Guest contributor |
Over 50 per cent of manufacturing companies say that AI and machine learning (ML) will have the biggest impact on their operations in the next five years, according to ARC Advisory Group’s Accelerate Digital Transformation with Industrial AI Initiatives and Technologies Survey. AI and ML are predicted to be more impactful than cloud infrastructure, clean energy technologies and physical robots.
The broadening of the industrial AI toolset beyond the specific use cases and limitations of generative AI is a key trend to watch. While generative AI is attracting significant attention, other AI techniques, such as causal AI, neuro-symbolic AI and traditional ML algorithms, are equally important for addressing the complex needs of the industrial sector. Causal AI, for example, helps to identify cause-and-effect relationships, enabling more explainable and trustworthy AI solutions. Neuro-symbolic AI combines the strengths of neural networks and symbolic AI, allowing for more accurate and transparent AI solutions for mathematical and engineering use cases.
One more trend to look out for is the competition for industrial-grade data scientists. The demand for AI experts who understand the nuances of manufacturing and industrial processes is growing exponentially. These specialists are essential for developing the accurate, explainable, predictable, low latency and domain-specific AI skills that the industrial sector needs to capitalise on the massive investments being made in edge and cloud infrastructure. They are crucial for bridging the gap between AI technology and practical application. The ARC survey confirms that organisations identified as ‘leaders’ are more likely to attract, recruit and train data scientists and AI specialists to advise them internally. Industrial organisations are increasingly establishing in-house Industrial AI Centers of Excellence (AI CoE) to attract, train and retain ‘industrial grade’ data scientists.
Colin Masson is the director of research at ARC Advisory Group
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