AI for Finance: Budgeting, Forecasting & Reporting (FP&A)
Finance teams were among the first to be told AI would change their jobs, and among the last to be shown how. Most have tried pasting a trial balance into ChatGPT, got a confident paragraph back, and quietly gone back to Excel. That instinct is right. The model does not know your chart of accounts, your cost drivers or why March is always strange. What it is genuinely good at is the slow, repetitive work around the numbers: cleaning exports, drafting formulas, stress-testing assumptions and turning a variance table into commentary a manager will actually read.
This two-day programme is built around that split. Day one covers AI-assisted data analysis and building a budget from explicit assumptions. Day two covers forecasting, scenario planning, Budget vs Actual variance analysis and AI-drafted management commentary, finishing with a case study where participants produce a complete monthly pack. Every exercise uses Excel alongside ChatGPT or Microsoft Copilot, whichever your organisation has approved. Managers who need the finance fundamentals first should start with Finance for Non-Finance Managers, and teams whose next step is presenting the results well can follow on with Executive Dashboard Development with Excel and AI.
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Programme Agenda
Day 1, 9:00 AM - 9:15 AM
Welcome and Programme Overview
Introduction to the session, objectives, and housekeeping.
Day 1, 9:15 AM - 10:15 AM
AI in the Finance Function: What It Does Well and Where It Fails
A realistic map of where generative AI helps in finance and where it does real damage. Large language models are good at language, structure and code, and unreliable at arithmetic, so the workflow always keeps the calculation in Excel and uses the assistant around it. ChatGPT, Copilot in Excel and similar tools compared on what finance teams actually need. Prompting patterns that work for finance tasks, and the checking habit that has to come with them. Hands-on: the same analysis request run three ways, with the errors found and explained.
Day 1, 10:15 AM - 10:30 AM
Break
Day 1, 10:30 AM - 12:30 PM
AI-Assisted Financial Data Analysis in Excel
Getting ledger and ERP exports into a shape worth analysing. Using AI to write and explain formulas, build Power Query steps, spot anomalies and summarise trends, without handing it data it should not see. Structuring a prompt so the assistant works from your column definitions rather than guessing. Hands-on: clean a messy general ledger export and produce a first-pass analysis of spend by cost centre.
Day 1, 12:30 PM - 1:30 PM
Lunch
Day 1, 1:30 PM - 3:15 PM
Budget Preparation and Budgeting Assumptions
A budget is a set of assumptions with numbers attached, and most budget arguments are really assumption arguments. Top-down against bottom-up, zero-based against incremental, and building from drivers such as headcount, volume and price instead of last year plus five percent. Using AI to challenge assumptions, surface ones nobody wrote down and document the basis for each line. Hands-on: build a departmental budget from an explicit assumptions sheet.
Day 1, 3:15 PM - 3:30 PM
Break
Day 1, 3:30 PM - 4:45 PM
Building a Driver-Based Budget Model
Linking the assumptions sheet to the budget so a change in one driver flows through correctly. Model structure that another person can audit: inputs, calculations and outputs kept apart, no hardcoded numbers buried in formulas. Using AI to review a model for broken links, inconsistent formulas and logic errors. Hands-on: extend the budget into a twelve-month phased model and have the assistant audit it.
Day 1, 4:45 PM - 5:00 PM
Day 1 Close
Recap and what to review before day two.
Day 2, 9:00 AM - 9:15 AM
Day 1 Review
Recap of day one and the questions it left open.
Day 2, 9:15 AM - 10:15 AM
Financial Forecasting with Excel and AI
Rolling forecasts against the static annual budget. Trend, seasonal and driver-based methods, and when each is appropriate. Excel's forecasting functions and Forecast Sheet, with AI used to explain the output and question it. Why a forecast needs a stated confidence range, not a single number. Hands-on: produce a rolling revenue and cost forecast from two years of monthly actuals.
Day 2, 10:15 AM - 10:30 AM
Break
Day 2, 10:30 AM - 12:30 PM
Scenario Planning and Sensitivity Analysis
Base, upside and downside cases built from driver changes rather than a flat percentage. Data tables, Scenario Manager and simple sensitivity tables. Using AI to brainstorm plausible scenarios, including the uncomfortable ones, and to describe the business story behind each. Hands-on: model three scenarios for the same budget and identify the two drivers that matter most.
Day 2, 12:30 PM - 1:30 PM
Lunch
Day 2, 1:30 PM - 3:15 PM
Budget vs Actual and Variance Analysis
Price, volume and mix variances explained without the textbook fog. Separating timing differences from permanent ones, and noise from signal. Setting materiality thresholds so the team investigates what matters. Using AI to flag the variances worth explaining and draft the first question to ask the budget holder. Hands-on: build a Budget vs Actual report with variance drivers identified.
Day 2, 3:15 PM - 3:30 PM
Break
Day 2, 3:30 PM - 4:45 PM
AI-Generated Commentary, Management Reporting and Responsible AI
Turning a variance table into commentary that says what happened, why, and what to do about it, then editing the AI draft until it is accurate and sounds like you. What belongs in a monthly management pack, and what gets cut. Responsible AI and data governance for finance: what can and cannot be pasted into a public model, PDPA obligations, approved tools, audit trail, and who is accountable when an AI-drafted number is wrong. Capstone case study: teams produce a complete monthly pack with forecast update, variance analysis and commentary, then present it.
Day 2, 4:45 PM - 5:00 PM
Wrap-Up and Q&A
Key takeaways, next steps, and close.
Key Outcomes
- Use ChatGPT or Copilot alongside Excel for financial analysis while keeping every calculation verifiable
- Clean and analyse ledger and ERP data faster with AI-written formulas and Power Query steps
- Build a driver-based budget from documented, challengeable assumptions
- Produce rolling forecasts with a stated range rather than a single number
- Model base, upside and downside scenarios and identify the drivers that move the result
- Analyse Budget vs Actual variances by price, volume, mix and timing
- Draft and edit AI-generated management commentary that is accurate and decision-ready
- Apply responsible AI and data governance rules to finance data, including PDPA obligations
Training Mode Physical / Online / Hybrid / e-learning
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Level Intermediate. Participants should be comfortable with Excel formulas and PivotTables and work with budgets or management accounts. No prior AI experience is required. Participants need a laptop with Excel and access to ChatGPT, Microsoft Copilot or a similar approved assistant.
Duration 2 Days (16 Hours) | 9:00 AM to 5:00 PM daily
Venue In-house at the client's premises, or delivered via the client's preferred platform (Microsoft Teams, Zoom, or equivalent)
Assessment A capstone case study in which teams produce a monthly management pack covering a forecast update, Budget vs Actual variance analysis and AI-assisted commentary, reviewed for accuracy and presented to the group
Certificate Certificate of Completion issued to all participants upon full attendance