What Are 4 Key Impacts of Artificial Intelligence (AI) in Heavy Industry?

Artificial Intelligence (AI) is ubiquitous and is impacting nearly every industry. For the heavy-asset industry, AI has already begun to positively impact how the industry can optimize labor and enhance productivity.  

Just how prominent has AI become? Here are a few statistics to understand AI trends in 2024:

• 25% of companies have adopted AI to address labor shortages. 
• Over 95% of executives agree that generative AI will revolutionize where and how AI is used. 
• The global AI market size is expected to grow 37% annually from 2023 to 2030.  
• Over 40% of business leaders report increased productivity through AI automation.  

In the oil and gas sector, 92% of companies are investing in AI or plan to in the next two years. Its impact is already being felt, with 50% of oil and gas executives saying they have already begun using AI to help solve challenges in their organizations. 

Why is there a rush to adopt AI, and how is it used in the heavy-asset industry? Let’s review the top four ways AI impacts businesses in heavy industry. 

1. Optimize Predictive Maintenance     

Equipment in the heavy-asset industry tends to be specialized and costly. Companies engage in preventative or predictive maintenance to ensure the machines are operating as intended.   

Predictive maintenance helps reduce maintenance costs by preventing unnecessary inspections and minimizing major repairs or replacements. By analyzing the data, predictive maintenance systems can alert maintenance teams about potential issues before they lead to equipment breakdowns. This early detection is key to avoiding unplanned downtime, which can be extremely costly in repairs and lost production. 

When should companies engage in predictive maintenance? The equipment manufacturer may have a suggested maintenance schedule. Companies may also rely on their employees’ intuition – they can ‘sense’ when the equipment is not performing. This unscientific method is ripe for failure. 

Another practical consideration may be operating budgets. Due to financial constraints, companies may try to stretch out the time between maintenance work on the equipment. Putting off the maintenance can make repairs far more costly than they would have been if done earlier.  

Harvard Business Review reports that Honeywell used AI to identify equipment displaying anomalies. This has helped the company detect potential problems earlier and “reduce[d] the maintenance time for Burj Khalifa’s [the world’s tallest building] mechanical assets by 40% while improving their availability to 99.95%.” 

How does AI help with predictive maintenance? AI algorithms can analyze sensor data from heavy machinery and equipment to predict potential failures before they occur. 

Without machine learning algorithms, technicians manually analyze data to detect signs of wear or failure and schedule maintenance to prevent breakdowns and extend equipment life. 

By eliminating the guesswork of predictive maintenance and applying AI, heavy asset industries can keep vital equipment up and running as needed and extend its life.  Unexpected outages are expensive. If orders are delayed and or go unfilled, the impact on a company’s reputation can be devastating.   

Greater insight into maintenance needs also empowers companies to decide how to apply their budgets and spend. Finally, more certainty about maintenance needs is helpful as companies strategize how to maximize the workforce. 

2. Enhance Site Safety  

In heavy-asset industries, where the stakes are high and the risks even higher, safety isn’t just a priority—it’s a necessity. With some of the highest injury and fatality rates among all sectors, these industries face unique challenges in protecting their workforce. 

Companies and employees are aware that their workplace is potentially dangerous. Many companies invest time and money into safety programs to minimize or eliminate safety issues. 

Is that money well spent? If injury or fatality rates are not on a consistent downward trend, the impact of safety programs is debatable.  

Safety issues tend to happen around and involving the same criteria. Some elements of the work process prove to be more prone to accidents. If employees are made aware of the issue, they are more likely to act cautiously and lessen the chance of a safety incident.  

When dealing with a contractor workforce who may be on and off many work sites, managing safety is more challenging as many may be unaware of each site’s safety challenges.  

Companies that invest in advanced safety programs, particularly those augmented by AI, have shown a significant return on investment. A study found that every dollar spent on safety programs yields up to six dollars in cost savings from avoided accidents and improved worker productivity. 

By analyzing real-time sensor data and video feeds, AI can alert companies to potential issues, enhance safety, and improve safety programs.  AI can identify potential safety hazards, alert workers, and prevent accidents.  

In addition, AI-powered systems monitor heavy assets and detect potential safety hazards or compliance violations in real time. By keeping assets operating as intended, they're less likely to be involved in a safety incident.  

While AI tools provide essential insights and warnings, integrating these technologies with human oversight ensures that safety measures are both proactive and responsive, leveraging the strengths of both technology and human judgment.

3. Automate Regulatory Compliance 

Frequent law changes, vast rule sets, and managing large, diverse workforces complicate regulatory compliance in heavy industry. Ensuring regulations are strictly followed can be challenging, particularly in large workplaces that include a significant percentage of contract workers. 

Before enforcing regulations, management needs to be clear on the nitty gritty. This is challenging since the rules are complex, they change regularly, and there are many. AI can help. 

For example, using AI in the oil and gas industry can address this challenge by automating data collection and analysis. Machine learning algorithms can sift through extensive documents and datasets, extracting relevant information and highlighting areas that need attention to meet regulatory requirements. 

Regulatory compliance also requires qualified people to perform the work. Ensuring employees are qualified when managing a large, contracted workforce is very challenging. The contractor may leave the company out of compliance (and out of contract). 

Even better, AI-driven analytics can monitor operations in real-time, providing continuous compliance monitoring. Any deviations from regulatory standards can trigger immediate alerts, enabling companies to take corrective actions promptly. 

Companies that maintain regulatory compliance can increase safety, avoid potential fines, and increase productivity. 

Advanced AI applications can go beyond data analysis by integrating with compliance management systems to provide real-time insights and actionable recommendations, ensuring compliance is both proactive and responsive. 

Navigating the complex maze of regulatory compliance can be formidable. AI systems play a pivotal role by efficiently tracking changes in regulations and ensuring that businesses are up-to-date and compliant. This automation seamlessly extends to reporting, where AI tools swiftly aggregate and analyze compliance data, generating reports that adhere to regulatory standards.  

In addition, generative AI can create realistic data models in stress-testing compliance systems. This is particularly useful to determine how a business might fare under various regulatory scenarios, including extreme conditions. By simulating different regulatory environments, businesses can better prepare for potential changes in compliance requirements.