Artificial intelligence (AI) has the potential to generate tangible efficiency and performance gains in the renewable energy sector, but most companies continue to struggle to translate technological ambition into value at scale. This is the main conclusion of the study “A Real-World Game Plan for AI in Renewable Energy”, by the Boston Consulting Group (BCG), according to which the adoption of AI solutions can enable 15% to 25% increases in worker productivity and improvements of one to three percentage points in energy yield, through greater asset availability and more effective operational execution.
According to the latest BCG AI Radar, energy and utilities companies plan to triple their investment in AI in 2026 compared with 2025 – the largest increase among all sectors analyzed, with the exception of insurers. Despite this momentum, value capture remains limited in many organizations.
According to the report, the bottleneck rarely results from a lack of use cases, but from the difficulty of scaling initiatives beyond the pilot phase, integrating them into real workflows and associating them from the outset with clearly defined business metrics.
The study emphasizes that, in the current context of margin pressure, rising costs and greater regulatory and operational uncertainty, renewable companies need to be particularly selective in how they allocate capital to AI.
Rather than overly broad programs or those centered only on technological experimentation, the priority should be on initiatives capable of producing visible returns over a six- to twelve-month horizon, with direct impact on critical performance indicators, namely asset availability, energy yield, operating cost per megawatt-hour, and efficiency in capital allocation.
According to BCG’s analysis, companies do not need to develop hundreds of applications to start generating impact. The consultant’s experience indicates that 10 to 15 use cases can capture between 60% and 70% of the total value potential, provided they are prioritized based on their operational and financial impact and not solely on internal visibility or enthusiasm for emerging technologies.
Among the main identified application areas are real-time operational monitoring, planning and forecasting asset performance, maintenance, field operations management, market intelligence for energy trading, and increased efficiency in support functions such as procurement, finance, and document management.
The opportunity is relevant across the entire value chain, but real returns tend to arise when solutions address concrete operational bottlenecks and are designed in light of the actual business conditions.
In asset-intensive sectors like renewables, value does not arise from processes redesigned in the abstract, but from the ability to remove bottlenecks that penalize productivity, availability and execution quality. This implies observing field operations, understanding how teams and systems actually work in practice, and designing solutions that respond to that reality, rather than assuming an idealized operating model. The study is explicit in stating that if an initiative cannot explain, right in the first week, which KPIs it will move and by what financial mechanism, it ceases to be an AI project and becomes a research exercise.
In one of the cases analyzed, a European utility with a substantial renewable portfolio managed to significantly improve the productivity of operational teams through an AI solution applied to planning, dispatching and rescheduling of field work orders.
The focus was on a specific problem: idle time between tasks, which averaged between one and two and a half hours per technician per day. The initiative was structured from the outset around financially understandable indicators – reduction of idle time, increased execution capacity and reduced need for additional staffing – and translated into a reduction of operating costs by several millions of euros.
The study also underscores the importance of balancing speed with control. Given the critical nature of energy infrastructures, the sector cannot adopt the spirit of “move fast and break things” typically associated with the tech industry.
The acceleration of AI must be supported by clear governance mechanisms: human oversight at critical points, non-negotiable safety rules, rollback mechanisms to reliable previous states in case of failure, and rigorous prioritization based on risk and value of each initiative. At the same time, companies cannot remain trapped in prolonged pilots without practical application, for fear of losing momentum and wasting the opportunity to create value.
Scaling AI beyond pilots also requires an appropriate operating model. The study highlights the effectiveness of a hub-and-spoke approach, which combines a central structure responsible for setting standards, prioritization and cross-cutting governance with decentralized business units that maintain ownership of the use cases and their implementation.
This balance between central discipline and proximity to the ground is identified as a determining factor in turning isolated initiatives into cross-cutting business impact.
In the Portuguese context, this agenda takes on particular relevance. As the country strengthens its commitment to the energy transition, increases renewable capacity and modernizes infrastructure, the importance of improving the performance of existing assets, increasing operational efficiency, and reinforcing execution capability in a sector characterized by high capital intensity and increasing complexity also grows.
In this framework, AI can play a relevant role not only in optimizing operations but also in strengthening the competitiveness and resilience of the energy system.
The central message of the report is clear: the potential of AI in renewable energy is significant, but its realization is far from automatic. This value will be captured by organizations capable of turning technological ambition into disciplined execution, measurable impact and scalable operations. In a sector where return, reliability and adaptability are increasingly recognized as critical factors, this capability could translate into a structural and lasting advantage.
The study is available in full here.
