Davos, Switzerland – Contemporary Amperex Technology Co., Limited (CATL) has been named a recipient of the World Economic Forum’s (WEF) 2026 MINDS Award, a distinction recognizing high-impact innovations in Artificial Intelligence. The award highlights CATL’s "Augmented Intelligence Leading Next-Generation Lithium-ion Battery Design" project, which the WEF cites as a new global benchmark for industrial AI application.

The MINDS (Meaningful, Intelligent, Novel, Deployable Solutions) program is an initiative by the WEF’s Centre for AI Excellence designed to identify technologies that drive safe and collaborative industrial transformation. CATL was one of only 15 organizations globally to pass the rigorous evaluation across strategy, talent, data, technology, and governance.

According to the official WEF report, the award recognizes CATL’s ability to turn a "deep reservoir of proprietary, multimodal data into a sustained AI advantage," effectively shifting battery cell engineering into a fully data-driven process.

The Shift from Trial-and-Error to Predictive Design

The automotive industry is currently facing compressed development timelines. With New Energy Vehicle (NEV) market penetration exceeding 50 percent and vehicle development cycles shortening to just 18 months, the pressure to deliver higher-quality, safer batteries is intense. Traditional lithium-ion development has historically relied on manual trial-and-error and the individual experience of engineers—a process characterized by high prototyping costs and prolonged testing periods.

CATL’s award-winning solution addresses these bottlenecks by moving from a "reverse design" methodology to forward prediction. By integrating the company’s vast resources in materials science, manufacturing, and equipment, CATL has deployed an intelligent design platform housed on an on-premises private cloud.

The system functions as a "digital engineer." It utilizes physics-based electrochemical models combined with agentic AI to automatically generate and refine design options. This closed-loop system, underpinned by over 50 million data records, allows for the rigorous evaluation of battery designs before physical production begins.

Performance Metrics and ‘Augmented Intelligence’

The capabilities of the platform are extensive. Trained on more than 100,000 specific design cases, 600TB of test data, and real-world aftermarket data from a wide spectrum of NEVs, the system reportedly achieves a prediction accuracy of up to 95 percent.

While manual design approaches can take weeks or months, CATL’s AI-driven optimization can generate design recommendations in seconds and complete virtual cell designs in minutes. This speed allows for dynamic adjustments to meet highly customized performance requirements without sacrificing reliability.

However, company leadership emphasizes that the technology is designed to assist, not replace, human expertise.

"The MINDS Award is a significant recognition of our exploration in intelligent battery design," stated Ni Jun, Chief Manufacturing Officer and Co-President of Engineering Manufacturing System at CATL. "In complex industrial scenarios like battery production, relying solely on 'Artificial Intelligence' can't solve core problems—I advocate more for 'Augmented Intelligence'. By integrating our intelligent design system with engineers' in-depth understanding... we can strengthen the human–AI partnership."

Industry Implications

The launch of the MINDS initiative reflects a broader shift in the AI sector from theoretical potential to concrete performance. As organizations seek practical adoption pathways for intelligent technologies, the WEF is using the program to highlight scalable, responsible deployment.

For CATL, this recognition underscores a commitment to moving AI from merely "enabling" existing processes to "creating" new possibilities in energy storage. By solving known engineering limitations and discovering new frontiers in materials science, the company aims to solidify the technical foundation necessary for the global energy transition.