What does AI implementation cost and where do you start?

We hear this question on nearly every first call with a client. The honest answer is: it depends — but not on luck or the vendor's mood, on three concrete things: the number of use cases you want to deploy, the state of your data, and the infrastructure it will run on. The good news is you don't have to build a massive platform for hundreds of thousands right away. A step-by-step approach works best, where every stage delivers measurable value and every next one is a decision based on facts, not faith in technology.

Step one is discovery, which we call AI Discovery. For a week or two we work with your business and IT: we map processes, check what data you have and in what shape, and collect ideas for AI applications. Then we assess each idea for potential return and implementation difficulty. What you get is a list of use cases ranked by ROI, an architecture recommendation and a roadmap. This is the moment when most companies see in black and white, for the first time, where AI will genuinely help them — and where it would just be an expensive toy.

Step two is a pilot. We pick the single most promising use case and build a working solution on your data — in 4 to 8 weeks. The key part: success metrics are agreed before the start, not after. If the pilot is meant to shorten ticket handling time, we measure handling time. If it's meant to speed up document search, we measure how many minutes users save each day. After the pilot you don't have to take our word for anything — you have numbers. And those numbers decide whether you invest further.

How do you pick the right use case for a pilot? We have a simple rule: look for a process that is frequent, repeatable and measurable at the same time. Frequent — because savings on a rare event will never cover the costs. Repeatable — because AI excels at patterns and struggles with situations that look different every time. Measurable — because without numbers you can't prove return on investment, to the board or to yourself. Ticket classification, document search, data extraction from invoices, standard customer replies — these are classic first pilots precisely because they meet all three conditions.

Speaking of pilots, it's worth knowing how a pilot differs from a prototype, because offers sometimes blur these terms deliberately. A prototype shows that something could work — usually on artificial data, without integrations and without users. A pilot actually works: on your data, connected to your systems, used daily by real employees. Only the latter gives you knowledge on which to base an investment decision. So when comparing offers, don't ask "will you build a demo" — ask "will the pilot run on our data, with our users".

Step three is production. We turn the successful pilot into a full solution: integrations with ERP, CRM or your ticketing system, company single sign-on, security hardening, monitoring, documentation required by the EU AI Act, user training. At this stage costs are fully predictable, because they're based on measured pilot results, not promises from a proposal. Only then comes scaling — the next use cases from the list created during discovery.

Now for what you're really asking about: the numbers. We won't give them here, and not out of convenience. AI implementation pricing is always individual, because the range is enormous — a knowledge assistant for twenty people on existing infrastructure is a different budget than an agent platform with dedicated GPUs and integrations with five systems for several hundred users. Quoting ranges without knowing your situation would be guessing. What we can promise is a quote after a short technical call — and a guarantee that we'll start with the cheapest stage that proves anything at all.

We'll also say plainly what mistake we see most often. Companies start by buying hardware or licenses before anyone has checked whether AI solves their real problem. They buy GPU servers because "you need them for AI", or annual subscriptions to tools nobody uses after three months. That's the wrong order. First discovery and a pilot at minimal cost, then purchasing decisions backed by measured returns. Hardware is bought for a specific, working load — not for hope.

The second common mistake is trying to do everything at once. A grand AI transformation program, ten use cases simultaneously, a year of work before anything reaches users. Such projects die halfway, because the organization loses patience and the budget runs out. One well-chosen pilot, delivered to the end, builds more trust in AI within a company than ten strategy presentations.

The third mistake is subtler: treating an AI implementation as a purely technological project. In practice, technology is half the work. The other half is people — winning the team over, training users, changing habits. We've seen technically excellent systems die because nobody thought about communication with employees: instead of a tool that makes work easier, they saw a threat to their jobs. That's why in every one of our deployments, training and communication are scheduled on par with development, and end users take part in testing from the pilot stage.

What does the typical timeline for the whole journey look like? For orientation: discovery takes 1–2 weeks, a pilot 4–8 weeks, and the move to production another 1–3 months depending on the number of integrations and security requirements. From the first conversation to a working system in production, roughly one quarter usually passes — not a year. That pace is possible precisely because we don't build everything at once, but add successive elements on a proven foundation.

And what if the pilot fails? That question deserves a straight answer too. It happens — and that's exactly what the pilot is for, so that failure costs weeks, not millions. If the metrics miss the target, you get full documentation, an analysis of the causes and a recommendation: fix and repeat, change the use case, or walk away. Any of those decisions is better than a months-long blind investment. Paradoxically, an honestly accounted failed pilot also builds trust — because it shows that decisions in your company are made on data.

What does the cost of an implementation actually consist of? It's worth splitting it into four parts, because each follows different logic. The first is advisory and analytical work — workshops, process mapping, the business case. The second is development: building the solution, integrations with your systems, testing. The third is infrastructure — hardware or cloud, licenses, environments. The fourth, often forgotten, is maintenance: monitoring, model updates, evolution, user support. When reviewing a quote, ask about all four, because an offer that only shows the build cost is telling you a quarter of the truth.

What drives the price the most? From our experience — three factors. First, the state of your data: if documents are organized and digitally available, the project starts fast; if they need to be scanned, cleaned and organized first, that's a separate stage. Second, integrations: every system the AI needs to talk to (ERP, CRM, ticketing, DMS) is additional work, and legacy systems without APIs can multiply it. Third, security and compliance requirements: deployment in a regulated industry costs more, because documentation, audits and security testing are real work, not a formality.

How do you assess whether the investment will pay back? A simple calculation we use in workshops: count how many hours a week your team loses on the task AI is meant to take over — searching for information, retyping data, classifying tickets, writing standard replies. Multiply by the hourly cost of labor and by 52 weeks. If the annual cost of the problem is clearly higher than the implementation cost, you have a business case. If it's comparable — look for a different use case. This simple calculation filters out most bad ideas before you spend the first euro.

It's also worth knowing that an AI implementation is not purely a cost — it often qualifies for co-funding. National and EU programs periodically support company digitalization and automation, and AI projects increasingly fit their criteria. We don't build the business case on this, but if funding is available — we help factor it into the plan.

One last piece of advice, perhaps the most important: choose a partner who tells you what AI won't do. The technology is being sold today with exaggerated promises, and the market is full of implementations that ended at an impressive demo. Ask about projects delivered to production, about measurable results, about what went wrong and what the vendor learned. A good partner isn't afraid of such questions — we ask them ourselves on the first call.

If you want to know the real cost for your company, let's start with a free 30-minute call with our architect. No sales deck — we'll ask about your processes, data and infrastructure, then tell you straight which application will pay back fastest and what it might cost. We'll also give you something the competition won't: honesty when AI simply doesn't pay off in a given spot.