RPA or AI agents: the difference and the right choice for SMEs

A business owner recently asked me if he could finally replace "that robot" with AI. He meant an RPA bot that pulled invoices from his inbox every night and dropped them into his accounting package. The bot did its job neatly for years, until a supplier changed the layout of its invoice and everything ground to a halt. Three days of manual work before anyone figured out why.

That is exactly where the choice between RPA and an AI agent starts. They are not synonyms, and it is not a question of old versus new either. They are two tools for two kinds of work. Mix them up and you build an expensive solution for the wrong problem.

What is RPA, really?

RPA stands for robotic process automation. The name sounds more impressive than the technology is: an RPA bot mimics the actions an employee performs with mouse and keyboard. Log in, copy a field, paste it into another screen, click save, on to the next row. No physical robot, no artificial intelligence, just software replaying a fixed click path you recorded up front.

That is the essence of RPA, and the reason it exists. A lot of business software does not talk to other business software. Your payroll system does not know your CRM, your webshop does not know your accounting tool. Instead of building an expensive integration, you put a bot in front of the screen that retypes the data the way a human would. Cheaper than an integration, faster to set up, and you leave the underlying systems untouched.

In practice you will run into RPA under different names: a "bot", a "digital worker", sometimes "workflow automation". The meaning is always the same: rule based automation of repetitive, predictable actions.

Where RPA is strong

RPA is at its best when the work meets three conditions: it is repetitive, the input is structured, and the steps do not change. Think of:

• Moving data between two systems that have no integration.
• Assembling standard reports from fixed sources.
• Processing invoices that always follow the same format.
• Bulk actions: creating hundreds of records, updating statuses, renaming files.

For this kind of work RPA is reliable and fast. A bot works continuously without making typos. If the volume is high and the path is stable, a well built bot pays for itself within months.

Concrete examples of RPA in SMEs

Let's get concrete, because in the abstract this stays slippery. An accounting firm that every morning pulls bank transactions from one system and drops them into the books of dozens of clients. A webshop moving orders from an ageing point of sale into its shipping software. An HR team that has to create a new hire in five systems at once. Each is repetitive and structured: exactly the work a bot does not choke on, and where the time saved counts immediately.

Where RPA breaks

The problem is in that word "stable". An RPA bot understands nothing. It does not know what an invoice is, it recognises a pattern of fields in a fixed spot. Move that spot, and it trips.

And in practice something is always changing. A supplier tweaks its invoice layout. A software vendor moves a button after an update. A field that was always filled comes in empty once. The bot does not notice, it either does the wrong thing or stops dead. That is called brittleness, and it is the biggest hidden cost of RPA: not the build, but the maintenance.

On top of that, RPA cannot judge. As soon as a step calls for interpretation, "which ledger account does this go on", "what does this customer mean in this email", "is this an exception", RPA gives up. You can keep stacking rules for the cases you know, but the exception you did not anticipate slips through every time.

The hidden costs of a bot

The build price of an RPA bot is rarely the problem. Maintenance is. Every bot is tied to a screen it does not control. Every update of that screen can make the bot fail. With one bot that is manageable. With thirty bots reaching across ten systems, you get what the industry calls "bot spaghetti": a web of automations no one fully understands anymore, that throws up an outage somewhere with every software change.

So the real question with RPA is not "can I automate this", because often you can. The question is: how often does the underlying environment change, and who fixes the bot when it fails? Underestimate that and you save three hours a week only to pay it back in unexpected outages.

What an AI agent does differently

This is where an AI agent is fundamentally different. Where RPA replays a fixed path, an agent works from a goal. You do not say "click here, copy that", you say "process this invoice correctly" and the agent decides the steps itself, reads unstructured text, weighs things up, and corrects itself when something is off.

So an agent can deal with variation where a bot gets stuck: an invoice in an unknown format, an email with a slightly different question, an exception that no rule ever covered. That makes it stronger, but also less predictable, and that is precisely why you should not put it on everything. What an AI agent actually is and how it works I wrote up in what is an AI agent. This piece is about the choice between the two.

The market is shifting, and why

The numbers show where things are going. The global RPA market grew to around 3.6 billion dollars in 2024, at a growth rate of about 14 percent: healthy, but clearly flattening out. The market for AI agents sat at roughly 7.6 billion dollars in 2025 and is heading for 11 billion in 2026, growth of more than 45 percent a year.

By the end of 2026 about 40 percent of business applications are expected to contain task specific AI agents, up from less than 5 percent a year earlier. Budgets are visibly shifting from rule based bots to systems that can reason.

Important: this does not mean RPA disappears. It means the line moves. Work that used to be nailed down with ever more complicated RPA rules is now going to agents that can handle the variation. The boring, stable middle stays perfectly fine bot work.

RPA or AI agent: a decision table

If you are unsure which category your work falls into, run through these questions.

The more answers land in the right column, the stronger the case for an agent. If everything is on the left, an AI agent is overkill: more expensive, slower and less predictable than a simple bot.

Almost always: a hybrid

In practice the question is rarely RPA or AI. The best solution combines the two. The bot does the stable, structured steps, the so called happy path. The agent steps in where the path deviates: an unknown format or a step that calls for judgement.

A concrete example. In invoice processing the bot pulls the invoices and books the known, fixed suppliers automatically. When an invoice comes in that does not fit the mould, the bot hands it to the agent, which reads the text, proposes the right ledger account and asks a human to sign off when in doubt. That way you combine the reliability of RPA with the flexibility of AI, without asking too much of either.

In practice the question is rarely RPA or AI. The bot does the stable steps, the agent steps in where the path deviates.

What this means for your business

For Dutch SMEs this matters more than it looks. According to CBS, in 2025 nearly 30 percent of SMEs with ten to 249 employees used at least one AI technology, and Dutch SMEs are among the European frontrunners in their investment plans: more than eight in ten want to invest more in AI in the coming years. The biggest brake is not money or technology, but a lack of knowledge: not knowing what belongs where.

That is exactly what this choice is about. A company that lets an AI agent loose on work that is really simple rule work pays too much and gets an unpredictable result. A company that tries to catch everything in RPA rules builds a house of cards that topples at the first layout change. The gain sits in the right tool on the right step.

How do you start?

Not with the technology, but with the process. Take one process that eats too much time today and go through it step by step: is this stable rule work or does it call for judgement? That distinction decides whether you need a bot, an agent or a combination.

For the practical side of rule based automation and where it pays off in the Dutch context, read more on the page about RPA and process automation. If you want to know how agents take over work that is too variable for a bot, take a look at AI agents and automation. And if you just want to know where most of your time disappears into manual work, the free AI scan gives you a first analysis based on your own situation.

The question is never "RPA or AI". The question is which slice of work deserves which approach.