Before You Deploy a Finance AI Agent, Watch This

You're evaluating AI agents for your finance function. You're focused on which model performs best. That's not where deployments go wrong.

This video walks you through why handing an autonomous agent traditional API access to your financial data creates consolidation gaps, governance failures, and audit trails that don't hold up - and how a dedicated MCP (Model Context Protocol) layer fixes each of those problems at the infrastructure level without touching your existing ERP setup.

For example, Datarails FinanceOS connects over 600 data sources to a governed semantic layer through a production-ready Finance MCP server, so your agents work with accurate, consolidated, permission-controlled data from the first query.

If you want AI analysis you can act on, the data infrastructure comes before the agent.

Chapters:
0:00 Introduction - why finance AI agents need an MCP layer
0:22 The problem with traditional API access
0:46 What raw API access really looks like
0:55 How an MCP layer fixes this
1:16 Do you need to replace your ERP?
1:24 Datarails FinanceOS in practice
1:41 The takeaway

#Finance #AI #MCP #FPandA #CFO,