MONITORING & METERING Harnessing AI to work quietly in the background
It seems that everyone wants to talk about the potential of AI, but how many realise that many of its most useful tools are already being exploited? Simon Dray explains how it is already enabling companies to analyse data and improve efficiency levels.
Simon Dray
energyassets.co.uk
A
Strategy director at Energy Assets
rtificial Intelligence has become a familiar phrase (dare we even say ‘buzzword’) in energy and
utilities conversations across the UK. It’s referenced in boardrooms, industry speaker panels, government frameworks and corporate strategy slide decks. In many organisations AI is being
explored with a cautious mix of curiosity and hesitation. There’s an enthusiasm present where consultants are engaged and roadmap strategies are drawn up, but it often stops there. It feels like there’s a collective need for a more concrete sense of confidence before AI is fully embraced into a ‘business-as-usual’ approach. Most of what’s being written about AI in the sector tends to focus on the ‘potential’ of AI without much to speak of in the way of case study or demonstrative results. There are pockets of the
industry where AI has already been successfully implemented and functions as part of daily operations. However, it often goes unlabelled. These software innovations and processes aren’t always presented as AI, even if that’s what they are. The emphasis is usually on the ‘human’ aspect – how it helps people address fixes, improvements or flags.
Overseeing data At Energy Assets, our approach to AI has been shaped by our experience on the ground for years now. The sector has always handled large datasets, but the scale is growing exponentially. It’s not unusual for a commercial or public sector customer to have dozens or even hundreds of meters reporting data every half hour. Multiply that across a full portfolio, and the total volume quickly becomes unmanageable without support. Our AMR DNA platform was designed with that in mind. The system processes 48 readings per day, per site. With over 10,000 sites under watch, the volume is significant. The platform runs quietly in the background, reviewing trends,
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Many of the most useful tools are already up and running, they just don't always make headlines
spotting variations and giving clients a view they can act on if they choose to. It minimises human oversight, meaning teams don’t need to dig through mountains of raw data to find the information that matters. Some clients use the platform to
fine-tune their operations, others lean on it more heavily, building it into their internal processes. It fits into scheduled reporting and feeds discussions between estates teams and site managers. It helps organisations understand how their buildings behave, without requiring specialist knowledge or skill sets to interpret the data.
Up and running This type of usage is routine now, but it hasn’t always been framed that way. AI in energy often gets discussed as if it’s still around the corner. In reality, many of the most useful tools are already up and running, they just don’t always make headlines. Most of the time, they don’t look like the high-concept innovation that gets the spotlight at trade shows or in policy papers. What they do offer is consistency.
They help with early intervention and give organisations the ability to notice things they might not otherwise catch. Details like overheating, sudden
consumption spikes or, changes to baseline use over time that often get lost without some form of automated review. When they’re surfaced, people can respond. That idea of responsiveness is key. AI is not about taking over decisions, it’s about improving visibility. We’ve seen it make a difference in retail, local authorities, education estates and healthcare. Each context brings its own quirks, but the underlying challenge is often the same: there’s a lot of data, and not enough time to sift through it.
Labels don’t matter There’s still a gap in how AI is understood across the sector. Some organisations are further along than others. Some have embedded systems and dedicated resource. Others are watching from the sidelines, unsure whether the investment will pay off. And then there are those who are already using it, without feeling the need to label it that way. It may be helpful to separate the
technology from the terminology. Many of the most effective tools don’t present themselves as AI, or indeed the opposite where they are advanced data capture and analysis systems but still require human intervention.
They’re positioned as energy management platforms, reporting systems, or automated analytics. The labels matter less than the outcomes when what people are really looking for is support in understanding how energy is being used, and where it might be wasted. At Energy Assets, we made a decision early on not to treat AI as a premium product, it’s simply part of how we deliver our service. That’s what’s helped us learn what works in real-world settings. It’s also allowed our systems to develop in response to genuine usage, not just test environments or controlled trials. In the coming years, more
organisations will likely move from exploration to implementation. The drivers are already there, especially with net zero targets tightening and budgets remaining under pressure. There’s growing awareness that small efficiencies, across large portfolios, can add up quickly. Tools that help with that will find their place. AI may still be a buzzword in some
spaces, but in others it’s already a quiet presence in the background. It doesn’t need to announce itself; it just needs to be reliable, adaptable, and able to keep pace with the sector it serves. ■
EIBI | JULY � AUGUST 2025
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