In the latest of his several brilliant books, Recommendation Engines, Michael Schrage examines what is perhaps the single most influential form of mass communication.

“The right recommendation at the right time is exactly what an Amazon, a Netflix, a Facebook, a Spotify, a Google, a LinkedIn, a Tinder, a TikTok, and a YouTube aspire to. That makes their ambitions transformative. Their massive, high-powered ‘recommendation engines’ not only algorithmically anticipate what ‘people like you’ desire, they nudge users to explore options and opportunities that might never have crossed their minds.”

In this context, I am again reminded of these observations by Steve Jobs: “Some people say, ‘Give customers what they want.’ But that’s not my approach. Our job is to figure out what they’re going to want before they do. I think Henry Ford once said, ‘If I’d asked customers what they wanted, they would have told me, “A faster horse!”‘ People don’t know what they want until you show it to them. That’s why I never rely on market research. Our task is to read things that are not yet on the page.”

When a recommendation 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.”

What you select and then re-purchase reveals your preferences. This information also suggests to others what else may be of interest to you. Of course, systems need to be in place for engine-driven processes that capture, organize, classify, and then distribute data where it will have the greatest value. The best decisions in any organization — at all levels and in all areas — are based on the best information available . The same is true of decisions made by individual consumers to whom recommendations are offered.

There will always be challenges and — to be sure — failures. The latter are viewed as learning opportunities in a workplace culture within which experimentation is most likely to thrive. Jeff Bezos: “If you double the number of experiments you do per year, you’re going to double your inventiveness.”

Microsoft researcher Amit Sharma: “Recommendation systems are nothing but ‘similarity hunters.'” I agree with Schrage that the search for similarity — powered by algorithmic innovation — “is an astonishingly robust platform for predicting — and proposing — the future.”

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Recommendation Engines is a must-read for anyone who wants to accelerate their personal growth and professional development. It is one of the most valuable volumes in the Essential Knowledge Series, published by The MIT Press (2020).

To learn more about Michael Schrage and his brilliant work, please click here.