Guest contributor |
More than 50% of manufacturing companies say that AI and Automatic Learning (ML) will have the greatest impact on their operations over the next five years, according to the Arc Advisory Group Accelerate digital transformation with a survey on initiatives and technologies of industrial AI initiatives. AI and ML should have more impact than cloudy infrastructure, clean energy technologies and physical robots.
The widening of the industrial AI tool set beyond the specific use cases and the limits of the generative AI is a key to monitoring. While generative AI draws significant attention, other AI techniques, such as causal AI, neuro-symbolic AI and traditional ML algorithms, are just as important to meet the complex needs of the industrial sector. Causal AI, for example, helps to identify cause and effect relationships, allowing more explanatory and trustworthy IA solutions. Neuro-symbolic AI combines the forces of neural networks and symbolic AI, allowing more precise and transparent AI solutions for mathematical and engineering cases.
Another tendency to seek is the assistance of scientists of industrial quality data. The demand for IA experts which includes the nuances of manufacturing and industrial processes increases exponentially. These specialists are essential for developing specific, explainable, predictable and weak skills specific to the field that the industrial sector needs to capitalize on massive investments made in Edge and Cloud infrastructure. They are crucial to fill the gap between AI technology and practical application. The ARC survey confirms that organizations identified as “managers” are more likely to attract, recruit and train scientists from data and AI specialists to advise them internally. Industrial organizations are increasingly establishing internal industrial industry centers (AI COE) to attract, train and keep scientists of “industrial quality” data.
Colin Masson is research director at Arc Advisory Group
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