AI automation or RPA, which one fits each case

The discussion is usually framed as a choice between technologies, when it's really a question about the nature of the process. What determines the tool is how predictable the work is.

The question that settles the decision

Is the process always done the same way, or does each case carry variations that require interpretation?

If the answer is "always the same", rule-based automation is cheaper, faster to implement and more predictable. If every case arrives differently — free text, changing formats, classification that requires judgement — that's where artificial intelligence earns its place.

Where each one performs

Rule-based automation works well when:

• Data arrives structured, in defined fields.
• Conditions can be written as if-this-then-that.
• The process changes little over the year.
• Volume is high and exceptions are few.

Typical cases: generating a recurring report, syncing records between two systems, triggering notifications, loading data from a fixed-format spreadsheet.

Artificial intelligence adds value when:

• The input is free text: an email, a complaint, a description.
• Documents come in variable formats, like invoices from different suppliers.
• Something needs classifying, summarizing or prioritizing with judgement.
• A reply or a draft has to be generated.

Typical cases: reading inbound email and routing it, extracting data from heterogeneous documents, summarizing conversations, drafting a first version of a response.

The combination usually wins

In practice, the best results come from workflows where AI handles the ambiguous stretch and rules do the rest.

A concrete example in accounts payable: an invoice arrives by email. AI extracts supplier, amount, dates and line items regardless of the PDF layout. From there, a rule-based flow validates against the purchase order, checks approval limits and posts to the accounting system. If something doesn't match, it routes to a person with the discrepancy spelled out.

Nobody had to choose between technologies: each does what it does best.

The hidden cost of automating

Before committing, look at three factors that never appear in the quote:

Maintenance. When the process changes, the automation needs adjusting. Rule-based flows are brittle against interface or format changes; AI-based ones tolerate variation better but need periodic quality review.

Exceptions. No real process has zero exceptions. Defining what happens with the odd 10% usually takes longer than automating the normal 90%, and it determines whether the project survives in production.

Change management. If the team doesn't trust the automation, they'll check everything manually and the saving disappears. Trust is built by showing results during the first weeks, not by running a training session.

How to choose the first process

Three practical criteria:

1. High volume and measurable time. You can calculate what it costs today.
2. Verifiable output. It's easy to notice when the result is wrong.
3. Stable process. It isn't about to change in the next six months.

If a process meets all three, it's a good candidate, regardless of which technology ends up solving it.

What to measure afterwards

The same indicators in both cases: share of cases processed without human intervention, time per case, error rate and number of exceptions. If the intervention rate doesn't fall after the first two months, the problem usually sits in the process definition, not in the tool you picked.

Frequently asked questions

Does AI replace the RPA tools we already have?

Not necessarily. RPA remains cheaper and more predictable for fixed-rule processes. AI joins where there is free text, variability or judgement involved, and it often plugs into the workflow you already run.

Which processes are not worth automating?

Low-volume ones, those that change every few months, and those requiring negotiation or professional judgement. In those cases the cost of building and maintaining the automation outweighs the benefit.

How long does it take to automate a typical process?

A narrow workflow with accessible data is usually live in four to eight weeks. What stretches timelines is almost always integration with legacy systems and defining exceptions, not the model itself.