
This course introduces a practical AI in finance framework, showing how finance leaders can drive measurable value now through four use cases, governance, and risk management.
Discover five ai techs for finance - large language models, machine learning, predictive analytics, robotic process automation, and natural language processing - and how they automate tasks and improve forecasting.
AI enables real-time, continuous forecasting by analyzing transaction-level data, detecting patterns, and running thousands of scenarios in parallel, reducing time and improving accuracy.
In contract review, AI analyzes contract volumes to extract key terms, flag non-standard clauses, monitor renewal dates, and build a searchable portfolio for faster review.
Learn how the close benefits from a hybrid model where ai handles processing, matching, drafting, and flagging, while accountants review exceptions and drive variance analysis and business partnering.
Discover how AI reduces external spend by moving technical accounting research, policy updates, market research, and process documentation in-house, delivering faster, higher-quality work at lower cost while preserving judgment.
Explore the full landscape of ai in finance beyond the four entry use cases, including planning and performance management as part of the five-category roadmap.
Explore 18 AI applications across finance, from FP&A, automated reporting, and cash flow optimization to profitability analysis, pricing, fraud detection, controls, treasury, and decision support for strategic deals.
Implement AI in finance by defining specific, measurable success criteria; assess capabilities; identify practical use cases; start with quick wins; experiment before scaling; and apply a one-page governance framework.
Explore a 30-day, five-step action plan to implement ai in finance, prioritizing use cases like forecasting, contract review, the close, and reducing external spend, with governance and a practical framework.
AI is already transforming the finance function. The question is whether you'll lead that transformation — or scramble to catch up.
This course was built for finance leaders who are skeptical by nature and convinced by evidence. Not technologists. Not consultants. Finance professionals who are accountable for results and want a practical framework — not a promise.
I'm Michael Hofer. I'm a CFO and AI Project Lead currently implementing AI inside a real finance organization. Everything in this course comes from doing it — not writing about it, not advising on it from the outside, but running real teams, closing real books, and executing AI projects under real constraints. That distinction matters.
What this course is:
A practical, evidence-based playbook covering the four AI use cases delivering measurable results in finance organizations right now — forecasting, contract review, accounting close automation, and reducing external advisory spend. A six-step framework for building AI capability with discipline. A governance model you can adapt and use immediately. And an honest look at the risks every finance leader must manage before deploying AI in consequential processes.
What this course is not:
This is not a course about prompt optimization or AI tool comparisons. It is not technical — you will not need a data science background, coding experience, or any prior AI knowledge. And it is not hype. The gap between what vendors promise and what organizations actually experience is real, and this course was built around the evidence, not the pitch deck.
By the end of this course, you will have:
A prioritized AI roadmap for your finance function. A success criterion for your first project. A governance framework your team can follow. And a 30-day action plan with a kickoff on the calendar.
The organizations winning with AI right now are not the ones with the biggest budgets. They are the ones that started.
This course exists to help you start.