Rockwell & Augury : Predicting Problems to Driving Action

In the last ten years, manufacturers have spent a lot of time and money developing sensors, connected machines and industrial analytics, but getting operational information to prompt action in time has long been a challenge. Factories are able to learn about equipment problems earlier than ever before, but it can take several disconnected systems and manual efforts to convert that information into maintenance activities and operational improvements. 

The next step in industrial AI is to close this gap.

In response to this evolution, Rockwell Automation and industrial AI firm Augury have revealed a strategic collaboration designed to seamlessly combine AI-driven machine health insights with maintenance and production processes. 

The partnership’s key feature is a new integration between Augury’s Reliability Agent and Rockwell’s AI-powered maintenance assistant, Fiix MAX. The technologies will be combined to provide an ongoing watch on machine condition, identify potential machinery problems, suggest corrective measures, and automatically assist in machine maintenance planning and work order execution. 

Industry context

Industrial AI is progressing quickly from descriptive analytics to agentic AI, which is intelligent software designed to be able to monitor, interpret, recommend decisions and support operational execution in current industrial processes. These AI agents are not meant to only provide alerts or dashboards but to empower operators and maintenance personnel to take action faster and more reliably based on their insights.

This is the reason that manufacturers are feeling pressure to increase asset reliability, overcome their workforce challenges and optimize productivity without increasing complexity. Although there are many investments made in digital technology, a lot of the data produced at industrial facilities isn’t getting utilized, as it’s not connected to the systems that make operational and maintenance decisions.

AI is thus being embedded in asset management, asset maintenance, and production platforms, making them more integrated in decision-making spaces for automation providers. With machine condition monitoring, predictive maintenance, CMMS, and production data all in one, manufacturers can focus on interventions that have an impact on the operation, instead of waiting for machines to fail.