Ronald
Coase nailed it back in 1937 when he identified scalable efficiency as the key
driver of the growth of large institutions. 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.
But
here’s the challenge. Scalable efficiency works best in stable environments
that are not evolving rapidly. It also assumes that the constituencies served
by these institutions will settle for standardized products and services that
meet the lowest common denominator of need.
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. Consumers are less and less
willing to settle for the standardized offerings that drove the success of
large institutions in the past. Ourresearch 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.
- John Hagel III,
- John Seely Brown
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