Planning for GenAI implementation in finance microcourse
CA ANZ
Last updated September 6, 2026
Planning for GenAI implementation in finance is a self-paced microcourse from Chartered Accountants Australia and New Zealand and a component of its Certificate in AI Fluency. It is about deciding where generative AI is worth applying, and preparing the ground before committing. Learners evaluate opportunities against strategic goals and likely impact, look at rethinking an established process rather than layering AI onto it unchanged, and work through getting data, processes and resources ready. The course then covers designing a full implementation plan — guidelines, change management, risk mitigation — and selecting a suitable tool for an accounting or finance application. It is a paid course, taken online and on demand.
Part 3 of 4 in CA ANZ's Certificate in AI Fluency
- Introduction to generative AI in finance microcourse
- Ethical use of generative AI microcourse
- Planning for GenAI implementation in finance microcourse (you are here)
- GenAI governance blueprint microcourse
What you'll learn
- Identifying and evaluating generative AI opportunities against strategic goals
- Reimagining an established process rather than layering AI onto it
- Preparing data, processes and resources for implementation
- Designing an implementation plan with guidelines, change management and risk mitigation
- Evaluating and selecting a generative AI solution for accounting or finance
Frequently asked questions about Planning for GenAI implementation in finance microcourse
Who is Planning for GenAI implementation in finance microcourse for?
Finance professionals and team leads planning a generative AI rollout in an accounting or finance function; sold on the Australian market, but the content is not jurisdiction-specific.
Is Planning for GenAI implementation in finance microcourse free?
No — Planning for GenAI implementation in finance microcourse is a paid course.
Why we suggest this course
For finance professionals moving from experimenting with generative AI to committing a team to it. The strongest element is the insistence on reimagining a process instead of bolting AI onto the existing one, which is where most adoption effort is quietly wasted. Data preparation and tool selection are treated as planning decisions rather than technical afterthoughts, which is the right place for them.