What is agentic AI and why chatbots fall short
Most IT teams already have some form of AI on their service desk — a chatbot answering common questions or automatic ticket classification by keyword. These tools save time, but they hit a ceiling: when something falls outside the trained scenario, they hand it to a human.
Agentic AI works differently. Instead of answering questions, it autonomously executes entire workflows — from diagnostics through remediation to ticket closure. An agent analyses context (logs, CMDB, incident history), decides on an approach, executes it via APIs, and verifies the result. If something doesn't add up, it escalates — not because it can't understand the question, but because it assessed the risk.
What agentic AI does in practice
In an ITSM context, agentic AI is most commonly deployed for:
- Automated triage and routing — the agent classifies incidents by priority, SLA, and user role, not just keywords
- Diagnostics and remediation — it compares incidents against historical data, suggests fixes, and for standard issues (password resets, access provisioning, VPN configuration) executes them end-to-end
- CMDB updates — during fulfilment, it automatically updates configuration items
- Knowledge article generation — after resolving an incident, it drafts an article for the knowledge base
- Proactive detection — it identifies patterns in monitoring data and creates incidents before users report them
Real-world numbers
The ITSM.tools 2025 AI survey found that 84% of respondents viewed AI in ITSM positively, with 61% confirming that corporate AI tools actively helped their work. Trust in AI grew for 59% of those surveyed. European organisations lagged behind — North American firms achieved three times greater efficiency gains and were over ten times more likely to have deployed self-healing capabilities.
In August 2025, Gartner predicted that by end of 2026, 40% of enterprise applications would feature task-specific AI agents — up from under 5% in 2025. Early enterprise deployments are already showing a 60% reduction in ticket volume, according to ITSM.tools.
Risks to take seriously
Not every project succeeds. Gartner also predicted (June 2025) that over 40% of agentic AI projects would be cancelled by the end of 2027. The reasons are predictable: unclear scope, poor CMDB data quality, missing governance, and attempting to automate processes that are themselves broken.
Agentic AI won't fix a bad process — it will automate it. Before deployment, you need clean data, defined escalation rules, and a clear governance model. Organisations that skip this step end up with an expensive system that makes mistakes faster than a human.
A sensible approach to agentic AI
Start where risk is lowest and volume highest — password resets, standard requests, L1 ticket triage. Measure MTTR and escalation volume before and after. Expand scope only where the agent demonstrably reduced workload without increasing error rates. Above all, treat agentic AI as a tool that needs the same quality process foundation as any other ITSM tool.