NEWS COMMENT


BEYOND THE DATA: WHAT RETAIL ENERGY CIOs ARE BETTING ON NEXT


By Joris Van Genechten, co-founder and VP of Product & Engineering, Gorilla. At EMC 25 in April 20-21 2026, under the theme ‘The Power Shift: Redefining Competitive Energy Markets’, retail energy technology leaders explored how IT is shaping competitive advantage in the energy sector. Their discussion reflected broader trends emerging, particularly around AI adoption, operational agility, and data-driven decision-making. The event brought together technology executives and advisors from


across the retail energy and generation landscape, including leaders from 174 Power Global, Chariot Energy JERA,, Direct Energy, and CG Infinity.


THE CIO’S ROLE HAS SHIFTED BEYOND TECHNOLOGY One of the clearest themes to emerge is how the role of the CIO has evolved


over the past decade. Technology leaders are no longer focused solely on infrastructure, system uptime, or software delivery. Increasingly, they are expected to influence commercial strategy and help shape business outcomes. Former Direct Energy technology executive Brian Hines pointed to the


changing nature of customer engagement as evidence of this shift. Moving away from call centres, digital engagement dominates today, with mobile applications becoming the primary touchpoint for customer interaction. Energy retailers are no longer benchmarked only against competitors in the utility sector. Customers increasingly compare every interaction to experiences delivered by digital-first companies across industries. As a result, CIOs are now expected to operate as business leaders as much


as technical leaders. Commercial awareness, operational understanding, and risk management are becoming just as important as technical expertise. The rise of AI has accelerated this trend. Boards are demanding guidance


on AI strategy, governance, and risk, often before organisations have fully defined what successful AI adoption should look like. Technology leaders increasingly find themselves responsible for translating broad AI ambitions into practical business applications.


DATA INTEGRATION IS BECOMING TABLE STAKES For much of the last decade, retail energy IT transformation has focused on


data integration and system modernisation. Many organisations have invested heavily in consolidating fragmented systems, improving data quality and creating more unified operational views. While this work remains essential, integrated data alone is no longer enough to create lasting differentiation. The next challenge is making organisational knowledge scalable. Companies


that can unify and access data quickly are likely to react more effectively to changing market conditions. Faster access to information creates operational speed, and AI systems can amplify that advantage. Increasingly, the real competitive edge may come from embedding


institutional expertise directly into operational systems. In retail energy, profitability often depends on nuanced knowledge developed over years of market participation. Pricing behaviour, hedging strategies, contract structures, regulatory interpretation, and customer segmentation all contain layers of context that are difficult to capture in traditional workflows. This expertise has remained concentrated within small groups of experienced employees, so how can organisations operationalise that knowledge more broadly? AI systems require broad access to organisational data in order to generate


useful insights at scale. At the same time, companies remain cautious about governance, security, and reliability. Many organisations are therefore trying to balance two competing priorities: enabling faster decision-making while maintaining strong controls around sensitive information and operational risk.


AI STILL DEPENDS ON HUMAN EXPERTISE Artificial intelligence is increasingly being viewed as an augmentation tool


rather than a replacement for experienced professionals. Many industry leaders warn that generic AI models often struggle in highly specialised industries such as retail energy. Market rules, regulatory structures, contract logic, and operational constraints vary across regions and organisations. Without sufficient domain context, AI-generated recommendations can appear credible while producing flawed conclusions. This concern is especially relevant in areas involving pricing, forecasting, portfolio management, and risk analysis. A more practical near-term model is one in which AI expands the capacity


of experienced teams rather than automating decisions independently. Traditionally, senior analysts or portfolio managers may only have time to


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review a limited number of contracts, accounts or market scenarios in depth. AI tools could potentially extend that analysis across much larger datasets while still leaving final validation to human experts. In power generation and other operational


environments, AI systems are increasingly capable of recommending maintenance procedures and troubleshooting. While these systems can improve efficiency, responsibility for operational decisions still rests with human operators. For many energy companies, this creates an important distinction between AI-assisted decision support and fully autonomous decision-making.


OUTCOME-BASED TECHNOLOGY DELIVERY Metrics such as churn reduction, pricing accuracy,


customer acquisition efficiency, or operational responsiveness are becoming the starting point for project planning. Under this model, implementation details become secondary to measurable business impact. This shift reflects broader changes in enterprise technology adoption. As


platforms become more configurable and AI capabilities evolve quickly, organisations are finding it harder to define fixed long-term requirements at the beginning of projects. Outcome-based approaches may also reduce implementation complexity by allowing technology teams to iterate around measurable operational goals rather than static specifications. Greater transparency around commercial objectives can also improve collaboration between internal teams and external partners.


THE NEXT MAJOR DISRUPTION REMAINS UNCLEAR Increasing market volatility, evolving regulations, and changing customer


expectations are all adding complexity to retail energy operations. Companies that can absorb new information and react quickly may gain a significant advantage. Others emphasise adaptability instead. Technology cycles are accelerating, and the rapid rise of generative AI illustrates how quickly industry assumptions can change. Organisations that build rigid architectures around a single technology model may struggle if the market shifts again. Another perspective centres on customer acquisition and digital engagement.


Search behaviour may change significantly as AI-powered interfaces alter how customers discover products and services online. Retailers that rely heavily on existing digital marketing channels may eventually need to rethink how they acquire and engage customers. Future success may depend less on predicting the exact source of disruption and more on building organisations capable of adapting continuously.


WHAT THIS MEANS FOR RETAIL ENERGY Retail energy technology strategy is moving beyond basic digitisation and


system integration. Reliable, integrated data remains foundational, but it is increasingly viewed as the starting point rather than the end goal. The larger challenge is enabling organisations to scale expertise, improve responsiveness, and make better decisions in complex and less predictable environments. AI is likely to play a significant role in that transition, particularly in


areas involving analysis, forecasting, operational support, and customer engagement. However, domain expertise remains critical. In highly specialised markets such as retail energy, human oversight continues to be essential for ensuring accuracy, accountability, and risk management. Whether disruption comes from regulation, customer behavior, AI


adoption, or market volatility, companies will likely need operating models and technology architectures capable of evolving quickly. Profitability often depends on how effectively organisations align pricing,


forecasting, hedging, billing, and customer operations. Improving those requires systems and workflows that can apply institutional knowledge consistently and at scale while allowing experienced professionals to focus on oversight, strategy, and decision-making. The companies best positioned for long-term success may be those


most capable of combining technology, expertise, and adaptability into a coherent operating model.


https://www.gorilla.co/ ENERGY & SUSTAINABILITY SOLUTIONS - Autumn 2026 5


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