MIT Sloan Management Review Article on Using Federated Machine Learning to Overcome the AI Scale Disadvantage

Deep pockets, access to talent, and massive investments in computing infrastructure only partly explain why most major breakthroughs in artificial intelligence have come from a select group of Big Tech companies that includes Amazon, Google, and Microsoft. What sets the tech giants apart from the many other businesses seeking to gain an edge from AI are the vast amounts of data they collect as platform operators. Amazon alone processes millions of transactions each month on its platform. All of that big data is a rich strategic resource that can be used to develop and train complex machine learning algorithms — but it’s a resource that is out of reach for most enterprises.

Access to big data allows for more sophisticated and better-performing AI and machine learning models, but many companies must make do with much smaller data sets. For smaller companies and those operating in traditional sectors like health care, manufacturing, or construction, a lack of data is the biggest impediment to venturing into AI. The digital divide between big and small-data organizations is a serious concern due to self-reinforcing data network effects, where more data leads to better AI tools, which help attract more customers who generate more data, and so forth.1 This gives bigger companies a strong competitive AI advantage, with small and midsize organizations struggling to keep up.

About the Author

Yannick Bammens is professor of strategy and innovation at Hasselt University in Belgium, where he coleads the AI4Business initiative. Paul Hünermund is assistant professor of strategy and innovation at Copenhagen Business School in Denmark, where he co-organizes the yearly Causal Data Science Meeting.

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