AI in FP&A
Diagnose the planning foundation before adding AI agents.
Before your company invests in AI, first diagnose whether the planning foundation is strong enough to produce reliable, explainable, and governable outcomes. AI can accelerate planning decisions, but it can also amplify weak business rules, broken hierarchies, inconsistent assumptions, and poor ownership if those issues are already present.
of CFOs say AI is integral to their finance transformation strategy*.
*Benchmarking the AI advantage in finance, IBM Institute for Business Value, May 2025.
Put AI to work in Finance
The CFO role has become more difficult and complex, but generative AI can help you navigate the challenges. This guidebook explores how CFOs can get the most from generative AI, how they can apply it, and what they need to make it a valuable addition to their finance function.
Our Framework
Four pillars we assess before any AI agent goes live
This is the lens we bring to every engagement: model logic, master data, governance, and adoption. An AI agent inherits whichever state these are already in.
The business rules and calculations that turn data into a plan - documented and understood beyond one person.
The hierarchies, dimensions, and reference data the model runs on - consistent, reconciled, and maintained on a cadence.
Clear ownership, controls, and an audit trail for every number that reaches a forecast or a board pack.
A planning process the business actually uses and trusts, not a shadow spreadsheet running alongside it.
Build the case
Build the business case before you build the agent.
Define a business case for your AI improvement initiatives. This business case should include three justifications:
Prioritize
Weigh benefit against risk so the highest-value, lowest-risk work goes first.
Align
Show how the initiative supports the broader finance and corporate strategy, not a standalone pilot.
Fund
Quantify it well enough to secure budget, and to defend it at renewal.
Quantify it in terms the business already tracks: cost, speed, and quality.
Model quality decides the outcome.
Across our planning model implementations, we've consistently seen the same outcome. When the underlying model is well structured and built on reliable data, AI agents can deliver meaningful productivity gains.
When the model lacks documentation or proper governance, they may produce answers quickly and confidently, but those answers become difficult to validate or defend in front of management or the board.
Get the foundation checked before an AI agent sits on top of it.
Not ready for a conversation yet? Start with the guidebook IBM wrote for finance leaders navigating this exact decision.