Predictive AI is often overshadowed by the buzz around generative AI, but it plays a crucial role in enhancing business operations by focusing on prediction rather than content generation. Companies leverage predictive models to make data-driven decisions on whom to contact, approve, test, or medicate, based on the likelihood of desired outcomes. Despite its importance, predictive AI faces cultural resistance because it deals with probabilities, which many find unexciting or complex. Unlike Han Solo’s aversion to odds in “The Empire Strikes Back,” modern businesses must embrace these probabilities to leverage predictive AI effectively.
To succeed with predictive AI, business professionals need a semi-technical understanding centered on three key aspects: what’s predicted, how well, and what’s done about it. Collaborating with data professionals is essential to determine the outcomes to predict, establish relevant metrics for model performance, and define actionable steps based on predictions. A lack of collaboration often leads to the failure of predictive AI projects. Therefore, business stakeholders must actively engage in these projects to ensure they are not only technically sound but also pragmatically aligned with business goals. By doing so, companies can overcome cultural barriers and fully realize the benefits of predictive AI.
