“How can I leverage the capabilities of disruptive technologies to accelerate my personal growth and professional development?”

That is a question many executives are now asking as a new calendar year proceeds, amidst what may well be the worst pandemic in human history. The disruptive technologies include artificial intelligence (AI); sensors and the Internet of Things (IOT); autonomous machines — robots, cobots, drones, and self-driving vehicles; distributed leaders and blockchains; virtual, augmented, and mixed reality; and connecting everything and everyone: 5G networks and satellite constellations. I view the disruption they continue to cause as both creative destruction (as Joseph Schumpeter characterizes it) and creative construction (as Michael Schrage suggests in his book).

In his just published book, Recommendation Engines , Michael Straight shares a wealth of information, insights, and counsel that can help almost anyone to answer that question. More specifically, he “defines, discusses, and details what recommendation engines are and what makes them special…[Then] places recommendations in broad historical contexts ranging from oracles and astrologers of antiquity to contemporary curators and self-help gurus…[Next he] reviews the history of recommendation engines themselves, their academic origins, commercial engines, and multitrillion-dollar global impact…[And then he explains] how recommendation engines work [and] examines the challenge of converting implicit, explicit, and side data into structures that can be algorithmically converted into recommendations…[before he] examines recommenders with user experience in mind…[and then] offers three brief incisive case studies that illustratively bundle the algorithmic and UX exposition…[before concluding] with apocalyptic/aspirational visions of possible, probable, and inevitable recommendation engines futures.”

Other than those who read Schrague’s new book, most executives know little (if anything) about HOW recommendation engines can help their organization to develop systems that drive knowledge transfers between and among those of greatest importance to its success. The best examples of enlightened companies include Amazon, Apple, Google, and Netflix. When one of their recommendations is right (i.e. appropriate), Schrage observes, it “offers ways of both understanding the world and understanding oneself. Recommenders prioritize the world’s most relevant options and choices for your consideration; those recommendations ostensibly reflect one’s tacit and explicit desires: that slice of the world that matters to you.”

The good news is that almost all other organizations — whatever their size and nature may be — can also help their own people as well as their customers to exchange suggestions and recommendations that are mutually beneficial. Years ago, Jackie Huba and Ben McConnell wrote a book in which they explain how to create what they characterize as “customer evangelists.”

Why not create employee evangelists? Why not create a total learning enterprise? Why not indeed.

Read Recommendation Engines and hope that your competitors don’t.

Those who share my high regard for this book are urged to check out Schrage’s previously published books. Also two others: Steve Brown’s The Innovation Ultimatum: How six strategic technologies will reshape every business in the 2020s, and, Daniel Kahneman’s Thinking, Fast and Slow.

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A research fellow with MIT Sloan School’s Initiative on the Digital Economy, Michael Schrage’s research, writing and advisory work focus on the behavioral economics of models, prototypes and metrics as strategic resources for managing innovation risk and opportunity. His published works include the  award-winning The Innovator’s Hypothesis (MIT Press 2014),  Who Do You Want Your Customers To Become? (Harvard Business Review Press 2012), and Serious Play (Harvard Business Review Press 2000).  His new book, Recommendation Engines, was recently published by MIT Press as part of its “Essential Knowledge” series. He’s run design workshops and executive education programs on innovation, experimentation and strategic measurement for organizations all over the world. To learn more about Michael and his work, please click here.

Here is a direct link to my review of Engines of Recommendation.