This transformation showcases how AI, automation, and intelligent workflows can revolutionize traditional infrastructure management. By turning data into actionable intelligence, the client now operates with greater agility, resilience, and confidence across its global pipeline network.

CHALLENGE

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Operating in over 70 countries, this oil and gas giant manages one of the largest and most complex pipeline networks in the world. With over 150,000 miles of critical infrastructure, the company faced increasing pressure to ensure operational continuity, safety, and environmental compliance.

Despite the company’s scale, its legacy Integrity Management System was heavily reliant on manual processes. Safety inspections occurred only once every four months, and anomaly detection could take 1–3 days to validate. This approach introduced several critical risks:

• Safety Risks and Delays: Delayed leak detection and failure to identify corrosion early increased the potential for pipeline bursts, environmental damage, and safety incidents.
• Unforeseen Downtime: Unplanned maintenance due to undetected anomalies often resulted in weeks of halted production, emergency repairs, and costly cleanups.
• Inefficient Manual Process: The existing manual process was time-consuming, error-prone, and non-scalable, making it inadequate for real-time asset health monitoring.

The company knew it needed a solution that would shift integrity management from reactive to predictive and from people-dependent to data-driven.

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SOLUTION

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The Solution: AI-Powered Integrity Intelligence at Scale
Aligned Automation was engaged to lead the transformation. The goal was to create a scalable, intelligent integrity management framework that could:

• Detect corrosion and anomalies in near real time
• Automate manual inspection workflows
• Extend asset lifecycle through predictive intervention

The solution leveraged Aligned Automation’s capabilities across AI/ML, advanced data analytics, and process transformation. Key components included:

• Workflow Automation: Manual tracking and logging were replaced with automated data collection and fault detection systems. Integrated alerts ensured operational teams were immediately notified of anomalies, enabling faster, coordinated responses.
• AI & Computer Vision:Drone-captured imagery was fed into machine learning models trained to detect early signs of corrosion and structural anomalies. This significantly expanded visual inspection coverage while reducing risk to human inspectors.
• Real-time Anomaly Detection: High-frequency data from sensors across the pipeline network was streamed into a central platform. AI models continuously analyzed this telemetry data, flagging unusual patterns within minutes, not days.
• Predictive Models: Using historical data, material degradation trends, and operational stress factors, predictive models were developed to identify assets most at risk. This allowed the client to shift from time-based to condition-based maintenance, maximizing ROI and asset health.

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What Changed

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This transformation showcases how AI, automation, and intelligent workflows can revolutionize traditional infrastructure management. By turning data into actionable intelligence, the client now operates with greater agility, resilience, and confidence across its global pipeline network.

The success of this initiative has opened the door for enterprise-wide applications of AI-powered anomaly detection, from offshore platforms to refineries and beyond.

Real-time visibility and predictive insight have shifted our maintenance culture from reactive firefighting to proactive optimization. This is the future of industrial asset management

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