In this episode of “Full Tech Ahead,” host Amanda Razani interviews Israel Ortiz, the VP and general manager at Accruent. They discuss the major operational crisis currently facing the manufacturing sector: the massive retirement wave of seasoned workers with 20 to 30 years of experience, leaving behind a novice workforce facing a steep knowledge gap.
Ortiz explains how combining asset management history with modern Internet of Things (IoT) sensor telemetry (measuring vibrations, temperature drops, and power fluctuations) enables AI-driven predictive maintenance. However, he cautions against blind trust in automated signals, emphasizing that senior domain experts must remain in the loop to validate findings.
By meticulously documenting work orders, cleaning historical data and embedding intelligent IoT sensors into newer production machinery, organizations can bridge the generational experience gap and achieve significant reductions in unplanned downtime.
Key Quotes
“The great experienced twenty, thirty-year workforce is deciding to move on to a better life... and that’s leaving behind the novices, the newer folks to try to figure out how to close that gap as soon as possible.”
“The smartest technicians out there... always say, ‘Oh, I just have to put my hand on that and feel the vibration and I know what to do.’ Well, you take someone brand new, there’s no way they know... That’s what connected IoT does.”
“The future will be us supervising that automation... let technology work for us, but let’s always stay in the loop to ensure it’s producing what we want.”
Takeaways
Bridge the Knowledge Drain via Asset Data: Seasoned technicians rely on unwritten, tactile intuition. To prevent this tribal knowledge from vanishing upon retirement, companies must rigorously document detailed work orders (annotating problem, cause, and remedy) to build rich historical datasets for AI models.
Transition to Predictive Maintenance: Integrating AI with IoT sensor streams (vibration, heat, power) shifts operations from reactive fixes or static preventive schedules to dynamic predictive maintenance. In one automotive manufacturing use case, this approach slashed maintenance overhead by 30% and dramatically boosted facility uptime.
Maintain Human Supervisory Control: Never trust predictive IoT signals blindly. Organizations should implement dedicated review cadences (e.g., weekly telemetry triage sessions) where human operators filter out false alarms and validate AI recommendations before taking equipment offline.
Enable the Novice Field Technician: The ultimate goal of modernizing manufacturing plants is field empowerment. By scanning a machine’s digital tag, a novice technician should immediately receive an AI-synthesized dossier containing past repair history, current sensor telemetry, and step-by-step diagnostic actions.
