I recently joined Hannah Sword, Director at BFY Group, and Fiona Bell, Commercial Director (DES) at Drax, for a webinar on the state of the B2B energy market. The conversation covered a lot of ground, and you can see the full webinar here.

A few themes kept pulling me back after the call ended. I wanted to set them down properly, because I think they get at something important about where this industry is right now and where it needs to go.

Growth is back

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. That's genuinely exciting.

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 your 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.

We saw it in the weeks leading up to the webinar. We saw a roughly 50% spike in platform usage among our own customers. Retailers were repricing in real time in response to global market movements. The pressure to be fast and accurate at the same time is already happening.

The core problem: most retailers are flying blind on margin.

Last year, we went out and spoke to a large number of our customers and asked a simple question: 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, quietly, margin erodes. Consumption comes in differently than forecasted. Non-commodity costs shift. By the time anyone notices, the damage is done and the contract is already loss-making.

The fix is not complicated in principle. You need to be able to see, at the contract level, what you are actually making versus what you forecast you 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.

That is what we are building at Gorilla, and it is the right thing to be building right now.

Going beyond visibility

Here is where I want to add something to the conversation we had on the webinar, because I think there is an important nuance that often gets lost.

Having customer-level margin data is necessary, but it does not automatically translate into better decisions. If you are looking at a portfolio of thousands of contracts, identifying which ones need attention - and what kind - is still a genuinely hard problem. 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? You need more than a number. You 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 comes up constantly in conversations about energy retail right now, usually in sweeping terms. I want to be specific about what I actually think.

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. I do not think AI replaces that. I do not think it should try to.

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 not a small thing. 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. Surfacing renewal opportunities, flagging at-risk contracts, explaining margin variances in plain language across a portfolio of thousands of accounts. These are things that were simply not practical before and are becoming practical now.

The data foundation

I want to be honest about one thing: AI will not save you if your underlying data is a mess. The prerequisite for any of the above is a single, reliable source of truth that connects your 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. 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. The question is how fast.

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