Guillaume Eymery, Chief Strategy Innovation & Digital Officer at Nexans, explains why AI is becoming a strategic tool for improving performance, safety and resilience across the electrification industry.
The global push towards decarbonisation is driving significant change across the electrification industry. As nations work to upgrade aging grids, scale renewable energy and deploy electrified technologies across homes, transport and industry, the systems underpinning energy supply are becoming more complex.
At the same time, artificial intelligence (AI) is evolving rapidly, reshaping how organisations understand risk, design infrastructure and manage large networks of physical and digital assets.
The development of electrification and software intelligence is becoming increasingly intertwined. The more electrified our world becomes, the greater the need for automation, data-driven management and predictive insight. As AI develops, it has the potential not only to process this complexity, but to help turn it into a more informed basis for decision-making.
For business leaders navigating this new era, AI represents a structural change rather than simply a technological upgrade. It affects engineering workflows, maintenance strategies, safety culture and even the resilience of critical national infrastructure. Understanding its impact is becoming increasingly important for those building reliable, secure and sustainable energy systems.
AI accelerates innovation and performance
Innovation in the electrification sector has traditionally been shaped by incremental improvements to materials, components and design processes. But AI is changing both the pace and scope of that innovation. Advanced algorithms can ingest and analyse engineering archives, testing parameters and environmental datasets that would take human teams years to evaluate. By generating design variations and simulating their performance, AI can help engineers identify solutions that are more resilient, more sustainable and more cost-effective before physical prototypes are created.
This shift is influencing how products such as cables, connectors, switching systems and renewable energy components are conceived. AI-assisted design tools are supporting the development of assets that are important to the energy transition, such as fire-resistant materials, lighter and more flexible components, and products that are easier to recycle. The ability to evaluate trade-offs between durability, efficiency, environmental impact and cost at an early stage is helping to shorten time to market and broaden the scope of innovation.
AI’s influence also extends beyond product development. Electrification systems generate large amounts of operational data, including sensor readings, environmental measurements, maintenance logs and performance histories. Together, these provide a strong basis for improving reliability. AI can detect anomalies within these datasets that human operators may struggle to identify, particularly at scale. By learning from patterns in equipment behaviour, environmental conditions and historical failures, AI can help predict when components are likely to degrade or malfunction. The result is maintenance that is proactive rather than reactive, helping to reduce unplanned downtime and extend the life of essential infrastructure.
For grid operators, AI offers an additional layer of intelligence that can support overall system stability. By combining weather forecasts, load data and real-time equipment performance, AI can help anticipate demand spikes, identify stress points on the network and adjust the flow of power to maintain balance. This level of automation is particularly relevant as renewable generation assets grow and the grid becomes more dynamic. With solar, wind and storage technologies introducing greater variability, AI offers analytical capability that can help orchestrate power flows efficiently and sustainably.
AI safeguards people and infrastructure
While innovation and performance are important drivers, safety remains the foundation of every electrification environment. Factories, substations, renewable energy farms and cable manufacturing sites all carry inherent risks, from high-voltage equipment to heavy machinery and complex operational workflows. AI is emerging as a useful tool in identifying and mitigating these risks before they become incidents. By examining historical safety records, near-miss data and real-time operational inputs, AI can detect subtle signals that may indicate unsafe behaviours, equipment anomalies or environmental hazards. These insights can help organisations intervene earlier, strengthen safety culture and reduce the likelihood of accidents.
In global operations, where teams may speak multiple languages and work across different regulatory frameworks, AI can also play a role in harmonising safety communication. Automated translation tools and intelligence-driven reporting systems can help ensure that critical information remains accessible and consistent across regions, reducing the risk of misinterpretation.
Beyond industrial sites, AI also contributes to the protection of critical national infrastructure. Electrical grids, renewable energy installations and underground cable networks face growing threats, primarily from extreme weather, ageing components and cyberattacks. Continuous monitoring powered by AI enables operators to identify unusual patterns in asset behaviour, shifts in environmental conditions or early signs of physical strain. These insights can support targeted repairs, asset replacement programmes and resilience planning, ultimately helping to prevent large-scale outages.
AI also has a role to play in strengthening the digital defences of increasingly connected energy systems. As smart grids expand, the attack surface for cyber threats widens. AI-driven security tools can detect unusual or suspicious network activity, identify vulnerabilities and respond to anomalies faster than would be possible through manual processes alone. This level of vigilance is becoming increasingly important as the industry grows more digitised and interdependent.
Human expertise and data quality remain critical
Although AI is reshaping the electrification landscape, its effectiveness depends on two critical factors: human expertise and high-quality data. Electrification organisations generate vast amounts of information across their operations, yet this data is often fragmented, outdated or locked away in isolated systems. Without structured, accurate and consistently maintained data, AI models cannot perform reliably. Many organisations are investing in data governance, standardisation and cleansing to ensure that AI-driven insights are trustworthy and actionable.
The value of AI becomes clearer when human expertise is integrated into its use. Engineers, planners, analysts and frontline operators bring essential context, judgement and ethical oversight that help ensure AI outputs are interpreted correctly and applied responsibly. This partnership is what enables organisations to make decisions that align with safety, sustainability and operational priorities.
As the energy transition accelerates, collaboration between people and advanced technology will play an important role in shaping the future of electrification. The organisations most likely to succeed will be those that combine advanced intelligence with strong domain knowledge to build systems that are safer and more resilient.
