The main difference between the two processes lies in the nature of the event being managed. Incidents are unplanned interruptions that affect the quality of a service, while service requests are formal and expected requests for a standard activity or service.

For this reason, incidents involve a higher level of risk and require timely management, whereas service requests involve a lower level of risk and do not require immediate escalation.

An omnichannel ticketing platform centralizes all communications from different business touchpoints by connecting them with CRM systems and other business applications.

Using cloud technologies, analytics tools and artificial intelligence, the platform manages the entire ticket lifecycle through a centralized workflow, providing operators with a single interface to handle every request.

A well-structured knowledge base enables self-service, allowing users to find answers without contacting support.

Advanced systems suggest relevant content during ticket creation, enabling ticket deflection. This reduces repetitive requests and allows IT agents to focus on more complex issues. Up-to-date documentation also speeds up the resolution of existing support tickets.

Consequently, an integrated knowledge base delivers tangible benefits across the entire support process, including:

• Significant reduction in ticket volume thanks to users being able to resolve issues independently, finding immediate answers without opening new requests.
• Greater efficiency of the support team, which can focus on truly complex issues, reducing operational workload and improving the quality of interventions.
• Faster resolution times (MTTR) thanks to always up-to-date and context-aware information, enabling tickets to be resolved more quickly and with fewer steps.

AI agents act as a first line of support in customer service, handling Level 1 requests using knowledge bases, documentation, and ticket history. The goal is to reduce the number of tickets reaching human agents (ticket deflection).

Practical example: An AI agent answers onboarding-related questions by leveraging official documentation, citing internal sources, and guiding users without human intervention.

The main difference between a Change Request and a Service Request lies in the impact, risk level, and approval process. Service Requests are used to handle standard operational needs and recurring user activities without the need to activate particularly complex procedures.

Change Requests, on the other hand, involve modifications that may affect IT infrastructure or business processes, such as system upgrades. For this reason, they require impact assessments, formal approvals by the Change Advisory Board (CAB), and updates to the related documentation.

• Business impact: It consists of analyzing the operational and economic consequences of the change, measurable through indicators such as business SLAs associated with impacted items, the number of users affected, and the relevance of privacy-related regulatory requirements.
• Probability of error or malfunction: This involves estimating, on scales ranging from “unlikely” to “frequent,” the chance that the implementation may fail or generate negative effects on the system, also based on historical data or expert experience.
• Technical complexity of the change: It evaluates the technological risks associated with the Change, considering factors such as the number of Configuration Items (CI) involved, possible system integrations, and the risk of downtime.
• Availability of a rollback plan: This involves verifying the existence and effectiveness of a recovery plan, particularly analyzing the time required and the technical complexity needed to restore the system to its previous state in case of failure.
• Maintenance window and resources involved: It concerns the operational planning of the Change, including the release plan, required lead time, expected downtime, and the level of involvement of technical teams.