AI automation for companies
In most companies the problem is not a shortage of ideas. It is that the team's time goes on work no human should be doing in the first place.
Copying data from one system into another. Sorting the shared inbox. Typing invoices by hand. Stitching a report together at the end of every month. None of it is difficult — that is exactly why it is expensive: it eats senior hours that would be worth far more elsewhere.
AI business process automation solves precisely this. The repetitive, rule-based steps move to the machine, while the judgement stays with the person who carries the responsibility. It is not a risky, all-or-nothing system replacement: we move process by process, each with its own measurable payback.
We have been building business systems at Develops Hungary since 2018, and over the last few years intelligent process automation has become the single most frequent request from our clients. This page is about how to choose the first process, what is genuinely worth automating, and what to expect in time and money.
Which process should you automate first?
A first project does not succeed because it uses the most impressive technology. It succeeds because you picked the right process. We ask three questions about every candidate, and the answers multiply together.
A good first candidate eats at least five to ten working hours a week, is documented well enough that two colleagues would describe it the same way, and its mistakes are still correctable. If you want that shortlist put together for your own company, that is exactly what happens during the Free AI Assessment.
Where AI automation pays off — use cases
These five areas cover the overwhelming majority of what companies ask us for. The impact ranges below are typical orders of magnitude from projects of this kind — your own numbers come out of the assessment.
RPA or AI-based automation — which one, when?
In practice the two are not rivals. The strongest solutions are hybrid: AI reads, classifies and extracts, then deterministic automation executes the steps in the target system — so the unpredictable part stays where you can measure it, and the transactional part stays exact. The rule of thumb is simple. If the input always arrives in the same format, do not pay for AI. If the input is human — written by a customer, a supplier or a colleague — do not force RPA onto it.
It fits your existing systems — nothing has to be replaced
The most common objection we hear is that automation would mean another migration project. It does not. The automation layer sits on top of what you already run — ERP, CRM, accounting software, document management, webshop, ticketing — and talks to each of them the way they can be talked to.
Where there is an API, we use it. Where there is none, we work with scheduled exports, a read-only database connection, an inbox-based handover, or a thin RPA layer on the user interface. We rarely meet a greenfield situation in a mid-sized company, and we do not design as if we did: legacy systems, an Excel that has been running the company for a decade and one process everybody handles slightly differently are the normal starting point, not an exception.
Two things matter for the long term. First, permissions are inherited rather than reinvented: the automation may not see more than the person it works for. Second, integrations are kept as separate connectors, so if you switch ERP in two years, only the connector is rewritten — not the automation logic. If your case needs deeper system-level work, that is the subject of Custom AI Integration.
Automation potential calculator
Type in your own numbers and you get the order of magnitude currently tied up in manual work. It is an estimate, not a quote — but it is usually enough to decide whether this is worth a conversation.
The calculation assumes a 60% automation share and 52 working weeks. Sixty percent is our typical range, but in practice it varies between roughly 30% and 85% depending on the process. The figure does not include the one-off implementation cost or the monthly running cost of the solution.
Frequently asked questions
How much does AI business process automation cost?
We work with ranges, not with a fixed price list. A single, well-bounded process — email triage, invoice extraction, one report — typically lands between HUF 1.5 and 4 million. Automation that touches several systems, with its own integrations and approval logic, is usually HUF 4 to 12 million. On top of that there is a running cost: model usage plus operations and monitoring, typically HUF 30,000 to 150,000 per month depending on volume. You get a fixed-price offer only after the assessment, because quoting a process we have not seen would be guesswork.
How long before we see results?
The assessment and process mapping take one to two weeks. The first working automation is usually live in production within four to eight weeks — deliberately on one narrow process, so the payback is measurable rather than promised. From there we extend in two- to four-week increments. If someone offers you a company-wide rollout in two weeks, they are selling a demo, not a system.
What do you need from us?
Three things. A process owner who knows how the work really happens and can spend two to four hours a week with us during the project. Access to the relevant systems, or an export from them. And a decision maker who can approve the change, because the hardest part of automation is never the technology — it is agreeing on what the process should look like from now on.
What happens to our data — GDPR and the EU AI Act?
Data stays in the EU, we sign a data processing agreement, and we use model providers with opt-out from training on your data by default. Where the content is sensitive — health, HR, financial records — we can run open models in your own environment or in a private cloud instance. Under the EU AI Act, ordinary business process automation falls into the minimal or limited risk category; what it requires from you in practice is transparency toward users and a record of where AI is in the loop. We hand over that documentation as part of the project.
What if the AI gets something wrong?
We assume it will, and design for it. Every automation has a confidence threshold: below it the case is routed to a person instead of being processed. Every decision is logged, so any output can be traced back to its input. And every process keeps a fallback path — if the automation is unavailable, the work continues the old way. Accuracy is measured on your own data before go-live, not claimed from a brochure.
Related services
Automation is one building block. If you want to see how it fits into the bigger picture, start here.
Write a few sentences about which process eats the most time. We come back within one business day with a concrete proposal and an honest price range.