Too many technologists, in every technology generation, argue that management needs to think more like programmers. This is not the case. Instead, tech professionals need to learn to talk to management. “The Business Case for AIby Kavita Ganesan, PhD, is good insight for managers who want to understand and control the complexities of implementing artificial intelligence (AI) systems in business.

I’m always skeptical of self-published books. Usually that means the books just aren’t good. However, sometimes, especially in non-fiction, it means editors have no idea what the subject matter is and are reluctant to work with people who aren’t “names”. This book is an example of the second option, and it will give management an introduction to the concepts surrounding AI and how to approach implementation in a way that will increase the likelihood of AI initiatives being successful.

The indication that the author mostly lives in the real world comes quickly. The first chapter is a good introduction to what matters to businesses when it comes to AI. Forget the technical focus, it’s about solving problems efficiently and cost-effectively.

Chapter 2, “What is AI?” isn’t bad either, although I disagree with the idea that machine learning (ML) is part of AI. Business Intelligence (BI) has advanced alongside IT performance that standard analytics provide insights that can be called ML, so that ML and AI overlap. This, however, is a religious argument and what Ganeson has to say about AI is on a good level for management understanding.

The intro’s weakest chapter is the fourth, where the sci-fi junkie in me must have sighed over “movies like ‘I Robot’.” Ummm, check your library.

The list of myths in this chapter is also a bit problematic. The first, on job loss, is the only area where it shows that the part of the real world the author exists in is not where most people find themselves. The AI ​​revolution is very different from the industrial revolution and previous technological revolutions. She talks about artificial general intelligence (AGI) and says that since it’s still a long way off, that means a lot of jobs won’t be lost. We don’t need AGI to replace jobs.

The next chapters are good for setting up examples of business processes that could be impacted by AI. I have a problem with which companies she decides to name and which ones remain anonymous, as it seems to involve customer protection. The best part was a good discussion of IT and manufacturing operations, but it could have been improved by discussing infrastructure operations such as pipelines and the power grid.

Part 3 (Chapters 7-9) is very good but, again, there are a few things to keep in mind. On page 117, six phases of the development life cycle are defined. I agree with them, but I want to point out that data acquisition and model development, phases 2 and 3, can be done somewhat in parallel. The things you learn from each can impact the other. The other issue is that the author seems to be using the warehouse incorrectly. Data warehouses have a specific and narrower purpose. When she uses the term, think of the data lake. The importance of logging, transactions and more is often overlooked, and the end of this section of the book has a good explanation of its importance.

The fourth part of the book is a set of chapters that delve deeper into the “finding AI projects” part of the analysis process, and is well presented.

The last section has two chapters. The first concerns “build v buy”. It’s no surprise that a consultant leans towards the build, it’s her livelihood. What managers need to understand is that companies are not as unique as they think. There are some unique things, but the vast majority of businesses are like other businesses. AI is a new technology and there are not enough easy-to-use tools to make a buying decision in many areas, but that will change over time. Managers must have a flexible understanding of the real-world equation and balance.

The last chapter is, as expected, a good summary and a return to the focus on business results. It continues the author’s use of good, simple graphics to show the points of his arguments. Regardless of the issues I mentioned above, the book does a great job of presenting the challenges of artificial intelligence from a business perspective. The book doesn’t dive deep into algorithms or other details that don’t matter to management, while it does provide a framework for looking at AI projects through a business lens that integrates technology into organizations in a way that doesn’t leave everything to the technologist. “The Business Case for AI” is a good primer for IT and operational managers to think about how to integrate artificial intelligence into their organizations.

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