This topic covers how enterprise AI platforms are being used to manage institutional assets and automate tokenized‑finance workflows—spanning portfolio construction, token issuance and lifecycle management, custody reconciliation, regulatory reporting and risk/AML monitoring. As of early 2026, institutions are combining foundation models, agent orchestration, low‑code workflow engines and governance tooling to link on‑chain events with internal ledgers and compliance processes. Key categories include AI data platforms and marketplaces (for model and dataset procurement), low‑code workflow platforms (to operationalize processes without heavy engineering), regulatory compliance tools (for KYC/AML, audit trails and reporting), and AI governance tools (for model risk, lineage, explainability and access controls). Representative tools: IBM watsonx Assistant for enterprise virtual agents and no‑code/developer automation; Mistral AI for enterprise‑oriented open and efficient foundation models with privacy and governance focus; Xilos for visibility and orchestration of agentic AI activity across services; Together AI for scalable training, fine‑tuning and inference of specialized models; Anthropic’s Claude family for conversational analysis and research workflows; Microsoft 365 Copilot for embedding insights across productivity apps; and Notion for knowledge, documentation and lightweight automation. Practical trends include increased use of private or fine‑tuned models to protect sensitive financial data, multi‑agent orchestrations to map complex token workflows, and stronger emphasis on auditability and model governance to satisfy regulators. Successful deployments prioritize data lineage, deterministic logs for on‑chain/off‑chain reconciliation, and low‑code connectors to custody and ledger systems—balancing automation with controls required for institutional risk management.