The increasing complexity of modern business operations pushes organizations to look beyond traditional robotic process automation (RPA). SS&C Blue Prism, a long-time provider of automation tools, is supporting enterprises as they move from rule-based workflows towards agentic automation, driven by artificial intelligence. For many businesses, this transition brings both anticipation and hesitation, especially as unstructured data and unpredictable scenarios become common in day-to-day activities. Balancing innovation with pragmatic risk management is a challenge, but SS&C Blue Prism aims to make the journey manageable, highlighting the comfort firms can take in moving at their own pace. As companies face mounting pressure to streamline processes and deliver faster outcomes, the migration from conventional RPA solutions to context-aware AI agents looks increasingly inevitable.
Recent discussions of SS&C Blue Prism’s approach show a clear evolution from earlier industry efforts, where RPA solutions handled largely repeatable and structured tasks. Past reports centered on the deployment and scaling challenges of digital workers but seldom addressed integrating advanced AI models into RPA ecosystems. Now, leaders at SS&C Blue Prism, such as Steven Colquitt and Brian Halpin, are candid about the hurdles in merging AI agents with longstanding automation practices, particularly regarding trust, compliance, and technical stability. There is also more emphasis on the organizational divide between automation and AI teams than before, pointing to the importance of integrating expertise for broader adoption of agentic automation.
Why Move Beyond Traditional RPA?
Traditional RPA has reached its limits as business needs demand more flexible and intelligent solutions. Real-life processes generate data in unpredictable formats and often require contextual decision-making, challenging the static, rules-based nature of legacy RPA systems. Steven Colquitt, Vice President of Software Engineering at SS&C Blue Prism, observes that current workflows go far beyond what classical bots were meant to manage, with input variation and changing outcomes that can only be navigated by AI-powered agents.
How Are AI Agents Changing Process Automation?
AI agents, especially those leveraging large language models (LLMs), now offer the capacity to interpret, reason, and respond to changing contexts within business documentation and interactions. Brian Halpin, Managing Director of Automation at SS&C Blue Prism, provides the example of credit reviews—tasks once handled line by line, now tackled holistically by AI for “answers” instead of mere data extraction. This approach shifts automation from rigid instruction-following to achieving outcomes with minimal intervention.
“We’re now saying we’re giving an AI agent the outcome that we want, but we’re not giving it the instructions on how to complete,”
Halpin explains, emphasizing the leap towards autonomy.
What Are the Challenges and Next Steps?
Despite this progress, fully autonomous agentic workflows are not yet widespread. Regulatory requirements, the unpredictability of LLMs, and concerns over auditability and security continue to slow full-scale adoption. Halpin acknowledges the learning curve ahead, and notes that AI and automation teams often operate in silos, presenting an additional barrier.
“There’s an awful lot of learning to happen before I think companies go fully autonomous and real agentic workflows [are] driven from that sort of non-deterministic perspective,”
Halpin states, highlighting the journey still underway.
To address these gaps, SS&C Blue Prism plans to release new technologies that facilitate embedding AI agents within organizational workflows, increase orchestration capabilities, and bridge the process efficiency divide between separated teams. The company’s participation in events like TechEx Global and its track record with over 3,500 digital workers and dozens of AI agents in production demonstrate both the complexity and scale of their ongoing automation initiatives. The hope is that these tools enable organizations to capture the next tier of process improvements by blending AI seamlessly into their existing operations.
The shift towards agentic automation requires organizations to rethink how they structure teams and align technology investments. Companies should focus on integrating automation and AI strategies, pilot new agentic workflows cautiously, and build trust in AI agents by setting up robust oversight and signage processes. Knowledge-sharing between automation centers of excellence and AI specialists can accelerate learning and minimize risk. Organizations that proceed methodically, balancing innovation and governance, are best positioned to capitalize on efficiency gains from the marriage of RPA and AI. As agentic automation matures, pragmatic steps to foster collaboration and maintain auditability will remain critical for sustainable enterprise adoption.
