“Working With AI,” Read It For The Case Studies

“Working With AI,” Read It For The Case Studies

This is going to be a soapbox article. “Working with AI,” Thomas H. Davenport and Steven M. Miller, is an MIT Press book that has some good points but overall loses out. As the headline states, this book has a nice spread of case studies, but the higher point they try to make is not correct.

Let’s start with the good. As mentioned, the first two-thirds of this book is a series of fairly short case studies that show the breadth of artificial intelligence (AI) adoption across the business world. AI is a very valuable tool and it is increasingly being used in many ways that impact business and citizens day-to-day. For those who want to understand the rapid spread of the technology, this book will help them see how adaptable AI is and how it is transforming knowledge and processes.

It’s that last part, however, where the authors miss, and they most likely do it intentionally. The repeated trope that AI won’t destroy jobs is used in a way that academics are providing to management to use in their own corporate messaging. Messaging, not reality. The AI ​​revolution is not an industrial revolution, and economies aren’t where they were in the Eighteenth Century.

The best way to talk about what will happen to jobs is to use one of the case studies and then a part of the polemic later in the book. One case study is about how underwriting has changed. AI is being used for the simplest of underwriting cases, the argument being that will help lower boredom of the experts and let them focus on the complex cases. An expert employee, however, does wonder how they will train the next generation of underwriters, since humans no longer will see the easiest ones, the policies on which people learn in order to prepare for the complex cases. It’s a valid point, but it’s short term.

The authors repeat a discussion of that challenge in a higher level policy chapter in the latter third of the book. While they propose that schools teach entry level knowledge for all impacted fields, a truly unrealistic concept, what they don’t do is admit to the reality of AI. The same owners and executives who are hollowing out the public school systems are looking at what the current AI systems are doing as only a beginning. So are the programmers and system designers.

The goal of the people building and buying the systems such as the ones mentioned in the case studies is to also replace the expert workers. Then there’s no need for the high cost of personnel that lowers the bottom line. You don’t need entry level workers if you don’t need experts either.

I love AI, but I understand that it is an enormous change in the way our societies will function, and not enough focus has been aimed at that. The earlier that we look at how the definition of work is changing, and how to make real changes to our educational system in order to help the dwindling middle-class adapt to those changes, the better chance we have of both integrating AI and upholding and enhancing our society.

Academics talking to executives are doing a good job of showing both the benefits of AI and how adoption is speeding up. However, it seems most academics who write books I’ve reviewed don’t really understand how artificial intelligence is going to change both business and society or they are intentionally ignoring the subject in order to sell books that can go on their curricula vitae.

This book is an example of that. It does a great job of describing the breadth of the tactical implementation of AI, and it does a poor job in looking at the strategic goals and impacts of artificial intelligence.

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