The integrated power market is undergoing rapid transformation. Increasing electrification, variable renewable energy sources and cross-border interconnections are creating a system that is more dynamic, decentralised and data-intensive than ever before. At the same time, the pressure to reduce emissions and improve energy efficiency is intensifying. Traditional planning and operational tools are struggling to keep pace with this complexity.

Artificial intelligence (AI) is emerging as a key enabler of smarter, more efficient energy systems. From predictive maintenance to real-time grid optimisation, AI offers powerful capabilities that can help Nordic power companies meet their climate goals while improving reliability and cost-effectiveness. This article explores how AI is being applied across the power value chain, what impact, risk and opportunity (IRO) considerations it raises, and what companies must do to build the competence and governance needed to deploy it responsibly.

The efficiency challenge in integrated power markets

Integrated power markets are designed to optimise energy flows across regions, technologies and timeframes. However, the increasing share of intermittent renewables, electrified demand and distributed resources introduces volatility and uncertainty. Forecasting errors, grid congestion and inefficient dispatch decisions lead to energy waste, higher costs and reduced system stability.

Efficiency is no longer just about minimising losses. It is about maximising the value of every kilowatt-hour produced, transmitted and consumed. This requires real-time decision-making, adaptive control and predictive insights, capabilities that AI is uniquely positioned to deliver.

Efficiency is no longer just about minimising losses. It is about maximising the value of every kilowatt-hour produced, transmitted and consumed. 

AI applications across the power value chain

AI is already being deployed in multiple areas of the power sector, with promising results.

In generation, AI enables predictive maintenance of turbines, solar panels and thermal units, reducing downtime and extending asset life. It also supports dynamic optimisation of generation portfolios based on weather forecasts, market signals and grid conditions.

In transmission and distribution, AI helps balance the grid by forecasting demand and generation more accurately, detecting faults before they occur and automating rerouting during outages. It can also support congestion management and voltage control in increasingly complex networks.

On the end user side, AI powers smart energy management systems that optimise usage in buildings, industrial facilities and homes. It enables personalised energy services and supports demand-side flexibility, which is critical for balancing renewable supply.

In market operations, AI is used for price forecasting, bidding strategies and portfolio optimisation. It helps producers and retailers navigate volatile markets and make better trading decisions.

Impact, risk and opportunity perspective

From an IRO perspective, AI introduces a new layer of strategic considerations.

The impact of AI can be transformative. It enables lower emissions through better resource utilisation, reduces operational costs and enhances system reliability. It also supports more inclusive energy access by enabling tailored services and decentralised solutions.

However, risks must be carefully managed. These include data privacy concerns, cybersecurity vulnerabilities, algorithmic bias and lack of transparency in decision-making. Overreliance on opaque systems can undermine trust and accountability.

The opportunity lies in using AI not just to automate existing processes, but to rethink how energy systems are designed and operated. Virtual power plants, flexibility markets and AI-driven asset management are examples of new business models emerging from this shift.

Governance, competence and talent needs

Deploying AI effectively requires more than technology. It demands a strategic approach to governance, competence and culture.

Companies must expand internal capabilities in data science, machine learning, energy systems modelling and software engineering. Cross-functional teams that combine technical expertise with operational insight are essential. Internal upskilling programs can help existing staff transition into AI-related roles, while partnerships with universities and research institutions can support innovation and recruitment.

Attracting talent in a competitive global market requires offering meaningful work, opportunities for impact and a collaborative environment. AI professionals are drawn to organisations that align with their values and offer a clear vision for digital transformation.

Governance frameworks must ensure ethical use of AI, transparency in algorithms and compliance with evolving regulations. Internal risk oversight should include AI-specific risk assessments and scenario planning.

AI as a lever for capacity gains and cost efficiency

One of the most compelling arguments for AI is its ability to unlock capacity gains without the need for costly new infrastructure. By optimising existing assets and operations, AI can defer or reduce capital expenditures while improving system performance.

For example, better forecasting and control can reduce reserve margins and peak loads, allowing more efficient use of generation and grid capacity. Predictive maintenance can extend asset life and reduce unplanned outages. Smart demand-side management can flatten load curves and reduce the need for backup generation.

Compared to traditional infrastructure investments, AI solutions are often faster to deploy, more scalable and more adaptable to changing conditions. While not a substitute for all physical upgrades, AI can significantly reshape capital planning and improve return on investment.

While not a substitute for all physical upgrades, AI can significantly reshape capital planning and improve return on investment.

Aligning capital with climate resilience

As a financial institution, we expect companies to integrate climate adaptation into their investment strategies. AI should be part of a broader approach to resilience, efficiency and sustainability.

Investment cases should include robust scenario analysis, clear strategies for infrastructure resilience and transparent reporting aligned with regulatory frameworks. We support projects that use AI to enhance grid flexibility, enhance energy efficiency, reduce emissions and deliver customer value.

We also encourage collaboration across the sector, between utilities, technology providers, regulators and research institutions, to accelerate learning and ensure responsible deployment.

AI is not just a digital tool. It is a strategic asset for energy efficiency and system resilience. Nordic power companies have an opportunity to lead the way in deploying AI to build smarter, cleaner and more adaptive energy systems.

Success will depend on integrating AI into core business strategies, building the right competencies and ensuring strong governance. As the power system evolves, those who embrace AI thoughtfully and boldly are well positioned to shape the future of energy in the Nordic region and beyond.

Lyse case study: An interview with Øyvind Feed

Two waves of responsible AI – First came governance, now comes competence

Lyse has always had a clear goal – to create new opportunities based on its own business. New technology and data have long been part of that journey. Underpinning everything are the company’s core values: team player, courageous, straightforward and responsible.

“As part of our focus on data science, several frameworks were established back in 2019 to ensure that we manage technology responsibly and in line with the group’s strategy. A key outcome of this work was the guideline ‘Ethical Use of Algorithms and Artificial Intelligence in the Lyse Group’, which was approved by group management in 2020,” says Øyvind Feed, CIO of Lyse AS.

When ChatGPT was launched in the autumn of 2022, Lyse carried out a risk assessment in accordance with this guideline. The assessment became the starting point for the establishment of the AI Council in 2023. The council was created by group management to ensure that Lyse adopts artificial intelligence in a safe and responsible way. At the same time, it was tasked with building competence and exploring new opportunities – with the expectation that the volume of AI services would increase as the technology moved from producing predictions to generating actual objects and content.

Practical implementation and future outlook

“Since its inception, the AI Council has reviewed 68 different use cases. It continuously assesses new proposals, defines ethical boundaries and builds a shared practice for AI use. In this way, many small, practical initiatives have been implemented in everyday operations – in customer service, invoicing and work support. More complex areas such as power markets and grid operations have been phased in gradually and in a controlled way,” Feed continues.

Lyse is now building further on this foundation, as more advanced AI tools are introduced to increase speed and quality in development and operations. The principles from the first wave remain firm: human oversight, accountability, traceability and clear boundaries.

At Lyse Produksjon, AI is now used together with optimisation models to manage power production as efficiently as possible.

“As the systems become more automated, people will still be responsible for building, training and monitoring the solutions,” says Feed.

“Another good example is the subsidiary Lnett. The group’s infrastructure company has adopted AI in the planning of maintenance and expansion. With millions of images as a basis, the systems interpret terrain and installations, and suggest, among other things, where wooden poles should – and should not – be used,” he adds.

Lyse practises responsible AI through regular updates of ethical guidelines and clearly defined boundaries and restrictions. This includes caution when it comes to meeting transcription and a firm ‘no’ to analysing colleagues.

Lyse is now preparing for the next wave. AI agents will transform how customers interact with the company digitally – and how relationships are built in the future.

Author

Name:
Marianne Bruvoll
Title:
Senior ESG Analyst
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