Wouldn’t it be nice if an algorithm could tell you when to develop a new business model or whether to enter a new market?

We’d be lying if we said that such an algorithm exists. It doesn’t, and we don’t imagine a time in the foreseeable future when algorithms (or other forms of artificial intelligence) will be able to answer such difficult strategic questions. But we do believe that something almost as interesting is emerging: a way for organizations to apply algorithmic principles to make frequent, calibrated adjustments to their business models, resource allocation processes, and structures—without direction from the top.

That’s a provocative claim, but it’s based on actual developments we’ve observed at internet companies like Google, Netflix, Amazon, and Alibaba. These enterprises have become extraordinarily good at automatically retooling their offerings for millions of individual customers, leveraging real-time data on their behavior. Those constant updates are, in fact, driven by algorithms, but the processes and technologies underlying the algorithms aren’t magic: It’s possible to pull them apart, see how they operate, and use that know-how in other settings. And that’s just what some of those same companies have started to do.

In this article, we’ll look first at how self-tuning algorithms are able to learn and adjust so effectively in complex, dynamic environments. Then we’ll examine how some organizations are applying self-tuning across their enterprises, using the Chinese e-commerce giant Alibaba as a case example.