When an AI audit makes sense
When AI came into the company through the back door: someone subscribed to a tool, someone else connected a chatbot, another person automated a process with a model nobody else understands. A few months later there are overlapping subscriptions, customer data flowing to services nobody reviewed and results nobody measures. The audit sorts that out before another dollar is spent.
What we review
- Inventory: which tools and models are used, by whom and for what.
- Real cost: subscriptions, usage-based spend and the hours spent maintaining it.
- Data: what leaves the company, to which provider, and under which terms.
- Outcomes: which processes improved measurably and which only look more modern.
- Risks: single-person dependencies, automations without error handling, unsupervised answers.
What you get
A report with three lists — keep, fix, switch off — each item with its reason and priority. It is not a catalogue of new tools to buy. Often the best recommendation an audit makes is to stop paying for something.
FAQ
- Do we need existing AI projects?
- Yes: an audit reviews what exists. If you do not use AI yet, what you need is a diagnostic of where to start.
- Does it cover regulatory compliance?
- It reviews data handling and governance risk, but it does not replace legal advice. Legal issues are flagged for a lawyer to validate.
Before you commission it
- AI systems audit: when to do it
- Score your business with AI (Spanish) — six questions, instant result, no email required.