AI for MSPs Playbook
Practical AI adoption strategies for managed service providers, from evaluation to implementation. Built by practitioners, not theorists.
45 pages · 6 frameworks · 9 real-world use cases · Built by MSP operators, reviewed by practitioners.
What's Inside
Everything an MSP operator needs to make informed decisions about AI adoption.
AI Evaluation Framework
Structured approach to assess which AI tools actually fit your MSP operations and client needs.
Includes a 6-layer operating model that maps AI concepts to MSP language: context, skills, tools, agents, tasks, and connectors.
Implementation Roadmap
Step-by-step plan for rolling out AI across your service delivery without disrupting existing workflows.
Covers the "start with what you already have" approach, discovery questions based on the Pareto principle, and a maturity progression model from Aware to Leading.
Risk and Compliance
Navigate data privacy, client consent, and compliance considerations before deploying AI in managed environments.
Addresses production failure modes, context corruption, quality gates, skill atrophy, and AI governance, not just checkbox compliance.
Use Case Library
Real-world AI applications for MSPs covering ticketing, monitoring, documentation, and client communication.
9 scored use cases including ticket triage, policy and SOW drafting, QBR prep, and client communication, each rated by complexity and expected impact.
Written by Practitioners
Henry Timm, Dawn Sizer, and David Sizer, the co-founders of Rocket Fuel Factory. Combined decades of hands-on MSP operations, cybersecurity delivery, and IT channel leadership. This playbook reflects how they actually think about AI adoption, not how vendors want you to think about it.
Shaped by the Community
This playbook was reviewed and strengthened by working MSP operators and security practitioners:
Jason Slagle — Reframed AI's role as additive to core MSP work, not a replacement. Contributed the Pareto-based discovery approach and SOW drafting use case.
Ashley Cooper — Pushed the governance model deeper with context corruption risks, skill atrophy, maturity regression, and ownership considerations.
Callen Sapien — Expanded governance with failure modes, quality gates, and maturity progression clarity.
Chris Johnson — Strengthened the governance taxonomy and framework alignment.
Dan Mitchell — Drove the addition of business analytics, pricing, change management, and vendor evaluation sections.
Portions of this playbook were drafted and assisted using an AI research assistant; all content was reviewed and verified by the human authors.
Common Questions
Ready to start your AI adoption journey?
Download the playbook now. No fluff, no vendor pitches. Just the frameworks and decision tools your team needs to move forward with confidence.