One of the most important value drivers of machine learning and artificial intelligence (ML/AI) technology isn’t the technology itself. It’s where a company deploys it. The company’s experience is that when investors realize they lack the technical skills to evaluate ML/AI technology, they tend to throw in the towel and take a target’s inflated claims at face value. That’s a mistake. The good news is that it only requires general business knowledge to assess one of ML/AI’s main value drivers: profit proximity.
Turns out, the main value of ML/AI comes from which type of business problems ML/AI is applied to. There are a few categories here and explain how each differs in its proximity to profit, which is what really drives value. There are three profit proximities: The quickest payoff is when ML/AI is applied to customer behavior. Medium-term payoff typically applies when ML/AI improves a product that makes a user’s life noticeably easier. Lastly, the longest-term payoff applies when ML/AI’s largest effect is reputational.
The prototypical example in the business-to-business space is any ML/AI application that falls under the general category of robotic process automation, where the improved ML/AI immediately increases user productivity. While user productivity is immediately improved, increased profit from this improved product must wait until customers subsequently buy more or resist switching to lower-priced competition, which can be weeks, months or years away.
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To support for AI technology and development for social impact, Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF) has established Artificial Intelligence World Society Innovation Network (AIWS.net) to develop positive AI for helping people achieve well-being and happiness, relieve them of resource constraints and arbitrary/inflexible rules and processes, and solve important issues, such as SDGs.