Artificial intelligence (AI) and machine learning (ML) have continued to garner vast amounts of attention as their impact resounds across multiple industries, manufacturing being no exception. Nearly 31% of enterprise respondents within the manufacturing sector from 451 Research’s Voice of the Enterprise: Internet of Things, Workloads and Key Projects survey claim that AI/ML technologies are a critical enabler to the success of their organization-wide IoT initiatives. Although AI/ML is currently being used to drive IoT initiatives for a mere third of manufacturing respondents, its influence is continuously growing, with the magnitude of its impact significant at both the industry and the organizational level and driving adoption.
Adoption of AI/ML is increasingly born of a need to remain competitive within the space, driven more strongly by industry-wide shifts in sentiment compared with organizational pressures.
As per 451 Research’s Voice of the Enterprise: AI & Machine Learning, Adoption and Use Cases survey, 90% of manufacturing respondents believe AI will have at least a slight impact on the industry over the next two years, while 75% believe the same about their own organization. Adoption of AI/ML is increasingly born of a need to remain competitive within the space, driven more strongly by industry-wide shifts in sentiment compared with organizational pressures. However, sentiments surrounding AI are positive, demonstrating a positive outlook and driver of adoption that will increasingly lead to greater overlap and co-deployments of IoT and AI/ML technologies over the next few years.
The 451 Take
Current state of industrial AI
Barriers to adoption
Skills gaps, budget constraints, tech integration
Drivers for adoption
Optimization, improving quality of products and/or systems, automation
The 451 Take
AI/ML is rising in importance as a critical enabling technology in manufacturing environments for the adoption and success of IoT deployments. As data generated by sensors and distributed systems becomes increasingly complex and continues to increase in volume, AI/ML is essential to make sense of it, allowing for the generation of critical insights from IoT deployments, driving key value for various manufacturing applications.
The concepts of AI/ML and the technology used is similar across all verticals, but industrial use is an edge first use case that has driven changes in how models are orchestrated and distributed. Machine optimization is a prime example where constant time series data needs to be evaluated on the fly, such as preventative maintenance and automation. That is not to say cloud or other compute venues in the IoT continuum do not have a place, as clearly comparing the Overall Equipment Effectiveness (OEE) of multiple factories globally located is not a pure edge computing use case. Despite pervasive inhibitors, deployment of industrial AI applications within manufacturing environments is anticipated to grow significantly over the next two to three years, driving demand for AI/ML applications at the edge, ultimately paving the way for advances in industrial robotics and broader use cases into the future.
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The resulting overlap between AI/ML and IoT technologies is at present generating felt value within industrial manufacturing environments and will only increase in size and scope in the near term.
The intersection of artificial
intelligence and manufacturing: