0
Step 0: Individual Augmentation
🤖 What AI Does
- ✓ IT staff use ChatGPT to draft runbook procedures and troubleshooting guides
- ✓ Generating scripts: PowerShell for AD, Bash for server maintenance, Python for log parsing
- ✓ Answering "how to" questions for SaaS app configurations
- ✓ Drafting change management documentation
- ✓ Writing incident reports and post-mortems
👤 What Humans Still Do
- • All infrastructure management and changes
- • User provisioning and deprovisioning
- • Security incident response
- • Vendor management and procurement
- • Network and server administration
- • Helpdesk support
🛠️ Tools & Tech
- → ChatGPT/Claude subscriptions
- → No integrations
👥 Role Changes
- ↻ None. IT staff individually faster at documentation and scripting.
⚠️ Key Risks
- ! AI-generated scripts run in production without proper testing
- ! Security configs generated by AI have vulnerabilities
- ! Shadow AI usage creates compliance gaps
🚪 Gate Criteria → Step 1
- ☐ >60% of IT team using AI for documentation/scripting
- ☐ AI-assisted scripts through standard change management before production
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1
Step 1: Structured Productivity
🤖 What AI Does
- ✓ Templates for incident response, change management, user provisioning checklists, vendor evaluation
- ✓ Helpdesk AI: first-line support bot handling password resets, VPN issues, common questions
- ✓ Automated KB article generation from resolved tickets
👤 What Humans Still Do
- • All infrastructure changes and approvals
- • Security architecture and policy decisions
- • Vendor negotiations and contracts
- • Complex troubleshooting
- • Strategic IT planning
🛠️ Tools & Tech
- → Enterprise AI with IT-specific templates
- → Helpdesk integration (ServiceNow/Jira Service Management)
- → ITSM workflow tool with AI layer
👥 Role Changes
- ↻ L1 helpdesk agents shift to "AI support supervisors"
- ↻ Sysadmins produce documentation 2-3x faster
- ↻ IT manager designates "IT AI Champion"
⚠️ Key Risks
- ! Helpdesk AI gives wrong answer → user takes harmful action
- ! Template-generated change requests create false sense of completeness
- ! IT staff become documentation factories
🚪 Gate Criteria → Step 2
- ☐ Helpdesk AI handling >40% of L1 tickets autonomously
- ☐ Change management documentation time reduced ≥50%
- ☐ All templates reviewed by IT security
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2
Step 2: Shared Knowledge Layer
🤖 What AI Does
- ✓ RAG over: network diagrams, server inventories, configuration baselines, incident reports, vendor docs, security policies
- ✓ "What's the firewall rule for traffic between trading systems and clearing network?"
- ✓ "When was the last time we patched market data servers?"
- ✓ Dependency mapping: "Show everything that depends on Service X"
- ✓ Asset inventory queries in natural language
👤 What Humans Still Do
- • Infrastructure architecture decisions
- • Security policy creation and enforcement
- • Vendor relationship management
- • Complex troubleshooting requiring system-level access
- • Maintaining the knowledge base
🛠️ Tools & Tech
- → Vector DB indexing CMDB data, network docs, incident history, runbooks
- → CMDB integration (ServiceNow)
- → Monitoring tool APIs
- → Asset management integration
👥 Role Changes
- ↻ L1/L2 support dramatically faster
- ↻ New IT hires productive in days instead of months
- ↻ Senior IT staff valued for knowledge contributions
⚠️ Key Risks
- ! Outdated infrastructure docs in RAG
- ! CMDB data quality issues (garbage in, garbage out)
- ! Security-sensitive information needs tight access control
🚪 Gate Criteria → Step 3
- ☐ >80% of infrastructure questions answerable via RAG
- ☐ CMDB integrated and accessible via AI
- ☐ IT onboarding time reduced ≥40%
- ☐ Access controls verified
↓
3
Step 3: Workflow Automation
🤖 What AI Does
- ✓ New employee (HR trigger) → auto-provisions: AD account, email, Slack, VPN, role-based access
- ✓ Employee terminated → auto-deprovisions all accounts within 15 minutes
- ✓ Security alert from SIEM → auto-triages: severity, affected systems, initial containment, pages on-call
- ✓ Server health degradation → auto-diagnoses, auto-scales, escalates unknowns
- ✓ Engineering needs new environment → auto-provisions cloud resources
👤 What Humans Still Do
- • Approve infrastructure changes above cost/risk threshold
- • Handle novel security incidents
- • Strategic IT decisions
- • Complex networking and architecture
- • Manage vendor relationships
🛠️ Tools & Tech
- → SOAR platform (Splunk SOAR, Palo Alto XSOAR)
- → Infrastructure as Code (Terraform) with AI configs
- → ITSM workflow automation
- → HR system integration for provisioning
- → Auto-scaling policies
👥 Role Changes
- ↻ L1 helpdesk role may be eliminated (AI handles >80%)
- ↻ L2 becomes "Automation Engineering"
- ↻ Security analyst: reviewing AI escalations, not every alert
- ↻ IT ops → "Platform Engineering"
⚠️ Key Risks
- ! Auto-deprovisioning hits wrong account (locks out trader mid-session)
- ! Security automation takes wrong containment action
- ! Over-automation without sufficient testing → cascading failures
🚪 Gate Criteria → Step 4
- ☐ Employee provisioning/deprovisioning automated end-to-end
- ☐ Security alert triage >70% automated
- ☐ Infrastructure auto-remediation for known issues
- ☐ Zero incidents caused by automation in 90 days
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4
Step 4: Monitoring & Consolidation
🤖 What AI Does
- ✓ Unified IT operations dashboard: system health, security posture, compliance, cost, satisfaction
- ✓ AIOps: anomaly detection across all infrastructure
- ✓ Automated capacity forecasting and cost optimization
- ✓ Security posture continuous assessment
- ✓ Vendor performance tracking against SLAs
👤 What Humans Still Do
- • IT strategy and budget decisions
- • Security architecture evolution
- • Vendor negotiations
- • Governance: automation scope management
- • Complex incident management
🛠️ Tools & Tech
- → AIOps platform (Moogsoft, BigPanda, or custom)
- → Unified monitoring (Datadog/Grafana)
- → Security posture management
- → Cost management (CloudHealth/Spot)
- → Automated compliance reporting
👥 Role Changes
- ↻ IT team consolidates around platform engineering and security
- ↻ CIO becomes data-driven decision maker
- ↻ Operations and security merge under unified AIOps
⚠️ Key Risks
- ! AIOps generates too many false positives
- ! Cost optimization impacts performance
- ! Unified dashboard creates single point of visibility failure
🚪 Gate Criteria → Step 5
- ☐ Single pane of glass for IT operations
- ☐ AIOps reducing alert noise by >60%
- ☐ Infrastructure costs optimized (documented savings)
- ☐ Compliance reporting automated
↓
5
Step 5: Personal Agent Teams
🤖 What AI Does
- ✓ Each IT staff has agent teams managing their domain: network agents, security agents, cloud agents
- ✓ Agents auto-remediate known issues 24/7
- ✓ One admin manages what previously required 3-4
- ✓ Continuous security monitoring and response
- ✓ Proactive capacity management
👤 What Humans Still Do
- • Architecture evolution
- • Novel threat response
- • Vendor strategy
- • Platform design decisions
- • Governance and compliance oversight
🛠️ Tools & Tech
- → Agent orchestration per admin
- → Domain-specific agents (network, security, cloud, identity)
- → Personal agent memory and preferences
👥 Role Changes
- ↻ IT staff becomes "Infrastructure Architects"
- ↻ One admin + agents = team of 3-4
- ↻ Security analysts focus on threat hunting, not alert triage
⚠️ Key Risks
- ! Agent actions create cascading infrastructure issues
- ! Over-reliance on agents for security response
- ! Loss of manual skills for disaster scenarios
🚪 Gate Criteria → Step 6
- ☐ Agent teams managing infrastructure for 3+ months
- ☐ MTTR improved ≥50%
- ☐ Zero outages from agent actions
↓
6
Step 6: Autonomous Department
🤖 What AI Does
- ✓ IT/OPS runs autonomously: infrastructure self-heals, security auto-responds, provisioning instant, costs auto-optimize
- ✓ Self-healing infrastructure handles 95%+ of issues without human intervention
- ✓ Continuous compliance monitoring and auto-remediation
- ✓ Dynamic resource allocation based on demand
👤 What Humans Still Do
- • Architecture evolution
- • Novel threats and zero-days
- • Strategic vendor decisions
- • Governance and audit
- • Disaster recovery planning
🛠️ Tools & Tech
- → Self-healing infrastructure platform
- → Autonomous security operations
- → Dynamic resource management
- → Continuous compliance engine
👥 Role Changes
- ↻ IT team: 2-3 platform architects + CISO
- ↻ From 8-12 people to 3-4 with better uptime
- ↻ All routine operations eliminated
⚠️ Key Risks
- ! Self-healing masks underlying problems
- ! Catastrophic failure without manual expertise
- ! Regulatory concerns about autonomous infrastructure
🚪 Gate Criteria → Step 7
- ☐ Autonomous IT operations for 6+ months
- ☐ Uptime >99.95%
- ☐ Security incidents auto-contained >90%
- ☐ Zero human-hours on routine operations
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7
Step 7: Autonomous Enterprise
🤖 What AI Does
- ✓ IT is the nervous system of the autonomous enterprise
- ✓ Every department's agents depend on IT infrastructure agents
- ✓ Self-evolving infrastructure adapts to company needs
- ✓ Predictive capacity management
- ✓ Continuous security evolution
👤 What Humans Still Do
- • Strategic architecture decisions
- • Security governance at highest level
- • Innovation and evaluation of new platforms
- • Regulatory compliance oversight
🛠️ Tools & Tech
- → Self-evolving infrastructure
- → Enterprise-wide agent orchestration backbone
- → Predictive systems
- → Autonomous security mesh
👥 Role Changes
- ↻ Humans: 2-3 platform architects + CISO
- ↻ IT is infrastructure, not a department
- ↻ All operational work handled by agents
⚠️ Key Risks
- ! Single point of systemic failure
- ! Loss of all manual operational knowledge
- ! Cascading agent failures across departments
🚪 Gate Criteria → Step 8
- ☐ Infrastructure supports full autonomous enterprise
- ☐ Self-evolution documented and governable
- ☐ Zero manual operational intervention for 12+ months