Renewable firms can boost profits from existing assets through AI-led maintenance: McKinsey
New Delhi, May 27
Renewable energy companies can significantly improve profits from existing wind and solar projects through better maintenance practices and AI-driven operations, instead of relying only on building new capacity, suggests a recent McKinsey report.
The report, titled "Renewables O&M reimagined: Boosting performance with AI and conventional levers", said renewable energy operators are increasingly facing pressure on profitability amid high interest rates, inflation, falling power prices and supply chain disruptions, making efficient operations and maintenance (O&M) a critical growth lever for the sector.
"Scale alone does not necessarily guarantee profitability," the report said, adding that "optimized maintenance strategies, smarter supplier contracting, and performance-enhancing technologies such as AI can meaningfully increase energy yields while reducing cost."
According to the report, operators that actively optimize O&M could realize value of "more than EUR 9 million per GW annually for onshore wind and approximately EUR 3.4 million per GW annually for solar PV."
McKinsey noted that many renewable companies have traditionally focused on lowering the cost of building new projects, while paying less attention to improving the performance and profitability of existing assets.
The report highlighted that the performance gap between top-performing and average renewable portfolios remains significant.
"The 2025 benchmarking results reveal a 12 to 15 per cent performance gap between median and top-performing portfolios," the report stated.
It added that "top-quartile portfolios for onshore wind outperform the median by 15 per cent," while lower-performing portfolios lag substantially behind due to operational inefficiencies.
According to McKinsey, a large part of the losses in renewable projects comes not from major equipment failures, but from everyday operational inefficiencies such as delayed maintenance, poor planning, resource shortages and inefficient contractor management.
The report said companies with top-performing renewable portfolios consistently focus on "operational excellence through technology" and use "technology-enabled processes to maximize availability and yield while minimizing costs."
The consultancy highlighted that AI is emerging as a key tool in improving renewable asset performance.
McKinsey explained that AI systems can help operators predict failures before breakdowns happen by analysing operational data, maintenance history and equipment performance patterns.
The report also noted that digital tools can improve planning and scheduling.
"Digitally supported planning tools can improve weekly and daily scheduling by optimizing crew routes, task bundling, and timing around constraints," the report said.
On maintenance execution, the report stated that "gen AI maintenance expert can act as a copilot with the technicians" by helping workers identify faults, troubleshoot recurring issues and prepare work orders more efficiently.
The report further highlighted that spare parts management remains one of the biggest cost challenges in renewable operations, especially when supply chains are disrupted.
"Inventory analytics... can optimize reorder points, safety stocks, and stocking locations," the report said, adding that such systems can reduce both downtime and excess inventory costs.
McKinsey also pointed to contractor management as a major area for improvement in the renewable sector, particularly because many operators outsource maintenance work.
"Gen AI can support contract analytics and high-volume invoice reconciliation, helping operators detect noncompliance and reduce value leakage," the report said.
The report cited a case study where a wind farm operator reviewed existing contracts and retendered maintenance agreements for three wind farms, resulting in a 29 per cent reduction in total O&M contract spending.
McKinsey said renewable companies now need to shift focus from simply expanding capacity to improving profitability from existing assets.
— ANI
Reader Comments
The 29% cost reduction from retendering maintenance contracts is impressive. Many Indian wind farms in Tamil Nadu and Maharashtra are still using outdated maintenance schedules. But I wonder if the upfront investment in AI systems is feasible for smaller operators. Hope the government provides some incentives for this.
Typical McKinsey report - all numbers and no ground reality. In India, the biggest challenge isn't AI but basic issues like getting technicians to remote wind farm sites, power theft from solar fields, and monsoon damage. Let's fix the basics first before talking about AI copilots for technicians. 🛠️
This is absolutely correct! I work in a solar O&M company in Andhra Pradesh, and we see daily losses due to poor planning and delayed maintenance. Our biggest issue is spare parts management - sometimes we wait weeks for a single inverter component. GenAI for inventory analytics would be a blessing if implemented properly.
Interesting perspective from an Indian context. McKinsey is right that scaling capacity without optimizing existing assets is a mistake. But €9 million per GW is a European figure. In India, with lower tariffs, the savings would be smaller but still significant. The real value might come from extended asset life rather than direct profit. 🤔
The performance gap between top and average portfolios (12-15%) is shocking. That's basically free energy being left on the table. With India's ambitious 500GW renewable target by 2030, we cannot afford such inefficiency. I hope SECI and other agencies mandate minimum O&M standards for all projects. 👏
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