AI is becoming a key driver of industry innovation and an engine of productivity. AI technologies are setting the stage for new forms of productivity that will create significant value. AI-enabled applications are characterized by abundant data, vast knowledge, and high fault tolerance. In the future, AI will improve its ability to understand and interact with the real world and transform the economy and society. Two paths are gradually taking shape in the field of industrial intelligence: the path of general-purpose foundation model and the path of dedicated small models. These two paths complement each other and are unleashing value in their respective applications.

The EU AI Act aims to ensure a high level of protection for health, safety, and fundamental rights in terms of legally protected interests, which means its broad objective is to make sure AI-based systems, AI-based products, and services are safe. The AI Act has a horizontal approach across sectors within EU competence, considering sectorial specificities and needs without prejudice to other relevant EU acquis. This risk-based approach is the fundamental idea behind the AI Act. The higher the risk, the stricter the rules.

In the past, it would usually take more than 10 years and billions of dollars to develop a new drug. The AI-assisted pharmaceutical pipeline powered by Huawei’s Pangu model has greatly shortened the development time for new drugs, significantly reduced costs, and opened up more space for innovation.

Despite misconceptions and fears, AI in healthcare is entering a data era, bringing existing knowledge to patients and doctors in a better way and generating new knowledge at a faster speed. We are motivated by what amount of benefit AI brings to patients and doctors. This is also a motivation for the industry—that generative AI is going to change the healthcare market with a huge margin.

AI has significantly transformed academic research over the past few years. It’s bringing a lot of benefits, especially in data analysis, literature review, hypothesis generation, automated repetitive tasks, etc. AI is not only enhancing the efficiency and effectiveness of academic research, but opening up new avenues for exploration and innovation. But at the same time, there are also some challenges brought about by using AI. Some of the challenges are about data bias, transparency, and ethical concerns. These challenges highlight the need for careful consideration and balanced integration of AI in academic research. To maintain research integrity, we must improve measures for journals and researchers.

At this roundtable, we released the GIO White Paper: AI-enabled Industrial Innovations. This white paper is a collection of success stories of AI-enabled industry applications. It serves as a useful reference for decision makers in the manufacturing sector, as these case studies are all selected from the automotive and manufacturing industries. With the help of AI, these industries have accelerated digitalization across their design, engineering, operation, and maintenance phases, which further fuels innovation. Some of these case studies are on mature applications, while most of them are on new applications that have passed the proof of concept (PoC) phase. Each case study describes the customer’s challenges, how AI helps address these challenges, and the results of AI enablement. The value of AI enablement is highlighted on a radar chart for each case study.