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The question isn't whether to use AI in FP&A. It's how.

A practical checklist for putting AI to work across close, forecasting, planning, and board reporting, without inheriting the failure mode.

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ebook cover - AI for FP&A
What you'll learn

Four moves from cautious to AI-native

Finance held out on AI for good reasons. This is how to adopt it without inheriting the failure mode: a confidently wrong number nobody can trace.

01

Get the foundations right

Where AI earns its place, why every number has to trace to the GL, and how a connected, governed, reconciled data layer makes or breaks every use case.

02

Close and forecast with AI

Use AI where the work is mechanical, treat its commentary as a hypothesis you correct, and demand forecasts that show their drivers and a range.

03

Compress planning, elevate reporting

Reconcile top-down targets against bottom-up builds, and keep the board narrative in the CFO's voice, starting from a sourced first draft, not a blank slide.

04

Redesign the operating model

Shift the team's time from assembly to judgment, organize agents on one governed layer, and place yourself on a three-tier maturity self-assessment.

INSIDE THE E-BOOK

15 questions across four sections

01

Foundations and Readiness

Where AI earns its place, and why every use case lives or dies on the data layer.

02

Close and Forecasting

Flux commentary, anomaly flags, driver-based scenarios, and where judgment leads.

03

Planning and Reporting

Compressing the planning calendar and drafting the board narrative, not just the numbers.

04

Operating Model and Governance

Redesigning where the team's time goes, the FP&Agent model, and a maturity self-assessment.

AI drafts the first pass. You make the call.

Cube · How to Use AI for FP&A

Who's behind it

Written by the team building the layer

Cube's four FP&Agents, the Data Manager, the Analyst, the Planner, and the Business Partner, work on a single governed data layer, so every draft, forecast, and board narrative traces back to the transaction behind it.

No black-box numbers. No re-keying the same data into five tools. Finance teams with clean, connected data can move from cautious to AI-native in quarters, not years, because the hard part is the data layer, not the model.

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The 15 questions to ask before you trust AI with the numbers, across close, forecasting, planning, and governance. Yours in one download.

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