The agentic AI leap toward autonomous and personalized service delivery

IT service request management, as a practice, has been on a path of continuous evolution. ITSM practitioners started out with legacy frameworks, defined by human-driven handoffs. The subsequent arrival of automation provided the necessary speed and structure, yet the experience remained limited by rigid rules.

Then came the first wave of AI in ITSM. Virtual chatbots began handling routine requests around the clock, like password resets. Machine learning models could detect patterns, classify, and route tickets to the right technician. Sentiment analysis gave service teams a pulse on user frustration before it escalated. It felt like a leap forward.

But even with AI in the mix, service request management followed a familiar pattern, where AI assisted automation stayed bound by rules with humans driving the better part of the service delivery process. Systems knew how to fulfil a request, not why it needed to be done.

Now, the rise of agentic AI is changing that. It introduces a more autonomous layer of intelligence with AI agents that can interpret intent, reason across data, and execute multi-step fulfilments without waiting for human input, all while continuously learning from context and outcomes.

In essence, service request management has evolved from manual coordination, to structured execution driven by automation and early AI. Service request management is now beginning to adopt more autonomous, context-aware capabilities through agentic AI.

To show how this shift transforms everyday service delivery, we’ll follow a simple use case: John, an employee, is planning a business trip. The underlying requirements are familiar—approvals, travel bookings, expense setup, and secure remote access—but the experience changes dramatically across three different service-delivery models:

• Human-driven service delivery
• Automation and early AI
• Agentic AI-powered orchestration

By cycling through these iterations of technology, we’ll see how service request management evolves from manual coordination, to intelligent automation, to truly autonomous, personalized service delivery.

The fragility of human-driven handoffs

Many organizations, despite the evolution of automation and AI, still rely on service delivery models where most coordination is manual. Often, approvals are buried in emails, updates are fragmented across chats, and teams operate on different information, with no shared source of truth.

In John's case, he first emails his manager, Kumar, for approval. Then, the Travel desk books his itinerary, Finance clears the credit card, but IT, not yet aware of John’s travel plans, is left out of the loop. However, when John downloads free VPN software to access corporate resources when traveling, he unknowingly violates security policies, and opens the door to potential risks that alerts the IT security team.

This scenario perfectly captures the fragility of human-driven workflows. When departments operate in silos and depend solely on manual coordination, even a simple service request can snowball into a security incident. With no automation to flag dependencies or enforce guardrails, every handoff introduces risk, delay, and inconsistency.

The age of automation and early AI

Automation brought much-needed structure and speed to service request management. Standardized workflows replaced ad hoc coordination, ensuring requests were routed, approved, and fulfilled with minimal friction. Then, a layer of AI entered the picture, making these processes smarter and more responsive.

When John submits his travel request, the email is automatically converted into a ticket while AI classifies and routes it for approval. His manager, Kumar, reviews and approves it instantly. From there, predefined enterprise service management (ESM) workflows take over, coordinating the flow of tasks across multiple departments. The Travel desk books the itinerary, Finance processes the corporate card and allowances, and IT readies the approved VPN software and provides John with tailored instructions for secure installation. Without this unified workflow, each task would unfold in isolation, lacking the cross-functional collaboration and context needed for a smooth experience.

Throughout this process, AI quietly improves efficiency:

• A virtual support agent assists John 24/7, guiding him through VPN setup or travel-related queries.
• Machine learning models handle categorization and task assignment.
• LLM-powered recommendations craft contextual updates and reminders.

This marks a clear improvement from what happened in the legacy system, where disconnected workflows and manual handoffs prevented IT from guiding employees effectively. Now, with automation and AI guiding the workflow, John receives the approved VPN software along with step-by-step instructions, leaving little room for missteps.

While this service delivery model is reasonably efficient, it remains reactive, with the final mile of execution still dependent on John. He must open the instructions, read through them, and install the approved VPN himself.

This dependency points to deeper structural issues in today’s service delivery landscape, issues that automation and AI can help solve, though not eliminate completely. The following are some of the persisting issues:

• Data silos still make end-to-end visibility and automation difficult.
• Workflows assume a one-size-fits-all approach to service fulfillment.
• Rule-based automations continue to dominate, limiting adaptability.
• Many touchpoints still depend on humans to interpret, approve, or act.

The agentic AI leap

As automation reaches its ceiling, the next frontier in service management lies in intelligence that can think for itself. Agentic AI marks that turning point, enabling systems to understand context, plan dynamically, and act autonomously across business functions. Human reliance drops dramatically, and the goal is no longer faster service delivery, but smarter, self-directed orchestration.

In this model, service delivery is powered by a network of goal-driven AI agents that possess three defining capabilities:

• Contextual awareness to interpret user intent.
• Dynamic reasoning to plan actions and resolve dependencies.
• Multi-agent coordination to execute complex workflows across systems.

Let’s revisit John’s travel request, this time through the lens of an Agentic AI-powered service request management.

• Natural language case extraction and intent detection:
  When John pings his manager on Microsoft Teams seeking approval to attend a the conference, an AI agent with access to the enterprise service management platform detects the intent, extracts details such as destination, duration, and purpose, and automatically creates a travel request. John's manager, receives an approval prompt within the same text conversation. Upon approval, multiple AI agents spring into action and execute coordinated downstream tasks.
• Multi-agent orchestration:
  A connected enterprise ecosystem enables this orchestration, as data is ingested in real time from the organization’s HRMS, ITSM, UEM, and ESM platforms. Leveraging this shared context, a Travel desk AI agent validates John’s eligibility, checks budget limits from Finance data, and confirms compliance with the corporate travel policy. It then generates an approved travel itinerary and attaches supporting documents to the service request. Simultaneously, a multi-agent workflow is automatically initiated in IT. One AI agent verifies John’s device encryption status, another checks alignment with the secure remote work policy, and a third adjusts his access privileges according to the principle of least privileges.
• Adaptive, hyper-personalized experience
  A custom self-service recommendation model trained on historical data dynamically updates John’s adaptive self-service interface to surface only the most relevant content—the travel checklist, VPN troubleshooting guide, corporate credit-card policy, and incident-reporting process.
• Continual governance: During travel, another AI agent continuously monitors endpoint health and behavior through the UEM dashboard, correlating them with risk data to update John’s risk score. If deviations or threats are detected, the system autonomously enforces conditional access policies, securing data without disrupting John’s work. When John returns, an AI-driven post-travel workflow initiates a device health check, verifies encryption and configuration compliance, and restores default access privileges. The process concludes with synchronized updates across systems, like the HRMS logs the travel completion, Finance reconciles expenses, and IT marks the request as resolved.

The evolution from automation to Agentic AI marks a shift from rigid execution to intelligent, outcome-driven service delivery. Here’s how the two differ:

Final thoughts

With Agentic AI, service request management evolves into a system of autonomous decisions, where AI agents can think, learn, and act by understanding the right context, and orchestrating cross-functional tasks. But even as autonomy grows, humans will remain in the loop to guide policies, validate exceptions, and steer high-impact decisions.

Organizations that embrace Agentic AI will move beyond incremental efficiency gains. They will build a service ecosystem that is anticipatory, resilient, and personalized at scale, with AI handling the operational heavy lifting and humans being involved in decision making. This way, employees will receive support that feels seamless, while IT teams are freed to focus on higher-value work.

This balance redefines what “great” looks like in service request management and sets a new benchmark for secure, intelligent, and human-aligned employee experience.