Artificial Intelligence and Business Strategy
In collaboration withBCG
It would be very, very helpful to know what the future holds for artificial intelligence in business. Unfortunately, it is also very, very hard to predict.
With this topic, our extrapolation heuristics may not work well. We tend to extrapolate linearly, expecting the pace of past progress to continue unchanged. That is unlikely to work with AI.
Consider the fun example of Joshua Browder, a 19-year-old who built a chatbot to help people fight parking tickets. It took him about three months to develop, and in the first 21 months, the application helped people win 160,000 of 250,000 cases — a 64% success rate.
The temptation to extrapolate from this is strong, leading to thoughts like:
- With another three months of effort, the rest of the cases could be won.
- This success is just in London and New York, but the same thing could be done in every other municipality.
- It works for parking tickets, so let’s apply the same approach to more important contexts.
Although appealing, the reality is that extrapolating the future success and progress of AI is not that straightforward.
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Technology has a long history of being pleasantly (and unpleasantly) nonlinear. For example, growth can be exponential, as it is with computing power or network effects. Or growth can be punctuated, with periods of rapid growth interspersed with relatively dormant periods.
The growth of AI in business is likely to similarly defy smooth, linear progression. It is difficult to build off of what has already happened to reliably determine what is likely to develop.
Why is this particularly difficult for AI? More so than the laws of robotics, three other laws may be important for AI in business:
The Pareto Principle: People focus on solving AI problems with the most potential benefits first. This is entirely rational; it just makes sense to invest efforts where the benefits are greater. The Pareto Principle describes the concept that 80% of the effects come from 20% of the causes. By focusing on a few scenarios (the 20%), AI can get solutions that address the majority of effects (~80%).
In the case of the parking tickets, the results of Freedom of Information Act requests indicated that appeals courts dismissed most parking tickets for any of just 12 reasons. As the result, the chatbot focused first on these 12 reasons.