The Great Acceleration

Two studies tell the story. Prior to the pandemic, we undertook one of the largest studies ever of enterprise systems and technology adoption. Encompassing C-level executives at more than 8,300 companies across twenty industries in twenty-two countries—half in information technology (IT) and half not—it included data on their IT systems strategies, their use of twenty-eight technologies, their approaches to talent and culture, and specific performance indicators between 2015 and 2023 (expected).¹ During the pandemic, we undertook a second study covering the same range of issues and encompassing 4,000 companies; twenty industries across twenty countries; and, again, C-level executives, half in IT and half not.²

To say that the pandemic accelerated the pace of tech adoption is to put it mildly. Compared to just before the pandemic, the pace picked up by 70 percent. First-time adoption of digital, AI, cloud, and related technologies averaged 63 percent. Put another way, for any given technology a firm had not adopted before Covid struck, there was a 63 percent chance they adopted it during the pandemic. And they often did it in a hurry. As the chief digital officer of a major European food manufacturer reported to us, “IT changes we planned to undertake over 12 to 18 months occurred in a matter of days.”

Yet, a large majority of companies used these technologies as a lifeline, not as engines of innovation. Before the pandemic, laggards and mediocre performers were already falling behind at an alarming rate: pre-Covid, the top 10 percent of leaders in technology adoption and innovation grew revenue at more than twice the rate of laggards—the bottom 25 percent. Why? Laggards adopt technologies unsystematically, isolate them in unconnected silos, and fail to harness their innovative potential. By contrast, leaders adopt a wide range of cutting-edge information technologies and weave them into “living systems” that blur boundaries, afford agile adaptability, and create seamless human-machine integration.

When Covid compelled companies to fast-forward, the gap between leaders and laggards widened. Leaders invested in these digital technologies at historic rates to respond to new operational challenges and rapidly shifting customer demands. They scaled their investments in key technologies such as cloud and AI. This helped them not only absorb impacts quickly, but also refocus on growth. They not only survived; they thrived—and pulled ahead of laggards at an even more astounding rate. In 2019, our landmark research on enterprise technology strategies and their impact on performance showed that tech leaders were growing revenues at twice the speed of tech laggards. In our second study, the top 10 percent of companies rocketed even further ahead, growing revenues at five times the speed of laggards. Now, following the pandemic, laggards could find themselves falling even further behind—dangerously so—despite their embrace of new technologies.

The bottom line: every business is now a technology business. At the same time, a radically human approach to technology innovation, often diametrically opposed to existing approaches, has now burst forth—and brought companies to a new moment of truth.

Innovation Loses Its Innocence

The breakthroughs we examine here arrived at a time of gathering pessimism about the technological road we had been traveling for the past decade. The 2010s dawned with an unprecedented burst of technology-driven innovation. AI promised driverless vehicles, error-free surgery, great leaps in productivity, and much else. The savvy use of social media helped elect America’s first Black president. Homes, buildings, energy grids, and even cities got “smart” and were getting smarter. Opportunities for disruption seemed boundless—every upstart company aspired to be the Uber or Airbnb of its industry.

But as the decade wore on, innovation lost its innocence. Autonomous vehicles sometimes turned deadly. A massive federal study of facial recognition systems widely used by law enforcement found that Asians and African Americans were up to 100 times more likely to be misidentified than white men. Malign actors appeared to hijack social media to influence US politics and subvert democracy across Europe. Cambridge Analytica harvested the personal data of 87 million people. By the end of the decade, countries and municipalities across the globe were using advances in facial recognition and datamining to create pervasive surveillance systems trained on billions of their citizens. In 2020 and 2021, key agencies throughout the US government were hacked. Ransomware attacks caused transportation problems, created gas shortages, hampered meat processing, and led to worries about food shortages.

Those were just the stories playing out in public. Other challenges emerging from the trajectory of innovation took place largely out of sight. Though seemingly more mundane than the headline-grabbing events, these developments were just as consequential for citizens, companies, and societies.

In many businesses and organizations, algorithms produced biased or unexplainable results that directly affected individuals applying for loans, seeking jobs, or confronting the criminal justice system. Among companies and institutions, “big” IT widened the gap between the haves and the have-nots, as innovations in IT architecture brought ten-fold growth each year in computing power. That’s more than 300,000-fold growth since 2012. Yet, startups, many companies, and academic labs don’t have the resources to pay for the exponentially increasing costs of training data-hungry AI systems to capture the potential of this growing computing power.

As lumbering legacy IT systems struggled to keep up with new technology, leaders found that making wise tech investments had become more difficult than ever. “Yes,” we often heard from CEOs in the course of our work, “we know we have to become a technology company, but what technology?”

The time was ripe for a new departure. But it would not be in the direction of more imposing technology and ever diminishing human involvement. Instead, the trajectory of innovation is taking a dramatic turn that is changing the terms of competition and charting a path toward a more workable future.

Radically Human—Innovation Turned Upside Down

The way in which humans interact with intelligent technologies is entering a third stage. In the first stage, AI was used to automate repetitive tasks. Humans were subservient to machines and often replaced by them, leading to dire predictions of a dystopian future of joblessness for many workers. Happily, the second stage proved the pessimists wrong. As we detailed in Human + Machine, a number of leading companies used AI to augment human capabilities, not replace them. These leaders defied the conventional expectation that technology would render people obsolete. They used the power of human-machine collaboration to transform mechanistic processes into highly adaptive and human-centered activities, transforming their businesses and their bottom lines. This collaborative stage, leveling the playing field between humans and machines, is now giving way to a third stage in which humans and the human are in the ascendant. Leading organizations are not only out-innovating their competitors but taking an even more decisive turn toward human-centered technology—a radically human turn, as we see it, that is upending the very nature of innovation as it was practiced over the previous decade.

This turn is radical in both senses of the word—revolutionary and rooted. Revolutionary in that it is rewriting the terms of competition. Rooted in that it engages with the deepest attributes of humans—how we understand, feel, and think. Intelligent technologies have long endowed us with superhuman abilities, but now they are beginning to encompass the inherently human. That includes not only our abilities but our fallibilities. Just as behavioral economists have incorporated human fallibility into the formerly “dismal science,” tech innovators are taking into account the biases and other human faults that have crept into previous generations of AI and related technologies.

This radically human approach is turning assumptions about the basic building blocks of innovation—intelligence, data, expertise, architecture, and strategy—upside down. Taken together, this upending of reigning assumptions in Intelligence, Data, Expertise, Architecture, Strategy—IDEAS—offers a new innovation framework for companies large and small that they can use to chart a new course to the future, turbocharge revenue growth, and prepare to compete in a world where the human—and the humane—will be the means by which companies will succeed and the measure by which they will be judged.

• Intelligence.   Technologies based only on deep learning have little sense of causality, space, time, or other fundamental concepts that human beings effortlessly call on to move through the world. Now a number of pioneering researchers and companies are creating applications and machines whose reasoning ability is adaptable and savvy—more like the way humans approach problems and tasks. For example, a new generation of robots can generalize in real-world settings like warehouses, manipulating items without being told what to do. Or consider “emotional AI,” which grew out of work with autistic children to help them understand and express their emotions. It is now evolving into onboard automobile AI that could be as effective in saving motorists’ lives as seatbelts. By leveraging the most powerful cognitive characteristics of humans—awareness and adaptability—these developments promise potentially more intelligent solutions to pressing commercial and social challenges.
• Data.   The voracious data appetite of deep learning and the need for massive infrastructure to support it has increasingly put AI out of reach for many organizations. In the future, however, we will have top-down systems that don’t require as much data and are faster, more flexible, and more affordable. Companies, like e-commerce retailer Wayfair, are effectively training AI in contexts where big and noisy data, like an enormous number of products, would previously drown out the small subset of relevant data. As AI continues to evolve, researchers and organizations are developing techniques ranging from data echoing, where a system reuses data; to active learning, where the system indicates what training data it needs; to synthetic data, where usable data is created where none exists. The size, shape, sources, and implementation of data are changing and, in the process, giving companies even more powerful insights and adding agility to their operations.
• Expertise.   The human turn in intelligent systems is upending many of the assumptions about the role of people and their expertise in the emerging technological ecosystem. This is one of the most consequential human turns of all: from machines “learning” by processing mountains of data to humans teaching machines based on human experience, perception, and intuition. Rather than training systems with bottom-up machine intelligence, people are guiding them with top-down human knowledge, imparting natural intelligence to what was previously artificial. At Royal Dutch Shell, for example, an engineer or other in-house expert puts a layer of high-level machine teaching on top of an AI drilling system to dramatically shorten the time it takes the system to figure out how to operate when conditions change. Tesla uses its hundreds of thousands of car owners to teach its Autopilot feature how humans drive in virtually any situation. Etsy, the online marketplace for vintage and handmade goods, has developed a product recommendation system based on aesthetics, a notoriously difficult challenge for AI, by having the company’s experts school the system in subjective notions of style. For almost any company, machine teaching unleashes the often-untapped expertise throughout the organization, allowing more people to use AI in new and sophisticated ways.
• Architecture.   Because all businesses are now, in effect, technology businesses, architecture matters more than ever. The conventional IT “stack” spans software applications, hardware, telecommunications, facilities, and data centers. But this conventional stack simply can’t handle today’s hyper-digital world of mobile computing, AI applications, the internet of things (IoT), and billions of devices. And it wasn’t designed to adapt to the human turn in intelligence, data, and expertise that is setting the new terms of innovation. In place of the rigid conventional stack, innovative companies are creating “living systems”—boundaryless, adaptable, and radically human architectures that bring an elegant simplicity to human-machine interaction. Epic Games, creator of the software framework called the “Unreal Engine,” is a great example. This architecture is fast and adaptable, allowing more than 8 million simultaneous users to engage in graphics-intensive game play in addition to collecting a large and steady stream of data for AI-enabled analytics. Harnessing the power and elasticity of the cloud and combining it with AI and edge computing, the human turn in architecture has ignited a new era of business where competition, no matter the industry, has become a battle between systems.
• Strategy.   Leading companies are pioneering a fundamentally new approach to strategy and, in the process, creating powerful engines of value creation. Enabled by intelligent technologies, these business models are built on an unprecedented integration of strategy and execution that advances both nearly simultaneously. Given the great acceleration in digital transformation and the speed with which new intelligent technologies are arriving, these companies know they can no longer afford to sequentially devise a strategy, experiment, and then execute. Their new approach is ushering in some novel business strategies. Among these strategies, three stand out: Forever Beta, Minimum Viable IDEA (MVI), and Co-lab. Forever Beta strategies are seen in products like the Tesla, digitally updateable through the cloud, allowing customers to see the value and utility of their purchase grow over time rather than fade. MVI strategies use one or more elements of the IDEAS framework to precisely target weak links in a traditional industry, provide a superior customer experience, and make immediate inroads in the market. Lemonade, an upstart online insurer, combined AI chatbots, machine learning, the cloud, and an approach that made the customer the linchpin of a unique human-in-the-loop process to eliminate the mutual distrust between insurance companies and customers. Co-lab strategies produce superior results in the sciences or other knowledge-intensive environments through human-guided, machine-driven discovery. Nowhere can the power of IDEAS and the marriage of strategy and execution be seen more clearly than in the remarkable part played by Moderna and Pfizer/BioNtech in the development of Covid-19 vaccines in record time.

With the IDEAS framework at hand, both technical and nontechnical executives can better understand each of the distinct elements of the emerging technology landscape and innovate in all areas of the business. Possibilities span everything from R&D and operations to talent management and business models. Executives with deep knowledge of tech may be inspired to further push the boundaries of their disciplines to capture even more value from digital transformation using the framework.

Most importantly, for specialists and nonspecialists alike, the IDEAS framework provides a common point of reference to guide business and technological initiatives—the radically human dimension that will increasingly drive competition.

Who Will Win in the New World of Radically Human Innovation?

To gauge how companies are faring in this emerging environment, we have drawn on both of our studies—pre-pandemic and post-pandemic—of enterprise systems and technology adoption. We have drawn also on targeted case studies of leading companies, our hands-on client experience, and extensive conversations with business leaders and innovators around the world that have grown out of Human + Machine.

In part one, “Transforming Innovation—The Power of IDEAS,” we explore the new approaches to intelligence, data, expertise, architecture, and strategy that are redefining innovation. In part two, “Competing for the Radically Human Future,” we explore how companies will use IDEAS to differentiate themselves along four key dimensions: talent, trust, experiences, and sustainability.

All four of the areas outlined in part two have been of concern to a greater or lesser degree for many companies throughout this century. Now they loom larger than ever. Tech-literate talent is in short supply; trust has been thrust to the fore by the pandemic; unique experiences, enhanced by technology, offer nearly limitless possibilities for customers, employees, and citizens alike; and sustainability grows more urgent daily.

What’s different now is the outsized role AI and related technologies play in how companies perform along these four dimensions of competitive difference. Companies of all kinds—traditionally tech oriented or not—will need to come to terms with the ways radically human technology and innovation are redefining these more-important-than-ever sources of differentiation. Talent, trust, experiences, and sustainability will be major components of every company’s value proposition, brand promise, and financial performance.

Who will come out ahead? The answer is far from obvious. The fact that leaders in our second survey were growing revenue five times faster than laggards might imply that we are moving into an era of winner-take-all competition. But in this second survey, conducted during the pandemic, we found a number of organizations that have been able to break previous performance barriers.

These “leapfroggers,” representing about 18 percent of our sample, have adopted advanced and emerging human-centered technologies and scaled them across their enterprises while fostering the right kind of organizational change needed to take advantage of these investments. And they have shifted their IT budgets from operations-related activity to innovation-related activity. Between 2018 and 2020, leapfroggers grew at four times the rate of laggards. During the pandemic, their growth rate was even higher than that of the average leader. By pursuing compressed transformation, these breakout companies demonstrate the possibility that even the least technologically advanced organizations can make enormous and profitable leaps forward.

A More Human—and Humane—Future

What could radically human technology mean for individuals and society? What role do companies play in helping society move toward a more general social prosperity? How can companies actively foster well-being for customers and communities? And do it all while aiming for financial success?

Just as we shape our tools, our tools shape us.³ This adage offers an excellent philosophical foundation to begin to think through these questions. From the hammer to the wheel to television to AI, each tool created for making or doing in turn forces us to rethink our environments and ourselves, to reconsider who we are and what’s possible for the future, for better or for worse.

In Human + Machine, we reported that leading companies aren’t pitting humans against machines in a fight for jobs. Instead, they are putting people and machines in symbiotic relationships, each pushing the other to achieve what neither could do on their own. The symbiosis between people and our tools is what undergirds the radically human IDEAS framework and what shines a light through the differentiators of talent, trust, experience, and sustainability.

While not all solutions should be tech solutions, modern technology is, without doubt, one of the most potent and scalable tools for accelerating positive change in our world, especially when coupled with strong policy and healthy guardrails for human safety and dignity. That today’s technology holds this kind of power is all the more reason to understand how to better shape it so as to better shape ourselves. This sentiment includes acting quickly to fix our tools as well as our own understanding of them, when they, or we, falter.

What follows are the stories of innovators, researchers, companies, and organizations that are putting new, radically human technologies to work for and with human beings and, in doing so, shaping our future tools, the world we live in, and ourselves. In some ways these stories are as old as civilization, and in some ways they’re brand new. All aim to inspire future trajectories of innovation through the very old story of doing well by doing good. What will it take to get us there together? This book will be your guide.