By Chris Royles, EMEA Field CTO at Cloudera.
Across all industries, there’s an agreement that innovations in AI have the potential to revolutionise a whole host of business processes. Specifically, in the manufacturing sector, the impact is profound.
The global AI market in manufacturing was valued at $3.2 billion in 2023 and is poised to grow to $20.8 billion by 2028. While some manufacturers are beginning to take AI from the lab to production, most have only just begun dipping their toes into the water.
Manufacturers must take a considered approach to AI deployment, weighing up the risks and ensuring that they have guardrails in place to mitigate them. At the same time, it’s crucial to establish the most valuable use cases. With so many practical applications of AI emerging every day, from chatbots to predictive maintenance, it’s difficult to know where to begin. The irony is, AI can help here too.
AI assisting AI
By using an LLM to conduct background research, manufacturers can gain a better understanding of where AI can provide the most value in just a few minutes. LLMs can also help to prioritise use cases and explain the benefits by answering questions such as:
- What are the top 10 use cases for AI in manufacturing? Here, manufacturers can quickly cut through the noise and gain insight into where AI can provide the most value. In manufacturing, popular AI use cases include supply chain optimisation, quality control, predictive analytics, energy management and customer insights.
- Can you classify these use cases by financial impact on revenue? One aim of enterprise AI is to increase revenue. So, the next logical step is to understand which use cases will have the biggest impact on revenue. For many manufacturers, supply chain optimisation will be top priority for revenue enhancement because it has a broad and deep impact across procurement, production, and distribution.
- Can you map these use cases against the EU AI Act’s risk categories? Globally, regulators are looking to govern the safe use of AI. But the EU is a step ahead of most, having recently passed the EU AI Act. So, mapping use cases against the EU AI Act can help manufacturers to understand the risks. Here, supply chain optimisation may be considered a limited risk because it mostly affects operational efficiency and does not directly impact safety. Whereas quality control would be considered high risk as it directly affects product safety and compliance.
- My manufacturing company operates in [insert locations]. Can you tell me which regulations these use cases could infringe upon? Outside of AI-specific regulations, there is also a mosaic of legislations that manufacturers must abide by, particularly if they operate internationally. LLMs can help shed light on the broad array of regulations that will apply to AI use cases. For example, FDA regulations for AI in medical devices would apply to some manufacturers. When the final product in a manufacturing process involving AI goes overseas, several additional considerations and use cases come into play, such as compliance with export regulations.
These questions can provide manufacturers with a great starting point. But it’s important to not take all this information at face value and should be supplemented with further research, in order to make any informed decisions. LLMs can provide references for their own findings which will provide some direction for further reading around potential use cases to present to the business.
Armed with this knowledge, manufacturers will have a better understanding of where AI can help them the most. But having the ability to utilise AI is one thing, successful deployment is another – it means more than understanding use cases, risks, and regulations.
Data-driven AI in manufacturing
To be truly effective, AI must have access to a complete set of high-quality manufacturing data, across design, manufacturing and operations, or it will provide questionable analysis and below-optimal results. However, in today’s hybrid, multi-cloud environments, data often sits in siloes, making it difficult to access. Across such vast, distributed environments, implementing consistent control and compliance is also a challenge.
This is why having a modern data architecture is important. By implementing a unified data platform, manufacturers can provide AI with data from across any environment – whether in the cloud or on-premise. Strict governance can also be enforced to ensure that this data doesn’t leak outside of an organisation and evoke the wrath of regulators.
How manufacturers can get the most from AI
As utilising AI in production becomes more common, prioritising use cases will be key to success. In the manufacturing industry, the tiniest improvements will enhance yields, so those companies that harness AI’s potential stand to be the winners. However, they need to first ensure they’re truly ready for enterprise AI, and not just following the crowd.
Establishing a modern data architecture is therefore critical for manufacturers aiming for AI success. Without adopting this approach, many AI projects are likely to fail before they get out of the starting blocks.