How AI can transform the materials value chain

About the event

Artificial intelligence (AI) represents a significant opportunity to accelerate innovation across the whole materials value chain. However, adoption remains low for several reasons but perhaps primarily due to limited awareness of the potential applications and solutions to materials businesses.

Do you want to understand how your materials business can benefit from AI?

Are you developing AI solutions for materials businesses?

Join us in this workshop to discuss the opportunities, barriers and interventions to encourage the materials value chain to reap the benefits from AI.

Why attend?

In this workshop, we will:

Highlight the range of applications for AI in the materials value chain – Case studies will be provided across a broad range of application areas including materials discovery, optimisation, production and end-of-life. You can network with those developing solutions in these areas.

Tour the Materials Processing Institute (MPI) to see examples of where AI has been put into practice – MPI is a not-for-profit research and innovation centre with a 75-year track record in developing new materials, processes and technologies. It illustrates a clear example of how AI can be applied in established processes, typical of many in the materials value chain.

Discuss the barriers to adoption and consider the interventions that can help overcome these barriers – Building on previous discussions and research, we will document and prioritise the barriers to adoption in different applications areas then work through the Innovate UK intervention canvas to uncover areas where Innovate UK can facilitate AI development, implementation and adoption.

Who should attend?

This event is ideal for:

• Organisations creating, developing and producing materials interested in how AI can help their operations
• AI developers creating solutions relevant for materials organisations
• Researchers in this field who can contribute to the workshop discussions and develop a greater understanding for the applicability of their research