HBR Guide to AI Basics for Managers
Various Contributors and HBR Editors
Harvard Business Review Press (January 2023)

“If robots take over the world, I’m going to work in the factory that makes them.” Ralph Kramden

Most of the volumes in the “HBR Guide to” series are anthologies of articles previously published in Harvard Business Review in which their authors share their insights concerning a major business subject such as Better Business Writing, Getting the Right Work Done, and Project Management.

As is also true of most volumes in other such series, notably HBR Essentials, HBR Must Reads, and HBR Management Tips, the HBR Guides offer substantial value in cutting-edge thinking from 20 or more sources in a single volume at a price (each at about $20-25 from Amazon in the paperbound edition) for a fraction of what article reprints would cost. As for this Guide, the total cost of reprints would be $214.80.  The paperbound edition of this volume costs only $21.95. That’s a steal.

What we have in this volume are 24 articles (including an Introduction and an Appendix) selected by HBR Editors. The authors focus on AI basics that can help managers to achieve learning objectives that include

o How AI works, what it is good at, and what it should never be expected to do
o What is essential to know about machine learning
o Which technologies best perform each essential AI task
o How to ramp up team members’ predictive analytics skills
o Understand how and why AI is a tool by which to assist rather than replace workers

o Who should be involved when making AI decisions
o How and why AI can scale risks as well as solutions
o How AI can help address inequity if users’ trust is earned
o Three critical conversations that can help mitigate ethical risks
o How learning through trial and error can lead to creative solutions

In or near the central business district of most major cities, there is a farmer’s market at which merchants (at least pre-COVID) offer slices of fresh fruit as samples of their wares. In that same spirit, I now offer a few brief excerpts from the material provided in this volume:

o “So, what should all of your employees be learning about AI? They should be able to answer these:

1. How does it work?
2. What is it good at?
3. What should it never do?”

“Three Questions About AI That Every Employee Should Be Able to Answer,” Emma Martinho-Truswell, (Pages 13-15)

* * *

o “The most common application of machine learning tools is to make predictions. Here are a few examples of prediction problems in a business:

• Making personalized recommendations for customers
• Forecasting long-term customer loyalty
• Anticipating the future performances of employees
• Rating the credit risk of loan [or credit] applicants”

“What Every Manager Should Know About Machine Learning,” Mike Yeomans (18-19)

* * *

o  “Humans need to perform three crucial roles. They must train machines to perform certain tasks’; explain the outcomes of those tasks, especially when the results are counterintuitive or controversial; and sustain the responsible use of machines (for example, preventing robots from harming humans)…

[TRAINING:] Machine learning algorithms must be taught how to perform the work they are designed to do…[EXPLAINING:] ] As AIs increasingly reach conclusions through processes that are opaque (the so-called black-box problem), they require human experts to explain their behavior to nonexpert users…[SUSTAINING:] In addition to having people who can explain AI outcomes, companies need ‘sustainers’ — employees who continually work to ensure that AI systems are functioning properly, safely, and responsibly.” (99)

“Organizations that use machines merely to displace workers through automation will miss the full potential of AI. Such a strategy is misguided from the get-go. Tomorrow’s leaders will instead be those who will embrace intelligence, transforming their operations, markets, their industries, and — no less important — their workforces.” (115-116)

“Collaborative Intelligence: Humans and AI Are Joining Forces,” H. James Wilson and Paul Daugherty

* * *
o Seven steps to operationalize data and AI ethics:

“1. Identify existing infrastructure that a data and AI ethics program can leverage.
2. Create a data and ethical risk framework that is tailored to your industry.
3. Change how you think about ethics by taking cues from the successes in health care.
4. Optimize guidance and tools for product managers.
5. Build organizational awareness.
6. Formally and informally incentivize employees to play a role in identifying AI ethical risks.
7. Monitor impacts and engage stakeholders.”

Reid Blackman, “A Practical Guide to Ethical AI” (159-164)

* * *

While reading business books such as this one, highlight key passages and keep a lined notebook near at hand in which to record your questions, comments, and page references.  These two simple tactics will facilitate, indeed expedite frequent review of key material later.

Here are a few concluding thoughts from the HBR Editors: “Whether you need to get up to speed quickly or need a refresher, or you’re working with an AI expert for the first time, the HBR Guide to AI Basics for Managers will give you the information and skills you need to succeed.”