Learn how FP&A analysts use AI to forecast faster without losing control of the numbers. This module teaches you to write structured AI prompts that generate a baseline forecast, read prediction intervals and confidence ranges in plain business terms, and evaluate AI output against simple statistical benchmarks using forecast accuracy metrics such as MAE, RMSE, and MAPE. You'll learn human-AI collaboration and forecast governance for financial planning and analysis (FP&A): when to accept, question, or override an AI forecast; how to document overrides with reason codes and audit logs; and how to produce a blended, explainable forecast that controllers, auditors, and finance leaders can trust. Ideal for anyone working on AI forecasting, rolling forecasts, demand and revenue forecasting, predictive analytics, and AI-assisted decision support.

Scenario analysis is a core financial planning and analysis (FP&A) skill for decision-making under uncertainty. This module teaches scenario analysis and scenario planning from the ground up: how to build base, best, and worst-case scenarios, how to translate business assumptions into quantitative driver inputs, and how to weight outcomes by probability to calculate an expected value. Learners connect each scenario to strategic risks, contingency plans, and risk registers, and they learn how scenario analysis differs from sensitivity analysis and everyday what-if analysis. It is ideal for finance professionals, analysts, and managers who want practical, beginner-friendly skills in scenario modeling, probability-weighted forecasting, risk management, budgeting and forecasting, and capital-allocation decision support — with no coding or advanced statistics required.

Learn to pinpoint what really moves your numbers and turn uncertainty into decisions leadership can act on. This module teaches sensitivity analysis and scenario planning for financial planning and analysis (FP&A): how sensitivity analysis differs from scenario analysis and from simple what-if changes, how to build one- and two-variable sensitivity tables (data tables) in a spreadsheet or planning tool, and how to rank drivers by impact using tornado-chart thinking. You will also use AI to stress-test assumptions, generate scenario variants, and curate a focused short list of base, best, and worst cases for executive and board review. Build practical skills in driver-based planning, forecasting, decision support, risk analysis, what-if modeling, and AI-assisted scenario generation — no coding required.