Redefining IT Support with AI: 10 use cases that scale
Published February 17, 2026
- Data & AI
Key takeaways
- AI is reshaping IT support by reducing ticket volumes, accelerating resolution times, and improving the user experience.
- Early benefits come from simple, fast-to-deploy use cases such as self-service and intelligent triage.
- More advanced applications enable a shift from reactive support to a preventive, proactive model.
- Sustainable value depends on strong foundations, both in terms of data quality and governance.
In a context where operational efficiency and user experience have become key differentiators, AI is opening up new performance levers for IT and Service Desk teams. From intelligent self-service and proactive incident detection to agent coaching and knowledge automation, these concrete use cases illustrate how AI is becoming a true transformation accelerator. Below is an overview of the 10 key use cases that are already redefining how organizations support, assist, and interact with their users.
1. Reducing ticket volumes (shift left and prevention)
2. Increasing agent efficiency (productivity and quality)
3. Enhancing the user experience (UX)
1. Reducing ticket volumes (shift left and prevention)
The evolution of IT support follows a clear shift-left trajectory, driven by the rise of self-service, richer knowledge bases, and the introduction of intelligent automation at Level 1. This trend has accelerated with generative AI and early automation capabilities that can resolve simple requests without human intervention. The result is a significant reduction in ticket volumes, notably through automated workflows.
A chatbot or voicebot understands the user’s request through natural interaction and resolves or guides frequent issues that do not require third-level intervention.
Impact : Lower L1 ticket volumes, shorter wait times, higher user satisfaction, and reduced low-value workload for Service Desk agents.
AI automatically groups similar tickets, detects abnormal spikes in key metrics, and triggers alerts for potential major incidents.
Impact
Faster response times, fewer duplicate tickets, reduced service disruption, and optimized operational costs.
AI converts recurring resolutions into draft knowledge base articles, suggests titles, troubleshooting and resolution steps, prerequisites, screenshots, and tags, and detects duplicates or obsolete content.
Impact
Automated knowledge management, faster self-service adoption, fewer recurring tickets, and improved onboarding for new agents.
AI analyzes incident, problem, and change tickets alongside monitoring data to identify recurring root causes, correlations (for example patch versus incident), and relevant preventive actions such as updates, fixes, or targeted communications.
Impact
Fewer incidents and recurrences, improved service stability and availability through targeted preventive actions, and reduced operational workload for IT teams.
2. Increasing agent efficiency (productivity and quality)
Automating repetitive tasks allows agents to focus on cases that require expertise and analysis. Combined with knowledge generation, multilingual support, and predictive assistance, this redistribution of effort reduces errors, accelerates resolution, and professionalizes the user experience.
AI suggests categories, sub-categories, priorities, and assignment groups based on detected signals such as major incidents, VIP users, or security-related requests.
Impact
Fewer routing errors, more relevant incident handling, and faster time-to-assign.
AI supports agents during incident resolution by leveraging the knowledge base and user context, suggesting relevant questions to ask and potential resolution paths.
Impact
Time savings for agents, fewer ticket reopenings, and more consistent responses.
AI compiles the full interaction history (chat, emails, actions) into a clear, actionable summary covering symptoms, environment, tests performed, results, and next steps.
Impact
Faster handovers between support teams, reduced information loss, and improved resolution times.
AI evaluates the quality of processed tickets (completeness, clarity, process compliance) and provides improvement suggestions directly to agents.
Impact
Greater standardization, fewer reopenings, and improved perceived service quality.
3. Enhancing the user experience (UX)
Real-time sentiment analysis helps support teams detect sensitive situations, adapt tone, prioritize requests, and accelerate resolution—an essential lever for de-escalating interactions and strengthening satisfaction. In parallel, real-time translation enables truly multilingual support: users communicate in their own language without friction, while agents receive instant, accurate translations.
AI captures and understands written and spoken interactions (chats, calls, meetings) to deliver live transcription, instant translation, and automated summaries. It enables multimodal interactions such as voice-to-text, text-to-voice, and voice-to-voice, directly integrated into collaboration tools.
Impact
Smoother interactions and more effective multilingual collaboration.
AI analyzes the tone of exchanges (chat, email) and behavioral signals such as follow-ups or wording to suggest personalized responses and resolutions.
Impact
Anticipates frustration, prevents escalations, and delivers sustained improvements to the user experience.
Conclusion
Integrating AI into IT support activities is a powerful lever for efficiency, service quality, and user experience improvement. To fully realize its potential, organizations must rely on solid technical and organizational foundations.
- A clear governance model is essential. It defines who decides, who validates, and how AI use cases are controlled. Without it, initiatives fragment and risks increase.
- A robust risk management framework is equally critical. It helps prevent misuse, ensures model transparency, and guarantees compliance with security, data protection, and ethical requirements—conditions that are fundamental to building trust.
- Reliable and accessible data is another cornerstone. AI performance depends on structured, high-quality information that is properly exposed: well-maintained knowledge bases, sufficiently documented tickets with clear descriptions, symptoms, and resolution actions. Without this, outcomes become inconsistent or unusable.
- An open and scalable technical architecture enables smooth integration of new tools, interoperability with existing systems, and industrialization of use cases—allowing organizations to move from local pilots to large-scale deployments.
Once these foundations are in place, organizations can activate accelerators such as prioritizing quick-ROI use cases, industrializing delivery through factory-style models, and supporting teams to drive adoption. By combining strong governance with rapid iteration, they can secure their AI initiatives while maximizing impact on interaction fluidity, ticket reduction, and overall support quality.
