AI TRAININGS · ISO/IEC 20000-1:2018 · ISO/IEC 42001:2023
AI in IT Service Management — ISO/IEC 20000-1
Introduce AI into service design, operation and improvement with measurable service value, controlled change, reliable knowledge and accountable human oversight.
Who should attend
AI in IT Service Management
- IT service management and SMS owners
- Service desk, incident and problem managers
- Change, release and configuration managers
- AIOps, automation, data and AI product teams
Learning outcomes
- 01
Select AI use cases against service value and risk
- 02
Define service requirements, SLAs and human escalation
- 03
Control AI-enabled incidents, requests, problems and changes
- 04
Protect knowledge, configuration and operational data
- 05
Measure automation quality, drift and customer impact
- 06
Build an auditable improvement backlog
Detailed training outline
8 modules, each tied to a working output.
Introduce AI into service design, operation and improvement with measurable service value, controlled change, reliable knowledge and accountable human oversight.
- 01
SMS baseline and AI operating model
Connect ISO/IEC 20000-1 service value and lifecycle requirements to AI-assisted work without implying that automation owns the process.
- 02
Use-case selection and service requirements
Evaluate virtual agents, ticket classification, summarization, correlation, prediction and knowledge generation against value, failure impact and customer expectations.
- 03
Service design, catalog and SLA controls
Define service boundaries, AI features, support hours, data dependencies, escalation targets, excluded decisions and measurable quality thresholds.
- 04
Knowledge, configuration and data integrity
Control authoritative sources, retrieval scope, article approval, configuration relationships, retention, provenance and correction of generated content.
- 05
Incident, request and problem workflows
Set confidence thresholds, mandatory review, safe automation limits, major-incident exclusion, feedback capture and problem-analysis evidence.
- 06
Model, prompt and service change control
Treat model, provider, retrieval, prompt and policy changes as controlled service changes with evaluation, approval, rollback and post-implementation review.
- 07
Supplier and platform governance
Assess provider terms, service levels, data use, sub-processors, regional availability, portability, exit, concentration and incident communication.
- 08
Measurement and continual improvement
Measure containment, resolution accuracy, reopen rate, unsafe-action prevention, escalation quality, customer effort, cost and performance drift.
Participant working pack
Participant working pack
Training supports competence and implementation planning. It is not certification, accredited auditor training, legal advice or a conformity assessment.
- AI service use-case scorecard
- RACI and human-escalation matrix
- AI service-catalog and SLA annex
- Knowledge provenance control sheet
- Model-change record template
- Supplier due-diligence checklist
- ITSM AI measurement dashboard
Authoritative references
Authoritative references
Working knowledge of the relevant management system is helpful; licensed standards remain the controlling source.
AI TRAININGS
Request dates and delivery format
Training supports competence and implementation planning. It is not certification, accredited auditor training, legal advice or a conformity assessment.