As energy systems become smarter and more digital, organisations must rethink how they plan transformation and build talent, including how to recruit for a fast-changing future, where AI supports hiring, decision-making, and workforce planning, but human judgment still matters. In addition, key questions will be addressed around what skills are truly needed in an Energy 5.0 world? Are we evolving recruitment practices or simply adding new tools? Discussions will evolve around how to build inclusive, tech-ready teams while navigating the risks and realities of human–AI collaboration.

Attendee insights:

Gain critical insights into what talent transformation means in the era of Energy 5.0, and how teams can embrace, navigate, and address the challenges of this new reality.

AI is contributing to the transformation of the energy sector through its utilisation of large data sets. According to BNEF’s net-zero scenario modeling “every 1% of additional efficiency in demand creates $1.3 trillion in value between 2020 and 2050 due to reduced investment needs”, with AI set to help achieve this by enabling greater energy efficiency and flexible demand. Machine learning, deep learning and generative AI are improving operations effectiveness, providing insights for lowering emissions, anticipating mechanical and supply chain malfunctions and driving substantial energy efficiency gains. However, scaling AI from successful pilots to broad implementation brings its own challenges. AI uptake and the use of associated high energy demand data centres - which are growing globally in number by the day (200TWh power is needed to meet global data centers demand according to Goldman Sachs) - are on track to outpace the power available to run them. What will be needed, both in the AI application and securing access to new energy supplies, to deliver on the promise of AI and its contribution to the energy transition?

Attendee insights:

Understand how AI is transforming business operations, its opportunities and challenges related to data centers and energy consumption needs.