Introduction
The recent era of chatbots and reactive AI is already evolving into agentic AI, systems that act, plan, and execute autonomously. Bain & Company (2025) describe agentic AI as a shift in technology, reshaping how organizations coordinate work, reason about tasks, and enable automation.
What is Agentic AI?
Agentic AI refers to systems that can set or receive high-level goals, break them into sub-tasks, plan and act, and adaptively learn from their environment (UiPath, 2025). In contrast, generative AI focuses on content creation, while rule-based AI automates fixed tasks (Aisera, 2025). For example, an AI agent might monitor a system ticket queue, diagnose root causes, trigger remediation workflows automatically, and update relevant systems with minimal human intervention (Aisera, 2025).
Real-World Use Cases
Agentic AI is applied in multiple aspects:
- IT/Service Desk: AI agents proactively monitor infrastructure, detect issues, and auto-resolve tickets (Aisera, 2025).
- Supply Chain & Logistics: Agents optimize inventory, predict disruptions, and reroute shipments without manual orchestration (Supply Chain Digital, 2025).
- Customer Service / Back-Office: Multi-step workflows, such as verifying transactions and updating CRM systems, can be handled autonomously (UiPath, 2025).
- Strategic Operations: Agentic AI enables “cognitive enterprises” that continuously sense, think, act, and learn (World Economic Forum, 2025).
Challenges and Risks
- Governance and Oversight: Autonomous agents require transparency, accountability, and auditing (Aisera, 2025).
- Data and Infrastructure Readiness: High-quality, integrated data is necessary (McKinsey & Company, 2025).
- Ethics and Bias: Autonomous decision-making raises ethical concerns (Aisera, 2025).
- Skill and Culture Shift: Teams must adapt to designing agent-oriented workflows rather than implementing static tools (CIO, 2025).
Preparing Your IT Team
Organizations can adopt a phased approach:
- Begin with pilot projects in domains with clear value.
- Build robust data and integration layers.
- Define governance and monitoring frameworks.
- Upskill teams in AI workflow design.
- Focus on orchestration architecture for multiple agents and systems (Moveworks, 2025).
Conclusion
Agentic AI is the next frontier of enterprise automation. Realizing its potential requires rethinking workflows, building strong foundations, and embedding governance to ensure safe, reliable, and
References
Aisera. (2025). Agentic AI blog. https://aisera.com/blog/agentic-ai/
Bain & Company. (2025). Building the foundation for agentic AI. https://www.bain.com/insights/building-the-foundation-for-agentic-ai-technology-report-2025
CIO. (2025). Realizing the full potential of agentic AI in the enterprise. https://www.cio.com/article/3989217/beyond-automation-realizing-the-full-potential-of-agentic-ai-in-the-enterprise.html
McKinsey & Company. (2025). Seizing the agentic AI advantage. https://www.mckinsey.com/capabilities/quantumblack/our-insights/seizing-the-agentic-ai-advantage
Moveworks. (2025). Agentic AI: The next evolution of enterprise AI. https://www.moveworks.com/us/en/resources/blog/agentic-ai-the-next-evolution-of-enterprise-ai
Supply Chain Digital. (2025). Top companies using agentic AI. https://supplychaindigital.com/technology/top-10-companies-agentic-ai
UiPath. (2025). Agentic AI overview. https://www.uipath.com/ai/agentic-ai
World Economic Forum. (2025). Cognitive enterprise: Agentic business revolution. https://www.weforum.org/stories/2025/06/cognitive-enterprise-agentic-business-revolution/