Claude’s new financial services release shows how powerful AI in finance is becoming. It also highlights what finance teams still need around it.
The ability to analyze data, work across documents and spreadsheets, draft outputs, and support complex finance workflows is genuinely impressive. For small businesses, solo operators, and lean finance teams, this can already be a major unlock.

But when we speak with finance teams in larger and growing companies, we hear a consistent pattern: they are excited about AI, but the real need is not just better analysis. It is getting finance work done safely, reliably, and with the right controls.

Finance teams tell us that the hard part is often connecting the right context: accounting data, approvals, budget ownership, CRM inputs, tax rules, entity-specific logics, and manager decisions. They also want AI outputs they can inspect, verify, and approve before anything consequential happens.
That distinction matters. AI can prepare, check, summarize, and recommend. But when the next step affects a payment, reimbursement, approval, accounting treatment, audit record, or ERP write-back, companies need more than a smart assistant. They need a governed finance workflow layer.

That is where the next phase of AI in finance will be won: connected data, encoded business logic, permissions, approval flows, audit trails, and human control over consequential actions.
Finance teams should start now. The best place to begin is with workflows where AI can safely prepare the work, while the company builds the operating layer needed to execute with confidence.

The companies that benefit most will be the ones that start learning now. Not by handing over consequential finance decisions on day one, but by putting AI into real workflows, understanding where it creates leverage, and building the controls needed to scale it safely.

How do you think about this? Would love to hear more thoughts

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