AI is spreading across corporate finance from the inside out, embedded in the software, reporting platforms and ERP systems that accounting teams already use. That makes governance, not budget, the gating factor on how fast finance teams can adopt these tools - especially in regulated industries where every number eventually lands in a shareholder filing.
Christie Kozlik, chief accounting officer at Accel Entertainment Inc., a distributed gaming company operating 29,000 slot machines across 4,700 locations in 10 states, said enthusiasm for new tools must be matched by a plan for checking what they produce. "It's one thing to turn on Claude. It's another thing to use it smart," Kozlik said. "It's another thing to actually get the output from it that you really want."
The trust gap in machine confidence
The core obstacle in finance is less about cost than trust. A model answers in microseconds and sounds equally certain whether it is right or wrong - a gap that Workiva Inc. has tracked in its midyear executive benchmark survey. Kozlik pointed out that human interactions carry signals AI cannot replicate. "Reading the body language - hearing how you're saying something - can go a long way in knowing if that output is correct or not correct," she said. "When you're talking about AI, you don't get that. You get the absolute confidence."
In response, the discipline required is deliberately old-fashioned. Every process gets a defined input, an expected output and a documented review. Internal and external auditors are brought to the table early rather than after the fact. "What is the input that I am feeding into this AI? What is the output that I'm expecting? What is the root cause that I'm trying to solve for?" Kozlik said. "Knowing those inputs, outputs and expectations becomes so important."
ROI measured against the close cycle
Return on investment gets the same structured treatment, measured against concrete metrics such as cutting the financial close from seven days to five. Kozlik's near-term targets are Securities and Exchange Commission reporting and the company's ERP systems buildout - both heavily audited areas where her team knows the data lineage. This approach to AI for Finance treats governance as a prerequisite, not an afterthought.
"When I keep saying data governance, it's probably more risk rating that data," Kozlik said. She described a practical method: take any 10-K or 10-Q, rate each footnote on whether it could be AI bot-ready, and build a game plan for each one. The process forces teams to confront exactly which data sources they trust before handing any output to a regulator.
Why this matters for finance professionals
For finance managers, the message is that AI adoption does not hinge on securing a larger technology budget. It hinges on documenting data provenance and building review workflows that auditors will accept. The teams moving fastest are the ones who can answer three questions before they start: what data is going in, what output is expected, and who verifies the result. For those building those disciplines, the path to shorter close cycles and faster SEC filings is already open.
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