Artificial intelligence (AI) is revolutionising the manufacturing industry, offering unprecedented value through various applications, including machine learning, generative AI, and more.
Machine learning algorithms can identifying potential issues before they cause downtime, optimising production schedules, and improving quality control.
Generative AI aids in design, innovation and productivity, creating efficient and novel solutions that traditional methods might overlook.
Additionally, AI-driven data analytics provide actionable insights, streamline supply chain management, and enable real-time decision-making. By infusing AI into IT systems, manufacturers can achieve greater efficiency, reduce costs, and maintain a competitive edge in a dynamic and changing market.
AI is a fast-moving field, and only a few can claim significant experience, especially with the recent disruption caused by generative AI which has fundamentally changed the way we will view and work with technology in the future.
My IT in manufacturing blog has more recently evolved towards a focus on AI.
While I am not an expert or a pioneer, every important journey has to start somewhere. I invite you to join me on this path of discovery as we explore AI in manufacturing in all its aspects.
When you have a hammer, everything can appear to be a nail! Generative AI (GenAI) is certainly a massive hammer, but using it for competitive advantage on your own data can sometimes be elusive, especially in business. How can GenAI be put to work on your company’s information without leaking all your proprietary secrets?
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 “experts” is increasing exponentially. In contrast, the number of engineers who actually know how to implement AI technology remains small.