THE INTERVIEW
30
Where do you see these opportunities for
machines to step in?
The first is the user experience. Some simple
examples are voice assistants and digital
assistants that already make customer care
more productive and our homes more efficient.
Those experiences are not possible with an
army of human beings behind them. You need
machine intelligence.
The second is business process improvement.
Every business process we have
today—financial, industrial, manufacturing,
healthcare—involves thinking tasks, and most
of them do not have enough human beings
with the expertise to do those thinking tasks.
Applying machine intelligence to take on just
some of the thinking, such as with radiology,
achieves a more effective operational outcome,
without running out of humans to do
the work.
Then the third is the creation of entirely
new industries. The autonomous vehicle industry
is the best example. Candidly, if you want
cars to drive themselves, you can’t do it by
adding more people.
All of these outcomes have different
degrees of complexity, but none of them are
possible without shifting most of the task—
maybe all of the task—to a machine environment
that operates with speed and efficiency.
The upside is enormous.
How do you address concerns about
job disruption?
All technology, all industrialization changes
disrupt jobs, full stop. AI will disrupt jobs. There
are jobs that will go away, and there will be
jobs created. But there’s also a third category,
and that’s improvement of the human condition.
What happens when machine intelligence
achieves its outcome, like making cars autonomous?
Or making healthcare more intuitive,
or making customer service easier? What
happens to the businesses that use them? It’s
very likely those businesses will grow because
some impediment to their growth suddenly
disappears, which allows them to reach more
customers and provide a better service.
It’s much more complex than just a
one-dimensional consideration of jobs. New
technology will always equal some job disruption—hopefully
more job creation—plus
a change in the human condition that is a
net positive and makes our existence happier
and better.
What is the biggest misconception
leaders have about their data?
Many leaders are wrestling with the idea that
you have to understand your data—what to collect
and what to keep—before you can develop
a data strategy. That’s not true; flip it around.
You have to pivot to the idea that it’s because
you don’t understand all of your data that you
should gather it and use tools like AI and machine
learning to figure out what it’s telling you.
If you don’t pivot, two things will happen.
One, your data strategy will probably never
happen because you’ll never really understand
all your data. Or two, you’ll throw away a bunch
of data that could’ve been really valuable in
getting better insight.
The other misconception people have is that
the data era equals big data. Big data allowed us
to use some new, but rudimentary tools to mine
lots of data to try to see patterns, then present