than with generative AI. To run more effectively, many business operations need prediction more than they need the generation of new content. That’s why predictive AI is the kind of AI companies turn to for improving the effectiveness of large-scale processes. Predictive modelswhom to contact, approve, test, warn, investigate, incarcerate, set up on a date or medicate. They target operational decisions: Market to those likely to buy. Approve for a loan those likely to pay on time.
But the world isn’t making it easy for predictive AI to succeed. First, genAI's current popularity leaves predictive AI a relatively unsung hero. Second, prediction boils down to probabilities and probabilities aren't sexy. A cultural aversion hinders companies from embracing them. For many business professionals, the topic of probability can seem at best boring and at worst arcane and complex.
But there's no way around it: Embracing predictive AI means becoming a business that acts probabilistically. We don't generally have absolute, highly-confident predictions. There's no magic crystal ball. But we do have: A number between 0 and 100 that expresses the expected chance of a certain outcome or behavior. That's a probability. And that's what you get from a predictive model generated by machine learning.
But if you take a deep breath and a good look, you'll find that predictive AI isn't hard to understand. The required upskilling is accessible, not arcane. Business professionals involved with predictive AI must ramp up onFor the first of these three, you work with data professionals to establish what outcome or behavior to put probabilities on, such as whether a customer will click, buy, lie, die, cancel their subscription or commit an act of fraud.
For the second, you must help establish which metrics to report on for determining whether an ML model is production-ready. This includessuch as the improvement to profit or savings a deployment is expected to deliver. Spoiler alert: accuracy is usually an. For example, if the model predicts that a customer will buy if contacted, then include that customer in a marketing campaign. If a transaction is predicted to be fraudulent, then block or audit it..