Redefining risk and reliability through human-AI collaboration

Let’s face it. When something breaks in energy operations, it’s rarely just one thing. A minor fault can snowball into a major outage. A skipped maintenance step might trigger an environmental incident. Suddenly, you’re in crisis mode. In a world shaped by aging infrastructure and climate volatility, reliability is no longer just a target. It’s a balancing act.

So, where does AI come in?

The future isn’t about full automation. It’s about collaboration, through a concept called the cognitive loop, a framework where humans and AI work together to understand, explain, and respond to operational risks. It’s not just about knowing what’s wrong, but understanding why it matters and how to act on it.

By putting operators and engineers at the centre, and equipping them with systems that learn and respond in context, this approach reshapes how we manage reliability. It moves away from static metrics and toward a dynamic model of trust, adaptability, and shared decision-making.

Section 1: market context – the reliability imperative in a decentralized era:

As the global energy transition accelerates, maintaining reliability across an increasingly fragmented and decarbonized infrastructure landscape is more challenging than ever.

According to the North American Electric Reliability Corporation, more than half of the United States faces an elevated risk of energy shortfalls during peak demand in 2025. This is driven by high renewable penetration, grid congestion, and aging infrastructure. Globally, utilities face similar pressures as they work to integrate solar, wind, LNG, and distributed storage while demand from electrification and data centers continues to rise.

Meanwhile, oil and gas operators are losing 20 to 50 million dollars annually from unplanned outages in mid-sized refineries. Renewables are also under strain, grappling with issues like basis risk, intermittency, and inverse price-volume dynamics.

The need for adaptive, real-time risk management is clear. But as digitalization scales, many reliability teams find that black-box AI models, however accurate, aren’t trusted. Without context, operators hesitate to act.

Section 2: technology deep dive - what is a cognitive loop?

The cognitive loop is a collaborative feedback system that allows humans and AI to learn together over time. Rather than issuing isolated alerts, cognitive systems provide context, respond to input, and evolve based on operational feedback. Unlike traditional automation platforms, cognitive AI systems are designed to:

• Explain their reasoning in operational terms
• Learn from operator feedback
• Reference contextual data like SOPs, logs, and physical models
• Align with domain-specific constraints

This creates a loop. AI identifies a risk, explains it, the operator responds, and the system refines its future responses. The result is not just smarter alerts, but systems that feel more like experienced colleagues than faceless machines.

Key features of cognitive reliability systems:

• Explainability: Outputs are delivered in plain language with links to supporting documents or historical trends
• Hybrid Modelling: Physical constraints are built into the machine learning process
• Soft-Sensing: Inferred signals are derived from hard data using multivariate analysis
• Operator Integration: Engineers can challenge or confirm AI conclusions, training the model over time

This model allows AI to support reliability not just as a fault detector, but as a reasoning partner.

Section 3: Real-world applications – learning from the field:

Wind farm risk management

In a European wind farm, a cognitive loop system was deployed to improve turbine fault response. Rather than flagging vibration anomalies in isolation, the system connected the alert to similar gearbox events, temperature conditions, and past interventions. The recommendation to derate a turbine came with a written explanation and supporting data, which operators reviewed and confirmed. The fault was addressed 72 hours earlier than in previous cases.

Refinery reliability

In Southeast Asia, a downstream operator faced high alert fatigue from predictive systems. By implementing a contextual AI platform that tied anomalies to equipment histories, incident reports, and manufacturer manuals, the refinery reduced false alarms by 38 per cent. Technicians began responding to fewer, more meaningful alerts, each with a clear rationale.

Gas plant emergency response

During a Texas heatwave, a combined-cycle gas plant used a cognitive system to manage load during a peak event. The system drew on archived response data, maintenance records, and weather forecasts to recommend a curtailment strategy. Operators acted with confidence, not because the AI was right, but because they understood its logic.

Section 4: Implementation roadmap – building the cognitive layer:

This requires more than model deployment. It demands a rethinking of system architecture, workflows, and operator relationships.

Deployment considerations

• Data Integration: Ensure historian, document, and sensor data are accessible.
• Human-Centric Interfaces: Build tools that let operators interact naturally with AI.
• Governance and Traceability: Ensure each output can be traced to inputs and documented logic.
• Cultural Adoption: Position AI as augmentation, not automation.
• Investment Timeline: Most pilots see results within 6 to 12 months.

The path to adoption starts with small, embedded pilots, typically focused on one system or risk vector, followed by phased scaling.

Conclusion:

The energy industry has no shortage of data or algorithms. What it often lacks is trust.

For AI to make a lasting impact on reliability, it must show its work. Systems that predict without explaining will be bypassed. Those who learn and evolve with their human counterparts will be embraced.

The cognitive loop is more than a framework. It’s a new mindset, one that treats reliability not as a static goal, but as a shared responsibility between people and machines. In a world defined by risk, volatility, and complexity, that kind of partnership may be our most reliable asset.

About the Author

Nikhil Davies is a technology leader with expertise spanning oil and gas, energy, and AI-driven industrial solutions. As Director at Applied Computing, he focuses on integrating explainable AI into high-stakes operational environments to improve reliability, reduce risk, and enhance decision-making. With a career bridging traditional energy operations and digital transformation, he has guided organizations through the challenges of grid modernization, decarbonization, and Industry 4.0 adoption. Nikhil is passionate about the intersection of human expertise and AI, advocating for collaborative systems that build trust and deliver measurable impact across critical infrastructure and industrial sectors. He can be reached at nikhil@appliedcomputing.com.

Mr. Nikhil Davies
Director | Applied Computing