Guide: How-to
What It Really Costs to Implement AI in a Business: What's Actually in the Price
NILG.AI · October 19, 2026
How Much Does It Cost to Implement AI in a Business: What's Actually in the Price
An AI project at a mid-sized company costs, in practice, between €15,000 and €150,000 end to end. The figures in this article are indicative order-of-magnitude estimates, not a price list: every project needs its own quote. The variation is not random. It depends on three factors: the state of the company's data before the project starts, the complexity of the process being automated, and whether the goal is a proof of concept or a real production solution.
The problem with almost every conversation about cost is that numbers appear without context. Comparing a document classification project with a churn prediction system for 500,000 customers makes no sense. They are different problems, with different levels of effort, and proposals with similar-looking price tags that only seem equivalent on a spreadsheet.
This article breaks down what is inside the price, where the money actually goes, and how to size a budget before talking to any vendor.
The Four Cost Phases of an AI Project
1. Diagnosis and Strategy
This is the phase most companies leave out of the initial budget and end up paying for twice. A proper diagnosis answers simple but hard questions: which process has the most business impact if automated, whether the existing data supports that, and what a measurable definition of success looks like.
It typically costs between €3,000 and €15,000. On smaller projects, it may be offered at no charge as part of a commercial proposal. But when it is free, it is rarely independent. A paid diagnosis is almost always more candid.
Without this phase, the risk is hiring someone who starts building without understanding the problem. The project gets more expensive halfway through because the scope shifts.
2. Development and Implementation
This is where the largest share of the budget goes. For a medium-complexity project, that means €20,000 to €80,000. For something more ambitious, such as a recommendation system or a computer vision model in an industrial setting, it can go beyond €150,000.
What is inside that number:
- Data preparation and cleaning (often the most time-consuming work)
- Model development and training
- Integration with the company's existing systems
- Testing and validation of real-world behaviour
- Deployment to production
Integration with legacy systems is consistently the most underestimated item. Companies running an older ERP, a fragmented CRM, or data spread across separate silos can see this phase take three times longer than estimated.
3. Internal Team Training
A production AI model that nobody in the company knows how to interpret is a system that will be abandoned. Not immediately. But within the first six months.
Basic training for non-technical teams costs between €2,000 and €8,000, depending on the number of people involved and the depth required. This is not an optional cost. It is what determines whether the prior investment generates returns or sits forgotten on a server.
4. Ongoing Maintenance and Monitoring
This is the cost that most often surprises first-time budgeters. An AI model is not like a static website. Data changes, customer behaviour changes, and models degrade.
Ongoing maintenance can be structured in several ways: a monthly retainer between €1,500 and €5,000, or annual support contracts with quarterly reviews. It is worth defining this phase from the start, so it does not come as a surprise when the initial project wraps up. To understand how NILG.AI structures this kind of ongoing support, see the AI consulting page.
The most common mistake is not budgeting for this at all. Then the model starts to degrade, nobody understands why, and the internal conclusion is that "the AI didn't work".
The Difference Between Paying Once and Paying Indefinitely
There are two cost models you will encounter in the market.
Phased project model: investment concentrated in the development phases, with maintenance priced separately. Suited to companies that want full budget control and a clear deliverable.
AI SaaS platforms: more predictable monthly costs, but with customisation limits. They work well for standard use cases. They fall short when a company's data or processes have specific characteristics that a generic platform cannot accommodate.
The relevant question is not "SaaS or consulting". It is: is the process I want to automate standard enough for a generic tool to handle? If yes, SaaS is probably cheaper and faster. If not, a custom solution is the only way to reach the outcome that justifies the investment.
At NILG.AI, some projects start with exactly this conversation. There are cases where the honest recommendation is "what you're looking for already exists as a product". There is no point building something that already exists.
The Hidden Costs Nobody Mentions in the Proposal
Some items rarely appear as line items but affect the final total.
Internal time from the client's team. An AI project requires genuine involvement from the company: alignment meetings, output validation, decisions about data. This has an opportunity cost. A team of three people involved at 20% capacity for four months is a real cost that does not appear on the consultancy's invoice.
Cloud infrastructure. If the model runs on AWS, Azure, or Google Cloud, there are infrastructure costs that scale with the volume of data processed. That might be €200 per month for a small project or €3,000 per month for a large-scale production system.
Data collection and labelling. When data exists but needs to be organised or annotated to train a supervised model, that work has a cost. In computer vision or NLP projects, it can account for 20 to 30% of the total budget.
Iterations after the first result. The first version of a model is rarely the final one. The process includes adjustments, metric reviews, and retraining. If the contract does not cover this explicitly, each iteration can generate additional charges.
How to Size the Budget Before You Start
A practical starting point: use the NILG.AI recoverable work calculator to estimate the financial impact of the process you want to automate before approaching vendors. Knowing the potential value of the project helps calibrate what makes sense to invest.
The other useful question before requesting proposals: what is the current cost of the problem you want to solve? If the manual process costs €80,000 per year in team time and operational errors, a €40,000 project that eliminates it has a clear payback. If it costs €10,000 per year, the same project rarely makes sense.
Concrete numbers are more useful than intuitions. Arriving at the first meeting with an estimate of the problem you have is the most effective way to judge whether the proposal you are shown is grounded in reality or shaped by the commercial ambitions of the person presenting it.
The next step, once the budget is calibrated, is evaluating incoming proposals against objective criteria: what is included, what is excluded, and who pays for iterations. You will find more practical guides on the NILG.AI blog.
Implementing AI is not cheap. But the cost of doing it poorly, or of not doing it while competitors already are, is consistently higher. The question is not whether the price is fair in the abstract. It is whether the expected return justifies the investment in the specific context of your business.