AI Training for FP&A | Forecast faster, model scenarios and tell the story behind the numbers
Sharper forecasts and scenario models built in hours, not weeks. Designed and delivered by CA Dhaval Kodilkar.
Clients include Larsen & Toubro, Safaricom, Bajaj Finserv, Indian Oil, Galderma, Abbott, Lanxess, Bajaj Finance, VE Commercial Vehicles (Eicher), UPL, Marico, WPP Media, IndusInd Bank, KPMG, Income Tax Appellate Tribunal (ITAT), NPCI, Mayfair Insurance, Copy Cat Group, Ramco Group, HCG Group, Clutterbot.
AI for FP&A training shows planning and analysis teams how to use generative AI and Excel Copilot for budgeting, rolling forecasts, variance analysis, driver-based planning and scenario modelling. The programme focuses on faster model building and better narrative, with checks that keep AI-generated numbers traceable to source data.
Built for your team.
- FP&A analysts and managers
- Business finance partners
- Budgeting and planning teams
- Heads of FP&A
- Group size
- 6 to 40 participants per cohort
- Prior knowledge
- None needed
Where the time goes today.
- The annual budget takes 8 to 12 weeks and is out of date by the first quarter.
- Variance analysis explains what moved but not why, so business leaders ignore it.
- Scenario requests from leadership take days because every model is rebuilt by hand.
- Driver assumptions sit in individual spreadsheets and cannot be audited.
- Analysts spend more time formatting decks than analysing performance.
6 modules, each with a hands-on lab.
- Module 1
AI in the planning cycle
Map where AI fits across budget, forecast and review, and where human judgement stays.
Lab: Mark up your planning calendar with AI-assisted steps.
- Module 2
Driver-based planning with AI
Identify and document revenue and cost drivers with AI-assisted analysis.
Lab: Build a driver tree for a product line from historical data.
- Module 3
Rolling forecasts
Use Excel Copilot and Python to refresh forecasts from actuals quickly.
Lab: Update a 12-month rolling forecast with three months of new actuals.
- Module 4
Variance analysis that explains why
Generate root-cause commentary linked to operational data.
Lab: Write price-volume-mix commentary for a revenue variance.
- Module 5
Scenario and sensitivity modelling
Create best, base and worst cases with clear assumptions in minutes.
Lab: Model a 10% input-cost shock and its effect on EBITDA.
- Module 6
Narrative reporting for leadership
Produce concise, consistent performance narratives for CXOs.
Lab: Turn a 20-slide review deck into a one-page leadership note.
What FP&A teams automate first.
| Workflow | Typical tool |
|---|---|
| Generate price-volume-mix variance commentary |  ClaudeClaude |
| Build sensitivity tables for key assumptions |  ExcelExcel with Copilot |
| Draft budget instructions and templates for business units |  Microsoft CopilotMicrosoft Copilot |
| Summarise competitor results calls for planning assumptions |  PerplexityPerplexity |
| Refresh a rolling forecast from monthly actuals |  PythonPython |
| Write a quarterly business review narrative |  ChatGPTChatGPT |
| Build interactive variance dashboards with natural-language queries |  Power BIPower BI Copilot |
| Document model logic and assumptions for audit and handover | ClaudeClaude |
- Excel
- Claude
- ChatGPT
- Power BI
- Perplexity
- Python
Choose the depth that fits.
| Format | Duration | Group size | Outcome | Typical deliverables |
|---|---|---|---|---|
| Executive briefing | 90 minutes to 2 hours | 4 to 25 | Shared understanding and priorities | Pre-read, oversight or priority list |
| Half-day workshop | 3 to 4 hours | Up to 40 | Core skills and 3 to 5 use cases | Prompt library, use case list |
| 1-day workshop | 6 to 7 hours | Up to 40 | Hands-on labs and 5 to 10 use cases | Prompt library, templates, 30/60/90-day plan |
| 2-day programme | 2 days | Up to 35 | Deeper labs, team-specific workflows | Templates, workflow designs, adoption plan |
| Multi-sprint programme | 4 to 8 weeks | Up to 30 | Use cases built and tested on live work | Working use cases, measured results |
| Train-the-trainer | 2 to 3 days | 6 to 15 | Internal champions who can run sessions | Trainer kit, slides, lab files |
Recent sessions.
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What your team keeps.
- 5 to 10 implementable use cases mapped to your workflows
- A prompt library for your team's tasks
- Workflow templates and checklists
- A one-page AI usage policy
- A 30/60/90-day implementation plan with owners
- Post-training Q&A support
AI training for FP&A: common questions.
Get a FP&A AI training proposal.
- Response within 1 business day
- Details used only to reply, never shared
- Trusted by teams at Larsen & Toubro, Safaricom, Bajaj Finserv