[This post originally appeared in HBR online on 3rd October, 2017]
by Ajay Agrawal, Joshua Gans and Avi Goldfarb
How will AI change strategy? That’s the single most common question the three of us are asked from corporate executives, and it’s not trivial to answer. AI is fundamentally a prediction technology. As advances in AI make prediction cheaper, economic theory dictates that we’ll use prediction more frequently and widely, and the value of complements to prediction – like human judgment – will rise. But what does all this mean for strategy?Here’s a thought experiment we’ve been using to answer that question. Most people are familiar with shopping at Amazon. Like with most online retailers, you visit their website, shop for items, place them in your “basket,” pay for them, and then Amazon ships them to you. Right now, Amazon’s business model is shopping-then-shipping.Most shoppers have noticed Amazon’s recommendation engine while they shop — it offers suggestions of items that their AI predicts you will want to buy. At present, Amazon’s AI does a reasonable job, considering the millions of items on offer. However, they are far from perfect. In our case, the AI accurately predicts what we want to buy about 5% of the time. In other words, we actually purchase about one out of every 20 items it recommends. Not bad!Now for the thought experiment. Imagine the Amazon AI collects more information about us: in addition to our searching and purchasing behavior on their website, it also collects other data it finds online, including social media, as well as offline, such as our shopping behavior at Whole Foods. It knows not only what we buy, but also what time we go to the store, which location we shop at, how we pay, and more.Now, imagine the AI uses that data to improve its predictions. We think of this sort of improvement as akin to turning up the volume knob on a speaker dial. But rather than volume, you’re turning up the AI’s prediction accuracy. What happens to Amazon’s strategy as their data scientists, engineers, and machine learning experts work tirelessly to dial up the accuracy on the prediction machine?At some point, as they turn the knob, the AI’s prediction accuracy crosses a threshold, such that it becomes in Amazon’s interest to change its 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. Every week, Amazon ships you boxes of items it predicts you will want, and then you shop in the comfort and convenience of your own home by choosing the items you wish to keep from the boxes they delivered.This approach offers two benefits to Amazon. First, the convenience of predictive shipping makes it much less likely that you purchase the items from a competing retailer as the products are conveniently delivered to your home before you buy them elsewhere. Second, predictive shipping nudges you to buy items that you were considering purchasing but might not have gotten around to. In both cases, Amazon gains a higher share-of-wallet. Turning the prediction dial up far enough 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 the product returns — perhaps a fleet of delivery-style trucks that do pick-ups once a week, conveniently collecting items that customers don’t want.If this is a better business model, then 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. For example, today we would return 95% 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....MORE
