Working with AI: Real Stories of Human-Machine Collaboration

by Thomas H. Davenport, Steven M. Miller

Two management and technology experts show that AI is not a job destroyer, exploring worker-AI collaboration in real-world work settings.

This book breaks through both the hype and the doom-and-gloom surrounding automation and the deployment of artificial intelligence-enabled--"smart"--systems at work. Management and technology experts Thomas Davenport and Steven Miller show that, contrary to widespread predictions, prescriptions, and denunciations, AI is not primarily a job destroyer. Rather, AI changes the way we work--by taking over some tasks but not entire jobs, freeing people to do other, more important and more challenging work. By offering detailed, real-world case studies of AI-augmented jobs in settings that range from finance to the factory floor, Davenport and Miller also show that AI in the workplace is not the stuff of futuristic speculation. It is happening now to many companies and workers.

These cases include a digital system for life insurance underwriting that analyzes applications and third-party data in real time, allowing human underwriters to focus on more complex cases; an intelligent telemedicine platform with a chat-based interface; a machine learning-system that identifies impending train maintenance issues by analyzing diesel fuel samples; and Flippy, a robotic assistant for fast food preparation. For each one, Davenport and Miller describe in detail the work context for the system, interviewing job incumbents, managers, and technology vendors. Short "insight" chapters draw out common themes and consider the implications of human collaboration with smart systems.

Genres: Technology, Artificial Intelligence, Nonfiction, Audiobook, Business, Science

310 pages, Kindle Edition

Published September 27, 2022

About the author

Tom Davenport holds the President's Chair in Information Technology and Management at Babson College. His books and articles on business process reengineering, knowledge management, attention management, knowledge worker productivity, and analytical competition helped to establish each of those business ideas. Over many years he's authored or co-authored nine books for Harvard Business Press, most recently Competing on Analytics: The New Science of Winning (2007) and Analytics at Work: Smarter Decisions, Better Results (2010). His byline has also appeared for publications such as Sloan Management Review, California Management Review, Financial Times, Information Week, CIO, and many others.

Davenport has an extensive background in research and has led research centers at Ernst & Young, McKinsey & Company, CSC Index, and the Accenture Institute of Strategic Change. Davenport holds a B.A. in sociology from Trinity University and M.A. and Ph.D. in sociology from Harvard University. For more from Tom Davenport, visit his website and follow his regular HBR blog.

Reviews

[Brian Clegg · Rating 3 out of 5]

There have been plenty of books on the use of artificial intelligence (AI) and how it will impact our lives from the dire warnings of Cathy O'Neil's Weapons of Math Destruction to Melanie Mitchell's in-depth exploration of the technology Artificial Intelligence, but in Working with AI, Thomas Davenport and Steven Miller give us a new viewpoint that is interesting, if a little worrying. If we compare writing about AI with the Star Wars movies, it's as if almost every AI book I've read so far, like the films, has been written from the viewpoint of the rebels. But this is a book that solidly takes the viewpoint of the empire.

Unfortunately, although it covers a fair number of AI applications, it's also written more like a business book that a popular science/technology book, and as such it's pretty dull. Anyone familiar with the business book genre will recognise that deadly moment when you get to a box that's a case study. It's going to be boring. This book contains 29 case studies, one after the other, by the end of which I was quietly groaning.

However, there were definitely some insights to be gained here. In that range of case studies, there were several standouts. The main thesis that Davenport and Miller are proposing is that, despite some issues, artificial intelligence will not destroy vast swathes of jobs, but will instead improve them by taking on the boring bits, not (on the whole) displacing humans, but working alongside them. Perhaps the best example of this was the robotic weed picker, which made a farm worker's job more interesting and did something that, frankly, no human really wants to do. Admittedly, in this kind of application there would be fewer humans employed, but it feels like a genuinely beneficial change.

What was worrying, though, in the dark side orientation of this book was that there was very little consideration of some of the other potential negatives of AI - in fact, it felt the authors were almost celebrating some of these. Several case studies highlighted this approach, for example one on using AI to support a help desk, a couple on making decisions on issuing insurance policies and mortgages and one on policing.

The help desk example felt particularly insidious. The idea was that the software monitored conversations between customers and the help desk to improve the quality of interactions. But apart from a passing mention of it, the authors don't really acknowledge the Big Brother aspect of software checking your every word, rating your performance and pushing you into conformity with the required groupthink. Similarly, we heard about all the advantages for the companies using software to decide if customers should be given an insurance policy or mortgage, but not the well-documented problems raised by opaque machine learning systems using entirely unsuitable data to reject individuals. The policing example is an infamous one, and the authors had to acknowledge there have been serious problems with such systems producing racist results and making particular areas even worse than they were before, but merely say this has to be avoided, without giving any evidence that this is even possible to do.

I'm sure Davenport and Miller thought they were doing something useful in focusing on the ways that AI will not necessarily replace human workers but rather would augment their abilities. But I don't think it's possible, as was done here, to ignore some of the other dangers of AI like lack of transparency, misuse of data, surveillance and more. You have to take the view across the board.

I'd suggest this book is important reading to get a balanced picture of AI, if you can cope with the kind of mangled business-speak sentences that crop up, such as 'She works particularly at the top of the prospect funnel, trying to move leads along in the sales process and operationalize a disciplined prospecting and selling process.' The book does illustrate a few examples where having an AI helper can be genuinely beneficial to workers. And plenty more where it can benefit companies to the disadvantage of either workers or customers. This is surely valuable data, whether you side with Luke Skywalker or Darth Vader.

[Raymond Goss · Rating 5 out of 5]

This book is about companies that have used AI techniques to solve real problems. In the authors' point of view, these implementations have not resulted in loss of jobs or other doom and gloom predictions that others have posing about using new technologies. Although this book came out just a few years ago, the author's team interviewed people who successfully implemented the technologies and the leaders at those companies. There is an inherent delay in the reporting of the successes and the actual implementations. Most of the implementations took place before 2020. That means that most of these implementation were before ChatGBT's gain in popularity and the recent hype over "AI." Some of the terms, like Robotic Process Automation (RPA), has now moved on to Agentic AI, but is basically the same capability, just slightly better. The book, while not a "How To" it does cover "What To Do" through tangible examples. Some of these examples might by used by the reader today or in the near future, especially the investing and medical tools.
One particular story about Seagate's use of Defect Detection using Google's AutoML (now Vertex) is identical to the implementation that I started worked on maybe 8-10 years ago and again at my current company, which acquired the capability via acquisition of my old company's factory. Only recently did we name the term Auto Defect Classification (ADC). I was surprised that Seagate uses the same term. I wonder which solution came first. Did Seagate copy our implementation? Or was our implementation influenced by Seagate's? Did Google share the solution with other companies? Either way, the solution saves thousands of dollars and earns millions in improved products. We saved on labor, and while we didn't directly lay anyone off, we definitely are not hiring as we rollout the solution to additional factories.
I can see a follow-up edition, with new emerging capabilities. I work with a team of engineers that have been rapidly deploying AI and ML tools, robotics, vision and other capabilities. Our list of successes and learnings from failures are also quite extensive.

[Arnoud De Meyer · Rating 4 out of 5]

I enjoyed reading the 21 short case stories about successful human-AI interaction, and the short chapters with the fairly straightforward but interesting lessons the two authors draw from these short caselets. I have been reading a lot about the application of AI in business and very often I see overhyped stories. These cases are about real achievements, sometimes minor, sometimes quite disruptive. I would recommend this book to anybody who wants to understand what AI in business can do today and I like it for its realism and down to earth approach. I also enjoyed that the case studies come from North America and Singapore (reflecting the location of the two authors). This makes it a lot less US centric and makes AI more tangible for some of us that don't live close to the centre of AI development.

[سليمان العوشن · Rating 3 out of 5]

يضعك هذا الكتاب في قلب التحوّلات الجارية في بيئة العمل بفعل الذكاء الاصطناعي، ويمنحك فهماً متوازنًا بين الفرص والتحديات. أعجبني كيف قُدمت الأمثلة الواقعية من مؤسسات كبرى لتوضيح كيفية دمج الذكاء الاصطناعي عمليًا، مما جعلك تشعر بأن الموضوع ليس خيالاً علمياً بل واقعًا ملموساً. أكثر ما جذبني هو تأكيد المؤلفَين على دور الإنسان في توجيه وتقييم قرارات الذكاء الاصطناعي، وهو ما يطمئنك بأن الآلة لم تُلغِ الحاجة للبشر، بل باتت تدفعهم نحو أدوار أكثر استراتيجية وتخصصًا. قد تشعر أحيانًا أن الطرح أكاديمي قليلاً أو يميل إلى التحليل المفصل، لكن ذلك لا ينتقص من قيمة الرؤى التي يقدمها. أنصحك بقراءته إن كنت قائداً أو محترفاً مهتماً بمستقبل العمل، فهو مرجع غني سيساعدك على فهم كيف تؤثر هذه التقنيات على المؤسسات، والأدوار، وحتى المهارات التي تحتاج إلى تطويرها مستقبلاً.

تابعني على شبكات التواصل الاجتماعي

سليمان العوشن

http://about.me/aloshan

[Simon · Rating 2 out of 5]

The first few case studies were all about sales, and even though each of the case studies were rather short at a few pages, I found them a bit of a drag to get through. While some of the later examples were more interesting, a lot of them were rather dull. There are a large number of case studies, probably with the goal of being comprehensive but there's a fair bit of overlap with several case studies being very similar. It's a bit of a chore to get through and the insights, future and conclusions sections seemed quite brief in comparison, with repeated affirmations that AI wasn't going to take everyone's jobs.

[Kevin Postlewaite · Rating 3 out of 5]

Disappointing. There are some interesting examples and some of those do include some interesting ML applications, some are simply automation examples. Many don't have enough technical detail to understand what has been implemented. I was hoping to hear of more examples where state-of-the-art large language models or transformer models were changing business processes but you won't find that here. You'll find lots of people confident that people "will always be needed" by workers who haven't yet been exposed to modern AI tools.

[Mikhail Filatov · Rating 2 out of 5]

Too many -around 30 -use cases, each 4-8 pages long.
A lot of these pages are about interviewed people biography and opinions. Very short-several bullets at the end-summary from the authors, usually without any additional information or insights.
Very few details about the system capabilities, intricacies or even basic functions.
A couple of use cases -about RPA- were interesting for me, so I put to stars vs. one

[Fountain Of Chris · Rating 4 out of 5]

Is it a bit dry? Yes. Does it have a lot of timely info very relevant to something that people involved at all levels of business should know about? Also, yes. Is it a little heavily weighted to the positives? Yes. Does it provide an EXTENSIVE list of ways that humans will be able to contribute at work despite the rise of AI? Also, yes. I think the 3s are a little harsh, and probably colored more by the format and the writing style than by the content.

[Arun Narayanaswamy · Rating 3 out of 5]

This book has very different style of writing. Very research type.
Book starts with a general outline of AI. Then goes into completely disconnected set of 20+ case studies.
Then comes the summary in terms of picking some benefits and strategies around ai and linking them to the case studies.
Though the case studies were good, they lacked depth. Some of these case studies could randomly be picked from white papers or blogs online and stitched into a story.
Weak but ok !

[Debbie · Rating 3 out of 5]

Thank you for the advance copy of your book. The case studies were well laid out and with enough variety. The insights and conclusions were straightforward. You can jump around the book depending on your needs.

[Tomas Nilsson · Rating 5 out of 5]

This is a very good business book on AI. A must read for educators, business people and policy makers interested in the topics around AI and technology.

[Jason · Rating 3 out of 5]

Really good examples, got a little repetitive

[Underscore · Rating 2 out of 5]

I really was hoping more out of these case studies but instead they felt slightly repetitive and expanding less into the technical side of ai and more into the ethical / reasoning / why territory. I don't think this book does anything but show pretty standard stuff with ai, it works great along side humans, we are a long time away from I'm forgetting the term used in the book, but ai that's at least on par with human intelligence and greater. I really wanted to like this book but this was a real struggle to finish.