Service systems are very good at recording activity. The call ended. The steps were completed. The technician left. The work order closed.
But none of that answers the question the customer actually cares about: Did the problem get fixed?
This month, In Plain Sight looks at the gap between recorded completion and verified resolution, and why repeat calls and repeat visits often begin after a case appears to be finished.
Technically, the installation was complete. The device was online, the app was connected, and the checklist was finished.
But the outcome was useless. The point of a video doorbell is not to appear online. It is to show who is standing at the door.
That is the difference between completing a process and resolving the customer's real need.
Send us your worst "everything said it was working" story. We are collecting the best Home CTO confessions for a future issue.
ABB's challenge was not a lack of technical knowledge. It was getting that knowledge in front of the physical problem quickly enough.
With TechSee, customers could show experts exactly what was happening through a standard mobile device, helping ABB decide when a visit was truly necessary.
A closed ticket records activity. A verified outcome creates value.
Read the ABB customer storyIn this WIRED story, a missing e-bike delivery turns into a months-long struggle across a retailer, carrier, bank, and multiple automated service systems.
The striking part is not simply that the bots were frustrating. It is that every organization had a process, but no one took ownership of the outcome.
Read it on WIRED ->Gartner found that 91% of customer service and support leaders feel executive pressure to implement AI in 2026.
But the priorities behind that pressure are revealing: higher customer satisfaction, greater operational efficiency, stronger first-contact resolution, and more successful self-service. The goal is not more AI activity. It is better service outcomes.
Read the Gartner survey ->Forrester's analysis of Zendesk Relate 2026 makes an important distinction: adding AI features is not the same as operationalizing AI across the service lifecycle.
For AI to resolve issues reliably, knowledge must include the rules, conditions, exceptions, actions, and escalation paths required to execute the work, not just documents written for people to interpret.
Read it on Forrester ->
A traditional service workflow ends when the instructions or checklist end. Visual Intelligence adds one more step: it checks the physical result.
That could mean confirming the correct cable, verifying a camera's field of view, or checking that a repair was completed correctly before the job closes.
Seeing the problem creates clarity. Verifying the outcome creates trust.
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