An accounting firm's bookkeeper spends 2 hours every day copying transaction data from bank statements into the firm's accounting software. Click, copy, paste, verify, repeat. The work requires zero professional judgment; it is pure data transfer between two systems that do not talk to each other. RPA automates exactly this kind of task. A software bot logs into the banking portal, extracts the transactions, formats them correctly, and enters them into the accounting system, doing in minutes what took hours.
Wikipedia defines RPA as "a form of business process automation that is based on software robots (bots) or artificial intelligence (AI) agents." Low-code vendor Appian puts it more simply: "If RPA imitates what a person does, AI imitates how a person thinks."
For most of the last decade, RPA was the default choice for business automation. It excels at structured, predictable tasks: data entry, form filling, report generation, file transfers, and system-to-system data migration. If a task follows the same steps every time with no variation, RPA handles it well.
The limitation of RPA is that it cannot think. When the bank statement format changes slightly, the RPA bot breaks. When a transaction does not fit the expected pattern, the bot either stops or enters incorrect data. When a new exception arises that was not programmed into the rules, the bot has no way to handle it. RPA is brittle: it works perfectly within its defined scope and fails outside of it.
This is where AI agents represent an evolution beyond RPA. TechTarget explains that "AI agents can perform tasks that involve unstructured data and require flexibility and decision-making," while RPA is limited to "structured data and predefined workflows." An AI agent reading a bank statement can handle format changes, categorize unusual transactions, and flag discrepancies for review. An RPA bot doing the same task will crash when the column headers move.
For business owners, the question is not RPA versus AI agents. It is understanding which tasks fit which technology. Pure data transfer between systems with fixed formats? RPA might still be the most cost-effective solution. Tasks that require reading unstructured documents, making judgment calls, or handling exceptions? AI agents are the right choice. Many businesses benefit from a combination, where RPA handles the predictable data movement and AI agents handle everything that requires reasoning.
The market is converging: RPA vendors are adding AI capabilities, and agent frameworks are increasingly taking on the structured work that was once RPA territory. The distinction between the two technologies is blurring. For a detailed comparison, see our guide to AI agents versus chatbots versus RPA.