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More thoughts on succeeding with AI

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I have written before on how a decisions-first approach is ideal for success with AI. After reading David Roe‘s article 11 Questions Organizations need to Ask Before Buying into AI I thought a few more comments were in order:

If you focus on decisions first and on how you must/could/want to make the decision, you can rapidly tell if you really need AI at all. Often business rules and simpler analytics are enough – but you need to know what decision you are trying to make before you can tell. Similarly if you don’t know what else, besides the AI, is going into the decision then you won’t be able to tell how much impact AI is going to have. It’s easy to have a compliance or policy constraint undermine the “lift” you get from AI.

The business case for most AI is “better decisions”. If you don’t know which decisions, and what counts as better, then your AI is just a gimmick. Know what decisions you are trying to improve and how before you begin to ensure your AI has a real business case.

Decision models are great for showing you what else goes into a decision besides AI. This let’s you see how exposed you are when the AI gets it wrong, how good your predictions need to be to be helpful and much more. Understand the context first and it’s easier to manage, and get support for, your AI plans.

Lastly integrate AI into your decisioning stack -make sure your business rules, predictive analytics, machine learning and AI can be integrated to deliver a single, better decision (based on a decision model).

If you want to learn more about decision modeling, contact us or come to our live online decision modeling with DMN training in March.

 

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