How RPA and AI Agents Unlock the True Potential of Business Processes

Companies debating between RPA and AI agents are missing the bigger opportunity: RPA-optimized processes provide the perfect foundation for AI agent success. If you’ve already invested in robotic process automation, this established automation infrastructure could be your secret weapon in the race to deploy truly intelligent, agentic artificial intelligence for business process improvements.

In brief:

• Stop treating RPA and AI agents like competitors. Think of RPA as your structured foundation and AI agents as the intelligent decision-makers that pick up where automation hits its limits. The result is RPA AI agents.
• Start with RPA handling your repetitive, rule-based tasks, then layer in AI agents when workflows get complex or need humanlike judgment.
• AI agents multiply what RPA already does well. Together, they create end-to-end workflows that actually finish the job instead of just moving it along.
• Look at processes where RPA already delivers value but still requires human intervention for complex decisions or unstructured data. These gaps are your golden opportunities to integrate AI agents and see immediate return on investment (ROI) on both technologies.

Innovative companies focused on automation and artificial intelligence (AI) might be debating robotic process automation (RPA) versus AI agents. However, this misses the bigger opportunity: using RPA as a foundation for AI agent success.

If you’ve already invested in RPA, this established automation infrastructure could be your secret weapon in the race to deploy truly intelligent, agentic AI for business process improvements.

Or if you’re currently evaluating and planning your AI agent strategy, RPA and AI agents can be fully integrated to transform business processes. It’s not always necessarily one or the other in every single scenario — rather, it’s how they can work together.

RPA as the Foundation for Integrating AI Agents

Many organizations default to believing the two are the same.

When it comes to differences between the two, automation architect Nick Rahn explains, “AI can handle unstructured information, such as classification and sentiment analysis, while RPA is much stronger at structured or repeatable information.”

However, they complement each other more than they act as always-separate functions. RPA is the perfect foundation for advanced AI agents because existing RPA infrastructure gives you a head start on using agents to advance and automate various business process improvements.

It’s helpful to take a phased approach to implementing an RPA and AI integration. Begin initial automation work through RPA, enhance it through AI agents, and then fully bridge the two together for a dynamic, intelligent workflow. Take a look at this process in action.

Phase 1: How RPA Creates AI Agent-Ready Environments

RPA excels at repetitive, structured, rule-based tasks, making it perfect for data entry, accounting, report creation, and more. RPAs create natural integration points and handoff moments, especially when a workflow requires judgment or involves unstructured data.

Once workflows become too intricate or complex, agentic AI can seamlessly step in to change inputs and make decisions. RPA’s structure creates a solid foundation for AI agents to be even more valuable and effective than before, making them the perfect complement to each other.

Phase 2: AI Agents Build on RPA’s Structure

In “AI Agentic Workflows: The Next Evolution of AI Agent Development,” Centric Consulting’s AI strategy director Joseph Ours defines an AI agentic workflow as “a structured sequence of tasks performed by multiple agents working together.”

AI agents use the boundaries, triggers, and outcome structure of RPA in highly predictable environments. They differ from chatbots, traditional AI, and even machine learning because these more traditional approaches are designed to handle a specific isolated task with a limited scope and provide outputs based on input data.

Agents are also extensible, meaning they can access and use varied data sources.

Compared to RPA, AI agents are:

• Autonomous
• Goal-oriented
• Collaborative
• Data-driven
• Able to mimic human intelligence and decision-making

Phase 3: The Bridge to Advanced Intelligence with Established Automation Frameworks

Together, these technologies work to automate and transform business processes. For example, combining the two could power an end-to-end workflow, with each tool picking up where the other leaves off.

RPA builds the essential foundational layer, and you introduce AI agents when linear, rule-based automation isn’t enough.

Let’s discuss how AI agents use this valuable infrastructure to multiply capabilities.

Transforming RPA Workflows With AI Agents: The Multiplier Effect

So, where exactly could AI agents step in to enhance RPA workflows? When processes become too complex, migrate from linear decision-making, or must interact in natural language with human workers, AI agents can provide a multiplier effect.

Enhanced Decision Intelligence: Moving Beyond Rule-Based Automation

RPA is excellent at data entry and updates. Still, when data becomes unstructured or disorganized, AI agents could aggregate and clean up PDFs, scanned documents, and images, for example — something traditional RPA couldn’t do with its standard, rule-based automation.

Adaptive Learning: How AI Agents Optimize RPA Processes Over Time

For ongoing processes, RPA will simply execute based on its instructions. However, as processes need to change and optimize, AI agents can step in to provide those intelligent recommendations.

For example, a financial services platform teaching a chatbot might need more conversational training when customers go beyond basic “yes” or “no” answers. Also, using new business process improvement tools like AI mapping can help improve these processes over time.

Human-AI Collaboration: RPA as the Interface Between Knowledge Workers and AI Agents

Any AI needs human oversight, interpretation, input, and improvement at some level. RPA can work as the bridge between knowledge workers and AI agents, adjusting based on new patterns and monitoring efficiency.

RPA and AI agents are undoubtedly more powerful when combined, so let’s discuss real-world implications for integrating them.

1. Implementation: Build on Your RPA AI Agent Advantage

Your business can use existing RPA investments to adopt AI agents faster. You likely already have some processes built with RPA’s rule-based workflows in finance, human resources (HR), or customer service. They might even work exceptionally well and alleviate the manual burden on your teams.

2. Assessment: Identify High-Impact Integration Opportunities

Start by identifying the best opportunities for improvement with AI agents. Criteria could include processes with major business effects on efficiency or revenue, ease of implementation, task complexity, or ROI potential. Look at your key business priorities, whether that’s revenue growth or customer retention, and assess processes based on those that can provide quick wins.

For example, at a healthcare organization, perhaps RPA has already revolutionized updating patient records after visits. However, an AI agent could take that work and improve medical billing and coding automation. AI agents could also use the significant amount of code needed to maintain accuracy, improve billing, and speed up claims processing while reducing the risk of coding errors.

3. Pilot Program: Start With Processes Already Built by RPA

Start your pilot programs with processes already built by RPA. This sets your team up for quick success and gives you rapid feedback.

If the process still has room for more intelligent decision-making and more intricate processes, it has a high impact on revenue and could massively reduce human error. Those factors might indicate a good test project for your business. AI agents can help complete RPA processes.

4. Scaling Strategy: Build an Integrated Automation Ecosystem

Here’s a step-by-step guide to building an integrated automation ecosystem, starting with your existing RPA investments.

• Identify the most high-impact and important business processes. Determine if they’re already improved with RPA.
• Look at RPA’s early success as a strong indicator of AI agents’ value. If RPA benefited the workflow, add AI where RPA fell short.
• Integrate your RPA and AI systems by standardizing data formats and creating shared dashboards to monitor performance.
• Start with one or two processes, and measure the impact in terms of efficiency, productivity, time savings, and cost savings.
• As RPA and AI agents scale, build a strong governance framework that establishes rules for data security and compliance and defines ownership across teams.
• Continuously review workflow, optimize processes, and monitor performance.

Use RPA and AI Agents to Transform Business Processes

Instead of deciding between RPA versus AI, companies should focus on complementary strategies to integrate them. Siloing them in different departments or processes misses the million-dollar opportunity to integrate them so they work in harmony.

Bridging them allows RPA AI agents to create truly scalable automation, use human knowledge workers where relevant, and create a foundation for continuous improvement.

Early adopters will enjoy compounded benefits and advantages, reducing costs, improving efficiency and productivity, and crafting a significant competitive advantage over others. In the future, RPA AI agents will be able to power end-to-end processes, such as customer support, product fulfillment, inventory management, onboarding, and compliance, by bridging rule-based process automation with AI’s intelligent decision-making.

To get started, fully optimize your RPA processes. Identify repetitive and rule-based tasks and assess your current IT infrastructure to ensure it can maintain both RPA and AI agents.

Then, identify the right RPA tool. UiPath, for example, helps build the foundation for training AI models. Once your workflows are live, continuously optimize and monitor performance while adjusting and monitoring successes.

Our operational modernization and AI experts are ready with an external perspective on fully integrating RPA and AI agents. Whether you need workshops on AI readiness or help integrating RPA and AI agents, work with Centric to maximize your investment. Let’s talk