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Thank you to everyone who attended my session at today's Future of Utilities event in London. With a packed room and engaged audience, I wanted to capture my thoughts from the session.
With 70 months left to reach a clean power grid, the entire energy ecosystem must accelerate its pace. Achieving this ambitious goal by 2030 will require transforming conventional approaches to energy demand, supply, and management.
As we strive to meet governmental targets for net-zero emissions, a faster, smarter approach is critical to manage the scale, uncertainty, and complexity that characterise today’s energy landscape.
To that end, there are several things we need to do across the energy system:
The traditional energy model, where supply dynamically meets predictable demand, is shifting to an environment where demand must adapt to fluctuating renewable energy sources like wind and solar.
This shift requires new strategies, as the grid faces the challenge of quadrupling network capacity in less than a decade, while integrating an ever-growing mix of distributed energy resources like electric vehicles, solar installations, and energy storage.
The success of the clean energy transition will be as much about how it is delivered as about what is delivered.
Leaders across the energy sector must prioritise agility and adaptability over rigid, incremental progress. A start-up speed mindset, combined with enterprise-scale resources, is essential to make rapid advancements without sacrificing reliability or resilience.
AI is, of course, the answer to all of these problems.
While advanced data and AI technologies can help us manage this complex landscape and solve challenges, the journey from experimentation to practical AI integration has often been slow.
Many organisations get stuck in the Proof of Concept (POC) phase without scaling up - leaving some in a POC graveyard. For AI to make a meaningful impact, companies must embed these technologies into their operations, focusing on tangible outcomes rather than standalone projects.
Instead, we need to consider what the priority challenge is and the minimal viable product to solve that challenge. How does this solution fit into your vision of the future? Is your landscape and environment fit to support this solution, with the right structures, processes and skills? Finally, how can this be scaled to solve other challenges?
This focus has underlined the importance of quality and accessible data that is shared across the ecosystem. The foundations simply cannot be bypassed if we’re to meet these lofty goals. Data connectivity and visibility across all levels of the energy system—from generation to customer use—are critical for whole-system intelligence.
Real-time data analytics enable predictive modelling, demand forecasting, and scenario planning, which are essential for balancing renewable sources with demand shifts. This “connected visibility” across both internal and external data sources allows for collaboration across sectors and efficient, responsive decision-making.
By connecting players from different parts of the energy system to share data, we can solve whole system problems, rather than just our own. With a collective approach, we can spread the risk and cost by solving problems together.
To succeed in this accelerated timeline, it’s crucial to think about what we can change now and take immediate action. Leaders need to focus on foundational principles and addressing the slowest part of their processes, not optimising what is already fast.
Investing in organisational transformation, particularly in upskilling teams for data and AI proficiency, ensures that innovations can be owned and managed internally, creating sustained momentum.
The mission to achieve clean power by 2030 is a challenging but achievable goal, provided the sector can adapt quickly enough. Energy leaders must shift from traditional, cautious approaches to bold, agile strategies. By embracing speed, data-driven innovation, and whole-system collaboration, we can make clean power a reality within the decade.
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