When operationalizing an AI system to improve delivery times, PMI-style AI project guidance stresses the importance of identifying constraints and assumptions early, before heavy investment in build-out. A preliminary feasibility study is the standard method to surface key performance constraints that might impact the AI solution. This includes analyzing current logistics processes, data availability and latency, network conditions, service-level expectations (e.g., maximum response times for route optimization), infrastructure capacity, and integration limits with existing systems.
A feasibility study helps the team clarify: what throughput is required, how frequently predictions must be updated, what real-time vs. batch constraints exist, and whether current hardware, APIs, and data pipelines can support those requirements. This aligns with PMI-CPMAI’s emphasis on evaluating technical, data, and organizational readiness before committing to full-scale deployment.
Benchmarking competitors (option A) may highlight external performance targets but does not systematically uncover the internal constraints. Implementing advanced visualization tools (option B) can help later with monitoring and communication but does not, by itself, identify constraints. Training employees on AI ethics (option D) is valuable from a governance standpoint, yet it does not address performance limitations. Thus, the method that directly meets the objective of identifying performance constraints is to conduct a preliminary feasibility study.
