What I Learned Listening: My First 90 Days and the Future of Maintenance

When I stepped into the CEO role at Limble 90 days ago, I made a commitment: I would spend my first quarter listening, not talking. I opened my calendar to our customers - the maintenance directors, reliability engineers, and plant managers. My goal wasn’t to pitch, it was to understand why, despite decades of software "innovation," so many operations still feel like they are fighting their tools rather than being fueled by them.

What I found is a fundamental disconnect. The maintenance industry has a data problem disguised as a technology problem. We’ve been consistently misread by software vendors who build for the executive reviewing the report, rather than the technician completing the work.

These 90 days have clarified three pillars that will define our mission to empower the humans who keep the world’s assets running.

Pillar 1: Closing the Execution Gap

Every leader I spoke with could pinpoint the exact moment their last CMMS failed: the moment the technicians stopped using it. In high-pressure environments, usability isn't a "nice-to-have" it’s the entire game.

Our research shows a 37-point work capture gap between high-discipline and low-discipline teams. This isn't a training issue; it's a design issue. When software respects the technician’s workflow, allowing them to close a job in three taps rather than 23, adoption follows and clean data is the result.

Pillar 2: Addressing the Universal Data Failure

Coming from twenty years at companies like Adobe, NetSuite and Simpro, I’ve seen a recurring pattern: companies invest in software, technicians quit using it because it is too complex and the data stops .

Maintenance and asset management is where this gap has the highest cost—a staggering $1.4T in annual downtime. According to our 2026 Benchmark Report, 72% of organizations (and 93% of enterprises) don't trust their own asset data. We aren't just building a place to store data; we are building a tool to ensure that data is accurate enough to drive action.

Pillar 3: Solving the AI Readiness Crisis

The most timely lesson from my first 90 days is that the industry is racing toward AI on a foundation it hasn't audited. Organizations with weak data discipline run 56% unplanned maintenance. If you train AI on that baseline, it will only "confidently" predict the wrong things.

The precondition for AI that works is a data foundation most organizations don't yet have. Our job is to provide the "Intelligence-to-Action" tools that make technicians the smartest people in the room before the first AI model is even deployed.

The Human Stakes

This mission is personal. Empowering the human means protecting their life outside the plant. A system fueled by clean data is the difference between a planned Tuesday repair and a 2:00 AM emergency call on a Saturday.

My listening tour doesn't end just because my first 90 days have. We will continue to measure our success not by features shipped, but by how effectively we support the people who keep the lights on and the lines moving.