As
the physical world continues to dovetail with machine learning and
artificial intelligence, Smart Systems will enable previously
unimagined capabilities for both the B2B and B2C worlds. After years of
frustrating fits and starts, the technology is here to integrate
people, processes, and data in ways that enable collective awareness
and better decision making. The question is whether business leadership
is ready to make the leap and grasp its potential.
EMERGING RESEARCH THEMES
We’ve
been reading the technology trends articles that always show up at the
turn of each year. These “portrayals of what’s to come” are perennially
popular because trend spotting seems to come as close as you can get to
foretelling the future.
And
it’s not a lie that all these isolated phenomena—machine learning,
blockchain, nano-medicine, robotic prosthetics, quantum computing,
etc.—have come up over the horizon and are hurtling toward us. But
after reading these yearly trends wrap-ups, we never believe that we’ve
seen a convincing portrait of the future.
Spotting
real trends is like watching waves break on the shore, one after the
other, while remaining unaware of the deep currents and invisible
undertows that cause this surface-reality. The specific trends change
from year to year but the impact of the stories is very predictable.
They always focus relentlessly on the technologies alone, whereas the
real future clearly lies in the complex inter-relationship of many
technological, human, business, and societal forces.
The
multiple parallel technologies behind the trends have not evolved in
isolation. In fact, they have grown up so inter-related and
inter-dependent that they not only reinforce each other but create
completely new compound effects.
This
phenomenon is not just about the impacts of technology on people,
business, and societies. It’s also about the impacts of people,
businesses and societies on technology development. Networks and
information technology’s most profound potential lies in its ability to
connect billions upon billions of smart things and people in a way that
will stretch the boundaries of today’s business and social systems, and
create the potential to change the way we work, learn, innovate and
entertain.
So,
rather than focusing on “point” technology trends, we are highlighting
what we like to call “emerging research themes” that examine the many
reciprocal impacts that are occurring between and among technologies,
people and society.
In his book,The Nature of Technology: What It Is and How It Evolves,
Brian Arthur introduced the idea of combinatorial evolution. Very
simply, each of our technologies is a system assembled from earlier
technologies. For example, the GPS and navigation systems we take for
granted in smartphones combine the predecessor technologies of
satellites, computing, radio receivers, transmitters and atomic clocks
into a new unified and infinitely more valuable technology.
Today,
multiple parallel technology developments appear to be increasingly
reinforcing and accelerating one another. Cloud infrastructure
resources are providing unprecedented computing scale. Mobile computing
devices are extending the reach of computing itself. Machine learning
and AI are bringing intelligence to diverse things, and embedded
systems and IoT technology are connecting and integrating a broad array
of physical and digital applications.
Each
of these technologies is powerful on its own, but “catalytic”
combinations of these capabilities are multiplying their impacts.
Human-connected devices and machine-connected IoT devices enable
exponentially more data. The cloud then enables us to capture, analyze
and model many phenomena through its computational capacity. This in
turn sets the stage for AI and machine learning tools to analyze and
capture new insights.
Interestingly,
the value of a new technology lies not just in what it does, but also
in what future technologies it leads to. Every new technology becomes a
building block for new innovations.
INVISIBLE BUSINESS AND INFORMATION AUTOMATION
Digitization,
AI, and machine learning are creating an economic and business world
that’s vast, automatic, and invisible. Information technology’s impact
on “autonomy” is moving ahead quickly. Business that once took place
primarily among computer-assisted humans is now being executed by ever
more complex adaptive systems without human intervention.
Inside
such systems, reliable and blindingly fast processors do what they are
very good at doing (and what people are very bad at doing): digesting
billions of data-points, interacting with each other about the data,
and controlling each other based upon the state of the data. All in a
matter of nanoseconds. Human beings cannot do this, nor should they.
This incessant stream of ongoing data is becoming automated. Business
is increasingly being conducted in an “invisible” unseen digital domain
that is quietly creating a parallel economy.
The
nature and behavior of this new invisible economy are concerns that
have yet to really take center stage—not only in business communities,
but in most governments and institutions, too.
THE DECENTRALIZATION OF EVERYTHING
As
we end the second decade of the 21st century, many of our biggest
challenges in society and business still originate directly from our
inability to creatively collaborate to solve many significant and very
threatening cross-border problems (pandemics, climate change,
availability of water and food, and many more).
But
just as tides shift according to the gravitational pull of the moon, we
also are seeing the emergence of a cycle of de-centralization and
distribution of resources. Powerful distributed technologies such as
IoT, edge computing, blockchain and more are once again demonstrating
the power of decentralized systems, relationships and interactions, and
potentially setting the stage for a new era of large-scale
collaboration and problem solving.
Just
as the extensible, technology-neutral nature of the Internet has
allowed it to scale so dramatically and gracefully with minimal central
administration, we need a similar approach to enabling problem solving
at scale for our most intractable problems.