Agentic AI is reshaping enterprise operations by introducing systems of autonomous AI agents that plan, act, and coordinate across business functions. These systems can execute workflows, make decisions, and drive outcomes at scale. Yet, realizing their potential requires more than deploying new tools—it demands a reorganization of how companies design work, define missions, and govern intelligence.
From Automation to Agency
Traditional automation technologies like robotic process automation (RPA) focus on repetitive, rule-based tasks. They excel at speed and consistency but fail when conditions change or exceptions occur. Agentic AI, in contrast, represents a shift from task execution to outcome achievement.
Built on large language model (LLM) technology, agentic AI systems operate more like digital colleagues than simple scripts. Specialized agents perform discrete functions—such as coding, document generation, or query handling—while coordinating agents orchestrate their collaboration toward defined business goals. Guided by human supervision, they operate across fragmented systems and data sources to deliver seamless, adaptive results.
How Agentic AI Works in Practice
A compelling example comes from Hitachi Digital, which deployed an agentic AI system to streamline HR operations. Each year, its HR team managed over 90,000 employee inquiries across multiple systems. Instead of migrating data into a single centralized system, Hitachi developed a multi-agent workflow.
“Skye,” the company’s HR AI agent, routes employee requests to specialized agents such as policy lookup, leave management, or IT help desk agents. These agents access relevant data, process tasks autonomously, and escalate exceptions for human validation. The outcome is a unified, responsive HR experience that cuts across silos and reduces manual workload dramatically.
Similarly, NTT DATA deployed agentic AI to handle requests for proposals (RFPs). Previously, preparing a complex RFP could take 20 people several weeks. With AI agents drafting proposals and compiling data, the process now takes minutes—improving efficiency more than threefold.
Three Design Imperatives for Success
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Design Around Outcomes and Assign Mission Owners
Most enterprises are structured around internal functions rather than customer journeys. Agentic AI demands the opposite. Companies should appoint mission owners who define key outcomes—such as “productive hires” or “faster onboarding”—and orchestrate both human and AI efforts toward them. The mission owner becomes accountable for cross-functional success, measuring results with outcome-based KPIs. -
Unlock Data Silos and Clarify Business Logic
Agentic AI does not require perfect, centralized data. Instead, it thrives on interoperable systems connected through APIs, metadata, and well-defined business logic. By codifying how work is done—decision flows, approval paths, and customer journeys—AI agents can navigate distributed systems autonomously.
European logistics firm Bigblue used this approach to resolve complex shipping issues. Its agentic AI agents handle 70% of customer tickets autonomously, integrating across 50 carriers, 500 brands, and multiple warehouses—reducing cost while improving satisfaction. -
Develop Leadership and Governance for Intelligent Systems
Agentic AI demands a new kind of leadership—one that combines technical literacy, change management, and trust in autonomous systems. Leaders must demystify AI for employees, create transparent feedback loops, and ensure governance is embedded from the outset.
Governance guardrails include audit logs, escalation protocols, approval workflows, and real-time monitoring. Organizations must define how AI agents handle exceptions, encode ethical principles, and maintain accountability.
Getting Started
The best place to begin is with a high-friction process a journey where outcomes matter but inefficiencies persist due to manual handoffs. Reframe it as a mission. Assign a mission owner with authority to align teams, map workflows, define success metrics, and deploy AI agents incrementally.
The data does not need to be perfect; it must simply be accessible and well contextualized. With structured governance and clear objectives, agentic AI can quickly reveal value through measurable improvements in cycle time, accuracy, and customer experience.
The Operating Model of the Future
Agentic AI represents a structural transformation, not a passing trend. By linking human expertise with autonomous reasoning systems, organizations can shift from reactive process management to proactive outcome orchestration. The companies that embrace this change will not only improve efficiency—they will define the next-generation operating model for intelligent enterprises.
Adapted by the IIGAI Editorial Team
Based on an article by Linda Mantia, Surojit Chatterjee, and Vivian S. Lee, Harvard Business Review.