From RPA to Agentic AI: Why Your Automation Strategy Needs an Upgrade
Your robots followed rules. Your next automation will chase outcomes.
SS&C Blue Prism manages over 3,500 digital workers across its enterprise. They have saved hundreds of millions in operational costs. And they are now teaching those same bots to think—not just click.
This is not a rip-and-replace story. It is an evolution. And for any business leader with RPA investments, it signals where enterprise automation is heading next.
The RPA Ceiling Is Real
Traditional robotic process automation excels at structured tasks: extract data from Form A, paste into System B, send notification C. Rule-based. Predictable. Reliable.
But modern business workflows do not cooperate:
- Unstructured data arrives from emails, PDFs, and chat logs
- Outcomes shift based on context
- Decisions require reasoning, not just pattern-matching
- LLMs hallucinate
- Models drift over time
- Underlying model changes alter responses unpredictably
- Regulations have not caught up
- Audit trails get complicated
- Audit existing RPA – What is working? What is hitting structured data limits?
- Identify reasoning gaps – Which processes need understanding, not just extraction?
- Start hybrid – Keep deterministic bots for structured tasks, add agents for exceptions
- Build governance – Before scaling, establish audit trails and human oversight
- Measure outcome-based ROI – Time-to-decision, exception resolution, end-to-end completion
As Steven Colquitt, VP of Software Engineering at SS&C Blue Prism, notes: Inputs can vary, outcomes can shift and decisions depend on context in real-time.
Credit agreements illustrate the gap perfectly. A traditional bot might extract 30 data points. But an agentic AI extracts answers—understanding context, identifying inconsistencies, and reasoning through exceptions.
What Agentic Actually Means for Operations
The shift sounds subtle but changes everything:
| Traditional RPA | Agentic Automation |
|---|---|
| Follow step-by-step instructions | Pursue defined outcomes |
| Extract structured data | Interpret unstructured content |
| Stop at exceptions | Reason through edge cases |
| Repeatable scripts | Adaptive problem-solving |
Brian Halpin, Managing Director of Automation at SS&C Blue Prism, explains the new paradigm: We are now saying we are giving an AI agent the outcome that we want, but we are not giving it the instructions on how to complete.
Instead of follow step one, two, three, four, five, the instruction becomes: Review this loan or Onboard this customer.
The Trust Problem Nobody Talks About
Here is the honest truth from someone running 35 AI agents in production: most enterprises are not ready for full autonomy.
Halpin is direct about why:
There is an awful lot of learning to happen before companies go fully autonomous, Halpin acknowledges. But then, there will be something else, right? There will be another model. So really, it is all a journey right now.
This is not pessimism—it is practical enterprise reality.
The Integration Challenge Inside Your Own Walls
Here is a pattern Halpin sees repeatedly: companies have built AI centers of excellence separate from their process automation teams.
The result? RPA teams are not even allowed to use AI tools.
The bridge-building opportunity: connecting these silos to capture the next 20-30% of automation potential across end-to-end processes.
What This Means for Your Next Steps
SS&C Blue Prisms roadmap offers a template:
The Bottom Line
RPA is not dead. But the ceiling is visible. Agentic automation represents the next layer of value—if approached with appropriate caution and governance.
The companies that win will not be those that abandon their automation investments. They will be the ones that teach those investments to think.
Want to see how AI agents could fit your specific workflows? Take our 2-minute assessment and get a tailored automation roadmap.
