Many business operations benefit from the use of predictive AI, which focuses on making decisions based on probabilities rather than generating new content. Predictive models help in deciding whom to contact, approve, test, warn, investigate, incarcerate, set up on a date or medicate. However, the current popularity of generative AI often overshadows predictive AI, and there is a cultural aversion to probabilities that hinders companies from fully embracing them.
Embracing predictive AI means becoming a business that acts probabilistically, as absolute predictions are rare. Predictive models provide a number between 0 and 100 that expresses the expected chance of a certain outcome or behavior. This approach may not be as exciting for some as traditional methods, but it can greatly improve the effectiveness of large-scale processes.
While there is a cultural backlash against probabilities, examples like the successful collaboration between quant and stakeholder in Moneyball show the potential benefits of predictive AI. However, there is a need for business professionals to develop a semi-technical understanding of predictive AI, focusing on what is predicted, how well it is predicted, and what actions are taken based on those predictions.
Collaboration between data professionals and business professionals is essential for the success of predictive AI projects. Establishing what outcomes to put probabilities on, determining the metrics for a model’s readiness, and deciding how to act on predictions are crucial steps. Business professionals must involve themselves in these details to provide a business-side perspective that ensures the project stays on track both technically and pragmatically.
Despite the potential benefits of predictive AI, many projects fail to reach deployment due to a lack of collaboration with business-side stakeholders. To improve the success rate of these projects, business professionals must be actively involved in all aspects of a predictive AI project, ensuring that their business perspective is considered throughout the process. By taking a hands-on approach and staying informed, business professionals can increase the likelihood of a successful deployment of predictive AI in their operations.
