From Skeptic to Advocate: Customer Stories on AI Adoption

Brent Soaper didn’t start as a believer. When UpKeep AI features first appeared, he called it a “parlor trick” — interesting enough to test, easy enough to dismiss. Within months, he’d gone from skeptic to using AI for roughly 80% of his workday. This is that story.

As IT Systems Manager at Welch Packaging — a 30-plus-location packaging company — Brent had already deployed UpKeep across multiple facilities before AI entered the picture. In this 45-minute conversation with UpKeep Customer Education Manager Meline, he walks through what changed: technicians who spent decades writing things down by hand, now using voice-fill on the mobile app. A new manager who built a spending dashboard by typing one sentence. A transportation workflow where someone’s full-day data-entry job now takes five seconds with a Studio OCR app.

The session covers the resistance (the technician willing to quit over switching to a CMMS), the adoption strategy (start with one workflow, hold hands where needed, then let the team run), and the practical payoff (fewer calls to IT, faster onboarding for new hires, and a maintenance team that leaves on time because they’re not doing paperwork after their shift).

“I’ve been able to do stuff in the last six months that I’ve been trying to get done for the last six years.”
— Brent Soaper, IT Systems Manager, Welch Packaging — on the compounding effect of AI tools

What You’ll Learn:

• What real AI skepticism looks like in a maintenance context — and the specific moments (a voice-filled work order, a one-sentence dashboard) that turned a skeptic into an advocate.

• How to sequence adoption across a team: start with one workflow, demonstrate it side-by-side, and identify the early runners who bring others along.

• Concrete Studio and Nova use cases already running in production at Welch Packaging — from OCR-powered work order entry on truck repair invoices to live dashboards built without a coder.

• Why new-hire onboarding with UpKeep AI is faster than it was before AI — and what it means that the people who don’t know the “old way” adopt faster than the ones who do.

• The prompting philosophy that works: start with “How did we do today?” and build from there — and why you’ll get better at asking as the model gets better at answering.