Program Overview
IT teams today sit at the center of enterprise transformation, yet their own workflows often remain manual, overloaded, and reactive. This program equips IT professionals with hands-on skills to leverage AI for faster troubleshooting, system monitoring, incident management, documentation, automation, and stakeholder communication. From using GenAI for scripting and coding assistance to deploying AI-driven analytics for network, application, and security visibility, participants learn how to transform day-to-day IT operations with practical, safe, and reliable AI adoption. The course also introduces governance, data security, and responsible use practices to ensure IT teams can lead by example before scaling AI capabilities across other business functions.
Features
- Apply AI tools to automate routine IT tasks, troubleshooting, documentation, and monitoring
- Use AI copilots to accelerate coding, scripting, debugging, and configuration tasks
- Enhance IT service delivery using AI-driven analytics, ticket intelligence, and proactive incident management
- Establish governance, data safety, and responsible AI practices for enterprise-wide adoption
Target audiences
- IT Operations, IT Infrastructure & Network Teams
- Developers, System Administrators, Cloud Engineers
- Cybersecurity, DevOps & Automation Engineers
- Service Desk & Support Teams
Curriculum
- 8 Sections
- 57 Lessons
- 1 Day
Expand all sectionsCollapse all sections
- Foundations of AI for IT Professionals7
- 1.1What AI actually means for IT teams (beyond hype)
- 1.2GenAI vs. ML vs. RPA: Where each technology fits in IT
- 1.3AI copilots and productivity tools
- 1.4AI in IT maturity model (Reactive → Preventive → Predictive → Autonomous IT)
- 1.5Role of IT as the AI adoption champion for the enterprise
- 1.6Quick demo: Using an AI assistant to summarize logs, tickets, and documentation
- 1.7Identify top 10 manual IT tasks that can be AI-assisted
- AI for IT Service Desk, Ticketing & Troubleshooting7
- 2.1AI for ticket triaging and routing
- 2.2Automating common L1/L2 responses
- 2.3AI-driven root cause suggestions
- 2.4Natural language search for knowledge base
- 2.5Reducing ticket noise with AI clustering & pattern detection
- 2.6Activity: Convert a real ticket into: automated response; troubleshooting steps; knowledge article
- 2.7Activity: Build an AI-powered service desk workflow
- AI for Coding, Scripting & DevOps Automation9
- 3.1Using AI for PowerShell, Python & Bash scripting
- 3.2Generating config files (YAML, JSON, Dockerfile)
- 3.3AI for code reviews & debugging
- 3.4Auto-generating CI/CD pipeline templates
- 3.5AI for infrastructure-as-code (Terraform/Ansible)
- 3.6Automating repetitive DevOps tasks
- 3.7Activity 1: Generate a script using AI (log cleanup, monitoring, backup, etc.)
- 3.8Activity 2: AI-assisted CI/CD pipeline build
- 3.9Activity 3: Debug an existing script using an AI copilot
- AI for Infrastructure, Network & Cloud Ops8
- 4.1AI for incident forecasting (CPU spikes, memory leaks, outages)
- 4.2AI anomaly detection for cloud and network logs
- 4.3Predictive maintenance for servers & network devices
- 4.4AI-based configuration verification and risk detection
- 4.5Cloud cost optimization using AI insights
- 4.6Activity 1: Diagnose infrastructure logs with an AI tool
- 4.7Activity 2: AI-generated cloud optimization recommendations
- 4.8Activity 3: Build an AI-enabled infra monitoring checklist
- AI for Cybersecurity & Threat Detection8
- 5.1AI for SOC: threat detection, log correlation, IOC analysis
- 5.2AI for vulnerability management & patch recommendations
- 5.3AI-based phishing detection & email analysis
- 5.4Using AI to generate security policies & incident reports
- 5.5Secure use of AI tools (data protection, safe prompting, boundaries)
- 5.6Activity 1: Analyze a suspicious email/log using AI
- 5.7Activity 2: Generate a risk assessment or incident report using an AI copilot
- 5.8Group Activity: Identify high-risk AI use-cases
- AI for Documentation, Communication & Knowledge Management8
- 6.1Auto-generating SOPs, release notes, technical docs
- 6.2Creating playbooks, runbooks, and troubleshooting guides
- 6.3AI for meeting summaries and project updates
- 6.4Preparing stakeholder briefs & executive summaries using AI
- 6.5Organizing knowledge bases with AI tagging
- 6.6Activity 1: Convert a technical change request → proper documentation
- 6.7Team Activity: Generate SOPs/playbooks using AI
- 6.8Group Interaction: Create an FAQ knowledge base for recurring issues
- AI Governance, Enterprise Security & Responsible Use7
- 7.1Data classification & access control for AI usage
- 7.2Prompt safety rules, boundary policies & compliance
- 7.3IT’s role in building an internal AI governance framework
- 7.4Guardrails for sensitive data, logs, credentials & customer info
- 7.5Defining best practices for org-wide AI rollout
- 7.6Identifying unsafe vs safe prompt
- 7.7Activity: Create an AI Acceptable Use Policy draft for your organization; Outline a governance checklist for rollout to other functions
- IT-Led Roadmap for Scaling AI Across the Enterprise3
Instructor
