How Much Does AI Process Automation Cost? Pricing and ROI in 2026 (from €3,500 net)
How much does AI process automation cost in 2026? Automating one business process starts from €3,500 net (typically €3,500–9,000), and an agent that runs a whole process from €6,000. You calculate ROI on your own numbers, not on our promise. You start with a free process scan.
Automating one business process with AI starts from €3,500 net. Typical ranges for a single process run €3,500–9,000 net, depending on integrations and volume. If the process crosses several systems and needs decisions, that is an agent from €6,000 net. You calculate ROI on your own numbers, not on our promise.
Quick answer
"AI process automation" has no single price, because the phrase covers very different scopes of work. At Syntalith we price them as separate lines, net:
- free process scan (€0): a 30-minute engineer call plus a written takeaway in two business days,
- automating one process (from €3,500 net): a system that handles a specific, repeatable process end to end, with system integration,
- agent that runs a process (from €6,000 net): performs multi-step tasks across your systems, within the boundaries you set, escalates exceptions, and leaves a trail,
- typical full implementations (€6,000–35,000 net): project pricing based on the process, integrations, and risk,
- maintenance (priced individually): hosting, monitoring, SLA, and changes after launch,
- ongoing AI model cost: at typical volumes usually a few cents per case, but calculated on real traffic, not fixed in advance.
If you want a portable document with architecture and a fixed quote before a bigger decision, the current price of the implementation specification is €1,200 net. The full, current price list for every line is on the Syntalith pricing page.
What automating a process costs (ranges per process)
This is not a price list for the whole market, just a way to read scope. The key column is the last one: it, not the name of the process, sets the price. The ranges are for one process taken to production, with integration, boundaries, and a trail.
| Process | Typical range (net) | Typical timeline | What raises the price |
|---|---|---|---|
| Email handling (triage, classification, draft replies) | €3,500–8,000 | 2–5 weeks | Number of case types, volume, what may be sent without a human, helpdesk or CRM integration. |
| Invoices and OCR (document reading, entry into a system) | €3,500–9,000 | 3–6 weeks | Scan quality, variety of supplier formats, accounting and ERP integration, exception validation. |
| Quoting (gathering data, generating a quote) | €4,500–12,000 | 3–7 weeks | Pricing complexity, variants, CRM and price-list integration, level of human approval. |
| Reporting (pulling data from systems, assembling a report) | €3,500–8,000 | 2–5 weeks | Number and quality of data sources, frequency, required accuracy. |
| Systems integration (wiring several systems, data flow) | €6,000–35,000 | 4–12 weeks | Legacy systems without APIs, number of integrations, sensitive data, governance requirements. |
We know email handling from production: we maintain such an automation for the shared Gmail inbox of a B2B services company, about 3,000 emails a month. We state only the input volume, without percentages, because without your data any such figure would be made up.
What it will pay back: run it on your own numbers
ROI from automation is not our promise, it is your substitution. Start with what the process costs you today:
Annual manual process cost =
hours per week on this process
x hourly rate of the people doing it
x 52Then calculate the payback. Subtract maintenance and model cost from the saving, because those are real lines after launch:
Monthly saving =
(hours recovered per month x hourly rate)
- monthly maintenance
- AI model cost
Months to payback =
build cost ÷ monthly savingThe result frames the price conversation. If the annual cost of manual work is lower than the cost of building and maintaining it, we will advise against building it. If it is clearly higher even under cautious assumptions, it is worth moving to a detailed specification. Add the team's quality-control time and a stabilization period after launch, and do not count revenue you cannot measure.
For context, not as a promise: McKinsey and MIT (August 2025) report that payback on AI investments in operations has shortened to 6–12 months, down from 12–18 months for leaders and 18–24 months for the rest in their earlier study. IDC (a Microsoft-sponsored study, January 2025) put the average return at $3.7 for every dollar spent on generative AI. That is self-reported survey data from other markets, with a huge spread (leaders up to 10.3x, laggards far less), so treat it as a reference point, not a number for your process.
What drives the price of automation
The price does not depend on whether you call something "automation," a "bot," or an "agent." It depends on five things.
Number and quality of integrations. Connecting one modern API is different work from integrating with a legacy system, an inbox, and Excel files. The price rises most where you have to work around a missing API or unusual data formats.
Volume. An automation that runs 50 times a day can be simple. One that runs thousands of times a day has to be observable: monitoring, queues, limits, and error handling.
Process stability. An automation works well where the process has rules and the exceptions can be named. If the company changes its workflow every week, order the process first, then automate it.
Data and security. Personal data, financial information, environment separation, SSO, or a DPA are not decorations. They are the conditions for going into production and a real part of the cost.
How much the system does without a human. An automation that drafts something for approval is cheaper and lower risk than one that sends decisions to customers or changes data in an ERP by itself. More on how that maps to budget is in the implementation pricing guide, and the ongoing token cost is broken down in what a running agent actually costs.
When automation is not enough and you need an agent
Automation handles one repeatable process in predictable steps: input, rule, result. That is enough for most single processes, which is why it starts from €3,500 net rather than at an agent's price.
An agent is needed only once the process stops being simple:
- it has many decision paths, not one rule,
- it crosses several systems and has to reason from context,
- it requires escalation of exceptions to a human and a trail of every decision.
Then the right purchase is an agent that runs the process (from €6,000 net), described in the piece on custom AI agents for business. How to tell one from the other step by step, we explain in the guide on what an AI agent is. Do not buy an agent just in case: Gartner (June 2025) predicts that over 40% of agentic AI projects will be cancelled by the end of 2027, mainly due to rising costs and unclear value. You buy an agent where automation genuinely falls short, not sooner.
When NOT to automate
Honestly: there are situations where automation is a bad purchase, however fashionable it is.
- Low, irregular volume. If the process happens rarely, the cost of building and maintaining it will not pay back even in an optimistic scenario. Manual handling can be cheaper.
- An unstable process. If the rules change every week and live in someone's head, write the process down on paper first. That is 80% of the work before AI even enters the picture.
- A ready SaaS is enough. Sometimes an existing off-the-shelf tool solves the problem at a fraction of the price. There is no point building a dedicated automation where a ready product already works.
There is also a harder truth worth hearing before you buy. McKinsey (November 2025) reports that more than 80% of companies see no measurable impact of generative AI on the enterprise-wide bottom line, and fewer than 10% of deployed use cases ever move past the pilot stage (McKinsey, June 2025). The reason rarely sits in the model. Usually the company automated a process that was not the bottleneck, or did not redesign the work around it. That is why we start with a scan and a number, not a tool. If any of these points fits your situation, we will say so plainly before you spend anything.
How to start
The cheapest sensible first step is to calculate the process, not to buy a tool.
- Book a free process scan and show one specific process.
- Prepare: who does the work, how many times a month, how long one case takes, which systems are in the path, and where the exceptions appear.
- After the call you get a recommendation: process automation, an agent, an implementation specification, or an honest "not worth it yet."
Book a free process scan | See pricing | AI automations