What Energy Leaders Need To Know About AI

Over the last several years, generative AI has massively disrupted the energy sector, revolutionizing everything from resource management to predictive maintenance. Generative AI has enabled energy companies to optimize production, enhance efficiency, and reduce costs. AI is already being utilized in over 50 applications within the energy sector, with estimates for its market potential reaching over $13 billion.

This disruption is not only driving innovation, but also reshaping the competitive landscape, putting pressure on established and traditional companies to adapt. One of these new challenges is around sustainability goals. AI workloads in data centers are energy intensive and account for 1-2% of global electricity demand – and this figure is expected to continue growing as AI becomes more ubiquitous.

Russell Reynolds Associates recently hosted the thirteenth installment of our Energy Matters series, bringing together two thought leaders in AI and energy innovation to debate how companies and leaders can capitalize on AI’s opportunities, whilst also tackling the challenges it poses to decarbonization.

Event Speakers

Ernesto Ciorra

Former Chief Innovability Officer, Enel

Tamin Saleh

Senior Partner, McKinsey

Technological innovation opportunities are abundant

While artificial intelligence has been in use for several years via data correlations and predictive models, generative AI uses deep learning to efficiently extract data and mimic human-like interactions. Its energy use cases have already enhanced predictive maintenance, with our panelists giving examples of cost reductions of over 50%. Energy companies are now able to digitize many aspects of their operations, further reducing maintenance costs.

Innovation often involves integrating existing technologies in new ways, and energy businesses should explore their value chains and data assets to identify these opportunities. For example, wireless energy transmission solutions which were initially developed for space missions, have been adapted for terrestrial use, therefore enabling remotely operated vehicles to recharge efficiently while out cleaning solar panels or doing maintenance.

With generative AI here to stay, leaders and businesses need to respond to new challenges

Digital is the “new oil:” Generative AI’s potential comes at a higher energy cost

There’s no doubt that AI is here to stay. However, the technology is also contributing to significantly increased emissions at a time when major energy users and providers are under serious scrutiny over carbon emissions. For example, some data center owners have experienced a rise of nearly 30% in emissions since 2020.

As such, energy companies will benefit from building their sustainability and AI approaches in tandem, drawing on each as a tool for optimizing the other.

Energy leaders need to invest in AI capabilities at every level

While most leaders view AI acumen as critical for the future, most lack confidence in both their personal and their organization’s ability to embrace the technology today.

According to Russell Reynolds Associates’ Global Leadership Monitor, leaders are noting significant gaps in their organization’s data quality, processes, and talent capabilities to guide AI use that’s both impactful and ethical. What’s more, only 37% of leaders are confident in their personal ability to implement AI in their organizations.

Without appropriate investments in reskilling and a leadership commitment to sustainable technology, this gap might take years to close.

Coordination and collaboration are key for deployment at scale

Stakeholders across startups, established companies, investors, and governments need to come together to drive change at a macro level. We know that collaboration between companies has led to successful climate outcomes.

For example, one panelist discussed an Australian waste management company that reduced pollution by 80% after convincing their water partner to change their ways of working. The company created a digital twin and studied its maintenance data for patterns, identifying 10-12 different actions that could be taken by humans in anticipation of major incidents, such as storms, providing evidence of pattern identification in their dataflow post-maintenance incidents.

Leaders need to prioritize partnership across the value chain, emphasizing their conviction to responsible AI in service of sustainability goals across communication channels.

How can energy leaders unlock AI innovation?

Model change at the top: In the age of AI-driven workforce transformation, the most important organizational changes are cultural. These mentality shifts need to come from—and be modeled by—leaders at the top. The energy industry needs to be open to change and new ways of doing things to fully unlock the potential of generative AI and other climate technologies. This requires that leaders and employees alike feel empowered to make decisions and that they are involved in their organization’s transformation.

Fail fast: Established companies need to adopt the mentality of ‘failing fast’ to uncover real game-changing technologies. Depending on the scale of the organization, thousands of technologies need to be screened and trialed for real gems to be identified and funneled down to a handful of successful pilots, which have the potential to translate into new organizational tools.

Invest in reskilling: Our Global Leadership Monitor revealed that only 22% of non-tech sectors agree they have the right technical skills or people in-house required to implement generative AI solutions. This will require training and new ways of working across all levels in an organization, successfully building collaboration between ecosystems. As such, creating clear communication plans around your internal and external AI use cases and policies, upskilling and reskilling programs, and how AI will drive long-term impact is key.

Work across sectors: The energy sector has an advantage in attracting and retaining talent. It is focused on climate and driving environmental sustainability. In order to fully leverage this, however, energy companies and established industrial players need to be open to introducing cross-sectoral talent to their teams and open to take on board learnings from different industries such as telecommunications, financial services and even consumer.

Embrace a transformation mindset: Companies that have been successful in leveraging AI have a chief executive, chief financial officer and chief human resources officer with a high level of conviction who have embraced technological innovations. Leaders with a high level of conviction do not necessarily come from organizations which are digitally native, but rather from industries and sectors which have undergone significant transformation. The lived experience of leading through transformation equips both executives and boards to manage through risks and capitalize on opportunities presented by AI. Having this representation at a senior level can also be a powerful retention and talent attraction tool for the best-in-class digital leaders across sectors.

Authors

Chris Nicholson is a senior member of Russell Reynolds Associates’ Global Industrial & Natural Resources Practice, covering the Energy markets. He is based in London. 

Abigail Skerrett is a member of Russell Reynolds Associates’ Global Industrial & Natural Resources Practice and leads the firm’s Global Energy Transition Practice. She is based in London. 

Irina Dascalu is a member of Russell Reynolds Associates’ Global Industrial & Natural Resources Research team. She is based in London.

Contact us to register your interest in future events