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Enterprises increasingly seek to optimise digital products using predictive artificial intelligence (AI) that anticipates customer behaviour rather than merely reporting it. This paper proposes and evaluates an integrated predictive-AI framework for enterprise product optimisation that unifies five capabilities usually pursued in isolation: behavioural modelling, customer-acquisition analytics, process intelligence, intent prediction, and personalised decision-making. The central thesis is that these capabilities compound when built on a shared feature and event foundation, converting fragmented analytics into a coherent optimisation loop. Using a design-science methodology triangulating a representative behavioural dataset, controlled predictive-model evaluation, and an A/B-style comparison against a non-personalised baseline, the study quantifies behavioural-prediction accuracy, acquisition-conversion uplift, process-bottleneck detection, intent-prediction performance, and the business effect of personalised decisioning.....