Middle Management: The Driving Force Behind AI Success in Manufacturing

Middle Management: The Crucial Link to AI Success in Manufacturing

Vietnam is making significant strides in advancing its AI capabilities through focused government strategies and major investments. The National Strategy on AI Development sets an ambitious goal to rank Vietnam among the top four ASEAN countries and within the global top 50 for AI research and applications by 2030. In 2024, Decree 182 introduced the Investment Support Fund, offering up to 50% of initial investment costs for semiconductor and AI R&D projects.

These initiatives signal a clear shift toward big investment and strategic risk-taking in AI development, urging manufacturing leaders to reconsider their AI investment plans despite economic challenges and geopolitical uncertainties.

The Data Challenge for Manufacturers

Minister of Information and Communications Nguyen Manh Hung highlighted AI as the core technology of the Fourth Industrial Revolution. However, sectors like pharmaceuticals, automotive, and food processing require advanced AI — including deep learning and 3D scanning — to meet complex use cases and compliance demands.

Such AI advancements depend heavily on high-quality, extensive data. Manufacturers must collaborate to share data securely and structure it effectively. Creating safe environments for data sharing is essential since AI thrives through both competition and cooperation. Globally, there’s a growing emphasis on privacy-preserving data infrastructures, including public datasets and national data libraries that support innovation and responsible AI development.

This data collaboration challenge is also internal. Research by Zebra Technologies found that nearly 20% of automotive AI machine vision leaders in Germany and the UK feel their systems could perform better. Similar concerns exist in Vietnam, where AI success hinges on data quality and availability. Addressing data issues is critical for AI to fulfill its promise.

Zebra Machine Vision: Turning Data into Value

Data generated at the edge of manufacturing operations can be transformed into valuable assets—whether for training AI models or refining production and inspection processes. Integrating data and AI opens the door to automation with smart cameras, sensors, and vision-guided robotics, freeing frontline workers to focus on growth areas.

Yet, manufacturing sites often operate in silos, limiting data sharing even for similar workflows. Variations in experience and time across teams complicate data quality efforts, alongside difficulties in recruiting skilled personnel. Consistent data storage, annotation, and usage for model training and testing are necessary to maximize AI effectiveness.

Manufacturers must overcome hesitations about cloud adoption, often due to concerns over privacy and intellectual property. Cloud-based solutions enable secure data uploading, labeling, and annotation across locations, offering scalability and computing power accessibility.

AI applications are already available for diverse use cases such as electric battery and semiconductor inspection, fresh food sorting, packaging compliance, serial number reading, and automotive defect detection. However, improving AI performance requires measuring ROI, setting realistic timelines, and ensuring data quality. Transforming data management alongside workforce training and operational adjustments takes time, but some AI solutions offer low or no-code setups for faster returns.

Why Middle Management Holds the Key

Who can champion AI adoption and push for meaningful change within manufacturing? Research shows that middle managers—many millennials aged 35 to 44—are the frontline leaders connecting the factory floor and senior executives. These managers report high levels of AI expertise and enthusiasm, with 62% indicating strong AI skills.

Middle managers are well-positioned to drive AI initiatives that boost productivity, automate processes, and improve quality. Yet, only 30% of senior leaders increase resources for growth initiatives during volatile times, and less than a third dedicate significant time to long-term growth.

Now is the time to rethink the role of middle management in prioritizing growth and AI adoption. Empowering frontline teams and ensuring visibility of assets allow these managers to lead AI-driven improvements effectively. The moment for hype is over; it's about practical action and results.

Manufacturing leaders ready to explore AI strategies and training options can find valuable resources at Complete AI Training, where courses cover a range of AI applications and skills relevant to manufacturing and beyond.