The best books on “managing technological innovation for mere mortals”

In this book, Professor Clayton Christensen explores the puzzle of why seemingly well-run companies fail. He shares many examples of companies that are well-managed, listen to their customers, act on market trends, and invest in R&D – i.e. appear to do all the right things – and yet fail. The Innovator’s Dilemma lays out why established companies often fail at disruptive technologies and how to tackle the inherent challenges. Disruptive technologies are technologies that underperform existing products in mainstream markets and have features that a few fringe customers value. They are often ignored by well-run companies precisely because their customers don’t care for them. But these technologies can improve quite dramatically and catch these companies off guard.

The author backs his theory with several examples of technologies that meet his definition of disruptive technologies (digital photography, PCs, etc.). While other theorists have pushed back on Christensen’s theory in recent years, I still consider the book as a must-read for anyone trying to understand innovation strategy.

This book by Steven Blank is a bible for anyone trying to understand how to build lean startups. The classic mistake that most entrepreneurs make is to go build a product soon after they develop a hypothesis about what customers want. By building products before customer discovery (i.e. verify customer needs and a scalable sales model), many products miss the mark and fail. The book explains how a lean start-up can figure out what customers want before proceeding to build products. This emphasis on a customer-centered approach rather than a product-centered approach can be the difference when it comes to finding product-market fit. A must-read for any founder!

This book lays out one of the most useful big-picture ways to put AI to use:  as a predictive tool. The central point of the book is that from a business/economic standpoint, AI increases accuracy and reduces the cost of making data-driven predictions. The benefit of reducing the cost of predicting in business is enormous and this book lays that out clearly. And the rest of it is focused on how that will affect the world around us. I recommend this book highly to any manager who is thinking through what AI means for their firm. The writing style is clear – not excessively technical. A great read for everyone!

This book provides an excellent description of the various kinds of machine learning approaches and asks the question of whether there will be a Master Algorithm, one single (universal) algorithm that learns all kinds of tasks from data. The author, Pedro Domingos, introduces the different approaches to building intelligence and the research tribes exploring them – Symbolists (with its foundations in Philosophy and Logic), Connectionists (foundations in Neuro/Cognitive Science), Evolutionaries (foundations in Evolutionary Biology), Bayesians (statistical foundations), and Analogizers (Psychology). He also introduces some of his own ideas on what the master machine learning algorithm might look like. It’s a really useful primer for those who are not deeply immersed in machine learning but it’s written for readers with at least a basic engineering and computer science background.

One of the biggest questions surrounding AI is the impact it will have on jobs. Just as manufacturing jobs are affected by mechanical automation, many white- and blue-collar jobs are going to be affected by AI-driven automation in the future. The question is whether AI will be like all technologies in the past (which have created more jobs than they have destroyed) or whether it is unique in its ability to automate and displace human jobs at a faster pace than the jobs it creates. Many commentators have asked the question and there are dozens of books exploring how AI will affect jobs.

Among all the books out there on the impact of AI-driven automation, this is one of my favorites. While offering a fundamentally optimistic take, the authors also warn the reader that our education systems will need to change and people will need to upskill themselves and public policy will need to keep up in order for us to successfully manage the transition. If you are under 40 years of age or have children who will be entering the workforce in the future, you should read this to understand how to adapt to AI.