by Ajay Agrawal
Agrawal is a professor of entrepreneurship and strategic
management at the University of Toronto's Rotman School
commentary is adapted from an interview conducted by Rik
Kirkland, the senior managing editor of McKinsey
Publishing, who is based in McKinsey's New York office.
Rotman School of
professor Ajay Agrawal
explains how AI changes the
cost of prediction
and what this means for
so many perspectives on the impact of artificial
intelligence (AI) flooding the business press,
it's becoming increasingly rare to find one that's
So when strategy professor Ajay Agrawal shared his
brilliantly simple view on AI, we stood up and took
Agrawal, who teaches at the University of Toronto's
Rotman School of Management and works with AI
start-ups at the Creative Destruction Lab (which he
founded), posits that AI serves a single, but
potentially transformative, economic purpose: it
significantly lowers the cost of prediction.
In his new book,
Prediction Machines - The
Simple Economics of Artificial Intelligence,
coauthored with professors Joshua Gans and Avi
Goldfarb, Agrawal explains how business leaders can
use this premise to figure out the most valuable
ways to apply AI in their organization.
The commentary here, which is adapted from a recent
interview with McKinsey's Rik Kirkland, summarizes
Agrawal's thesis. Consider it a CEO guide to parsing
and prioritizing AI opportunities.
effects of falling costs
When looking at
artificial intelligence (AI)
from the perspective of
economics, we ask the same, single question that we ask with any
What does it reduce
the cost of?
Economists are good at
taking the fun and wizardry out of technology and leaving us with
this dry but illuminating question.
The answer reveals why AI
is so important relative to many other exciting technologies. AI can
be recast as causing a drop in the cost of a first-order input into
many activities in business and our lives - prediction.
We can look at the example of another technology, semiconductors, to
understand the profound changes that occur when technology drops the
cost of a useful input.
the cost of arithmetic, and as they did this, three things happened.
First, we started
using more arithmetic for applications that already
leveraged arithmetic as an input. In the '60s, these were
largely government and military applications.
Later, we started
doing more calculations for functions such as demand
forecasting because these calculations were now easier and
started using this cheaper arithmetic to solve problems that
hadn't traditionally been framed as arithmetic problems.
For example, we
used to solve for the creation of photographic images by
employing chemistry (film-based photography).
arithmetic became cheaper, we began using arithmetic-based
solutions in the design of cameras and image reproduction
The third thing
that happened as the cost of arithmetic fell was that it
changed the value of other things - the value of
arithmetic's complements went up and the value of its
substitutes went down.
So, in the case
of photography, the complements were the software and
hardware used in digital cameras.
The value of
these increased because we used more of them, while the
value of substitutes, the components of film-based cameras,
went down because we started using less and less of them.
powers of prediction
As the cost of prediction continues to drop, we'll use more of it
for traditional prediction problems such as inventory management
because we can predict faster, cheaper, and better.
At the same time, we'll
start using prediction to solve problems that we haven't
historically thought of as prediction problems.
For example, we never thought of autonomous driving as a prediction
problem. Traditionally, engineers programmed an autonomous vehicle
to move around in a controlled environment, such as a factory or
warehouse, by telling it what to do in certain situations - if a
human walks in front of the vehicle (then stop) or if a shelf is
empty (then move to the next shelf).
But we could never put
those vehicles on a city street because there are too many ifs - if
it's dark, if it's rainy, if a child runs into the street, if an
oncoming vehicle has its blinker on.
No matter how many lines
of code we write, we couldn't cover all the potential 'ifs.'
autonomous driving with prediction
Today we can reframe autonomous driving as a prediction problem.
Then an AI simply needs
to predict the answer to one question: What would a good human
driver do? There are a limited set of actions we can take when
We can turn right or
left, brake or accelerate - that's it. So, to teach an AI to drive,
we put a human in a vehicle and tell the human to drive while the AI
is figuratively sitting beside the human watching.
Since the AI doesn't have
eyes and ears like we do, we give it cameras, radar, and light
detection and ranging (LIDAR).
The AI takes the input
data as it comes in through its "eyes" and looks over to the human
and tries to predict,
"What will the human
The AI makes a lot of
mistakes at first.
But it learns from its
mistakes and updates its model every time it incorrectly predicts an
action the human will take. Its predictions start getting better and
better until it becomes so good at predicting what a human would do
that we don't need the human to do it anymore.
The AI can perform the
importance of data, judgment, and action
As in the case of arithmetic, when the price of prediction drops,
the value of its substitutes will go down and the value of its
complements will go up.
The main substitute for
machine prediction is human prediction.
As humans, we make all
kinds of predictions in our business and daily lives. However, we're
pretty noisy thinkers, and we have all kinds of
cognitive biases, so we're quite poor at prediction.
AI will become a much
better predictor than humans are, and as the quality of AI
prediction goes up, the value of human prediction will fall.
prediction means for human judgment
But, at the same time, the value of prediction's complements will go
The complement that's
been covered in the press most is data, with people using phrases
such as "data is the new oil." That's absolutely true - data is an
important complement to prediction, so as the cost of prediction
falls, the value of a company's data goes up.
But there are other complements to prediction that have been
discussed a lot less frequently.
One is human
We use both
prediction and judgment to make decisions. We've never
really unbundled those aspects of decision making before -
we usually think of human decision making as a single step.
unbundling decision making. The machine's doing the
prediction, making the distinct role of judgment in decision
So as the value
of human prediction falls, the value of human judgment goes
up because AI doesn't do judgment - it can only make
predictions and then hand them off to a human to use his or
her judgment to determine what to do with those predictions.
complement to prediction is action.
valuable only in the context of some action that they lead
to. So, for example, one of the start-ups we work with at
the Creative Destruction Lab built a very good
demand-forecasting AI for perishable food such as yogurt.
accuracy, this prediction machine is worth zero in the
absence of a grocery retailer deciding how much yogurt to
owning data as an asset, many incumbents also own the
experiment for the top team
One approach to
pinpoint ways to use AI in business is to review
organizational workflows - the processes of turning inputs into
outputs - and break them down into tasks.
Then, look for the tasks
that have a significant prediction component that would benefit from
a prediction machine.
Next, determine the
return on investment for building a prediction machine to do each
task, and simply rank those tasks in order from top to bottom.
Many of the AIs created out of this exercise will be
efficiency-enhancing tools that will give the company some kind of a
lift - possibly a 1 percent to 10 percent increase in
EBITDA or some other measure of
identify areas of AI disruption - and value
However, to anticipate which AI tools will go beyond increasing
efficiency and instead lead to transformation, we employ an exercise
called "science fictioning."
We take each AI tool and
imagine it as a radio volume knob, and as you turn the knob, rather
than turning up the volume, you are instead turning up the
prediction accuracy of the AI.
To see how this works, imagine applying the exercise to Amazon's
recommendation engine. We've found its tool to be about 5 percent
accurate, meaning that out of every 20 things it recommends, we buy
one of them and not the other 19.
That accuracy sounds
lousy, but when you consider that the tool pulls 20 items from
Amazon's catalog of millions of items and out of those 20 we buy
one, it's not that bad.
Every day people in Amazon's machine-learning group are working to
crank up that prediction-accuracy knob. You can imagine that knob is
currently at about two out of ten. If they to crank it to a four or
a five, we'll now buy five or seven out of 20 things.
There's some number at
which Amazon might think,
"We are now
sufficiently good at predicting what you want to buy. Why are we
waiting for you to shop at all? We'll just ship it."
By doing this, Amazon
could increase its share of wallet for two reasons.
The first is that it
preempts you from buying those goods from its competitors, either
online or offline.
The second is that, if
you were wavering on buying something, now that it's on your porch
you might think,
"Well, I might as
well just keep it."
This demonstrates that by
doing only one thing - turning up the prediction-accuracy knob - the
change made by AI goes from one that's incremental (offering
recommendations on the website) to one that's transformational:
business model flips from shopping and then shipping to shipping
and then shopping.
imperatives for harnessing the power of low-cost prediction
There are several things leaders can do to position their
organizations to maximize the benefits of prediction machines.
Develop a thesis on time to AI impact
The single most important question executives in every
industry need to ask themselves is: How fast do I think the
knob will turn for a particularly valuable AI application in
If you think it
will take 20 years to turn that knob to the transformational
point, then you'll make a very different set of investments
today than if you think it will take three years.
Looking at the investments various companies are already
making can give you an idea of their thesis on how soon the
knob will hit the transformation point.
DeepMind for over half a billion dollars at
a time when the company was generating almost no revenue. It
was a start-up that was training an AI to play Atari games.
Google clearly had a thesis on how fast the knob would turn.
So if I were a CEO in any industry right now, my number-one
job would be to work with my leadership team to develop a
thesis for each of the key areas in my organization on how
fast the dial will turn.
Recognize that AI progress will likely be exponential
As executives develop their thesis on timing, it's important
to recognize that the progress in AI will in many cases be
exponential rather than linear.
progress in a wide range of applications (e.g., vision,
natural language, motion control) over the last 12 months
was faster than in the 12 months prior. The level of
investment is increasing rapidly. The quality-adjusted cost
of sensors is falling exponentially.
And the amount of
data being generated is increasing exponentially.
Trust the machines
In most cases, when AIs are properly designed and deployed,
they're better predictors than humans are.
And yet we're
often still reluctant to hand over the reins of prediction
to machines. For example, there have been studies comparing
human recruiters to AI-powered recruiters that predict which
candidates will perform best in a job.
was measured 12, 18, and 24 months later, the recruits
selected by the AI outperformed those selected by the human
recruiters, on average.
evidence, human recruiters still often override the
recommendations provided by AIs when making real hiring
Where AIs have demonstrated superior performance in
prediction, companies must carefully consider the conditions
under which to empower humans to exercise their discretion
to override the AI.
Know what you want to predict
I work at a business school, so, using my domain as an
example, if you read business-school brochures, they're
usually quite vague in terms of what they're looking for in
They might say,
"We want the best students."
Well, what does
"best" mean? Does it mean best in academic performance?
Social skills? Potential for social impact? Something else?
The organizations that will benefit most from AI will be the
ones that are able to most clearly and accurately specify
We're going to
see a lot of the currently fuzzy mission statements become
much clearer. The companies that are able to sharpen their
visions the most will reap the most benefits from AI.
Due to the
methods used to train AIs, AI effectiveness is directly tied
to goal-specification clarity.
Manage the learning loop
What makes AI so powerful is its ability to learn. Normally
we think of labor as being learners and of capital as being
Now, with AI, we
have capital that learns. Companies need to ensure that
information flows into decisions, they follow decisions to
an outcome, and then they learn from the outcome and feed
that learning back into the system.
learning loop will be more valuable than ever before.
In response to a surge of advances in AI by other countries,
particularly China, Robert Work, a former deputy secretary of
defense, was recently quoted in a New York Times
"This is a Sputnik
He was, of course,
referencing America's catch-up reaction to the Soviet Union's
launching of Sputnik I, the world's first earth-orbiting satellite,
This initiated the space
race, led to the creation of NASA, and resulted in the Americans
landing on the moon in 1969.
This sentiment for defense applies broadly today.
Organizations in every
industry will soon face their own Sputnik moment. The best leaders,
be they visionary or operationally oriented, will seize this moment
to lead their organizations through the most disruptive period they
will experience in their professional lives.
They will recognize the
magnitude of the opportunity, and they will transform their
organizations and industries.
And as long as proper
care is exercised, we'll be better off for it.