A profound transformation
The Energy and Utilities industry is undergoing a profound transformation, driven by the rising demand for clean, affordable and reliable energy sources as well as the rapid development of new technologies.
The International Energy Agency has referred to AI and Energy as "the new power couple." Indeed, the AI-Energy relationship holds immense potential for addressing current industry challenges as data and AI are becoming more and more essential tools to optimize operations, enhance performance, reduce costs, improve safety and enable innovation.
Strategy& convened experts from 18 countries, representing leading players in the Energy and Technology industries, for a Global Roundtable in Rome.
The purpose was to discuss the (R)evolution of AI applications in the Energy Industry and to share insights on how they are adopting AI within their companies.
The discussions revealed significant highlights that can be categorized into five key points. The first two relate to the current state of AI in the Energy industry, while the remaining three address future challenges necessary to realize AI's full potential in the coming years.
Today’s status
Emerging Use Cases
- Energy players have developed and implemented various use cases focusing primarily on assets, processes and customers.
- Notably, there is a convergence on the uses and applications from the asset perspective. Particularly, several Energy industry players have been experimenting with various use cases (e.g., anomaly detection for wind and solar assets, predictive maintenance systems, and grid/plant optimization algorithms).
- Significant activity is also registered in the space of process optimization use cases. Instead, progress on use cases optimizing the customer experience and interaction have been hindered to date by difficulties in outcome measurability, fear of compromising customer relationships and limited technology penetration in involved areas (e.g., customer care).
Change Management Processes
- Companies are implementing initial change management initiatives to strengthen the role of AI within organizations. These initiatives encompass several aspects of the organization, from recruiting highly specialized professionals who have recently emerged in the job market to integrating these individuals into multifunctional teams.
- Additionally, it addresses aspects of corporate culture, aiming to disseminate an interest in and predisposition for innovative solutions throughout the organizational fabric.
- However, there is a broad consensus that these initiatives need to be extended and conducted within a systematic program.
3 future steps to overcome AI challenges and realize its full potential
Outcome measurement
- Effective outcome measurement procedures and standards are crucial for unlocking investments to scale AI use cases to specific applications.
- Some financial services players, such as US and European Banks, have started implementing specific methodologies to prioritize use cases based on their expected financial contribution, capture their expected profitability impact into financial plans and MBOs and measure systematically the actual value-added once the use case is implemented. These experiences may serve as practical examples for Energy players.
People upskilling
- To fully exploit the potential of AI, organizations must become AI-enabled, ensuring that most employees can maximize the value derived from AI use cases. On one hand, hiring processes need to evolve targeting new recruiting pools and incorporating value propositions aligned to the new professional figures. These new professionals (e.g., data scientists and engineers) need to be included in all key processes by re-adapting the structure of project / development teams.
- Finally, the whole organization needs to be onboarded in the AI journey with tailored training and communication efforts encouraging proactivity and adoption. We have seen such organic programs being undertaken in different sectors such as banking and industrial manufacturing which can serve as examples for Energy players.
Common Platforms
- The development of AI solutions can be hindered by various factors such as substantial investments in physical assets, technical complexities and limited data availability for the development of effective use cases.
- Various industries have demonstrated the efficacy of collaborative platforms in supporting the large-scale adoption of innovative solutions. Notable examples of successful cooperations from other industries include e-roaming platforms in electric mobility and digital payments providers.
- Strategy& has identified three different possible modes of collaboration, both within and across industry players, that can help energy companies overcome these challenges.
Conclusions
Overall, a significant potential for AI in transforming the Energy sector emerged during the Roundtable. In such context, Energy players will need adequate support to define a robust value creation framework, prepare for ensuring adoption at scale and identify effective development approaches and partnership opportunities. Tackling these challenges early-on will provide significant competitive advantage and unlock the full potential of AI.
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