Aidentyx’s Joe Lee joined Reliability Radio to discuss how practical AI for maintenance can help reliability teams move from equipment alerts to root cause analysis, connected operational knowledge, and faster action.
AI for Maintenance and Reliability
Aidentyx’s Joe Lee, Senior Vice President of Product Marketing, recently joined host Brendon Russ on Reliability Radio for a conversation about moving beyond AI hype and applying AI to real maintenance and reliability challenges.
In the episode, recorded during The RELIABILITY® Conference 2026, Joe explains how AI can help industrial teams shorten the path from detecting a potential equipment problem to understanding its cause and taking action.
AI for Maintenance: Moving Beyond the Alert
An equipment alert is important, but it is only the beginning of the maintenance process. After receiving an alert, an engineer may still need to inspect the equipment, review historical data, identify the root cause, locate the correct maintenance procedure, confirm parts and personnel availability, and create a work order.
When these steps rely on manual searches across disconnected systems, the time from alert to repair can extend from days to weeks.
During the interview, Joe describes a practical AI-assisted workflow that brings these steps together. Starting with an equipment alert, an engineer can review why the alarm occurred, use AI to investigate possible root causes, retrieve relevant maintenance knowledge, and receive a recommended response.
How AI Supports Maintenance and Reliability Teams
AI can support maintenance and reliability teams across the workflow—not only by detecting potential equipment problems, but by helping engineers investigate root causes, retrieve relevant maintenance history and procedures, understand operational impact, and determine the next appropriate action. When AI works across connected equipment, maintenance, and operational data, teams can reduce the manual search between an alert and a repair decision.
Connecting Maintenance Knowledge to Action
The effectiveness of this workflow depends on connecting the knowledge that already exists across the organization. This may include:
- Equipment manuals and maintenance procedures
- Historical repair records
- Previous fault and alarm data
- Comments and observations from maintenance teams
- Parts and work-order information
By organizing this information into a connected knowledge base, AI can compare a new alert with previous events, identify relevant context, and recommend how the issue may be addressed. It can also help surface the appropriate maintenance procedure and information needed to begin the repair process.
This approach reflects a central direction for manufacturing AI: moving beyond isolated alerts and dashboards toward systems that connect data, knowledge, and workflows.
Human Expertise Remains Essential
Joe also emphasizes that human expertise remains part of the process. AI can accelerate analysis, retrieve knowledge, and assist with recommendations, but maintenance and reliability professionals still provide the domain knowledge needed to validate results and confirm the appropriate action.
As teams interact with these systems, their feedback can also become reusable knowledge—helping strengthen future analysis and recommendations.
Start Small and Build on Proven Value
For organizations beginning their AI journey, Joe’s recommendation is practical: start small, demonstrate value with a focused use case, and expand from there.
At Aidentyx, this philosophy is reflected in the PODO® AI Framework, which connects shared manufacturing context, reusable AI skills, purpose-built applications, and AI Engineering Agents to help industrial teams detect issues, diagnose causes and operational impact, and coordinate the next action.
The full Reliability Radio conversation explores practical applications of AI in maintenance and reliability, workflow automation, knowledge management, root cause analysis, and the continuing role of human expertise.
Listen to the full episode on ReliabilityWeb →
Reliability Radio EP 370 is hosted by Brendon Russ and published by ReliabilityWeb.
AI for maintenance, explained.
A closer look at how AI can help maintenance and reliability teams move from equipment alerts to diagnosis and action.
What is AI for maintenance?
AI for maintenance uses equipment data, maintenance history, operational context, and engineering knowledge to help teams identify potential issues, investigate causes, understand operational impact, and determine appropriate maintenance actions. The goal is to help engineers move more efficiently from an equipment signal or alert to an informed response.
How can AI help maintenance and reliability teams?
AI can support maintenance and reliability teams by bringing together condition data, historical failures, maintenance records, procedures, and plant context. This can reduce the manual search between detecting an issue and understanding what happened, why it matters, and what action should come next.
Can AI help with root cause analysis and work orders?
Yes. AI can help engineers investigate likely root causes by connecting equipment behavior with maintenance history, operational events, procedures, and related plant information. Once the issue is understood, AI can also support the next steps by surfacing recommended actions, relevant procedures, required resources, and information that can be carried into maintenance workflows and work orders.
For maintenance and reliability teams, this alert-to-action workflow is a practical example of how AI-powered asset performance management can extend beyond condition monitoring. Built on the PODO® AI Framework , AI Engineering Agents can work across equipment data, maintenance history, operational context, and engineering knowledge to help teams diagnose issues and coordinate the next action.
Related: [Read our TRC 2026 recap →]