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Dr. Michael Watson
Northwestern University

Supply Chain by Design

Dr. Michael Watson, one of the industry’s foremost experts on supply chain network design and advanced analytics, is a columnist and subject matter expert (SME) for Supply Chain Digest.

Dr. Watson, of Northwestern University, was the lead author of the just released book Supply Chain Network Design, co-authored with Sara Lewis, Peter Cacioppi, and Jay Jayaraman, all of IBM. (See Supply Chain Network Design – the Book.)

In addition to teaching at Northwestern, Watson is a founding partner at Opex Analytics. 

August 8, 2018

Why Business Leaders should think of AI as an Umbrella Term

The First in a Series of Articles on Artificial Intelligence (AI): The Next Will Explain how the Term has Evolved and why it is a Good Thing

Watson Says...

A good way to add clarity is to closely examine how the industry’s thought leaders define it.

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“AI” is a tech buzzword that seems to take on a new meaning each time you hear it. This causes confusion for many business leaders who struggle to put it into perspective for their specific needs. A good way to add clarity is to closely examine how the industry’s thought leaders define it.

Jeff Bezos, CEO of Amazon, is convinced AI’s amazing advances will generate improvements for every business. He discusses two layers of AI.1 The first layer: the “showy” new things people see and are amazed by— AI recognizing people, driving vehicles and understanding spoken language. The second layer:  all the algorithms and changes happening “beneath the surface” that allow operations to run more efficiently— things like better recommendation engines for online shopping and better forecasting for inventory management.

Andrew Ng, a professor at Stanford University and a Thought Leader in the AI space, uses the term “AI”2 as an umbrella term to encompass a whole range of algorithms, accomplishing things like optimizing web search, targeting advertisements, approving consumer loans and routing delivery drivers. 

Amy Webb, a professor of strategic foresight at the NYU Stern School of Business and the Founder of the Future Today Institute, published a document on AI trends that defined AI as having a computer “do things that normally require human intelligence.” She admits that this is an “extremely large, broad field.”

The 2018 book Prediction Machines uses the term AI to encompass all algorithms that allow us to make better predictions. The book’s authors define predictions very broadly and make a strong case that almost all decisions have some element of a prediction. They claim that the AI movement is about drastically reducing the cost of making these predictions. This will lead to a much wider use of AI. The implication is that business leaders need to adjust to this reality so that competitors or start-ups don’t beat them to it and take away market share or profit margins.

Tom Davenport played a key role in the initial rise of the term “analytics” with his book and article about ‘Competing on Analytics’ published in 2007.  In recent years he has begun writing more about the role of AI as well. In 2015 (before the term “AI” was widely used), he wrote an article about how Schneider National (a large trucking company) was augmenting human planners with intelligent machines. This was done solely with optimization technology. In 2018, he wrote another article about the uses of AI in the real world for process automation, cognitive insight, and cognitive engagement. If you look under the hood, you’ll find many different types of tools and algorithms working together to enable each of these transformations. 

A recent
Economist article addressed AI in the supply chain. The figure to the left shows the value that can be unlocked from AI in Operations (according to a McKinsey study). In the article, the Economist lumps many different algorithms and approaches into AI. This includes inventory management, forecasting demand, better routing and predictive maintenance.

As we can see from these examples, AI is being used as an umbrella term to encompasses a wide variety of  algorithms and approaches. This comes as no surprise. The field is moving rapidly, and so many tools and algorithms seem to fit well within definitions of “intelligence” that it makes sense to include them under this same umbrella.

The most important realization to be aware of is that the definition of AI that suits your business may be entirely different for others. For example, computer vision may be an essential AI advancement at Google while Amazon’s key AI application could be demand forecasting. Your business might be transformed by a different AI algorithm altogether.  The bottom line is that AI is behind many of the most influential transformations in business but exactly how it is applied in each instance is unique. The power behind AI is what you make of it.


Stay tuned for the next article in this series will explain the how we got to the term AI and why this is a good thing.  You may also want to check out my recently released white paper on Reinventing your Business with AI that cover these topics and much more.  (This article first appeared on our Medium Blog.)



1. For the Bezos quote, see this video:, from the 21:00 to the 23:15 minute mark. Note that he uses the terms “AI” and “machine learning” almost as synonyms.

2. For the NG quote, see this video:, from about the 4:50 to the 5:35 mark, where he defines web search, advertising, approving a consumer loan, and estimating the time and routing a driver to your home for take out food delivery as all being powered by AI. The talk goes further and hits other industries.

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