How to Prepare for AI in Your Operations

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

 

The most common industrial application of AI today is for equipment monitoring — especially large assets like pumps and compressors — to help predict potential failures. Many industrial facilities already use asset management software. AI enhances the functionality of this software by analyzing historical and real-time data to identify and mitigate risks. 

 

Beyond maintenance applications, AI is also increasingly being used for process optimization and complex problem-solving tasks that advanced process controls alone can’t handle.

 

Some may wonder why AI is necessary when advanced process control (APC) already exists. The reason is that APC relies on first-principle models, meaning it performs well when processes follow known scientific laws and can be accurately modeled. However, where processes are highly variable, difficult to model or require multiple assumptions, AI can adapt and find patterns that traditional APC cannot. 

 

This doesn’t mean APC is no longer relevant. But it is important to realize that existing APC technologies can be complemented by AI, improving decision-making in areas where conventional modeling falls short.

At Hargrove Controls & Automation, we’re seeing industrial companies consider adopting AI when they face large or complex process issues, especially long-standing 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 and plant-wide challenges, using data collection and aggregation to analyze complex systems at a facility-wide level.  A standout example of AI implementation that Hargrove participated in involved a refinery that collected historical data to build a neural network controller. This system provided control set points to the distributed control system (DCS), allowing for new process optimizations. It also incorporated cost data, enabling AI to adjust production strategies for greater efficiency and profitability.

However, we see 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. 

 

Helping to offset this gap between large and small industrial companies is the fact that control system vendors are actively embracing AI and expanding resources for customers.

 

This accessibility means manufacturers should start preparing now. Maximizing data collection is key to AI use, even if your planned adoption of AI may still be years away. 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.

 

The best way to prepare for AI is to ensure your operations data are well structured and accessible for future AI applications. 

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.

 

It’s also important to realize that 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 is another 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.