Aspire Thought Leadership! Ever wondered about artificial intelligence in business?. Find out more on what has changed with artificial intelligence in
Everyone has had or will soon have an AI moment. We have grown accustomed to a media saturated with stories of new technologies that will change our lives. We are so used to the constant drumbeat of technology news that we numbly recite that ‘the only thing immune to change is change itself ’. Until we have our artificial intelligence in business moment. Then we realize that this technology is different.
Some computer scientists experienced their AI moment in 2012, when a student team from the University of Toronto delivered such an impressive win in the visual object-recognition competition ImageNet that the following year, all top finalists used the then-novel ‘deep learning’ approach to compete. These scientists recognized that object recognition is more than just a game; it actually enables machines to ‘see’.
Some technology CEOs experienced their AI moment when they read the headline in January 2014 that Google had paid more than $600 million to acquire UK-based DeepMind, even though the start-up had generated negligible revenue relative to the purchase price but had demonstrated that its AI had learned on its own, without being programmed to play Atari video games with super-human performance.
Our own AI moment came in 2012, when a trickle and then a surge in the number of early-stage AI companies employing state-of-the-art machine-learning techniques applied to the Creative Destruction Lab at the Rotman School of Management. The applications spanned industries drug discovery, customer value proposition, customer service, manufacturing, quality assurance, retail, medical devices. The technology was both powerful and general purpose, creating significant value across a wide range of applications.
We set to work understanding what this meant in economic terms. We knew that AI would be subject to the same economics as any other technology. The technology itself is, simply put, amazing. Early on, famed venture capitalist Steve Jurvetson quipped: “Just about any product that you experience in the next five years that seems like magic will almost certainly be built by these algorithms.” We understand and sympathize with Jurvetson’s characterization of AI applications as ‘magical’; but as economists, our job is to take seemingly-magical ideas and make them simple, clear and practical.
Let’s start with the basics: prices. When the price of something falls, we use more of it. That is simple Economics, and it is happening right now. AI is everywhere packed into your phone’s apps, optimizing your electricity grid, and replacing your stock portfolio managers. Soon it may be driving you around or lying packages to your house. Where others see transformational innovation, we see a simple fall in price. But it is more than that. To understand how AI will affect your organization, you need to know precisely what price has changed and how that change will cascade throughout the broader economy. Only then can you build a plan of action.
Economic history has taught us that the impact of major innovations is often felt in the most unexpected places. Consider the story of the commercial internet in 1995. While most of us were watching Seinfeld, Microsoft released Windows 95, its first multitasking operating system. That same year, the U.S. government removed the final restrictions to carrying commercial traffic on the internet, and Netscape the browser’s inventor celebrated its initial public ofering (IPO). This marked an inflection point when the Internet transitioned from a technological curiosity to a commercial tidal wave that would wash over the economy.
Netscape’s IPO valued the company at more than $3 billion, even though it had not generated any significant profit. Venture capital investors valued start-ups in the millions even if they were ‘pre-revenue’. Freshly-minted MBAs turned down lucrative traditional jobs to prospect on the web. As the effects of the Internet began to spread across industries, technology advocates stopped referring to it as a new technology and began referring to it as the New Economy. The Internet had transcended the technology and permeated human activity at a fundamental level, and everyone from politicians to corporate executives, investors, entrepreneurs and major news organizations started using the term.
Everyone, that is, except economists. To us, this looked like the regular old economy. To be sure, some important changes had occurred: Goods and services could now be distributed digitally causing digital disruption; communication was easy; and you could find information with the click of a search button. But you could do all of these things before. What had really changed was that you could now do them cheaply. The rise of the Internet led to a significant drop in the cost of distribution, communication and search, forcing industries for a change in their digital transformation strategy.
Re-framing technological advances as ‘a shift from expensive to cheap’ or ‘from scarce to abundant’ is invaluable for thinking about how they will affect your business agility. The first time you used Google, you may remember being mesmerized by its seemingly magical ability to access information. From the economist’s perspective, Google made search cheap, and when that happened, companies that made money selling search through other means (e.g., the Yellow Pages, travel agents, classified ads) found themselves in a competitive crisis. At the same time, companies that relied on people finding them (e.g., self publishing authors, sellers of obscure collectibles, homegrown moviemakers) prospered.
This change in the relative costs of certain activities radically influenced some business models and even transformed industries. However, economic laws did not change: We could still understand everything in terms of supply and demand, and could set pricing strategy, inform policy and anticipate the future using of-the-shelf economic principles and even help in forming premium pricing strategy on how to fight a price war.
What might be affected when a new technology makes something cheap is not always obvious, whether the technology is artificial light, steam power, the automobile or computing. Tim Bresnahan, a Stanford economist and one of our mentors, has pointed out that computers do arithmetic and nothing more, and that the commercialization of computers made arithmetic cheap. When arithmetic became cheap, not only did we use more of it for traditional applications, but we also used it for applications that were not traditionally associated with arithmetic like music.
AI will be economically signiicant precisely because it will make something very important much cheaper. Right now, you may be thinking about intellect, reasoning or thought itself; you might be imagining robots all over or non-corporeal beings, such as the friendly computers in Star Trek, letting you avoid the need to think. Lovelace had the same thought, but quickly dismissed it. At least insofar as a computer was concerned, she wrote, it “had no pretensions to originate anything. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths.”
What will new AI technologies make so cheap? Prediction. Therefore, as Economics tells us, not only are we going to start doing more prediction, but we are going to see it emerge in surprising new places.
We will spare you the details when we emphasize that each of these methods is actually about prediction using information you have to generate information you don’t have. The challenge is to identify situations in which prediction will be valuable, and then igure out how to benefit as much as possible from that prediction. As indicated, when arithmetic became cheap, we started using it on problems that weren’t traditional arithmetic problems. Whereas we once created photography with chemistry, we transitioned to an arithmetic-based solution: digital cameras. A digital image is actually just a string of zeros and ones that can be reassembled into a viewable image.
The same goes for prediction, which is used for a wide variety of traditional tasks, from inventory management to demand forecasting. And because it is becoming cheaper, it is being used for non-traditional prediction problems. Kathryn Howe, of Integrate. ai, calls this ability to see a problem and re-frame it as a prediction problem ‘AI insight’, and today, engineers all over the world are acquiring it.
For example, we are transforming transportation into a prediction problem. Autonomous vehicles have existed in controlled environments for over two decades. They were limited, however, to places with detailed floorplans such as factories and warehouses, so that engineers could design robots to maneuver with basic ‘if-then’ logical intelligence. For example, ‘If a person walks in front of the vehicle, stop’; ‘if the shelf is empty, move to the next one’.
However, autonomous vehicles could not function outside of a highly predictable, controlled environment until engineers re-framed navigation as a prediction problem. Instead of telling the machine what to do in each circumstance, engineers recognized they could instead focus on a single prediction problem: What would a human do? Now, companies are investing billions of dollars in training machines to drive autonomously in uncontrolled environments.
Imagine an AI sitting in the car with a human driver. The human drives for millions of miles, receiving data about the environment through its eyes and ears, processing that data with the human brain, and then acting in response to the incoming data. Engineers are basically giving the AI its own eyes and ears by outfitting the car with sensors (e.g., cameras, radar, lasers). So, the AI observes the incoming data as the human drives and simultaneously observes the human’s actions. When particular environmental data comes in, does the human turn right, brake or accelerate? The more the AI observes the human, the better it becomes at predicting the specific action a driver should take.
Critically, when an input such as prediction becomes cheap, it can enhance the value matrix of other things. Economists call these ‘complements’. Just as a drop in the cost of coffee increases the value of sugar and cream, for autonomous vehicles, a drop in the cost of prediction increases the value of sensors to capture data on the vehicle’s surroundings. For example, in 2017, Intel paid more than $15 billion for the Israeli startup Mobileye, primarily for its data-collection technology that allows vehicles to effectively see objects (stop signs, people, etc.) and markings (lanes, roads). When prediction is cheap, there will be more prediction and more complements to prediction, and these two simple economic forces will drive the opportunities that prediction machines create.
During the shopping process, Amazon’s big data in retail AI offers suggestions of items that it predicts you will want to buy. The AI does a reasonable job, but it is far from perfect. In our case, we actually purchase about one of every 20 items it recommends. However, considering the millions of items on offer, that’s not a bad ratio.
Imagine that the Amazon AI collects more information about us over time and uses that data to improve its strategic pricing predictions an improvement akin to turning up the volume knob on a speaker dial. At some point, as it ‘turns the knob’, the AI’s prediction accuracy crosses a threshold, changing Amazon’s penetration pricing strategy: The prediction aspect becomes sufficiently accurate that it is suddenly more profitable for Amazon to ship you the goods that it predicts you will want, rather than wait for you to order them. Cranking up the prediction dial changes Amazon’s business model from ‘shopping-then-shipping’ to ‘shipping then shopping’.
Of course, shoppers would not want to deal with the hassle of returning all the items they don’t want. So, Amazon would invest in infrastructure for product returns perhaps a fleet of trucks that do pickups once a week, conveniently collecting items that customers don’t want.
If this is a better business model, why hasn’t Amazon done it already? Because if implemented today, the cost of collecting and handling returned items would outweigh the increase in revenue from a greater share of wallet. As indicated, today we would return 95 per cent of the items Amazon ships to us, which would be annoying for us and costly for the company. Its predictive ability isn’t yet good enough for Amazon to adopt this new model.
We can imagine a scenario where Amazon adopts this new retail pricing strategies even before the prediction accuracy is good enough to make it profitable because the company anticipates that at some point, it will be profitable. By launching sooner, Amazon’s AI would get more data sooner and improve faster. Of course, Amazon realizes that the sooner it starts, the harder it will be for competitors to catch up: Better predictions will attract more shoppers, more shoppers will generate more data to train the AI, more data will lead to better predictions, and so on, creating a virtuous cycle. Adopting too early could be costly, but adopting too late could be fatal.
Our point is not that Amazon will or should do this although skeptical readers may be surprised to learn that Amazon obtained a U.S. patent for ‘anticipatory shipping’ in 2013. The salient insight is that turning up the prediction dial can have a significant impact on different pricing strategies. In this example, it shifts Amazon’s business model from shopping-then-shipping to shipping then shopping, generates the incentive to vertically integrate into operating a service for product returns (including a fleet of trucks), and accelerates the timing of investment.
Leaders across industries would do well to invest in gathering intelligence on how fast and how far the dial on the prediction machines will turn for their particular sector and applications. They should also invest in developing a thesis about the strategic options created from turning the dial. To get started on this ‘science fictioning’ exercise, close your eyes, imagine putting your fingers on the dial of your prediction machine, and in the immortal words of Spinal Tap turn it up to eleven.
Some computer scientists experienced their AI moment in 2012, when a student team from the University of Toronto delivered such an impressive win in the visual object-recognition competition ImageNet that the following year, all top finalists used the then-novel ‘deep learning’ approach to compete. These scientists recognized that object recognition is more than just a game; it actually enables machines to ‘see’.
Some technology CEOs experienced their AI moment when they read the headline in January 2014 that Google had paid more than $600 million to acquire UK-based DeepMind, even though the start-up had generated negligible revenue relative to the purchase price but had demonstrated that its AI had learned on its own, without being programmed to play Atari video games with super-human performance.
Our own AI moment came in 2012, when a trickle and then a surge in the number of early-stage AI companies employing state-of-the-art machine-learning techniques applied to the Creative Destruction Lab at the Rotman School of Management. The applications spanned industries drug discovery, customer value proposition, customer service, manufacturing, quality assurance, retail, medical devices. The technology was both powerful and general purpose, creating significant value across a wide range of applications.
We set to work understanding what this meant in economic terms. We knew that AI would be subject to the same economics as any other technology. The technology itself is, simply put, amazing. Early on, famed venture capitalist Steve Jurvetson quipped: “Just about any product that you experience in the next five years that seems like magic will almost certainly be built by these algorithms.” We understand and sympathize with Jurvetson’s characterization of AI applications as ‘magical’; but as economists, our job is to take seemingly-magical ideas and make them simple, clear and practical.
Artificial intelligence in business - Cutting Through The Hype
Economists view the world differently than most people. We see everything through a framework governed by forces such as supply and demand, production and consumption, prices and costs. Although economists often disagree with each other, we do so in the context of a common framework. We argue about assumptions and interpretations but not about fundamental concepts, like the roles of scarcity and competition in setting price optimization. This approach to viewing the world gives us a unique vantage point. On the negative side, it doesn’t make us popular at dinner parties. On the positive side, it provides useful clarity for informing business decisions.Let’s start with the basics: prices. When the price of something falls, we use more of it. That is simple Economics, and it is happening right now. AI is everywhere packed into your phone’s apps, optimizing your electricity grid, and replacing your stock portfolio managers. Soon it may be driving you around or lying packages to your house. Where others see transformational innovation, we see a simple fall in price. But it is more than that. To understand how AI will affect your organization, you need to know precisely what price has changed and how that change will cascade throughout the broader economy. Only then can you build a plan of action.
Economic history has taught us that the impact of major innovations is often felt in the most unexpected places. Consider the story of the commercial internet in 1995. While most of us were watching Seinfeld, Microsoft released Windows 95, its first multitasking operating system. That same year, the U.S. government removed the final restrictions to carrying commercial traffic on the internet, and Netscape the browser’s inventor celebrated its initial public ofering (IPO). This marked an inflection point when the Internet transitioned from a technological curiosity to a commercial tidal wave that would wash over the economy.
Netscape’s IPO valued the company at more than $3 billion, even though it had not generated any significant profit. Venture capital investors valued start-ups in the millions even if they were ‘pre-revenue’. Freshly-minted MBAs turned down lucrative traditional jobs to prospect on the web. As the effects of the Internet began to spread across industries, technology advocates stopped referring to it as a new technology and began referring to it as the New Economy. The Internet had transcended the technology and permeated human activity at a fundamental level, and everyone from politicians to corporate executives, investors, entrepreneurs and major news organizations started using the term.
Everyone, that is, except economists. To us, this looked like the regular old economy. To be sure, some important changes had occurred: Goods and services could now be distributed digitally causing digital disruption; communication was easy; and you could find information with the click of a search button. But you could do all of these things before. What had really changed was that you could now do them cheaply. The rise of the Internet led to a significant drop in the cost of distribution, communication and search, forcing industries for a change in their digital transformation strategy.
Re-framing technological advances as ‘a shift from expensive to cheap’ or ‘from scarce to abundant’ is invaluable for thinking about how they will affect your business agility. The first time you used Google, you may remember being mesmerized by its seemingly magical ability to access information. From the economist’s perspective, Google made search cheap, and when that happened, companies that made money selling search through other means (e.g., the Yellow Pages, travel agents, classified ads) found themselves in a competitive crisis. At the same time, companies that relied on people finding them (e.g., self publishing authors, sellers of obscure collectibles, homegrown moviemakers) prospered.
This change in the relative costs of certain activities radically influenced some business models and even transformed industries. However, economic laws did not change: We could still understand everything in terms of supply and demand, and could set pricing strategy, inform policy and anticipate the future using of-the-shelf economic principles and even help in forming premium pricing strategy on how to fight a price war.
Artificial intelligence in business - Cheap Means Everywhere
Chances are you are reading this under some kind of artificial light. Moreover, you probably never thought about whether using that light for reading was ‘worth it’. Light is so cheap that we use it with abandon. But, as economist William Nordhaus meticulously explored, in the early 1800s it would have cost you 400 times what you are paying now for the same amount of light. At that price, you would think twice before using artificial light to read. The subsequent drop in the price of light lit up the world: Not only did it turn night into day, but it allowed us to live and work in big buildings that natural light could not penetrate. [mindfulness in the workplace] Virtually nothing we have today would be possible had the cost of artificial light not collapsed to almost nothing.What might be affected when a new technology makes something cheap is not always obvious, whether the technology is artificial light, steam power, the automobile or computing. Tim Bresnahan, a Stanford economist and one of our mentors, has pointed out that computers do arithmetic and nothing more, and that the commercialization of computers made arithmetic cheap. When arithmetic became cheap, not only did we use more of it for traditional applications, but we also used it for applications that were not traditionally associated with arithmetic like music.
AI will be economically signiicant precisely because it will make something very important much cheaper. Right now, you may be thinking about intellect, reasoning or thought itself; you might be imagining robots all over or non-corporeal beings, such as the friendly computers in Star Trek, letting you avoid the need to think. Lovelace had the same thought, but quickly dismissed it. At least insofar as a computer was concerned, she wrote, it “had no pretensions to originate anything. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths.”
What will new AI technologies make so cheap? Prediction. Therefore, as Economics tells us, not only are we going to start doing more prediction, but we are going to see it emerge in surprising new places.
How Cheap Prediction Creates Value
Prediction is the process of filling in missing information. It takes information you have often called ‘data’ and uses it to generate information that you don’t have. Much discussion about AI emphasizes the variety of prediction techniques using increasingly obscure names and labels: classification, decision trees, neural networks, topological enterprise data management and analysis, reinforcement learning, and so on. These techniques are important for the technologists interested in implementing AI for a particular prediction problem.We will spare you the details when we emphasize that each of these methods is actually about prediction using information you have to generate information you don’t have. The challenge is to identify situations in which prediction will be valuable, and then igure out how to benefit as much as possible from that prediction. As indicated, when arithmetic became cheap, we started using it on problems that weren’t traditional arithmetic problems. Whereas we once created photography with chemistry, we transitioned to an arithmetic-based solution: digital cameras. A digital image is actually just a string of zeros and ones that can be reassembled into a viewable image.
The same goes for prediction, which is used for a wide variety of traditional tasks, from inventory management to demand forecasting. And because it is becoming cheaper, it is being used for non-traditional prediction problems. Kathryn Howe, of Integrate. ai, calls this ability to see a problem and re-frame it as a prediction problem ‘AI insight’, and today, engineers all over the world are acquiring it.
For example, we are transforming transportation into a prediction problem. Autonomous vehicles have existed in controlled environments for over two decades. They were limited, however, to places with detailed floorplans such as factories and warehouses, so that engineers could design robots to maneuver with basic ‘if-then’ logical intelligence. For example, ‘If a person walks in front of the vehicle, stop’; ‘if the shelf is empty, move to the next one’.
However, autonomous vehicles could not function outside of a highly predictable, controlled environment until engineers re-framed navigation as a prediction problem. Instead of telling the machine what to do in each circumstance, engineers recognized they could instead focus on a single prediction problem: What would a human do? Now, companies are investing billions of dollars in training machines to drive autonomously in uncontrolled environments.
Imagine an AI sitting in the car with a human driver. The human drives for millions of miles, receiving data about the environment through its eyes and ears, processing that data with the human brain, and then acting in response to the incoming data. Engineers are basically giving the AI its own eyes and ears by outfitting the car with sensors (e.g., cameras, radar, lasers). So, the AI observes the incoming data as the human drives and simultaneously observes the human’s actions. When particular environmental data comes in, does the human turn right, brake or accelerate? The more the AI observes the human, the better it becomes at predicting the specific action a driver should take.
Critically, when an input such as prediction becomes cheap, it can enhance the value matrix of other things. Economists call these ‘complements’. Just as a drop in the cost of coffee increases the value of sugar and cream, for autonomous vehicles, a drop in the cost of prediction increases the value of sensors to capture data on the vehicle’s surroundings. For example, in 2017, Intel paid more than $15 billion for the Israeli startup Mobileye, primarily for its data-collection technology that allows vehicles to effectively see objects (stop signs, people, etc.) and markings (lanes, roads). When prediction is cheap, there will be more prediction and more complements to prediction, and these two simple economic forces will drive the opportunities that prediction machines create.
From Cheap to Strategy
The single most common question corporate executives ask us about implications of artificial intelligence in business strategy. We use a thought experiment to answer that question. Most people are familiar with shopping at Amazon. As with most online retailers, you visit its website, shop for items, place them in your cart, pay for them, and then Amazon ships them to you. Amazon’s current business model is ‘shopping-then-shipping’.During the shopping process, Amazon’s big data in retail AI offers suggestions of items that it predicts you will want to buy. The AI does a reasonable job, but it is far from perfect. In our case, we actually purchase about one of every 20 items it recommends. However, considering the millions of items on offer, that’s not a bad ratio.
Imagine that the Amazon AI collects more information about us over time and uses that data to improve its strategic pricing predictions an improvement akin to turning up the volume knob on a speaker dial. At some point, as it ‘turns the knob’, the AI’s prediction accuracy crosses a threshold, changing Amazon’s penetration pricing strategy: The prediction aspect becomes sufficiently accurate that it is suddenly more profitable for Amazon to ship you the goods that it predicts you will want, rather than wait for you to order them. Cranking up the prediction dial changes Amazon’s business model from ‘shopping-then-shipping’ to ‘shipping then shopping’.
Of course, shoppers would not want to deal with the hassle of returning all the items they don’t want. So, Amazon would invest in infrastructure for product returns perhaps a fleet of trucks that do pickups once a week, conveniently collecting items that customers don’t want.
If this is a better business model, why hasn’t Amazon done it already? Because if implemented today, the cost of collecting and handling returned items would outweigh the increase in revenue from a greater share of wallet. As indicated, today we would return 95 per cent of the items Amazon ships to us, which would be annoying for us and costly for the company. Its predictive ability isn’t yet good enough for Amazon to adopt this new model.
We can imagine a scenario where Amazon adopts this new retail pricing strategies even before the prediction accuracy is good enough to make it profitable because the company anticipates that at some point, it will be profitable. By launching sooner, Amazon’s AI would get more data sooner and improve faster. Of course, Amazon realizes that the sooner it starts, the harder it will be for competitors to catch up: Better predictions will attract more shoppers, more shoppers will generate more data to train the AI, more data will lead to better predictions, and so on, creating a virtuous cycle. Adopting too early could be costly, but adopting too late could be fatal.
Our point is not that Amazon will or should do this although skeptical readers may be surprised to learn that Amazon obtained a U.S. patent for ‘anticipatory shipping’ in 2013. The salient insight is that turning up the prediction dial can have a significant impact on different pricing strategies. In this example, it shifts Amazon’s business model from shopping-then-shipping to shipping then shopping, generates the incentive to vertically integrate into operating a service for product returns (including a fleet of trucks), and accelerates the timing of investment.
Leaders across industries would do well to invest in gathering intelligence on how fast and how far the dial on the prediction machines will turn for their particular sector and applications. They should also invest in developing a thesis about the strategic options created from turning the dial. To get started on this ‘science fictioning’ exercise, close your eyes, imagine putting your fingers on the dial of your prediction machine, and in the immortal words of Spinal Tap turn it up to eleven.
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