RPA definition
Robotic process automation (RPA) is software that automates repetitive, rule-based computer tasks by mimicking how people use applications: clicking, typing, copying data between systems, reading screens and filling forms. Software robots, or bots, follow scripted workflows across existing applications without needing APIs, which makes RPA useful for legacy systems and high-volume back-office processes.
How does RPA work?
Developers or trained business users design a workflow in a visual studio, often starting by recording a person doing the task. The bot identifies screen elements through UI selectors, keyboard and mouse actions, and optical character recognition where needed. Attended bots run on an employee's desktop and help with parts of a task on request. Unattended bots run on servers or virtual machines on a schedule or trigger, processing work queues with no person present.
An orchestrator manages the bot fleet: scheduling runs, distributing work, storing credentials securely, retrying failures and logging every action for audit. Worked example: a bot downloads supplier invoices from a shared mailbox, extracts fields with document AI, matches each invoice to a purchase order in the ERP, posts the matched ones and sends exceptions to an accounts payable clerk with the reason attached.
Popular RPA tools
- UiPath: a broad automation platform with studio, orchestrator and document understanding.
- Automation Anywhere: cloud-based automation with AI-powered document processing.
- Microsoft Power Automate: desktop flows plus cloud workflows tied to Microsoft 365.
- Blue Prism: enterprise RPA with strong governance features.
- Robot Framework and open-source RPA libraries for developer-led automation.
RPA vs API integration vs AI agents
API integration connects systems directly through stable interfaces and is more reliable and faster than any screen-based bot, so it should be the first choice when APIs exist. RPA fills the gap where they do not: legacy desktop applications, government portals and vendor systems with no integration options. AI agents add judgment, reading unstructured requests and deciding what to do, but they are less predictable. Mature automation programs combine all three according to the task. Choose per step, not per project.
Good RPA candidates and common pitfalls
The best candidates are rule-based, high-volume, digital-input processes with stable screens and few exceptions: invoice entry, payroll data transfers, claims intake, account reconciliations and report generation. Avoid automating a broken process as it stands; simplify it first, or the bot will faithfully reproduce the waste. Measure the current process first so savings can be proven later.
- Brittle bots that break whenever an application's screen layout changes.
- Bot sprawl without ownership, documentation or monitoring.
- Credentials stored insecurely or shared between bots and people.
- Underestimated maintenance, which continues for the life of each bot.
- Measuring bots deployed instead of hours saved and errors avoided.
- Ignoring exceptions, so unusual cases silently fail or pile up.
Intelligent automation
Modern automation programs pair RPA with document AI to read invoices, forms and emails, with language models to classify requests and draft responses, and with human-in-the-loop review for low-confidence cases. This combination, often called intelligent automation or hyperautomation, extends automation to processes that classic rule-based bots could not handle. Nexzem designs automation that uses APIs wherever possible, RPA where it is the only option and AI where judgment is needed, with monitoring and clear owners for every bot.