Winning in the Industrial AI Era: Yokogawa's Role and Vision
The ARC Advisory Group Singapore Forum, held on 7 August 2025, centered on the theme "Winning in the Industrial AI Era." Yokogawa, as a Platinum Sponsor for the second consecutive year, highlighted the growing significance of artificial intelligence (AI) and emerging technologies in industrial automation. Over 180 attendees gathered to explore how AI is reshaping the manufacturing landscape.
AI-First Strategy: Transforming Industrial Operations
Chai Kah Ming, Yokogawa’s Head of Smart Manufacturing and Executive Consultant, delivered a thought-provoking presentation titled “AI-First Strategy: Industrial Game-Changing Solutions to Deliver Key Value.” With extensive experience leading more than 30 Industry 4.0 projects, Chai focused on how AI can be applied to industrial operations while prioritizing sustainability.
In his session, Chai outlined that “AI-First” doesn’t equate to “AI-Only.” Instead, it’s about integrating AI into existing industrial systems to improve decision-making and enhance process optimization. The key is to identify the right use cases for AI, so it can complement legacy systems like DCS, PLCs, and ERP, rather than replacing them.
Understanding the Stages of Industrial Autonomy
Manufacturing today is at different stages of autonomy, with many factories still operating in a semi-automated state. However, the first movers are gradually transitioning toward full autonomy. Yokogawa’s “IA2IA” (Industrial Automation to Industrial Autonomy) approach visualizes this journey as a series of stages, emphasizing the incremental move toward fully autonomous operations.
AI in Industrial Automation: A Powerful Tool, Not a Replacement
Chai stressed that AI’s role in industrial transformation is to enhance, not replace, human capabilities. AI acts as a powerful tool to solve complex problems and improve efficiency. Industrial systems, such as MES and ERP, can remain intact, while AI coordinates across OT, IT, and ET layers. Human oversight is always maintained, with AI operating in various modes—ranging from human-in-the-loop to fully autonomous under safety guardrails.
Composability and Digital Twins: A Modular Approach to AI Adoption
Chai also introduced the concept of composable digital twins, combined with embedded AI, to extend intelligence across the organization. The modular approach allows companies to start small with a specific use case and gradually build out AI and digital twin capabilities. This flexibility makes it easier for organizations to scale their digital transformation without needing to fully digitize or connect all systems upfront.
Real-World Use Cases: AI Delivering Tangible Results
Autonomous Process Control in Chemical Manufacturing
One standout example shared by Chai involved a Japanese chemical plant using AI to optimize a complex distillation process. The plant faced significant challenges due to tight control requirements and process dynamics that traditional methods could not stabilize. Yokogawa’s reinforcement learning-based FKDPP AI model enabled the plant to reduce steam usage by 40%, optimize energy efficiency, and maintain consistent product quality over three years.
AI-Powered Digital Twin for Asset Management
In another case, a large mining company with multiple sites and over 3,000 controllers struggled with asset management due to resource constraints. By deploying a digital twin integrated with AI, Yokogawa enabled real-time monitoring and predictive maintenance, optimizing the performance of automation assets. The system allowed maintenance teams to act proactively, significantly improving operations and reliability across sites.
The Future of AI: A Trusted Partner in Industrial Transformation
Chai concluded his presentation by reflecting on the future of AI in industry. The transition from human-driven decision-making to AI-enabled autonomy will be gradual, with humans maintaining oversight throughout. He outlined best practices for implementing AI, including aligning use cases with business goals, building composable systems, ensuring safety and compliance, and starting with modular solutions that can scale toward autonomy.
Conclusion: Harnessing AI's Potential for Responsible Industrial Transformation
The ultimate aim of industrial AI is not to replace human expertise but to augment it. By leveraging AI’s scale, speed, and analytical power, while keeping human oversight focused on purpose and ethics, organizations can navigate the complexities of industrial transformation responsibly and effectively.