Enterprise AI platforms for supply‑chain, digital twins and industrial automation combine agentic automation, cloud ML/GenAI, edge vision, 3D model generation and data governance to automate decision loops across physical networks. As of 2026‑02‑13 this area is driven by persistent supply‑chain volatility, wider adoption of digital twins for simulation and planning, matured edge compute for real‑time vision and inspection, and stronger regulatory and enterprise demand for explainability and vendor governance. Key product patterns include: specialized AI agents that automate domain workflows (CargoBrain for air‑cargo pricing and operations; StackAI, MindStudio and Relevance AI for no‑code/low‑code agent design and deployment), unified cloud ML/GenAI platforms for model lifecycle and production (Vertex AI), enterprise virtual assistants and orchestration (IBM watsonx Assistant), open/efficient model providers and production stacks (Mistral AI), and agentic infrastructure with observability (Xilos). Governance and risk controls are addressed by platforms like Monitaur, and underlying AI data platforms and 3D/edge toolchains enable model training, digital twin creation, and on‑device inference. Typical industrial use cases include predictive maintenance and anomaly detection, automated pricing and routing in logistics, real‑time visual inspection at the edge, scenario testing with digital twins, and end‑to‑end process automation via multi‑agent workflows. Practical selection hinges on integration with existing OT/IT, latency and edge constraints, data platform maturity, model governance, and the balance between no‑code speed and developer control. This convergence favors modular stacks—agent frameworks + cloud model ops + edge vision + 3D model tooling + robust data governance—allowing enterprises to incrementally deploy resilient, auditable AI across supply‑chain and industrial operations.