Dates and location
Pricing
Hours
Dates and location
Pricing
Hours
Description
Generative AI is changing what it means to work in finance. The mechanical work is increasingly absorbed by the tools, and the professional's value is shifting from preparing numbers to directing, verifying, and standing behind AI-assisted work.
This course moves participants from reactive to proactive, enabling participants to direct AI toward the outcomes they want. They will also learn the discipline that makes AI-assisted work professionally defensible, with every piece of lab work carrying an audit trail.
Through hands-on exercises using leading generative AI tools, participants will learn how to save time on routine finance work, build confidence in using AI responsibly, strengthen their ability to verify and explain AI-assisted outputs, and leave with practical methods they can apply immediately to their work.
Technology Requirements: To fully participate in the hands-on exercises, participants must have access to a paid ChatGPT or Claude plan (Pro or equivalent). All tools are browser-based, with no downloads or special permissions required.
Schedule
Session 1 – October 27, 2026, 9:00–11:00 a.m.
Session 2 – November 10, 2026, 9:00–11:00 a.m.
Session 3 – November 24, 2026, 9:00–11:00 a.m.
Note: This course consists of three sessions throughout October and November. Participants must attend all three sessions.
Course Details
Objective:
Learn how generative AI can dramatically reduce the time spent preparing and analyzing finance data while establishing audit ready practices.
What We'll Cover:
- Generative AI fundamentals and common causes of failure.
- Data governance, privacy, and responsible AI use.
- The evolving role of finance professionals in AI-assisted workflows.
- Creating and maintaining an AI audit trail.
Hands-on Lab:
Clean and analyze a flawed transaction dataset using AI, uncovering spending insights and anomalies while documenting each step in an AI audit trail.
Trust It: Insight from the Unstructured
Objective:
Learn how to use AI to extract reliable insights from unstructured documents while applying the verification practices needed for audit-ready, professional analysis.
What We'll Cover:
- How AI processes information, where it excels, and where it is prone to errors.
- Common risks such as inaccurate extractions, misattributed figures, and reconciliation issues.
- Verification techniques, including source citations, control totals, independent checks, and challenge testing.
- Advanced AI audit trails, including source references, validation steps, and confidence assessments.
Hands-on Lab:
Analyze a multi-document finance package that includes financial statements, contracts, analyst commentary, and email correspondence. Participants extract key data with citations, reconcile conflicting information, perform verification checks, and identify a deliberately planted inconsistency while documenting their work in an AI audit trail.
Objective:
Learn how to move from retrospective analysis to forward-looking decision-making by directing AI toward outcomes, applying governance best practices, and creating repeatable AI-enabled workflows.
What We'll Cover:
- Using objectives and key results to guide AI work more effectively than prescriptive instructions.
- Understanding agentic AI, current capabilities, and the skills needed to leverage future AI workflows.
- Practical AI governance, including data classification, tool selection, access controls, and review processes.
- Building sustainable AI practices through workflow documentation, reuse, and impact measurement.
Hands-on Lab:
Use historical billing and transaction data to build a forward-looking forecast with AI. Participants define objectives and success criteria, test assumptions through scenario analysis, and generate a fully documented audit trail. They then convert their approach into a reusable workflow and create a personal 90-day action plan to apply AI-driven efficiencies and measure results.
Key Takeaways
By the end of this session, participants will have gained:
- Practical AI fluency: Hands-on capability with generative AI on real finance work such as messy data, dense documents, and forecasting.
- A new way of directing AI: How to lead AI by objective and desired results rather than rigid step-by-step.
- Professional trust and defensibility: Verification techniques and an audit-trail discipline that make AI-assisted work documented, sourced, and reviewable.
- A clear, plain-language understanding of agentic AI workflows: what they are, where they're heading, and how the skills built in this course become the foundation for them.
Who Will Benefit
Finance professionals such as controllers, analysts, treasurers, accountants, and finance leaders. The course is deliberately role-neutral: it’s about how each participant's work evolves, not about any one seat at the table.
How to Access the Course
To access your course, visit the CPA Ontario Blackboard site and sign in using the same username and password used for the Registration Portal. You can also access your course through the Blackboard Learn app (iOS or Android).
Important: Course access begins on the date of purchase and remains available for the Access Time specified for the course under Dates and Location. Please review the access time before purchasing. Note that it may take up to 15 minutes after registration for the course to appear in Blackboard.
Registration, cancellation, withdrawal, and other CPA Ontario PD policies can be found here.
Speaker(s):
Vishen Maharaj is Director, AI & Machine Learning at MNP Digital, where he advises organizations on how to move from AI experimentation to practical, measurable business outcomes. His work spans AI strategy, governance, solution design, and implementation, with a particular focus on helping finance and executive teams understand where generative and agentic AI can meaningfully change how work gets done.
Vishen brings a combination of AI leadership and financial advisory experience, allowing him to connect emerging technology with the realities of finance, risk, governance, and decision-making. He works with organizations across industries including financial services, real estate and construction, manufacturing, and the public sector. His approach is hands-on and business-focused, helping professionals build the skills to direct, validate, and responsibly use AI in their day-to-day work.