Industrial leaders are under increasing pressure to boost efficiency, reduce downtime, meet sustainability targets, and make better use of existing assets. But with tight budgets and lean teams, how can you achieve these goals without adding complexity?
Enter Industrial AI.
This guide is designed for non-technical leaders and decision-makers who want to understand what Industrial AI is, how it delivers value, and how to get started.
Industrial AI refers to the application of artificial intelligence technologies to improve operations, maintenance, and decision-making in industrial settings. It uses historical and real-time data from sensors, equipment, and systems to identify patterns, detect anomalies, and make accurate predictions.
Think of it as a smart assistant that never sleeps, constantly monitoring your operations and alerting your team to potential issues or opportunities before they become problems.
Industrial AI helps businesses:
Early detection of faults, not detected easily by condition monitoring software, allows for planned interventions and minimal downtime. Just like the first ever failure of the Produced Water Pump on a new FPSO, the early prediction provided time for repairs, avoiding a £1M plant trip.
AI analyses data across multiple processes or equipment to determine factors that are leading to reduced output, higher energy consumption or repeated reliability issues. In one example, an energy producer used industrial AI to compare two air supply units as they were experiencing reduced energy production and high energy consumption. The AI was able to pin point an issue which resulted in plant heat retention and significant annual fuel cost savings.
AI can help operators stay safe and compliant, detecting root causes of issues so operators can control flaring, leaks or contamination. An oil and gas operator in the middle east achieved a 40% reduction in flaring, reducing their emissions, improving the sites utilization of its fuel and power and generating savings of $527K per annum.
AI can assist operators understand how much they can push their assets without impacting reliability, when to hold off on routine maintenance activities and how to extend the life of an asset. One North Sea oil and gas operator was able to use AI on its late life assets to maintain the uptime and integrity of their platforms, reducing downtime.
Industrial AI learns from the data that’s constantly being produced by plant equipment, identifying deviations from normal operation. Teams that can access, understand and apply these insights benefit, working more efficiently and effectively. Setting up alerts and notifications helps operators plan ahead of time, and spend less time firefighting issues, as can be seen here in this example of a simple AI alert before a Produced Water Filter clogs.
In many cases, the ROI from predictive maintenance and process optimisation can be realised within months. See the business case for industrial ai infographic.
“In a conventional way, we would form a team to look at the problem, and it would take weeks to fix that problem… technically you couldn’t get this particular decision to be made, as fast as that.” Head of Offshore Operations
Sensors and systems collect real-time operational data.
The AI platform cleans and structures the data.
AI algorithms learn from past performance and outcomes. No-coding required.
The system provides insights, forecasts, or alerts.
Your team takes proactive steps to prevent downtime or inefficiency.
Modern platforms like OPUS use no-code AutoML and MLOps tools so your engineers can build and manage these AI models themselves—no data science team required.
• "AI is too complex for our team." Modern industrial AI platforms are designed for use by engineers and operators—not data scientists.
• "We don’t have enough data." You likely already collect valuable time-series data from equipment, sensors and control systems.
• "AI takes years to implement." Many AI use cases—such as predictive maintenance—can be implemented in weeks or months.
"It took our focus group two weeks to form an action plan. When we met with VROC, the VROC model gave all the problems that we needed to focus on in less than ten minutes. This helped the engineers pinpoint the problem." Manager, Oil and Gas
Predictive maintenance is a proactive, forward-thinking approach that aims to prevent equipment failures before they happen. Companies can gain valuable insights into the health and performance of their assets, identifying potential issues and taking preventive action. By analyzing historical data and monitoring equipment conditions in real-time, these solutions can predict when a failure is likely to occur and recommend preventive action. More on predictive maintenance
Resolving complex operational challenges may be the key to untapping significant savings and value for many industrial businesses. Subject matter experts can build models of their operational processes and systems and uncover insights a wide range of challenges, including equipment output, cost reduction, energy management and forecasting. More on Process Optimization
Detect hazards and risky behaviour in real time with CCTV and AI. Real-time alerts lead to the prevention of incidents and compliance with a range of HSE policies. Learn about Watchworks AI
These applications can apply across oil & gas, mining, manufacturing, water utilities, and other asset-intensive sectors
A successful Industrial AI initiative usually involves:
• Engineers and Operators who understand the processes
• Maintenance and Reliability Teams who act on insights
• IT or OT teams who help with system access and integration
• Business Leaders who sponsor and scale the initiative
Importantly, you don’t need a team of data scientists to get started.
"Let everyone use it, don't restrict to any process engineer or operation engineer, give everybody access including business planners, let everyone use it. Because the beauty of this is that it will open the eyes of the importance of AI in Oil and Gas." Manager, Oil and Gas.
2. Engage your operations and engineering team to validate the idea
3. Select a no-code AI platform that suits your existing systems and team capabilities. Thinking of building your own platform? Read Build vs Buy
4. Run a pilot and monitor early results.
5. Scale successful models across the enterprise. See our tips on how to scale Industrial AI.
Tips on how to scale Industrial AIIf you have data-generating assets (SCADA, PLCs, historians), and recurring challenges like downtime or energy waste, you're likely ready. If you can’t access your data or you lack data, Discover IoT provides an end-to-end solution with IoT sensors, monitoring and AI apps for specific equipment types.
Discover IoTModern platforms are designed to be used by process and maintenance engineers with minimal training. VROC provides teams with training and on-going support to ensure they benefit in full from the investment.
AutoML stands for Automated Machine Learning. It automates the process of building and selecting the best models—so your team can focus on results, not coding.
Automated Machine Learning with VROC’s AI PlatformIndustrial AI platforms can be deployed on-premises or in secure cloud environments, with strict access controls and data policies. VROC is both SOC2 and ISO 27001 compliant.
Why data quality, silos, delays and dark data could be stalling your digital transformation
Read ArticleComparison of 'build it inhouse' vs 'outsource' approaches to artificial intelligence
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The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even.
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