Dirty Data, AI Agents.
AI agents are only as good as the data underneath them. Here's what that means for your financial close and AP automation workflows.
AI Agents Break on Bad Data — And Your Chart of Accounts Is Probably Bad Data
OpsMill just raised $14M specifically to solve one problem: AI agents fail when the underlying data isn't trustworthy. They cut deployment times from five days to under an hour by cleaning up the data layer first. That same failure mode is sitting inside every accounting firm running AI on messy ERP exports, inconsistent GL codes, or duplicate vendor records. The agent isn't the bottleneck — your data structure is.
Before you buy any AI automation tool, audit the data it will actually touch. The tool will only be as smart as what you feed it.The Accountants Who Will Stay Billable Are the Ones Who Can Diagnose AI Failures
When an AI agent misclassifies a transaction or closes a period with a reconciling error, someone has to find it, explain it, and fix it. That skill — knowing where automation breaks and why — is worth more right now than knowing how to run the automation itself. Think of it like the difference between driving a car and knowing what a transmission actually does: most people can drive, but the mechanic gets paid when the car stops. Start documenting every time an AI tool in your workflow produces a wrong output and why.
Keep a running log of every AI error you catch this month. That list becomes your diagnostic expertise — and your negotiating leverage.Infrahub-Style Data Lineage Thinking Should Come to Your Financial Close Stack
OpsMill's Infrahub platform solves a specific problem: it creates a single source of truth for infrastructure data so AI agents stop making decisions on stale or conflicting information. The same problem exists in financial close — GL data lives in the ERP, reconciliations live in spreadsheets, supporting docs live in email, and your AI automation is reading all three without knowing which one is right. You don't need Infrahub specifically, but you do need to ask your current close or AP tool one question: what is the authoritative data source for each step, and how does the system know?
If your AP or close automation vendor can't explain their data lineage, your exception rate will stay high and you won't know why.The Firms Getting Hurt by AI Aren't the Slow Adopters — They're the Fast Ones With Bad Foundations
Everyone assumes the risk is falling behind on AI adoption. The real near-term risk is deploying AI automation on top of unstructured, inconsistent data and then billing clients for the output. KPIT Technologies just reported a 33% profit drop despite 12% revenue growth — operating costs scaled faster than the efficiency gains did. That's what premature automation looks like financially. Firms that clean up their data models and standardize their workflows before layering in AI will run higher margins than firms that moved fast and are now managing a high-volume error correction process.
Slow adoption with clean data beats fast adoption with dirty data. Every time.