Source: HBR.org/ John Hagel III and John Seely Brown
Summary. Ronald Coase nailed it back in 1937 when he identified scalable efficiency as the key driver of the growth of large institutions. But here’s the challenge. Scalable efficiency works best in stable environments that are not evolving rapidly. Today we live in a world that is increasingly shaped by exponentially improving digital technologies that are accelerating change, increasing uncertainty, and driving performance pressure on a global scale. There still is a compelling rationale for large institutions, but it’s a very different one from scalable efficiency. It’s scalable learning. In a world that is more rapidly changing and where our needs are evolving at an accelerating rate, the institutions that are most likely to thrive will be those that provide an opportunity to learn faster together. Scalable learning offers the potential to shift away from a “diminishing returns” model to an “increasing returns” model where the more people who join together to learn faster, the more rapidly value gets created.
It’s far easier and cheaper to coordinate the activities of a large number of people if they’re within one institution rather than spread out across many independent organizations. Scalable efficiency assumes that the constituencies served by these institutions will settle for standardized products and services that meet the lowest common denominator of need.
Consumers are less and less willing to settle for the standardized offerings that drove the success of large institutions in the past. Our research into the long-term decline of return on assets for all public companies in the US from 1965 to today (it’s gone down by 75%) is just one indicator of this pressure. Another indicator is the shrinking life span of companies on the S&P 500. A third is the declining rates of trust indicated by the Edelman Trust Barometer — as the gap grows between what we want and expect and what we receive, our trust in the ability of these institutions to serve our needs erodes.
To reverse these trends, we need to move beyond narrow discussions of product or service innovation, or even more sophisticated conversations about process innovation or business model innovation. Instead, we need to talk about institutional innovation, or re-thinking the rationale for why we have institutions to begin with.
We believe there still is a compelling rationale for large institutions, but it’s a very different one from scalable efficiency. It’s scalable learning. In a world that is more rapidly changing and where our needs are evolving at an accelerating rate, the institutions that are most likely to thrive will be those that provide an opportunity to learn faster together.
We’re not talking about sharing existing knowledge more effectively (although there’s certainly a lot of opportunity there). In a world of exponential change, existing knowledge depreciates at an accelerating rate. The most powerful learning in this kind of world involves creating new knowledge. This kind of learning does not occur in a training room; it occurs on the job, in the day-to-day work environment.
For example, our informal survey of where employees are spending their time in major departments across large companies suggests that 60-70% of their time is consumed in “exception handling” – addressing unexpected events that the existing processes can’t handle. These exceptions are a great opportunity to create new knowledge – how to handle something never anticipated. Yet, today this work is generally done inefficiently – workers struggle to find each other and to access the relevant data and analytics required to resolve the exception. Once they resolve the exception, what they did and learned is largely lost to the rest of the organization.
Moreover, most organizations seem to use digital technology to simply automate tasks and eliminate people. But scalable learning harnesses technology to augment the capabilities of people. Routine tasks do need to be automated, but for the purpose of freeing up people to explore new approaches to create even more value. In this context, one key dimension of learning is for workers to discover how to more effectively use increasingly powerful digital tools in specific contexts. Historical studies of the Industrial Revolution have shown that there was a significant lag between the introduction of new industrial machinery into the workplace and resulting productivity improvements because it took time for workers to develop the skills required to get the most value out of the machinery – skills that could only be taught in a very limited form because they had to be adapted to specific contexts and needs.
Scalable learning not only helps people inside the institution learn faster. It also scales learning by connecting with others outside the institution and building deep, trust-based relationships that can help all participants to learn faster by working together. For example, a number of entrepreneurial motorcycle companies in Chongqing, China have created product design networks connecting a large number of technologists and component vendors and helping them to work together to improve the designs of the components in ways that have led to significant cost reduction while maintaining or improving product performance and reliability.
What if we went further, redesigning our work environments (physical, virtual, and management systems) to help accelerate learning and performance improvement on the job? We have not been able to find a single company that has undertaken this in a systematic and holistic way. We did find some intriguing examples of companies that have introduced interesting elements into the work environment to accelerate learning. For example, Intuit has deployed experimentation platforms throughout the company to encourage employees to try and test new approaches to deliver more value while managing the risk associated with these experiments.
In institutions driven by scalable efficiency, it is the responsibility of the individual to fit into the assigned tasks and roles required by the institution. In institutions driven by scalable learning, the institutions must find ways to evolve and adapt to the needs of the individuals within their organization.
Scalable efficiency doesn’t just demand conformity among the individuals within the institution. It also seeks conformity among those it serves – that’s the path to scalable efficiency.
Scalable learning on the other hand is driven by the desire to learn more about those who are being served by the institutions and then to provide ever more value to those constituencies by tailoring products and services to address the individual and evolving needs of those being served. That learning is a prerequisite to understanding how to deliver more and more value to those being served. Becoming more responsive to the evolving unique needs of the individuals being served by institutions could help to restore the trust that has been eroding.
Not only could innovating around scalable learning help to rebuild trust in our institutions, it could also lead to a profound shift in the nature of performance improvement. The scalable efficiency institutional model is inherently a diminishing returns model – the more efficient these institutions become, the longer and harder they will need to work to get the next increment of performance improvement. Scalable learning, on the other hand, for the first time offers the potential to shift to an increasing returns model where the more people who join together to learn faster, the more rapidly value gets created.