TL;DR
- Many organizations collect large volumes of machine data but still struggle to reduce avoidable failures.
- Reliability teams need actionable insights, not more raw data or disconnected alerts.
- AI can accelerate reliability workflows, but only when it is paired with the right data and operational context.
- Purpose-built industrial AI solutions help teams identify common faults and prioritize corrective actions.
- Technologies such as AMS Asset Monitor and AMS Optics help reliability teams focus on higher-value work while maintaining predictive maintenance programs.
Industrial organizations have invested heavily in digital transformation and condition monitoring technologies, generating unprecedented visibility into the health of critical assets. Yet many reliability teams continue to face the same challenge: converting growing volumes of data into timely decisions that improve uptime and reduce risk. As experienced personnel become harder to find and teams are expected to do more with fewer resources, actionable intelligence has become far more valuable than raw information.
We’re buried in data. In nearly every facility, across every industry, digital transformation has brought incredible visibility into machine performance. So why do so many organizations struggle to lock in predictive maintenance strategies? Why do so many avoidable failures pop up and create so much unplanned downtime?
In a recent article in Manufacturing Today, Emerson’s resident machinery health expert Drew Mackley explained that reliability teams still struggle because they need the right tools and strategies to turn that data into actionable information. Today’s lean teams are stretched thin. If they have mountains of raw data from assets but few or no data analysts with the time to dig into that data for insight, they will rarely be able to make significant changes.
“Operations only need information they can actually act on.”
The value of asset data is realized only when it is presented with enough context to support confident decision-making.
Teams can collect data, and even turn it into alerts, but that data must go to the right people and must include the right context and guidance to help them know what to do about it.
AI Needs Guidance Too
So, if the problem is reams of data without anyone to analyze it, why not just force feed that data into a standalone AI tool and have it determine what the spectrums and waveforms mean?
There’s no doubt AI has come a long way and is becoming a powerful tool to help reliability teams make the most of their data, but, as Drew explains, AI, too, requires underlying data, and more importantly, context, to help turn data into decisions. A disconnected, third-party tool will typically struggle to give good recommendations because of limited underlying data.
“AI is only as good as what it’s been taught. It doesn’t create or reason in the way people sometimes imagine. It recognises patterns based on what it has already seen. If your sensing technology isn’t collecting the right information, no amount of AI is going to identify a fault that simply isn’t being measured.”
Effective AI depends on accurate sensing technologies, quality data, and the operational context necessary to generate meaningful recommendations.
That’s not to say that AI isn’t a useful tool in reliability. Teams simply need to invest in fit-for-purpose industrial AI technologies. Today, many of the most powerful reliability solutions incorporate on-board AI to make it a seamless part of the reliability workflow. Tools like the AMS Asset Monitor leverage embedded AI-driven insights built around Emerson’s decades of industry expertise to alert personnel to common faults for a wide range of assets like fans, motors, gearboxes, pumps, and other rotating machinery.
New Tools Drive New Strategies
When teams implement fit-for-purpose industrial AI like that in the AMS Asset Monitor, or the even more powerful AI tools in Emerson’s AMS Optics, reliability teams can begin to approach their tasks differently. Instead of relying entirely on manual rounds and time-consuming analysis, teams can instead focus on higher-level tasks that deliver more value without sacrificing predictive maintenance.
“Predictive maintenance describes part of the process, but the real objective is understanding the health of our assets and then taking the right corrective action. There’s a workflow around that, from identifying a problem to planning maintenance and managing the risk to production.”
Successful reliability programs focus on the complete asset health workflow, from detection through corrective action and risk management.
The technology is attainable, affordable, and intuitive, Mackley explains. By starting with trusted technology from an automation partner with decades of experience, teams can easily implement tools, including AI, that help turn data into action.
Make the most of data
The challenge facing many reliability teams today is no longer collecting data. It is transforming that data into timely, actionable decisions that improve asset performance and reduce unplanned downtime. Purpose-built industrial AI solutions help bridge that gap by combining quality data, embedded expertise, and practical workflows that support maintenance teams from fault detection through corrective action. As reliability programs continue to evolve, organizations that successfully turn information into action will be best positioned to improve uptime, efficiency, and operational resilience.
