One of the core topics within the machine learning space that I have studied happens to be ROI or, more to the point, an examination of just how well spent money would be in the space. You can certainly spend money on machine learning and AI related efforts as a part of a think tank, an independent lab, or a pure research institution. Within corporate spaces, pure research and development is one thing, but generally to expend the precious capital resources of an institution, organization, or group, you want to know that some type of return will be accumulated from those activities. It’s inherent to the nature of the venture into running a corporation compared to running some other type of organization.
ROI for machine learning is a topic that deserves consideration. You can evaluate a variety of potential machine learning use cases to solve problems. Going from being a special product to an operationalized business process that utilizes technology to get things done will be dependent on the ROI associated with the use case. If it costs more to do it, then you could reasonably expect that use case is going to make most responsible actors pump the brakes on the project. However, it appears that a lot of use cases went forward anyway.
I spent some time trying to find a good accounting of how much money has been spent on machine learning projects overall and how many of them have actually yielded solid ROI. You probably will not be all that surprised to learn that very few rigorous studies exist of successful ROI in the machine learning space. If some of those types of studies exist and I just missed them during my search, then by all means feel free to share them in the comments and let me know [1]. It won’t hurt my feelings or anything, and I’d actually be a little bit relieved that research on the subject exists.
Before we conclude here, I do want to share one paper that does seem to be directly addressing this question. It has only been cited by three other papers since 2020. I had hoped it would lead me to a cluster of academic research. That was not the case. The search for solid academic research on machine learning ROI is still ongoing.
Mizgajski, J., Szymczak, A., Morzy, M., Augustyniak, Ł., Szymański, P., & Żelasko, P. (2020). Return on investment in machine learning: Crossing the chasm between academia and business. Foundations of Computing and Decision Sciences, 45(4), 281–304. https://sciendo.com/article/10.2478/fcds-2020-0015
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“We Talked To A VP At Microsoft - WAN Show December 23, 2022”
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What’s next for The Lindahl Letter?
Week 102: ML pracademics
Week 103: Rethinking the future of ML
Week 104: That 2nd year of posting recap
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