AI automation platforms span a wide range of capabilities, from lightweight task automation and robotic process automation (RPA) to model orchestration, decisioning engines, and human-in-the-loop systems. For buyers, the important question is not which platform is the most feature-rich on paper, but which one maps to their operational realities: data access patterns, legacy system integration, governance and compliance needs, latency and throughput requirements, and the team’s skill set. Practical considerations include low-code vs developer-first interfaces, prebuilt connectors to CRMs and databases, versioning and rollback for models and workflows, observability and alerting for live automations, and role-based controls for safe experimentation. Cost models (per automation, per API call, or seat-based) and vendor lock-in risk also shape long-term outcomes. This comparison focuses on real use cases — order-to-cash automation, intelligent document processing, conversational assistants that escalate to humans, and routine back-office workflows — and clarifies which platform capabilities matter for each. We also discuss operational readiness: testing/QA processes, audit trails for decisions, and incident response patterns. The goal is to give product leaders, ops managers, and platform engineers a framework to prioritize features, assess implementation effort, and estimate ROI so they can choose a platform that delivers reliable, maintainable automation rather than short-lived proofs of concept.