ORGANIZATIONAL SUPERPOWERS

 

A participant at one of my workshops came up to me a few months afterward to relate the following story, which I am paraphrasing: “Knowing Lean Startup principles makes me feel like I have superpowers. Even though I’m just a junior employee, when I meet with corporate VPs and GMs in my large company, I ask them simple questions and very quickly help them see how their projects are based on fundamental hypotheses that are testable. In minutes, I can lay out a plan they could follow to scientifically validate their plans before it’s too late. They consistently respond with ‘Wow, you are brilliant. We’ve never thought to apply that level of rigor to our thinking about new products before.’ ”

As a result of these interactions, he has developed a reputation within his large company as a brilliant employee. This has been good for his career but very frustrating for him personally. Why? Because although he is quite brilliant, his insights into flawed product plans are due not to his special intelligence but to having a theory that allows him to predict what will happen and propose alternatives. He is frustrated because the managers he is pitching his ideas to do not see the system. They wrongly conclude that the key to success is finding brilliant people like him to put on their teams. They are failing to see the opportunity he is really presenting them: to achieve better results systematically by changing their beliefs about how innovation happens.

Putting the System First: Some Dangers

 

Like Taylor before us, our challenge is to persuade the managers of modern corporations to put the system first. However, Taylorism should act as a cautionary tale, and it is important to learn the lessons of history as we bring these new ideas to a more mainstream audience.

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Taylor is remembered for his focus on systematic practice rather than individual brilliance. Here is the full quote from The Principles of Scientific Management that includes the famous line about putting the system first:

In the future it will be appreciated that our leaders must be trained right as well as born right, and that no great man can (with the old system of personal management) hope to compete with a number of ordinary men who have been properly organized so as efficiently to cooperate.

In the past the man has been first; in the future the system must be first. This in no sense, however, implies that great men are not needed. On the contrary, the first object of any good system must be that of developing first-class men; and under systematic management the best man rises to the top more certainly and more rapidly than ever before.3

 

Unfortunately, Taylor’s insistence that scientific management does not stand in opposition to finding and promoting the best individuals was quickly forgotten. In fact, the productivity gains to be had through the early scientific management tactics, such as time and motion study, task-plus-bonus, and especially functional foremanship (the forerunner of today’s functional departments), were so significant that subsequent generations of managers lost sight of the importance of the people who were implementing them.

This has led to two problems: (1) business systems became overly rigid and thereby failed to take advantage of the adaptability, creativity, and wisdom of individual workers, and (2) there has been an overemphasis on planning, prevention, and procedure, which enable organizations to achieve consistent results in a mostly static world. On the factory floor, these problems have been tackled head on by the lean manufacturing movement, and those lessons have spread throughout many modern corporations. And yet in new product development, entrepreneurship, and innovation work in general we are still using an outdated framework.

My hope is that the Lean Startup movement will not fall into the same reductionist trap. We are just beginning to uncover the rules that govern entrepreneurship, a method that can improve the odds of startup success, and a systematic approach to building new and innovative products. This in no way diminishes the traditional entrepreneurial virtues: the primacy of vision, the willingness to take bold risks, and the courage required in the face of overwhelming odds. Our society needs the creativity and vision of entrepreneurs more than ever. In fact, it is precisely because these are such precious resources that we cannot afford to waste them.

Product Development Pseudoscience

 

I believe that if Taylor were alive today, he would chuckle at what constitutes the management of entrepreneurs and innovators. Although we harness the labor of scientists and engineers who would have dazzled any early-twentieth-century person with their feats of technical wizardry, the management practices we use to organize them are generally devoid of scientific rigor. In fact, I would go so far as to call them pseudoscience.

We routinely green-light new projects more on the basis of intuition than facts. As we’ve seen throughout this book, that is not the root cause of the problem. All innovation begins with vision. It’s what happens next that is critical. As we’ve seen, too many innovation teams engage in success theater, selectively finding data that support their vision rather than exposing the elements of the vision to true experiments, or, even worse, staying in stealth mode to create a data-free zone for unlimited “experimentation” that is devoid of customer feedback or external accountability of any kind. Anytime a team attempts to demonstrate cause and effect by placing highlights on a graph of gross metrics, it is engaging in pseudoscience. How do we know that the proposed cause and effect is true? Anytime a team attempts to justify its failures by resorting to learning as an excuse, it is engaged in pseudoscience as well.

If learning has taken place in one iteration cycle, let us demonstrate it by turning it into validated learning in the next cycle. Only by building a model of customer behavior and then showing our ability to use our product or service to change it over time can we establish real facts about the validity of our vision.

Throughout our celebration of the success of the Lean Startup movement, a note of caution is essential. We cannot afford to have our success breed a new pseudoscience around pivots, MVPs, and the like. This was the fate of scientific management, and in the end, I believe, that set back its cause by decades. Science came to stand for the victory of routine work over creative work, mechanization over humanity, and plans over agility. Later movements had to be spawned to correct those deficiencies.

Taylor believed in many things that he dubbed scientific but that our modern eyes perceive as mere prejudice. He believed in the inherent superiority in both intelligence and character of aristocratic men over the working classes and the superiority of men over women; he also thought that lower-status people should be supervised strictly by their betters. These beliefs are part and parcel of Taylor’s time, and it is tempting to forgive him for having been blind to them.

Yet when our time is viewed through the lens of future practice, what prejudices will be revealed? In what forces do we place undue faith? What might we risk losing sight of with this initial success of our movement?

It is with these questions that I wish to close. As gratifying as it is for me to see the Lean Startup movement gain fame and recognition, it is far more important that we be right in our prescriptions. What is known so far is just the tip of the iceberg. What is needed is a massive project to discover how to unlock the vast stores of potential that are hidden in plain sight in our modern workforce. If we stopped wasting people’s time, what would they do with it? We have no real concept of what is possible.

Starting in the late 1880s, Taylor began a program of experimentation to discover the optimal way to cut steel. In the course of that research, which lasted more than twenty-five years, he and his colleagues performed more than twenty thousand individual experiments. What is remarkable about this project is that it had no academic backing, no government R&D budget. Its entire cost was paid by industry out of the immediate profits generated from the higher productivity the experiments enabled. This was only one experimental program to uncover the hidden productivity in just one kind of work. Other scientific management disciples spent years investigating bricklaying, farming, and even shoveling. They were obsessed with learning the truth and were not satisfied with the folk wisdom of craftspersons or the parables of experts.

Can any of us imagine a modern knowledge-work manager with the same level of interest in the methods his or her employees use? How much of our current innovation work is guided by catchphrases that lack a scientific foundation?

A New Research Program

 

What comparable research programs could we be engaged in to discover how to work more effectively?

For one thing, we have very little understanding of what stimulates productivity under conditions of extreme uncertainty. Luckily, with cycle times falling everywhere, we have many opportunities to test new approaches. Thus, I propose that we create startup testing labs that could put all manner of product development methodologies to the test.

How might those tests be conducted? We could bring in small cross-functional teams, perhaps beginning with product and engineering, and have them work to solve problems by using different development methodologies. We could begin with problems with clear right answers, perhaps drawn from the many international programming competitions that have developed databases of well-defined problems with clear solutions. These competitions also provide a clear baseline of how long it should take for various problems to be solved so that we could establish clearly the individual problem-solving prowess of the experimental subjects.

Using this kind of setup for calibration, we could begin to vary the conditions of the experiments. The challenge will be to increase the level of uncertainty about what the right answer is while still being able to measure the quality of the outcome objectively. Perhaps we could use real-world customer problems and then have real consumers test the output of the teams’ work. Or perhaps we could go so far as to build minimum viable products for solving the same set of problems over and over again to quantify which produces the best customer conversion rates.

We also could vary the all-important cycle time by choosing more or less complex development platforms and distribution channels to test the impact of those factors on the true productivity of the teams.

Most of all, we need to develop clear methods for holding teams accountable for validated learning. I have proposed one method in this book: innovation accounting using a well-defined financial model and engine of growth. However, it is naive to assume that this is the best possible method. As it is adopted in more and more companies, undoubtedly new techniques will be suggested, and we need to be able to evaluate the new ideas as rigorously as possible.

All these questions raise the possibilities of public-private partnerships between research universities and the entrepreneurial communities they seek to foster. It also suggests that universities may be able to add value in more ways than by being simply financial investors or creators of startup incubators, as is the current trend. My prediction is that wherever this research is conducted will become an epicenter of new entrepreneurial practice, and universities conducting this research therefore may be able to achieve a much higher level of commercialization of their basic research activities.4