Article
Agentic AI in Finance Operations: What a CFO Should Demand Before Signing Off
Finance leaders are being sold AI solutions by vendors who know more about the technology than they do about finance operations. Here is the framework for evaluating what matters, what does not, and what questions to ask before the contract is signed.
The Vendor Problem
Every major software vendor now offers AI capabilities in their finance modules. Every emerging vendor is pitching AI-native finance solutions. The claims are consistent: faster close, automated reconciliation, intelligent forecasting, reduced headcount requirement. The evidence for these claims varies enormously, and most CFOs do not yet have a reliable framework for evaluating it.
This is not because finance leaders are unsophisticated. It is because the technology is new, the vendor market is crowded and competitive, and the standards for what "AI-enabled" actually means in a finance context have not yet stabilised. A solution that uses AI to generate a commentary on financial results is technically AI-enabled. So is a solution that autonomously processes invoices end-to-end without human review. These are not the same thing, and the distinction matters enormously for what you will actually get.
The Five Questions That Separate Real from Promising
1. What can the system do without a human approving each step?
This is the agentic question. An AI assistant that helps a finance professional work faster is valuable. An AI agent that can process a transaction end-to-end, without human review at each stage, is transformative. Most vendors describe their product in terms that blur this distinction. Ask directly: in a standard case, what is the last human action required before the transaction is complete?
Good answers are specific. "The agent processes invoices that match a three-way check and routes to a human anything outside tolerance X" is a real answer. "Our AI significantly reduces the manual workload" is not an answer to this question.
2. How does it handle exceptions, and what is the exception rate in your environment?
Every AI finance solution handles standard cases well. The operational reality of finance is that standard cases are a fraction of the workload. The interesting question is what happens when a vendor invoice has a discrepancy, when a currency rate creates an unexpected variance, when a payment terms dispute needs resolution, when a GL code is ambiguous.
Ask for the exception rate in deployments comparable to your environment -- in terms of transaction volume, ERP platform, and industry. Ask to see how exceptions are presented to human reviewers. The quality of exception handling often tells you more about a solution's maturity than its performance on standard cases.
3. What data does it need, and what does it need from our ERP team to get it?
AI finance solutions require data. The question is where that data comes from, how clean it needs to be, and what integration work is required to connect the AI solution to your existing systems. Vendors who underestimate the integration requirement are the most common source of delays in AI finance deployments.
Ask for a list of every system the solution integrates with and every data field it requires from each. Ask what happens if a required field is not available or not consistently populated. Ask what the typical integration timeline has been in comparable deployments. The answers will tell you whether the integration is a solved problem or an open one.
"The question is not whether the AI works in a demo. The question is whether it works in your environment, with your data, connected to your systems, in the first six months."
4. How is accuracy measured, and what is the accuracy rate in production?
Finance is a high-accuracy domain. An accounts payable error that gets through to payment is not an acceptable operational outcome, regardless of how many transactions the system processed correctly. Ask for accuracy rates in production, not in demos or test environments. Ask how accuracy is defined -- is a transaction counted as accurate when it is processed correctly, or only when no human correction is required after the fact?
Be specifically interested in accuracy rates in the first ninety days of a deployment. This is when the model is calibrating to your specific patterns and when error rates are typically highest. Knowing what to expect, and what support the vendor provides during this period, is important before you commit.
5. What does the audit trail look like?
Finance operations have audit and compliance requirements that AI solutions must meet. The agent's decision log -- what information it considered, what rule it applied, what it did -- is not optional. It is a compliance requirement. Ask to see the audit trail for a processed transaction. Ask whether the trail meets the specific requirements of your audit framework. Ask who is responsible if the audit trail is challenged.
The Deployment Sequence That Works
Based on Elixir deployments across enterprise finance functions, the sequence that consistently produces the best outcome starts with a single, well-defined use case where the data is clean, the rules are clear, and the volume is high enough to generate meaningful performance data quickly. Accounts payable invoice matching is the most common starting point. It is not the most transformative use case, but it is the one that builds the confidence, the integration infrastructure, and the operational discipline that more ambitious use cases require.
From invoice matching, the path typically extends to expense reconciliation, then to period-close automation, then to cash flow forecasting. Each step uses the integration layer and the governance framework built in the previous step. Each step generates return that justifies the next investment. The CFOs who try to deploy comprehensive AI finance transformation in a single phase are the ones who end up with the most expensive pilots.
