When choosing an environment tooperationalize an AI/ML solution, PMI-CPMAI guidance stresses starting fromstakeholders and end-user interactions, then deriving technical choices (infrastructure, deployment model, integration pattern) from those needs. Identifyingwho the end users are, how they will interact with the system, and in which workflows and channelsis crucial. This includes understanding whether the AI will be consumed via dashboards, embedded in existing applications, via APIs, or as decision support in specific business processes.
Once these interaction patterns are clear, the project manager and technical team can determine environment needs: latency requirements, availability, integration points, security boundaries, on-prem vs. cloud, edge vs. centralized deployment, and needed tooling for monitoring and MLOps. Scalability (option A), cost (option B), and compliance (option D) are all important factors, but they aresecondary considerationsthat should be evaluated in the context of how users will actually use the system.
PMI’s AI lifecycle view emphasizes that environment and architecture decisions must berequirements-driven, not purely cost- or technology-driven. Therefore, the project manager should firstidentify the end users and their interactionswith the solution (option C) as the basis for selecting the most suitable operational environment.
