AI in Finance.
What AI actually changes for finance work — what's real, what's hype, and where it already earns its keep.
AI in finance is the practical question of what artificial intelligence actually changes for finance work — and what it doesn’t. Today it lands first on the routine, high-volume paths: transaction coding, reconciliations, variance commentary, and contract and invoice extraction, in a draft-and-review pattern where the model produces the first pass and a human owns the judgment and the sign-off.
What it doesn’t change is accountability — when the model is wrong, the controller still owns the number. Strategy of Finance covers what’s working in production, what’s still vapor, and where AI earns its keep — close speed, FP&A turnaround, spend analysis — alongside the guardrails (the three-eyes principle, exception logging, and reserving model output for cheap-to-verify cases) that keep a confident-but-wrong answer from compounding into an audit finding.
Essays 11
Episodes 2
Related questions
Reviewed- What does AI actually change in a finance function today?
- Mostly the routine, high-volume paths first — transaction coding, reconciliations, variance commentary, and contract and invoice extraction. The pattern is 'draft-and-review': AI produces the first pass, a human owns the judgment and the sign-off. What it doesn't change is accountability — when the model is wrong, the controller still owns the number. The near-term ROI shows up in close speed, FP&A turnaround, and procurement spend analysis, not in replacing the people who interpret the output.
- Will AI replace finance jobs?
- It replaces tasks faster than roles. The most exposed work is the repeatable, rules-based middle — the parts of FP&A, AP/AR, and reporting that are pattern-matching dressed up as judgment. The work that compounds in value is the opposite: judgment under uncertainty, narrative for boards and investors, and knowing which questions to ask. Finance professionals who move up the judgment curve get leverage from AI; those who stay in the rote middle get compressed by it.
- Where should a finance team start with AI?
- Where the work is high-volume, low-ambiguity, and easy to check: reconciliations, spend categorization, contract data extraction, and first-draft variance narratives. Keep a human in the loop, log every exception, and measure the share of volume handled end-to-end without escalation — that number, not the demo, tells you whether it's working. Avoid betting first on the high-stakes, low-verifiability cases (forecasting the number, board guidance) where a confident-but-wrong model does the most damage.
- What's the biggest risk of AI in finance?
- Automating a process you don't understand. AI will happily produce a clean, confident, wrong answer, and finance is exactly the domain where that compounds — into a misstated forecast, a controls gap, or an audit finding. The guardrails are old-fashioned: the three-eyes principle (the parties who do, check, and approve a transaction are different), exception logging, and reserving model output for places where the answer is cheap to verify.