Vestas inspects its blades with drones and AI: predictive maintenance is the quiet AI already paying off

By Petróleo y Energía · November 10, 2026.

Vestas, one of the major wind turbine manufacturers, inspects its turbine blades with drones and analyzes the images with artificial intelligence. According to the outlet, the system detects damage faster and more accurately, and reduces technicians' exposure to work at height.

The process, as described by the source, has four steps. A drone captures hundreds of images from different angles. A digital platform standardizes the data. The AI analyzes it and pre-categorizes potential damage. Finally, specialists validate the findings and decide whether to repair immediately, intervene later or simply monitor.

The figure that justifies the effort: according to the article, severe leading-edge erosion (the edge of the blade that first cuts through the air) can reduce a wind turbine's annual output by up to 5%. Five percent may seem small, but it repeats year after year on every turbine in a wind farm.

This is not new. According to the source, Vestas has used drones since 2016 with InspecTools in North America. In 2018 it expanded the collaboration globally and incorporated AI to automate the analysis. The article provides no figures on savings, model accuracy or number of turbines inspected. As a result, the benefits of “less downtime and lower costs” are a qualitative claim we cannot quantify.

**Our take.** This is the least-discussed side of AI and the one that most resembles its promise. There is no chatbot and no autonomous agent. There is a model that classifies images and a human who decides. The AI does the tedious, high-volume work, and the expert retains judgment. It is the “augmented AI” pattern we advocate for in employment: it changes which part of the work holds value; the trade does not disappear.

There is also an effect on people. Working at height on a blade is hazardous. Having a drone do the survey and sending the technician up only when needed is a real workplace safety improvement, not marketing. In the short term, it may indeed reduce manual inspection hours; that transition deserves attention.

The most interesting part is the economics. Turning unplanned repairs into scheduled ones cuts costs and production losses. Every percentage point recovered from a turbine is additional clean energy without building anything new. More broadly, predictive maintenance is one of the industrial applications of AI with the clearest return.

In the long-term thesis, abundance requires cheap and plentiful energy. Solar already surpasses nuclear in global generation, according to another piece from the same outlet. But producing more is not enough: the existing fleet must be maintained efficiently for decades. That unglamorous task is where AI delivers value today, without waiting for any superintelligence.

One caveat: the article appears close to corporate communications and offers no independent data. Its benefits should be taken as company claims, not measured results.