AI strategy and pilot implementation for an MSP
Scoped a focused AI pilot, deployed it inside one operations team, and stayed through rollout so the framework actually stuck.
Challenge
Handled IT had AI on the roadmap but no clear answer on where to start. Vendor pitches looked identical, internal experiments stalled at the demo stage, and leadership needed a way to evaluate AI without committing to a full platform. The real risk wasn't trying AI — it was running another six-month pilot that produced nothing measurable.
Approach
We ran a focused AI Clarity Audit to identify the single highest-ROI workflow inside their operations team, then built and deployed the pilot with that team — not as a parallel side project. Stayed embedded through rollout: training, prompt tuning, and post-launch optimization. The pilot now serves as the template for how Handled rolls AI into the next function.
Result
- 1 pilot team running AI in production
- ~4 wks from audit to deployed pilot
- 0 stalled experiments
Details
The audit drew a clean line between AI workflows that would move a number for an MSP and ones that would just generate slideware. We picked the one with the clearest before/after and built it directly with the team that was going to use it daily.
Rollout was the part most pilots skip — the team had support during the first weeks of live use, with adjustments made against real traffic instead of canned demo scenarios. That’s what made the framework portable: Handled now has a process for rolling AI into the next function without our team in the room.
In their words
“Brought GAW in to figure out where AI actually fits in our ops, not just where we could shove a chatbot. They scoped the pilot team, deployed it, and stayed through rollout. We have a working framework now, not a slide deck.”
