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Session Length
~17 min
Adaptive Checks
15 questions
Transfer Probes
8
Robotic Process Automation (RPA) is a technology that uses software robots, or 'bots,' to automate repetitive, rule-based tasks that are typically performed by humans interacting with digital systems. These bots can mimic human actions such as clicking buttons, typing data, reading screen content, copying and pasting information, and navigating between applications. RPA operates at the user interface level, meaning it does not require deep integration with underlying systems or databases, which makes it relatively fast and inexpensive to deploy compared to traditional enterprise software integration approaches.
The RPA market has grown rapidly since the mid-2010s, driven by organizations seeking to reduce costs, improve accuracy, and free human workers from monotonous tasks. Leading RPA platforms such as UiPath, Automation Anywhere, and Blue Prism provide visual development environments where business analysts and developers can design automation workflows using drag-and-drop interfaces. RPA implementations range from simple attended automations that assist individual workers to complex unattended automations that run independently on virtual machines, processing thousands of transactions without human intervention.
Modern RPA is increasingly converging with artificial intelligence and machine learning to form what is known as Intelligent Automation or Hyperautomation. While traditional RPA excels at structured, predictable tasks, the addition of AI capabilities such as natural language processing, computer vision, and document understanding enables bots to handle semi-structured and unstructured data. This evolution allows organizations to automate more complex end-to-end business processes, moving beyond simple task automation toward true digital transformation of operations across finance, human resources, supply chain, healthcare, and many other domains.
One step at a time.
A software program configured to execute a sequence of automated steps that replicate human interactions with digital systems. Bots can log into applications, enter data, perform calculations, and move data between systems without human intervention.
Example: A bot that logs into an email system, downloads invoice attachments, extracts key fields, and enters the data into an accounting application every morning.
An RPA deployment model where bots work alongside human users on their desktops, activated by the user to assist with specific tasks. The bot and human collaborate in real time, with the human making judgment calls while the bot handles repetitive steps.
Example: A customer service agent clicks a button to trigger a bot that automatically pulls up a caller's account history, recent orders, and open tickets across three different systems.
An RPA deployment model where bots run independently on servers or virtual machines without human interaction, typically scheduled or triggered by specific events. These bots can operate 24/7 and process high volumes of transactions.
Example: A bot that runs every night at midnight to reconcile thousands of financial transactions between a company's ERP system and its bank statements.
An analytical technique that uses event log data from enterprise systems to discover, visualize, and analyze actual business processes. In RPA, process mining helps identify which processes are the best candidates for automation based on volume, frequency, and standardization.
Example: A process mining tool analyzes six months of ERP system logs and reveals that the purchase order approval workflow involves 12 manual steps and takes an average of four days, making it a prime RPA candidate.
A centralized management platform that schedules, monitors, and controls the execution of RPA bots across an organization. It manages bot queues, handles exceptions, distributes workloads, and provides dashboards for tracking automation performance.
Example: UiPath Orchestrator assigns incoming invoice processing tasks to available bots, monitors their progress, and alerts the operations team if any bot encounters an error it cannot resolve.
The use of AI technologies such as optical character recognition (OCR), natural language processing, and machine learning to extract, classify, and validate data from unstructured or semi-structured documents like invoices, contracts, and forms.
Example: An IDP system reads scanned paper invoices in various formats, identifies the vendor name, invoice number, line items, and total amount, and feeds the structured data to an RPA bot for entry into the accounting system.
A dedicated organizational unit responsible for establishing RPA governance, best practices, standards, and a pipeline of automation opportunities. The CoE typically includes business analysts, RPA developers, solution architects, and change management specialists.
Example: A bank's RPA Center of Excellence evaluates automation requests from all departments, prioritizes them by expected ROI, develops the bots, and manages the entire automation lifecycle from design through production support.
The mechanisms built into RPA workflows to detect, manage, and resolve errors or unexpected situations that arise during bot execution. Proper exception handling ensures bots fail gracefully, log errors, and route issues to humans when necessary.
Example: When a bot encounters a customer record with a missing zip code during data migration, it logs the exception, places the record in a review queue for a human to fix, and continues processing the remaining records.
More terms are available in the glossary.
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