The tension emerging at the heart of the B2B energy retail market
Photo by Volker Thimm on PexelsGrowth is back in B2B energy retail, but it’s become glaringly apparent that ambition has outpaced infrastructure.
Most retailers are pricing deals, winning them and then handing them over to operations with little visibility into what actually happens to margin from that point on. With the systems and commercial intelligence needed to grow sustainably falling short, the contracts become loss-making and retailers quietly bleed margin, and all without them knowing until it’s too late.
Growth is bouncing back
And they are the ones who can roll out a new product end to end, including pricing it, selling it, billing it and forecasting it, without a multi-year IT project.
After a few years of consolidation, caution and shorter-term contracts, the appetite for growth in B2B energy retail is clearly returning. We’re seeing energy retailers across the UK and Europe trying to enter new segments, launching new product structures, and chasing volume again.
But growth in a compressed-margin environment is a very different challenge from growth in a stable one. When competition increases, margins shrink. When customers are demanding more sophisticated products, including flex contracts, corporate PPAs and time-of-use structures, the complexity of getting pricing right increases dramatically. Doing both at once, growing aggressively while protecting profitability at the contract level, requires a level of commercial intelligence that most retailers simply don’t have yet.
It’s been happening over the past few weeks, and it’s still happening now. Retailers are repricing in real time in response to global market movements. The pressure to be fast and accurate at the same time is constantly building.
Most retailers are flying blind on margins
In 2025, we went out and spoke to a large number of our customers and them: where do you actually need help steering your margin? The answer, almost universally, was customer-level margin visibility.
That might sound like a narrow technical problem, but it isn’t. Right now, the majority of energy retailers price a deal, win it, and then essentially hand it over the fence to operations, assuming it will be profitable. The pricing team modelled it that way, the forecasting team agreed. Everyone is aligned.
And then the margin erodes – and quietly. Consumption doesn’t come in as forecasted, non-commodity costs shift, but by the time anyone notices, the damage is done and the contract is already loss-making.
In principle, the fix is not complicated. Retailers need to be able to see, at the contract level, what they are actually making versus what they forecasted they would make, and understand the gap clearly enough to act on it. That visibility has to span commodity costs, actual consumption and non-commodity costs together. It has to be live, not a monthly finance report.
But it needs to go beyond visibility. Having customer-level margin data is necessary, but it does not automatically translate into better decisions. When retailers look at a portfolio of thousands of contracts, identifying which ones need attention – and what kind – is still a big challenge. Is a margin deviation a data error? A consumption shift that warrants a product change? A signal to proactively reach out to a customer ahead of renewal? They need more than a number. They need the number to surface the right action, for the right person, at the right time.
This is the problem beneath the visibility problem, and it is where the real commercial value lies.
The role AI will play
AI frequently crops up in conversations about energy retail, usually in sweeping terms. In B2B energy, relationships, experience and judgment matter enormously. The ability to explain a complex shift in non-commodity costs to a customer or to navigate a sensitive renewal conversation is something that typically takes years to develop. AI does not – and should not – replace that.
What AI can do is compress that learning curve dramatically. A pricing analyst two years into their career, equipped with the right tools, can start performing at the level of someone with ten or fifteen years of experience behind them. That is a genuine competitive advantage for any retailer that gets it right.
And then there is the accelerator case: analysis that previously took an analyst three weeks can now be produced in seconds. Things that were previously not practical now are, like surfacing renewal opportunities, flagging at-risk contracts, or explaining margin variances in plain language across a portfolio of thousands of accounts.
The data foundation
To be clear, AI will not save retailers if their underlying data is a mess. The prerequisite for any of the above is a single, reliable source of truth that connects pricing, forecasting, hedging, billing and sales functions. Most retailers today still have those functions running on different logic and different systems. That is the root cause of the margin problem, and it is what has to be solved first.
The retailers who are furthest ahead right now are those that invested early in that data infrastructure. They are the ones who can push pricebooks multiple times a day instead of once a week. And they are the ones who can roll out a new product end to end, including pricing it, selling it, billing it and forecasting it, without a multi-year IT project.
That is the direction the entire market needs to move, so it’s just a matter of how fast.
