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Phase One: Critical Mass Summarize| Homework Help Website

CHAPTER 6

PHASE ONE: CRITICAL MASS

Every August, GE has its annual officers’ meeting in Crotonville, New York, at the company’s executive training facility. It was at that meeting, in 2012, that I spoke to the company’s highest-level executives about The Lean Startup at Beth Comstock and Jeff Immelt’s invitation. The talk was followed in the second half of the day by a workshop with the Series X team—and a few dozen GE officers in the back of the room “just to observe.” These were pivotal meetings, in which I pleaded utter ignorance of diesel engines (and they were kind enough to listen to me anyway), as I described in the Introduction. They turned out to be the beginning of GE’s transformation journey.

I made my presentation on the ground level of an auditorium-style lecture hall. Rising above me in stadium seating were roughly two hundred executives—all skeptics. As Beth Comstock, who shared the stage with me that day, describes them: “A lot of these people were engineers and finance people. They run regions. They’re functional leaders. They crossed their arms. They shifted. You could just see the thought bubble over their heads, like, ‘Okay smarty-pants software dude. You can change a software order fifty times in a day, but try that in a jet engine.’ ”

It was no accident that Series X had been picked as the first project to test; a huge multiplatform engine is about as far away from software as you can get. The thinking was that if we could get this project operating in a new way, there would be no limit to Lean Startup applications company-wide, which aligned perfectly with the company’s desire to simplify its way of working across its many businesses.

 

SERIES X: “RAISE YOUR HAND IF YOU BELIEVE THIS FORECAST.”

Hours after addressing the officers, I found myself in a business school–type classroom elsewhere in the building along with engineers representing the businesses involved in the Series X engine development, the CEOs of each of those

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businesses, plus a small cross-functional group of top-level executives who’d orchestrated my visit that day. And don’t forget the GE officers there “just to observe.”

We had gathered to try to answer one of Jeff Immelt’s most persistent questions: “Why is it taking me five years to get a Series X engine?”

I kicked off the workshop by asking the Series X team to walk us all through their five-year business plan. Cory Nelson, the GM for the Series X program at the time,1

describes the scene far better than I can: “I tell people it was like free falling.” My role was to ask questions about what the team actually knew versus what they had guessed. What do we know about how this product will work? Who are the customers, and how do we know they will want it? What aspects of the timeline are determined by the laws of physics versus GE’s internal processes? (From our discussion in Chapter 4, you’ll recognize these as leap-of-faith assumptions.)

The team proceeded to present the currently approved business case for the Series X, including a revenue forecast with graph bars going up and to the right with such velocity that the chart showed this as-yet-unbuilt engine making literally billions of dollars a year for GE as far into the future as thirty years hence. Beth Comstock recalls: “It was like all the business plans we see, with a hockey stick that is going to grow to the moon in five years, and everything is going to be perfect.”

I remember thinking to myself, “I may not know very much about diesel engines, but this business plan looks awfully familiar—it’s like every startup’s ludicrous fantasy plan. Step into my office!” So I made a simple request of the room: “Raise your hand if you believe this forecast.”

I am not making this up: Everybody in the room raised a hand! And, to be honest, they seemed a little irritated at the question, and at having to explain to the software guy who knew nothing about engines that they never would have invested millions of dollars in this plan if they didn’t believe in it. After all, this team had already spent months collecting requirements. The best and brightest minds in the company had already thoroughly vetted this project and approved it. You can see why they saw my question as an insult to their intelligence.

But I kept going, this time pointing to one specific bar on the chart: “Seriously, who really believes that in the year 2028, you’re going to make exactly this many billion dollars from this engine?”

This time, no one raised a hand. Everyone knows you can’t predict the future with that kind of accuracy. And yet

many of the talented executives in the room had forged successful careers by doing precisely that.

After a prolonged moment of discomfort, we moved on. The team told me that their main competitor in this space had a long history of market domination with a

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product that was technically inferior to what the Series X could be. GE planned to build something that was 20 to 30 percent more energy efficient and use this superiority to convince customers to switch.

Buried in a footnote to the appendix of their business plan was a tiny detail: The key to the competitor’s success was its network of local franchises, which meant it had in place a huge support system that fostered customer relationships. Obviously this was a serious competitive advantage, so I asked the team what their plan was for distribution. “We’re going to go build our own distribution network,” they told me. “Do you know how to do that?” I asked. “Have you done it before?” And, most important, “When are you going to do it?” The answer to this last question was most telling of all. “After the product is done.”

What that meant was that the team would spend five years building a product and then another chunk of time setting up a distribution network, all for a product that by then might have been designed nearly a decade earlier.

Still, the question loomed over the room. Why does it take so long to build this engine?

I don’t want to undersell the technical challenges that led to the original five-year plan. The specifications required an audacious engineering effort that combined a difficult set of design parameters with the need for a new mass-production facility and global supply chain. A lot of brilliant people had done real, hard work to ensure that the plan was feasible and technically viable.

But a large part of the technical difficulty of this project was driven by the specifications themselves. Remember that this product had to support five distinct uses in very different physical terrains (visualize how different the circumstances are at sea, in stationary drilling, on a train, for power generation, and in mobile fracking). The uses for the engine were based on a series of assumptions about the size of the market, competitors’ offerings, and the financial gains to be had by supporting so many different customers all at once.

These “requirements” had been gathered using traditional market-research techniques. But surveys and focus groups are not experiments. Customers don’t always know what they want, though they are often more than happy to tell you anyway. The incentives that govern most customer research promote more rather than fewer requirements (especially when you use a third party).

And just because we can serve multiple customer segments with the same product doesn’t mean we have to. (In fact, this is a typical source of scope creep. In order to create a more alluring fantasy plan, we increase the degree of difficulty for the engineers.) If we could find a way to make the technical requirements easier, maybe we could find a way to shorten the cycle time.

There were also many questions about the plan’s commercial assumptions. One

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of the executives present, Steve Liguori, then GE’s executive director of global innovation and new models, recalled, “We had a whole list of these leap-of-faith assumptions around the marketplace and the customer. What percentage gains is the customer looking for? Are you going to sell it with a direct sales force or an indirect sales force? Are you going to sell it or lease it or rent it? Are you going to pay for distribution? We had about two dozen of these questions, and it turns out that when we asked the team how many of them they thought they could answer, it was only two of the twenty-four.” Liguori recalls this as the “aha moment.” The company had been so focused on the technical risks—Can this product be built?—that it hadn’t focused on the marketing and sales-related risks—Should this product be built?

Since the best way to test market assumptions is to get something out to customers, I made what was, to the room, a really radical suggestion: an MVP diesel engine. This team was trying to design a piece of equipment that would work in multiple contexts. As a result of this complexity, not only did it not have a specific target customer, it was caught up in the budgeting and political constraints that accompany such a multifaceted project. What would happen if we decided to target only one use case at first and make the engineering problem easier?

The room went a little wild. The engineers said it couldn’t be done. Then one of them made a joke: “It’s not literally impossible, though. I mean, I could do it by going to our competitor, buying one of their engines, painting over the logo, and putting ours on.” Cue the nervous chuckles in the room.

Of course, they never would have actually done this, but the joke led to a conversation about which of the five uses was the easiest to build. The marine application had to be waterproof. The mobile fracking application needed wheels. Ultimately, the team arrived at a stationary power generator as the simplest technical prospect. One of the engineers thought this could cut their cycle time from five years to two.

“Five years to two is a pretty good improvement,” I said. “But let’s keep going. In this new time line, how long would it take to build that first engine?” This question seemed to once again cause some irritation in the room. The participants started to painstakingly explain to me the economics of mass production. It’s the same amount of work to set up a factory and supply chain no matter how many engines you subsequently produce.

I apologized once again. “Forgive my ignorance, but I’m not asking about one line of engines. How long would it take you to produce just one single unit? You must have a testing process, right?” They did, and it required that the first working prototype be done and tested within the first year. When I asked if anyone in the room had a customer who might be interested in buying the first prototype, one of the VPs present suddenly said, “I’ve got someone who comes into my office every month asking for that. I’m pretty sure they’d buy it.”

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Now the energy in the room was starting to shift. We’d gone from five years to one year for putting a real product into the hands of a real customer. But the team kept going. “You know, if you just want to sell one engine, to that one specific customer,” said one engineer, “we don’t even need to build anything new. We could modify one of our existing products.” Everyone in the room stared in disbelief. It turned out that there was an engine called the 616 that, with a few adjustments, would meet the specs for just the power generation use. (Of course, the 616 wouldn’t be nearly as profitable as the proposed Series X, as it had the wrong weight and cost profile. But since we were literally talking about a single engine, I asked, could we afford to sell the modified 616 at the lower Series X price—just to test demand? The GE balance sheet could take the hit.)

This new MVP was literally an order of magnitude faster than the original plan: from more than five years to fewer than six months.

In the course of just a few hours—by asking a few deceptively simple questions— we’d dramatically cut the project’s cycle time and found a way for this team to learn quickly. And, if they decided to pursue this course, we could potentially be on track to save the company millions of dollars. What if it turned out that that first customer didn’t want to buy the MVP? What if the lack of a service and support network was a deal killer? Wouldn’t you want to know that now rather than five years from now?

I’ll be honest: I was getting pretty excited. It seemed like a perfect ending. Or was it? As the workshop wound to its conclusion, one of the executives in the

back of the room couldn’t stand it anymore. He had been mostly silent so far, but finally he decided to speak up: “What is the point,” he asked, “of selling just one engine to one customer?” From his point of view, we had just gone from talking about a project potentially worth billions to one worth practically nothing.

His objections continued. Even putting aside the futility of selling only one engine, targeting only one customer use effectively lowered the target market for this product by 80 percent. What would that do to the ROI profile of this investment?

I’ll never forget what happened next. “You’re right,” I said. “If we don’t need to learn anything, if you believe in this plan and its attendant forecast that we looked at a few minutes ago, then what I’m describing is a waste of time. Testing is a distraction from the real work of executing to plan.” I kid you not—this executive looked satisfied.

And that would have been the end of my time at GE, except for the fact that several of his peers objected. Hadn’t we just admitted a few minutes ago that we weren’t entirely sure that this forecast was accurate? The executives themselves started to brainstorm all the things that could go wrong that might be revealed by this MVP: What if the customer’s requirements are different? What if the service

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and support needs are more difficult than we anticipate? What if the customer’s physical environment is more demanding? What if the customer doesn’t trust our brand in this new market segment?

When the conversation shifted from “What does this outsider think?” to “What do we, ourselves, think?” it was a whole new ball game.

Even the most senior technical leader in the entire company at the time, Mark Little, who was then senior vice president and chief technology officer of GE Global Research, had a change of heart. He was the person the engineers in the room most looked up to, and whose skepticism—voiced quite clearly earlier in the day—had them most worried. He ended our workshop by saying something that stunned the room: “I get it now. I am the problem.” He truly understood that for the company to move faster in the way that Jeff Immelt wanted, he, along with every other leader, had to adapt. The standard processes were holding back growth, and he, as a guardian of process, had to make a change.

“What was really important and interesting to me was that the workshop changed the attitude of the team from one of being really scared about making a mistake to being engaged and thoughtful and willing to take a risk and try stuff, and it got the management team to think more about testing assumptions than creating failures,” he recalled. “That was very liberating.”

This was not the end of the story, as you’ll see. The Series X team turned into one of the many pilot projects for the program we came to call FastWorks. The team got the test engine to market dramatically sooner and immediately got an order for five engines. During the time they would have been doing stealth R&D in the conventional process, waiting for what Mark Little calls “the big bang,” they were gaining market insights and earning revenue from their MVP.

We’ll come back to the role of coaches in helping teams learn these methods in the next chapter. But for a moment, I want to dwell on an important fact. During this workshop—and the months of coaching that followed—no one had to tell these engineers what to do. Not me, not Beth Comstock, not Mark Little, not even Jeff Immelt. Once presented with the right framework for rethinking their assumptions, the engineers came up with the new plan through their own analysis and their own insights. It became obvious to everyone in the room that this method had worked and that the team had arrived at an outcome that the company would not have been able to get to any other way.

“The Lean Startup just simplified it,” Cory Nelson recounts. “We were trying to drag around so much complexity and so much overhead. The Lean Startup said, ‘Don’t make it so hard. Take it a step at a time.’ Let’s get an engine out there, let’s go learn some things, then let’s pivot when we need to. There may be some intermediate stops along the way. It’s not going to be a straight line to get there, but it’s having the faith that you’re going to figure out a way to get there.”

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PHASE ONE: COMMON PATTERNS

At GE, the transformation began with the single project I just told you about: the Series X engine. Of course, not every company has a multiplatform engine on tap, so the way the company launched their efforts to change was, in that sense, unique. But in many ways, the project was absolutely typical of the start of a transformation process. No matter what size or kind of company, the earliest stages of implementing this new way of working are local, ad hoc, and chaotic. Early adopters experiment with new approaches, sometimes an external or internal coach may help a handful of individual teams. The beginning of a Startup Way transformation is very grassroots. It makes progress, one project at a time, in service of proving a larger thesis, both to management (top-down) and to the teams doing the testing (bottom-up). It looks different depending on the organization, but I’ve noticed certain common patterns that recur in many different kinds of organizations:

Start with a limited number of projects and build from there in order to create a comprehensive set of cases, stories, and results to show how the new method works in this particular organization. Create dedicated, cross-functional teams to undertake the pilot projects in order to embed functional diversity from the start. Create a growth board–type system that allows executives to make swift, clear decisions about the projects presented to them. Teach early teams how to design Lean Startup–type experiments that help them plot a course through uncertain terrain. Use the right startup-style metrics to measure the results of those experiments. Build a network of leaders in the organization who can help resolve problems that come up as the new way of working comes into conflict with entrenched methods. Work by exception at the start in order to move forward quickly and to defer deep changes to organizational structures until later phases. Translate the new concepts into company-specific language and tools.

 

THE ENERGY FOR TRANSFORMATION

The steps above are, obviously, no small amount of work to undertake. Where do organizations get the motivation to embark on a Startup Way type of transformation? I have seen three distinct driving forces behind this kind of change:

1. CRISIS: Sometimes, a crisis forces change. Earlier I recounted the story of how the very public meltdown of HealthCare.gov—a crisis of the highest order—was the catalyst for real change at numerous agencies across the federal government, beginning with an epic lesson in what can happen if you rely on

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traditional “safe” management methods.

2. STRATEGY: Other times, a new organizational strategy clearly necessitates a new

way of working. At GE and Intuit, change was driven from the very top by a recognition that new strategic imperatives required a dramatic overhaul. This can work only when the most senior leaders in the company have bought into the new approach and are determined to see it through. It is also not the kind of decision that can be made lightly, which is why it becomes critical, after the first stages, to demonstrate how the new methods function and to lay the groundwork for full mobilization across the entire organization.

3. HYPERGROWTH: Success can be its own form of crisis. When a startup achieves product/market fit, it can be forced to grow extremely rapidly. As legendary Silicon Valley investor Marc Andreessen, also founder of Netscape and general partner of the VC firm Andreessen Horowitz, put it (in one of the startup movement’s most famous pieces of writing):

In a great market—a market with lots of real potential customers—the market pulls product out of the startup….And you can always feel product/market fit when it’s happening. The customers are buying the product just as fast as you can make it—or usage is growing just as fast as you can add more servers. Money from customers is piling up in your company checking account. You’re hiring sales and customer support staff as fast as you can. Reporters are calling because they’ve heard about your hot new thing and they want to talk to you about it. You start getting entrepreneur of the year awards from Harvard Business School. Investment bankers are staking out your house.2

What all three of those scenarios have in common is that they unleash a tremendous amount of energy. Like breaking the nuclear bonds of an atom, this discharge must be carefully managed. The kind of energy released in each scenario is different, but once it’s unlocked, what happens next follows the same pattern. If the bonds are broken randomly, without the apparatus to manage the energy, the process can be tremendously destructive. Those who have a way to turn that energy into productive change are at a decisive advantage.

 

HOW THE SUCCESS OF SERIES X INFLUENCED GE

The Series X workshop kicked off a transformation process. After its success, we continued to coach new teams until we had a critical mass that touched every combination of function, region, and business unit in the entire company. The early participants were not chosen at random. Nor was the work they did an end in itself—although it was real, important work. Rather, these initial proofs of concept

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were designed to demonstrate to senior management that this new way of working would be viable across the organization.

GE made an early key decision that was a big driver of FastWorks’s later success. The CEO appointed a cross-functional team of senior executives to oversee the initiative. Comprising the top executives across each of the core disciplines of engineering, marketing, HR, IT, and finance,3 this team served as a kind of steering committee. (Later we would formally organize it as a growth board.) Appointing the right people to be in charge of the effort is critical.

Jeff Immelt immediately recognized that they were on to something important, and not just for new products. Beth Comstock recalled his excitement after the Series X report. “Jeff said, ‘You see, we can do something here. Can we go beyond product scope? Can we use this to go after bureaucracy?’ ” As Viv Goldstein remembers, “That was the entire intent with the Series X engine—to get a single proof point on the board that said, ‘Can it work in a very complicated, very difficult environment?’ And if it could, then what do we do?”

What we did was start training more teams. First, one team at a time. Then four at a time, and then batches of eight, including both new products and new process projects. There were teams focused around refrigerators and engines and neonatal incubators, along with a corporate process redesign team, an enterprise resource planning (ERP) project for manufacturing supply chains, an IT project, and an HR hiring project. Each of these projects was chosen intentionally.

The goal was to test as many kinds of teams and functions as possible to show that the FastWorks methodology could work company-wide on a broad cross- section of lines of business, functions, and geographic regions. This critical mass eventually set off a chain reaction of change throughout the company, sparked and driven by the confident buy-in of senior management.

 

1. Start Small

This first phase is about looking at the results of early projects and asking what went well and what went poorly. Which behaviors and practices support experimentation and entrepreneurial behavior? Which employees have proven to be change agents who will help to scale these efforts?

At GE the scale of the program was determined by how many people the company wanted to train at each stage. The total number of people affected in the early teams that we coached was a tiny fraction of GE’s 300,000-plus workforce. At a startup, the scale of the program is determined by the size of the company.

The fact is, there is no such thing as a sixty-person company. There’s only a sixty- person company on its way to becoming a sixty-five-person company, then a

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hundred-person company, then six hundred, then six thousand, depending on the rate of growth. That is why it’s important to gradually weave what worked well for a young company into the overall management process—to be integrative rather than retroactive. As Patrick Malatack of Twilio puts it: “The failure pattern is: as you scale your business, you stop applying what you had to do out of necessity when you were smaller. You stop experimenting the way you had to experiment because you didn’t have enough resources to go do a three-year project that goes nowhere. As you grow your organization, you need to make sure that you are able to still continue to experiment and try new things.” As Twilio has grown from thirty-five people to six hundred and fifty, they’ve worked hard to keep the early structures in place. “It’s strange how the size of your organization creates this failure pattern for you if you’re not careful,” Malatack says.

 

TRANSFORMING THE FEDERAL GOVERNMENT

The story I told you about HealthCare.gov and Mikey Dickerson may sound like the beginning of the government’s transformation story, but it’s actually the middle. Long before HealthCare.gov, innovative pilot projects and teams were being tested by Todd Park and others, along with a group of young technologists from President Obama’s transition team. They were trying to find ways to implement desperately needed tech reform. Among them was Haley Van Dyck, who had arrived in Washington after working on the presidential campaign as part of the technology team developing and deploying the campaign’s mobile and text messaging platforms, the first of their kind in politics. Now she and many of her colleagues were in D.C. with “a very similar mandate of using technology to connect citizens to the government, instead of voters to the campaign.”

On his first full day in office, President Obama signed the Memorandum on Transparency and Open Government. A few months later, he appointed Aneesh Chopra, then the secretary of technology for the state of Virginia, to the newly created post of chief technology officer of the United States. Together with Vivek Kundra, America’s first chief information officer, and later Jeffrey Zients, the new chief performance officer, Chopra would be responsible for “promot[ing] technological innovation to help achieve our most urgent priorities.” It would be done within a sprawling, interconnected organization made up of dozens of agencies employing 2.8 million people using computer systems dating back to the 1950s: the federal government.4

Teams were parachuted into a few agencies under the name “New Media” offices. A team was also set up within the White House to improve digital communication and civic engagement with the public, building on the campaign’s great success.

These were wild, experimental days and, as is typical of Phase One, quite chaotic. There was little to no organization or coordination among teams, no consistent

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structure in terms of who was reporting to whom or even agreement over what people’s individual missions were.

But these pioneers also learned that there was a real place for technology and startup talent within government. It was the first time anyone was able to gather real evidence for this idea at scale. (A previous lean version of HealthCare.gov built before the failed launch of the official site barely saw the light of day—it was too small to matter!) Van Dyck herself became part of a successful team at the Federal Communications Commission (FCC). As is almost always the case, her team discovered plenty of people inside the agency who were ready for change. They just had had no system for supporting it. If it had not been for these early efforts, there could not have been an effective HealthCare.gov rescue.

The Presidential Innovation Fellows Program After several early successes, Todd Park, who had been promoted to federal CTO after Aneesh Chopra stepped down, proposed a program called the Presidential Innovation Fellows (PIFs), in which leaders from the tech industry would do “tours of duty,” in partnership with civil servants inside the agencies, to tackle specific problems in government that seemed intractable. The idea was to combine the experience and expertise of inside stakeholders with the skills and talents of outside entrepreneurs, designers, and engineers, just as Park would himself do during the HealthCare.gov meltdown and rescue. “What we said was,” Park explained, “what are you trying to do? What kinds of capabilities and skills do you want to bring in from the outside to help you? Let’s form a team that has your best people on it and bring people in from the outside who have the skills you want, and then have that team execute [operations] in a Lean Startup mode to do more than either could do separately and deliver successfully against that mission.”5

The program itself was an experiment. No one knew if it would even be possible to get Silicon Valley people into government, so that became the team’s first hypothesis. To test it, Park got on a plane and announced the new program at TechCrunch Disrupt, a gathering of entrepreneurs, investors, hackers, and tech fans.

The response was overwhelming—nearly seven hundred people applied.6 Park ended up selecting eighteen fellows for that first class and “just throwing them into a small number of projects, and [we] were off to the races to see the kind of results it delivered,” says Van Dyck. As of 2017, 112 fellows had been through the program, more than half of whom had stayed on in the federal government to continue their work.7

The PIF program was the government’s version of the FastWorks pilot projects we did at GE. It was created not only to do important work, but to continue gathering evidence that this new way of working could take hold in a wide variety of agencies and across a huge range of projects.

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2. Build Dedicated, Cross-Functional Teams

The goal of building cross-functional teams is to harness and share collaborative energy from various disciplines within the organization, allowing functional diversity to grow over time. Chances are, the initial teams won’t have the ideal mix of functions, but it’s important to assemble as many necessary functions as possible. Sometimes this means including people who aren’t officially on the team but are willing to volunteer their time and expertise.

On one team I worked with at a large corporation, the leader wanted an industrial designer on the team full-time. But this team didn’t have the budget or political capital to get someone with the right skills assigned to it. Design was considered a separate function from product in this company, so there was a lot of resistance to assigning someone from a rival function. Convincing leaders not only to build truly cross-functional teams but to make them fully dedicated is one of the most significant challenges I typically face when I work with companies of any size. This was a perfect example.

However, the leader of this team knew a designer who believed in his vision, so he approached that person and asked if she would move her desk to the room where the team had set up. This designer wasn’t working for the team or officially assigned to it. She was not being paid out of the team’s budget. She was just a committed volunteer sitting nearby so that when questions arose, team members could consult her. This team was also working on physical prototypes, and her proximity to the process allowed her to intervene if she saw something she knew wouldn’t work.

Of course, not every team needs an industrial designer, just as some won’t need IT or legal support but might need engineering, marketing, or sales. The key is to identify which functions each team requires to make progress.

Susana Jurado Apruzzese, head of the innovation portfolio at Telefónica, says that one of the biggest challenges her company faces is the transfer of knowledge in innovation projects from the innovation area to the business unit for commercialization. To take the success of a project to the next step—namely, to market—the project must be transferred to sales and marketing. Jurado Apruzzese finds that including the business side on her team early on makes getting their buy- in much easier. It’s also an ideal way to make sure that sales and marketing are well versed in the knowledge around the product so that they fully understand what it is they’re selling when the time comes.

“We have realized that unless you are involving the business unit in terms of being a sponsor or a stakeholder from the very beginning, it’s not going to work as well, because they don’t feel like the product is theirs,” says Jurado Apruzzese.

 

WHAT TO DO IF A FUNCTION ISN’T REPRESENTED

Most organizations are resistant to working cross-functionally. The politics and

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budgeting issues alone can derail the initiative. But in the early days, these kinds of failures can turn into valuable learning opportunities for the organization.

During my first months at GE, I worked with a health care team in the company’s Life Sciences business that was developing a very advanced product that they planned to commercialize over several years at a cost of $35 million. This complex, FDA-regulated, technologically intensive device had been in serious R&D for many years, and the company finally felt the technology was mature enough for commercialization.

After going through the FastWorks process, the team decided to build an MVP that they could show a specific customer within a few weeks—instead of waiting for years. They built the prototype, which was nonfunctional but showed how the device would both look and work, and set up a meeting with their customer.

The night before the big reveal, I got an urgent phone call from the team. “Legal said no to our experiment,” they fretted. This team, naturally, did not have a lawyer on board, so they relied on the company’s legal function for approvals.

Because of the nature of their work, they’d known they’d eventually have to consult legal for approval, but they hadn’t planned ahead. Imagine if they had enlisted legal expertise from the start from someone who knew all along that there was no real risk to a patient or anyone else in this MVP—that there was no liability involved until the customer actually said yes to the product and then, eighteen months later, paid money for it. Eighteen months is a long time to resolve any liability issues. They got their project approved, but it required a last-minute exception that added a lot of tension to the process.

 

FUNCTIONAL AMBASSADORS

I want to highlight one additional critical aspect of cross-functionality. Functional team members serve not only as the team’s conscience in their particular area of expertise, but also as enthusiastic ambassadors. As the Startup Way of working starts to spread, it’s important to have people on board who can go back to their colleagues in each function and tell them about the new method.

Ambassadors also act as translators who can explain their role in terms others on the team can understand. I experienced this firsthand with a team at a major manufacturer. An engineer was brought in to ensure that the rigor of the process wasn’t lost as the team transitioned to experimenting and making MVPs. He understood the principles so organically that he was able to translate them into very technical, mechanical engineering terms that were foreign to me but easily understood by the team. We would often have him meet with teams, who said, “We’d love to do this, but we can’t compromise our new-product development process.” His response? “I helped write that process for our division. Here’s how to rethink it to ensure that safety and compliance standards are met, even as we change the mechanics of how we work.”

3. Wield the Golden Sword

At GE, we held “report out” sessions at the end of every three-day training for the teams in Phase One. The second part of the presentation was like a corporate version of the TV show Deal or No Deal. We would explain what was required in order to make the new plan succeed. I encouraged every team to be honest and ask for what was really needed, not the usual corporate-speak padded estimates.

To the surprise of senior leaders, teams rarely asked for more funding. What they usually requested was air cover and clearing away of bureaucratic obstacles. One team needed its team reduced—from a twenty-five-person committee of part-timers to a five-person dedicated team. Other teams needed experts from other functions assigned on a full-time basis. And many of them simply needed senior leadership’s assurance that if they worked in this new way they wouldn’t be eaten alive by middle managers. Using this process, they generally got what they asked for in an extremely efficient manner.

Over the years, I’ve been repeatedly amazed at how many “impossible” problems could be solved by using the simple process I call the “Golden Sword” because it cuts through bureaucracy in one swift stroke. It comes into play during meetings between teams and executives, and it goes like this. The team presents an offer to the senior leaders, saying here is what you get: faster cycle time, more insight into what’s happening on the ground, and a promise to solve the problem fully and control spending along the way. And here is what it will cost you: air cover, secure funding, and cross-functional collaborators. From the point of view of most executives, that’s a true bargain. Greater accountability, greater confidence that the team will deliver real results, and all for the low, low cost of some political maneuvering, which is one thing they excel at.

Of course, getting what they want doesn’t mean a team will automatically succeed. When we launched the first cohort of projects at one company, I had a conversation with executives in which I explained, with all due respect, it would be a triumph if even one team succeeded. True to corporate form there was a lot of pressure to ensure a 100 percent success rate. This kind of thinking, of course, is incompatible with startup thinking, which understands failure and experimentation as part of the methodology. Wielding the Golden Sword helps leadership become a part of that process.

 

TRANSPARENCY AND A BACKUP POWER SYSTEM

One GE team I worked with was working on a next-generation uninterruptible power supply (UPS)—a system that gets sold to big data centers and ensures that if there’s a utility outage the system has power until a second generator is available.

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The team believed they could build a more efficient system using a higher-voltage architecture. Their plan, when they came into the workshop, was to spend three years and roughly $10 million, followed by a huge public launch.

This was one of the first GE teams to organize an internal board. They would have regular pivot-or-persevere meetings in which they would assess the latest MVP they were considering, and the board would make funding decisions based on asking them questions about what they’d learned and how.

Following our initial workshop, the team agreed to build an MVP in three months instead of three years. The team spent a few weeks on electrical diagrams to ensure they could build the product. Then came the moment of truth: A customer requested a proposal, saw the diagrams, and instantly rejected them. The team tried again with another customer, then another. When the rejections piled up, they knew their plan was fundamentally off.

It was no small thing to admit this to their executive sponsor. But, luckily, the Golden Sword process made it easier to keep the conversation focused on what the team had learned following each customer visit. Once they got up the courage to admit this, the team was able to pivot several times and ultimately come up with a new system that turned out to be a winner—and bore only a tangential relationship to their original product specifications.

 

84. Design a Good Experiment

In order for an experiment to tell us what we need to know, i.e., whether it’s worth continuing, it needs to have certain features. Teams don’t do experiments just to see what might happen (if they did, they’d always be successful because something will always happen!). They do them to gain knowledge by measuring customer actions, not just what customers say. Every experiment should have:

A clear falsifiable hypothesis. Without a clear vision of what is supposed to happen, we can’t judge success or failure. And if you cannot fail, you cannot learn. An obvious next action. Build-measure-learn is a cycle, which means every experiment should lead directly to a follow-on action. One experiment is never enough to draw the necessary conclusions. Only a series of experiments can reveal the truth. Strict risk containment. What’s the worst that can happen? is usually a question we ask flippantly. But here we really need to know the answer—and make sure we can live with it. The goal isn’t to prevent anything bad from happening. It’s to make sure, by modifying the experiment, that whatever that bad thing is isn’t disastrous. Risk containment strategies include restricting the number of

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customers who are exposed; not putting the corporate brand on the MVP; not compromising safety or compliance (even better, having a compliance expert on the team); giving the customer a more-than-money-back guarantee; offering to pay extra penalties for non-performance. Commit to always making it right for the customer, no matter the cost (remember, you won’t have very many customers at first). A tie between what is measured and at least one LOFA. If we’re not using an experiment to test an assumption, it’s not giving us useful information.

 

THE CONNECTED CAR

After the meeting with Toyota executives I described in Chapter 1 took place, Matt Kresse, a researcher at the company’s innovation hub, the InfoTechnology Center (ITC), and those same executives agreed to the idea of a Lean Startup project. In March of 2013, Kresse and Vinuth Rai, director of the Toyota InfoTechnology Center, began a series of experiments designed to discover and develop state-of-the- art technology for an Internet-connected car.

Their first step was to test an assumption: They ran an ad on Craigslist under the heading “Do you hate your commute?,” inviting people to come into the research center and complain about their current driving experience. Within an hour, three hundred people had responded. “It was an immediate and very overwhelming response,” Kresse remembers. “We didn’t build anything until we heard the main pain points customers were expressing. It was, I think, the first time we were getting this raw data from users. It felt so good, because we had been operating mostly in a lablike environment where the setting is very sterile. That’s not very conducive for someone to give you an honest response, so this was pretty refreshing.”

The team offered five of the thirty people they brought in for interviews a prototype device to put in their cars for a month and told them that if they liked it they could keep it; if not, they’d receive $100 for their participation. This MVP was nothing more than an Android tablet with a very basic navigation system connected to an inexpensive microcontroller that was wired into the ignition and steering controls and packaged with a Toyota faceplate. “You’ve got to get stuff in front of people, get feedback from people early on,” Kresse remembers thinking.

It was the first time Kresse’s group was able to test their ideas with real consumers. They tracked which applications drivers were using in real time. They then met with people periodically to find out what each individual liked and didn’t. “We were in this process of rapidly reiterating the applications,” says Kresse. When the month was up, 60 percent of the people who tried the prototype navigation system wanted to keep it, and 40 percent of them said they’d recommend it to another person.

This kind of data got the attention of Toyota executives. Again, there was very

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little risk involved—the key to a smart MVP. Kresse and Rai weren’t launching anything outside of the incubator-style ITC. But once their work was recognized, they got the go-ahead to start working with the company’s product groups.

In November of 2016, Toyota launched its new connected “Mobility Service Platform” (MSPF) within its Toyota Connected business, of which Shigeki Tomoyama is president. They’ve come a long way from a humble Craigslist post seeking frustrated drivers.

 

BUSINESS MODEL EXPERIMENTS

One of the early teams I worked with at GE was designing a new gas turbine for a combined cycle power plant. Their goal was to build something that would be 5 percent more efficient than anything else on the market. They predicted it would take about four years to produce and would require the creation of a new supply chain. As they were getting into the planning, a team member said: “Hold on. Four years from now, the competitors’ efficiency will have improved, so let’s make sure that we extrapolate out the new efficiency target and be 5 percent better than that.” Fair enough. But then they had to reestimate and required six years until launch, at which point someone else added, “Wait a minute—in six years, won’t the efficiency need to be even greater?” Before anything had actually happened, the team was already trying to anticipate how they’d know whether they’d have market-leading efficiency by the time the engine was done. It was a downward spiral into near- infinite scope creep.

Their solution was to come up with a new business model. In the old model, GE’s main job was to sell equipment. If and when customers came back for maintenance, try to sell them new efficiencies and upgrades. The team imagined a new model: include upgrades up front and commit to future improvements. There was also a clause that stipulated that GE would be responsible for damages related to any missed deadlines.

As one team member summarized the value proposition, “What if we approached the customer and said, ‘Instead of waiting ten years for a turbine that’s 5 percent more efficient, what if we sold you one right now that is pretty good, the added value being that every year from now on, we’ll offer you an upgrade to replace the blades and fans, and tune the turbine to increase efficiency,’ ” the team brainstormed. “ ‘You’ll have the option, once a year, for us to install these parts, and we’ll have a pre-existing contract that says we get paid for every point of efficiency we drive with each new upgrade.’ ”

We took this plan to the executives, who were excited about it. It offered a dramatically faster cycle-to-market time, and every iteration would provide greater efficiency. The customer is able to buy a turbine with best-in-class efficiency in perpetuity. Then someone asked the fatal question: “Is the revenue that we make

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each year from installing the upgraded parts product revenue or service revenue?” Naïvely, I said, “Who cares? Revenue is revenue.” But the answer mattered to

them—a lot. It represented a turf war within the company that couldn’t be resolved at the time. Still, the team kept at it, and the customer feedback remained positive. Ultimately, the executives came around to understanding that they needed to think about what was right for the customer rather than relying on traditional paradigms. Guy DeLeonardo, gas turbine product manager for GE Power, recalls, “It took the threat of losing a one-billion-dollar deal with a major utility company and valued customer for the two division leaders (the turf owners) to find a way to resolve this. You have to understand, this is how we had worked for the past thirty-plus years.”

With the new model in place, the team launched the 7HA.02 Gas Turbine, which is now an industry leader for product efficiency and had $2 billion in sales in 2016. The team can add new innovations annually, knowing that customers want and will pay for them because the commercial terms have been agreed on in advance. “It doesn’t matter where the revenue goes to the customer,” says DeLeonardo. “We got out of our own way to do what’s right for the customer.”

When experimenting with business models,9 here are a few things to keep in mind:

Whose balance sheet should the product be on? Does it really make sense to force a small business or a consumer to pay cash up front? Why make a distinction between product and service? If products are designed to require periodic maintenance, why not take responsibility for providing it? Should a company profit from a product that may not actually fulfill the customer’s needs? By charging only when the product performs—per-use or performance-based compensation—the company stays fully aligned with the customer’s needs. Cycle times are faster when the company controls every aspect of service delivery. Can we take responsibility for intermediate steps in order to bring new innovations to market faster? When a company puts itself on the customer’s side of the transaction (we profit only when they profit), we are able to discover more ways to add value. Are new competitive dynamics available to gain market share? For example, in GE’s commercial lighting division, they have building-maintenance contracts that charge per socket rather than per bulb. GE is responsible for keeping the socket filled and operational. Every socket covered by a long-term agreement of this type effectively shrinks the total available market that is eligible for old- fashioned competitors to pursue.

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In Lean Startup terms, we understand cycle time as build-measure-learn, which means that sometimes nontechnical parts of the cycle can be collapsed through business model changes. One product team I worked with kept running into a problem. After their product was designed and built, it took a year or more for customers to be given the chance to buy the new model. Why? Because distributors had to purchase the new product and revamp their showrooms in order to display it. Many distributors found this expensive and had little incentive to do it frequently. In markets with few distributors, there was little pressure on distributors to have the latest models on display. Nobody on either side of the divide found this arrangement strange—it was simply how the industry had always operated. And in terms of the long-term profitability of each product, it probably made little difference. Still, from a learning and cycle-time point of view, this setup is quite expensive. Compared to the cost of developing a new product, the cost of helping the distributor is quite modest. Why not move showroom costs onto our balance sheet?

5. Create New Ways to Measure Success

In this first phase of transformation, the organization is setting up cross-functional teams and doing experiments. But how do the teams know if they’re succeeding? Through the use of leading indicators that measure validated learning.

 

LEADING INDICATORS

Leading indicators come in many forms. Their purpose is to track signs that the process is working at the team level. The goal is to show that the probability of something good happening is increasing. For example, one executive I worked with was very focused on cycle time as a leading indicator for success. He was happy if his product teams achieved an order-of-magnitude improvement in cycle time, even if they didn’t produce other tangible benefits immediately. He was convinced that just getting to market faster and learning from customers sooner would eventually produce better commercial outcomes. More often than not, he was right. And this conviction allowed his teams to become more bold in their thinking.

Another good early indicator is customer satisfaction and engagement. As Todd Jackson, VP of product at Dropbox, learned, “Having early passionate users who advocate for your product means they will also tell other users. The number one best form of marketing is word of mouth.”

The customer of one GE team was annoyed to learn that a perfect product would take another five or ten years to deliver. After going through FastWorks and coming up with a new plan, the team went back to this customer and said, “Instead of bringing you perfection, how about we bring you something that is still significantly better than what you have now but is just a start? We can bring it to

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you next year instead of in five years if you would help us by becoming an engaged part of the process.” The customer was excited enough by this idea to want to start collaborating immediately, an experience the team had never had before. They hadn’t done anything yet except have a conversation, but already their relationship with the customer had changed fundamentally. They knew they were on the right track.

The excitement at making new connections with real customers and seeing the potential there ties in to another important leading indicator: team morale. Change is hard, but it can also be contagious. Enthusiasm for a new way of working can make a huge impression on other people. Often one exposure to a truly engaged team is enough to get people saying things like, “I want my whole team to work like that,” or even, “I wish my whole division thought and acted that way.” Morale is powerful.

Notice a universal pattern in all of these stories: In every case, senior leadership has a point of view—and conviction—that the leading indicators the change agents are working toward point to good things ahead. Without this agreement, all the experimentation in the world in Phase One is for naught. Of course, a lot of work happens in later phases to confirm that these leading indicators do, in fact, signal positive business outcomes. But without leadership conviction, there’s no way to get to those later phases.

 

METRICS

A little farther along on the path of experimentation, it’s important to create metrics to measure the success of entrepreneurial projects. This entails replacing traditional metrics—often ROI—with validated learning: scientifically gathered information based on frequent experiments. For example, when the Dropbox team was building Paper, in order not to repeat the mistakes of Carousel and Mailbox, they looked at two critical behaviors: “We were disciplined about signing up first users who were representative of our entire customer and user base,” Aditya Agarwal explains. “We made sure we weren’t siloing ourselves.”

 

Dropbox’s two basic metrics were:

1. Virality. “We did not want Paper to become a single-user tool. If someone was using Paper to replace [to-do list software] Evernote, we weren’t interested in that. We needed it to spread and be collaborative.”

2. Week-two retention. “We invited someone, they tried it. Did they come back in week two?”

Metrics need not be complicated. Jeff Smith was hired into the role of CIO at IBM in 2014 to lead an agile transformation in the IT division. He says, “We used to measure too many damn things that did not have anything to do with the actual

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business value created.” Now they have a list of four: (1) how fast a team can get a new task done; (2) how many tasks a team can complete in the course of one regular work cycle (however long that is); (3) how long it takes for a task pulled out of the backlog lineup to get into production; and (4) how long a task has been sitting in the backlog, which includes pruning projects that have been made irrelevant by the passage of time.

“The simpler your metrics, the simpler your goals,” Smith says. “If everyone can understand it without a manual, people start getting better at a faster rate.”

This is true no matter what the context. As Brian Lefler, a software engineer who transitioned into government IT, says, “Software companies have plenty of parts of their company that don’t make obvious, clear money. At Google, when I worked in Ads it was very clear—I knew how much money we brought in divided by the number of people on my engineering team. But it wasn’t that way when I worked in Amazon Ordering, which was a cost center. We were either costing our salaries or we were costing our salaries plus all sales missed, because we were broken. We figured out how to measure the success of teams so we had a proxy for market interactions.”

When Lefler is working on a project for the federal government, partnering with an agency, these are some of the metrics he likes to use at the outset instead of just asking, “How did this week go?”:

How many bugs were there? How often was the system up? How many minutes did it take someone to process his or her form? How many cards (in the case of an immigration project involving green cards) did we print today?

Metrics are critical, Lefler says, because “when leadership can’t measure results, the common response is to require all decisions to go up to their level. The first- order effect of better measurements is better decision making. The second-order effect is that leadership gives high-performing teams the autonomy to act faster and focus their attention on the right things.”

 

6. Work by Exception

Every team that’s working in the Startup Way needs to have someone in company leadership they can call upon, when needed, to resolve the toughest problems they face when interacting with the wider organization. The lack of such a person can be fatal to an internal startup at worst and cause a huge waste of time at best. Without such a sponsor, the team will have to spend precious resources explaining, navigating, and apologizing for their methods to others in the organization.

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These executive sponsors fall into two different categories. One is the executive sponsor for the company’s change agents. At GE those change agents were Viv Goldstein and Janice Semper, who on a daily basis drove the larger project to get the company to change its ways. The dominant concern of change agents is to make sure the program moves from Phase One to Phase Two. They’re the “boots on the ground,” so they need cover from above, often from someone who has the ear of the CEO, to make sure the change keeps moving forward. The other role played by executive sponsors is as provider of cover for the individual teams. In a large organization, the executive sponsor can be anyone in a position to clear obstacles for the individual pilot teams. It need not be the same sponsor for every team—but every team needs at least one.

In The Lean Startup, I emphasized that what people commonly think of as “protecting the startup from the big bad parent corporation” is actually backward. The issue is how to convince the parent company—and its nervous middle managers —that whatever the startup is doing is safe. That’s why innovation methods that rely on secrecy rarely work more than once. The only path toward company-wide transformation is to innovate out in the open. But then, how do we clear these obstacles?

In a startup, even one that has grown to thousands of people, the person who plays the role of executive sponsor is often the founder or co-founder, who has both the moral authority and the connections to wave problems away. Todd Jackson of Dropbox, who has seen this in action both there and at Google, says: “You have to have the founders or the CEO say, ‘Nope. We’re investing in this.’ And that has to come from him or her, because if it doesn’t, the project will likely get swallowed up by the internal amount of energy and internal inertia that goes into the core product.”

In a larger organization, the executive sponsor must be senior enough to clear obstacles, but not too senior to be unable to meet with individual teams.

 

WHO IS LEGAL?

At one tech company, I worked with a team that wanted to bring a new software product to market in a number of different countries. Their original plan was to launch globally and with great fanfare after eighteen months. During their Lean Startup training, they realized they could gauge interest in those countries much faster with Facebook ads. Their goal was to see if they could get people to enter their credit card numbers and commit to pre-ordering the software before too much time and money had been spent building the new versions.

Everyone agreed on the plan. Then, all at once, they froze. What about legal? In a rush, the team presented a series of arguments for why “legal” wouldn’t allow

this experiment. They argued that there was no way they’d be permitted to take a

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credit card number without actually shipping something. And, once they had the credit card numbers, they’d have to make sure those numbers were kept safe from hackers, since they wouldn’t be using the company’s usual systems for processing. They needed a way for customers to explicitly agree that if the software was never shipped, they would get a refund. There was a laundry list of rules and compliance issues the team saw as insurmountable. They were ready to go back to the original plan.

So then I asked a dumb question: “Who is legal?” Who had told them about all of these ironclad rules? They were stumped. As in many organizations, their fear was a part of the culture that had been handed down and passed around for years.

Then I asked who we could call to find out whether their fears were actually well founded. Remember, this was software, not a medical device or a jet engine. No one’s life or livelihood was at risk, so it seemed worth exploring whether there was a way to run this experiment.

The team decided that the only person who could really answer their question was the division’s general counsel. At my insistence, we placed the call and cowered around the speakerphone in a conference room, waiting for “legal” to answer. When the GC answered, the team presented their question in the worst possible way: “Do you mind if we incur unlimited liability to the company by taking people’s credit card numbers and charging them for a product we may never ship?” You can guess what the answer to that was.

Before the GC could launch into a full lecture, I interrupted. “Sir, apologies for the confusion, but what this team actually wants to do is take credit card numbers from no more than one hundred customers, each of whom would be charged a maximum of $29.95 if and when the product finally ships.” To which the general counsel replied, “You’re telling me that the total liability to the company is three thousand dollars, even in the worst-case scenario?” When we replied yes, he said, “Do you realize you’ve already spent more money than that wasting my time with this phone call? Of course you can run that experiment. Goodbye.”

The team exploded with delighted disbelief. Their experiment had been approved. It was a one-time exception, but the experience offered a glimpse of a new way of working. Only much later, in Phase Three, did this company adopt a more systemic policy. For Phase One, simple exceptions would suffice.

 

ALL HANDS ON DECK—OR NOT

What happened to that software team frequently occurs in Phase One. It’s not always going to be smooth sailing; conflicts will arise in the organization with both existing systems and people. I call this the “all-hands-on-deck problem.” A company has an issue—perhaps the quarter isn’t going well, or the company is about to raise financing and the numbers aren’t where they want them to be. The person in charge

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tells the CEO, and the CEO reports the problem to the board with alarm: “We need all hands on deck! Every person in this company is now dedicated, one hundred percent, to solving this problem!” But what about the small teams dedicated to innovations not tied to the current quarter? There’s something supremely unsatisfying about “almost all hands on deck,” or giving only “99 percent effort” to an urgent problem. This causes many innovation projects to be canceled.

This is precisely the kind of situation in which executive sponsors are critical to making sure the transformation isn’t stalled by conflict and the clash of systems. They can both protect the innovation teams and reassure everyone else that rising to the call in the company’s hour of need is also the right thing to do. It’s one of the ways in which organizations begin to build the capability to do both kinds of work simultaneously. Without this, long-term, sustained growth is impossible.

7. Translate This Way of Working into Terms the Organization Can Understand

One of the most important things an organization needs to do in the early stages of a transformation is to make the process its own. That includes talking about it in language that makes sense for each individual company. As Beth Comstock says of FastWorks (a name that drew on GE’s tagline “Imagination at Work”): “I think with any company, you have to make it your own. We took the best of what was brought to us and adapted it, and I think that’s part of the story, too. We added other tools, like a more disciplined growth-board process inspired by venture capital funding and cultural sayings. I think if you judge a culture by their communication, by the words they use, that’s how you know you’ve had a change.” Remember, it’s not even called “lean manufacturing” at Toyota—it’s the Toyota Production System.

Learning to work in this new way is not about the rigid adoption of a series of practices; it’s about finding the ways the tools can be adapted and applied to each specific company. When people go to Intuit looking for a model of how to bring innovation into their companies—the company’s Design for Delight innovation process has been hugely successful—Bennett Blank, innovation leader at Intuit, explains, “They say ‘What can we replicate?’ ” His answer? “The first thing I always say is, ‘You can’t replicate. Run your own experiments, apply everything to your own process, and then you’ll discover what works in your organization.’ ” This is really good advice, and it’s what has made it possible for the organizations we’ve been talking about in this chapter to move forward so successfully. In this book, I’ve attempted to draw attention to the similarities between programs like FastWorks, Design for Delight, and the U.S. government’s USDS. But each of these programs is still distinctively different. They reflect the culture and character at their parent organization.

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As word has leaked out about my work with GE, I’ve fielded calls regularly from companies that want to replicate GE’s early FastWorks success. But I don’t run a consulting company, so I’m frequently asked whom a company should hire to make this change happen.

I tell companies to put someone already on their staff directly in charge of this initiative and to give that person the necessary resources. I believe this is the only way to make a change like this permanent. It has to come from within the organization and be seen as an indigenous development. It has to be designed by people who truly understand the company culture and the levers that make it work. It’s fine to have coaches help along the way, but an outsider pushing the organization to change is doomed to failure. As CEO of Hootsuite Ryan Holmes says, “Bad processes won’t fix themselves. They often lurk in a power vacuum; frontline employees don’t have the authority to make changes, while senior leaders overlook these issues or assume they’re someone else’s problem. That’s why it’s so helpful to put someone in charge, even if it’s not an official or full-time role—it gives employees somebody to go to.”10

It was precisely this realization that led Janice Semper to approach her boss and insist that she be put onto GE’s transformation project full time. Not only did the individual teams lack someone to lead them, but GE as a whole was trying to effect massive change without an authoritative guide.

About three months after the first group of eight teams I worked with at GE went back to their businesses, Semper and Viv Goldstein asked them to return to company headquarters for an update on how they were doing implementing lean practices. Semper and Goldstein, who both had other roles at the company, had been charged by the executive team with figuring out the next steps in this process everyone was so excited about. “What Viv and I expected to hear was, ‘Hey, everything’s going great!’ and some good ideas on how we could scale it,” Semper recalls. “What we heard was, ‘Wow, this was really hard.’ Consistently, when they went back into the businesses, it was like organ reject. They were thinking and beginning to work in a different way, but nobody around them understood what they were doing, why they were doing it, or how.”

That was the moment when Semper knew that this was about more than just training a few people and waiting for the message to spread. “We realized we needed to redefine and re-articulate how we wanted our employees to think and act and lead.” Semper’s and Goldstein’s jobs from then on were solely focused on helping to create the culture that would support a new way of working. They became co- founders of what would soon be named FastWorks. “We started to look at the process and think about how to take the essence and the root of that process and apply that to GE and make it work for us here,” Semper says. This realization led to much larger changes in the coming months.

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PUTTING IT ALL TOGETHER: THE GE BELIEFS

After Janice Semper realized that GE needed to redefine and re-articulate how they wanted employees to think and act (“You can’t just train people and expect that everything’s going to be fine”), she asked herself a critical question: “What are the levers for change?” One of them was the company’s long-held GE Values, a list of principles that, as Semper describes them, are “growth values. Historically, they were very deeply entrenched in our HR processes, our talent processes, how we recruited people, how we developed them, how we assessed them.” These tenets served as the company’s “north star,” and GE knew that if they really wanted to change the way people worked, they needed to find “a new North Star.”

Rather than tinker with the old list, they decided to start fresh. “This is not incremental,” Semper remembers thinking. “This is a leap, a distinct repositioning of the way we need to work.”

After looking at other companies that had embraced the principles Semper and Goldstein wanted to articulate—faster, simpler, more customer driven—the team began drafting. They weren’t looking to replicate anyone else’s ideas but to educate themselves on what was possible.

They narrowed their “MVP” list to twelve major characteristics of companies that were achieving the kinds of results GE was seeking. Next, Semper’s team decided to get validation from their “customers”—in this case, GE’s own employees. They took their new ideas and engaged directly with “the officers, top two hundred leaders, and four thousand employees in GE’s entry-level leadership program.” They posed two questions:

Which of these twelve traits is GE good at? Where, among these twelve, are our biggest gaps?

The data was unequivocal: There were seven things almost everyone agreed the company was already good at, and five they all believed GE needed to improve upon in order to thrive. Semper’s team zeroed in on the company’s weaknesses, which became the focus of its new priorities. “They were very much centered around being much more customer and user driven rather than product driven,” Semper says. “They were about being simple and lean and operating with speed and experimenting after creating the best teams possible with voices from all parts of the organization.”

When Semper’s team started drafting the actual principles, they again took them out to employees for feedback. To distinguish them from GE’s old values, they decided to call them something different. “We decided on ‘GE Beliefs,’ because it’s meant to capture emotion and spirit, not just intellect. People have to feel this in every fiber of their being, because it’s not just about applying a new process. It’s

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about changing your mindset—your paradigm. How you think about things. Then from there, the behaviors follow.”

After a few rounds of changes and feedback, they launched the GE Beliefs at the annual officers’ meeting in August of 2014:

1. Customers determine our success. 2. Stay lean to go fast. 3. Learn and adapt to win. 4. Empower and inspire each other. 5. Deliver results in an uncertain world.

The reason GE was able to tackle changes at this level, at this stage, was because the transformation was driven very early on by people completely dedicated to making it happen. I’ve told you this story because, as I mentioned in the Introduction, it’s one I saw firsthand. But it’s not only a story about GE. It’s about how dedicated founders—selected sometimes by deliberate choice, sometimes by accident—are the engine that powers entrepreneurship within an organization. Every company has levers that make it run. All it takes to pull them is courage.

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CHAPTER 7

PHASE TWO: SCALING UP

So what does the tipping point of the Startup Way look like? When and how do companies catalyze their early successes into scaled-up deployment?

As I mentioned previously, sometimes the process is part of a planned transformation. Sometimes it’s prompted by crisis, be it positive, like massive growth in an early-stage startup, or negative, as with the federal government following the HealthCare.gov debacle.

Regardless of how, when the moment for decisive action arrives, the hard work and preparation of Phase One pays off. The steep upward slope of the S curve of Phase Two is no time to be taking baby steps and learning new theories. With luck, when the time comes, the new playbook has been battle-tested, or at least probed and prodded a little bit. Because as much as we like to complain that our organizations move like molasses, when change happens, it can happen surprisingly fast.

It may sound strange to compare the exciting sky’s-the-limit potential of a Facebook or Dropbox to the quieter rollout of a new corporate initiative in an established company, but having seen both up close, I can attest to some surprising parallels.

Transformation unleashes a huge amount of latent creativity and energy. It makes impossible-seeming things suddenly possible. The key is to be ready.

 

PHASE TWO: COMMON PATTERNS

This phase is all about rapid scaling and deployment of the methods, identified through the efforts of Phase One, that are right for an organization. At GE, we went from showing that these ideas worked in one functional domain to proving they could be applied in any domain, through individual projects. We proved that this

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