How AI Is helping B2B energy retailers overcome decades-old challenges

B2B energy retailers sit at one of the most complex intersections in the modern economy. They buy and sell a commodity whose price changes every hour, serve millions of customers with highly varied consumption patterns and operate inside tight regulatory frameworks that differ by region. In recent years, volatility has pushed margins sharply upward and downward, but industry analysis now suggests profitability will lighten as we move into 2026-27. 

The challenge most retailers face is that the tools they rely on haven’t kept pace with this 

volatility. Many still run critical pricing, forecasting and risk decisions on disjointed systems or spreadsheets held together by institutional memory. 

This gap between market complexity and decision-making capability has become a structural challenge. It’s one that has eroded margins, slowed down innovation and made it almost impossible for teams to understand where profitability is truly created or lost. But the emergence of Energy Margin Intelligence (EMI) marks a meaningful shift in how retailers can operate in this environment. 

What is Energy Margin Intelligence?  

EMI applies advanced AI and decision intelligence to unify data, automate the most error-prone workflows and give commercial teams the real-time visibility they need to take action. Rather than wrestling with siloed systems for pricing, forecasting, hedging and portfolio analysis, retailers can finally see the full margin picture – past, present and future – in one place. This creates a foundation for faster and more confident decisions, from launching new propositions to responding to volatile market movements. 

More importantly, EMI doesn’t replace human expertise; it amplifies it. Energy retail is full of 

nuanced judgement calls that benefit from experience. What AI can do is eliminate noise like manual processing, reconciliation steps and the late-night spreadsheet firefighting many retailers know all too well. Without these distractions, experts can focus on where they bring the most value. They can move away from reactive, backward-looking processes toward a proactive, intelligence-led operating model.  

It’s time for positive change  

For an industry that has spent years trying to modernise under the weight of legacy systems, this shift is overdue. As energy markets become more dynamic, with electrification, flexible demand and renewables pushing new complexity into retail operations, the ability to manage margin with precision will define who thrives and who struggles. Upcoming structural changes like surging data-centre demand will also only widen the gap between retailers who have real-time margin intelligence and those relying on static models or spreadsheets. 

For many retailers, margin erosion doesn’t always come from big market shocks. They’re far 

more likely to see it accumulate through hundreds of small, hidden leaks: settlement adjustments, imbalance drift, hedging timing, incorrect metadata and shifting non-commodity charges just to name a few. EMI surfaces these micro-impacts before they silently eat into profitability.  

Applied AI is reshaping traditional sectors. As an industry that wasn’t built for extreme volatility but now has the tools to navigate it, energy retail is a prime example. With Energy Margin Intelligence, retailers are hitting two birds with one stone: solving the problems of today as well as building the capabilities they need for the next decade of the energy transition. This new approach turns margin from a backward-looking finance number into a shared, real-time commercial discipline.