What org design works best for AI in FP&A at a public company?

Four separate roles with clear accountabilities: an AI FP&A Lead inside FP&A (deployment and business outcomes), a Model Governance Owner (usually the Chief Accounting Officer or Controller) accountable for model inventory, human-in-the-loop policy, and audit evidence, a Data Owner (Finance Systems or FinOps) accountable for source-of-record and lineage, and a Business Sponsor (VP FP&A or Divisional CFO) accountable for cycle-time KPIs. Deployment velocity and governance must sit in different people, and the CFO should be the accountable executive, not the operator.

Should a public-company CFO create an AI Center of Excellence in year one?

No. In our experience a standalone Finance AI CoE created before there are 5+ live use cases across 3+ business units becomes a demo-and-slides function disconnected from the close calendar. Put the AI FP&A Lead inside FP&A with a dotted line to the CIO and a formal RACI with the Controller. Formalize a CoE only once real production usage justifies it, and even then scope it to standards, not delivery.

What data inputs actually feed AI in FP&A?

Four classes in priority order: structured financial data (GL, sub-ledger, consolidation, planning) pulled from source-of-record with lineage; structured operational data (CRM pipeline, HRIS headcount, billing usage, supply-chain volumes); semi-structured business context (board decks, QBRs, contract summaries) ingested via structured retrieval with source citations; and unstructured supplier or market data used only for scenario framing and clearly labeled as external-and-unverified. No unverified external data should ever contribute to a computed number that appears in a reported filing.

What data contracts should the Data Owner sign in the first 90 days?

Six per source system: source-of-record designation, refresh cadence, schema-change notice (minimum 14 days), data quality SLA with an on-call owner, historical restatement policy, and access controls. Without these, most 'AI accuracy' problems are actually data problems the deployment can't diagnose.

How do you get FP&A analysts to actually use AI tools?

Answer three legitimate questions on day one — will this get me fired (no, and here's the JD change), who owns it when it's wrong (the human reviewer, always), and do I still need to check the math (yes, for the first year). Then run the 5-step adoption sequence: pick a real close-cycle deliverable, ship it in parallel for one quarter, switch to AI-first with human review in quarter two, expand narrowly in quarter three, and formalize the operating model in quarter four. Add AI-adjusted cycle time to the FP&A scorecard and create a senior IC track for analysts who become expert prompt designers and model reviewers.

How long does it take to get AI in FP&A into production at a public company?

Twelve months to reach a defensible production state: quarter one for org design and the first-use-case data contracts, quarter two for parallel-run adoption on the first close-cycle deliverable (target >70% acceptance), quarter three for AI-first with human review plus a second use case, and quarter four for the formal operating model, KPI dashboard, and external-auditor walk-through. At the end of year one, expect two use cases in production against a real close cycle — not a broad rollout.

AI in FP&A: A Public-Company CFO's Operator Playbook (Org Design, Data Inputs, Change Management)

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