Predictive Task Prioritization for Generative AI: Anticipating Future Request Probability and Computational Value to Optimize Execution Order

The increasing deployment of generative artificial intelligence (AI) systems has introduced increasingly complex computational workloads involving proactive response generation, model preparation, KV cache construction, intermediate representation computation, inference optimization, and other speculative computational activities. As predictive computing infrastructures become capable of anticipating future computational demand, they may generate large collections of candidate tasks that compete for limited GPU capacity, memory, execution time, and financial resources. Predicting future demand and preparing computational resources, however, do not independently determine which tasks should be executed first. When multiple potentially useful tasks are available, the system must determine their relative execution priorities according to the probability of future requests, the potential computational benefits of successful preparation, and the opportunity costs of consuming resources. This paper introduces Predictive Task Prioritization for Generative AI (PTP-GAI), a conceptual framework for dynamically assigning execution priorities to anticipated computational tasks according to their predicted future demand and expected computational value. Rather than treating candidate tasks equally or prioritizing them exclusively according to arrival order, computational size, or historical frequency, PTP-GAI estimates the probability that each task will be requested within a specified future horizon and evaluates the potential benefits of preparing it before demand materializes. The framework integrates future request probability estimation, expected request frequency prediction, computational savings estimation, reuse probability assessment, task urgency modeling, resource consumption evaluation, and adaptive priority assignment. A central component of the proposed framework is a predictive priority function that combines the expected probability of future task execution with its potential net computational benefit. The framework further distinguishes between the probability that a task will be requested, the probability that its prepared output will be successfully reused, and the computational value obtained when reuse occurs. These factors represent different sources of uncertainty and should not be treated as interchangeable. A constrained task scheduling formulation is developed to maximize expected aggregate computational benefit while accounting for GPU capacity, memory limitations, execution deadlines, task dependencies, and service-level requirements. Additional mechanisms address priority updates, task expiration, prediction errors, resource contention, and starvation prevention. The proposed architecture can operate alongside predictive demand forecasting, predictive computational orchestration, and economic-aware predictive computing. Its primary responsibility is not to predict aggregate GPU utilization, select economically favorable execution periods, or independently generate future responses, but to determine the relative importance and execution order of candidate computational tasks. An experimental methodology is established to compare the proposed approach with FIFO scheduling, static priority scheduling, probability-only scheduling, benefit-only scheduling, and conventional cost-aware task selection. Evaluation metrics include realized computational savings, priority prediction effectiveness, task completion rates, resource utilization, scheduling latency, and computational expenditure per successfully prepared workload. This research establishes predictive task prioritization as a distinct decision-making component of predictive computing infrastructure. Its central hypothesis is that combining future request probability with expected computational benefit can improve the allocation of limited preparation resources compared with scheduling strategies that consider these factors independently. The practical effectiveness of the framework remains subject to empirical validation under realistic and temporally varying generative AI workloads.