Robotic Process Automation (RPA) is a term most business and IT leaders are familiar with. For years, it’s been the go-to solution for automating repetitive, rule-based tasks across finance, HR, operations, and beyond. But recently, a newer concept has entered the conversation: AI agents, sometimes referred to as agentic AI. Unlike RPA, which automates fixed processes, AI agents are autonomous software entities that can reason, learn, and take actions in pursuit of specific goals. They’re not just automating tasks – they’re making decisions, adapting to changing inputs, and collaborating with humans and systems in more dynamic ways.
Interest in agentic AI is growing rapidly. Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, resulting in a 30% reduction in operational costs. As organizations begin exploring these increasingly intelligent and flexible tools, the conversation is shifting from task automation to goal-directed autonomy. And with that shift comes a timely question: Will AI agents replace RPA?
The answer isn't a simple yes or no. To understand the future of RPA, we need to explore what it does well today, how AI agents are changing automation, and where businesses are heading next.
What is RPA and why it still matters
Robotic Process Automation (RPA) refers to software bots designed to automate structured, rule-based tasks. Instead of relying on people for repetitive work, it takes on activities such as copying and pasting data, validating fields, or logging into systems. These are tasks that consume time but follow predictable patterns.
A classic example is claims processing for healthcare insurers. Payer operations teams often receive claims data in multiple formats, then manually extract details such as member information, provider IDs, procedure codes, and billed amounts before entering them into a core adjudication system. Depending on complexity, this can take anywhere from 30 minutes to several hours per claim. RPA bots can replicate these steps by monitoring inbound claim submissions, extracting data with OCR, and validating it against policy rules automatically.
Key benefits of RPA:
- Reduces manual workload
- Improves accuracy by eliminating human errors
- Completes repetitive tasks faster
- Integrates well with legacy systems without requiring major infrastructure changes
For many organizations, especially those with older applications, RPA continues to deliver strong business value and long-term cost savings.
RPA vs AI agents: what’s the difference?
RPA works well for automating predictable, structured processes. But it has limitations when exceptions or unfamiliar inputs arise. That’s where agentic AI, or intelligent process automation, steps in.
RPA is best described as:
- Rule-based: Bots follow explicit instructions that define exactly what to do step by step.
- Linear and step-driven: Processes unfold in a fixed sequence, with little flexibility if something unexpected happens.
- Designed for repetitive processes: Ideal for tasks like data entry, system logins, or form filling that rarely change.
Agentic AI agents expand automation by:
- Learning from data and patterns: Instead of relying only on rules, they improve accuracy and efficiency over time.
- Adapting to new scenarios: They can recognize unfamiliar formats or inputs and adjust their approach dynamically.
- Making context-based decisions: With minimal human input, they interpret context to choose the right action.
- Handling exceptions intelligently: Complex or unusual cases are no longer immediate roadblocks.
When invoices or claims data arrive in a familiar format, RPA is highly effective—it can extract amounts, member IDs, and billing codes and enter them into the adjudication system with speed and accuracy. But if that invoice comes in a new layout from an unfamiliar provider, a traditional RPA bot may stall because it does not recognize the format. An AI agent, however, can analyze the document structure, understand the context, and determine how to capture the right fields. With each interaction, it improves its ability to handle variations, learning and adapting over time.
This progression also enables assisted automation. Bots handle the bulk of routine claims work, while AI agents step in for exceptions, escalating only the most complex or unusual cases to human staff. The result is faster turnaround times, fewer errors, and better alignment between automation and human oversight.
Complementary automation: how RPA and AI agents work together
So, will AI agents replace RPA? Not entirely.
What’s changing is how RPA is used. Instead of serving as a standalone solution, it is increasingly part of a larger, more intelligent automation strategy. AI agents complement RPA by handling the unpredictable, while RPA manages the routine—together forming a powerful and resilient automation strategy. This combination enhances efficiency, accuracy, and speed, allowing employees to focus on higher-value work.
The following use cases highlight this synergy in action:
Prior authorization
The prior authorization process is critical for controlling costs and ensuring appropriate care, yet it often involves manual data entry, document review, and coordination between providers and payers.
- RPA handles routine work, such as extracting patient and provider information and entering it into authorization systems, reducing processing time and errors.
- AI agents add an intelligent layer, analyzing requests, flagging incomplete or inconsistent information, and suggesting decisions based on historical patterns, accelerating turnaround while maintaining compliance.
Grievances and appeals
Timely, accurate responses to complaints and appeal requests are essential for member satisfaction and regulatory compliance.
- RPA extracts data from appeal documents, routes cases to the appropriate team, and updates tracking systems automatically.
- AI agents enhance the process by categorizing appeals, assessing case complexity, and drafting initial responses for review, helping teams resolve issues faster and more consistently.
Customer service and call center operations
Seamless support across multiple channels is key to member engagement.
- AI agents handle inquiries via phone, email, IVR, and SMS, providing 24/7 assistance.
- They create and triage support tickets for claims, benefits, coverage questions, and basic troubleshooting such as account verification or status updates.
- Only the most complex or sensitive cases are escalated to human agents, ensuring efficiency without compromising service quality.
Business considerations: choosing the right tool
Now is the right time to evaluate your automation tools. Look for opportunities to extend RPA with AI where it makes sense and build a foundation that supports both efficiency and innovation.
Before deciding on RPA, AI agents, or a combination of both, organizations should assess:
- Process complexity: Are you automating standard tasks or managing exceptions?
- Scalability: Will the automation need to adapt over time?
- Impact on teams: Will it shift job responsibilities or create opportunities for higher-value work?
- Budget and infrastructure: RPA often has a lower barrier to entry, while AI agents may require more data and training upfront but offer greater flexibility.
Both RPA and AI agents can reduce costs, increase productivity, and improve customer service. The key is to select the right tool, or combination of tools, for your business needs, rather than chasing technology trends.
Next steps: where to begin with smarter automation
If you're still relying on manual processes or only using basic RPA, now is the time to evaluate how AI agents can extend your automation strategy. Getting started with intelligent automation doesn’t need to be complicated. A simple approach can help you move from exploration to measurable results:
- Review where you are today: Identify which processes are still manual and where RPA is already in use.
- Spot opportunities for improvement: Look for high-volume or exception-heavy tasks that could benefit from smarter automation.
- Start small, then expand: Pilot one use case to prove value before scaling across other departments.
- Plan for growth: Build a roadmap that combines RPA for routine work and AI agents for more complex, adaptive tasks.
At Alithya, we help organizations across industries take these steps with confidence. Whether you’re optimizing existing RPA, exploring AI agents, or building a long-term automation strategy, our team can guide you from assessment through implementation.
Contact us to schedule an automation readiness assessment or learn more about our AI-enabled solutions.