An AI agent can reschedule a repair crew in seconds, but it can’t decide which customer deserves to wait. Draw those lines before you buy the platform, because the software will only enforce the ones you’ve already agreed on.
Buying an AI governance platform before making your business decisions is like installing a fancy thermostat before your family agrees on a temperature. Lovely gadget. Still going to be a fight.
Case in point: A commercial HVAC client of mine runs an AI agent on repeat service calls. Repair doesn’t hold? The agent catches it, keeps the original tech off the job, finds someone qualified, reshuffles the schedule, and tells the customer when the truck is coming. Nobody lifts a finger. Impressive!
But should that customer get credit for the hassle? And what happens when protecting one big account starts bumping three smaller ones? Those aren’t scheduling questions anymore. They’re money and relationship questions, and nobody has decided who owns them.
You can see how the argument starts. Dispatch wants to get the truck out. The account manager wants to keep the customer happy. Finance wants to know who is paying for the extra visit. All reasonable. Until the agent starts making those trade-offs on their behalf.
So, for now, a human signs off on concessions, and we’re testing whether the agent can even see its ripple effects.
And that took conversations with people who don’t normally sit in the same room to talk about a service call. Not because the technology was particularly mysterious. Because someone had to decide what the agent could promise, what it could spend, and whose problem it became when one customer’s fix created another customer’s delay.
In my experience, getting operations, finance, customer service, and IT to agree on those guardrails is harder than configuring the platform. Every. Single. Time.
The tool enforces the lines. You still must draw them. Have you?
