From Predictive Maintenance to Process Optimization: AI’s Growing Impact on Industrial Automation

By Ryan Grove, Hargrove Controls & Automation Engineer

In industrial automation, new technologies are often adopted gradually due to safety and environmental concerns. Despite this cautious approach, AI has become increasingly mainstream and accessible in recent years. It represents the next major step for the industry, offering capabilities to improve production and efficiency in ways that conventional tools cannot.

How AI Is Used Today

The most common industrial application of AI is currently equipment monitoring, especially large assets like pumps and compressors that help predict potential failures. Many sites already use asset management software and AI enhances functionality by analyzing historical and real-time data to identify and mitigate risks. Beyond maintenance, AI is used for process optimization and complex problem-solving tasks that advanced process controls alone can’t handle.

Why Companies Are Turning to AI

Clients consider adopting AI when they face large or complex process issues, especially longstanding problems that traditional methods have struggled to resolve. When other approaches prove ineffective, AI offers a viable solution with its ability to use historical data to develop targeted strategies to help clients meet their goals.

For larger companies, AI is valuable for multi-unit or plant-wide challenges, using data collection and aggregation to analyze complex systems beyond individual equipment. However, smaller companies often struggle to leverage AI because they lack the infrastructure for comprehensive data collection and the resources needed to invest in AI-powered analytics platforms. Without access to these tools—or the engineering effort to integrate them effectively—they face greater challenges in solving complex issues.

One standout example of AI implementation was at a refinery that collected historical data to build a neural network controller. This system provided control set points to the distributed control system (DCS), enabling new optimizations. It also incorporated cost data, enabling AI to adjust production strategies for greater efficiency and profitability.

Why AI Over Advanced Process Control?

Some may wonder why AI is necessary when advanced process control (APC) already exists. APC relies on first-principle models, meaning it performs well when processes follow known scientific laws and can be accurately modeled. However, in some cases where processes are highly variable, difficult to model, or require multiple assumptions, AI can adapt and find patterns that traditional APC cannot. Existing APC can be complemented by AI, improving decision-making in areas where conventional modeling falls short.

The Expanding Role of AI in Industrial Automation

A surprising development is how accessible AI has become, with control system vendors actively embracing it and expanding resources for customers. AI wasn’t even part of the conversation a just few years ago.

This accessibility means manufacturers should start preparing now. Maximizing data collection is key, even if AI adoption is still years away. Most facilities collect data but don’t analyze or use the majority of it in an impactful way. Since AI relies on historical and real-time data, sites without robust data collection will struggle to implement AI effectively when they are ready to adopt it.

Selecting the Right AI Solution

Not every AI platform is suited for industrial applications. Many AI developers specialize in data science but lack control system expertise, leading to solutions that don’t prioritize reliability and safety. Manufacturers should choose AI solutions from vendors who understand their specific manufacturing processes and control systems to make sure the system functions as intended.

One of the most exciting technologies in industrial automation is the digital twin, which uses historical data and computational models to create a virtual replica of a plant or process. While not inherently AI-driven, digital twins can be paired with AI for tasks like predictive modeling and optimization. This allows manufacturers to test modifications, run scenarios, and explore optimizations without risking production, safety, or environmental impact.

Preparing for AI Adoption

The best way to prepare for AI is to ensure that data is well structured and accessible for future AI applications. Many in the industrial world are cautious about change, but those who take steps now to align their data practices with AI capabilities will be more prepared when AI becomes standard.

Many facilities allocate their budget to maintenance and sensor upgrades instead of AI because they don’t see an immediate economic impact from AI tools. As AI solutions become more accessible and affordable, those who invest in organizing their data and upgrading infrastructure now will be in the best position to benefit in the future.

Addressing Resistance to AI

In an industrial facility, operators are often among those who are most resistant to AI because it feels like a black box to them, making decisions they can’t easily explain. When implementing AI tools, thorough training, clear communication, and rigorous testing are key to building trust and ensuring a smooth rollout. If AI causes problems early on, regaining trust can be difficult.

Cybersecurity Considerations in AI

Cybersecurity is an important consideration when adopting AI in industrial settings, particularly when using third-party, cloud-based AI solutions. Connecting control systems to external AI platforms can introduce vulnerabilities, increasing the risk of cyber threats. As AI adoption grows, manufacturers should work closely with their IT and OT security teams to ensure that AI solutions align with industry standard cybersecurity protocols and network safeguards.

Final Thoughts

AI is transforming industrial automation, offering new ways to optimize processes, improve efficiency, and solve complex challenges. As AI adoption grows, manufacturers that prioritize structured data collection and select AI platforms suited for industrial applications will be best positioned to take advantage of its full potential.

While challenges exist, the increasing availability and affordability of AI make it an investment worth considering. Companies that take proactive steps today will gain a competitive edge as AI becomes a standard tool in industrial operations. Our Team can help. Contact us today.