Transition of RPA to Agentic AI Systems

Organizations are now transitioning to the fifth industrial revolution, which is redefining the way of working, with a focus on the convergence of human, workflow automation, state machine-based automations, and Agentic Artificial Intelligence (Agentic AI). Robotic Process Automation (RPA) focuses on workflow and state machine-based automations for rule-based tasks, along with event-based tasks and those that are repetitive or mundane. In contrast, Agentic AI enables machines to make decisions, adapt, and learn autonomously. RPA focuses on attended or unattended automations that provide output by automatically performing a predetermined sequence of actions depending on a sequence of events or designed business rules.

RPA is primarily suited to structured, rule-based processes; the success rate decreases with increasing workflow complexity and dynamicity. Recently, most vendors have added features like self-healing of robots, applications, systems, and process-level complex exception handling scenarios to increase the automation success rate. Despite these enhancements, pre-defined rule-based decisions in RPA workflows aren’t accurate for dynamic scenarios. Hence, there is a need to integrate Agentic AI into RPA workflows to increase the success rate and stability of automation solutions. Agentic AI integrations with RPA bots can operate beyond pre-defined rules; they can leverage Large Language Models (LLMs) to address complex and unpredictable scenarios.

The powerful combination of RPA and Agentic AI can automate a broader range of complex processes. Due to its deterministic nature, RPA provides accuracy that can be further amplified by Agentic AI’s intelligence and self-healing capabilities. Moreover, a powerful combination of RPA and Agentic AI can handle dynamic and complex scenarios with ease.

Drivers of RPA to RPA + Agentic AI Transitions

Few Use Cases of RPA and Agentic Artificial Intelligence

Key Challenges

The transition from RPA to RPA + Agentic AI offers the advantage of Intelligent Automation, but it also introduces inherent problems to solve. Here are some key challenges that companies may face:

An Approach to Accelerate Agentic Process Automation

True enterprise-grade Agentic AI is rarely implemented. There are different levels of AI maturity, ranging from RPA to Copilots or AI Agents, Multi-Agentic Systems, and Autonomous workforces. Most organizations are currently using Copilots or RPA, often adding minimal intelligence by integrating with Agentic systems. However, a shift is needed from RPA to RPA + Agentic AI, Multi-Agent Systems, and Autonomous systems; these combinations are seldom implemented.

As next steps, we can address these key challenges in transitioning from RPA to RPA + Agentic AI by upgrading new systems, investing in technology and talent, ensuring robust data & security compliance, and enhancing infrastructure. The transition from RPA to RPA + Agentic AI systems, i.e., Intelligent Automation, marks a crucial landscape of digital transformations for organizations. While RPA successfully delivered ROI by automating mundane, rule-based tasks, with increasing complexity and dynamicity, it requires the Agentic AI fabrication within RPA workflows to build more adaptive and self-healing automation workflows.

Transition to RPA + Agentic AI can help organizations tackle the broad spectrum of unstructured and complex processes, where RPA is the foundation for rule-based tasks and Agentic AI enhances the ability of automation workflow to manage complex and unstructured process flows, make context-aware decisions, adapt, and learn continuously. Finally, the goal is not to replace RPA with Agentic & AI, but to transition to RPA + Agentic AI that creates a symbolic relationship where these technologies complement each other.

The Platform Advantage: Powering Agentic Process Automation

Realizing these benefits requires a unified platform approach, such as EdgeVerve AI Next, to streamline AI adoption and ensure long-term success. This approach minimizes project overruns, accelerates deployments, and delivers consistent outcomes through:

Further, the value of the platform for specific organizational setups/delivery models such as Global Business Services (GBS) could be enhanced through curated platform-based offerings. For instance, EdgeVerve AI Next Operations & Service Management (OSM) is an Agentic-powered platform built on AI Next to drive AI-led transformation of GBS at speed and scale. It’s packed with powerful capabilities—from AI-driven service request handling and intelligent case management—to dynamic workforce allocation and insightful dashboards.

From the above benefits, it’s clear that a unified platform approach can help accelerate the transition from RPA to RPA + Agentic AI and ensure optimal ROI on automation investments.