AI automation of business processes
Classic automation handles whatever can be written as a rule. AI automation takes on what cannot: reading an invoice from a supplier you have never seen before, sorting a hundred emails by what they are actually about, and pulling out the fields somebody currently retypes by hand. Anything the model is unsure about goes to a person. It never argues.
Who this is for
- Someone retypes data daily from documents that look different every time
- Orders, questions and complaints land in one inbox and a human sorts them
- You tried writing rules and ended up with a hundred exceptions nobody maintains
- Volume is growing and the only answer you can see is hiring more admin staff
Where AI does the most work in a process
- Reading documents. Invoices, delivery notes, contracts and forms. The model finds the amounts, dates and line items no matter how a given supplier lays them out.
- Sorting the inbox. Incoming mail is classified by intent, prioritised and routed to whoever should handle it, with a draft reply attached.
- Extraction into structure. Free text becomes fields you can write into a system, not another paragraph for someone to read.
- Checking and matching. Does the invoice match the order and the goods received note? Discrepancies surface, the rest passes through.
- Proposing a decision. AI does not decide on its own, it prepares the case with reasoning. A human confirms or overrides, and the system learns from that.
What you get
- A process that runs itself, plus an exceptions dashboard where your team only handles what cannot be decided automatically
- A confidence score on every output and the source it came from, so you can trace why it turned out that way
- An escalation threshold set by what a mistake costs you, not by some default from a tutorial
- Integration with the systems you already run, over APIs; for closed software, a safe RPA layer
- An evaluation set built from your real cases and a measured accuracy figure, not a demo on five hand picked samples
When I will talk you out of it
Not every process wants AI, and it is cheaper to find that out now than after deployment.
- Under fifteen cases a day. The saving will not cover the running cost. Usually the answer is to simplify the process, not automate it.
- When a rule would do. If the input always has the same shape, a rule is cheaper, faster and more reliable. I will send you to classic automation and integrations.
- When nobody catches the mistakes. In processes where a bad decision only shows up a year later there is nothing to escalate, and the risk outweighs the saving.
Parameters
FAQ
What is the difference between AI automation and ordinary automation?+
Ordinary automation does what can be written as a rule: when an order arrives, write it into the warehouse. AI fits where a rule cannot be written because the input differs every time. Every supplier's invoice looks different, every customer email is phrased differently. In most deployments both layers run side by side, AI reads and decides, rules execute.
What does AI automation cost?+
Deployment starts at CZK 25,000 for one bounded process. On top of that come running costs, meaning model calls, which at ordinary volumes land in the hundreds to low thousands of CZK a month. Exact numbers come in writing after an intro call. Full pricing is on a separate page.
What happens when the AI gets it wrong?+
That is designed in from the start. Every output carries a confidence score and the source it was derived from. When the model is unsure it does not push the case through, it hands it to a person. That escalation threshold is set by what an error costs in your process.
Will our data leave the company?+
Not without your knowledge. I use APIs with no data retention, or a model running on your own infrastructure when the nature of the data calls for it. I will sign an NDA before looking at any system, and what goes where is written down, not agreed verbally.
At what volume does AI automation pay off?+
The practical threshold is around fifteen repeated cases a day. Below that it is usually cheaper to simplify the process than to automate it, and I will tell you so. Above it, deployment typically pays back within a few months.
Do we have to replace our systems?+
No. The AI layer sits on top of what you already have and talks to your systems over APIs. For software with no API a safe RPA layer works. Replacing an ERP because of AI is almost always a needlessly expensive route.
Related
- Automation & integrations, when connecting systems with rules is enough
- AI agents & LLM applications, when it should talk to people, not just process documents
- Data, RAG & backend, when AI needs to reach into company knowledge
- Pricing, prices and lead times for everything in one place
Tell me which process eats the most manual time at your company and I will tell you whether AI fits it, or whether there is a cheaper way.
Write to me, I reply within hours.