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AI technology is rapidly entering its 2.0 era. As the technology matures, generative AI is steadily moving out of the laboratory and into the stage of industrial deployment. According to forecasts from multiple research institutions, the global generative AI application market has entered a period of rapid growth, with market size expected to expand from US$11.3 billion in 2023 to US$51.9 billion in 2028, representing a compound annual growth rate (CAGR) of 35.6%. In 2023, North America remained the largest market globally, with a 34.8% share; however, Asia showed the strongest growth momentum, accounting for a 22.1% market share and with an expected CAGR of 41.7%. These numbers highlight the Asian region’s key role in generative AI in the future.  

This growth momentum is fueled by the broadening of industrial applications on all fronts. A McKinsey report notes that generative AI has shown significant revenue growth potential across a wide range of industries and corporate functions. For example, the application of generative AI can effectively enhance efficiency and innovation in marketing & sales, in customer service of banking/insurance/telecommunications, as well as in product R&D in advanced electronics & semiconductors/manufacturing/ pharmaceutical/healthcare. Generative AI is also accelerating automation in the fields of education, legal services, business management, community services, and creative arts. Notably, the higher a worker’s education level, the greater the potential for AI to support or optimize their role. This underscores the profound impact of generative AI on knowledge-intensive industries.

Amid this wave of AI-driven industrial upgrade, the green transition has emerged as a focal point for all parties. Whether it is discriminative AI or generative AI, it has the potential to help industries achieve their net-zero goals. In the short term, use cases aimed at energy saving and carbon reduction are especially urgent. In light of the global push toward net-zero emissions, Taiwan’s export-oriented industries will inevitably face challenges— particularly the energy-intensive sectors such as petrochemicals, electronics, steel, cement, textiles, and paper manufacturing. The government has also announced plans to invest NT$900 billion by 2030 in its push to achieve net-zero emissions by 2050. 

The integration and application of AI and big data provides an effective path to low-carbon manufacturing. In manufacturing, the combination of AI with big data can optimize production processes, predict quality, and enable intelligent scheduling to improve energy efficiency. Meanwhile, carbon emissions and energy consumption can be reduced through process decarbonization, resource allocation optimization, and equipment health management. For example, Industrial Technology Research Institute (ITRI) assisted CPC Corporation to apply AI technology to their naphtha cracking process. Through the use of data analysis and parameter recommendations, the plant was able to effectively cut down energy consumption and carbon emissions by about 20,000 metric tons p.a., without compromising product quality. 

In the medium term, industry-specific use cases will bring wide-ranging green business opportunities. In the energy sector, AI can analyze the optimal combination of solar power generation and end-user consumption, or simulate the environment and distribution of wind turbines for offshore wind farms in order to configure for maximum generation efficiency. In the semiconductor industry, some domestic companies are applying AI algorithms and GPU computing to photomask generation, significantly shortening development time and cost in addition to delivering energy savings.

Looking ahead, generative AI will become a driving force in the green transition. Some companies have already introduced generative AI into smart manufacturing environments. For example, by using Vtubers (Virtual YouTubers) to quickly generate a large variety of content, or by integrating small language models (SLMs) and AI Agents, so that AI can learn industry-specific knowledge and workflows to optimize processing sequences and schedules thereby improving efficiency and reducing costs.

In summary, generative AI is not only an engine of corporate innovation but also a catalyst for achieving sustainability goals. Those who can effectively integrate AI technology with green transition strategies will gain a competitive advantage in the global market and create a new paradigm that balances performance and sustainability.