Artificial
intelligence in business: Separating the real from the hype
The
potential for AI to infuse business and value chains across various industries
is greater than ever before—but where should executives start?
Typically understood as being all about robots and whiz-bang machines, artificial intelligence (AI)
can be tough for executives to wrap their business minds around. In this
episode of the McKinsey Podcast, senior partner Peter Breuer and
McKinsey Global Institute partner Michael Chui speak with McKinsey Publishing’s
Simon London about burgeoning business applications of artificial intelligence,
the line between hype and true use cases for AI, and how business leaders can
separate one from the other.
Artificial intelligence in business:
Separating the real from the hype
Podcast transcript
Simon London: Hello and welcome to
the McKinsey Podcast. I’m Simon London with McKinsey Publishing.
Today, we’re going to be talking about artificial intelligence. It’s certainly
a hot topic in the business press and also starting to attract quite a lot of
attention in the mainstream media.
You’ve probably read
pieces about everything from killer robots to the impact of AI on jobs. But
what exactly is artificial intelligence? Just as important, what isn’t it? How
can companies put artificial intelligence to work today in ways that are
useful?
I’m joined today by two
McKinsey partners who advise clients and conduct research on these issues. I
notice they also have PhDs in adjacent fields, so as a liberal arts major, I’m
finding this somewhat intimidating. First we have Peter Breuer, a senior
partner based in Cologne, in Germany. Hello, Peter.
Peter Breuer: Hello.
Simon London: And we have Michael Chui, a
partner with the McKinsey Global Institute, based in San Francisco. Hello,
Michael.
Michael Chui: Hi, Simon. Pleasure to be
with you today.
Simon London: Good. Let’s start, if you don’t
mind, by defining our terms. When we talk about artificial intelligence, or AI, what do we mean? Michael, why don’t you just
give us your view.
Michael Chui: It’s interesting, this term
is actually not a new term. It was first used over half a century ago. But
basically it refers to using machines to do things that we consider to be
“intelligent”—being able to either simulate or do things that we describe
people as doing with their cognitive faculties.
Simon London: Peter, anything you’d like to
add to that?
Peter Breuer: As Michael pointed out, the
term was invented by Alan Turing in 1950. So it’s actually a pretty well-known
field. We have seen a little bit of an acceleration lately because of two main
factors.
Number one, the
computational power is going upward with exponential growth. And, number two,
the available data is going upward with exponential growth. Therefore you’ll
see methodologies around machine learning and now going even deeper into deep
learning with new neural networks that are applied to those vast amounts of
data. So that you see, to some extent, the exponential growth in data, in
computational power, leads now to the AI hype or AI development.
Simon London: Michael, in a report that we
published this summer, a McKinsey Global Institute report, we talked about
there being five technology systems of which machine learning is just part of
it. Did you want to run us quickly through what those five are?
Michael Chui: Earlier this year we surveyed over 3,000 different business executives around the world to
understand the degree to which they were deploying these types of technologies.
They’re broad families of technologies, and they overlap a bit, but they are
where some of the recent advancements and developments have been happening.
One of them is around
physical AI, and so that’s robotics and autonomous vehicles. We’re seeing a lot
of interesting things happen there. Second, computer vision—whether it’s image
processing, video processing, et cetera—the deep-learning systems that Peter
made reference to have made a lot of advancements there.
Similarly, around
natural-language processing, whether it’s spoken language particularly, which
is interesting, but also written language, we’re seeing a lot of
natural-language work being done. Also, virtual agents or conversational
interfaces. It’s a bit of an extension on natural language, which is more of a
feature, but this is the ability for systems to roughly converse with you
whether by voice or online through chats.
Finally, machine
learning actually has tremendous applicability beyond the application of the
other types of technologies I just mentioned. And hopefully we’ll have an
opportunity to talk more about that.
Simon London: Great. That’s really helpful.
At least we know the territory we’re dealing with here. Maybe we can bring it
even closer to reality with, as I go about my daily life, I suspect I’m already
running into artificial intelligence in action. Are there things, Peter, that
you see in your daily life that people will recognize are powered by AI?
Peter Breuer: I think we all do, actually.
With our smartphones, we all have supercomputers at our fingertips. Some of the
elements that Michael mentioned, you can experience in your daily life. The
improved spell-checks that you have when you’re typing an email or a message in
your smartphone, this is all powered by machine learning.
Michael also mentioned
language, the spoken word. You will notice that your Siri or Google Assistant
learns every day, and the understanding becomes better every day the more you
use it. That’s obviously machine learning in
the background.
Most of us followed the
exciting introduction of the new iPhone X, and there you also saw in the press
conference, it’s all about machine learning now for face recognition, applied
also, machine learning in face recognition to unlock your phone. So, I think we
all experience it already with our smartphones, and going forward, we’ll see
much more of it.
Michael Chui: What we’re starting to see is
these AI technologies underpinning a lot of the things, all the online and
mobile services that we’re now increasingly taking advantage of. So, for
instance, in e-commerce or media, when systems are providing you with
suggestions for things you might be interested in, things you might be
interested in reading or things you might be interested in buying—the
next-product-to-buy use case, as we describe it—increasingly, those types of
systems are powered not only by statistical methods, but by some of these AI
technologies as well, hopefully bringing consumers closer to the things that
they’d be most interested in.
Simon London: I’m going to throw one more
into the pot there. I’m lucky enough to live in the city of Mountain View in
Silicon Valley. There are a surprising number of self-driving cars out on the road. They’re training. They’re collecting data. These
aren’t things you can yet buy. But I don’t think there’s a day goes past where
on my morning commute in and out of the office, I don’t see a self-driving car.
It’s interesting talking to the technologists, because self-driving cars bring
together all of the things that we’ve been talking about. They have machine
vision. They have robotics. A lot of what’s powering them, of course, under the
virtual hood is machine learning. So I think that’s probably something else
that will be coming to us, before too long at least. What about in a work
setting, as we’re working with clients on artificial intelligence, what are
some of the more interesting business applications that we’re seeing now?
Peter Breuer: Autonomous driving itself
is a pretty broad spectrum. You’ll find a development into different levels of
autonomous driving. We would typically talk about five different levels of
autonomous driving. The automotive carmakers are currently experimenting with
what we would call Level Four autonomous driving, which means under certain
circumstances, the legal liability, when you run into an accident, the legal
liability would be with the OEM, and not with the driver anymore, which is
obviously quite disruptive. Here in Germany our legal authorities are thinking
about the next level of law to cope with that challenge.
Other than autonomous
driving, what you’ll see in other industries—for example in healthcare, there
are experiments with long short-term memory networks, which are currently on
the level that cancer detection is on par with experienced medical doctors,
which is also extremely exciting. Again, with that exponential development, we
will see soon that machines are better in cancer detection in MRI and X-ray
pictures, better than experienced medical doctors, which is quite disruptive
also.
Simon London: Michael, anything that you
see out there, in client work, which is novel and blows you away?
Michael Chui: One of the remarkable things
is the degree to which this is an extension of the things that we’ve seen in
data and analytics before. As Peter made reference to, one of the enabling
factors for machine learning to take hold, there’s large amounts of data. We’ve
seen more and more data collected by companies and our clients, whether it be
transactional data, voice data, or data from the Internet of Things in
the physical world. When you have all that data, you can extend the work you’ve
done in analytics with these AI techniques.
Take, for example,
forecasting a huge and important problem in all kinds of fields, particularly
manufacturing, but supply chain, et cetera. And I think if you talk to any
executive who has to deal with forecasting and ask them, “Could your forecast
be any better?” they would inevitably say, “Absolutely, it could be.” With the
amount of data now that we’re able to collect, when we bring some of these
techniques to bear, we can significantly improve the accuracy of forecast in
many cases. By the way, what’s interesting about that is by bringing these
techniques, say, deep learning and training, these networks, in order to
increase the accuracy of forecasts, you can often multiply that accuracy
increase by bringing more data to bear, so external data from outside the
organization, more fine-grain data from consumers.
This is just one business
problem for which we’re already applying data and analytics. Yet when we bring more data, and particularly when we
bring AI techniques, we can still materially improve the performance against
this problem. Then when we think across the entire value chain within an
organization, there’s almost no place where these technologies, where AI can’t
improve performance.
Simon London: Something I’ve heard you talk
about in other contexts and other forums, Michael: you mentioned a wonderful
application of artificial intelligence, particularly computer vision, at the
mine face in mining. Do you want to say a little bit about that? I thought that
was fascinating.
Michael Chui: Another perhaps surprising
application of AI is in the field of mining, where in many cases
you’re using explosives to blow material off the face. Then you have a choice
to make as to whether you want to use more explosives to create smaller pieces,
or whether you can take bigger pieces back and mill them down. What we’re
starting to see is the use of AI analyzing video analytics to look at the
pieces that come after a charge has been deployed, and then optimize across the
entire system to improve throughput as well as efficiency across the entire
mine.
Simon London: That’s great. That really
underlines that practically every industry, probably every industry, can make
use of AI technology. There are use cases right across the value chain and
across the operations of most companies.
I’m going to put my
journalist hat on for a minute and check us here on hype. You mentioned, Peter,
that to some extent artificial intelligence is going through a bit of a hype
cycle. From what we’re saying, there are a lot of applications and a lot of
industries, and a lot of value is at stake. This is very, very real. Do we think
AI is overhyped? And if so, how?
Peter Breuer: I would say yes and no. We
tried to basically define what AI is and what AI is not at the very beginning.
I mean, I have a PhD in mathematics; to be honest we have not been
superprecise, right? I would say today we are in a phase where we of course
have applications which we would call narrow AI. Those are very specific tasks
that machines today can do better than human beings. There’s always that
example of chess or Go now being played more successfully by machines than by
human beings. Then of course there’s that question about a general AI, where
you have a broader spectrum of capabilities that can be managed by a machine.
We are not there yet. However, we should not forget the speed of development is
exponential.
The human brain is not
wired to understand what exponential growth means. But we face exponential
development here in those key technologies. It is coming much, much faster than
we can imagine. Therefore I would say it is a little bit of overhype, but also
it’s coming extremely fast.
Simon London: Michael, how would you answer
that same question? Hype or no?
Michael Chui: There’s definitely a lot of
hype. But I think what we also see is that hype’s not always bad. It does get
people’s attention. It can sometimes end up with overinflated expectations in
the short term. But in the long term, we do think that there is huge potential.
We’re starting to see a
lot of investment, which reflects the understanding of that potential in some
of our research that we publish. Something in the neighborhood of $26 billion to almost $40 billion invested in AI in 2016, in the previous calendar year,
much of that with the tech giants and some of it from start-ups in terms of
these external investments. That reflects the cutting edge in terms of where we
see real potential value to be created. Now, that said, when we look to what
extent these technologies are actually being used in production, a very small
percentage of companies are either deploying this at scale or within their core
processes.
What we expect to see,
as we have with other technology trends that we’ve identified that truly have
the potential to create value, is that that adoption will start to increase
over time, and we’ll see more value capture over time.
We, in fact, have other
research looking at the potential pace and rate of automation, including technologies
such as AI, over time. When you incorporate all of the factors, which include
the technology development, as well as requiring a positive business case, as
well as the natural S-curve of adoption, we’d describe it as being slow in
macro but fast in micro.
It might take decades
for the full impact of these technologies, even the ones that have already been
developed, to propagate through the economy. On the other hand, if you’re a
company that needs to compete against a competitor who is using these
technologies to compete against you, that will feel very, very fast.
Or if it’s going to
affect you as an individual worker, that could happen quickly as well. So I
think that what’s incumbent upon business leaders is to understand this
technology, understand how they can use this as a competitive weapon, because
again, while it may take a long time for the entire economy to change as a
result of AI, it can change a business case very, very quickly.
Peter Breuer: Michael, I can only agree to
what you say. But this also means that any CEO of a large company—if he’s not
on the journey, or she’s not on the journey, already—they should move quickly.
What we also said at the very beginning, we talk about the spectrum of big
data, analytics, machine learning, deep learning, artificial intelligence.
My strong suggestion to
business leaders would be start your analytics transformation now if you have not already. This
will require you to build capabilities, build technology, start the change in
the organization, which will also be necessary to ultimately go into AI-enabled
processes and AI-enabled business.
Michael Chui: Absolutely. I think this is one
of those fields where there’s a learning curve. The sooner you get started on
the learning curve, the quicker you’ll reach higher levels.
Simon London: I wonder whether there’s a
case for a portfolio-of-initiatives approach, where you’re looking at the stuff
that you can do here and now, but you’re also looking at the second horizon,
even potentially getting smart about the third horizon of where the field is
going. Do we think that’s something that is smart?
Peter Breuer: Yeah. I think so. The
fundamentals are pretty much the same anyway. We talk about a new breed of data
scientists, data engineers that you need. We talk about new technologies and
new IT, and if you build those fundamentals, of course our suggestion would be
to take the right use cases in the right point in time, which is the portfolio
approach. And by getting started now with the easier and simpler use cases,
that also prepares you to take the more advanced use cases in the future.
Michael Chui: I couldn’t agree more. One of
the things that’s easy to do when there’s that much hype is to listen to a
salesperson and buy what’s in their bag, to use a term. Actually, what’s
incredibly important is to look at your own business, understand where you want
to compete and understand where this technology can create the most value for
you. It might be in an operations case like predictive maintenance if you’re
competing on the base of your operations.
If you’re a
sales-and-marketing-oriented organization, then perhaps the next product to buy
or marketing mix or one of these other problems might be the place where AI can
have the most impact. So looking across the broad portfolio and understanding
where you should focus your energies is incredibly valuable.
One of the other things
that’s important is as executives and leaders, even if you’re not an IT leader
or an analytics leader, this has to be led from the top—and that is what we
found in our surveys and what we found in our client work—if you’re really
going to move the needle in performance.
You don’t have to be
the data scientist. You don’t have to be the roboticist or the AI expert in
order to make sure that this has impact in your organization. That executive
leadership we do think is important.
Peter Breuer: I tend to say that empowering
companies to become analytics or AI driven is 50 percent about AI and 50
percent about [changing employees’ mind-sets]. The second 50 percent, in many
cases, is forgotten because everybody’s so excited about computers and robots.
We tend to forget that
we still have employees in the huge organization, for the time being, that we
need to train in those techniques. That top-down-led change needs to trickle
down to every employee ultimately. They need to embrace the new technologies
and the new opportunities. Only then will you see the impact in your business.
Simon London: I do want to challenge you on
the point that, as a general manager, surely you need to get a little bit smart
on the techniques. You may not need to be a data scientist, but I would imagine
that to have conversations with data scientists and to be able to think about
the applications and the use cases in the business, and assess them in an
intelligent way, and stress test them, you probably need to know just a little
bit. How deep do you need to go as a general manager? What are the ways that
you can do it?
Peter Breuer: There is a very important
capability that I think is required here, which I would call the translator
role. It is true that, on the one hand, you have the data scientists deep into machine
learning and similar techniques.
On the other hand, you
have the business with managers and sometimes line managers. But there is a
translation required. For the time being, the manager will make the decision,
and he or she will only make the decision based on better techniques like AI or
machine learning if he or she trusts in what the machine provides as a
suggestion.
That trust needs to be
built, and some level of understanding is required. We all know the terminology
“black box,” and we don’t like to trust the black box. Translators are required
to shed some light into that black box and make it a glass box, so that line
managers develop the trust and build their decisions based on the
recommendation coming from the machine.
Michael Chui: One of the other things that
we’ve discovered is incredibly important is that enabling power of data in
order to have the data sets to train these systems. I had the privilege
to talk with Andrew Ng, who’s a premier AI researcher.
He said the companies
that are taking AI seriously are engaged in multiyear, multidimensional chess
games to collect the data they need in order to compete. And I think if you’re
in a traditional industry and you’re not thinking about competitors who are
competing on the basis of chess—of these multidimensional, multiyear chess
games, to find data, to compete against you—you’re going to fall behind. That’s
an important capability and an important mind-set to bring to this problem too.
Simon London: Super. I’m afraid we’re out
of time, which is disappointing. I could talk about this for much longer. But
thank you, Peter Breuer and Michael Chui for a fascinating discussion. If you’d
like to learn more, please visit McKinsey.com. You’ll find a special page of
resources about artificial intelligence with more to come over the next few
months. Thank you for listening today.
NOVEMBER2017 https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/artificial-intelligence-in-business-separating-the-real-from-the-hype?cid=podcast-eml-alt-mip-mck-oth-1711
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