Agentic AI isn’t about automating tasks—it’s about removing the need for workflows altogether.
Enterprise workflows were built to manage complexity. When objectives couldn’t be handled by one person or system, organizations broke them down into linear sequences of tasks assigned to different roles. This structure made sense when coordination was manual and context had to be passed step by step. Robotic Process Automation (RPA) thrived in this environment by automating individual steps, but it never questioned the structure itself.
Now, agentic AI changes the equation. Systems can perceive context, make decisions, and collaborate dynamically. The workflow isn’t just optimized—it’s bypassed. That shift has deep implications for how enterprises measure ROI, design systems, and think about automation.
1. RPA’s Legacy: Task-Level Thinking That Limits AI Design
RPA taught teams to see work as a string of discrete steps. Each step could be mapped, scripted, and automated. This mindset carried over into how many design AI agents today—each agent framed as a substitute for a task.
The problem is that this approach misses the point of agentic AI. When agents can perceive context and interact with one another, they don’t need to follow a rigid sequence. They can operate in parallel, resolve exceptions, and collapse steps into coordinated decisions.
Enterprises that treat agents as task bots will end up with fragmented systems that replicate old inefficiencies. The real value lies in redesigning the work itself.
2. Why Workflows Persist—and Why They Shouldn’t
Workflows exist because traditional systems couldn’t handle complexity without structure. Routing work from intake to validation to adjudication was the only way to ensure accountability and control. But that structure also created delays, handoffs, and bottlenecks.
Agentic AI removes the need for that structure. In a claims process, for example, agents can simultaneously gather data, flag anomalies, consult policies, and resolve issues. The process doesn’t move step by step—it unfolds as a coordinated interaction.
The impact is not just speed. It’s a shift in how work is understood. Instead of optimizing each step, enterprises can eliminate many of them entirely.
3. The Wrong Metric: Why Time Saved Per Task Misses the Mark
RPA success was often measured in hours saved per task. That made sense when automation was scoped narrowly. But with agentic AI, that metric becomes misleading.
Compressing a 3-week process into 3 minutes isn’t about saving time on individual steps—it’s about removing the need for those steps. The ROI comes from collapsing the structure, not just accelerating it.
Enterprises that continue to count task-level savings will understate the impact of agentic systems. Worse, they’ll design for the wrong outcomes.
4. Designing for Interaction, Not Substitution
Agentic systems work best when agents are designed to interact, not just execute. That means giving them access to shared context, allowing them to negotiate outcomes, and enabling them to coordinate in real time.
This requires a different design mindset. Instead of scripting tasks, teams need to define roles, permissions, and protocols for interaction. The system becomes a network, not a pipeline.
Enterprises that make this shift will unlock new capabilities—like exception handling without escalation, or policy enforcement without manual review.
5. Reimagining Governance and Risk in Agentic Systems
Removing workflows doesn’t mean removing control. It means shifting governance from process enforcement to outcome assurance. That requires new tools for monitoring agent behavior, validating decisions, and tracing interactions.
Risk management also changes. Instead of auditing each step, enterprises need to audit the network—how agents made decisions, what data they used, and how exceptions were resolved.
This opens the door to more adaptive compliance, where policies are enforced dynamically and exceptions are resolved without delay.
6. From Automation to System Redesign: The Real ROI
The real ROI of agentic AI isn’t in automating tasks—it’s in redesigning systems. That means fewer handoffs, faster resolution, and more resilient processes. It also means better use of human expertise, which can be reserved for edge cases and oversight.
Enterprises that focus on system-level redesign will see gains in speed, accuracy, and scalability. Those that stay focused on task substitution will plateau quickly.
7. What to Stop Doing: Legacy Automation Patterns That Hold You Back
Many teams still start with process maps, identify tasks, and assign agents to automate them. That’s a legacy pattern. It locks in old assumptions and limits what agents can do.
Instead, start with the outcome. Ask what decisions need to be made, what data is required, and how agents can collaborate to get there. Build the system around that.
This shift isn’t just technical—it’s cultural. It requires retraining teams to think in terms of interaction, not execution.
Rethinking Automation: From Steps to Systems
Agentic AI isn’t a better RPA. It’s a different way of working. The goal isn’t to automate steps—it’s to remove the need for steps. That requires a shift in how enterprises design, measure, and govern their systems.
Leaders who embrace this shift will build faster, more adaptive, and more resilient organizations. Those who don’t will keep optimizing workflows that no longer need to exist.
We’d love to hear from you: what’s been the hardest part of moving from task-based automation to agent-led systems—and where have you seen the biggest shift in how work gets done?