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A short reading path on what AI changes in corporate finance: the controls around data, the economics of a workflow, and the judgment behind a decision. Choose the questions closest to your work.

For CFOs and controllers

• Data lineage is a practical AI bottleneck — trace how an answer connects to its source.
• Adapting model risk policy for language models — rethink inventory, review, and ownership for AI systems.
• Small AI errors can drift into important decisions — focus on plausible errors that can pass a quick review.

For FP&A and finance operators

• Embedded, point solution, or neither? — choose a response to AI already arriving in the stack.
• AI agents in FP&A: where judgment still matters — RACI, approval gates, and reversible outputs before automation expands.
• The continuous close: start with reconciliation — separate faster processing from faster review.
• How to measure AI ROI in finance pilots — connect pilot economics to measurable workflow outcomes.

For analysts building judgment

• How AI changes the FP&A learning path — consider how automation changes the learning path.
• A durable skill for new FP&A analysts — focus on the work that depends on interpretation and judgment.

For builders and reviewers

• Why the statement workbench is static on purpose — keep the checkable path separate from generative narrative.
• Walking the variance workbench — a 30-minute hiring-screen inspection path.
• What a LatAm SAP cutover taught me — Experience-labeled systems-change lessons (no confidential metrics).
• A tiny eval harness for finance claim drift — practice spotting value, period, unit, and definition mismatches.

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