Short answer: start with a repetitive, frequent process with clear rules, measure how much time and how many errors it costs today, automate just that stretch with a person supervising and expand when the data justifies it. Avoid automating “everything” at once.
Classic automation and AI automation
Traditional automation (rules like “if A happens, do B”) works very well with structured data and stable processes. AI adds the ability to handle messy information: emails, PDFs, conversations or documents. The most effective approach is often to combine both: rules for what is predictable and AI to interpret what is ambiguous.
Five processes with a typically good return
- Document intake. Invoices, delivery notes or orders received by email that someone types in by hand.
- Classifying and answering enquiries. Information or support requests that repeat.
- Lead qualification. Prioritising leads and recording their details in the CRM.
- Periodic reports. Consolidating data from several sources and generating a commented draft.
- Reconciliations and checks. Cross-matching lists and flagging discrepancies for review.
A method in 6 steps
- Inventory. List the tasks that take the most time and how often they repeat.
- Prioritise by volume, simplicity and risk of error.
- Measure the starting point: minutes per task, errors, delays.
- A narrow prototype on one stretch of the process, with real data.
- A supervised pilot for a few weeks, logging incidents.
- Scale only when the savings and quality hold.
Common mistakes
- Automating a process nobody has described clearly.
- Not leaving a person to handle exceptions.
- Not measuring beforehand: afterwards you cannot prove the saving.
- Choosing the tool before the problem.
- Ignoring data protection and permissions.
How to calculate the return
Multiply the time the task takes today by how often it happens each month and by the hourly cost of the person who does it. Compare that with the cost of implementation and maintenance. Add the errors avoided and the response time gained, which sometimes weigh more than the hours.
Frequently asked questions
Do I need to code?
For simple cases there are no-code tools. When there are several systems, sensitive data or complex logic, bespoke and well-documented development is advisable.
How long does a first pilot take?
A narrow process can have a pilot in a few weeks; the timeline depends on the data available and the integrations.
What if the AI gets it wrong?
That is why it is designed with human review at critical points, logs and confidence thresholds that route doubtful cases to a person.
If you want to identify your best candidates for automation, see IGLOBALIA’s applied AI agency or talk to us.
