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ServiceNow is the only leader in the Gartner® Magic Quadrant™ for AI Applications in ITSM 2025

23. October 2025· 2 Min. read Copy link Share on LinkedIn

AI for IT excellence

ServiceNow as the sole leader: significance of the award

Why ServiceNow is ahead in 2025: AI platform, data, and agentic AI

  • Native AI instead of add-ons: ServiceNow pursues a consistent AI-first strategy. AI is deeply integrated into the platform, which reduces complexity, accelerates adoption, and increases the impact of automated processes.
  • Multi-LLM approach for maximum flexibility: With Now LLM, in combination with models such as GPT, Gemini, or Claude, companies gain freedom of choice instead of dependency. AI can thus be tailored precisely to requirements – without vendor lock-in.
  • Agentic AI as a driver for autonomous IT: 2025 will be dominated by agentic AI. AI agents will not only support IT teams, but also act independently, prioritize, recommend, or solve tasks automatically—all under clear governance rules.
  • Workflow Data Fabric: Thanks to zero-copy connectors, ServiceNow can use data from any system in real time via the Workflow Data Fabric. The result: clean data, consistent workflows, better decisions.

Benefits for IT managers and companies

  • Reduced operational load through automated routine tasks and AI-supported workflows.
  • Higher service quality through faster, consistent, and traceable solutions.
  • Informed decisions in real time instead of reactive analyses.
  • Costs and risks under control through more stable and scalable service processes.
  • Modern service experience for employees thanks to fast, accurate support.

Outlook: The future of ITSM is autonomous and AI-powered

Further inspiration: E-book on AI in ITSM

E-Book: “Modernize IT services and operations with AI” — Why AI is Essential for ITSM
E-Book ServiceNow: “Modernize IT services and operations with AI” — Why AI is essential for ITSM

FROX: ServiceNow partner in Switzerland

Frequently asked questions:

What are AI agents?
AI agents are AI-powered software components that can do more than analyze or answer tasks; they can execute them in a goal-oriented way. They understand requests, evaluate information, make rule-based or AI-supported decisions, and then trigger specific actions in processes, workflows, or systems. Unlike traditional chatbots, AI agents do not simply respond to individual questions. They can plan multiple steps, consider context, use data from different sources, and independently handle tasks within defined guardrails.
What is the difference between GenAI and AI agents?
Generative AI creates content such as text, summaries, answers, code, or recommendations. AI agents go one step further: They use generative AI, decision logic, data, and integrations to actively execute tasks. Example: GenAI can summarize a support ticket. An AI agent can also check which category fits, analyze similar cases, prepare solution recommendations, start a workflow, or route the ticket to the right team.
What is the difference between a chatbot and an AI agent?
A chatbot typically answers questions or conducts simple dialogs. An AI agent can also plan tasks, evaluate information, support decisions, and trigger actions in connected systems or workflows. While a chatbot is often focused on communication, an AI agent combines communication, contextual understanding, automation, and process execution.
What benefits do AI agents offer companies?
AI agents can reduce repetitive tasks, shorten processing times, and relieve employees in their day-to-day work. They help teams find information faster, execute processes more consistently, and handle service requests more efficiently. AI agents are especially valuable wherever many similar tasks, large volumes of data, or complex process chains come together. Examples include IT Service Management, Customer Service, HR, Finance, Field Service, Operations, and Enterprise Service Management.
Where can AI agents be used?
AI agents are suitable for many areas of the enterprise where requests, decisions, data, and workflows are interconnected. Typical use cases include IT service, customer service, HR processes, knowledge management, process automation, incident management, request fulfillment, change management, AIOps, and case management. For example, they can classify tickets, suggest knowledge articles, process service requests, prepare approvals, analyze incidents, or guide employees through complex processes.
Do AI agents replace employees?
AI agents are primarily intended to support and relieve employees. They take over repetitive, time-consuming, or highly standardized tasks so that specialists have more time for complex decisions, customer interaction, and value-adding activities. In many scenarios, AI agents therefore do not work fully autonomously, but rather act as assistance systems with clear responsibilities, escalation rules, and human oversight.
How do companies get started with AI agents?
The best way to get started is with clearly defined use cases that deliver measurable value while remaining easy to control. Suitable examples include frequent support requests, ticket classification, knowledge management, summaries, simple approval processes, or standardized service requests. It is important not to focus on technology alone, but to consider processes, data, roles, responsibilities, and governance from the very beginning.

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Manuel Röttele

Senior Account Executive and specialist for service management and the ServiceNow plattform

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