How Does Predictive AI Create Business Value? A Configurational Analysis From a Task‐Technology Fit Perspective

This paper investigates how AI integrators drive business value across organisational contexts by leveraging predictive AI to reduce uncertainty. We integrate the task‐technology fit (TTF) framework with context‐mechanism‐outcome (CMO) logic to explain how AI integrators align predictive AI capabilities with business decision needs. Drawing on a qualitative multicase study of 23 predictive AI projects implemented by seven global AI integrators for client organisations, we identify the following three recurring configurations: (1) risk and propensity scoring for targeted decision‐making, (2) forecasting and resource‐planning optimisation to reduce volatility and (3) anomaly detection to enhance operational reliability and fraud prevention. Value creation depends on integrators' ability to orchestrate a socio‐technical fit between predictive mechanisms and decision tasks, including actionability and ownership of intervention routines for scoring, temporal fit for forecasting and trust and interpretability for anomaly detection. From a practical perspective, we provide a comprehensive, evidence‐based value creation framework that enables AI integrators and client managers to scope, embed and scale predictive AI deployments.