Artificial Intelligence in Machinery

The artificial intelligence (AI) for machinery applications report gives an overview of the technology, application, and markets for AI on the machinery level.  The research is based on 40 plus expert interviews plus an online survey.  We feature market size, as well as strategies for adoption and go into a deep dive on individual machinery segments.

Artificial Intelligence (AI) in Machinery: Changing Everything

Around 2009, people began talking about the fourth industrial revolution, Industrial IoT, and other related concepts.  However, in retrospect, the second and third industrial revolutions largely just replaced human muscle and manual labor with machines and computers that basically repeat pre-programmed behavior.  While the fourth industrial revolution increased the level of digitalization, until recently, even the most educated machines and computers did not make human-like decisions.  Now, with AI entering the plant floor, we’re finally starting to use digital technology to replace not only muscles but also brains.  Most experts agree that while AI will become deeply embedded across industrial and other applications and initial use cases have emerged, AI in manufacturing today is still a niche technology.  

Today, depending on your perspective, your thoughts on AI probably fall somewhere between the technology being an abstract threat or possibility, and a real-world solution with concrete use cases where you may not even know it is at work.  In the future, AI will be so deeply embedded in everyday life and in automation that we won’t even recognize that it is there for most of the time. 

In order to enable something that groundbreaking, it is crucial to identify the right solution for the right machine type.  Machine tools offer other possibilities; other than robots or material handling applications—and not only are the applications different, but also the AI techniques used.  The report features these characteristics, next to the individual market potential, and also lists a number of strategies of how to deal with the most common roadblocks, when implementing AI.

AI in Machinery Strategic Issues

In addition to providing a five-year market forecast, the AI for machinery applications market research provides detailed quantitative current market data and addresses key strategic issues as follows.

• The market penetration of AI in machinery is still in its infancy.  Machine builders are restructuring their business models to accommodate this new technology, while end users are working on operation-specific use cases for their machinery.  However, the market has moved beyond the AI hype cycle into tangible products and applications.  For each user and machine builder it is necessary to address the most common hurdles decisively.
• When it comes to adopting new technologies on the plant floor, the human factor is the most important.  ARC has identified these three major strategies to help make the workforce appreciate the introduction of AI:
  • Clear Benefits for Human Workers: Make the benefits to workers transparent, ease operations, and avoid direct conflict with existing work routines.  For example, AI can ease post-project paperwork or generate reports using speech recognition, reducing time and effort.
  • Involve Key Stakeholders: Resistance to AI solutions depends on AI team leadership and stakeholder involvement.  While corporate executives and advanced research groups typically show little resistance, most of the time, AI project teams are cross functional, involving multiple layers of the hierarchy.  This is especially true when it comes to labeling the data; the mid-management and people on the plant floor are needed.
  • Right Application for Right Client: In general, initially at least, end users are likely to push back more against AI for applications that don’t impinge on their core value proposition. 
• Skepticism about AI is huge.  It is important to offer a reliable solution that provides a clear benefit.  On the other hand, these use cases need to be part of a broader AI strategy and roadmap, so that one can leverage the full potential of AI.  For example, in mining machinery, the main requirement is to minimize downtime as the industry is capital intensive.  Currently, mining machinery is integrated with machine learning to support applications for data aggregation, processing, and predictive maintenance.  Older mining machines can be upgraded to increase machine sophistication, which can be achieved at low cost.  Many suppliers in partnership with AI solution vendors can provide these solutions for end users.
• Other applications include machine perception, which is available for mobile machines, and is partly autonomous.  The latter is mostly used for hazardous underground mining.