The global renewable energy sector is currently navigating a complex intersection of surging demand and a critical shortage of skilled labor. As the United States and international markets strive to meet ambitious decarbonization targets, the physical reality of building utility-scale solar farms has become a primary bottleneck. To address these challenges, leading engineering, procurement, and construction (EPC) firms are increasingly turning to advanced automation. Burns & McDonnell, a prominent global EPC contractor, recently announced a significant milestone in this technological shift through its partnership with Gritt, an AI and robotics firm. This collaboration marks a transition from experimental pilot programs to the active deployment of AI-powered machines on large-scale infrastructure projects, signaling a new era for the solar industry.
The Strategic Shift Toward Automation in Renewable Infrastructure
The adoption of robotics in solar construction is driven by a necessity to improve safety, enhance project predictability, and mitigate the impact of a dwindling workforce. According to industry reports from the Solar Energy Industries Association (SEIA), the U.S. solar industry will need to more than double its workforce by 2030 to reach the Biden administration’s climate goals. However, the labor-intensive nature of solar installation—which involves repetitive heavy lifting in often harsh outdoor environments—makes recruitment and retention difficult.
Burns & McDonnell’s partnership with Gritt represents a proactive response to these market pressures. Over the past year, the two companies have conducted rigorous evaluations of AI-powered robotics in real-world construction environments. These tests were designed to determine if smart machines could handle the nuances of varied terrain, fluctuating weather conditions, and the precise mechanics required for solar module placement. Following a successful testing phase, the technology was fully deployed this summer at the Gibson City Solar Project in McLean County, Illinois.
Case Study: The Gibson City Solar Project
The Gibson City Solar Project, a 346-megawatt (MW) facility developed by Earthrise Energy, served as the primary proving ground for the Gritt-powered robotic systems. The project is a massive undertaking, utilizing NextPower terrain-following trackers, a Shoals above-ground collection system, and SMA inverters. In such a high-capacity environment, the sheer volume of components to be installed is staggering.
During the construction phase, Gritt’s machines were tasked with the lifting and placement of solar modules. This is a critical function because a single utility-scale solar panel can weigh between 60 and 80 pounds. For human crews, the process involves bending, lifting, and securing these panels hundreds of times per day, leading to significant physical strain and a higher risk of musculoskeletal injuries. By delegating these repetitive tasks to AI-driven robots, Burns & McDonnell was able to streamline the installation process while significantly reducing the physical burden on its human workforce.
Adam Bernardi, the renewables business line director at Burns & McDonnell, emphasized that the move toward technology is about optimizing how teams work rather than replacing them. "Solar construction is very repetitive work, lifting 80-pound modules day after day," Bernardi noted. He explained that as installations grow in scale, technology provides a way to work "safer, faster, and smarter," allowing projects to be delivered with a level of predictability that traditional manual labor cannot always guarantee.
Technical Foundations: How AI Learns on the Construction Site
The technology provided by Gritt is not a static robotic arm but a sophisticated AI system that integrates with standard construction equipment. These systems are designed to be "site-agnostic," meaning they can be attached to existing machinery and adapt to the specific requirements of a location. This flexibility is essential for outdoor construction, where no two sites are identical.
Gritt’s AI utilizes machine learning to treat every deployment as a "classroom." The robots are equipped with sensors and computer vision that allow them to analyze site logistics, terrain challenges, and changing weather conditions in real-time. This continuous feedback loop ensures that the system becomes more efficient with every module placed. This adaptability is what separates modern AI robotics from the fixed-path automation seen in factory assembly lines. On a solar farm, the ground may be uneven, and the wind may shift; the AI must be capable of compensating for these variables to ensure the precise alignment of panels on their tracking systems.
The Success of Maximo: A Parallel in Solar Innovation
Burns & McDonnell is not alone in its pursuit of robotic efficiency. The AES Corporation, a global power company, has pioneered its own solution known as Maximo. This AI-enabled solar installation robot has already reached a historic milestone by installing more than 100 MW of utility-scale solar capacity at the Bellefield solar and storage project in Kern County, California.
Maximo was developed using Amazon Web Services (AWS) tools, specifically AWS RoboMaker, which allows developers to run large-scale simulations in the cloud before deploying robots in the field. This digital-twin approach ensures that the robot is prepared for a variety of lighting and climate conditions. In its deployment at the Bellefield site—which will eventually be one of the largest solar-plus-storage facilities in the United States at 2,000 MW—AES scaled its operations from a single unit to a coordinated fleet of four Maximo robots working in parallel.
The data from the Maximo deployment provides a clear picture of the efficiency gains possible through automation. AES reported that Maximo version 3.0 units can install modules at a rate exceeding one per minute. When integrated with human teams, the output reached as many as 24 modules per shift hour per person—nearly double the productivity of traditional installation methods. Chris Shelton, president of Maximo, stated that reaching the 100 MW milestone at a single site proves that intelligent field robotics can deliver consistent, utility-scale results.
The Human-Robot Collaboration Model
A recurring theme among industry leaders is the insistence that robotics will augment, not replace, human labor. The construction industry has long relied on skilled craft, and project managers argue that the human element remains indispensable for complex decision-making and site management.
Jami Stone, a construction project manager at Burns & McDonnell, highlighted that learning to operate and work alongside these technologies expands the toolkit available to construction professionals. This shift suggests a transition in the labor market: while the demand for pure physical labor may decrease, the demand for "technician-operators" who can manage robotic fleets will likely rise. By removing the "dull, dirty, and dangerous" aspects of the job, companies hope to make solar construction a more attractive career path for a younger, tech-savvy generation.
Economic and Industry Implications
The broader implications of these technological advancements are profound for the global energy transition. The "time-to-power" metric—the speed at which a project can move from groundbreaking to delivering electricity to the grid—is a critical factor for utility companies and investors. Robotic automation offers a solution to the volatility of construction schedules. Unlike human crews, robots do not suffer from heat exhaustion and can maintain a consistent pace regardless of the time of day, provided they are maintained.
Furthermore, the predictability offered by AI systems helps in financial modeling. When an EPC can guarantee a specific installation rate per day, the financial risks associated with project delays are mitigated. This is particularly important as solar projects move into more challenging geographic regions with steeper slopes or less stable soil, where manual installation becomes exponentially more difficult.
Chronology of Key Milestones in Solar Automation
- 2023: Burns & McDonnell enters a formal partnership with Gritt to begin a year-long testing phase of AI robotics in diverse field environments.
- Late 2023: AES Corporation begins field validation of Maximo at the Oak Ridge Solar project in Louisiana and Cavalier Solar in Virginia.
- Spring 2024: AES announces that Maximo has successfully installed 100 MW at the Bellefield project in California, transitioning the tech from validation to commercial production.
- Summer 2024: Burns & McDonnell completes the full-scale deployment of Gritt robotics at the 346 MW Gibson City Solar Project in Illinois.
- September 2024: The Gibson City Solar Project reaches commercial operations, marking a successful end-to-end integration of robotic assistance in a major utility-scale project.
Conclusion: A New Standard for Global Energy Construction
As the renewable energy pipeline continues to grow, the integration of AI and robotics is moving from a luxury to a requirement. The successful deployments at Gibson City and Bellefield demonstrate that the technology is now mature enough to handle the rigors of utility-scale construction. By combining the precision and endurance of machines with the expertise and oversight of human professionals, the solar industry is finding a path forward through its labor and productivity challenges.
The lessons learned from these projects will likely ripple across other sectors of infrastructure construction, including wind energy and battery storage. For now, the collaboration between firms like Burns & McDonnell, Gritt, and AES serves as a blueprint for how the energy industry can scale up rapidly to meet the demands of a low-carbon future. The "classroom" of the construction site is yielding results that suggest the future of solar is not just in the panels themselves, but in the intelligent machines that place them.
