2026 Can be the Year that AI Fixes EV Charging Software

While electric vehicles (EVs) have achieved near parity with their ICE counterparts in terms of reliability, speed, and everyday practicality, the charging infrastructure supporting them still lags behind. Hardware is improving but charging reliability remains low—often cited at below 80% uptime—due to complex software failures, poor remote diagnostics, and opaque payment systems. With the help of AI, 2026 can be the year that sees EV charging infrastructure finally live up to expectations.

This year has seen EVs and AI go on starkly differing trajectories. Growth of EVs in the U.S. has slowed and consumers saw incentives like the Federal tax credit sunsetted, while dealers have reported slower turnaround of EV inventory. AI, on the other hand, moved beyond hype in 2025, is now being integrated into the world’s largest businesses, and most importantly, has generated tangible financial benefits.

With AI assistance, the EV industry has a golden opportunity to make 2026 the year that EV charging became a strong point in the story of the transition to clean transportation, rather than a hurdle.

Numerous data points have been collected by EV industry watchers that point to EV charging infrastructure being the factor holding back the transition away from ICE vehicles. The latest annual reliability report from ChargerHelp found that only 71% of charging attempts on non-Tesla chargers were successful, and 35% of those failures occur on chargers that appear to be fully operational and available. Additionally, a Consumer Reports survey found that 21% of all chargers they inspected had an issue of some sort. And a 2024 survey from nonprofit Plug In America found that 68 percent of EV drivers have had an experience with a malfunctioning or broken EV charger within the previous 12 months.

Despite frequent hardware faults, EV charger operators must also address critical software shortcomings—such as inaccurate availability, connection issues, or system vulnerabilities—to ensure a seamless charging experience for the public.

Using AI as the foundational element, the EV charging software stack must consist of four well-integrated pillars: 1) It needs to seamlessly integrate chargers with their energy source (for example, the utility grid or solar power), 2) it needs to provide a robust management platform with constant connectivity both to the charger operator and the driver, 3) it needs to be so intuitive and easy to use that it creates a very positive driver experience, and 4) it needs to facilitate excellent AI-assisted customer support.

Seamless Grid Integration and Optimization

Good EV charging software must not merely connect EV chargers to their power source but also must ensure seamless operation and control of energy flows. AI is critical here, enabling sophisticated predictive charging and dynamic load balancing. Software must be designed to optimize energy flows for charging to take advantage of renewable energy and manage grid electricity to keep costs low. By intelligently integrating charging schedules with on-site solar generation and battery storage, users can significantly reduce their energy costs and minimize their reliance on the traditional grid during periods of peak demand. Chargers must have designed-in reliability, managed autonomously by AI, that assures they do not stop working during a reboot or over-the-air firmware update as so frequently happens.

Robust Management Solution with Predictive Maintenance

Beyond the operational experience, the backbone of a successful EV charging network lies in its robust charge management software. AI transforms this pillar from reactive monitoring to proactive prevention. This system needs to provide operators with real-time visibility into the status of their chargers, enabling proactive maintenance and swift issue resolution. Intelligent diagnostics and remote monitoring are crucial for identifying and correcting potential problems before they impact users. Furthermore, the software should facilitate efficient energy management across the network, optimize load balancing, and provide valuable data analytics on usage patterns, enabling informed decisions about network expansion and resource allocation.

This system needs to provide operators with real-time visibility through constant connectivity, but more importantly, AI-driven predictive maintenance algorithms must analyze raw data (communication faults, power fluctuations, event monitoring) to predict failures days or weeks before they occur. This intelligent diagnostic capability enables remote, pre-emptive maintenance, drastically reducing downtime and site visits, and ensuring consistent uptime that current systems cannot match. Furthermore, AI facilitates efficient energy management by dynamically optimizing load balancing across large networks based on real-time usage patterns.

Positive Driver Experience and Real-Time Routing

Third, the driver’s experience with the charging infrastructure is perhaps most important and cannot be overlooked.  Like gasoline pumps, EV chargers must be intuitive and easy to use, work every time, and facilitate simple and straightforward payments. AI-driven software design can prioritize ease of use, create clean interfaces, help locate available chargers, and initiate and complete a charging session. Machine learning models can analyze real-time charging status, historical occupancy, and even traffic data to provide accurate, personalized, real-time user routing. Minimizing friction points, such as confusing interfaces, unreliable payment systems, and unclear charging status updates, is crucial for building driver confidence and encouraging EV adoption.

Excellent Customer Support and Remote Diagnostics

Finally, addressing the industry’s well-documented reliability concerns requires more than just functional hardware and competent software. It demands excellent support systems that are readily available, responsive, and effective in resolving issues. AI can enable self-service and remote-first support, turning human agents into escalation specialists. AI-powered diagnostic tools can simplify complex technical errors into clear, actionable steps for both drivers and non-technical staff. This enables remote troubleshooting, automates the resolution of most issues, and allows live agents, armed with remote photo diagnostics and real-time logs, to resolve the remaining complex problems quickly, rebuilding the trust required for mass EV adoption.