Fuelling the future

Our experts explore how AI makes dynamic pricing smarter, balancing regulation, profit, and customer experience in a changing UK energy market.

The podcast explores how real-time pricing, mandated by new UK regulations, can move from being a compliance requirement into a genuine strategic advantage. Our experts discuss how technology enables businesses to achieve not only safe pricing but also smart pricing.

Key areas discussed:

The importance of clean, integrated data from multiple sources

Digital twins and creating a virtual model of the site

Leveraging AI and automation with the right guardrails in place

Ensuring compliance and robust auditing

The discussion highlights the outcomes and benefits of dynamic pricing when supported by a strong technology foundation. With the right approach, decisions become explainable, auditable, and fair. Transparency and accountability are essential—dynamic pricing is about aligning costs with market signals, sustainability goals, and customer value.

The episode concludes with practical insights into effective implementation, demonstrating how dynamic pricing presents a significant opportunity for businesses.

Transcript

Akrash Chaudhary: Hi, everyone, and welcome to Fuelling the Future, our new podcast on how technology is reshaping retail energy. I'm Akrash. If you manage forecourts, lead EV charging networks, or run retail operations in the energy space, you are in the right place. In the first episode, we'll be diving into AI-powered dynamic pricing with new UK rules now requiring near real-time price updates. The question is, if you have to change prices fast, why not make them smart too?

This is about more than algorithms. It's about using AI to protect margins, respond to regulation, and most importantly, give customers better value, especially as EV adoption accelerates. I'm joined by two brilliant guests, Jaz and Matt, who bring deep expertise in this area and AI. Hi, both.

Jasdav Chahal: Hello, Akrash.

Matthew Griffiths: Hi, Akrash.

Jasdav Chahal: Very pleased to be here.

Akrash Chaudhary: Jaz, let's start with you. What's driving this shift right now, and why should leaders pay attention in 2025?

Jasdav Chahal: Well, thank you, Akrash. In my opinion, I'd say there are three big reasons. The first one being profitability. If you look at global benchmarks from firms like BCG, they clearly show up to 8% uplift in gross margin when fuel and retail prices are dynamically optimized. Second, I would say is the UK regulation based on the fuel finder open data scheme, which means forecourts will now have to publish the pump price updates within 30 minutes. If you're required to make rapid fast updates, we should make them smart and strategic, and real-time.

Third, I'd say, in my opinion, is UK proof points. If you look at the Centre for Net Zero, they ran a trial with over 110,000 EV drivers, and one of the key observations that really stood out was off-peak charging. When it was discounted by 40%, the usage had a massive jump up to 117%. Now, this is proof that dynamic pricing isn't just theoretical. It actually changes behaviour, it changes mindsets. Matt, anything you'd like to add.

[00:02:31] Matthew Griffiths: Yes. Thanks, Jaz. I think we're at a point where it's quite exciting. This kind of dynamic pricing is really going to change things. I think the key thing is when it's done responsibly, it's fully legal and it's actually beneficial both to retailers and to the end consumer. Some recent guidance from the competition and market authority is clear. It's encouraging the use of pricing algorithms, but it's also setting the bar high for retailers in terms of the control mechanisms. It's wanting the setting of pricing to be transparent. It's wanting it to be explainable and it's also putting in place guardrails to protect everybody from anti-competitive behaviour.

For me, that's where AI can really be a force for good. It's not around manipulating the prices in the back end. It's really there to respond to what's happening in the market, taking on board the real-world signals, and having clear logic and audit trails to really back up those pricing decisions. I think the myth of pricing AI being risky is not there anymore. The truth is if it's done right, it helps reduce risk, but it's also gives a clear reason and record of why the pricing is being done.

I think if we're changing prices every 30 minutes, we need a system that's going to be smart, fair, and accountable. That's where autonomous AI comes in and really adds the value. I think a key thing is this activity is not just about chasing margins for the retailers. It's about making sure the price is aligned to what's happening in the market, making sure it's greener. Looking at changing prices when out of hours, when demand is a bit softer, and it makes more sense for the customer as well. I think that's my point of view. Probably back to you, Akrash.

Akrash Chaudhary: Thanks a lot, Matt and Jaz. That's a really strong case. Especially when you consider both the business upside and the regulatory tailwinds. Just touching upon the last point there, Matt, I think it's good for the viewers to just get into the engine room, right? From a tech point of view, what's actually powering these capabilities behind the scenes?

Matthew Griffiths: I think that's interesting to explore. We can look at this probably four clear layers of the architecture that really build up the capability to give them both smart pricing and safe pricing. I think the first area is really the data. The way that real-time pricing is going to work is having clean data, data from different places, such as the fuel prices, the electricity rates, carbon impacts. Actually, looking at what the footfall is within the retail site and looking at other factors such as the weather. Using all of that information can really drive a guaranteed, predictable price and makes it more relevant for the retailer, but also to the consumer.

I think second to that is really building a picture of the site itself in the digital world. This is where the concept of digital twins comes in, and that's where we can look to simulate the effects of pricing on that retail site. Really using that data to simulate scenarios such as when it's sunny, when it's cold and wet, and look at different times of the day. That means that everything that is going to be potentially changed at site is first simulated, first tested to see the impact, so it's not just a guess. It's based on fact.

I think the third thing is around actually making those pricing changes. Quite often, that's been a manual activity. The use of AI and automation will help those recommendations really get to the forecourt much quicker, both for benefit for the retailer, but also the consumer. It's key that, in that process, that there are guardrails to make sure that those prices are being checked, and if there's any concern from the AI that there is a human in that process to validate and give the green light before the pricing goes to site.

Finally, I think all of this landscape around pricing is compliance. Making sure this is all wrapped up in a layer to ensure that any pricing change that's made at site is clearly logged, it's clearly timestamped, it's explained why that pricing was done and what the reasoning for that was. That both protects the retailer. You've got a clear visibility of why something happens, if there's any challenges, but also it supports the customer to know that things are being done fairly. I guess I'll hand it over to you, Jaz. What do you think on this topic?

Jasdav Chahal: Thanks, Matt. I think from our perspective, audit logs are pretty fundamental in safeguarding the integrity of any business operation. I think, as directors, we've got a duty not only to act in the best interests of the organization, but also to remain fully accountable for the decisions we make, especially around pricing, why it's changed. I think that's where having a clear timestamp, audit trail means we can precisely explain why a price has changed, what were the factors influencing that decision, and who was involved.

This level of transparency, I think, is very critical not only to reassure stakeholders, but also to demonstrate that decisions were made independently and ethically without any form of collusion or anti-behaviour. Just that helps build that trust with your end customer, and more importantly, in a regulatory environment where scrutiny is increasing and audit logs actually provide that essential backbone which supports fair practice, and enables as leaders to stand by decisions and with confidence. It's not just about compliance. I'd say it's about protecting the reputation, trust, and long-term value of the business. Akrash?

Akrash Chaudhary: Thanks a lot, both. It was quite powerful. We discussed the four golden pillars that you need to have from a technology standpoint of view and obviously the entire thing revolving around compliance. Thanks for your insights on that, Jaz. Essentially, we are talking about a fully autonomous pricing agent working within set boundaries and that's also leaving a digital paper trail that keeps leaders, customers, everyone protected and compliant. In a way that kind of sets the scene, and as Matthew touched upon, it sets the bar quite high.

Let's talk more from the outcomes perspective. Let's talk about the value to business and customers, and the impact that it causes. Jaz, what's the value from a commercial and a customer lens?

Jasdav Chahal: I think it's a massive upside in terms of impact. Firstly, financially, any AI-led dynamic pricing surely helps boost fuel and retail margins, and that offers real-time discounted EV tariffs plus variety of offers around what's customers' predicted dwell time if that's high. Secondly, coming onto socially, it helps bring that equality and equity in. Many drivers can't charge at home. Having that dynamic public pricing gives them a fair access to off-peak energy.

Just thinking wider, more from a CSR, Corporate Social Responsibility, aspect as well. Last, I'd say, is sustainability. This demands to greener hours, so wind-rich overnight windows helping both the grid and ESG goals.

Akrash Chaudhary: Thanks for that, Jaz. I think it's quite compelling from a business and a customer standpoint of view, especially around retail stores where they're trying to up their sales because that's a major factor for the revenue, and if that's combined, then it helps deliver hyper personalized offers. Imagine just turning up and then not only do you get the best tariff on the fuel, but then you also get these offers come in based on your preferences on, say, suppose I'm in for a croissant or a cup of coffee, my favourite, say, mocha, that just turns up. It's quite powerful that way. Over to you, Matt. What is your view on this?

Matthew Griffiths: Thanks, Akrash. I think from a more technical and operations standpoint, it locks a new layer of operational intelligence that we didn't have before. I think every pricing event, every change made, isn't just about the price on the pump; it's a key piece of data. Over time, that data starts building up a much richer picture around your retail operations, what time of day you see the best uptake, how pricing is sensitive to change in terms of location, weather, and other factors.

I think the kind of insights that you get from this go well beyond pricing. You start to see the effects, the influence of what it does to your retail operations. It can start driving predictive maintenance, so you can see when you've got laws and really make your business fit for purpose at the time that it needs to be. I think one of the key things is customer experience is that the pricing that is taking place is based on, as I said, a number of factors. Those can actually be turned into marketing messages to the customer to understand why the pricing might be cheaper at certain points a day.

You can give the environmental angle, they get to see what's really driving that behaviour, and I think that also can give better brand loyalty to the retailer. I think, finally, we know retailers are always battling with the margins, keeping things at the right price point, but also, this intelligence is key to make sure people are still loyal to the brand, that the operations are up and running, so that you can keep that right price point. I guess that's my view, Akrash. Back to you.

Akrash Chaudhary: Thanks a lot, Matt. As we have seen from both of your insights that this is a good blend of financial, social, there's sustainability value added as well, and this is what retailers are aiming for. Even though the old discussion started off with pricing, but we see that it's quite important, and as price is king, it really touches upon each of these key elements. It's not just theory. As we went through all these different sections, we do understand it's real, it's measurable, it's all about how fast we actually bring this change on.

If I'm a retail leader and I'm listening to this, one of the questions I will have is: How do I actually start? What's the rollout plan, and what should I actually watch out for? For that, we recommend a three-step journey. The first one is piloting in a shadow mode. Obviously, we don't want to just switch on and, just rely on AI to generate pricing recommendations without actually comparing them against the actuals or even simulating some of these changes that might occur.

First thing is always a pilot and that in a shadow mode. The second one is to phase out that rollout. By phasing out, I mean choose a domain, we would recommend EV charging because that's where customer expectations are already there to support variable pricing. We see that even in our homes, we have packages which enable us to save some costs if we stick to off-peak charging and then scale to the traditional fuel and in-store pricing. Again, this ties into having that vision and then having a roadmap initially just to help out phase it in a manner which supports your business outcomes.

Third, which it's actually quite important, it's communicating with clarity. Matt, you touched upon this point earlier, which is quite important is customers should know when and why prices vary. As an example, brands that explain Green Saver hours, or say late-night EV boost, they'll build loyalty. It comes across with a marketing message attached to it. It's not just confusion or you're not just someone knocking this off, saying it's just some buzzword out there, but it'll build that whole customer trust. Just going back to you, Matt, from a technical standpoint, how should leaders be looking at this?

Matthew Griffiths: Yes, Akrash. Spot on with those phases, and I think from a technology standpoint, it's all around getting the foundations right. Like I said earlier, the data is key to this initiative. The first thing, focus on your data, get it as good as you can. Whatever you feed into your AI will drive the quality of the outputs. Making sure that you're getting accurate readings from your site in terms of your pricing, that's already there, and what's coming back from your pumps, your energy meters, et cetera.

If you don't get those foundations right, the recommendations you get will probably have faults, and it will lead to more intervention and correction from a human, and slows down that pricing journey. I think second is, once you've got the data, is making sure you've got the guardrails in place so you're not just handing over the control to AI, the retailer is still in control, but it's making sure that the boundaries are there to control the AI and understand that those processes that run autonomously are well understood, they are audited, and that there's the ability to bring a human into the loop to validate those pricing choices and have that check.

I think what an analogy I've seen talked about is think about those guardrails like bumpers on a bowling lane. The AI is in the middle, it's the bowling ball, it's got the freedom to go down the lane, but there's still that control on the outside to keep it in check. Then I think, lastly, which we've touched on a number of times, is that auditability, that traceability. Making sure every action that the AI is doing is fully captured when it happened, what it did, what drove it to make those decisions. So that, if there are any queries, you can always go back to that record, understand, and actually you can use that same information to drive improvements in the model and the behaviour going forward.

I think it's clear that having your audit ready it's not just a checkbox; it's the backbone of A, the trust of the retailer in the AI, but fundamentally the customer in the retailer.

Akrash Chaudhary: Thanks a lot, Matt, for your views. Dynamic pricing is no longer a future ambition based on all what we have discussed right now. It's a present opportunity, especially since, in today's world, we have transparency and speed. There are now baseline requirements. It's a double-edged sword. It's also a competitive edge. Then, if not done right, it can also cause a detrimental impact on the overall business.

If it's done right, it'll lift profit, it'll deepen customer loyalty, and it'll also support the energy transition. Jaz, Matt, thanks once again for your wonderful insights. To our listeners, if you're thinking about AI and pricing, now is the time to pilot, measure, and scale. Stay tuned for our next episode, where we will explore driving intelligent operations with AI. Thanks once again for joining us.

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