AI is spreading across corporate finance from the inside out, coming from the software, reporting platforms and the ERP systems that accounting teams already run. That makes governance, not budget, the gating factor on how fast teams can move.
In regulated industries the calculation is sharper, because every number eventually lands in a filing that shareholders and regulators read. That means that enthusiasm for new tools has to be matched by a plan for checking what they produce, according to Christie Kozlik (pictured), chief accounting officer at Accel Entertainment Inc., a distributed gaming company.
“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.”
Kozlik spoke with theCUBE’s Krista Case and co-host Alison Kosik at Amplify, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed building trust in AI outputs and finding real return on investment in finance and accounting. (* Disclosure below.)
Trust and governance steer AI toward ERP systems
Accel operates 29,000 slot machines in 4,700 locations across 10 states, with data arriving from dozens of source systems, Kozlik noted. The obstacle in finance is less cost than trust, since a model answers in microseconds and sounds equally certain whether it is right or wrong — a gap Workiva Inc. has tracked in its midyear executive benchmark survey.
“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, with internal and external auditors 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 gets the same treatment, measured against a metric such as cutting the financial close from seven days to five. Her near-term targets are Securities and Exchange Commission reporting and the company’s ERP systems buildout, both heavily audited areas where her team knows where the data comes from.
“When I keep saying data governance, it’s probably more risk rating that data,” Kozlik said. “If you look at anybody’s [10-K] or [10-Q], rate each footnote. Could this one be AI bot-ready? Yes, no, one, two, three – and then talk through each one of them. Put a game plan together for each one of them.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Amplify:
(* Disclosure: Workiva sponsored this segment of theCUBE. Neither Workiva nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
