AI for FP&A... FP&AI?
As FP&A professionals, we all know it’s time to get on board with AI tools—these things aren’t going anywhere. But where do we even start, and how do we avoid the frustration that comes from diving in without a map?
Let’s be honest, how many of us have tried some AI tool, only to feel like we were trying to program a microwave without instructions?
The truth is, with the right guidance, AI can become as easy as reaching for that trusty tool in your pocket or handing off tasks to your virtual assistant. Yes, it’s awkward at first, but like any habit, it gets easier with regular use, and soon enough, you’ll be wondering how you ever worked without it.
As a fellow FP&A professional, I’m on this AI journey too, and I’ve started seeing the productivity boosts we’ve been promised. But let’s clear something up: we used to think of AI in terms of machine learning and predictive analytics—what we now call "Traditional" AI. Sure, it’s powerful, but let’s face it, it doesn’t exactly come with a user-friendly manual. GenAI, on the other hand, feels more approachable but still leaves us wondering where to begin.
So, let’s roll up our sleeves and figure this out together!
"Traditional" AI or GenAI for FP&A?
Let's do both!
"Traditional" AI
Focuses on pattern recognition, decision-making, and automation using structured data. It uses predefined rules and algorithms (e.g., decision trees, machine learning) to perform tasks like classification, prediction, or optimization. Traditional AI excels at repetitive tasks and working with well-defined problems. This is like an advanced tool for analyzing financial data and improving decision-making. It helps automate tasks such as forecasting, budgeting, or variance analysis by recognizing patterns in historical data. For example, it can quickly identify trends in expenses or revenues and flag potential risks, helping you optimize financial planning and decision-making.
Generative AI
Is designed to create new content (text, images, code, etc.) based on prompts. It uses advanced models, like large language models (LLMs), to understand and generate creative outputs. Instead of following predefined rules, GenAI can simulate human-like creativity, allowing it to generate anything from conversation to art. Examples include tools like GPT-4 or DALL·E. Think of this as a creative partner in your financial work. Instead of just analyzing data, GenAI can generate new insights, reports, or even suggest potential strategies based on the financial data you provide. You could ask it to draft a financial report or create a presentation based on company performance, and it will generate a customized version. It helps you move beyond just looking at past data to creating something new, like possible financial scenarios or narratives for business strategy.
FP&A professionals can boost their daily productivity and conduct both regular and ad-hoc analysis with the help of GenAI, while Traditional AI requires specialized knowledge in machine learning and specific tools to be effective. But both are applicable! Below are some key AI applications for FP&A.
GenAI
Traditional AI
Revenue Forecasting
Generate multiple forecast scenarios with narratives, or provide recommendations based on prompts. Automate report generation for different scenarios.
Use machine learning models to analyze historical data and predict future revenue trends based on established patterns.
GenAI
Traditional AI
Expense Analysis
Generate detailed narratives on expense trends or offer potential optimizations based on patterns in the data.
Analyze patterns in historical expenses to identify cost-saving opportunities or forecast future expenses.
GenAI
Traditional AI
Cash Flow Management
Create automated insights or scenarios for future cash flows based on historical data and user input.
Use predictive models to analyze cash inflows and outflows, providing future cash balance estimates and flagging risks.
GenAI
Traditional AI
Scenario Planning
Generate 'what-if' scenarios quickly with new data points, assisting in exploring multiple financial outcomes.
Traditional AI models can simulate different scenarios based on historical data, but require predefined rules or input assumptions.
GenAI
Traditional AI
Variance Analysis
Automatically generate insights, summaries, and potential explanations for variances between actuals and forecasts.
Compare actual financial results against forecasts using anomaly detection or pattern recognition to identify discrepancies.
GenAI
Traditional AI
Financial Reporting
Automate the generation of detailed financial reports, including narratives for presentations and stakeholder communication.
Automate the process of gathering and compiling financial data for reports, and use tools to analyze trends.
GenAI
Traditional AI
Driver-Based Planning
Quickly generate new plans based on drivers or assumptions and create insights on how changes in key drivers can affect financial outcomes.
Use machine learning to analyze key financial drivers and create detailed plans based on historical data trends and relationships.
GenAI
Traditional AI
Sensitivity Analysis
Automatically generate sensitivity reports showing how changes in different variables affect financial outcomes. Provide insights on the most critical factors.
Use predefined models to simulate how changes in variables like revenue or costs impact overall financial performance.
GenAI
Traditional AI
Working Capital Analysis
Generate narratives around working capital performance, suggest improvements based on patterns, and create automated projections for liquidity.
Analyze historical working capital data to predict future liquidity needs and flag potential cash flow risks using predictive models.
GenAI
Traditional AI
CapEx Planning
Create detailed CapEx scenarios, project financial impacts, and provide narratives or recommendations for capital allocation decisions.
Use machine learning models to predict capital expenditure outcomes based on historical data, assisting in CapEx budgeting and planning.
