📘 SOP: AI-Driven ITSM Operations (2025 Edition)

Version: 1.0
Owner: IT Service Management / IT Project Manager
Approver: IT Director / CIO
Tools: ServiceNow / Jira / Freshservice + ChatGPT + Copilot + Azure OpenAI

1️⃣ Purpose

This SOP defines the procedures for operating an AI-enabled ITSM environment, including:

AI-based ticket classification
Auto-triage
AI-generated knowledge articles
Auto-resolution of Level-1 issues
SLA prediction and risk scoring
Ticket summarization and reporting

The goal is to ensure standardized, predictable, and efficient operations using AI and GenAI.

2️⃣ Scope

This SOP applies to:

Incident Management
Service Request Management
Problem Management
Knowledge Management
Alert Monitoring
L1/L2 Operations
Reporting & Dashboards

Not in scope:

DevOps CI/CD pipelines
Non-IT business processes

3️⃣ Roles & Responsibilities

Service Desk (L1)

Monitor AI-generated ticket queues
Validate AI classification
Approve/Reject AI auto-resolution suggestions
Provide training feedback to AI model

Technical Support (L2/L3)

Handle escalated tickets
Validate AI-generated KB articles
Update problem database for AI learning

AI Engineer / Automation Expert

Maintain AI models
Improve accuracy
Monitor automated rule performance
Manage GenAI prompts and configurations

ITSM Process Owner

Ensure ITIL alignment
Review SLAs and KPIs
Approve automation workflows

IT Security Team

Ensure compliance
Approve data masking policies
Monitor AI logs and access

4️⃣ Process Workflow (Step-by-Step)

4.1 Incident Logging & AI Classification

Step 1: Ticket Creation

Ticket may originate from:

Email
Portal
Chatbot
Monitoring alerts
API integrations

Step 2: AI Classification

AI automatically predicts:

Category
Sub-category
Priority
Assignment group
SLA timer

Step 3: L1 Validation

L1 agent validates the AI classification:

*Action*	*Condition*
Approve	Classification ≥ 85% confidence
Modify	Category mismatch / error
Reject	Confidence < 60%

Audit Note: All overrides are logged.

4.2 AI-based Auto-Assignment

AI assigns the ticket to:

Best-fit engineer
Based on last 90-day performance
Available work capacity
Skill matrix

L1 only monitors.

4.3 AI-L1 Auto-Resolution

AI suggests resolution steps for:

Password reset
VPN issues
Outlook problems
Printer & network issues
Access-related FAQs

L1 Responsibility:

Execute suggested steps
Confirm issue resolved
If unresolved → escalate to L2

4.4 Ticket Summarization (For L2/L3)

AI auto-generates:

Ticket history
Impact summary
Resolution attempts
Recommended next steps

This reduces L2 analysis time by 40–50%.

4.5 Knowledge Article Generation

AI automatically drafts KB using:

Ticket description
Resolutions
Screenshots / logs

L2 Responsibility:

Approve
Edit
Publish

Knowledge Manager Responsibility:

Review monthly
Archive outdated articles

4.6 SLA Prediction & Escalation

AI predicts:

Tickets likely to breach
Tickets needing escalation
Tickets with incorrect assignment

L1/L2 must review within 30 minutes.

4.7 Problem Management Integration

AI detects patterns:

Repeated incidents
High-frequency categories
Known errors

L3 creates a Problem ticket if:

≥ 5 similar tickets occur in 48 hours
Any major incident is logged

5️⃣ Alert Monitoring SOP (AI-Ops)

Step 1: Alert Ingestion

Monitoring tools → AI → Noise reduction

Step 2: Correlation

AI correlates alerts based on:

Host
Timestamp
Logs
Service impact

Step 3: Action

AI triggers:

Ticket creation
Auto-remediation script
Notification

Step 4: L2 Approval

L2 must approve high-risk actions:

Restart service
Resource allocation
Cleanup scripts

6️⃣ Escalation Matrix

*Condition*	*Owner*	*SLA*
Ticket unresolved 30 mins after AI suggestion	L1 → L2	30 mins
Classification mismatch	AI → L1 → AI Team	Daily
KB rejected >2 times	L2 → KB Manager	24 hrs
SLA breach predicted	L1 → L2 → Manager	Immediate

7️⃣ Compliance & Security Requirements

Mandatory:

PII masking in AI prompts
Role-based access to AI tools
All AI-generated actions logged
GDPR / RBI / NIST alignment
No raw customer data fed to LLMs

Monthly Audit:

Model accuracy report
Logic drift detection
Error case samples
Review override logs

8️⃣ KPIs to Track

*KPI*	*Target*
Auto-classification accuracy	≥ 85%
Manual effort reduction	≥ 50%
L1 auto-resolution	≥ 35%
SLA compliance	≥ 95%
Alert noise reduction	≥ 40%
KB automation coverage	≥ 80%

9️⃣ SOP Review & Version Control

SOP must be reviewed every 6 months
Changes approved by Process Owner
Versioning controlled by PMO

✍️ Author

Raju Ambhore - IT Project Manager & Blogger |Advocating Sustainable Technology & Ethical Digital Practice.