Adding to the challenges surrounding the use of artificial intelligence in manufacturing are the number of ways in which it can be utilized. While this type of flexibility is usually seen as a benefit, it can also create new debates on how to budget time and resources for all the potential applications and the impact it will have on the workforce.
I recently interviewed Rob Ratterman CEO and co-founder of Waites, a leading provider of industrial sensor technology. He shares his thoughts on how to best implement AI, how manufacturers are doing in their efforts to use these tools and the impact it should have on workers and OT environments.
Jeff Reinke, editorial director: The momentum behind AI implementation in manufacturing has not slowed. What do you feel are some of the biggest missteps that companies are taking in their eagerness to put AI to work?
Rob Ratterman, CEO, Waites: Your biggest problem with how you’re implementing AI (this thing you don’t even understand and cannot figure out how to do a single thing with) is that you’re not using it in every area of your business.
Successful AI integration lies in its ability to optimize operations and improve automation by analyzing massive amounts of accurate and reliable data. If the AI model fails to capture all data or a comprehensive picture of the data, the analysis it provides will be incorrect and/or unsuitable.
Not only is this unhelpful from a business standpoint, but it also will likely cause larger complications down the line if manufacturing leaders need to spend time and money understanding why the AI they’ve integrated isn’t working at full capacity.
JR: A common perception is that AI replaces people and, therefore, jobs. How would you recommend introducing new AI initiatives to the workforce while quelling concerns over being replaced?
RR: AI is sustainable and successful when it works in tandem with the workforce. Companies introducing new AI initiatives to their workforce should emphasize this point and demonstrate that AI is more of an assistant than a replacement.
As skilled labor becomes increasingly difficult to source, the ability to automate diagnostics and prioritize repairs is a force multiplier for companies eager to remain competitive. When it comes to maintenance teams in particular, by leveraging predictive maintenance technology (PdM) and AI, leaner teams can operate with precision and confidence without compromising on uptime, safety or performance.
Through the assistance of AI, maintenance teams can focus their time and energy on delivering clear, prescriptive insights instead of chasing false alarms or performing routine inspections on healthy equipment. Technicians can respond to real issues before they escalate and become more proactive than reactive. That shift in mindset empowers teams to drive meaningful impact and build more resilient operations.
JR: If you had to pinpoint one driving force that should fuel all AI strategies and implementations, what would it be?
RR: Like all technologies before, AI should be a tool used by humans — not on humans — to ultimately improve people's lives and work.
Companies implementing AI should use the “human-in-the-loop” approach as a guiding force, as it helps ensure employees are not feeling replaced or encouraged to produce lower-quality work because AI is assisting them.
JR: There’s an abundance of AI tools and providers in the marketplace right now. What selection advice would you offer in sorting through all these options?
RR: When selecting the correct AI tool for your business, it’s essential to find solutions that work across your entire manufacturing chain. Because AI analyzes large swaths of data, it’s a strategic imperative to have a tool that can reflect the entire production chain.
Implementing AI does not have a long learning curve or require massive infrastructure changes - it just requires the right partner. By working with a partner that offers tools to support an entire manufacturing chain, manufacturers can unify maintenance across every facility. Incorporating a scalable maintenance program also helps in navigating the transition to AI-enabled workflows as it allows AI to grow more sustainably.
JR: There’s been a lot of talk lately about the AI bubble popping. What is your take?
RR: If AI is introduced to workflows correctly, we likely won’t see the AI bubble pop. When companies use humans in conjunction with AI, they scale and optimize their operations more sustainably.
AI in manufacturing is just getting started, so companies should take the time to implement AI in a way that works best for their workflows and employees. By starting small and taking steps to reach full AI optimization, companies can implement AI in a way that is more impactful and sustainable.
In short, introducing AI to a work process won’t be successful if the technology is not properly implemented with human collaboration and buy-in. Using AI to support current operations is a longstanding goal that will contribute to sustainable business practices.
JR: Outside of AI, what do you see as some of the biggest trends impacting manufacturing?
RR: Predictive maintenance (PdM) has gained popularity on factory floors for its ability to reduce downtime and improve operational efficiency. Prescriptive maintenance (RxM), however, is the latest trend we’ll see improving manufacturing alongside AI.
Prescriptive maintenance determines the treatment protocol, optimal intervention window and expected outcome of a predicted maintenance operation. Additionally, this maintenance method helps address potential breakdowns by providing actionable, data-driven recommendations on how to address any specific issue maintenance teams may be alerted to before they become a larger challenge.
This ultimately improves ROI for companies looking to make significant efficiency gains in their reliability strategies. I predict prescriptive maintenance will work hand-in-hand with predictive maintenance in having the biggest impact on manufacturing in 2026.
