The growth in AI software capabilities is transforming how industrial companies harvest and derive value from manufacturing data. Starting with suppliers to engineering through operations, machine learning algorithms are revolutionizing manufacturing, driving unprecedented efficiency, accuracy, and insights.
However, and while industrial AI has many benefits, there is an increasing gap between the revenue expectations implied by the aggressive AI infrastructure build-out and actual revenue growth in the industrial AI ecosystem due to a still slow adoption of AI.
The slow implementation of AI/ML technologies in manufacturing is mainly attributable to a lack of industrial AI use cases (or a lack of support by ops and management) and concerns around data security and model accuracy due to the high stakes in production processes and the proprietary nature of manufacturing data. In addition, and compared to other industries, manufacturers are more cautious ensuring correct results from AI automation outputs that govern process integrity and product quality.
Finally, and most critically, manufacturers have serious concerns regarding the costs of using a new technology that is often outside their scope of expertise, and which requires a dependency on high cost and scarce labor resources (data scientists) to maintain. Manufacturers are in need of a self-serviceable AI deployment and usage model that reduces these barriers to entry.
Software and analytics companies in our focus address these challenges and strive to put AI to work effectively and to mitigate AI risks. By leveraging simplified data management platforms (including MLOps) and ML/Deep Learning methodologies further backed by deep domain expertise and application knowledge around the use cases, they provide the data and analytics infrastructure and actionable AI/ML-powered insights needed to drive real-world AI use cases within a clearly defined customer specification framework.
Finally, and by making small and fast steps through pilots, testing, and simulations up to final model deployment –AI/ML-powered analytics must deliver quantifiable outcomes and clear business benefits for industrial customer within their operational framework.