You hear about AI from all sides, on LinkedIn everyone is boasting about an assistant, and you feel you're falling behind. At the same time you're afraid you'll spend €30,000 on a project that ends up in the drawer like three before it. An AI audit is a boring but the cheapest insurance against exactly that: a structured mapping of your company that tells you in black and white where AI will save hours and euros and where it's just an expensive toy. In this article you'll get the exact course of an audit, a scoring framework for choosing the first use case, a self-assessment checklist, indicative prices and an honest paragraph about when an audit isn't worth it.
What is an AI audit and what is it definitely not?
An AI audit is a diagnosis of your processes, data and systems with one goal: to find the places where AI or automation will bring a measurable return. The output is not enthusiasm, but a list of opportunities sorted by ROI, with an estimate of costs, savings and risks.
What an audit is not:
- It's not a presentation about what ChatGPT can do. You can watch that for free on YouTube.
- It's not buying a tool. Buying a licence and hoping it will use itself is the most expensive way to change nothing.
- It's not a one-off workshop full of buzzwords without numbers and without a plan.
A good AI readiness audit is so concrete that afterwards you can say: "We'll deploy this in 6 weeks, it will cost X, it will save Y hours a month and it will pay off in Z months."
How do you know your company needs an audit?
Typical signals we see repeatedly:
- People retype data from one system into another – from e-mail into Excel, from Excel into accounting.
- Shadow AI has appeared: employees are already secretly pasting company data, price lists and e-mails into ChatGPT because it helps them – and no one knows where that data goes.
- You have one or two pilot projects behind you that never made it to production.
- Reporting takes one person three days at the end of the month, and when they're on holiday, you have no figures.
- Customer support keeps answering the same questions and looks for answers in company documents that no one can properly find.
- You're growing, but instead of the margin growing, the number of people in administration grows.
If at least three fit, the audit will almost certainly pay for itself.
How does an AI audit proceed step by step?
A realistic AI audit of a company with 40–150 employees takes two to four weeks. It proceeds in six phases:
- Process mapping (3–5 days). Interviews with people from operations, finance and sales. We record how work actually flows – not how it's drawn in a directive.
- Measuring time and costs (3–5 days). To each step we assign a frequency, time and hourly rate. Without numbers it's not an audit, just an opinion.
- Scoring the opportunities (2–3 days). Each candidate use case gets a score according to the framework below.
- Data and technical readiness (2–4 days). Where the data lives, in what quality, whether the systems can be connected via API.
- Risks and compliance (1–2 days). GDPR, where company data may and may not be, basic guardrails according to the EU AI Act.
- Roadmap and business case (2–3 days). The order of deployment, an estimate of costs, savings and return.
A good audit always separates quick wins (within 6 weeks) from larger projects that require connecting systems or deeper process automation.
How to choose the first use case? A scoring framework
The most common mistake is to start where it's "cool", not where the return is. Use a simple framework – score each factor 1 (low) to 5 (high) and multiply the values:
Score = Volume × Frequency × Error rate × Data availability × Ease of integration
| Factor | 1 point | 3 points | 5 points |
|---|---|---|---|
| Volume (how many actions) | a few a month | dozens a week | hundreds a day |
| Frequency | irregularly | weekly | daily |
| Error rate today | almost none | occasional errors | expensive errors often |
| Data availability | in people's heads | in scattered files | in a system with an API |
| Ease of integration | impossible | medium | easy |
A use case with high volume, daily frequency and data available via API almost always beats a "clever" idea for which you have no data. If you want to quickly calculate the saving, the AI savings calculator will help.
Which processes bring the greatest saving?
Indicative figures from practice with small and medium companies:
| Process | Estimated time saving | Deployment difficulty |
|---|---|---|
| Extracting documents and invoices (OCR + AI) | 40–70% | low–medium |
| Customer support (draft replies, triage) | 30–50% | medium |
| Reporting and monthly statements | 50–80% | low–medium |
| Creating quotes and pricing | 30–60% | medium |
| Internal search across company documents (RAG) | 20–40% | medium–higher |
Extracting documents is the most common starting point – we analyzed it in detail in the article on invoice extraction via OCR and AI. Internal search runs on the RAG principle (retrieval-augmented generation), where the AI answers exclusively from your documents, not from the internet.
Why do most AI projects fail on the data?
Not on the model, on the data. If invoices exist only as scanned PDFs in e-mails, if customers are in three different Excel files and in the ERP you have three versions of the same product, no model will save it.
Data readiness means:
- The data exists in a structured form, not just in people's heads.
- The systems have an API or at least exports that can be connected to.
- You have a single source of truth – not five spreadsheets that everyone edits their own way.
That's why an audit often reveals that the first step is not AI but order in the data and connecting systems. It's boring, but without it the AI project falls apart in the third month.
What risks and compliance need to be addressed?
Practically, without legal ballast:
- GDPR: personal data must not leave uncontrolled into tools outside the EU. The solution is usually a company account with training on your data turned off, or locally/EU-hosted models.
- Company data in external tools: if employees use shadow AI, your price lists and contracts may already be somewhere. The audit names this and sets up a safe alternative.
- EU AI Act: most common company use cases (support assistant, document extraction, reporting) fall into the low-risk category with minimal obligations. A problem arises only in sensitive areas such as evaluating people. With ordinary automation there's nothing to fear, just document it.
How much does an AI audit cost and when is it not worth it?
Indicative ranges for the Slovak market:
| Scope | Price (indicative) | Duration | Expected return |
|---|---|---|---|
| Quick audit (small company, 1–2 processes) | €800–2,000 | 1–2 weeks | 2–4 months |
| Standard AI audit (10–150 people) | €2,500–6,000 | 2–4 weeks | 3–6 months |
| Pilot / MVP of the first use case | €4,000–15,000 | 4–8 weeks | 4–9 months |
| Full deployment after the pilot | from €10,000 | according to scope | 6–18 months |
Honestly – when an audit isn't worth it:
- The company has up to 5 people and one clear problem. Then skip the audit and build a small pilot right away.
- You have no digital data and aren't willing to put things in order. AI won't save that.
- You're looking for a reason not to use AI. An audit won't give you an excuse, it will give you a decision.
What mistakes do companies make most often with AI projects?
- They start with a tool instead of a problem ("we bought AI, what do we do with it").
- They automate a process that is bad – they get faster chaos.
- They don't determine who is responsible for the project, and the project dissolves.
- They don't measure the state before deployment, so they can't prove the saving.
- They want everything at once instead of one use case that runs to the end.
A good AI strategy for small and medium companies is the opposite of a big bang: one process, one measurable result, then another.
What does the audit output look like on paper?
After the audit you get a concrete document, not a presentation full of pictures:
- A map of processes with times and costs.
- A list of opportunities sorted by score and ROI.
- An estimate of savings in hours and euros for each use case.
- An assessment of data and technical readiness (AI readiness).
- An overview of risks and compliance measures.
- A roadmap for 3–12 months with prices and the order of deployment.
With this document you can decide even without us – and that's exactly the point.
Checklist: what to ask yourself even before the audit
- Which three activities do my people do most often and would most gladly not do?
- Where do we retype data daily from one place to another?
- Which error cost us the most money last year?
- Do we have data in a system with an API, or in Excel files and e-mails?
- Is anyone at our company already using ChatGPT with company data – and do we know about it?
- How many hours a month do reporting and invoicing eat up?
A practical example: a wholesaler with 40 people
A wholesaler with 40 employees received around 900 supplier invoices a month. Two people manually retyped them into the ERP – roughly 120 hours a month in total, plus fixing errors when matching them with orders.
The audit revealed that the invoices arrive in PDF and that an API exists in the system. The score of document extraction was the highest of all candidates. An OCR + AI flow was deployed that extracts the data, matches it with the order and prepares it for posting. Accuracy on the checked fields exceeded 95%.
Result: of the 120 hours, approximately 35 hours of checking a month remained. The freed-up capacity went to complaints and sales. The project's payback was under half a year. We described a similar scenario in the article on accounting automation.
Frequently asked questions (FAQ)
What is a company AI audit?
An AI audit is a structured mapping of a company's processes, data and systems that determines where AI and automation will bring a measurable saving of time and money, and where they don't make sense. The output is a list of opportunities sorted by return and a deployment roadmap.
How long does an AI audit take?
For a company with 10 to 150 employees, a standard AI audit takes two to four weeks. A smaller audit focused on one or two processes can be managed in one to two weeks.
How much does an AI audit cost?
Roughly €800 to €6,000 depending on the company's size and scope. With a well-chosen first use case, the investment in the audit typically returns within three to six months.
When is an AI audit not worth it?
If you have a very small company with one clear problem, it's worth skipping the audit and building a small pilot right away. It's also not worth it if you have no digital data and aren't willing to put it in order.
How do I find out whether my company is ready for AI?
Look at whether you have data in a system with an API, whether people retype data manually and whether anyone is using AI secretly. Three or more such signals mean an AI readiness audit will almost certainly pay off.
Summary
- An AI audit is the cheapest way to find out where AI will save and where it's just an expensive toy.
- Choose the use case by score (volume × frequency × error rate × data × integration), not by what's popular.
- Most projects fail on the data and disorder in the systems – address that first.
- GDPR and EU AI Act risks are manageable with ordinary automation, they just need to be addressed and documented.
- Honestly: for a small company with one problem, skip the audit and test a pilot right away.
Want to know where AI will really save you time and money? Take a look at our AI solutions for companies or arrange a free consultation right away – we'll get back to you within 24 hours and tell you straight whether an audit is worth it in your case.
