Reimagining the Enterprise with Agentic AI

In one of my previous articles, I elucidated the difference between Traditional AI, Generative AI, and Agentic AI. To briefly recap:

• Traditional AI analyzes data, makes predictions, basis on human inputs. Humans must act on its outputs

• Generative AI creates new content such as text, images, or code. It still relies on human inputs

• Agentic AI goes further. It can plan, decide, and execute multi-step tasks across systems. It adapts based on results and works toward defined goals with limited supervision

Agentic AI represents a shift from “Assisted intelligence” to “Autonomous execution.”

What Agentic AI Means for the Enterprise

Agentic AI can act as a digital operator inside the organization. It can plan, coordinate, and execute work across systems, workflows, and teams. Instead of handling single tasks, Agentic AI takes ownership of multi-step, goal-driven processes. This reduces operational bottlenecks, speeds up decisions, and allows human teams to focus on strategy and innovation.

High-Impact Use Cases to Begin With

Based on practical experience leading large transformation programs, the following areas are strong starting points:

• Cross-Department Orchestration - Automating multi-step workflows across Customer Service, Supply Chain, HR, and Finance

• Revenue Assurance - Reconciling transactions, detecting billing discrepancies, and preventing revenue leakage

• Autonomous Support Operations - Monitoring IT/OT systems, identifying anomalies, triggering fixes, and escalating when needed

• Data Governance & Integrity - Validating pipelines, enforcing SLAs, tracking schema changes, and analyzing downstream impact

• Decision Support & Execution - Preparing insights, running simulations, and executing decisions with human approval at critical points

These use cases provide measurable value while maintaining manageable risk.

Practical Challenges and Risks

Deploying Agentic AI is not simple specially in traditional enterprises mainly due to:

• Integration with legacy systems

• Managing complex and changing workflows

• Ensuring high-quality, reliable data

• Designing strong feedback and monitoring mechanisms

There are also following risks:

• Unintended actions if agents misinterpret goals

• Regulatory and compliance violations without proper controls

• Lack of explainability, reducing trust

• Over-automation, leading to loss of oversight

To manage these risks, organizations must implement:

• Confidence thresholds

• Human-in-the-loop controls

• Audit logs

• Clear governance frameworks

Starting small, monitoring closely, and scaling gradually ensures safe and measurable value creation.

Implementation Approach: The Strangler Fig Pattern

Because Agentic AI introduces significant change, a phased approach is essential. Basis my experience of driving large scale digital transformation, I recommend using the ‘Strangler Fig Pattern’, a strategy for gradually replacing or enhancing legacy systems without disruption. Just as a strangler fig vine grows around a tree and slowly replaces it, a new AI capability can be layered around existing systems.

With this approach:

• Agentic AI starts as an overlay

• It gradually takes control of specific workflows

• Legacy processes are retired over time

This enables safe experimentation, continuous learning, and controlled scaling — without disrupting business operations.

Expected Benefits

When implemented correctly, Agentic AI delivers:

• Operational Efficiency – Reduced manual work and faster multi-system processing

• Scalability – Ability to handle complex workflows without proportional headcount growth

• Business Outcome Focus – Improved SLA compliance, reduced revenue leakage, and better collaboration

• Improved Data Quality – Continuous monitoring and automated corrections

Managing Change: The ADKAR Model

Technology transformation is only successful when people adopt it. The ADKAR model (Awareness, Desire, Knowledge, Ability, Reinforcement) provides a structured way to guide change:

• Awareness – Clearly communicate why Agentic AI is needed

• Desire – Build stakeholder motivation and support

• Knowledge – Train teams to work effectively with AI agents

• Ability – Provide tools, governance, and safe environments to apply it

• Reinforcement – Measure adoption and ensure sustained success

Final Thoughts

Agentic AI is not just another automation tool. It is a shift toward autonomous, outcome-driven operations. When implemented with strong governance, ethical controls, and human oversight, Agentic AI can accelerate decision-making, scale operations efficiently, improve data integrity and optimize cross-functional processes.

Remember, the goal should not be to replace humans, but to create a balanced model where technology executes intelligently, and humans provide direction, accountability, and strategic judgment.  Enterprises that adopt this approach thoughtfully will move closer to becoming truly autonomous, resilient, and future-ready organizations.

Disclaimer: Views expressed here are the author’s own and not necessarily those of financialexpress.com.