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. Right now, Amazon’s business model is “shopping then shipping.”
During the shopping process, Amazon’s artificial intelligence offers suggestions of items that it predicts you will want to buy. The AI does a reasonable job. However, it is far from perfect. In our case, we found informally that the AI accurately predicts what we want to buy about five per cent of the time. We actually purchase about one of every 20 items it recommends. Considering the millions of items on offer, that’s not bad!
Imagine that the Amazon AI collects more information about us and uses that data to improve its predictions, an improvement akin to turning up the volume knob on a speaker dial. But rather than volume, it’s turning up the AI’s prediction accuracy.
At some point, as it turns the knob, the AI’s prediction accuracy crosses a threshold, changing Amazon’s business model. The prediction becomes sufficiently accurate that it becomes more profitable for Amazon to ship you the goods that it predicts you will want rather than wait for you to order them.
With that, you won’t need to go to other retailers, and the fact that the item has arrived at your home may well nudge you to buy more. Amazon gains a higher share of wallet. Clearly, this is great for Amazon, but it is also great for you. Amazon ships before you shop, which, if all goes well, saves you the task of shopping entirely. 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 have to invest in infrastructure for the product returns – perhaps a fleet of delivery-style trucks that do pickups once a week, conveniently collecting items that customers don’t want.
If this is a better business model, then why hasn’t Amazon implemented it already? Because if it did so today, the cost of collecting and handling returned items would outweigh the increase in revenue from a greater share of wallet. Using our example, we would return 95 per cent of the items it ships to us. That is annoying for us and costly for Amazon.
The prediction isn’t good enough for Amazon to adopt the new model. But we can imagine a scenario where Amazon adopts the new strategy 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 acquires more data and improves its predictions faster. 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 the company obtained a U.S. patent for “anticipatory shipping” in 2013. Instead, the salient insight is the impact on strategy. In this example, it shifts Amazon’s business model from “shopping then shipping” to “shipping then shopping,” generates the incentive to operate a service for product returns (including a fleet of trucks) and accelerates the timing of investment.
All this is due simply to turning up the dial on the prediction machine. What does this mean for your strategy? First, you must invest in gathering intelligence on how fast and how far the dial on the prediction machines will turn for your sector and the work you do. Second, you must invest in developing a thesis about the strategic options created from turning the dial.
To get started on this “science-fiction” exercise, close your eyes, imagine putting your fingers on the dial of your prediction machine and, in the immortal words of Nigel Tufnel from Spinal Tap, turn it to 11.
Excerpted from Prediction Machines: The Simple Economics of Artificial Intelligence, by Ajay Agrawal, Joshua Gans and Avi Goldfarb, professors at U of T’s Rotman School of Management.
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