Vendors love to tout a big reduction in close time. The question that should always follow — and almost never does — hits on the other side of the equation: what happened to accuracy? If a tool can classify 99% of something correctly from a tax perspective, is that actually good, if it’s a black box and that remaining 1% means every single output still needs a human review? I’ve seen this exact situation play out: a tool that nailed transaction analysis in the 90% range, and the team still couldn’t use any of it, because AI doesn’t hand you a confidence score. There was no way to know which outputs fell in that remaining 8 to 10%, so every single one had to be reviewed anyway. The efficiency gain evaporated.
This is exactly where finance and accounting teams are unusually well positioned to help, because of how deep our understanding of controls, process, and policy already runs. Technical AI risk shows up as an error rate or a benchmark score. The people actually making decisions are thinking in dollars, timelines, and reputation. That mismatch is why real risks end up buried in an appendix nobody reads.
The translation habit that actually works has three parts. Start with the plain technical fact — a benchmark score, a stated limitation — and stay as close to independently verifiable sources as you can. State it exactly as it is; it’s something someone can check. Second, tie it to a real cost. What does this actually cost the organization if it goes wrong? Back to that classification example: misclassify something on a tax return and you’re potentially looking at material misstatements, interest charges, a real dollar figure — not a vague sense of risk. Third, size the likelihood. There’s been a lot of AI doomerism lately, including a well-known claim from someone at an AI lab putting real odds on an existential risk. Whatever you think of that number, it illustrates the point: people make decisions differently once a probability is attached. Is the risk you’re describing a 1% chance of a $1 million issue, or a 30% chance? Those are very different conversations, and naming the number is what lets someone else actually reason about it instead of just reacting to your tone.
Put together, that’s the whole formula: if this happens, here’s what it costs us, and here’s how likely it is. On current benchmarks, task-based success rates for even strong models tend to sit in the low-to-mid 50s — better than a coin flip, not dramatically so. Translated into business terms: roughly one in three outputs needs a human catch before it goes out the door. That’s a sentence a non-technical stakeholder can actually act on, and from there you can talk about what governance actually reduces that risk — human-in-the-loop checkpoints, or maybe AI isn’t the right tool for that particular step at all.
The same translation applies to context window risk. Long AI sessions can silently lose track of earlier information. Framed as “long chats are a bad idea,” that lands as an arbitrary rule. Framed as “here’s the specific risk this creates and what a mistake here would cost,” it actually sticks.
One more habit worth building: every efficiency claim needs a paired question. Time saved? Ask what happened to accuracy. Everyone’s adopted a tool? That’s not the same as it actually working — measure the outcome, not just the usage.
Key takeaways
- Translate every technical AI risk into three things: probability, dollar impact, and the specific decision it should change.
- A bare percentage or technical term, on its own, either gets ignored as too technical or wildly overreacted to.
- Always pair a vendor’s efficiency claim with the accuracy and review-step question before treating it as a win.
- You’re likely one of the only people in the room who can make this translation — that’s a real, valuable skill finance and accounting professionals already have the instincts for.
- The goal isn’t to make AI risk sound scarier or safer than it is — it’s to make it sizeable enough that someone can actually act on it.
Want the CPE credit? Take the full lesson on EverydayCPE and earn 0.2 CPE credits: Turning a Technical AI Risk Into a One-Slide Business Risk
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