Here is an excerpt from an article written by Megan Beck and Barry Libert for Harvard Business Review and the HBR Blog Network. To read the complete article, check out the wealth of free resources, obtain subscription information, and receive HBR email alerts, please click here.

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Platform business models are booming—becoming bigger and more powerful than ever. Just consider that a few tweets from the president caused Amazon’s market capitalization to fall by about $40 billion, or that Russian influencers were able to reach 126 million people through Facebook. At OpenMatters, we spend a lot of time studying network orchestration—business models where companies facilitate relationships and interactions, rather than serving up all the products, services, and pieces of content themselves. Think Facebook, Uber, Pinterest, Alibaba, Airbnb, and the myriad “unicorns” that are being showered in investor dollars. These companies are groundbreaking, leveraging networks effects and near-zero scaling cost to trounce competition or define new markets. However, not all platform plays work—the business model alone isn’t sufficient for success. There are lots of things that can make a platform succeed or fail, of course, but an increasingly central aspect of a successful platform strategy is machine learning.

The network-based business model, the role of being a “connector,” is not new. We had matchmakers before we had Match.com, message boards (real ones!) before we had Twitter, and recruiters before we had LinkedIn. Moving to an online model is a big improvement—enabling rapid and incredibly low-cost scaling. This is valuable for a network that wants to attract millions of users, but this sort of scale brings problems of its own. Large-scale platforms are too complex, and the use cases too varied for simple algorithms to manage them. Because what do you get when you pile up a million resumes, dating profiles, or 280-character thoughts? A mess. A jumble that is far too big to be valuable to your customers, and even too big for a team of employees to clean up. And within this jumble of connections, misuse and manipulation can be easily hidden.

Traditionally, the way around this is to have humans write clever algorithms to sort through the mess. Today it’s machine learning that solves this problem, by organizing what the network offers up, bringing users carefully tailored results, and flagging bad behavior. This is true for networks of any type, whether they are sharing ideas, services, relationships, or even tangible offerings.

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Megan Beck is Chief Product and Insights Officer at OpenMatters, a machine learning startup, and a digital researcher at the SEI Center at Wharton. She is the coauthor of The Network Imperative: How to Survive and Grow in the Age of Digital Business Models.

Barry Libert is a board member and CEO adviser focused on platforms and networks. He is chairman of Open Matters, a machine learning company. He is also the coauthor of The Network Imperative: How to Survive and Grow in the Age of Digital Business Models.