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As co-founder of MaintainX, I believe that manufacturers are falling into an "AI trap" by rushing to implement artificial intelligence solutions without proper understanding or foundation. In this article, I explain that while AI will reshape manufacturing within five years, most people tasked with bringing AI strategies forward don't really understand the technology or how it works. This leads to failed implementations and wasted resources.

The key to success lies in building a strong data foundation that combines both machine data (from sensors/IoT) and human-generated insights (from operators and maintenance records) before jumping into AI adoption. At MaintainX, we see this pattern emerging across the industry, where pressure from boards and FOMO (fear of missing out) are driving hasty AI implementations that ultimately fail to deliver value.

Read the entire article here.

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The article discusses the importance of capturing both machine data and human-generated insights for successful AI implementation. In your experience, what are the biggest challenges in collecting and integrating these two types of data effectively? How have you overcome these challenges in your organization?

Discuss in the comments below. 👇

 

The largest issue I have found is the human factor. Having to depend on people, not all people, to input correct values consistently. Over time, people get complacent and tend to “pencil whip” input and this skews the true results. One way to overcome this, is to rotate the personnel responsible as well as follow up for critical information. Using monthly audits at the time of scheduled PM helps to follow the track of the machines. This also allows the operators and technicians to work together which helps to cement the need for proper information. If the operator knows why it is important, they are more apt to fulfill their part. 

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