Leading AI models, like ChatGPT-5, continue to get faster and better. But finance teams still cannot — and should not — trust it to close the books.
This is not because the models lack intelligence, it's because they lack the needed context and integrity to truly be "finance-grade."
For instance, an AI model may not know that debits must equal credits — always. Or that cash flow from operations has to tie back to net income and working capital. Today's AI models don't have finance-native guardrails that recognize journal entries that violate cash flow identities. They lack verifiable finance reasoning graphs that reveal a number's origination and the logic used to put it there. There are not yet external assurance standards for auditors to meet to sign off on AI-generated narratives. The list goes on.
Sure, AI models can draft plausible entries and smart reports, but they have no inherent sense of whether they broke accounting logic. As such, it can look good, but still be wrong.
In finance, there's no place for wrong. Every action must be explainable, auditable and defensible. That's how I define "finance-grade." And while AI tools are getting faster and better at pieces of the finance team's work, it still doesn't make a finance system safe enough for go-it-alone AI.
Pillars of finance
We've rebuilt systems before. Think about it: Pilots didn't disappear from cockpits once autopilot arrived. Instead, cockpits were redesigned and the role of the pilot was redefined. Trust in autopilot rose because the entire system — of autopilot and pilot — proved trustworthy. Finance is at that moment now with AI.
To get to finance-grade AI, I break it down to four key buckets:
- Control: This entails traceable outputs, enforceable constraints and systems that can be audited. When things can be verified, they can be trusted.
- Integration: Many companies face fragmented data, disconnected analysis tools, too many spreadsheets and too many manual workflows. AI was not built to jump over such crevasses and work its machine reasoning. "Garbage in, garbage out" remains true even now that AI is on the scene. You need data that is clean, correctly curated and explainable so AI can integrate with it.
- Reliability: The world changes all the time, but so do policies, interest rates, exchange rates and so on. If AI models don't keep up — and they won't — the work they did an hour or day ago will no longer be optimal when you pull a trigger. Any automated workflow needs guardrails to allow human intervention. This means stop rules and other red flags that signal need for human oversight. You want intervention before payments are wrongly made or outdated forecasts infuse sales teams' targets — not just after.
- Accountability: It needs to be clear who owns decisions. Finance teams, like teams in all industries, are starting to use more AI agents to work autonomously. As they do this in finance, roles for human workers change too. Controllers become control architects. Reviewers look for exceptions. Auditors check systems, not just outputs. Still, it needs to be clear who owns every decision so that if one goes off track, there's a way to accountability and correction.
Planning for the inevitable
While AI is not yet, on its own, "finance-ready," it will get there. Increased capabilities are already in motion. They'll arrive even faster once the infrastructure is in place to house them.
In the meantime, finance leaders need to take steps for the short and long term.
For the quarter ahead, if you want to prove that AI belongs in your finance team, try it on something that causes your team pain and offer relief that scales.
Start with the mundane: chasing receipts, approvals, last-minute clarifications. These are simple tasks that suck time and energy out of highly skilled finance people. Give these tasks to AI agents trained to understand urgency, context and policy. They won't ask, "Is this right?" but they will ask, "Is this overdue or out of policy?" AI is great at taking action on domains where it can propose before a human approves and domains where logs track every message, action and verification.
Procurement is another likely target for an AI pilot. There's often a lot of rules around procurement — and a lot of grief for employees to know and follow them. Imagine an intelligent assistant that starts where the employee is — with a natural-language request — and guides them through the procurement process. It figures out whether to raise a purchase order or fund a card. It collects approvals based on pre-set logic. It gives finance visibility before the money moves. The end result is that something gets correctly procured and purchased within policy rules.
By addressing your finance team's pain points, you'll engage human employees in the value of having automation make their lives easier and their jobs more fulfilling. Your finance team will love an AI agent that nudges employees for receipts instead of having to do it themselves. As you amass ROI, you'll also amass employee belief that AI is a worthy colleague. That's how trust scales.
For the year ahead
Plan bigger and go wider as you consider the year ahead. Be ready for a scenario in which trust in AI builds steadily along with the tool's capabilities and one in which AI moves really fast and you need to keep up.
With the first one, assume AI adoption will mirror other enterprise technologies. Take the time now to design control environments. Ask audit and risk to give feedback so that, when automation scales, trust does, too. Document everything so you know how to tweak as you go.
With the second one, assume AI reliability leaps ahead of the controls you've built into your infrastructure. Prepare now. Get guardrails approved before you need them. This way, when the tech is ready, your system and team will be, too.
No AI will ever remove the need for trust. In fact, as machines do more tasks, the trust bar goes even higher. Invest now in things that will build that trust: provenance, constraints, clean data, clear roles, human-in-the-loop intervention. AI will then be finance-ready.
