APAC Engineers Accelerate AI Adoption Across Industrial Applications

Engineers across Asia-Pacific are moving artificial intelligence from experimentation into wider industrial deployment, with process automation, predictive maintenance and anomaly detection among the most common applications.

According to the fifth annual Avnet Insights survey, AI is becoming increasingly embedded in products and engineering solutions across the region as companies look to improve efficiency, reliability and competitiveness.

Globally, 56% of engineers said they are already shipping products incorporating AI, representing a 33% increase from the previous year. Adoption is particularly strong in China, where AI is being integrated across sectors including automotive, industrial automation and smart manufacturing.

Industrial AI Moves Beyond Experimentation

Across APAC, engineers are increasingly moving from AI trials towards practical applications that can deliver measurable operational benefits.

The most widely adopted AI applications identified in the survey include:

• Process automation – 42%
• Predictive maintenance – 28%
• Fault and anomaly detection – 28%

These applications reflect the growing role of AI within manufacturing and other asset-intensive industries where companies are looking to improve productivity, equipment reliability and operational efficiency.

China continues to lead the region in large-scale AI deployment, while Japan is taking a more reliability-focused approach, with emphasis on practical applications and long product lifecycles.

Other APAC markets are also steadily expanding AI adoption as manufacturers introduce more intelligent systems into production and engineering environments.

ASEAN Manufacturing Accelerates AI Deployment

Southeast Asia is expected to play an increasingly important role in the region’s industrial AI growth.

Tan Aik Hoon, Regional President for Avnet South Asia, Korea and Avnet United, said the findings provide a useful framework for markets including Singapore, Malaysia and Vietnam as ASEAN strengthens its position as a global manufacturing hub.

Manufacturers in the region are increasingly evaluating AI applications based on their ability to support commercial and operational outcomes rather than experimentation alone.

Avnet said it is seeing a shift from AI trials towards industrial-scale deployment across the ASEAN manufacturing corridor.

Edge AI and Machine Learning Gain Importance

Engineers are also increasingly combining Edge AI with machine learning.

Globally, 64% of engineers surveyed said they prioritise both technologies equally, reflecting growing demand for AI systems that can process and analyse information closer to where industrial data is generated.

This approach is particularly relevant to industrial environments where machines, sensors and production systems continuously generate operational data.

As AI becomes more integrated into industrial equipment and manufacturing processes, engineering teams are also having to manage increasingly complex requirements around system design, data and deployment.

Data Quality and Maintenance Remain Key Challenges

Despite accelerating adoption, engineers continue to face several barriers when implementing AI.

The survey identified continuous maintenance, cited by 54% of respondents, and data quality, cited by 46%, as two of the most significant challenges.

These challenges become increasingly important as companies move from limited AI projects towards larger-scale production environments.

Industrial AI systems require reliable data and ongoing optimisation to maintain performance as equipment, operating conditions and production requirements change.

Companies are also investing in skills development to support wider adoption.

The survey found that engineering teams are prioritising capabilities in areas including model optimisation (17%) and data analysis (16%).

AI Enters the Industrial Deployment Phase

The findings point to a broader shift in how AI is being adopted across Asia-Pacific.

Rather than focusing primarily on experimental projects, manufacturers and engineering teams are increasingly deploying AI within operational systems where it can support automation, predictive maintenance and equipment monitoring.

As adoption expands across the ASEAN manufacturing corridor and other APAC markets, the ability to manage data quality, maintain AI models and develop the necessary engineering skills will remain important to scaling these technologies successfully.

For industrial companies, AI adoption is increasingly moving from proving that the technology works towards integrating it reliably into everyday engineering and manufacturing operations.