What it really costs to automate a process with AI (with real price ranges)

One of the most common questions we receive before an initial diagnostic is 'how much does something like this cost?'. The honest answer is that it depends on variables that are only known after analysing the process, the available data and the required integration. But that does not mean there are no indicative ranges. Here are the ones we work with in real projects in Spain.

The cost of automating a process with AI fundamentally depends on three factors: the complexity of the process (how many exceptions and variants it has), the quality and accessibility of data (whether it is in a structured system or in PDFs and emails), and the required integrations (how many systems need connecting and whether they have an available API). A process with clean data and an available API can cost 5 times less than the same process with unstructured data and legacy systems with no standard integration.

Basic document automation (€8,000–€18,000)

Extraction and classification of data from structured documents (invoices, delivery notes, forms), validation against an existing system and transfer to ERP or database. Includes extraction model, integration with one target system and an exceptions review panel. Typical timeline: 6-10 weeks.

Operational process automation with an agent (€20,000–€45,000)

An AI agent that manages a complete flow: receives inputs from different sources, makes decisions based on business rules, executes actions in connected systems (ERP, CRM, email, calendar) and escalates cases requiring human review. Includes flow design, multiple integrations, exception logic and monitoring. Typical timeline: 12-20 weeks.

Multi-agent system with complex logic (€45,000–€90,000+)

Multiple coordinated agents for processes that cross different departments, with complex business logic, integrations with legacy systems and audit or traceability requirements. Projects of this type typically also include team training and complete technical documentation. Typical timeline: 4-9 months.

• Data quality: clean structured data vs unstructured data in PDF or email multiplies cost by 2-3x
• API availability: systems with documented APIs vs RPA integrations or scraping over legacy interfaces
• Number of exceptions: a process with few well-defined exceptions is much cheaper than one with complex business logic
• Regulatory requirements: sectors with audit, traceability or compliance requirements add governance cost
• Training and change management: if the project includes team adaptation or process redesign
• Maintenance: AI models need periodic retraining or adjustment as the business changes

• Hours of 'prior research' that produce no concrete deliverable
• Licences for the supplier's proprietary platforms without an explained open source alternative
• First-year maintenance paid upfront with no defined SLA
• Generic AI training not specific to your process and tools

• Does the budget clearly break down hours for design, development, integration and QA?
• Does it include a post-launch stabilisation period with a defined SLA?
• Does it specify which technologies are used and whether they create supplier lock-in?
• Is the budget variation range explained with concrete assumptions?
• Does it include verifiable deliverables at each phase, not only at project end?

The cost of an AI automation project must always be compared against the current cost of the manual process: person-hours, error rate, cycle time and opportunity cost. Without that comparative analysis, any figure sounds expensive or cheap without real context.