Short answer
An AI operations audit is a structured review of how a business actually works, ending in a ranked list of tasks worth automating with estimated time saved, required data and build effort for each. The output is a decision document rather than a technology recommendation, and a common finding is that a process needs fixing before it can be automated.
Also called: AI readiness audit, AI opportunity assessment
The method is undramatic. Sit with the people doing the work, write down each repeated task with its frequency and duration, then check what each one would need: where the data lives, whether it is machine-readable, who owns the system it sits in, and what a wrong output would cost. Scoring value against effort produces the ranking, and the ranking often contradicts the intuition the team started with.
The finding that saves the most money is frequently a negative one. A process that three departments perform differently cannot be automated coherently until they agree on one version, and paying to encode the disagreement in software is how automation projects grow expensive without becoming useful. An audit that recommends fixing a form, changing an approval route or cleaning a customer list before any model is involved has done its job.
Common questions
What does an AI audit produce?
A written inventory of the repeated work in the business, each item scored for time cost, data readiness and risk, with a shortlist of candidates in build order and a rough estimate for each. It should be usable as a plan by any competent developer, not only by the team that wrote it.
Where this comes up in our work
Related terms
Workflow Automation
Workflow automation is software that runs a defined sequence of business steps without a person moving data between systems: a submitted form creates a record, notifies the right team, files the attachment and schedules a follow-up.
AI Agent
An AI agent is a system that uses a language model to decide which actions to take, then takes them through connected tools: reading a database, sending an email, updating a booking record.
Human-in-the-Loop
Human-in-the-loop is a design pattern where an AI system prepares work and a person approves, edits or rejects it before it takes effect: drafted replies queued for a one-click send, extracted invoice fields shown for confirmation.
Inference Cost
Inference cost is what a business pays each time a model produces an output, billed by most providers per token of input and output, where a token is roughly three-quarters of a word.
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