Industrial digital twins and plant AI platforms combine real‑time OT data, 3D simulation, and AI agents to model, monitor, and automate manufacturing and processing plants. This topic covers end‑to‑end stacks that fuse AI data platforms (cleaning, curation, observability), AI automation platforms (LLM/agent orchestration and no‑code assistants), and 3D model generation and simulation tools used to build actionable virtual plants. As of early 2026, adoption is driven by demands for operational resilience, energy efficiency, and faster root‑cause analysis. Key trends include hybrid on‑prem/cloud deployments to protect sensitive process data, GPU‑accelerated simulation for high‑fidelity 3D twins, and multimodal models that ingest sensor streams, CAD assets, and manuals. Data curation and governance have become central: platforms that turn raw logs and PDFs into model‑ready datasets are prerequisites for reliable plant AI. Representative tooling spans multiple layers: DatologyAI and similar data‑curation services prepare training data; PDF.ai and diagram tools like Fluig convert documentation into searchable, structured knowledge; IBM watsonx Assistant and MindStudio enable enterprise virtual agents and no‑code automation for operators; LangChain and PolyAI support developer‑driven agent orchestration and voice‑first interfaces; Together AI supplies GPU training/inference infrastructure; JetBrains AI Assistant boosts developer productivity during integration. 3D generation and simulation stacks (including NVIDIA’s real‑time tools and rival offerings) underpin visualization, what‑if simulation and closed‑loop testing. Putting these elements together yields plant AI that supports predictive maintenance, SOP automation, and scenario testing, but success depends on disciplined data pipelines, model validation against plant physics, latency‑aware architectures, and clear governance to manage risk and ensure operational safety.