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Managing AI Decision-Making – Published on HBR

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I am super-excited to announce that an article I have been working on with Michael Ross has just been published on Harvard Business Review – Managing AI Decision-Making Tools

The nature of micro-decisions requires some level of automation, particularly for real-time and higher-volume decisions. Automation is enabled by algorithms (the rules, predictions, constraints, and logic that determine how a micro-decision is made). And these decision-making algorithms are often described as artificial intelligence (AI). The critical question is, how do human managers manage these types of algorithm-powered systems. An autonomous system is conceptually very easy. Imagine a driverless car without a steering wheel. The driver simply tells the car where to go and hopes for the best. But the moment there’s a steering wheel, you have a problem. You must inform the driver when they might want to intervene, how they can intervene, and how much notice you will give them when the need to intervene arises. You must think carefully about the information you will present to the driver to help them make an appropriate intervention.

The core of the article is to discuss the different ways people and automated decision-making can interact – is the human in the loop, on the loop or out of the loop?

We build a lot of decisioning solutions for clients and I’ve been working in this space a long time. Our DecisionsFirstTM approach emphasizes continuous improvement, and how the human managers of the domain interact with the system, to ensure deep and ongoing business enablement. We have found that making choices about the best management options is key to success with automating these kinds of micro decisions and to the use of artificial intelligence (AI) and machine learning (ML) more generally.

Enjoy the article. Drop me a link or connect to me on LinkedIn if you have questions!

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