EV charging networks increase AI adoption to improve uptime and efficiency

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EV charging firms prioritize AI-driven reliability and predictive maintenance
EV charging firms prioritize AI-driven reliability and predictive maintenance

As the electric vehicle industry expands rapidly, charging network operators are now focusing more on reliability, uptime and operational efficiency rather than only adding new infrastructure, according to a new industry report.

The 2026 State of EV Charging Network Operators Report by Driivz surveyed 300 senior EV charging professionals across North America and Europe, including executives from the US, Canada, Germany, France, the Netherlands, the UK and Norway.

The report found that 59% of operators identified charger reliability and stability as the industry’s biggest challenge, marking the first time it has overtaken energy constraints. Another 48% said site energy limitations continue to remain a major concern.

Operators are increasingly turning to artificial intelligence to improve network operations and profitability. Around 69% of respondents said predictive maintenance is one of the top AI use cases for operational improvement, while 72% highlighted anomaly detection and 70% pointed to pricing optimization.

The report stated that operators are using connected charger data and energy system insights to remotely detect faults, identify abnormal performance and prevent chargers from going offline before failures occur.

In terms of profitability, 59% of respondents said increased charger utilization is the biggest growth driver, while 44% said network uptime and reliability will play a major role in boosting profits over the next 12 months.

The report also revealed that 52% of operators plan to improve network monitoring and automation in 2026, while another 52% aim to enhance maintenance efficiency. Additionally, 60% said they will focus on smart energy procurement and load management to reduce operational costs.

Driivz CEO Shiri Levi-Laor said the EV charging industry is moving beyond deployment and entering a phase focused on operational intelligence, where reliability, utilization and data quality are becoming central priorities.

However, the report highlighted several barriers to wider AI adoption. Around 63% of respondents cited poor data quality or limited data availability as a key challenge, while 62% pointed to integration complexities with existing systems. Another 55% raised concerns around cost and unclear return on investment.

The study also found that 64% of operators described their cybersecurity readiness as robust or very robust, although gaps remain in areas such as fraud detection, threat monitoring and vulnerability scanning.

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