What is robotic process automation (RPA)?

Robotic Process Automation (RPA) uses software robots to automate structured, rule-based tasks like data entry, form processing, and system integrations. Originally focused on eliminating repetitive work, RPA has evolved to become a critical execution capability for agentic automation—a new form of automation that brings AI agents, robots, and people together in an orchestrated process to automate complex, end-to-end processes across enterprise systems.

Robotic Process Automation (RPA) uses software robots to handle repetitive, rule-based tasks like entering data, moving files, or processing transactions—quickly and accurately. By mimicking human actions in interacting with screens and systems, RPA helps enterprises streamline operations, reduce errors, and free up employees for more meaningful work.  

Though full-scale enterprise RPA has only been around for about a decade, it has seen rapid global adoption. RPA technology and RPA tools are being used by enterprises in every industry for thousands of use cases and applications. Millions of robots are now working across industries—saving time, cutting costs, and accelerating digital transformation around the world.  

And now as the age of artificial intelligence (AI) has dawned, RPA is expanding its reach and impact far beyond automating repetitive tasks. RPA has emerged as an essential capability for agentic automation—a new form of automation that leverages intelligent AI agents that can plan, adapt, and make decisions autonomously. RPA robots have emerged as a powerful execution arm for AI agents, completing tasks quickly, efficiently, predictably, and securely.  

RPA’s evolution encompasses three phases:   Phase 1: Task Automation (2010s)  

Initially, RPA focused on automating individual repetitive tasks. Early adopters used RPA software and desktop automation tools to eliminate manual data entry, file transfers, and simple calculations. Enterprise-grade platforms emerged around 2017, enabling organizations to scale these automations across departments and accomplish comprehensive business process automation while maintaining security and governance.  

Phase 2: Intelligent Automation (2018-2022)  

As AI technology advanced, RPA integrated with machine learning, natural language processing, and more sophisticated computer vision. This enabled automation of semi-structured processes involving complex tasks, including invoice processing, email classification, and document analysis.  This phase also saw the introduction of more sophisticated RPA solutions and capabilities, such as intelligent document processing (IDP) and process mining. 

Phase 3: Agentic Automation (2023-Present)   

Today, RPA not only continues to automate tasks and power intelligent automation—it also serves as the execution layer for agentic automation, turning the plans and reasoning of AI agents into tangible actions across complex, intelligent workflows. Agents handle sophisticated problem-solving and adaptation;  RPA ensures reliable execution across enterprise systems; and people provide oversight and direction. This collaboration between agents, robots, and people enables end-to-end automation of complete business processes, not just individual tasks.  

Example: AI agents, RPA robots, and the Order-to-Cash Process  

Imagine a global enterprise automating its order-to-cash process:  

1. AI agent intake: An AI agent monitors incoming customer emails and portals. It extracts order details using document understanding and determines which workflow to trigger based on customer profile, credit history, and pricing rules.  

2. Decision and routing: Based on the customer’s order, the agent checks credit thresholds and product availability. If all checks pass, it instructs RPA robots to proceed. If not, it escalates the issue to a human for review.  

3. Robot execution: Robots log into the ERP system, create the sales order, generate the invoice, and update inventory—all following defined business rules, without human intervention.  

4. Human-in-the-loop: If a discrepancy or exception occurs (e.g., a price mismatch or out-of-stock item), the agent routes the case to a human for resolution, then resumes the automation once the issue is cleared.  

5. Continuous optimization: Over time, process mining and analytics tools help refine the agent’s decision models, improving efficiency and accuracy. 

To implement RPA at enterprise scale, you will need an automation platform that supports the full automation lifecycle, from discovering opportunities to deploying, scaling, governing and—critically important--orchestrating intelligent workflows across the enterprise. The right RPA platform will position you to scale, govern, and evolve automation as a strategic capability across the enterprise both today and in the future. 

Start with development. A strong RPA platform should offer low-code tools for business users alongside a robust set of capabilities for professional developers—like support for version control, reusable components, advanced debugging, and automated testing to accelerate quality at scale. AI-powered process discovery helps uncover the best opportunities for automation, while prebuilt connectors make integration with enterprise systems fast and reliable.   

Look for a robust automation ecosystem. Open interoperability and flexibility are key. The platform must enable UI- API-, and AI-based integration and automation. It should be able to integrate quickly and seamlessly with leading enterprise technologies. And as companies adopt agentic AI, it needs to be able to work with and orchestrate a wide range of third-party AI agents. To accelerate time to value, ensure the availability of a wide range of prebuilt automations and connectors as well as complete solutions for specific industries (e.g., financial services or healthcare) and business functions (e.g., inventory management, pricing, or customer care).   

Orchestration is where real enterprise value is unlocked. As organizations move into agentic automation, platforms must do more than run robots—they must intelligently orchestrate the work of thousands of robots, AI agents, and people across long-running, workflows across mixed ecosystems. This means dynamically assigning tasks, managing context, escalating exceptions, and ensuring alignment to business outcomes—all in real time and at scale.   

Centralized management is a must. Look for capabilities like role-based access control, audit trails, exception handling, and unified dashboards to monitor performance, manage compliance, and maintain security across your automation landscape.  

Intelligence is also essential. From intelligent document processing and embedded decision logic to native integration with AI agents, the platform should empower you to build automations that adapt, learn, and deliver measurable results. Real-time analytics and optimization tools ensure continuous improvement and help demonstrate ROI.  

Finally, the platform should be flexible and deployment-ready—whether on premises, in the cloud, or hybrid. It should support both attended and unattended robots, remote environments, and mobile workforces.  

Some have suggested that AI agents could replace RPA robots. But what’s actually happening is more collaborative than competitive. As businesses adopt AI agents and generative AI to drive decision-making, they increasingly rely on RPA to carry out those decisions reliably across enterprise systems. 

RPA plays a critical role in intelligent workflows and integrated automation ecosystems. It delivers secure, auditable, and efficient execution, especially where systems lack APIs or require structured user interactions. Its value lies in reliability, governance, and the ability to operate at scale. 

Far from being outdated, RPA is evolving. It now supports intelligent, outcome-based automation by bridging AI with real-world applications. AI agents may analyze and decide, but RPA ensures those insights are acted on across legacy systems, virtual environments, and business-critical platforms. 

As automation expands, RPA provides a stable foundation. It enables enterprise teams—not just IT—to build and manage automations using intuitive tools, while ensuring compliance, scalability, and operational alignment. 

In an agentic future, RPA won’t fade into the background. It will continue to anchor execution within AI-driven ecosystems, connecting intelligence to impact and strategy to system-level action.