Thought Leaders
How Agentic AI Elevates Automation into Enterprise Strategy
A large enterprise recently completed a multi-year automation program with RPA, low-code platforms, and early AI pilots. On paper, the results looked impressive, but workflows still relied heavily on manual reviews. The lesson was clear: automating isolated tasks in pockets does not transform the way work gets done. Unfortunately, this is the reality for many organizations. McKinsey reports that 78 percent of companies now use generative AI in at least one function, up from 55 percent a year earlier. Yet adoption has not translated into enterprise-wide impact. The answer lies in rethinking automation design through Agentic AI.
For example, a large U.S. insurer combined existing automations with an AI-enabled document intelligence workflow in underwriting and policy operations. Turnaround times improved, manual reviews decreased, and decision quality became more consistent, with human-in-the-loop retained for exceptions and at key checkpoints for oversight. The outcome showed that pairing automation with AI delivers organization-wide results that tool-specific initiatives do not.
Why the Old Way Falls Short
In many enterprises, automation is pursued in silos, with other automation tools, such as, RPA separate from low-code and AI, housed in a standalone Center of Excellence (CoE). This separation duplicates effort, increases complexity, and limits end-to-end automation, thereby hindering enterprise-level effectiveness. Automation and AI go hand in hand, and a process-first approach treats them as complementary, deploying them together for each workflow and amplifying their impact. For instance, in invoice processing, RPA typically automates a portion of the steps. Adding AI-driven document processing raises coverage, yet exceptions still require human validation. Introducing Agentic AI, which deploys agents that reason, learn, and act across systems, further reduces manual intervention and increases overall automation. This example shows why a combined, process-led approach is more effective than separate programs.
How Agentic AI Changes the Game
Agentic AI represents the next step in the evolution of automation, providing a new tool to increase the Straight Through Processing (STP) numbers. It shifts automation from task completion to outcome delivery. While rule-based systems provide baseline efficiency, agents add adaptability by connecting across functions, interpreting structured and unstructured data, and suggesting next best actions. Routine cases can proceed with minimal supervision, while exceptions, policy-sensitive steps, and ambiguous situations can still be routed to human reviewers. In customer service, agents draft responses and next actions for teams to approve and escalate complex requests as needed. In finance, they prepare reconciliations, flag anomalies, and recommend adjustments for approval. In operations, they forecast disruptions and suggest workload changes that supervisors confirm. The result is reduced human intervention per case, faster cycles, and improved customer and employee experiences – all desired benefits of automation with accountability preserved.
Common Mistakes in Scaling Agentic Automation
Despite its promise, organizations often encounter challenges when scaling Agentic automation. The most frequent mistakes include:
- Lack of a clear “why”: Some enterprises adopt Agentic AI to follow a trend rather than to solve a defined business problem. Without clarity on objectives, implementations risk having a low impact and leading to disappointment.
- Treating automation and AI as separate tracks: Many organizations fail to see them as parts of the same continuum. Viewing them together enables phased maturity, where Agentic AI builds on existing automation.
- Overextending the scope: Even when enterprises have a valid reason to adopt Agentic AI, they sometimes attempt to apply it to every workflow. Not all processes justify the cost or complexity of Agentic automation. Prioritizing the right use cases is essential to protect ROI.
- Skipping process assessment: Without evaluating current workflows and identifying where Agentic AI adds the most value, enterprises risk misalignment. Some processes are better suited to traditional automation, while others benefit more from Agentic approaches. Equally important is embedding governance and responsible AI practices from the outset. Ignoring security, compliance, or oversight risks undermines trust and slows adoption.
How to Scale with Intent
Identifying the right use cases is key – shortlist three to five high-value processes where delays come from decisions and exceptions. Then define outcomes before the build begins. Implement in phases: use existing automation for system actions, add AI for perception tasks such as document understanding and classification, and apply agentic orchestration where context must be interpreted and actions proposed. Maintain oversight at policy and exception checkpoints, with documented approval paths and ownership, and run a limited pilot to validate improvements and compliance.
After early wins, move from individual deployments to a reusable and governed capability. Provide shared connectors to core systems, an orchestration layer for agents, model and data governance, monitoring and audit trails, and access controls aligned to risk. Establish a regular operating rhythm for change management, training, and performance reviews so teams can adapt it with minimal configuration. Scale to adjacent workflows that can reuse the same components and guardrails, and fund expansion only on verified results to maintain momentum and trust.
The Future of Enterprise Automation
Agentic AI is not simply another layer of automation, nor is it entirely automation in itself. Alhough it is becoming the operating model for enterprises, automation and AI must converge to create systems that decide, act, and improve. Success will also depend on embedding governance, security, and ethical oversight, ensuring that scale is achieved with trust and accountability.
Organizations that treat Agentic AI as a strategic lever for resilience and agility will outpace those that adopt it as a passing trend. The choice is clear: enterprises that scale with discipline, clarity, and measurable outcomes will define how work gets done in the years ahead. Process is still king.
