On the Road to AI, Don’t Ask “Are We There Yet?”

Businesses that put in the effort to create an artificially intelligent business may see amazing returns at first — but there are good reasons to expect those to diminish.

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Artificial Intelligence and Business Strategy

The Artificial Intelligence and Business Strategy initiative explores the growing use of artificial intelligence in the business landscape. The exploration looks specifically at how AI is affecting the development and execution of strategy in organizations.

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BCG
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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.

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.

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Topics

Artificial Intelligence and Business Strategy

The Artificial Intelligence and Business Strategy initiative explores the growing use of artificial intelligence in the business landscape. The exploration looks specifically at how AI is affecting the development and execution of strategy in organizations.

In collaboration with

BCG
See All Articles in This Section

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Comments (2)
alex Kashko
We may never achieve a totally artificially intelligent business, but all we need is a business that is smarter than humans and that may be acheivable relatively quickly.  

At that point the AI itself can take over development
Michael Zeldich
The programmed AI cannot solve the problems connected with decision making in prematurely unknown situation and never will reach human level of creativity.
The only way to create of an artificial systems capable to make decision in prematurely unknown situation and be more creative than human is in designing of the artificial subjective systems.The artificial subjective systems is the new class of the artificial systems, which will be able to accrue a professional skills without any needs in programming.
What reason to spend billion of dollars on such unsolvable by programming tasks, as developing SDV, or corporate management, for examples, if it is known that finding a finite set of algorithms capable to manage behavior of  very complex systems in a complex ever changing environment is impossible.

We are creative because subjective. 
The same is true for artificial systems as well.
They are should be built subjective to be creative.



Best,
Mike