Artificial intelligence (AI) is becoming established across all sectors, also in the solar industry – especially for operation and maintenance (O&M). At the Solar Quality Summit 2026, the debate was not about whether AI is coming, but rather what use cases are actually reliable, fundable and can be safely rolled out.
While in recent years the focus has been on anomaly detection, predictive maintenance and automated error diagnostics, it is becoming increasingly clear that the success of AI depends less on algorithms, but rather on the conditions it is used in. And when it comes to these conditions, two aspects are key: interoperability and data quality.
A major hurdle to the introduction of AI in O&M is interoperability. In practice, operators, O&M service providers and asset managers frequently work with a number of different systems:
These systems often do not communicate with each other well enough, though. Data is stored in silos; interfaces are either nonexistent or not standardized, and important information cannot be consistently collated. This is a grave problem for AI applications – AI models can only deliver reliable findings when technical, operational and historical data about plants, components and maintenance events are connected with each other. This makes interoperability more than an IT issue – interoperability is a basic requirement of scalable AI solutions in O&M.
The industry is currently developing standardized application programming interfaces (APIs), communication protocols and AI agents that can communicate across platforms.
The second deciding factor is the quality of data – AI models are only as good as the data they are fed and trained with. Many PV portfolios run on incomplete, inconsistent or incorrect data sets. Structured information on replacements, degradation rates, error classifications and previous service work is often missing. Measurement errors, sensor gaps and the inconsistent naming of components and events add to the confusion. Consequently, models struggle to identify patterns effectively, leading to false alarms and an unreliable basis for operational decision-making.
The most important AI development in PV O&M is therefore not merely the development of more powerful models, but the professionalization of the entire data ecosystem. Companies are increasingly investing in standardized data structures, better system integration and clean data pipelines. This will determine whether AI can be a driving force behind O&M efficiency.
It is becoming increasingly clear that the successful implementation of AI in the solar industry depends not just on new algorithms, but – more importantly – on interoperable systems and a reliable data basis. Here lies one of the central factors for bringing lasting improvements in the efficiency, availability and profitability of PV. Intersolar Europe offers the ideal platform to make these developments visible, discuss best practices and encourage dialog between technology providers, operators and O&M service providers. Unlocking AI’s full potential in the solar industry requires combining innovations that are currently isolated within the supply chain.