Section outline

  • Introduction

    Target Audience: Finance department employees (Analysts, Accountants, Managers, Controllers) with minimal technical AI knowledge.
    Prerequisites: Basic proficiency with Excel and familiarity with standard finance processes (AP, AR, Reporting, etc.).
    Overall Goal: To demystify AI and equip the finance team with the knowledge and practical skills to identify, evaluate, and implement AI-powered tools to increase efficiency, accuracy, and strategic insight.

  • Module 0: Pre-Course Onboarding

    • Purpose: Level-set and alleviate anxiety.
    • Content:
      • A short (15-min) video: "What is AI in Plain English?"
      • A glossary of key terms: Algorithm, Automation, Machine Learning, NLP, etc.
      • A simple survey: "What's your biggest pain point in your daily work?" (This helps tailor examples).
    Activities: 1

  • Module 1: Foundations: Demystifying AI for Finance

    • Learning Objective: Understand what AI and automation are, why they matter for finance, and dispel common myths.
    • Content:
      • What AI Is (and What It Isn't): Moving beyond science fiction. AI as a pattern-recognition tool.
      • Key Concepts Made Simple:
        • Machine Learning (ML): Teaching computers to learn from data.
        • Natural Language Processing (NLP): How computers understand text and speech (e.g., reading contracts, emails).
        • Robotic Process Automation (RPA): The "digital clerk" for repetitive, rule-based tasks.
      • The AI Spectrum in Finance: From simple task automation (Macros) to predictive analytics.
      • Why Now? The convergence of data, computing power, and accessible tools.
    Activities: 0

  • Module 2: The Fuel for AI: Data Fundamentals

    • Learning Objective: Recognize the importance of data quality and structure for any AI/automation project.
    • Content:
      • The Golden Rule: "Garbage In, Garbage Out."
      • Data Hygiene Best Practices: Consistency, formatting, and reducing manual entry errors.
      • Identifying Data Sources: ERP systems (SAP, Oracle), spreadsheets, emails, PDFs.
      • Practical Exercise: Cleaning a sample dataset in Excel (using Text-to-Columns, Remove Duplicates, Filters).
    Activities: 0

  • Module 3: Intelligent Automation in Action (The "Digital Clerk")

    • Learning Objective: Identify processes ripe for automation and understand how RPA and other tools work.
    • Content:
      • Identifying Automatable Tasks: High-volume, repetitive, rule-based processes.
        • Examples:
          • Invoice Processing (Data entry from PDFs/emails)
          • Account Reconciliations
          • Report Generation and Distribution
          • Expense Report Auditing
      • Introduction to Key Tools:
        • RPA Tools: UiPath, Automation Anywhere, Blue Prism (showcased with demo videos).
        • Low-Code/No-Code Platforms: Microsoft Power Automate, Zapier.
      • Hands-On Lab (Basic): Building a simple automation with Microsoft Power Automate to save email attachments to a SharePoint folder and notify a team.
    Activities: 0

  • Module 4: AI-Powered Analytics and Forecasting

    • Learning Objective: Understand how AI can enhance financial analysis, forecasting, and decision support.
    • Content:
      • Beyond Pivot Tables: Introduction to predictive vs. descriptive analytics.
      • Forecasting with AI: How machine learning can improve accuracy in demand planning, cash flow forecasting, and budgeting by detecting complex patterns.
      • Anomaly & Fraud Detection: How AI flags unusual transactions or patterns in real-time.
      • Demo: Using built-in AI features in familiar tools:
        • Excel: Ideas feature (automatically highlights trends and patterns).
        • Power BI: AI Visuals (Key Influencer, Decomposition Tree).
    Activities: 0

  • Module 5: Practical Toolbox: AI Tools You Can Use Today

    • Learning Objective: Gain hands-on experience with AI features in existing software (Microsoft/Google Suite).
    • Content:
      • Microsoft 365 Suite:
        • Excel: Forecast Sheet, Data Types (Stocks & Geography), Ideas.
        • Power Query: Automating data transformation and blending.
        • Power Point: Designer and AI-powered presentation tips.
      • Generative AI for Finance:
        • Responsible Use of ChatGPT & Copilot: For brainstorming, drafting reports, summarizing long documents (e.g., new accounting standards), and generating code for basic data analysis.
        • Critical Disclaimer: Data privacy, security, and hallucination. Never input sensitive, non-public company data.
      • Specialized FinTech: Brief overview of platforms like Vena Solutions (planning) or HighRadius (AR automation).
    Activities: 0

  • Module 6: Implementation and Change Management

    • Learning Objective: Learn how to propose, pilot, and manage an AI/automation project within the finance team.
    • Content:
      • How to Start Small: Finding a pilot project with a high chance of success.
      • Building a Business Case: Calculating ROI (time saved, error reduction).
      • Working with IT: Understanding security, compliance, and integration needs.
      • The Human Element: Upskilling, managing change, and focusing on value-added work.
    Activities: 0