Written by Ronald van Loon, CEO & Principal Analyst at Intelligent World

The rapid evolution of technology has paved the way for industries to undergo a significant transformation. By leveraging cutting-edge solutions, businesses can optimize their operations, enhance efficiency, and drive innovation.

I’m Ronald van Loon, we’ll take an in-depth look at how intelligent transformation leverages AI and other technologies to significantly impact multiple industries.

Evolution of artificial intelligence in business strategy

Artificial Intelligence (AI) has emerged as a game-changer in technology. With its ability to process vast amounts of data and make informed decisions, AI is revolutionizing industries by offering strategic insights and automating complex tasks. One such revolution is Huawei’s recently announced All Intelligence strategy, built to focus on providing the immense computing power required to train foundation models tailored for diverse industries. By enhancing the synergy between hardware, software, and cloud solutions, businesses can cater to the varied AI computing needs of different sectors.

Tailored solutions for diverse industries

The need for industry-specific solutions has never been more evident. By fostering collaboration with partners and stakeholders, technology providers can develop cutting-edge solutions that cater to the unique requirements of sectors such as finance, government, manufacturing, electric power, and railways. These solutions, grounded in intelligent transformation architecture, are designed to address the challenges, and capitalize on the opportunities inherent in each industry.

An example of this collaboration, and the results stemming from it, is the partnership between the China West Airport Group (CWAG) and Huawei. Together, they have incubated 35 intelligent solutions. Among these, the intelligent ground handling solution stands out, boasting a 20% improvement in efficiency and a 17% reduction in turnaround times. Another noteworthy solution is the AI-assisted flight planning optimization model, which seamlessly balances supply and demand in airport operations.

Advancements in meteorology

Accurate weather prediction is vital for various sectors, from agriculture to transportation. With the introduction of advanced weather models, meteorologists can predict weather patterns with greater accuracy and in a fraction of the time previously required, resulting in 20% greater accuracy in rainfall forecasting in some cases.

For example, the Pangu Weather AI model, developed by the Huawei Cloud team, is a high-precision numerical weather prediction model that aims to provide accurate weather forecasts. Designed to predict various weather phenomena, such as typhoons, rainstorms, and other severe weather events, the model is critically important. It aids in minimizing the impact of adverse weather conditions on various sectors, such as agriculture, aviation, and public safety, by providing timely and precise weather information and  also plays a vital role in disaster preparedness and management.

The technological landscape is evolving at an unprecedented pace, fostering innovations that are reshaping industries. By harnessing the power of AI and tailored solutions, businesses can drive intelligent transformation across sectors, ensuring a brighter and more efficient future.

Revolutionising the mining industry

The mining sector, characterized by its challenging work environments, stands to benefit immensely from technological advancements in AI application. By implementing visual recognition technologies, mining operations can achieve higher accuracy levels, ensuring the safety and efficiency of underground workers. Furthermore, Shandong Energy Group, one of China’s largest coal mining companies, has leveraged transformative AI, resulting in an impressive increase in production, adding an extra 8,000 tons of cleaned coal annually at their Jining-based coal mine.

Driving innovation

Autonomous driving represents the pinnacle of vehicular innovation. However, the challenges associated with unexpected scenarios, have hindered its widespread adoption. Nevertheless, advanced AI models using digital twins, or models that reflect real-world physical situations, has drastically reduced the learning curve for autonomous driving systems, cutting down the time from over two weeks to under two days. This approach not only enhances the safety of autonomous vehicles but also accelerates their development and deployment.