Turning complex energy data into competitive advantage with AI

Energy companies understand their data's potential but face significant hurdles in fully harnessing its value. Three critical challenges restrict widespread data-driven transformation: outdated IT infrastructure, inconsistent data quality, and a critical skills shortage.

Legacy IT systems isolate data within departments, creating fragmented and inaccessible silos. Such compartmentalisation disrupts data flow and collaboration, limiting the ability to respond swiftly to market dynamics. Industry reports indicate nearly half of energy firms struggle to realise the full potential of their digital investments due to outdated infrastructures.

Inconsistent data quality further compounds these challenges. The variety of formats and devices collecting data often leads to unreliable or outdated information, complicating real-time decision-making and eroding organisational agility.

Moreover, the industry faces a significant shortage of personnel skilled in interpreting and leveraging complex datasets. The widening gap between demand and availability of analytics professionals underscores the urgent need for targeted training and talent development initiatives.

Addressing these barriers unlocks substantial opportunities. Predictive maintenance, traditionally dependent on periodic checks and historical data, is notably improved by AI. AI-driven predictive maintenance continuously analyses real-time operational data, accurately forecasting potential equipment failures and optimising maintenance schedules. This approach significantly reduces downtime, lowers maintenance costs, and extends asset lifespan.

AI-powered personalisation is transforming customer engagement. By analysing granular consumer behaviour data, energy providers can offer personalised tariffs and proactive alerts, enhancing customer satisfaction and loyalty.

Similarly, AI profoundly impacts energy trading, an area increasingly characterised by complexity and volatility. Advanced analytics help traders rapidly interpret large datasets, including market trends, weather forecasts, and geopolitical events. This empowers traders with precise, actionable insights, minimising risk and maximising profitability.

AI-driven analytics also underpin sustainability initiatives, enabling efficient renewable energy integration, precise demand forecasting, and optimised energy storage management, all key capabilities for meeting ambitious Net Zero objectives.

Many leading energy companies are already demonstrating the strategic benefits of effectively harnessing their data. Their experiences provide both inspiration and insights into converting complexity into competitive advantage.

• Enel Green Power started unifying its fragmented global data streams early, significantly enhancing predictive maintenance and operational efficiency across its renewable assets.
• E.ON has integrated AI across multiple organisational functions, including digital grids, improving operational efficiency, customer service, and sustainability outcomes.
• SSE is leveraging generative AI together with PwC to revolutionise audit processes by automating routine tasks, allowing teams to concentrate on strategic decision-making, highlighting AI's potential in optimising traditional practices.

Achieving transformative outcomes in data and AI requires more than technological advancements alone. Strategic collaboration with external experts significantly accelerates progress, offering essential technical expertise, strategic insights, and fresh perspectives.

Facing rapid growth and data fragmentation, Ilmatar collaborated with us to implement a comprehensive, cloud-based data platform. This transition streamlined their operations, provided immediate access to critical insights, improved operational efficiency, and enhanced investor relations.

Similarly, our partnership with Fortum evolved into a comprehensive strategic initiative, embedding advanced analytics and AI deeply within their organisational processes. Deploying sophisticated tools like Apollo, a powerful AI-driven platform designed for energy trading optimisation and asset management, significantly accelerated Fortum’s deployment of new capabilities by approximately 75%. Importantly, internal capability-building ensured sustainable innovation and fostered a lasting culture of continuous improvement.

Our collaboration with Vattenfall highlights the strategic value external partners contribute through AI prototyping and internal skill development, reinforcing the importance of strategic guidance beyond technological implementation.

Building on our experience in the energy sector, the successful transformations have led us to some key factors to effectively navigate and realise the benefits of data-driven innovation:

• Build Solid Data Foundations: Establish reliable, unified data platforms before advancing into AI-driven decision-making.
• Balance Quick Wins with Strategic Goals: Address immediate business needs to build internal trust while steadily progressing towards long-term transformational objectives.
• Secure Strong Leadership and Cultural Alignment: Engage committed executives and foster an organisational culture receptive to change, sustaining momentum through transformation.
• Prioritise Internal Capability and Knowledge Transfer: Embed expertise within teams, empowering continuous innovation and digital maturity.
• Leverage Agile, Flexible Technologies: Employ agile methodologies and adaptable solutions, including AI and low-code platforms, for rapid, scalable results.

A notable observation from our conversations with energy companies is their heavy investment in improving data infrastructure and quality. While essential, organisations must continue experimenting and innovating simultaneously to explore emerging technologies and applications.

The future belongs to energy companies that effectively leverage data-driven insights. Organisations must strategically identify innovative and commercially impactful ways to utilise data, benefiting significantly from external expertise. Strategic partnerships can help utilities quickly achieve clarity, deploy practical solutions, and realise tangible outcomes.

Leaders embracing this strategic, collaborative approach won't just navigate the evolving energy landscape – they will shape it.