John Hagel is a regular contributor for Techonomy and a director with Deloitte. He and John Seely Brown, co-chairs of Deloitte’s Center for the Edge, recently published a report tackling one of Techonomy’s central themes: How can institutions adapt to exponential technology change and reorganize themselves for “scalable efficiency?” Techonomy’s Adam Ludwig interviewed Hagel by email about the key ways organizations can foster an innovative environment for learning and transformation.
Other than reading your report, how do institutions figure out how to adapt their practices for better innovation?
Our analysis of long-term return-on-asset trends for all public companies in the United States suggests that most institutions are failing to adapt their practices for better innovation. Return on assets for all U.S. public companies has declined by 75% since 1965 and there is no sign of this turning around. This suggests a growing mismatch between the institutions and practices we have and the mounting performance pressure of a rapidly evolving global economy. To the extent that there has been any adaptation, it has generally occurred by watching and learning from smaller entrepreneurial companies and exploring the capabilities of new generations of digital technologies.
Can you give some examples of big companies learning from smaller, more entrepreneurial ones?
AT&T has accelerated learning and time-to-market and developed a pipeline of innovation by engaging an ecosystem of external developers, venture capitalists, and others through the AT&T Foundry. The foundries—in Silicon Valley, Israel, and Dallas—serve as spaces for external (and internal) teams to collaborate and for small companies to get an audience with AT&T. AT&T benefits from access to talent. For example, one Foundry company, Intucell, improved AT&T’s call retention and throughput speeds by 10 percent and decreased overloading by 15 percent.
SAP has developed an online platform, the SAP Community Network, which engages over two million participants, many from much smaller channel partners and application development companies. These participants contribute rich insights into application development and support. As they build relationships with each other, they are increasingly coming together in globally distributed teams to design and develop entirely new applications that run on SAP’s platform.
What happens if institutions fail to build new systems that foster innovation?
A basic measure of performance is survival rates. Dick Foster, a consultant, has looked at the average time a company spends on Standard & Poor’s Index of leading U.S. companies. Back in 1937, at the height of the Depression, if a company made it on to this prestigious list, it remained there for an average of 75 years. By the 2000s, that lifespan had declined by 80% to 15 years, and if a company fell off the list, it typically did not glide off gracefully into the sunset. Instead, it usually experienced a significant crisis or acquisition that ended its independent existence.
How do institutions find the sweet spot between keeping up with the pace of change and allowing the space and time for “tinkering” that innovation requires?
That’s precisely the issue. Most companies are still locked into a rationale of scalable efficiency; the goal is to squeeze out all friction and inefficiency. But this is a game of diminishing returns. Each increment of performance improvement is harder and takes longer to achieve. The model also views tinkering with suspicion because, after all, it is not very predictable or efficient. If we shift from a rationale of scalable efficiency to one of scalable learning—much more appropriate for a rapidly changing world with increasing uncertainty—we create opportunities to systematically design in the space and time for tinkering that innovation and accelerated learning requires. In fact, it’s no longer an opportunity; it’s an imperative to drive accelerated learning.
What are some examples of recent changes that have transformed how institutions innovate?
New generations of digital technology—cloud computing, social software, and big data analytics—are making it far easier and more economically feasible to mobilize large ecosystems with thousands and, in some cases, millions of participants in ways that can help the participants to learn faster. The current forms of crowdsourcing that tap into external expertise are only a very limited example of the potential to accelerate learning. Crowdsourcing typically involves relatively narrowly-defined transactions. Someone posts a problem or a challenge that can be tightly framed, and when someone comes up with an answer the transaction ends. But there are even more powerful ways to accelerate and sustain learning. What if large numbers of participants came together over an extended period of time to define new challenges, and then worked together to come up with creative new approaches that build on the efforts of others and rapidly iterate towards better and better solutions? The technologies are now in place to support massively scalable global learning platforms of this type.
What are the biggest ways that new digital infrastructures, combined with globalization and economic policy, have upended traditional ways of doing business?
Digital infrastructures and long-term shifts towards greater economic liberalization (freer movement of people, goods, money, and ideas across national and industry boundaries) have combined to systematically undermine barriers to entry and movement on a global scale. At one level, this intensifies competition. By a measure that economists use, competitive intensity in the U.S. since 1965 has more than doubled.
But that’s just the beginning. These forces have not only intensified competition, they have fundamentally changed the basis of competition. Over the last century, the key to creating economic value resided in knowledge stocks. We became successful as companies by developing proprietary knowledge, carefully guarding that knowledge to make sure that no one else could access it and then, as efficiently as possible, extracting the value from that knowledge and delivering it to the marketplace.
But in a world marked by accelerating change and growing uncertainty, something interesting happens. Knowledge stocks depreciate at an accelerating rate. What you know today becomes obsolete at a faster and faster rate. In that kind of world, the key to creating economic value shifts to more effectively participating in a larger number of more diverse knowledge flows so that you can learn faster and refresh your knowledge stocks at a faster and faster rate. Most of our institutions today are organized to succeed in a world of knowledge stocks, not a world of knowledge flows.
What are the biggest dangers facing companies?
In a world of rapid change and unexpected events there’s a significant danger that we fall into a reactive mode, simply sensing and responding to the latest developments and scrambling to keep up. Our horizons shrink and we lose any sight of longer-term trends and opportunities. The result is that we spread ourselves much too thinly across too many initiatives and increase the likelihood that we will get blind-sided by longer trends that appear to come out of left field.
One key way to accelerate learning is to adopt a “zoom out, zoom in” approach. It encourages executives to look ahead 10-20 years and develop a high-level perspective, but not a detailed blueprint, of what kind of company they will need to be successful on that horizon. Then it pulls back to a 6-12 month horizon and asks, “What two or three operating initiatives appear to have the greatest potential in accelerating movement towards that longer-term destination?” Then, every 6-12 months, step back and reflect on progress to date.