11
Discovery
Manage Innovation for the Digital Economy

As we have seen throughout this book, the future of work will be fundamentally different when machines do everything. Discovering this future—through formal R&D processes and informal “ground-up” continuous improvement—is the focus of this last chapter dedicated to outlining our AHEAD model.

Discovery, aka blue-sky innovation, related to systems of intelligence and the digital economy, is both a catalyst for and an outcome of undertaking the preceding steps of AHEAD. Automating, instrumenting, enhancing, and making products and services abundant will all allow your organization to discover opportunities that were never before visible or addressable.

Discovery itself, though, is a philosophy, a rigorous practice, an openness to the future, an understanding that innovation can't be a side project—that is, something “nice-to-have” or a rounding error of spend buried deep in a 10-Q report. Discovery is central to remaining relevant in the great digital build-out that lies before us. While machines will do more and more of our current work, the process of innovation will allow us to discover entirely new things to do (with and without machines) that are impossible to imagine and hard to predict, but they will be at the core of what we do in the future.

We've touched on a number of historical figures throughout this book: Henry Ford, Thomas Edison, Ned Ludd, and many others. We start this chapter by looking at another far less well-known one: an inventor whose humble technological breakthrough 186 years ago has had an outsized impact across the world (particularly on Sunday afternoons at 4:00 PM), the evolution of which has something to teach us not only about how innovation works but how we can and should have faith that the unknown future will produce amazing opportunities.

Think for a moment about today's global sports industry, now worth up to $620 billion a year. 1 Factor in ancillary revenues (hot dogs sold at games, taxi and subway rides to sports arenas, ibuprofen taken after your team gets knocked out in the first round of the playoffs), and the total amount of money sports generates is probably way north of that figure.

All that spending flows, initially, from an English engineer's 1827 invention, the lawn mower. Edwin Budding came up with the idea for a small, hand-pushed machine as an alternative to the scythe—the traditional backbreaking way of cutting grass—after seeing how a blade in a cylinder was used in a local cloth mill.

To be sure, sports existed well before the lawn mower: Thomas Lord of Lord's Cricket Ground 2 fame was playing professional cricket in the late 18th century, and, don't forget, Jane Austen was writing about baseball in 1797. 3 But there was no “sports industry” as such. Clearing spaces to play cricket with a scythe was difficult, and thus they were few and far between; early types of footballs were kicked around on mainly muddy thoroughfares.

Budding's lawn mower, as shown in Figure 11.1 , allowed grass to be cut easily and uniformly, and what had historically been simply fields of uncut long grass became a newly defined space: a “playing field.” Over the subsequent decades, into the previously unthought-of vacuum of that open space, rushed idea after idea about things that could be done there. Very quickly an explosion of sporting innovation occurred: football/soccer (the Football Association formed in 1863), rugby (the Rugby Football Union formed in 1871), tennis (the Wimbledon Championship was first played in 1877), croquet (the first all-comers' meeting was in 1868). All these sports emerged to take place on the grass playing field.

Everything that we sports fans live and breathe stems from that original foundational innovation that created that “space.”

A cartoon sketch depictin Mr. Budding and the first lawn mower.

Figure 11.1 Mr. Budding and the First Lawn Mower

If Edwin Budding could come back now and see the impact his lawn mower has had on the world, he would, no doubt, be astonished. It's unlikely that he could have imagined when he sold his first machine to Regent's Park Zoological Garden that 138 years later, a young professional footballer named Cristiano Ronaldo would have 200 million social media followers, all because he kicks a smallish ball around on a field of cut grass. 4

Ronaldo's fame and all of the jobs and money associated with sports across the world originate from Budding's machine. The lawn mower is the foundation upon which the sports industry is built.

Systems of intelligence are the latest set of technologies creating foundations on which new industries and new jobs are being and will be built. Jobs of the future that today we simply can't imagine—just as Budding wouldn't have been able to extrapolate from his machine the rise of Ronaldo, or the commentator who talks about Ronaldo, or the equipment manufacturer that supplies Ronaldo, or the designer who created his logo. What one might call the Budding Effect.

Today, a new economy is emerging with a raft of job categories that even a few years ago would have been hard to predict: social-media consultants, search-engine optimizers, full-stack engineers, Perl developers, digital prophets, hackers-in-residence, content curators, chief happiness officers, innovation sherpas, clue shredders, pixel czars, chief ninjas. These are all jobs that the tech economy's equivalent to Edwin Budding (Babbage, Flowers, Turing, Noyce, Hopper, Gates, Andreessen, and Zuckerberg?) could not have imagined.

Though the notion of “build it and they will come” was largely discredited in the dot-com bust of the early 2000s, faith that the future will produce opportunity is paramount to any company or anyone navigating uncertain times. Otherwise, you might as well just keep on trying to take cost out of your old machines, your old business models, the old S-curves.

Therefore, the question you and your teams should be asking is: What are our lawn mowers? We're already seeing digital leaders place discovery bets on what their lawn mowers might be.

Mark Zuckerberg paid $2 billion for Oculus VR (developer of the Oculus Rift virtual-reality headset) in 2014, but acknowledged that the acquisition's focus on gaming was just a start:

Virtual reality was once the dream of science fiction. But the internet was also once a dream, and so were computers and smartphones. The future is coming, and we have a chance to build it together. I can't wait to start working with the whole team at Oculus to bring this future to the world, and to unlock new worlds for all of us. 5

AI is changing our world already, but in reality we've only begun to scratch the surface of where it will take us over the next 20, 50, or 100 years. As Robert High, the chief technology officer of IBM Watson, told us:

Our work on cognitive computing—about amplifying human cognition—is at a very early stage. I don't want it [the AI technology] to be as good as a human; I want it to be better. Economic value is going to drive the progression and evolution of these cognitive systems to a form of intelligence that I don't think we would recognize as being similar to human intelligence. It's not going to be a replication of human intelligence. It's going to be a replication of some small portion of human intelligence, and then a whole bunch of other forms of intelligence that we don't necessarily recognize today but which will be more beneficial economically. Think about astronomers and how much more they've learned by being able to create sensors in the infrared range, or the X-ray range, or ranges of the spectrum that humans aren't able to perceive. It wasn't necessary for humans to perceive it, wasn't necessary for human evolution or even survival. But it is extremely useful for understanding how the universe works.

Your job is to imagine the new forms of value you can create with the new machines of the new revolution. AI may be a strange lawn mower (one, we hope, not related to Stephen King's The Lawnmower Man ), but we trust you get the point. The Budding Effect has played out time after time throughout history, and it is playing out again now. Institutionalizing the role and importance of being open to the fruits of innovation is a hugely important role that you, as a leader of the future, even though you may not be the head of your company's formal R&D department, need to play.

In a world of cloud services, of open APIs, platforms, and crowdsourcing, every organization has access to readily affordable tools (that a short time ago were prohibitively expensive) with which to build the future of their work. The leaders, individual and corporate, in the digital build-out will be those who leverage the new “means of production”—those who break away from business as usual and choose to develop “business unusual.” This is how you do that.

R&D Without AI Is No R&D at All

Soon, the new machine will be your platform of innovation. Once you are instrumenting, automating, tracking, and analyzing the core operations of your business and applying machine learning, innovation opportunities will be consistently unearthed. These will not be based on the informed opinions of individuals, whether executives or R&D staff, but rather on empiricism, on what's actually occurring in the business.

Now, “innovation” is a rich term, with many different attributes and applications. But the new machine can be applied to your R&D in numerous areas, including the following:

With product innovation, for example, your team will gain continual insight as to how your products are (and aren't) being used, as to what customer frustration points exist, and where the obvious areas for improvement (both incremental and fundamental) exist.

The idea of performing R&D-based innovation without an AI platform will soon be viewed as nothing short of guessing. People in your company will look at the traditional centralized R&D group within corporate and begin to ask, “Who are those strange wizards?” We will eventually wonder, “How did we think having a few smart people in a room at corporate headquarters would deliver constant innovation?” It will seem…silly.

Why? How can a small group of people in one location compete with the insight and the learning of the AI platforms we've described in previous chapters? Once you have automated, haloed, and enhanced your company's activities, the associated AI engines can be applied to innovation. Your R&D process and current R&D team will be greatly enhanced by the application of the new machine, primarily because it radically accelerates the scale and speed of the innovation process.

Innovation at the Velocity and Scale of AI

As Netflix continues to grow, it is attempting, essentially, to create something entirely new—the world's first global television station. But this raises many significant challenges in developing content that will appeal to a wide variety of international customers. After all, it's difficult enough for a local television station to understand the preferences of viewers in a specific metropolitan area; such a station's programming managers live in that very community, with their fingers continuously on the pulse of its culture, demographics, preferences, and sensibilities. How could Netflix's team, based in Northern California, possibly understand the nuanced viewing preferences of customers in places like Bavaria, Australia's Northern Territory, or Japan's Okinawa Island? With the aid of the new machine, it's actually quite easy.

Netflix's programming managers deploy their algorithms to inform them about what is (and isn't) working with specific customer demographics around the globe. In fact, they are constantly surprised, finding their personal assumptions are usually quite wrong. Upon launching into Europe, for example, the Netflix team presumed that age was the key determinant of what customers would watch. Wrong.

Todd Yellin, head of product innovation at Netflix, said, “We thought the 19-year-old guy and the 70-year-old woman have such different tastes that personalization would be easy. But the truth is, 19-year-old guys like to watch documentaries about wedding dresses. Hitting play just once on the Netflix service, that's a far more powerful signal than your age and gender.” 6 Similarly, geography doesn't matter as much as the Netflix team originally expected. Common sense told them that specific regions of the world would have very particular tastes. Wrong again. For example, many Japanese anime films are consumed outside of Japan. Concluded Yellin, “Now we have one big global algorithm, which is super helpful because it leverages all the tastes of all consumers around the world.”

Had Netflix pursued a more traditional approach to R&D to understand global customer preferences (i.e., talking to studio heads, hiring high-priced consultants around the world, taking action based on the informed opinions of its most experienced staff), it likely would have made some very expensive mistakes. After some global failures, Netflix executives might have concluded, “Our model just doesn't work over there.” Instead, by continually leveraging the insights generated by the new machine, the company benefits from a highly detailed roadmap for its ongoing expansion.

Innovation fueled by a system of intelligence is also fast. This rapid-fire, machine-based learning sits at the core of inventor and futurist Ray Kurzweil's Law of Accelerating Returns. In short, Kurzweil argues that humans learn at a linear rate, while machines now learn at an exponential pace. As such, when the new machine is soon widely adopted, the rate of human progress in the 21st century (as defined by the cumulative growth of human knowledge and the pace of innovation) will be at least 1,000 times the average rate of the 20th century. 7

Now, will this thousand-fold speed improvement actually come to fruition? Probably not; there are many carbon-based factors that slow down innovation (e.g., people's opinions, ideas, and emotions, as well as many of the organizational inertial issues we've previously highlighted). So let's be conservative and bring Kurzweil's prediction down not just a bit, but a lot. Even if we lower the machine-based innovation rate by two orders of magnitude, that still means your R&D process will move at 10 times its current rate. This is a level of scale and speed that traditional R&D simply could never provide.

Discovery Is Hard, but Not as Hard as Being Irrelevant

If you need further ammunition to argue the importance of being open to discovery, the views of the world's largest investor, Larry Fink, may be useful. Fink, whose firm, BlackRock, Inc., has $4.6 trillion under management, is not the sort of guy you'd expect to be interested in anything other than next quarter's results. Yet Fink recently sent a letter to the CEOs of S&P 500 companies and large European corporations, stating, “We are asking that every CEO lay out for shareholders each year a strategic framework for long-term value creation…. Today's culture of quarterly earnings hysteria is totally contrary to the long-term approach we need.” 10

Fink's letter says, in essence, that companies need to recognize that an overemphasis on optimizing old approaches without giving enough priority to new approaches is having deleterious effects; indeed, the letter goes on to say “companies have not sufficiently educated [their shareholders] about…how technology and other innovations are impacting their business.”

This type of insight is important in every organization that is trying to invent the future of their work. Living with failure is a prerequisite of creating long-term value. In his field guide to the start-up world of California, Chaos Monkeys: Obscene Fortune and Random Failure in Silicon Valley , former Facebook product manager Antonio Garcia-Martinez expresses the view that 90% of the 75% of companies that don't achieve “escape velocity” fail to do so because they simply give up; start-up life is so tough.

Discovery is a hard, unforgiving place to pitch a tent. There are no guarantees. Living with failure is contrary to so many personal and corporate instincts. Yet without this attitude—this resilience —your future probably looks an awful lot like your present, a present that is certainly soon to be your past. And that's not a future at all.

What to Do on Monday? Don't Short Human Imagination

Discovery can be a risk. Invest too much in the wrong ideas, and you go broke. Wait for somebody else to do it, and you can miss the market opportunity of a lifetime. Countless books have already been written about innovation methods, grandiose plans, and the future of society. Our goal here, however, is to give some practical advice wrapped in a single bold strategy.

That is, don't short human imagination. Succeeding in an era in which machines do almost everything also means accepting and believing that humans will have plenty to do.

If, as some techno-dystopians believe, machines will render humans irrelevant in the near future, the fundamental DNA of Homo sapiens that has propelled us forward for millennia will have run dry. What is that DNA attribute? It is curiosity , something that is the key defining characteristic of intelligence (as it is currently manifested in our human form). From our first words to our first steps, to our first journey, it is intrinsic to our very being to want to know who, what, why, and where. Nobody tells us to ask questions. No parent, or teacher, or TV show, or social media feed tells us or programs us to want to know what's up. We just do.

When computers start asking questions like “Just what do you think you're doing, Dave?,” then we should start worrying. But that eventuality is as far off in the future as it was 50 years ago, when the first prophets of AI-induced human calamity became vocal. Until then, humans will continue to ask questions, be curious, imagine, and build, all the while using the new machine.

The trick is figuring out how to identify and nurture the next big (or small) idea, while balancing this against the reality of how investments are made in large and medium-sized companies. In fact, whenever we bring up discovery with clients, their first reflex is to assert, “But we don't have any money!”

With that in mind, the following subsections describe some things you can do to help balance the budget while still benefiting from the results of wisely conducted discovery.

Apply Digital Kaizen

So what's the best practice for bringing about this new form of innovation? We find too many managers looking for “the next great breakthrough” or, in baseball terms, the grand slam. That doesn't work.

The opposite approach is to ask how the new machine adds the most value—that is, by looking for continuous, incremental improvements or looking to hit singles on a consistent basis. This is akin to the Japanese concept of kaizen , which translates as “change for better” but is implemented as small, continuous improvements that in time have a large impact.

Through digital Kaizen—leveraging the new machine to be on the lookout for incremental improvements—the R&D function can be revolutionized. For example:

  • In product innovation, AI platforms can help monitor a fleet of machines, enabling fast recognition of which components fail prematurely and why. This would then inform the next generation of engineering, as well as field service operations.
  • With process innovation, AI will monitor an instrumented workflow and quickly recognize existing bottlenecks and recommend new approaches.
  • With customer-led innovation, ongoing reviews of how customers are actually using your products will inform product management and pricing schemes.

These are the areas where the new machine can best be initially focused in R&D. They are clear examples of digital Kaizen, a series of small discoveries that over time can change the basis of competition.

For example, nothing is more important to colleges and universities than students—in particular, keeping them as students. The University of Kentucky (in Lexington) is dealing with retention in real-time by applying a bit of digital Kaizen. The school has employed a real-time data analytics platform and a team of data scientists to develop a predictive scoring system that provides here-and-now insight into individual student engagement levels. Data is collected by asking students short survey questions when they log into the school portal, perhaps whether they've purchased all their textbooks or to rate their stress level on a scale of 1 to 5. In just five weeks, the school collected data from more than 40,000 individual students. The insights gained helped increase freshman-to-sophomore retention by 1.3%—an apparently incremental improvement with a significant downstream impact. 11

The best part: with a breakthrough innovation, your competitors will quickly look to neutralize it in the market. But with digital Kaizen, your moves are stealthy and thus difficult to replicate.

It's easy to get a case of whiplash when making realistic plans for discovery. By their very nature, specific ROIs and well-intended Gantt charts tend to inspire frustration when applied to discovery. The best partial antidote is to balance near-term realistic innovation—digital Kaizen, or perhaps “little d” discovery—with your own lawn mower or moon shot idea—“Big D” Discovery.

Digital Kaizen focuses on incremental moves for meaningful impact, a near-term project for which you can take responsibility. The path should be clear by now: cut costs with automation, instrument everything, and harvest the data “exhaust.”

Your digital moon shot might be a project based on Blockchain, or quantum computing, or even artificial super-intelligence. These are bigger bets that, if you're thinking like a VC, can be taken in small steps over time.

The trick here is to balance both approaches.

Let Hits Pay for Misses

Another of the key principles of discovery is the notion best encapsulated by the scriptwriter William Goldman in his 1983 book about the film industry, Adventures in the Screen Trade: “nobody knows anything.”

Your goal should be to become a Know-It-All business via instrumentation, sensors, big data, and analytics. However, the inherent unpredictability of the future is likely a permanent truth. Nobody really knows what will work on the road ahead; not even Netflix. Goldman says that people, if they're lucky, make “educated guesses.” Code Halos can help educate us to make better guesses while making ignorance less acceptable, but we should be humble enough to know that we can't really know. This is particularly true in the wide open spaces now being created by our new lawn mowers.

To deal with this reality, you need to take a leaf out of Hollywood's book and structure your discovery-related efforts around the core idea that “hits pay for misses.” Estimates suggest that 70% of movies lose money. 12 (Only estimates exist because movie industry accounting is surrounded by a long-standing omertá. ) Similar ratios exist in other creative industries, such as music, books, and theater. Even the most revered actors or novelists have plenty of turkeys on their résumés. It's unlikely that Robert DeNiro's 2011 bomb, New Year's Eve , will feature heavily on the sad day The New York Times runs his obituary.

Of course the tech industry is full of its own misses: Microsoft's Vista, Facebook's phone, Apple's Lisa; even the mighty stumble a lot of the time. Shikhar Ghosh of Harvard Business School believes 75% of venture-backed firms in the United States don't return their investors' capital. 13 Technology funding, both in the VC world and inside large companies, is expressly set up so that 70% (and higher) failure rates can be tolerated.

In thinking like a VC, recognize that success is determined by active portfolio management. It's not about putting all of one's money on one idea but making lots of investments, many of which actually don't pan out. According to data from a prominent investment firm, “Around half of all investments returned less than the original investment.” 14 The analysis also notes that a paltry “6% of deals…made up 60% of total returns.”

This may be sobering info. You may ask, “How can we get returns when the smart money does so poorly?” Well, you can move those odds in your favor, but the key is managing a portfolio, not avoiding failure. In fact, in the study cited, a fund gets better returns not by having fewer failures but by having more really big hits. In other words, they had to take more risks to get the best returns.

It turns out the best VCs (those with the highest and most consistent returns) achieve their success not just through superior management of their individual start-ups (i.e., finding great entrepreneurs with fantastic ideas and providing them with the right funding and support). More important, as evidenced by the data, is the simple fact that they place more bets and are willing to fail more often.

Even if you're not in a formal R&D role related to discovery, this means you should establish a portfolio of initiatives focused on discovery, with a clear life cycle methodology that manages these initiatives from inception through to ultimate success or failure. Some companies have initiated groups of entrepreneurially minded employees and allowed them to propose ideas for new products or services, secure financing and management support, build out a team to establish the ideas as a going concern in the external marketplace, and then attempt to grow it to a mature enough point that it can “graduate” to become part of the revenue-driving engine of the overall company. This helps balance digital Kaizen—discovering process-level innovation—with more blue-sky discovery: that is, seeking the next big hit. Toyota is doing just this—balancing investment in its traditional models while simultaneously working on driverless cars.

In addition to this approach being run across your company, you can also scale it down to the portfolio of things that you are directly responsible for in whatever part of the business you operate. If you run a finance function, actively seek ideas from your team on how to reduce month-end book-closing cycles; if you get 10 ideas, one or two of them might be worth exploring further. If you run a sales function, ask for recommendations for new tools that can increase conversion rates. Again, most of the suggestions will likely go nowhere, but semi-formalizing a culture that encourages seeking out new ideas is a powerful way of signaling that business as usual isn't the only business you're in.

Leave the Past Behind

Through our work with clients, we repeatedly hear variations on this lament: “We love your ideas. We'd love to build Code Halos and introduce systems of intelligence. We know we have to migrate to our digital future. But … We have such ingrained, complex, mission-critical systems that we can't change; we dare not risk cutting ‘Wire X’ because we simply don't know what will happen if we do.”

Discovery initiatives in many Fortune 500 companies are hampered because of their expensive, sunk-cost, legacy technology. In an era when the competitive advantages of technology have never been greater, these organizations are maintaining (at huge expense) systems that are a terrible competitive disadvantage.

The reason for this is they don't have the ability to turn old stuff off.

Anyone who's been around corporate IT for a while knows that the concept of decommissioning systems (i.e., sunsetting them, killing them, turning them off ) is entirely alien. Even when new systems, apps, and processes are developed, these typically “sit” on top of their predecessors. Systems are built on top of systems, and before you know it, you have a logical architecture that is the proverbial plate of spaghetti.

There has never been a culture in enterprise IT of throwing things away when they're past their sell-by date. No brownie points accrue to those who question the value of old systems or choose to wade into the often complicated and uncomfortable (potentially career-limiting) political issues around such matters: “Fred commissioned that app. I don't want to tell him, an executive VP, that the system's no good anymore.”

On occasion, finance executives have tried to introduce ideas like “zero-based budgeting” to get IT to take a cold, hard look at the existing footprint but to little avail. Y2K proved that old systems never die. What little glory there is in IT comes from building new stuff, not dealing with old systems.

There are psychological dynamics in play as well, above and beyond spreadsheet dynamics. After all, shutting down old systems can feel like throwing away money. The Japanese writer Marie Kondo, whose book The Life-Changing Magic of Tidying Up has turned into an unlikely sales phenomenon (4.5 million copies sold and counting), says that the money you spent on the item was the price of the joy it generated while you used it. 20 If the item no longer “sparks joy,” there is no need to hang onto it.

In accounting-speak, we might say the item is “fully depreciated.” And a lot of IT is “fully depreciated,” hardly generating any “joy” at all.

This is a problem. Without clearing away systems and processes that are no longer fit for purpose or soon won't be, organizations are undermining their ability to find the budget, time, resources, and energy to invest in the future. Given that most IT budgets increase by small margins year after year, it's unlikely that maintaining business as usual will lead companies to prioritize building and leveraging new machines.

Turning off old IT is difficult, of course, but strategies to deal with this conundrum do exist. Given the mission-critical nature of many IT systems, caution understandably must rule. No one would be too happy if American Airlines made mistakes with flight management software while we were at 35,000 feet. However, the reality is that unless IT (and business) executives get honest about the need to turn apps, systems, and processes off , they (and the companies they serve) are going to simply sit and watch the incredible opportunities of this new golden age slip further and further away.

As we move away from systems of record to systems of intelligence, the time is right again to consider which elements of the past will be useful in the future. As we illustrated in Chapter 4 , systems of record do have a place in the new machines that you need to build, but not in their entirety, not all of them, and not as they are currently configured. Before you take another step forward, examine—or reexamine—your current IT and process portfolio with a steely eye. Chances are good you'll need to retain outside experts to give you the unvarnished truth. Most important, recognize that the cycles of technological innovation that we've examined throughout this book are moving so fast that it is entirely illogical to imagine that systems built 10, 20, or 30 years ago will withstand the competitive onslaught from systems built with the latest tools, techniques, and raw materials.

Play the Wayback Game

If you think discovery is something that's theoretically desirable but practically impossible, it's time to reconsider. They say a picture is worth 1,000 words, so here's a 90-second exercise that helps prove the point. Open a browser and check out the Internet Archive Wayback Machine (at https://archive.org/web/ ). As the name says, it's a nonprofit group with over 500 billion Web pages archived from the past decades. Then, just pick a company you know and check it out; poke around. Big companies including HP, Kodak, and IBM have loads of archived pages.

Initially, it's kind of fun in an “Oh, wow, do you remember film !?” way, but it's also fuel for a much deeper notion. The design and content of Kodak's website in 1997 is 20 years old, but it feels as dated as a wool bathing suit or penny-farthing bicycle. These sites were state-of-the-art in every way, but the world has changed miraculously since then. Those of us who were there at the time know we had no idea of what was to come. Humans simply have trouble extrapolating into the future. We're quite happy to be (rightfully) proud of our current moments. We believe we're at the zenith of development (technically, socially, etc.). We are hardwired to think of ourselves as being at the apex, but in reality we're just at the highest camp on the side of an infinitely high mountain. We simply can't envision how things could be in five or ten years.

One other lesson to learn from the Wayback Game is just how fast technology has changed. Many companies that ruled the day not too long ago—HP, CSC, EDS, Compaq, Gateway, Research in Motion, Nokia, etc.—are now either struggling or gone. If you are in the tech industry, this is business as usual, but as we've shown, digital is moving on to “work that matters.” Banks, insurance companies, retailers, health care providers, and so forth must become used to a tech-sector velocity of change.

Create Your Own Budding Effect

Machines will do more and more, and the companies leading the change will thrive in a winner-take-most digital economy. Discovery shouldn't be passed on to your competitors or your successors. With the new machine, you have all the tools and resources for discovery. It's your challenge, and your opportunity.

Throughout the book, we've talked about great innovators like Watt, Ford, and Jobs, and we've also mentioned lesser ones like Edwin Budding. We believe that in the next 20 years there will be a whole new set of iconic innovators who transform their industries. We don't know who they are, where they are from, or what they will do. Nobody does.

However, with great confidence, we can predict that they will connect the Three M's and apply the principles of the AHEAD model to focus on the work that matters. Central to their generative acts will be the belief that something better can be created. The true core of discovery is, after all, hope.

Notes