AI automation that actually reduces the workload.
Instead of just promising to.
Dometrics automates recurring processes with AI where they demonstrably cost time or cause errors: sorting enquiries, extracting data from documents, transferring data between systems, preparing drafts. Every process comes with a target metric, an approval stage and ongoing operation.
- Trigger New email to anfrage@ Sales inbox
- AI Identify request and data Company, product, quantity, requested date
- System Create contact in CRM incl. territory and owner
- AI Prepare draft reply from product data and price list
- Approval Approval by sales back office one click, corrections feed back
Where time is lost in day-to-day operations.
The most expensive processes are rarely the complicated ones. They are the simple ones that happen a hundred times a week and tie up qualified people.
Copy and paste between systems
Data from emails into the CRM, from the shop into accounting, from forms into spreadsheets. Every transfer costs minutes and produces typos.
The inbox is the task list
Enquiries, orders, complaints and supplier mail all land in the same inbox. Sorting and forwarding is a part-time job nobody ever advertised.
Documents are retyped
Invoices, delivery notes, orders and data sheets arrive as PDFs. The information in them is captured by hand, although it could be read by machine.
AI tools without a process
Staff use ChatGPT on their own initiative, with customer data in the prompt and without rules. That is a data protection risk and remains an isolated effect with no impact on operations.
One process, one metric. Then the next.
Automation does not start with a tool, but with the question of which process costs the most time and how that can be measured.
- 01 · Inventory
Map the processes
Which tasks repeat, how often, how long, with which systems? Measured rather than estimated.
- 02 · Assessment
Calculate the leverage
Frequency times duration times cost of errors gives the order. At the top is what pays off fastest.
- 03 · Pilot
Automate one process
One process, one target metric, one approval stage. The AI prepares, a person confirms, until the quality is right.
- 04 · Integration
Connect systems
Connection to inbox, CRM, shop, ERP or accounting via APIs and workflow platforms.
- 05 · Operation
Monitor and expand
Error rates, costs and lead times in the dashboard. Models and processes are refined continuously.
AI licence versus automated process.
A chat subscription for every employee is the most common entry point into AI. It is also the one where the benefit is hardest to prove.
| Criterion | AI tool licence | Automated process |
|---|---|---|
| Impact | Depends on who uses it and how | Runs the same way for every case |
| Measurability | Perceived time savings | Lead time, error rate and cost per case |
| Data | Ends up in prompts, often without rules | Flows in a controlled way between defined systems |
| Systems | Results have to be transferred by hand | Writes directly into CRM, shop or accounting |
| Control | No approval stage | Human in the loop until quality is proven |
Which processes are suitable and how the benefit adds up.
AI automation is often discussed as a question of tools: which model, which platform, which provider? The more important question comes first. Which process in the business costs the most time, happens most often and follows rules that can be described? Only once this question has been answered with numbers is it worth choosing a tool.
Which processes are suitable
A good candidate meets three conditions: it occurs often, it follows recognisable rules, and an error can be detected and corrected. The following overview shows typical examples from small and medium-sized businesses.
| Area | Typical process | What the AI takes over |
|---|---|---|
| Sales | Enquiry by email or form | Identify the request, enter data into the CRM, prepare a draft reply |
| Customer service | Recurring questions | Answer from the knowledge base, handover for special cases |
| Purchasing and accounting | Invoices and delivery notes as PDFs | Extract line items, reconcile, submit for approval |
| Industry and B2B | Enquiries with drawings and bills of materials | Extract line items, prepare the costing |
| eCommerce | Product data and returns | Complete texts and attributes, analyse return reasons |
| Marketing | Reports and analyses | Consolidate figures, detect and comment on deviations |
Human in the loop
No process starts fully automated. In the first phase, the AI prepares and a person confirms every result. This produces a reliable error rate based on real cases. Only once it falls below the agreed threshold does approval drop to spot checks. For cases with legal or financial implications, human approval remains in place permanently.
Important: Customer data does not belong in private AI accounts. Giving staff AI tools requires a policy: which tools, which data, which accounts. Since February 2025, the AI Act has also required that staff have sufficient AI literacy [2].
The business case
Every pilot is preceded by a calculation with real numbers. An example: a sales back office handles 600 enquiries per month. Capturing, assigning and forwarding takes eight minutes per enquiry. If automation takes over six of those minutes, 60 hours per month are freed up. Against this stand the one-off build and ongoing costs for model, platform and operation. If the project does not pay off within a reasonable period, it is not implemented.
Operation is part of the product
Language models change, APIs change, processes in the business change. An automation without operation works on day one and then quietly loses quality. That is why monitoring, cost control and regular readjustment are part of every project. The architecture is built so that a model can be switched without rebuilding the process.
Key point: The value of an automation lies not in the model, but in the process, the data and the measurement. These three belong to the company; the model is interchangeable.
How to assess the benefit of an AI tool before buying it is described in the guide Evaluating AI tools. A concrete example from industry is automated quote creation. For companies in Vienna, the digitalisation funding of the Wirtschaftsagentur Wien can cover half of the eligible costs [3].
What an automation project includes.
- Process inventory with frequency, duration and cost of errors per process
- Prioritised list of automation candidates with a business case
- Workflows on platforms such as n8n or Make, or as a custom application
- Connection to inbox, CRM, shop, ERP and accounting via APIs
- Language models via EU endpoints or on dedicated infrastructure
- Approval stages and logging of every automated decision
- Internal AI policy and training as a contribution to AI literacy under Art. 4 of the AI Act
- Dashboard with lead times, error rates and cost per case
How the project runs
Inventory
Workshops and analysis of inbox, tickets and system logs. Result: the ten most expensive recurring processes.
Pilot
The process with the greatest leverage is automated, initially with human approval of every result.
Expansion
Once the pilot meets the target metric, the approval rate drops and the next process follows.
Operation
Monitoring, model changes where needed, cost control and adjustment to changing processes.
Frequently asked questions
Which processes are suitable for AI automation?
Processes that occur frequently, follow clear rules and work with text or documents: classifying and routing enquiries, extracting data from PDFs, preparing replies or quotes, reconciling data between systems. Rare one-off cases and decisions with a high degree of discretion are less suitable.
What is the difference between automation and AI automation?
Classic automation works with fixed rules and structured data, for example an order that triggers an invoice. AI automation can also process unstructured content: understand a freely worded email, read a PDF, draft a reply. In practice, the two are combined.
Which tools does Dometrics use?
Depending on requirements, workflow platforms such as n8n, which can also be self-hosted in the EU, Make or custom applications, combined with language models via EU endpoints or open-weight models on dedicated infrastructure. What matters is that data and processes stay with the company and the model remains interchangeable.
What does the AI Act require of companies that use AI?
Since 2 February 2025, companies must ensure under Art. 4 that staff working with AI systems have sufficient AI literacy. Since 2 August 2026, transparency obligations apply, for example for chatbots. Internal automations generally do not fall under the high-risk rules, but they should be documented.
How quickly does AI automation pay off?
The business case before the project shows this: hours saved times hourly rate plus avoided error costs, minus build and operating costs. A pilot is only started if this calculation pays off within a reasonable period. If it does not, Dometrics says so up front.
Does AI automation replace employees?
Automation by Dometrics is about tasks, not jobs. What gets automated is transferring, sorting and retyping. The time gained flows into advice, sales and quality, work for which companies struggle to find enough people anyway.
Is AI automation eligible for funding?
Often, yes, provided the project is part of a coherent digitalisation plan. In Vienna, the Wirtschaftsagentur Wien (the city's business agency) funds such projects at 50 percent of eligible costs as of October 2026, with applications open until 31 December 2026.
