AI for Finance Leaders: Forecasting and Decisions

Apply generative AI to financial analysis, forecasting, scenario planning, reporting, and governance

Created byDr. Law Payne

Last updated 10/2026

English

What you'll learn

- Build a practical AI Finance strategy connected to meaningful business outcomes.
- Diagnose workflows, evidence gaps, assumptions, risks, and decision bottlenecks.
- Use AI for research, analysis, generation, and coordination without delegating accountability.
- Apply human review, verification, privacy, and quality controls at the right moments.
- Measure efficiency, quality, user response, business value, and organizational learning.
- Develop a ninety-day roadmap for implementation, governance, and responsible scale.

Course content

1 section • 5 lectures • 48m total length

The AI-Augmented Finance Function9:50
Learning objective: Explain and apply decision support, data lineage, materiality, control environment, human accountability in a realistic professional or business context.

Lecture description:

This lecture explains decision support, data lineage, materiality, control environment, human accountability. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Financial Analysis and Management Insight9:50
Learning objective: Analyze and apply variance analysis, driver trees, unit economics, working capital, narrative discipline in a realistic professional or business context.

Lecture description:

This lecture explains variance analysis, driver trees, unit economics, working capital, narrative discipline. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Forecasting Scenarios and Strategic Planning9:43
Learning objective: Analyze and apply forecast architecture, assumption registers, scenario design, sensitivity analysis, decision triggers in a realistic professional or business context.

Lecture description:

This lecture explains forecast architecture, assumption registers, scenario design, sensitivity analysis, decision triggers. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Executive Reporting and Business Partnership9:36
Learning objective: Explain and apply audience needs, visual hierarchy, management narratives, challenge questions, action tracking in a realistic professional or business context.

Lecture description:

This lecture explains audience needs, visual hierarchy, management narratives, challenge questions, action tracking. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.
Risk Controls Governance and the Finance Roadmap9:35
Learning objective: Evaluate and apply model risk, privacy, verification, segregation of duties, adoption roadmap in a realistic professional or business context.

Lecture description:

This lecture explains model risk, privacy, verification, segregation of duties, adoption roadmap. Students work through a practical framework, apply it to a continuing business case, examine common failure modes, and complete an exercise they can use immediately.

Requirements

- No coding experience is required unless a lecture explicitly demonstrates a technical workflow.
- Access to relevant AI tools is helpful but not required to understand the frameworks.
- Bring a real business, client, team, project, or professional objective for the exercises.
- Be willing to test assumptions and verify AI-generated claims.

Description

This course contains the use of artificial intelligence.

Artificial intelligence creates value when it improves a real decision, workflow, or customer outcome. AI for Finance Leaders: Forecasting and Decisions provides a structured system for professionals who want to accelerate analysis and communication while preserving model discipline, traceability, controls, and professional judgment.

This course is designed for finance leaders, business owners, analysts, controllers, consultants, and managers responsible for financial decisions. It moves beyond disconnected prompts and tool demonstrations. Students learn how to define outcomes, organize evidence, design practical workflows, evaluate quality, preserve human accountability, and connect AI activity to measurable results.

Across five substantial lectures, Dr. Law Payne explains the strategic logic and practical execution of AI Finance. Every lecture includes a decision framework, a continuing business case, quality controls, implementation guidance, and an assignment that learners can apply to an organization, client, career, or business.

The course emphasizes responsible practice. Students learn where AI can accelerate research, analysis, generation, and coordination, as well as where privacy, bias, accuracy, security, or reputation require stronger safeguards. The aim is not maximum automation. The aim is better performance with visible ownership.

Dr. Law Payne is a business educator, growth strategist, nine-figure entrepreneur, published author, and Doctor of Business Administration specializing in Organizational Leadership and Development. His experience includes more than one billion dollars in managed sales and more than fifteen years of teaching, coaching, and mentoring across business, psychology, leadership, fitness, and education.

By the end of the course, students will have a working AI Finance operating system and a ninety-day plan for disciplined implementation.

Who this course is for:

- Finance leaders, business owners, analysts, controllers, consultants, and managers responsible for financial decisions.
- Professionals who want structured workflows instead of prompt collections.
- Teams seeking practical AI adoption with human accountability.
- Learners who want to connect AI activity to customer, operational, or financial outcomes.