIATI And Confirmation Bias

Kotter’s point about emotive vs. analytic arguments is, of course, completely valid. The fundamental error he makes is that he focuses on that particular aspect to the exclusion of everything else. Daniel Kahneman calls this WYSIATI, or “what you see is all there is.” Once you get tunnel vision on a particular fact or idea, it’s hard to see anything else.

Consider this thought experiment: You go to a conference featuring a powerful, emotive presentation on the need to combat climate change. You see glaciers melting, polar bears losing their habitat and young children starving from drought. Then you go back to the office, fired up and ready to do something about it, but everyone else has a strong argument against acting on climate change.

What is likely to happen next? You convince you co-workers—including your bosses— about the urgency of the crisis? Or, surrounded by skeptics, your conviction begins to wane? When all we see is the poor polar bears and starving in an echo chamber of likeminded people, we forget about other considerations, but that doesn’t mean that’s all there is.

An issue related to WYSIATI is confirmation bias. Kotter proudly points out that he worked with Deloitte to conduct extensive research for his book. Amazingly, after analyzing over 200 interviews, he ended up with the same 8-step process he cited in his earlier work. So what was the purpose of the research, to gain actual insights or to confirm what he thought he already knew?

Perhaps not surprisingly, after decades of organizations applying Kotter’s ideas about change McKinsey still finds that more than two-thirds of transformational efforts fail. Maybe there is actually more to change than communication strategy.

2. Halo Effects And Confounding Variables

One of the most popular modes of analysis that business thinkers use is to examine successful companies and see what they do differently. A number of bestselling management books, such as In Search of Excellence, have used this method. Unfortunately, when doing so they often fall prey to a cognitive bias known as the halo effect.

For example, in 2000, before the dotcom crash, Cisco was flying high. A profile in Fortune reported it to have an unparalleled culture with highly motivated employees. But just one year later, when the market tanked, the very same publication described it as “cocksure” and “naive.” Did the “culture,” under the very same leadership, really change that much in a year? Or did the perceptions of its performance change?

Cisco had a highly motivated and, some would say, aggressive sales force. When the company was doing well, analysts assumed it was their aggressiveness that produced good results and when its fortunes changed, that same aggressive behavior was blamed for its failures. This is what’s known as a confounding error, the fact that an aggressive sales force correlated with specific results doesn’t mean that the aggressive sales force caused the results.

Every organization has things which it does differently, that are idiosyncratic to its management and culture. In some market contexts those traits will be advantageous, in other environments they may not be. It takes work—and some humility—to separate what’s truly a success factor, what’s merely fit for a narrow purpose and what’s not really relevant.

3. Survivorship Bias

Business school professors and consultants gain fame—not to mention large fees—when they are able to define a novel concept or success factor. If you are able to isolate one thing that organizations should do differently, you have a powerful product to sell. A single powerful insight can make an entire career, which is probably why so many cut corners.

For example, in their study of 108 companies, distinguished INSEAD professors W. Chan Kim and Renée Mauborgne found that “blue ocean” products, those in new categories without competition, far outperform those in the more competitive “red ocean” markets. Their book, Blue Ocean Strategy, was an immediate hit, selling over 3.5 million copies.

Bain consultants Chris Zook and James Allen’ book, Profit from the Core, boasted even more extensive research encompassing 200 case studies, a database of 1,854 companies, 100 interviews of senior executives and an “extensive review” of existing literature. They found that firms that focused on their ”core” far outperformed those who strayed.

It doesn’t take too much thinking to start seeing problems. How can you both “focus on your core” and seek out “blue oceans”? It betrays logic that both strategies could outperform one another. Also, how do you define “core?” Core markets? Core capabilities? Core customers? While it’s true that “blue ocean” markets lack competitors, they don’t have any customers either. Who do you sell to?

Yet there is an even bigger, more insidious problem called survivorship bias. Notice how “research” doesn’t include firms that went out of business because there were no customers in those “blue oceans” or because they failed to diversify outside of their “core.” The data only pertains to those that survived.

It’s hard to think of any other field where researchers could get away with such obviously careless work. Can you imagine medical research that didn’t include patients that died, or airplane research that didn’t include the flights that crashed? Suffice it to say that since the two books were published two decades ago, they’ve shown no capacity to predict whether a business will succeed or fail.

Don’t Believe Everything You Think

When I’m finishing up a book, I send out sections to be fact-checked by experts and those who have first-person knowledge of events. I’m always amazed at how much I get wrong. In some cases, I make truly egregious errors about facts I should have known (or did know, but failed to take into account). It can be an incredibly humbling process.

That’s why it’s so important to not to believe everything you think, there are simply too many ways to get things wrong. As Richard Feynman put it, “The first principle is that you must not fool yourself—and you are the easiest person to fool.” I would also add a second principle that just because you’ve managed to fool others, doesn’t mean you’ve gotten it right.

Unfortunately, so many of the popular management ideas today come from people who never actually operated a business, such as business school professors and consultants. These are often people who’ve never failed. They’ve been told that they’re smart all their lives and expect others to be impressed by their ideas, not to examine them thoroughly.

The problem with so much business thinking today is that there is an appalling lack of rigor. That’s the only way that obviously flawed ideas such as “blue oceans,” “profiting from the core” and John Kotter’s ideas about change management are able to gain traction. It’s hard to imagine any other field with such a complete lack of quality control.

That’s why I send out fact checks, because I know how likely I am to think foolish and inaccurate things. I’ve also noticed that I tend to be most wrong when I think I’ve come up with something brilliant. Much as Tolstoy wrote about families, there are infinitely more ways to get things wrong than to get things right.

— Article courtesy of the Digital Tonto blog
— Image credit: Pexels

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Putting Human Agency at the Center of Decision-Making

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Putting Human Agency at the Center of Decision-Making

GUEST POST from Greg Satell

We live in an automated age. From the news we read and the items we shop for, to who we date and what companies we choose to work for, algorithms help drive every facet of modern life. Such rapid technological advancement has led some to predict that we’re headed for a jobless future, where there is no more need for humans.

Yet in their recent book Radically Human, Accenture’s Paul Daugherty and H. James Wilson argue exactly the opposite. In their work guiding technology strategy for many of the world’s top corporations, they have found that, in many cases, the robots need us more than we need them. Automation is no panacea.

For over a century, pundits have been trying to apply an engineering mindset to human affairs with the hope of taking a more “scientific approach.” So far, those efforts have failed. In reality, these ideas have less to do with science than denying the value of human agency and limiting the impact of human judgment. We need to stop making the same mistake.

The Myth Of Shareholder Value

In 1970, the economist Milton Friedman proposed a radical idea. He argued that corporate CEOs should not take into account the interests of the communities they serve, but that their only social responsibility was to increase shareholder value. While ridiculed by many at the time, by the 1980s Friedman’s idea became accepted doctrine.

In particular, what irked Friedman was that managers would exercise judgment with respect to the objectives of the organization. “the key point is that, in his capacity as a corporate executive, the manager is the agent of the individuals who own the corporation … and his primary responsibility is to them,” he wrote.

The problem is that boiling down the success of an enterprise to the single variable of shareholder value avoids important questions. What do we mean by “value?” Is short term value more important than long-term value? Do owners value only share price or do they also value other things, like technological progress and a healthy environment?

Avoiding tough questions leaves significant problems unsolved, which may be one reason that, since Friedman’s essay, our well-being has declined significantly. Our economy has become markedly less productive, less competitive and less dynamic. Purchasing power for most people has stagnated. By just about every metric, we’re worse off.

How The Consumer Welfare Standard Undermines Consumer Welfare

In 1978, the legal scholar Robert Bork published the Antitrust Paradox in which he argued against the rule of reason standard for antitrust cases that required judges to use their discretion when deciding what constitutes a practice that “unreasonably” restricts trade. In its place, he suggested a consumer welfare standard, which would only take into account whether the consumer was harmed by higher prices.

Much like Friedman, Bork didn’t like the idea of depending on subjective human judgment. How could we trust judges to decide what is “reasonable” without a clear and objective standard? If the government is going to block business activity, he argued, it should have to prove, through stringent economic analysis, that harm is being done.

Yet as Lina Kahn pointed out in a now-famous paper titled Amazon’s Antitrust Paradox, consumers can be harmed even as prices are lowered. If Amazon is allowed to control the online retail infrastructure, including logistics, hosting, marketing, etc., then trade is restricted, free markets are undermined and the consumer will be harmed.

To understand why, you only need to look at the recent baby formula shortage, in which only three firms dominate the market and, the leader, Abbott, is the exclusive supplier in many markets. Not only is it highly likely that the lack of competition contributed to lax quality standards at Abbott’s plant in Sturgis, Michigan, but once it went offline because of contamination, there weren’t enough suppliers to fill the gap.

These aren’t isolated examples, but indicative of a much larger and growing crisis. An article in Harvard Business Review details how the vast majority of industries are concentrated in just a few dominant players. A more extensive analysis by the Federal Reserve bank shows how the lack of competition leads to lower business dynamism and less productivity.

“Great Power” Politics

In early March, the prominent political scientist John Mearsheimer gave an interview to The New Yorker in which he argued that the United States had erred greatly in its support of Ukraine. According to his theory, we should recognize Russia’s role as a great power and its right to dictate certain things to its smaller and weaker neighbor.

Today, the idea that America should have left Ukraine at the mercy of Russia seems not only morally questionable, but patently absurd. Not only has the brutality of the Russian forces horrified the world, their incompetence has laid bare the fecklessness of the the Putin regime. How could such a respected expert of foreign affairs get things so wrong?

Once again, the failure to recognize human agency is a key culprit. In Mearsheimer’s view, which he calls, “realism,” only “great powers” have a say in world affairs and they will work to further their interests. He believes that by not recognizing Russia’s desire to subjugate other nations in its orbit, America and its allies are being silly and impractical.

Hopefully, we can learn some lessons from the war in Ukraine. Strategy is not a game of chess, in which we move inert pieces around a board. People have the power to make choices. Ukraine chose to undertake tough reforms and arm itself. Russia chose an autocracy which rewarded loyalty over competence. That, more than anything else, has driven events.
The Real World Isn’t An Algorithm

A joke began circulating in the late 1970s, often attributed to management consultant Warren Bennis, that the factory of the future will have only two employees, a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment. Today, even with offshoring, about 10% of Americans work in factories.

When you scratch below the surface, the joke has less to do with technological advancement than it does with derision and control. Bennis wasn’t just any business consultant, but a renowned expert on leadership, who wrote books, published articles in top journals and even advised presidents. That he would promote the view, even as a joke, that leaders should deny agency to employees is as troubling as it is telling.

If you believe that human judgment is a liability rather than an asset, you manage accordingly. You treat employees as cogs in a machine rather than partners in a shared enterprise. You invest in offshoring rather than up-skilling, schedule shifts without regard to people’s lives, deny benefits such as parental leave. We’ve seen where that’s gotten us—lower productivity, worsening mental health and a society that is more unequal and less just.

We need to get back to the business of being human. Our economy should serve our people, not the other way around. The success of a society needs to be measured by the well-being of those who live in it. If we increase GDP, but our air and water are more polluted, our children less educated, we live unhappy lives and die deaths of despair, what have we really gained?

— Article courtesy of the Digital Tonto blog
— Image credits: Pixabay

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Top 10 Human-Centered Change & Innovation Articles of March 2025

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Top 10 Human-Centered Change & Innovation Articles of March 2025 Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are March’s ten most popular innovation posts:

Turning Bold Ideas into Tangible Results — by Robyn Bolton
Leading Through Complexity and Uncertainty — by Greg Satell
Empathy is a Vital Tool for Stronger Teams — by Stefan Lindegaard
The Role Platforms Play in Business Networks — by Geoffrey A. Moore
Inspiring Innovation — by John Bessant
Six Keys to Effective Teamwork — by David Burkus
Product-Lifecycle Management 2.0 — by Dr. Matthew Heim
5 Business Myths You Cannot Afford to Believe — by Shep Hyken
What Great Ideas Feel Like — by Mike Shipulski
Better Decision Making at Speed — by Mike Shipulski

BONUS – Here are five more strong articles published in February that continue to resonate with people:

The Worst British Customer Experiences of 2024 — by Braden Kelley
Your Innovation Team Doesn’t Have to be Dead — by Robyn Bolton
Unlocking Trapped Value with AI — by Geoffrey A. Moore
Be Better or Be Different? — by Shep Hyken
Ten Reasons Every B2B Company Needs an Evangelist — by Braden Kelley and Art Inteligencia

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

SPECIAL BONUS: While supplies last, you can get the hardcover version of my first bestselling book Stoking Your Innovation Bonfire for 44% OFF until Amazon runs out of stock or changes the price. This deal won’t last long, so grab your copy while it lasts!

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last four years:

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Disagreements Can Be a Good Thing

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Disagreements Can Be a Good Thing

GUEST POST from Mike Shipulski

When you have nothing to say, don’t say it.

But, when you have something to say, you must say it.

When you think your response might be taken the wrong way, it will.

When you take care to respond effectively, your response might be taken the wrong way.

When you have disagreement, there’s objective evidence that at least two people are thinking for themselves.

When you have disagreement, confrontation is optional.

When you have disagreement, everyone can be right, even if just a little.

When you have disagreement, that says nothing about the people doing the disagreeing.

When you have disagreement at high decibels, that’s an argument.

When you have disagreement, disagreeing on all points is a choice.

When you have disagreement, if you listen to sharpen your response, it’s a death spiral.

When you have disagreement, it’s best to disagree wholeheartedly and respectfully.

When you have disagreement, if you listen to understand, there’s hope.

When you have disagreement, it’s a disagreement about ideas and not moral character.

When you have disagreement, intentions matter.

When you have disagreement, decision quality skyrockets.

When you have disagreement, thank your partner in crime for sharing their truth.

When you have disagreement, there is sufficient trust to support the disagreement.

When you have disagreement, sometimes you don’t, but you don’t know it.

When you have disagreement, converging on a single point of view is not the objective.

When you have disagreement about ethics, you may be working at the wrong company.

When you have disagreement, there are no sides, only people doing their best.

When you have disagreement, the objective is understanding.

When you have disagreement, it’s the right thing to have.

When you have disagreement, there may be disagreement on the topic of the disagreement.

When you have disagreement, you are a contributing member, even if you stay quiet.

When you have disagreement, why not be agreeable?

When you have disagreement, it’s okay to change your mind.

When you have disagreement, you may learn something about yourself.

Image credit: Unsplash

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Top 10 Human-Centered Change & Innovation Articles of May 2023

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Top 10 Human-Centered Change & Innovation Articles of May 2023 Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are May’s ten most popular innovation posts:

A 90% Project Failure Rate Means You’re Doing it Wrong — by Mike Shipulski
‘Innovation’ is Killing Innovation. How Do We Save It? — by Robyn Bolton
Sustaining Imagination is Hard — by Braden Kelley
Unintended Consequences. The Hidden Risk of Fast-Paced Innovation — by Pete Foley
8 Strategies to Future-Proofing Your Business & Gaining Competitive Advantage — by Teresa Spangler
How to Determine if Your Problem is Worth Solving — by Mike Shipulski
Sprint Toward the Innovation Action — by Mike Shipulski
Moneyball and the Beginning, Middle, and End of Innovation — by Robyn Bolton
A Shortcut to Making Strategic Trade-Offs — by Geoffrey A. Moore
3 Innovation Types Not What You Think They Are — by Robyn Bolton

BONUS – Here are five more strong articles published in April that continue to resonate with people:

A Global Perspective on Psychological Safety — by Stefan Lindegaard
Customer Service is a Team Sport — by Shep Hyken
A Flop is Not a Failure — by John Bessant
Unlocking the Power of Imagination — by Teresa Spangler
Reasons Change Management Frequently Fails — by Greg Satell

Have something to contribute?

P.S. Here are our Top 40 Innovation Bloggers lists from the last three years:

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Involving Employees in Decision-Making Processes

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Involving Employees in Decision-Making Processes

GUEST POST from Art Inteligencia

In the rapidly evolving landscape of 2025, the traditional top-down organizational structure is increasingly becoming a relic of the past. Organizations that thrive are those that recognize their most valuable asset isn’t their technology or their capital, but their people. And for people to truly be an asset, they must be empowered, engaged, and intimately involved in the decisions that shape their work and the future of the enterprise.

For decades, I’ve championed the cause of human-centered innovation. My message has consistently been that true innovation doesn’t happen in a vacuum, nor does it emerge solely from a corner office. It bubbles up from the collective intelligence, diverse perspectives, and lived experiences of every individual within an organization. This is why involving employees in decision-making processes isn’t just a “nice-to-have”; it’s a strategic imperative for resilience, agility, and sustained competitive advantage.

Why the Time is Now: The Unarguable Case for Empowerment

The arguments for employee involvement are stronger than ever. The velocity of change demands faster, more informed decisions. The complexity of modern business challenges often outstrips the capacity of a small leadership team to fully grasp. When you bring your entire workforce into the decision-making fold, you unlock a cascade of benefits that are simply non-negotiable for future success:

Enhanced Decision Quality: Diverse perspectives lead to a more comprehensive understanding of problems and a wider array of potential solutions. Those closest to the work often possess the most accurate, granular insights.
Increased Buy-in and Implementation Success: When employees are integral to crafting the solution, they inherently own it. This dramatically reduces resistance to change, accelerates adoption, and embeds solutions deeply within the operational fabric.
Boosted Employee Engagement and Morale: Feeling valued, heard, and impactful is a fundamental human need. Involvement fosters a profound sense of purpose, psychological safety, and belonging, creating a truly vibrant workplace.
Improved Innovation and Problem-Solving: A culture of authentic participation naturally encourages creative thinking, challenges the status quo, and cultivates a proactive, solution-oriented approach to identifying and addressing complex challenges.
Reduced Turnover: Empowered employees are happier, more fulfilled employees. They are significantly more likely to stay with an organization that respects their intelligence, values their contributions, and invests in their growth.

Beyond the Suggestion Box: Practical Approaches for Leaders

So, how do organizations move beyond token gestures and truly integrate employees into decision-making? It requires a fundamental shift in mindset from control to collaboration, and a steadfast commitment to structured, intentional processes. For leaders, this means:

Cultivating Radical Transparency: Lay the groundwork by openly sharing context, challenges, and strategic goals. Employees can only contribute meaningfully if they understand the big picture. Transparency builds trust and enables truly informed contributions.
Empowering Cross-Functional Teams and Task Forces: For specific projects or complex problems, convene diverse teams with representatives from all affected departments and levels. Grant these teams genuine autonomy to research, analyze, propose solutions, and even execute pilot programs.
Leveraging Democratic Idea Generation Platforms: Utilize modern digital platforms (like enterprise social networks, dedicated innovation portals, or AI-powered ideation tools) where employees can submit ideas, collaboratively refine them, and democratically vote on their merit. This democratizes innovation.
Implementing Participatory Budgeting: Involve teams or departments in decisions about how their operational budgets are allocated. This fosters a heightened sense of accountability, strategic thinking, and ownership at every level.
Hosting Open Forums and Deliberative Dialogues: Create regular, facilitated opportunities for two-way dialogue between leadership and employees. These aren’t just Q&A sessions; they’re platforms for inviting challenging questions, candid feedback, and strategic suggestions on key organizational directions.
Embracing “Wisdom of Crowds” Methodologies: For complex, high-stakes decisions, engage a representative sample of employees in structured deliberative polling exercises. This scientifically-backed approach gauges collective sentiment, uncovers hidden insights, and can often predict outcomes more accurately than small expert groups.

Case Study 1: “AgileSphere Innovations” – Redefining Product Roadmap for a Hyper-Competitive Market

AgileSphere Innovations, a leading enterprise software provider, faced a common challenge in 2023: their product roadmap was often perceived as being dictated by a few senior executives, leading to internal misalignment, delayed feature adoption, and occasional missed market opportunities in an increasingly competitive landscape.

Instead of the usual top-down annual planning cycle, AgileSphere launched “Co-Create the Future.” They implemented a quarterly “Innovation Sprint” where every employee, regardless of role or seniority, was invited to submit product feature ideas, improvements, and even entirely new product concepts. These ideas were then collaboratively refined, discussed, and voted upon within an internal, gamified ideation platform. The top 50 ideas would then be pitched in a company-wide virtual “Shark Tank” style event, judged by a diverse panel of executives and randomly selected employees. The winning ideas directly influenced the next quarter’s product roadmap, with allocated resources and dedicated, self-organizing teams formed around them.

Outcome: Within 18 months, AgileSphere reported a remarkable 30% increase in employee engagement scores related to “feeling heard” and “impact on company direction.” Crucially, three of their most successful product launches in 2024 originated directly from employee submissions through this process, including a groundbreaking AI-powered analytics dashboard that captured significant market share, validating the power of collective intelligence.

Case Study 2: “EcoHarvest Foods” – Optimizing Supply Chain Resilience in a Volatile World

EcoHarvest Foods, a sustainable food distributor operating across North America, experienced significant and costly disruptions in their supply chain during the global events of the early 2020s. Recognizing that the frontline workers in their warehouses, logistics, and procurement departments held invaluable operational knowledge often overlooked, they initiated “The Ground Up Initiative” in late 2022.

This initiative involved creating regional “Resilience Circles” – self-managing, cross-functional groups of 8-12 employees who met bi-weekly. Their mandate was to identify supply chain vulnerabilities, propose alternative sourcing strategies, and streamline internal processes. These circles were given genuine autonomy to pilot small-scale improvements and report their findings directly to a senior leadership steering committee. EcoHarvest also implemented a “Reverse Mentoring” program, where younger, digitally native employees mentored senior leaders on emerging technologies like blockchain for traceability and AI for demand forecasting, bridging critical knowledge gaps.

Outcome: By mid-2024, EcoHarvest Foods had reduced supply chain lead times by an average of 15% and diversified their critical supplier base by 25%, significantly enhancing their resilience against future disruptions. The initiative also led to a 10% reduction in operational waste through employee-identified process efficiencies, proving that empowering those closest to the problem leads to tangible, bottom-line results and a more sustainable enterprise.

Navigating the Path: Addressing Challenges and Empowering Leaders

While the benefits are clear, implementing broad employee involvement isn’t without its challenges. Leaders must be prepared to address:

Fear of Ceding Control: This is perhaps the biggest hurdle. Leaders must understand that empowering doesn’t mean losing control, but rather amplifying influence through shared ownership.
Information Overload: As more voices contribute, managing the influx of information requires robust digital tools and clear facilitation processes.
Ensuring Equitable Participation: Not everyone is comfortable speaking up. Leaders need to actively foster an inclusive environment where all voices feel safe and encouraged to contribute, leveraging anonymous feedback channels where appropriate.
Managing Expectations: Not every idea can be implemented. Transparent communication about why certain decisions are made, even when an employee’s specific idea isn’t chosen, is crucial.
Decision Fatigue: While involvement is good, not every decision requires broad consensus. Leaders must discern when broad input is vital versus when efficient, executive decision-making is necessary.

For leaders, this shift requires new muscles: active listening, empathetic facilitation, skillful synthesis of diverse inputs, and a genuine belief in the wisdom of their collective workforce. Invest in leadership development that focuses on coaching, collaboration, and building psychological safety.

Your Next Step: Ignite the Power Within

The future belongs to the organizations that democratize decision-making. Don’t wait for a crisis to realize the untapped potential within your workforce. Begin today by identifying one key decision area where employee input could be transformative. Open the dialogue. Trust your people. And watch as engagement soars, innovation accelerates, and your organization becomes truly future-proof. The journey to a human-centered enterprise starts with empowering every voice.

Extra Extra: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.

Image credit: Pixabay

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Reversible versus Irreversible Decisions

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Reversible versus Irreversible Decisions

GUEST POST from Farnham Street

We often think that collecting as much information as possible will help us make the best decisions. Sometimes that’s true, but sometimes it hamstrings our progress. Other times it can be flat out dangerous.

***

Many of the most successful people adopt simple, versatile decision-making heuristics to remove the need for deliberation in particular situations.

One heuristic might be defaulting to saying no, as Steve Jobs did. Or saying no to any decision that requires a calculator or computer, as Warren Buffett does. Or it might mean reasoning from first principles, as Elon Musk does. Jeff Bezos, the founder of Amazon.com, has another one we can add to our toolbox. He asks himself, is this a reversible or irreversible decision?

If a decision is reversible, we can make it fast and without perfect information. If a decision is irreversible, we had better slow down the decision-making process and ensure that we consider ample information and understand the problem as thoroughly as we can.

Bezos used this heuristic to make the decision to found Amazon. He recognized that if Amazon failed, he could return to his prior job. He would still have learned a lot and would not regret trying. The decision was reversible, so he took a risk. The heuristic served him well and continues to pay off when he makes decisions.

Decisions Amidst Uncertainty

Let’s say you decide to try a new restaurant after reading a review online. Having never been there before, you cannot know if the food will be good or if the atmosphere will be dreary. But you use the incomplete information from the review to make a decision, recognizing that it’s not a big deal if you don’t like the restaurant.

In other situations, the uncertainty is a little riskier. You might decide to take a particular job, not knowing what the company culture is like or how you will feel about the work after the honeymoon period ends.

Reversible decisions can be made fast and without obsessing over finding complete information. We can be prepared to extract wisdom from the experience with little cost if the decision doesn’t work out. Frequently, it’s not worth the time and energy required to gather more information and look for flawless answers. Although your research might make your decision 5% better, you might miss an opportunity.

Reversible decisions are not an excuse to act reckless or be ill-informed, but rather are a belief that we should adapt the frameworks of our decisions to the types of decisions we are making. Reversible decisions don’t need to be made the same way as irreversible decisions.

The ability to make decisions fast is a competitive advantage. One major advantage that start-ups have is that they can move with velocity, whereas established incumbents typically move with speed. The difference between the two is meaningful and often means the difference between success and failure.

Speed is measured as distance over time. If we’re headed from New York to LA on an airplane and we take off from JFK and circle around New York for three hours, we’re moving with a lot of speed, but we’re not getting anywhere. Speed doesn’t care if you are moving toward your goals or not. Velocity, on the other hand, measures displacement over time. To have velocity, you need to be moving toward your goal.

This heuristic explains why start-ups making quick decisions have an advantage over incumbents. That advantage is magnified by environmental factors, such as the pace of change. The faster the pace of environmental change, the more advantage will accrue to people making quick decisions because those people can learn faster.

Decisions provide us with data, which can then make our future decisions better. The faster we can cycle through the OODA loop, the better. This framework isn’t a one-off to apply to certain situations; it is a heuristic that needs to be an integral part of a decision-making toolkit.

With practice, we also get better at recognizing bad decisions and pivoting, rather than sticking with past choices due to the sunk costs fallacy. Equally important, we can stop viewing mistakes or small failures as disastrous. Instead, view them as information that informs future decisions.

“A good plan, violently executed now, is better than a perfect plan next week.”

— General George Patton

Bezos compares decisions to doors. Reversible decisions are doors that open both ways. Irreversible decisions are doors that allow passage in only one direction; if you walk through, you are stuck there. Most decisions are the former and can be reversed (even though we can never recover the invested time and resources). Going through a reversible door gives us information: we know what’s on the other side.

In his shareholder letter, Bezos writes:

Some decisions are consequential and irreversible or nearly irreversible – one-way doors – and these decisions must be made methodically, carefully, slowly, with great deliberation and consultation. If you walk through and don’t like what you see on the other side, you can’t get back to where you were before. We can call these Type 1 decisions. But most decisions aren’t like that – they are changeable, reversible – they’re two-way doors. If you’ve made a suboptimal Type 2 decision, you don’t have to live with the consequences for that long. You can reopen the door and go back through. Type 2 decisions can and should be made quickly by high judgment individuals or small groups.

As organizations get larger, there seems to be a tendency to use the heavy-weight Type 1 decision-making process on most decisions, including many Type 2 decisions. The end result of this is slowness, unthoughtful risk aversion, failure to experiment sufficiently, and consequently diminished invention. We’ll have to figure out how to fight that tendency.

Bezos gives the example of the launch of one-hour delivery to those willing to pay extra. This service launched less than four months after the idea was first developed. In 111 days, the team “built a customer-facing app, secured a location for an urban warehouse, determined which 25,000 items to sell, got those items stocked, recruited and onboarded new staff, tested, iterated, designed new software for internal use – both a warehouse management system and a driver-facing app – and launched in time for the holidays.”

As further guidance, Bezos considers 70% certainty to be the cut-off point where it is appropriate to make a decision. That means acting once we have 70% of the required information, instead of waiting longer. Making a decision at 70% certainty and then course-correcting is a lot more effective than waiting for 90% certainty.

In Blink: The Power of Thinking Without Thinking, Malcolm Gladwell explains why decision-making under uncertainty can be so effective. We usually assume that more information leads to better decisions — if a doctor proposes additional tests, we tend to believe they will lead to a better outcome. Gladwell disagrees: “In fact, you need to know very little to find the underlying signature of a complex phenomenon. All you need is evidence of the ECG, blood pressure, fluid in the lungs, and an unstable angina. That’s a radical statement.”

In medicine, as in many areas, more information does not necessarily ensure improved outcomes. To illustrate this, Gladwell gives the example of a man arriving at a hospital with intermittent chest pains. His vital signs show no risk factors, yet his lifestyle does and he had heart surgery two years earlier. If a doctor looks at all the available information, it may seem that the man needs admitting to the hospital. But the additional factors, beyond the vital signs, are not important in the short term. In the long run, he is at serious risk of developing heart disease. Gladwell writes,

… the role of those other factors is so small in determining what is happening to the man right now that an accurate diagnosis can be made without them. In fact, … that extra information is more than useless. It’s harmful. It confuses the issues. What screws up doctors when they are trying to predict heart attacks is that they take too much information into account.

We can all learn from Bezos’s approach, which has helped him to build an enormous company while retaining the tempo of a start-up. Bezos uses his heuristic to fight the stasis that sets in within many large organizations. It is about being effective, not about following the norm of slow decisions.

Once you understand that reversible decisions are in fact reversible you can start to see them as opportunities to increase the pace of your learning. At a corporate level, allowing employees to make and learn from reversible decisions helps you move at the pace of a start-up. After all, if someone is moving with speed, you’re going to pass them when you move with velocity.

The biggest risk to irreversible decisions is deciding before you need to. The biggest risk to reversible ones is waiting until the last minute. Make reversible decisions as soon as possible and make irreversible decisions as late as possible.

This article originally appeared on Farnham Street

Image credits: Pixabay, Farnham Street

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Decision-Making Under Uncertainty

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Lessons from Top Innovators

Decision-Making Under Uncertainty

GUEST POST from Chateau G Pato

In a rapidly changing world, the ability to make robust decisions under uncertainty has become a defining trait of successful innovators. This capability does not merely hinge on intuition or foresight; it draws from a calculated approach that blends informed risk-taking, flexibility, and an acute sense of opportunity. As we delve into the stories of leading innovators, we uncover key lessons that can bolster decision-making amid ambiguity and turbulence.

Key Principles of Innovative Decision-Making

Embrace Ambiguity: Innovators thrive by accepting that the absence of complete information is not a barrier but a gateway to opportunity.
Prototype and Iterate: Learning through rapid prototyping and iteration helps gauge what works, reducing risks in the process.
Rely on Diverse Perspectives: Diverse teams bring a range of insights, fostering comprehensive decision-making that anticipates various outcomes.
Value of Failure: Treating failure as a stepping stone rather than a setback is essential in refining strategies and inspiring breakthroughs.

Case Study: SpaceX – Launching Dreams Amidst Uncertainty

Elon Musk’s SpaceX is a prime example of decision-making under extreme uncertainty. In the company’s early days, the prospects of commercial spaceflight were riddled with unknowns. The use of Falcon 1 rocket was met with skepticism, three consecutive failures, and dwindling finances. However, Musk demonstrated a profound belief in a calculated approach to risk-taking; he reinvested in refining technologies with a fourth successful launch as the outcome.

The SpaceX team embraced iteration with rigor. Every failure was meticulously analyzed, and the resultant insights were applied to subsequent designs. This culture of resilience and learning has enabled SpaceX to not only survive repeated adversities but also lead in reusable rocket technology, fundamentally changing the dynamics of aerospace sectors. Their unwavering commitment illustrates that embracing failure and preserving a vision are crucial elements of navigating uncertainty.

Case Study: Airbnb – Redefining the Travel Industry

Airbnb’s journey began at a time when the notion of home-sharing was largely unrealized. Founders Brian Chesky and Joe Gebbia faced significant uncertainties ranging from legal issues to trust deficits among users. Despite these challenges, they saw potential in leveraging the untouched resource of spare rooms to forge a new market.

Their decision-making process was heavily influenced by flexibility and listening to users. The founders prioritized user feedback, transforming invaluable insights into functional platform changes. To tackle trust issues, Airbnb introduced a review system and a range of host/guest assurances, which significantly increased user confidence and adoption.

This case underscores the importance of responsive pivoting and incremental innovation, which eventually helped Airbnb disrupt the travel industry and establish a new modality of travel accommodation amid initial market skepticism.

Conclusion

Top innovators, like those at SpaceX and Airbnb, exemplify decision-making under uncertainty through their strategic approaches to experimentation, collaboration, and adaptation. By embodying the principles of embracing ambiguity, valuing diverse insights, and fostering an iterative mindset, they navigate uncertainties not as obstacles but as part of the growth process.

As industry leaders continue to face unpredictable environments, adopting these lessons will be central to cultivating robust innovation strategies, sustaining growth, and crafting transformative impacts on the world.

In this article, I’ve featured two case studies — SpaceX’s use of iteration and resilience in rocket development and Airbnb’s strategic adaptation in the hospitality sector. Both scenarios highlight the importance of calculated risk, flexibility, and the readiness to learn from both successes and setbacks, providing valuable lessons on decision-making under uncertainty.

SPECIAL BONUS: The very best change planners use a visual, collaborative approach to create their deliverables. A methodology and tools like those in Change Planning Toolkit™ can empower anyone to become great change planners themselves.

Image credit: Pexels

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Leveraging AI to Drive Smarter Decision-Making in the Workplace

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Leveraging AI to Drive Smarter Decision-Making in the Workplace

GUEST POST from Art Inteligencia

In today’s fast-paced and data-driven world, organizations are constantly challenged to make smarter decisions at an increasingly rapid rate. As a human-centered design professional, I firmly believe that Artificial Intelligence (AI) holds immense potential in transforming the workplace, enabling decision-makers to unlock unprecedented insights and steer their organizations towards success. In this thought leadership article, we will explore the benefits of leveraging AI in decision-making through two compelling case studies that demonstrate its transformative power.

Case Study 1: Enhancing Customer Experience with AI-powered Insights

One of the key areas where AI is revolutionizing decision-making is in optimizing customer experiences. A leading e-commerce company, “SuperStore,” adopted AI-powered analytics to delve deeper into their customer data and gain actionable insights. By leveraging AI algorithms, they analyzed vast amounts of customer purchase history, preferences, and demographic information. Consequently, they identified customers’ propensity to purchase certain items, enabling them to personalize recommendations and offers dynamically.

SuperStore observed a substantial increase in conversion rates and customer satisfaction as a result of this AI-powered decision-making. With the ability to understand customer behavior patterns and predict preferences, they successfully exceeded their customers’ expectations. Furthermore, the insights obtained from AI algorithms provided valuable guidance in optimizing marketing strategies, product placements, and inventory management decisions, yielding significant business growth.

This case study highlights how AI-driven decision-making tools can harness vast amounts of customer data to create unparalleled customer experiences, boosting sales and establishing a competitive edge.

Case Study 2: Improving Operational Efficiency through AI-powered Automation

Another area where AI is revolutionizing decision-making is in streamlining operational processes. A global manufacturing firm, “SmartCorp,” sought to leverage AI to enhance operational efficiency and reduce costs. They implemented an AI-driven automation system that analyzed real-time production data from various sources and generated real-time alerts for potential anomalies or bottlenecks.

The AI system enabled SmartCorp to detect deviations from standard processes and critical inefficiencies promptly. Production managers were provided with actionable insights that enabled them to make data-driven decisions in real-time, such as adjusting production rates, identifying maintenance needs, and optimizing resource allocation. With the aid of AI, SmartCorp experienced a substantial decrease in downtime, a reduction in errors, and a significant increase in overall productivity.

This case study showcases how AI-powered decision-making supports organizations in transforming their operational landscape. The ability to automate and analyze vast amounts of data in real-time empowers decision-makers to proactively identify and address issues as they arise, optimizing operational efficiency and driving remarkable business outcomes.

Conclusion

AI represents a powerful opportunity for organizations to unlock new levels of productivity, efficiency, and success by harnessing data-driven decision-making. The case studies of SuperStore and SmartCorp demonstrate the profound impact that AI can have on enhancing customer experiences and improving operational efficiency. By leveraging the potential of AI, decision-makers can confidently navigate the complexities of today’s business landscape, ensuring smarter decisions, and ultimately propelling their organizations toward a prosperous future.

Bottom line: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.

Image credit: Unsplash

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Balancing Data-Driven Decision Making with Intuition in Innovation

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Balancing Data-Driven Decision Making with Intuition in Innovation

GUEST POST from Art Inteligencia

In the fast-paced world of innovation, leaders are often faced with the challenge of making critical decisions that can determine the success or failure of their initiatives. The rise of big data and advanced analytics has given organizations the tools to drive decisions based on empirical evidence. However, the role of intuition—those gut feelings honed by experience and tacit knowledge—remains irreplaceable. In this article, we will explore how to balance data-driven decision making with intuition, providing insights through two revealing case studies.

Case Study 1: Apple and the iPhone

When Steve Jobs introduced the iPhone in 2007, it revolutionized mobile technology. But this groundbreaking innovation wasn’t solely the product of data-driven decision making.

Data-Driven Insights

Apple analyzed the shortcomings of existing mobile phones in terms of user experience and functionality.
Market data indicated a growing interest in smartphones with internet capabilities, touchscreens, and multimedia features.
Advanced analytics helped Apple understand usage patterns, which influenced design elements like the touchscreen interface.

Intuitive Leadership

Steve Jobs’ intuition played a critical role in deciding to pursue the development of the iPhone despite potential risks.
He envisioned a device that combined a phone, an iPod, and an internet communicator, a concept unheard of at the time.
Jobs made bold decisions on user experience features based on his instinctual understanding of what users would love, rather than what traditional market research might suggest.

The iPhone’s success illustrates how data-driven insights and intuitive leadership can complement each other to bring about transformative innovation.

Case Study 2: Netflix’s Transition to Streaming

Netflix has become synonymous with streaming entertainment, but the company’s journey from DVD rental service to streaming giant was not an obvious path.

Data-Driven Insights

Netflix leveraged data from its DVD rental service to understand customer preferences and viewing habits.
Subscriber data indicated a shift in consumer demand towards digital content delivery, driven by increasing internet speeds and access to devices.
Advanced algorithms and predictive analytics were used to recommend content, enhancing user engagement and satisfaction.

Intuitive Leadership

Reed Hastings, co-founder, and CEO of Netflix relied on his intuition when deciding to invest heavily in streaming technology, a risky move at that time.
Hastings intuitively understood that consumer behavior was shifting towards a preference for on-demand content, even when the data was still emerging.
His vision for the future of entertainment included producing original content, an idea driven in equal parts by intuition and data analytics of viewing trends.

By balancing data insights with intuitive foresight, Netflix was able to successfully pivot its business model, fundamentally changing the entertainment landscape.

Strategies for Balancing Data and Intuition

Embrace Collaborative Decision-Making: Encourage teams to integrate both data and intuition when making decisions. Promote discussions that leverage diverse perspectives and experiences.
Cultivate a Test-and-Learn Culture: Implement policies that allow for experimentation based on intuition while using data to validate or refine these ideas.
Leverage Technology Wisely: Use advanced analytics tools to gather actionable insights, but don’t let them overshadow the value of human intuition and creativity.
Continuous Learning and Adaptation: Encourage ongoing learning for leaders and teams to enhance their intuitive abilities and stay updated with data analytics advancements.

Conclusion

In the quest for innovation, it is not a question of choosing between data-driven decision making and intuition. Rather, the key lies in finding the right balance, where data provides a solid foundation for insights and intuition injects creativity and foresight into the decision-making process. The cases of Apple and Netflix illustrate how the fusion of data and intuition can lead to groundbreaking innovations that redefine markets and industries. By adopting strategies that honor both elements, organizations can navigate uncertainty and foster a culture of sustained innovation.

Image credit: Pixabay

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