This topic surveys the leading AI platforms used in finance and accounting, focusing on how LLMs, agentic automation, and data/model orchestration are being applied to bookkeeping, financial close, forecasting and ERP workflows. As of April 2026, organizations are moving beyond point tools to combinations of conversational assistants, automated ML, and orchestration layers that ensure auditability and data governance. Key offerings span use cases and scales: Bookeeping.ai’s Paula targets small businesses and bookkeeping teams by automating routine journal entries and reconciliation; Claude (Anthropic) and Google Gemini power conversational and analytical assistants for query-driven reporting and narrative explanations; Adept’s agentic systems automate multistep tasks inside software interfaces; DataRobot provides automated ML for forecasting and anomaly detection; Oracle embeds AI in ERP suites for transactional automation and controls; IBM watsonx (Assistant and Orchestrate) supplies enterprise virtual agents and multi-agent orchestration to integrate assistants, workflows and governance. Marketplaces and AI data platforms increasingly surface connectors, certified models and deployment controls for finance-specific workloads. Trends shaping selection include the rise of prebuilt integrations (for example, accounting platforms integrating conversational models), stronger expectations for explainability and audit trails, and a shift toward orchestration-first architectures that combine agents, RPA, and ML models. Procurement now weighs not only model capability but data provenance, compliance with audit/regulatory requirements, and ease of integration with ERPs and general ledgers. This overview helps finance and IT teams evaluate which mix of assistant, automation, and data-platform capabilities best fits use cases from small‑business bookkeeping to enterprise financial close and forecasting.