Artificial intelligence has quickly become one of the most talked-about, researched, and marketed technologies of our time. From machine learning and autonomous decision-making, AI is often touted as the next episode of the digital transformation – Industry 5.0. Despite the huge potential of applying AI to control system design in industrial and manufacturing settings, companies are struggling to translate this theoretical revolution into measurable value on the plant floor.
This seeming conflict between theory and application of AI isn’t because the technology lacks potential – much of the time it’s because we’re often placing the cart ahead of the horse. Successful implementation of AI-driven automation systems requires the right use cases, the right data, and robust integration with OT. Experimentation with this new technology can turn into tangible results with a perspective balancing present capabilities with future potential.
Where AI Thrives: Augmenting Automated Processes
Industrial AI systems operate in unique ecosystems compared to consumer or enterprise AI. Safety, efficiency, and reliability are integral, and factors such as physical constraints, equipment limitations, and real-time operational dynamics makes designing an effective, autonomous AI automation system difficult. As such, it’s better when AI is used to augment control systems and human operators, not replace them.
However, that’s not to say the capabilities of a smart manufacturing intelligence system aren’t inherently – and tangibly – valuable to manufacturers. Where AI is delivering ROI today:
Predictive Maintenance & Equipment Health
The most developed and widely adopted use case for AI in industrial automation systems is for predictive maintenance. Predictive algorithms can use data from vibrations, temperature and pressure, and power data from IoT sensors to detect early signs of possible failures, necessary maintenance, or approaching end-of-life/obsolescence.
- ROI: Extended asset lifetime, lower maintenance and repair costs, reduced downtime
- Tip: AI outputs should be embedded into existing SCADA or maintenance workflows rather than as a standalone dashboard.
System Agility in High-Variability Operations
Where AI excels is in learning, managing, and optimizing complex, interconnected processes that are affected by stochastic variables. In industries like food and beverage, energy, chemicals, and pharmaceuticals, AI can identify optimal operating windows that are difficult to model with only traditional control system strategies.
- ROI: Reduced energy usage per product, optimized throughput and quality control, yield improvement
- Tip: AI should operate as an advisory or supervisory layer to existing PID or model-based control that recommends adjustments or alerts to suboptimal conditions.
Anomaly Identification & Quality Monitoring
Machine learning automation technology is increasingly able to identify subtle variations in quality parameters such as imbalances in a metal rolling line or temperature control in a chilled mixer. 3D vision technology can detect packing defects such as misshapen bottles on a beverage line or improperly stacked lumber on a pallet.
- ROI: Pattern recognition in batch processes, machine vision to detect defects, early alerts for off-spec trends in production
- Tip: The value here lies not just in catching defects, as operators can often identify defects. However, AI is able to identify the upstream problems that lead to quality issues, preventing defects and their costly effects.
Facility Energy Management, Forecasting, & Optimization
Rising energy costs, sustainability initiatives, and increasing regulations for energy usage, waste, and emissions are driving the adoption of AI-driven energy management systems. AI can use historical energy usage data to learn production schedules and consumption patterns, which it can then integrate with weather data and periods of peak grid demand to forecast usage and recommend strategies to reduce peak loads and energy intensity where it’s not needed.
- ROI: Reduced energy costs, ability to adapt to changing regulations, smaller environmental impact
- Tip: These AI solutions are especially effective when integrated with facility energy management systems and real-time process control.
Where AI Struggles: Learning Limitations & Autonomy
People across industry, academics, and society are concerned about AI’s ability to gain full autonomy on complex tasks in a way that essentially replicates human capabilities. In the manufacturing industry, that simply isn’t the case. Despite its strengths, AI control systems are not (yet) a universal automation solution:
Not Skilled at Fully Autonomous Control
It’s very rare to find an implemented AI system that attempts to independently control automated industrial processes without any operator oversight, and that’s because it’s risky. The potential cost of failures like safety incidents, regulatory violations, and equipment damage outweighs the potential benefits.
It’s Only As Smart As the Data It’s Given
AI models are only as intelligent as the data they consume – and they need a lot of really clean, high-resolution data. Inconsistent instrumentation, missing pieces from legacy black box systems, and poorly structured historical data are common barriers. Operators must consider that AI’s recommendations may not always have the necessary context.
AI Projects are Prone to Overdesign
Custom control system architecture is one thing; an advanced automation system can be tweaked and applied across assets and sites because there are definite specs and so finite customization options. However, AI can learn, so will “drift” into a narrower and narrower trajectory as it’s trained, becoming over-specialized. AI projects that rely on highly-customized models not designed with a clear scaling path will fail to deliver ROI.
How to Maximize ROI on a Smart Automation Solution?
AI’s greatest strength is serving as a toolkit to augment and update automation systems, so the most successful AI deployments must be focused, measurable, and tightly integrated with existing control system architecture. The crux of success is less about algorithms and more about project fundamentals:
- Data readiness: clean, contextualized OT data, incorporating FEED study results
- Deployment strategy: clear decisions regarding cloud processing vs edge computing
- System integration: seamless integration with existing control system design
- Change management: communication throughout project and training for operators
AI delivers value when it supports operators and the existing process automation architecture, not when it overwhelms and complicates. Automation and control system integrators play a major role in making AI actionable by bridging the gap between advanced data analytics software and real-world operational goals and constraints, including:
- Aligning AI initiatives with operational goals and KPIs
- Designing architectures that scale across sites and assets
- Ensuring cybersecurity and system reliability
- Integrating AI outputs with PLCs, DCS, SCADA, and MES systems
Without a comprehensive, end-to-end strategy and the right automation services company as a partner, even the most advanced AI won’t reap ROI.
Balancing Present AI Capabilities with Future Potential
AI is already on the plant floor delivering ROI in industrial automation systems across the globe, but only when it’s been applied thoughtfully, pragmatically, and – perhaps most importantly – with deference. The most successful projects treat AI as a way to enable better operations, not something to hand control off to as a replacement for human expertise.
With focus on practical applications, strong data foundations, and tight integration with existing automation systems, manufacturers can turn the ephemeral hype of industrial AI into tangible return.