Every Autonomous IT story from a vendor follows the same three‑act script: services and operations are manual and reactive...then AI and automation are implemented.. then zero‑touch nirvana is achieved. It sounds elegant—and is almost useless when trying to make real progress. The reality inside DT organizations is messier and non‑linear. Virtual agents fail to deflect tickets because theknowledge base is thin. AIOps surfaces alerts that cannot be acted on because the CMDB is incomplete or unmapped. Change risk scores mislead because incident data is noisy. The work does not fail for lack of AI; it fails for lack of foundations and sequence. To address this, a coalition of customer admins, implementation partners, product SMEs, and operations teams came together to map what actually works. The result is a living Autonomous IT map built from real journeys, with prerequisites, intersections, and compounding effects made explicit—not a polished marketing storyboard.
The Framework: Insight → Automation → Autonomy Across environments, a consistent pattern emerges:
- Insight – Activate foundations, establish visibility, and understand the environment. Mindset: “We can see and measure.”
- Automation – Layer AI and workflow automation onto those foundations to reduce toil. Mindset: “AI helps humans work faster.”
- Autonomy – Allow AI to act independently within guardrails, with humans in an oversight role. Mindset: “AI works, humans oversee.”
Sequence matters. Weak Insight leads to Automation built on bad data. Rushing to Autonomy without mature Automation means trusting AI that has not earned that trust. Garbage in still produces garbage out - just faster. What the Map Reveals: Prerequisites Compound When these journeys are plotted side by side, one conclusion stands out: earlier work in one area becomes the foundation for later value in another.
- Solid Incident Management and knowledge practices (“Zero Touch” at the Insight stage) create the clean data needed for accurate, AI‑driven change risk scoring (“Zero Outages” in Automation).
- Event ingestion and alert baselining (“Zero Outages” in Insight) provide clear signal so policy‑driven remediation can be expanded into Security and Compliance.
- Effective virtual agent deflection (“Zero Touch” in Automation) frees service desk and operations capacity to focus on proactive stability work (“Zero Outages”).
This is why “just start anywhere” so often fails. The order of work is not rigid, but prerequisites are real. The map is designed to show where an organization is, what comes next, and why that next move unlocks disproportionate value. The Core Journeys: Zero Touch and Zero Service Outages The map is organized into two core journeys that reinforce each other, plus expansionpaths. Zero Touch / Tier‑1 IT Support Objective: reduce effort on the service desk and improve end‑user experience.
- Insight: Stand up core ITSM workflows (Incident, Problem, Change, Request, Knowledge), consolidate entry points through portals, connect the CMDB to endpoint tools, and build dashboards that reveal top ticket drivers. This phase answers a basic question: what work is actually flowing through the system?
- Automation: Add intelligence - virtual and voice agents for omni‑channel support, AI‑assisted triage and categorization, automated assignment, knowledge graph capabilities, and Digital Employee Experience (DEX) for endpoint monitoring. Routine issues begin to resolve with minimal human touch.
- Autonomy: Introduce AI agents that handle end‑to‑end resolutions under governance. Autonomous workers triage and resolve common issues, DEX auto‑healing corrects endpoint problems proactively, the knowledge base continuously improves, and an AI control layer provides oversight and guardrails. Support scales without linear headcount growth, and human effort concentrates on exceptions.
The by‑product is not only a more efficient service desk, but also cleaner incident data and freed‑up capacity—both criticalprerequisites for operational stability. Zero Service Outages Objective: increase service reliability, reduce high‑priority incidents, and accelerate safe change.
- Insight: Run Discovery across on‑prem and cloud infrastructure, use Service Mapping to expose dependencies and blast radius, normalize alerts via Event Management, formalize Major Incident Management, and build operational dashboards to baseline MTTR, MTTA, and MTTD. The environment becomes observable.
- Automation: Deploy AIOps capabilities such as metric anomaly detection, log analytics, telemetry collectors, and real‑time operator worklists. Enhance Service Mapping with ML‑driven suggestions. Use AI‑assisted change planning, run playbooks for remediation, and define SLIs/SLOs and error budgets. Alert noise drops, changes become safer, and response time improves.
- Autonomy: Move from reactive and assisted operations to predictive, self‑healing behaviors. Automation runs remediation playbooks for known patterns without waiting for humans, low‑risk changes receive auto‑approval, and error budgets can automatically throttle deployments. AI Control Tower‑style governance ensures visibility and compliance with guardrails. Outages are increasingly prevented rather than simply resolved faster.
This journey, in turn, strengthens the foundations required for further automation in Security Operations, Compliance, and portfolio‑level decision making. Expansion Paths: Security, Compliance, and Strategy, Once Zero Touch and Zero Service Outages are in motion, the same foundations—clean data, trusted CMDB, accurate service maps, disciplined change, and automated workflows—become a launchpad.
- Security & Compliance: Trusted configuration and change data supports faster impact analysis, coordinated incident response, and audit evidence that is effectively generated by normal operations.
- Strategic Portfolio Management: Reliable service health, change, and release data tie day‑to‑day operations to strategic choices—enabling investment decisions, prioritization, and resource planning grounded in actual operational capacity.
The Autonomous IT map does not argue for a rush into every adjacent domain. Instead, it makes clear that early work in support and operations is not a cul‑de‑sac, but the infrastructure for broader autonomy across DT and security. This is a living map, not a finished playbook: as AI capabilities mature and more organizations experiment with autonomous workflows, new patterns, prerequisites, and anti‑patterns will surface, continually reshaping the journeys, phases, and expansion paths. What is new is not the idea of AI in DT, but a shared, realistic map that makes the path visible- foundations first, automation second, autonomy where the system has earned it.
The Cover Photo summarizes that journey; the attached PDF provides a more detailed view.
We will continue to iterate and post updated drafts here, and we welcome your feedback on what resonates, what we missed, and where you need the most help.
