28% of Taiwan’s Electronics and Information Manufacturing Industry Has Implemented AI
The Industrial Technology Research Institute (ITRI) recently released a survey titled “AI Adoption in Taiwan’s Electronics and Information Manufacturing Industry,” which reveals that 28% of Taiwan’s electronics and information manufacturing companies have implemented AI, while 46% are in the planning stage. In terms of sub-industries, the development of AI applications is more advanced in the PCB, optoelectronic materials, and components sectors, while other electronic components, computer, and peripheral equipment industries are progressing more slowly.
The survey indicates that large enterprises are ahead of SMEs in AI deployment, but SMEs have accelerated their efforts, with a projected compound annual growth rate (CAGR) of 26% in AI investment from 2024 to 2026. ITRI industry analyst Zhang Jiafu stated that the primary goals for manufacturers adopting AI are to improve performance and reduce costs, with key performance indicators including yield rate, production capacity, time-to-market, and cost.
AI Investment Continues to Grow, with Hardware Spending Taking the Largest Share
The survey results show that companies that have implemented AI in 2024 are expected to invest an average of NT$2.09 million, with this figure projected to rise to NT$2.36 million in 2025 and NT$2.61 million in 2026, representing an 11.5% CAGR over three years. Approximately 40% of businesses are continuing to increase AI investment, with 46% estimated to increase their budgets in 2025 and 39% expected to raise their investments in 2026.
Average AI Investment Forecast for Companies That Have Implemented AI
Source: Future Business (Data from ITRI MIC)
Regarding resource allocation, in 2025, hardware expenditure will account for the largest share of AI investments in the manufacturing industry, reaching 46%, followed by software (42%), while services (12%) will have the smallest share. This suggests that Taiwan’s automation sector has strong competitiveness in hardware, and future business opportunities are promising.
Resource Allocation for AI Investments in the Manufacturing Industry in 2025
Source: Future Business (Data from ITRI MIC)
Observation 1: Discriminative AI Is the Mainstream, Generative AI’s Potential Still to Be Developed
ITRI MIC pointed out that discriminative AI remains the mainstream in AI technology investment for the manufacturing industry, with a 73% budget allocation for discriminative AI in 2025, far exceeding the 27% allocated for generative AI. In 2026, the investment share for generative AI is expected to slightly increase to 29%. When focusing on the current AI applications in manufacturing units, the number of companies adopting discriminative AI is 1.6 times that of those using generative AI.
Budget Allocation for Discriminative AI and Generative AI in Manufacturing in 2025
Source: Future Business (Data from ITRI MIC)
Although generative AI applications are currently mostly limited to “product development report generation” and have relatively low satisfaction levels, with the development of AI agents and human-machine collaboration technologies, generative AI is expected to expand into more manufacturing and production processes in the future. Zhang Jiafu suggested that solution providers should continue to develop relevant applications to seize emerging market opportunities.
Observation 2: Quality Control and Production Are the Mainstream AI Applications
Half of the top ten AI applications are related to manufacturing production, with the top three being defect detection, defect image labeling, and production process improvement. In the future, the demand for AI in manufacturing and production departments will remain the highest, followed by product development and quality inspection departments, indicating that the level of intelligence in these departments will further widen the gap with other departments.
According to the ITRI MIC survey, the top ten AI applications are as follows: defect detection, defect image labeling, production process improvement, product development reports, root cause analysis of defects, production scheduling planning, detection of design flaws, safety accident analysis, process parameter optimization, and analysis of production issues.
Observation 3: IT Department as the Core Driver of AI Implementation
In companies that have implemented AI, the IT department leads the development progress with an implementation rate of 60%, indicating that the IT department is generally the driver of digital transformation in enterprises. The next two leading departments are manufacturing production and product quality inspection, which are also the areas with the highest concentration of AI applications.
AI Adoption Satisfaction and Challenges: Data Is the Core Issue
The survey shows that companies have varying levels of satisfaction with the results of AI adoption, with the most noticeable improvements being increased revenue, alleviation of labor shortages, and reduced costs. However, satisfaction with “improving problem predictability” is relatively low. Zhang Jiafu analyzed that the AI prediction ability did not meet expectations, possibly due to factors such as market supply and demand fluctuations, the political and economic environment, and the company’s own data preparedness.
ITRI MIC pointed out that data-related issues remain the biggest challenge in AI development for the manufacturing industry, with 80% of companies that have implemented AI facing data-related difficulties. Large enterprises, in particular, face more complicated data management issues due to their organizational structures. Furthermore, companies still in the planning stage of AI adoption mainly face the challenges of high costs and difficulty in assessing the benefits.
Industry analyst Zhang Jiafu stated that data readiness can only be assessed through practical implementation. Insufficient data can lead to poor model performance, while excessive data without proper governance can prevent AI from reaching its full potential. Companies should plan AI application scenarios first, then plan the required data to ensure the right amount, quality, and governance of data to enhance AI application accuracy and effectiveness.
Article reproduced with permission from: Future Business