MSP Agentic AI
Assessment · 01

Is your operating model ready for AI agents?

Most “AI readiness” is hype. This measures the thing that actually decides it: whether your data, processes, automation, skills, tooling, and governance are structured enough for agents to run the repeatable work — safely.

What it measures — the 6 dimensions

You rate each on how it is today (a mirror, not a grade). For every one, the app already holds the “what good looks like” standard you can browse and adopt.

  • Data & Documentation Readiness

    Can an AI agent ground on your data? Is your environment and service knowledge documented and structured — or locked in people's heads?

    What good looks like · A structured, documented backbone — records, runbooks, KB, and CMDB — that an agent can retrieve and reason over.
  • Process Standardization

    Are your workflows defined and repeatable, so an agent can execute them the same way every time — not improvised per technician?

    What good looks like · Defined workflow recipes + statuses + closure codes that an agent can follow deterministically.
  • Automation Leverage

    Is recurring work automated at the source, or re-solved by hand each time — so growth means hiring rather than capability?

    What good looks like · A maintained event→action automation library removes recurring work at the source, and agents draft the rest.
  • AI Skills & Team

    Does your team have the AI/automation skills — prompt engineering, LLM ops, agent integration, RAG, MCP, AI safety — to build and run agentic workflows?

    What good looks like · A team with the modern AI-ops skill set, mapped to roles, that can build, evaluate, and operate agents.
  • AI Tooling & Integration

    Is AI/automation tooling in your stack, and is your PSA + data integration-ready (APIs, MCP) so agents can actually act, not just chat?

    What good looks like · AI/automation tooling in the stack and integration-ready systems (APIs / MCP) agents can read and write through.
  • AI Governance & Safety

    Do you govern AI use responsibly — safety guardrails, human-in-the-loop on destructive actions, data privacy in AI pipelines, and an AI-use policy?

    What good looks like · An AI-use policy, output validation + PII handling, and human-in-the-loop gates on destructive actions — adoption you can defend to a client or auditor.

The 5 readiness levels

Where you land is your headline level; your lowest-rated dimension is the constraint holding you back — you're only as repeatable as your weakest critical area.

  • Level 1 — Ad-hoc

    AI is unused or one-off. Work is done by hand ticket-by-ticket; nothing about the operating model is structured for an agent to read or act on.

  • Level 2 — Exploring

    Isolated experiments — a chatbot here, a few RMM scripts there. No standardization, and the data an agent would need to ground on is scattered and unstructured.

  • Level 3 — Operationalizing

    AI and automation are embedded in some workflows. Core processes and environment data are documented enough to ground on, and the team is deliberately building AI skills.

  • Level 4 — Scaling

    Agents handle recurring work across the operating model — triage, dispatch, resolution drafting, documentation — on a standardized, governed, measured footing.

  • Level 5 — Autonomous

    AI agents run the repeatable work end-to-end with human oversight by exception. Throughput scales with capability, not headcount, and the system improves continuously.

Why it matters

Agents don't fail because the model is weak; they fail because the operation underneath them is unstructured — tribal knowledge an agent can't read, processes that change per technician, no guardrails on destructive actions. The fastest path up is rarely “more AI”; it's structuring your data, standardizing your processes, and governing the rollout so agents can act. This shows you exactly which of those is your bottleneck.

What you get

  • Your headline maturity level across all 6 dimensions.
  • The single constraint holding you back — and the targeted next-steps to lift it, cross-linked to the reference.
  • An evidence check: where your MSP Profile contradicts a high self-rating, the assessment challenges it (it never overwrites your answer).
  • A branded PDF report to download and share with your team.

See where your MSP stands — in minutes.

No install, free to explore. Pick an assessment and get a tailored read on your gaps and your maturity.

Open the reference →