Artificial Intelligence in Manufacturing

Industrial AI agents, unlike general LLMs, are designed for specific tasks, leveraging plant-specific knowledge graphs for context. This approach provides more reliable and predictable outcomes, which is required for industrial automation and control.

Graph technology helps manufacturing leaders manage complex, unstructured data by connecting equipment, procedures, and performance records. This article introduces how graphs can reveal valuable insights, enable predictive AI, and optimise processes by capturing relationships across datasets.

Generative AI (GenAI) is poised to transform industries like energy, chemicals, oil and gas, and mining by predicting supply chain disruption and optimising operations. Despite the tech bubble bursting, investor confidence in Industrial AI startups is growing, but true AI value requires more than surface-level integrations.

Edge AI holds many benefits for industrial applications. However, the complexity of deploying these technologies can be high, especially when considering hardware standardisation, software architecture, model development, training and deployment, organisational factors, and finally maintaining the models.

As the hype around AI intensifies, the number of use-cases is increasing exponentially. In contrast,  the number of people who actually know how to implement AI technology in industry remains small.