Agentic AI for Finance
About This Course
Agentic artificial intelligence (AI) changes how organisations plan, fund and govern digital investments. When AI systems can plan, call tools and execute multi-step tasks, the cost structure can move beyond a fixed software licence into variable consumption across inference, orchestration, data access, tooling, monitoring and human oversight. Finance teams that budget for AI like a conventional Software-as-a-Service (SaaS) subscription may struggle to forecast costs, attribute spend accurately and explain AI return on investment to senior management.
This course is designed for finance professionals who are required to build AI investment cases, track AI spend, assess total cost of ownership, and report on AI initiatives. Participants will apply a practical Agentic Profit and Loss Framework to cost attribution, budgeting, financial operations (FinOps)-style tagging, build-versus-buy analysis and board-level AI Return-on-Investment (ROI) reporting.
By the end of the course, participants will have a working model for costing an agentic AI deployment, a finance-ready approach to tagging and chargeback, and a structured method for comparing cloud, vendor, build, buy and on-premises options on a like-for-like economic basis.
What You'll Learn
- Explain why conventional software return on investment (ROI) models understate agentic artificial intelligence (AI) cost and value
- Build a cost-attribution model covering inference, orchestration, tooling and human oversight
- Avoid the most common business-case errors, including undercounted oversight cost
Financial Operations (FinOps) and Total Cost of Ownership
- Apply FinOps tagging and chargeback principles to agentic AI run-time spend
- Compare build, buy and on-premises deployment paths on a common cost basis
- Calculate the usage volume at which each deployment path becomes economic
Budgeting and Board Reporting
- Construct a budget for variable inference and agent run-time spend
- Apply a capability capitalisation model to agentic AI investment
- Present an AI investment case in financial language a board and Chief Executive Officer (CEO) will accept
Entry Requirements
- Participants should have at least 1–2 years of experience in finance, financial planning and analysis, business finance, financial control, procurement, project finance, technology finance, transformation, investment evaluation or related roles. Participants should be familiar with basic budgeting, cost analysis, return-on-investment concepts and business-case preparation.
- Prior coding experience is not required. Basic awareness of generative artificial intelligence (AI) or enterprise AI use cases will be helpful.
- This course is not intended for participants seeking hands-on AI model development, prompt engineering as a primary skill, or technical cloud engineering certification.