GMGT 610

Financial Planning and Analysis for Managers: Incorporating Ethical AI

Format

Hybrid (Online Sync + Async)

Weekly Hours

4-5 hours

Credits

3 Credits

Prerequisites

Admission to the M.S. in Global Engagement Management program or executive training

Course Description

This graduate course equips managers with advanced competencies in financial planning and analysis (FP&A) while critically examining the integration of artificial intelligence tools within ethical frameworks. Students will develop expertise in budgeting, forecasting, variance analysis, and strategic financial decision-making, while simultaneously evaluating how AI technologies can enhance—and complicate—these traditional management functions. The course adopts a critical lens toward AI adoption, examining algorithmic bias, data governance, transparency requirements, and the ethical implications of automated financial decision-making in global business contexts. Students will engage with real-world case studies from diverse regional contexts, reflecting the program's emphasis on global engagement and cross-cultural business management.

Learning Outcomes

Upon successful completion of this course, students will be able to:
1

Analyze organizational financial performance using advanced FP&A frameworks, including variance analysis, scenario modeling, and key performance indicators (KPIs), to inform strategic management decisions.

2

Evaluate the capabilities, limitations, and ethical implications of AI-powered tools in financial planning and analysis contexts, demonstrating critical judgment in technology adoption decisions.

3

Design comprehensive budgets and financial forecasts that integrate traditional methodologies with AI-enhanced analytics while maintaining transparency and accountability standards.

4

Apply ethical AI governance frameworks to financial decision-making processes, identifying and mitigating risks related to algorithmic bias, data privacy, and stakeholder trust in global business environments.

5

Synthesize financial insights and AI-generated analyses into coherent strategic recommendations, communicating complex information effectively to diverse stakeholder audiences across cultural contexts.

6

Critique organizational AI adoption strategies in FP&A functions, proposing evidence-based improvements that balance operational efficiency with ethical responsibility and regulatory compliance.

Weekly Schedule

15-week course schedule with topics, content, and assessment due dates
1

Introduction to FP&A in the AI Era

Bragg Ch. 1-2; Davenport Ch. 1; Course orientation

Academic Integrity Module
2

Financial Statement Analysis Foundations

Bragg Ch. 3-4; MIT OCW Finance Theory selections

3

Budgeting Processes and Best Practices

Bragg Ch. 5-6; CFI FP&A modules

Case Analysis #1
4

Forecasting Methods: Traditional and AI-Enhanced

Bragg Ch. 7; Davenport Ch. 2-3

5

Variance Analysis and Performance Management

Bragg Ch. 8-9; Case study readings

AI Tool Exploration Report #1
6

Introduction to AI in Finance: Capabilities and Limitations

Davenport Ch. 4-5; Stanford AI Index selections

7

Ethical Frameworks for AI in Business

OECD AI Principles; EU Ethics Guidelines

Case Analysis #2; Midterm Exam
8

Algorithmic Bias in Financial Decision-Making

Davenport Ch. 6; Supplementary case studies

Final Project Proposal Due
9

Data Governance and Privacy in FP&A

GDPR financial services guidance; Industry reports

10

Global Perspectives: AI Regulation Across Regions

Regional regulatory comparison readings

AI Tool Exploration Report #2
11

Strategic Financial Decision-Making with AI

Davenport Ch. 7-8; Bragg Ch. 10-11

Final Project Progress Checkpoint
12

Communicating Financial Insights to Stakeholders

Bragg Ch. 12; Presentation skills resources

13

Implementing Ethical AI Governance in Organizations

Davenport Ch. 9-10; Implementation frameworks

Case Analysis #3
14

Future Directions: Emerging AI in FP&A

Current industry publications; Guest speaker TBA

Final Written Report Due
15

Final Presentations and Course Synthesis

Peer review preparation; Reflection

Final Presentation; Peer Evaluations

Assessments

Course assessments and their weight in the final grade

Case Analyses (3 x 10%)

30%

Three case analyses applying FP&A concepts and ethical AI frameworks (1,500-2,000 words each)

Due: Weeks 3, 7, 13

Final Project: Ethical AI FP&A Implementation Plan

30%

Comprehensive implementation plan (4,000-5,000 words + executive presentation)

Due: Week 15

Midterm Examination

20%

Assessment of FP&A fundamentals and foundational AI concepts from Weeks 1-6

Due: Week 7

Participation and Professional Engagement

10%

Active participation in synchronous sessions, discussion forums, and weekly reflection activities

Due: Ongoing

AI Tool Exploration Reports (2 x 5%)

10%

Critical evaluation of AI-powered financial analysis tools (800-1,000 words each)

Due: Weeks 5, 10

Course Resources

Required readings and open educational resources

Required Texts

Financial Analysis: A Controller's Guide (3rd ed.)

Bragg, S. M. (2024) - Wiley

ISBN: 978-1394203185

All-in on AI: How Smart Companies Win Big with Artificial Intelligence

Davenport, T. H., & Mittal, N. (2023) - Harvard Business Review Press

ISBN: 978-1647824679

Open Educational Resources

MIT OpenCourseWare: Finance Theory I
OECD AI Principles and Governance Framework
European Commission Ethics Guidelines for Trustworthy AI
Stanford University Human-Centered AI Institute: AI Index Report
Corporate Finance Institute: FP&A Resources