By James Proctor, Co-Founder and Managing Director, The Inteq Group
No. Higher accuracy reduces how often an AI agent is wrong. It does nothing to reduce what being wrong costs, and autonomy must be governed by the second number, not the first. Expected loss is frequency multiplied by severity, and for decisions whose severity is unbounded or irreversible, no achievable accuracy brings the product inside an acceptable range. That is the entire argument in two sentences, and it is remarkable how much of the current AI conversation is engineered to keep executives from doing this simple multiplication.
What Does Accuracy Actually Buy?
Accuracy is a frequency lever, and a valuable one. Moving a decision class from 97 to 99.5 percent transforms the economics of routine, recoverable work: fewer exceptions, less rework, cleaner flow. Wherever consequence is bounded, small, and reversible, accuracy gains translate almost directly into autonomy headroom, and organizations should take that headroom deliberately. The category error begins when the same logic is walked into decision classes whose failure mode is not an inconvenience but an event: an unrecoverable transfer, a public commitment, a regulatory breach. Frequency improvements cannot repair a severity problem, any more than a safer car justifies removing the guardrail on a cliff road.
Why Can't More Nines Fix Consequence?
Two reasons, one arithmetic and one structural. The arithmetic: at operational volume, impressive percentages describe regular events. A 99.9 percent accurate decision executed five thousand times a day is five errors a day, every day. Whether that is trivial or catastrophic depends entirely on severity, which the accuracy figure does not carry. The structural reason is worse and almost never discussed: agent errors are not independent the way human errors are. Ten thousand human clerks fail idiosyncratically, one distraction at a time. An agent fails systematically: a flawed pattern does not produce one bad decision, it produces the same bad decision at full volume until detected, which converts severity from a single-case number into a correlated-event number. Title insurance and escrow is where this stops being abstract. An escrow operation can and should push document assembly, lien search synthesis, and closing preparation toward high autonomy; the work is complex, and errors are catchable and correctable downstream. The wire disbursement is different in kind. A 99.99 percent accurate disbursement agent misdirects one wire in ten thousand, wires are effectively unrecoverable, and a single misdirection can be a family's entire home proceeds. The disbursement decision is mechanically the simplest step in the closing, and it is the one that stays human at any accuracy level, because its severity, not its difficulty, is the governing fact.
“Accuracy sets the frequency of errors. Consequence sets their price.”
What Question Should Replace 'How Accurate Is It?'
Now the part the market will not enjoy: the accuracy arms race is a decoy, and vendors selling nines are answering a question nobody governing an operation should be leading with. Benchmark charts are procurement theater. The governing questions are the ones no benchmark can answer for you: what does a wrong decision in this class cost, can it be reversed, how fast would we detect it, and how correlated would the failures be at our volume? Answer those and the autonomy decision usually makes itself, at which point accuracy resumes its proper role, as the evidence that moves decision classes up through levels the consequence analysis already defined. Buy accuracy. Just refuse to let it buy authority.
Teaching teams to run consequence arithmetic before capability evaluation, and to hold that line in vendor conversations, is a core outcome of our agentic AI training courses, and it changes procurement behavior permanently.
