Utility Week

14 www.utilityweek.co.uk/fLeX But with the new system functioning well, Watson is directing efforts to the next big staging post of Northumbrian Water's transformation – managing its assets. "We've got 54,000km of pipes, several hundred treatment works, so having good network intelligence, and improving maintenance regimes, is the other big job that we do." Here the goal is to improve the life of mobile maintenance workers, which will be brought about by upgrading the organisation's planning and scheduling system and improving the repository for asset data to provide a basis for better collection, machine learning and analytics. By way of example, he points to the work of the ten-strong data science team, which has developed an algorithm to apply machine learning to predict problems in sewage pumping stations and leakage. "For sewage pumping stations, we can predict about eight times out of ten when there is likely to be a problem, and we've reduced our pollution incidents of that type by 80 per cent," he says. e last piece of the maintenance jigsaw, which overlaps with the customer programme that has been deployed, is to "take operational calls on the same system as the billing, meaning we've got a 360-degree view of all the interactions we have with the customer". Transformation journey So where is Northumbrian on its transformation journey? "We started the transformation four years ago, and I think we've got another couple of years to go. We've made a significant investment in putting a new architecture in place, but we know that we can never afford to stand still. We are using agile methods to continually enhance the customer and employee experiences that we have created." What does he think is crucial to this successful transformation? Watson doesn't hesitate: "Investing in our people – we've spent 30 per cent of the transformation budget on our people and on including our people in the programme." Watson says companies often make the mistake of skimping on training people properly or trying to bring existing staff on board. "It's easy to cut that bit, or spend a bit less, but I give the board of this company a lot of credit for realising that we need to put our people into the programme." And the challenges going forward? "It's about our employees getting used to doing things in new ways, quickly adapting to new tools, and getting used to the fact that change just keeps coming, and it's normal. "When I first arrived four years ago, people would say 'can we just slow the pace of change down a little bit?' But nobody is saying that any more, they've got used to the fact that we're just going to have to live with it." Q What sort of technologies are you getting excited about at the moment? e possibilities that AI brings, across our business, are big – water, wastewater, customer engagement. I'm very excited about what that can do to improve this business. And a little bit further away is digital twins, and the ability to run simulations that allow us to make better decisions across our business (see more on page 26). At the moment we're using machine learning to improve our maintenance regimes, and predict when we need to intervene in the assets in our network. We're increasingly looking at how to use it in the customer engagement arena – for example, could we use AI to hook up // When I first arrived four years ago, people would say 'can we just slow the pace of change down a little bit?' But nobody is saying that any more, they've got used to the fact that we're just going to have to live with it // I N T E R V I E W the customer with the person who would deliver them the best experience? Q How far do you think AI will go? In the medium term, I think it's going to help people to make better decisions. We'll be using it to supplement people and their knowledge. In the long run though, I think it will go further. inking ten years ahead, will our fleet be driven by people, or will it be autonomous? I would suspect the latter. Will people be talking to people or will they be talking to machines? I think quite a lot of them will be speaking to machines, and they won't even know it. Q How much time and effort are you putting in to developing AI at the moment? Quite a lot. I've got three people in my team put aside to look at this. And when I go out to conferences and things like that, that's mostly what I'm focusing my attention on. Q Any career moments that defined you or changed your outlook on the way you manage people or your ambition, or anything like that? When I was at GE Capital in California, they put me on to a post-merger and acquisition team of six people. I was the person from technology. It was very intense, we had 180 days to integrate a business. It forces you to take a very holistic view of a business. And I learned a lot doing that. I would be redesigning a sales incentive scheme one day, writing job descriptions the next, looking at technology. And I think that kind of experience made me more rounded. Q What did you take away from the experience in Turkey? I worked in Turkey for five-and-a-half years on a transformation programme. Turkey is a very young country – the average age is 27 – so the breadth of decisions I had to make as an experienced person was a real game-changer. Also, being able to adapt and understand, culturally, what bits you can influence and what bits you can't was an important skill I picked up along the way. I often asked the question 'is this Vodafone Turkey, or is this Turkey?' Because you might be able to change the first, but you definitely can't change the culture of the country. So, it's really important you can learn the context in which you're operating and accept what you can and can't influence. It's important to nudge the corporate culture along, and I try to do that here."

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