The field of Artificial Intelligence (AI) has become so compelling to business and technology professionals that Forrester estimates 58% are researching AI today. There is a problem though: Only 12% are actually using it.
So, what’s the hold-up? If AI has the power to improve operational efficiency, optimize equipment performance, minimize waste, and reduce maintenance costs, why isn’t every manufacturer buying-in?
According to an MIT Sloan Management Review piece, there is a gap between ambition and execution. The study used data from more than 3,000 executives and technology experts on their AI ambitions, and found that the main barriers to adoption included:
- Security concerns
- Lack of leadership support
- Competing investment priorities
- Lack of general technology capabilities
- Unclear or no business case for AI applications
- Attracting, acquiring, and developing the right AI talent
It’s clear that many organizations are unsure whether the juice is worth the squeeze. But whether it’s a lack of leadership support or need for AI talent, manufacturers should start by identifying the business potential, workplace implications, and industry context for themselves. Many companies with a passive view towards AI, for instance, rely heavily on external resources to get their AI efforts going. On the other end of the spectrum, companies who are considered AI pioneers are primarily building AI-related skills through training and hiring.
But no matter where your company lies on the adoption spectrum, AI has the power to enable collaborative and natural interactions between people and machines that extend the human ability to sense, learn, and understand.
The amount of data coming from connected devices and sensors makes it nearly impossible for a human operator to actually contextualize this information. AI makes this data valuable so that decision-makers can use it to understand the effects of a change before implementing something new. But AI goes much deeper than contextualizing big data, these systems can also flag anomalies to make recommendations while at the same time learning from that data. Ultimately, this helps companies to make the shift from mere predictive maintenance to predictive intelligence.
Imagine a factory asset being able to better predict potential failures, which in turn reduces downtime and provides employees with far superior tools to manage the quality of your manufacturing process.
Companies Using AI with Great Success
Honeywell is a great example of a large organization focusing on practical human + machine solutions today. From automated sorting for warehouses to automating in-store retail systems, Honeywell is ahead of the curve. They even offer a maintenance and inspection automation solution for logistics/industrial markets. And these offerings are paying off big time, increasing productivity by up to 35%, reducing errors by up to 25%, and reducing training time by up to 50%. By simplifying task management, reducing human error, ensuring compliance, and giving workers remote safety monitoring capabilities, there are massive opportunities for manufacturers to raise the collective bar.
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