Migrating from RPA to Real-Time Agentic Automation

Tim Butchart, CEO TrustPortal, September 2025

With Real-Time Agentic Automation, $$ saved and Productivity increases

Robotic Process Automation (RPA) promised much: “digital workers” that could handle the repetitive keystrokes and system hops that slowed back-office operations. To a degree, it delivered. But the limitations became clear almost as quickly as the pilots went live. Deloitte found that only 3 per cent of adopters ever reached enterprise scale; EY noted that as many as half of RPA projects stalled or failed. The problem was seldom the tools themselves, but rather their fragility and the difficulty of maintaining brittle scripts across dynamic environments.

A decade on, firms that built islands of automation find themselves short of what the market now demands: immediacy. Customers expect responses in the moment, not after a nightly batch run. Finance functions need reconciliations in minutes, not days. HR teams cannot wait for manual workflows to clear. IT operations require systems that heal as issues arise. The successor technology, at least in ambition, is Real-Time Agentic Automation: systems that can perceive signals, make decisions, and take action across enterprise applications as events occur.

The approach is beginning to appear in analyst forecasts and enterprise roadmaps alike. By the end of the decade, a meaningful proportion of everyday operational decisions are likely to be delegated to such real-time automation agents.

What “real-time” adds

Unlike task bots, real-time agentic automation agents do four things:

• listen continuously to events and signals;

• apply policies and models to decide;

• act securely through APIs, workflows and business applications;

• learn from outcomes, within explicit guardrails.

This is not a “better bot”, but a shift in operating model: from patching process fragments to creating an event-driven fabric that runs alongside human staff.

The business case

Evidence from early deployments points to three areas of return:

1. Speed. In customer-facing operations, reductions in average handle time of 20-65 per cent are reported; in finance and supply chain, cycle times for reconciliations or approvals fall from days to minutes.

2. Scale. Where RPA incurs cost with each additional bot or exception, agentic automation scales elastically with event volumes, whether in IT operations monitoring or HR onboarding flows.

3. Closing the loop. Traditional analytics often stop at “insights”. Agents can act, turning recommendations into execution; from reallocating stock in logistics to initiating order recovery in ecommerce.

   
   

By the numbers: Independent benchmarks and live deployments indicate cycle-time reductions of 40-90% when real-time agentic automation is applied across full business processes; from finance reconciliations and HR onboarding to supply-chain approvals and IT service incidents. In customer service, pilots report 20–65% cuts in handle time, 5-10 point gains in first-call resolution, and overall productivity uplifts of 15-20% per agent.

Exhibit 1: Cycle-time compression across Finance, HR, Supply Chain and IT Ops

Exhibit 2: Contact-centre deployments show 15-20% productivity uplift per agent, alongside cost savings and FCR gains

The lessons of RPA

RPA’s shortcomings are well catalogued: low rates of enterprise scaling; fragility when user interfaces change; the creation of siloed automations without orchestration. It is worth recalling these not to dismiss the gains; many firms did achieve rapid payback on structured tasks but to ensure the next wave avoids repeating the same traps.

Agentic versus “agent-washing”

Vendors are already rebadging old wares. True agentic automation should be goal seeking, tool using, stateful, and governed. Anything less risks being marketing gloss.

The architectural shift

Today’s RPA estates typically consist of work queues, scheduled bots, and brittle selectors. Tomorrow’s “agent fabric” is envisaged as an event backbone with orchestration, governed API access, human-in-the-loop oversight, and auditable logs.

A cautious migration path

Pragmatism suggests:

1. Inventory existing automations.

2. Establish an event backbone.

3. Wrap existing actions as callable tools.

4. Target high-latency processes across functions; from complaints handling in CX to claims triage, invoice matching, or IT service desk escalation.

5. Pilot with metrics, then scale selectively.

6. Retire brittle UI-scraping bots as alternatives mature.

   
   

A simple ROI illustration for a 200 agent service operation suggests potential savings in the £200,000/£300,000 range against platform costs of £80,000/£120,000; a net return that would satisfy most finance chiefs, if replicated at scale. Comparable logic applies in back-office domains, where faster cycle times translate into lower working capital, reduced error rates, and less manual rework.

Governance matters

Unchecked, automation agents risk compounding errors at speed. Controls are essential: policy first execution, continuous logging, human intervention above thresholds, and formal change management with versioning and rollback.

What becomes of RPA?

Stable bots handling routine transactions may persist. The more fragile automations, particularly those relying on UI scraping, are likely to be phased out or absorbed into the broader agentic fabric.

A 90-day approach

Weeks 1-2: baseline and prioritise processes by value.

Weeks 3-6: deploy backbone and tool wrapping.

Weeks 7-10: pilot with guardrails.

Weeks 11-13: expand or halt, depending on evidence.

TrustPortal in practice

TrustPortal has already put these principles into operation. At EDF Energy, real-time agentic automation streamlined service workflows, cutting resolution times from days to minutes. Telefónica applied the model at telecoms scale, integrating legacy systems without wholesale replacement and achieving double-digit gains in productivity. Other deployments, from insurers processing claims to financial services reconciling transactions, show similar patterns: stable return on investment, rapid cycle-time compression, and improved employee experience through reduced swivelchair effort.

Conclusion

RPA automated yesterday’s chores. Real-time agentic automation promises to manage operations as they happen. The transition is unlikely to be smooth, nor is every claim credible but the prize is clear enough: faster, more resilient, and ultimately more scalable operations. Firms that approach the shift with discipline may finally break through the ceilings that held back the first wave of automation.

Not to be confused with “Agentic AI” The term “agentic” is also used in a different, more controversial context. Agentic AI refers to autonomous, open-ended software agents often powered by large language models. These systems attempt to plan, reason and act in unstructured environments and have attracted both investment and scepticism. Real-Time Agentic Automation, by contrast, is narrower and more pragmatic. It refers to governed, event-driven agents that act instantly within enterprise guardrails: listening to signals, applying policies, and executing via APIs and core applications. It is designed to augment staff, not replace them; to integrate with existing IT estates, not bypass them. The distinction is crucial for enterprises seeking reliability rather than experimentation.

(c)TrustPortal 2025/TB