Agentic AI: Unlocking the Future of Business Operations

Athikom Kanchanavibhu, Executive Vice President, Digital & Technology Transformation, Mitr Phol Group

As organizations navigate the dynamic terrain of digital transformation, the spotlight increasingly falls on Agentic AI, a revolutionary advancement that combines the power of Robotic Process Automation (RPA) and Generative AI. This evolution of intelligent automation introduces a new paradigm: systems capable of semi-autonomous decision-making and proactive task execution, far beyond traditional automation’s rule-based confines.

For business and technology leaders, the opportunities are immense—streamlining operations, enhancing customer experiences, and driving productivity. However, these opportunities come with challenges. How can businesses ensure Agentic AI aligns with strategic goals, integrates seamlessly with legacy systems, and operates within ethical boundaries? Let’s explore the answers to these pressing questions and outline actionable steps to make Agentic AI a transformative force within your organization.

Aligning Agentic Ai With Strategic Goals

One of the greatest risks of any technology initiative is the tendency to devolve into isolated, low-impact projects. To avoid this pitfall, businesses must adopt a strategic approach to Agentic AI. This begins with a deep understanding of the technology itself. Unlike standalone RPA or Generative AI, Agentic AI combines these capabilities into intelligent agents that can adapt, learn, and operate in complex environments.

“Agentic-AI Represents A Profound Shift In How Businesses Operate, Blending The Efficiency Of RPA With The Adaptability Of Generative AI”

1. Build a Foundation: The journey to Agentic AI starts with establishing foundational technologies. Organizations must ensure that RPA and Generative AI are implemented across key business functions, transforming fragmented processes into cohesive digital workflows.

2. Inventory and Prioritize Business Processes: A comprehensive inventory of existing processes is essential. Identify processes that are partially digitized, repetitive, and integral to your strategic objectives. By creating a consolidated list of candidates, businesses can focus on processes that are ripe for transformation.

3. Develop a Roadmap: With clear priorities, leaders can craft a roadmap to scale Agentic AI. A phased approach ensures that each implementation delivers measurable value, builds momentum, and aligns with organizational goals.

Critical Success Factors: Legacy Systems And Digitization

A common misconception is that legacy systems hinder the deployment of advanced AI solutions. While modernization is ideal, Agentic AI’s roots in RPA make it adept at bridging gaps between legacy systems and modern platforms. 1. Work with Legacy Systems: Legacy systems need not be replaced immediately. Agentic AI agents can interface with these systems using RPA’s capability to emulate user interactions. This allows organizations to integrate AI solutions without the disruption of a complete overhaul. 2. Focus on End-to-End Digitization: The more significant obstacle lies in physical activities still embedded in business processes. For Agentic AI to deliver its full potential, organizations must digitize workflows end-to-end. This includes converting physical records into digital formats and automating manual tasks. 3. Incremental Modernization: While Agentic AI can function in tandem with legacy systems, incremental modernization— updating platforms and implementing APIs—should remain a long-term goal.

Governance And Ethical Considerations

As organizations scale Agentic AI, governance and ethical considerations become paramount. Poorly managed implementations can result in breaches of data privacy, biased decision-making, and non-compliance with regulations. 1. Establish Governance Frameworks: Data governance and AI governance must be integral to enterprise GRC (governance, risk, and compliance) structures. Develop policies that address data classification, ethical AI usage, and model transparency. 2. Ensure Data Privacy: For customer-facing applications, strong data privacy measures are non-negotiable. Encryption, access controls, and continuous monitoring safeguard sensitive information and build trust. 3. Manage AI Risks: Conduct bias audits and implement explainable AI frameworks. This not only reduces risks but also enhances the credibility of AI-driven decisions.

Building Ai-Ready Workforce

Technology alone cannot drive transformation. A successful Agentic AI strategy requires a workforce that is skilled, adaptable, and confident in leveraging these tools. 1. Familiarize Employees: With Foundational Technologies Begin with RPA and Generative AI. As employees integrate these tools into daily workflows, they will develop the familiarity needed to adopt Agentic AI.

2. Create Awareness Programs: Host workshops, webinars, and innovation days to demonstrate the potential of Agentic AI. Encourage employees to explore how these tools can improve efficiency and simplify tasks.

3. Start with MVPs: Pilot projects in areas like HR or finance allow employees to see immediate benefits. Quick wins build enthusiasm and serve as a blueprint for enterprise-wide adoption.

Measuring Impact: Key Metrics

To ensure long-term success, organizations must establish clear metrics to measure the impact of Agentic AI initiatives. These metrics should align with the organization’s balanced scorecard, reflecting both financial and operational outcomes.

1. Financial Returns: Cost reduction, productivity gains, and revenue growth are primary indicators of success. Regularly track these metrics to assess the value generated by AI investments.

2. Customer Satisfaction: For customer-centric industries, metrics like Net Promoter Score (NPS) and customer sentiment provide valuable insights into the effectiveness of AI-driven processes.

3. Adoption Metrics: Monitor leading indicators such as the number of employees using AI tools or the volume of AI-powered transactions. These metrics offer early signals of success and identify areas requiring improvement.

4. Continuous Improvement: If results fall short, refine models, retrain employees, and adjust governance frameworks. A continuous feedback loop ensures agility and resilience.

Conclusion

A gentic AI represents a profound shift in how businesses operate, blending the efficiency of RPA with the adaptability of Generative AI. By aligning initiatives with strategic goals, addressing governance concerns, and fostering an AI-ready workforce, organizations can unlock unparalleled value.

The journey to Agentic AI is not just about technology—it’s about creating a culture of innovation and resilience. Leaders who embrace this paradigm will be better equipped to navigate the complexities of modern business, driving sustainable growth and operational excellence in an increasingly competitive world.