The conversation around Industrial AI has shifted. It’s no longer about whether the technology works. It’s about whether organizations are set up to act on it.
We sat down with Pat Sinicropi, AVP, Strategic Sales, and Brian Crum, Senior Director, Product Management, following LNS Research’s The Productivity Event, an annual gathering of COOs and senior operations leaders from some of the world’s most productive companies, to get their take on what’s holding manufacturers back and what the ones getting it right are doing differently.
1. You just spent two days with COOs from some of the world’s most productive companies. What was the one thing that kept coming up?
Brian: AI was everywhere, but that wasn’t the interesting part. The more interesting conversation was how leaders need to redesign work so AI can actually drive productivity. The room was asking hard questions about operating models, governance, decision rights, and how to move from experimentation to scaled impact.
Pat: Decision latency. Leaders are looking for ways to compress the time from signal to action, and that compression is ultimately where business value gets created.
2. Most industrial manufacturers are sitting on more data than they know what to do with, and there’s still a significant lag between getting a signal and acting on it. What’s getting in the way?
Pat: Critical decision infrastructure. The gaps aren’t just in the data; they’re in the operational systems, tools, and processes that should be turning that data into action.
Brian: The issue is not a lack of data or even a lack of intelligence. Insights are still trapped across disconnected systems, dashboards, and workflows. Too often, the right signal does not reach the right person in time to change the outcome. That is the real gap: turning intelligence into action before downtime, quality loss, or waste occurs.
3. Adoption of digital tools kept coming up as one of the biggest challenges. What are you hearing from industrial manufacturers about what’s holding them back?
Brian: A lot of tools still ask frontline teams to do more work, not less. More dashboards, more alerts, more places to check, more interpretation required. That creates cognitive overload. Adoption improves when technology fits the way people already work, helps them make better decisions faster, and earns trust by being clear and actionable.
Pat: Scale. Identifying the right tools remains a focus, but what’s paramount is meeting teams where they are and integrating seamlessly into their daily workflows. The technology has to fit the work, not the other way around.
4. What does closing the insight-to-action gap look like in practice, and what are the industrial manufacturers getting it right doing differently?
Brian: It starts with a clear productivity problem, not a technology project. The leaders getting this right are asking: What decision are we trying to improve? Who needs to act? How fast do they need to act? What systems need to be connected? When clean machine data, operational context, and clear workflows come together, teams can move from signal to corrective action much faster.
5. Industrial AI investment is accelerating, but results vary widely. What has to change, in technology, in culture, or in how teams are structured, for it to deliver?
Pat: Start with the people. Leaders need to intimately understand their employees’ pain points, operational cadences, and daily workflows before anything else.
Brian: Leaders need to move beyond dashboards and alerts toward execution. That means AI has to be built around specific roles, jobs to be done, and the real workflows of reliability, maintenance, and operations teams. That is the idea behind the Industrial AI Workforce: role-based agents that help turn signals into trusted recommendations and frontline action.
6. This week showed that AI means something different depending on who you’re talking to. When an industrial manufacturer is evaluating what to invest in, how do they make sense of it all?
Brian: Start with what the technology does for the person using it. Does it help a reliability leader prevent failures? Does it help maintenance teams act faster? Does it help operations keep production running? Augury’s core Machine Health offering is built on more than 15 years of industrial experience and more than 1 billion hours of machine data. The Industrial AI Workforce builds on that foundation with role-based agents designed to help teams move from insight to action faster.
Ready to close the gap?
The manufacturers winning with Industrial AI aren’t waiting for the perfect technology stack. They’re starting with the right problems, the right people, and the right partners.
If you’re thinking about what it takes to get there in your organization, we’d love to talk. Request a demo with our team.
