The only thing hotter than AI innovation is the number of AI books. From strategy to deeply technical, experts are also riding the generative AI wave. To cull this year’s list, I focused on books published in the last two years with the themes of data, analytics, and AI.
I scoured lists and reviews on Amazon, solicited ideas from social networks, and am continuously reading. It’s true: I’ll throw in a Liane Moriarty or Percival Everett to satisfy both sides of my brain.
Unfortunately, time is not limitless, so while I liked many data analytics books, I’m being ruthless about sticking to a list of 10 musts. The list is sorted alphabetically by title. If you think I overlooked a critical one, do let me know! We learn best from one another.
Tune into The Data Chief podcast to hear from three of the authors on this year’s list, and check out last year's podcast guests to round out your reading list. Be sure to check out our 2023, 2022, and 2021 must-read lists.
Books About Data and AI: My Top Picks for 2025
1. The AI-Driven Leader by Geoff Woods
Geoff Wood’s book is an essential guide for executives, focusing on how to translate AI into tangible business impact. He advises leaders to view AI as a strategic partner to escape operational overwhelm and make smarter decisions that outpace the competition.
Woods offers a framework for better prompts, CRIT: Context, Role, Interview, Task—for crafting effective prompts. He also warns against abdicating judgment to AI: “You’re the driver, AI is in the passenger seat.”
With Wood’s experience as a strategist and growth leader, business executives will appreciate his grounding of AI as a business tool rather than a shiny innovation.
Hear Geoff on The Data Chief podcast here, and get your book here.
2. Co-Intelligence: Living and Working with AI by Ethan Mollack
Ethan Mollack’s book remains a best seller on AI, and as an associate professor at Wharton, he makes AI approachable for the masses. He offers 4 core principles:
1. Invite AI to the table
2. Keep humans in the loop
3. Treat AI like a person
4. That our current AI is the worst it will ever be (or, as I say, AI is in the dial-up days of the internet).
Mollack elaborates on the different roles AI may play as a co-worker, coach, or tutor. I appreciate his clarity in writing, and no matter how magically seeming, a reminder of AI’s origins:
“They are trained on our cultural history, and reinforcement learning from humans aligns them to our goals. They carry our biases and are created out of a complex mix of idealism, entrepreneurial spirit, and yes, exploitation of the work and labor of others.”
Get your copy of Ethan Mollack’s book here.
3. The Data Governance Handbook by Wendy S. Batchelder
A CDAO once said that “data observability” was a new term to make “data governance” more sexy. I have suggested rebranding data governance to data enablement, as too often people see governance as code for “no,” as in “no access, no, you can’t do that.” And yet, data governance ensures both trusted data and protection from dangerous data breaches.
As a three-time CDAO, Wendy Batchelder takes a pragmatic approach to data governance. The book includes important lessons on how to “build a coalition of advocates” and that governance should be “embedded into how you operate as a company” rather than viewed as overhead or bureaucracy.
Bonus: even if you prefer to read print as I do, the publisher Packt will send you a PDF for a digital copy that I could readily load into NotebookLM.
Hear Wendy S. Batchelder on The Data Chief here, and get your book here.
4. The Data Hero by Malcom Hawker
As a former Gartner research analyst and CDAO coach myself, I have followed Malcom Hawker’s role from Gartner to CDO advisor at master data management company Profisee. The Data Hero is Hawker’s first book, and his unique take is that many of the mistakes data leaders make a mistake in mindset.
Just as we talk about data as "garbage in, garbage out," Hawker believes we need to replace anti-hero mindsets with more empowering, growth-oriented beliefs. This hero mindset challenges the status quo and encourages strategic risk-taking rather than risk avoidance. Product management disciplines are also core to his book.
Hear Malcom Hawker on The Data Chief here, and get his book here.
5. Generative AI in Practice by Bernard Marr
Bernard Marr is a futurist and prolific author with more than 20 books in our industry. His latest book covers the important topics of which jobs will be most impacted, the highest priority use cases within specific industries, and what the future looks like as Generative AI rapidly expands.
Marr lays out how data professionals and citizens can best prepare for this transformative technology. He believes data analysts will be freed from the drudgery of creating dashboards and instead will work on higher-value tasks such as data curation.
Listen to Bernard Marr on The Data Chief podcast here, and get your copy here.
6. Mastering AI by Jeremy Kahn
I especially liked Jeremy Kahn’s book for how he covered both the history of AI and the societal impact. He provides a significant amount of research on how other technical innovations have shaped our ability to read, think critically, and process information. For example, English cab drivers who must memorize the maze of streets have a larger hippocampus than the general population.
Kahn fears that generative AI, with its ready answers, may enfeeble us and cause social skills to atrophy. And yet, he believes that “the organizations that come out on top will be those that are best at creating a positive feedback loop between people, data, and AI.”
Listen to Jeremy Kahn on The Data Chief podcast here, and get your copy here.
7. Taming Silicon Valley by Dr. Gary Marcus
Dr. Gary Marcus has been coding and building AI systems since childhood, combining his hands-on expertise with his industry knowledge to deliver a book that is a must-read. He breaks down the 12 biggest risks to society from generative AI that is evolving in real time. He is bluntly critical about the “moral descent of Silicon Valley” and calls for tighter regulation.
And yet, with regulation moving more slowly than the innovation in this space, this is why he wants citizens to take more ownership and be educated about this transformative technology that Dr. Marcus describes as “a mess, seductive but unreliable.”
Hear Dr. Gary Marcus on The Data Chief here, and get your book here.
8. Unmasking AI: My Mission to Protect What Is Human in a World of Machines by Dr. Joy Buolamwini
My hope for this book is that:
All business analytics programs in universities have it as required reading.
All responsible AI and AI ethics committees within corporations include it as pre-read homework.
Tech companies working with data and AI understand the fundamentals here and weave best practices into design reviews and culture.
I’ve followed Dr. Joy Buolamwini’s work from when she was a graduate student at MIT and first discovered bias in facial recognition programs. The book dives into her background as a poet and engineer and how the “coded gaze” led to her work on facial recognition bias.
For data lovers, you will appreciate her analysis of training data sets and how what the industry considers ‘gold standards’ only further exacerbate bias at scale.
This is a must-read for society as a whole. Get your copy of Dr. Joy Buolamwini’s book here.
9. Scaling Responsible AI, From Enthusiasm to Execution by Noelle Russell
Catching the title of this book on a social media post was enough to put it on my radar. Noelle Russell is an AI pioneer who began her journey working with Jeff Bezos and the early Alexa team. As the world implements AI, we must think about the impact on humanity and how to scale responsibly.
Russell has a unique perspective as a woman, Latina, and caregiver that has shaped her contribution to building tech and now advising boards and customers. Noelle provides a framework for AI that is both responsible and profitable through her POET framework.
Listen to Noelle Russell on The Data Chief here, and get her book here.
10. Your AI Survival Guide: Scraped Knees, Bruised Elbows, and Lessons Learned from Real-World AI Deployments by Sol Rashidi
The AI Survival Guide is a perfect combination of memoir, strategy, and real-world best practices for identifying the highest value applications of AI. Sol Rashidi’s book reveals her journey into AI, beginning with IBM Watson and later as CDAIO at multiple iconic companies.
She lays bare why the CDAO and CDO are perhaps the loneliest of jobs, yet one of the most critical roles of our times, as AI challenges business models and humanity itself. Rashidi provides a number of frameworks to prioritize use cases, assess readiness, and understand people’s motivations in embracing change or blocking efforts.
Hear more from Sol Rashidi on The Data Chief author episode here, and get your copy here.
You can also check out her earlier appearance on the podcast while she was CDAO at Estee Lauder here.
Bonus Reads: Noteworthy, Just Published, or Coming Soon
From Data to Dollars: Turning Data Strategy into Business Value, by Julia Bardmesser
The Path to AGI, by John Thompson
Successful AI Product Creation: A 9-Step Framework by Shub Agarwal
Nexus, by Yuval Harari
Happy reading!
