Flex 3: The AI edition – Utility Week Flex 3: The AI edition - Utility Week

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Those who have delved online to ask, “Will a robot take my job?” might take comfort that the sort of engineering and scientific expertise many of those working in utilities possess is unlikely to be replaced by machines any time soon, according to a study by the Martin School at Oxford University.

But that belies the profound impact that AI and machine learning will have on the operations of water and energy companies and their staff going forward. From bolstering resilience, to facilitating the move to a smart grid – and moving the dial on customer engagement – we’re already seeing a plethora of applications. This third issue of Flex drills down into many of these exciting developments and provides a comprehensive round-up of how utility firms are harnessing today’s advanced computer fire power, coupled with sensors and big data, to analyse, predict and engage.

United Utilities is applying software, for example, to optimise how clean water is pumped around the North West so that it saves energy. On the other side of the Pennines, Yorkshire Water is one of several water companies putting the technology to work detecting leaks.

As we also report, the power of AI to balance the supply and demand of electricity will help the transition to a more decentralised ecosystem fed by EVs and battery storage.

The application of AI is still in its early days, and there are inevitably challenges to be overcome – not least legacy infrastructure where investment in sensors and other IT systems to collect the necessary data will be costly.

Neither is it foolproof to apply AI to improve customer engagement. But get it right and the rewards are there to reap, greater productivity and happier customers conversing with voice assistants or chatbots. Meanwhile, staff can be freed to deal with complicated queries or more complex tasks.

As Northumbrian’s CIO Nigel Watson observes, in the longer term who knows where the technology will lead – autonomously driven fleets and machines talking to machines. But what is apparent to Watson and others featured in Flex is that the possibilities AI brings to improve utilities are immense.

Denise Chevin, Editor, Flex


Download the full issue of Flex, May, 2019



Northumbrian Water chief information officer Nigel Watson

By Denise Chevin

Northumbrian Water’s chief information officer on the potential of AI and digital twins and why the company is spending 30 per cent of its transformation budget on its people


Nigel Watson likes a challenge. When we spoke before Easter, he was gearing up to run the London Marathon and keeping his fingers crossed for cooler weather after an unseasonably warm spell.

“I’m not really built to run marathons, but I have trained for it, so I’m going to give it a go,” says Northumbrian Water’s chief information officer.

“It’s not going to be fast, but I’m hopeful that I’ll get round,” he says with a calm optimism. “I’m just hoping it’s not too hot!”

Near ideal weather conditions and gutsy determination saw Watson over the finishing line in a very respectable five hours and 23 minutes. But despite his aches, Watson was back at his desk in Durham the next day.

“What we’re trying to become is the fastest learning water company on the globe. And having the digital signals that allow us to quickly pick up on the effects of climate change, or the impact on customers, is why we have that ambition”

It’s this kind of conviction and energy, combined with crystal clear vision, that Watson is putting to work to make Northumbrian Water the “most digital water company in the world”.

“What we’re trying to become is the fastest learning water company on the globe. And having the digital signals that allow us to quickly pick up on the effects of climate change, or the impact on customers, is why we have that ambition.”

Watson arrived at Northumbrian Water in 2015 after seven-and-a-half years at Vodafone. He was initially brought on board as a consultant, and was then offered the CIO role when his boss departed a year later. His multi-varied career has seen him in business and operational and transformational roles, working far afield – including in California, Australia and Turkey. This breadth of experience and his rise from a Youth Training Opportunity Scheme at the age of 17 for the Eastern Electricity Board – and later in life an MBA – have furnished him with an ability to deal with people at all levels and in all cultures.

Joined-up thinking

Certainly, that is a vital asset in Watson’s role at Northumbrian Water, which has unusually wide bandwidth. The structure affords the opportunity to integrate operations that sometimes suffer the right-hand, left-hand syndrome other organisations are prone to, and harness and develop technologies that play to the strengths of more joined-up thinking. But like any change, it all requires a mixture of charm and persuasion – and a bit of stiff talking.

So, while Watson has the responsibility for all the usual IT functions and aspects such as cyber security, his domain also includes operational technology – the kit used across the business in areas such as treatment works to control and monitor water and wastewater. Bringing these two functions together brings benefits to its approach to cyber security and to collect vital data in the cloud that allows Northumbrian Water to use machine learning algorithms to learn, and to predict when bits of kit are going to fail. For example, Northumbrian can now predict sewage pump failure in eight out of ten cases, he says.

Watson retained his role of transformation programme director, and he and his team have just finished delivery of new platforms to support customer engagement. They are now about to deploy the first phase of intelligent asset management.

“The core team is 170, and then on transformation we’ve got about another 150 people. ‘Innovation’ we don’t hire people for, because we’ve made that everyone’s job,” he explains.

In addition, as the person responsible for driving innovation across the business, he instigated and is the sponsor of Northumbrian Water’s highly successful Innovation Festival at Newcastle race course, where last year 2,000 people attended from 500 different organisations. The third annual event takes place from 8 to 12 July and there are plans afoot to introduce a spin-off in September.

The festival provides an ideal vehicle for design sprints, which the organisation uses to try to solve complex problems. A proposal for a new digital underground map of utility infrastructure was one outcome from one of last year’s sprints.

“We had BT, Northern Gas Networks, Northern Powergrid and ourselves in the tent. We built three areas of Newcastle on this underground map, because we just wanted to know if we could do it. After the festival, we went out and with Sunderland City Council and Ordnance Survey built an underground map of the area. And we’ve taken that to government and we’re very pleased they are funding the next stage.” It has now been announced that the government’s geospatial commission is set to create a digital map of the UK’s pipe network.

He cites the success of the innovation festival as one of his key milestones since joining Northumbrian Water. In two events alone it has resulted in taking 76 ideas back into the business.

Another major highlight and a key staging post on the transformation programme is the rollout of a new customer system. The old one had 24 years on the clock, and while it was still functioning, the number of support staff who knew about it was rapidly diminishing.

“The catalyst was technology replacement, but we turned it into transformation of the customer experience, with a new CRM and billing system, omni-channel customer engagement platform, home calls, tweets and chat messaging,” he explains.

As a result, customers can now be offered more flexible payment options, and when they ring up their water provider it will recognise their phone number and pull up previous engagement and account history.

He admits it’s not revolutionary – but changing any billing system is a huge undertaking and it’s always a relief when it’s over and working well.

“We still have more work to do with our own internal teams, and we can go further in delivering an even better customer experience, but we absolutely have the right platforms in place to do that right now.”

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.

The 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.”

What sort of technologies are you getting excited about at the moment?

The 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.

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 the customer with the person who would deliver them the best experience?

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. Thinking 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.

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.

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.

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.”



What bit of tech would you like for your birthday?

I’m going to go with a bit of work tech – if someone was to deliver me a fully functioning digital twin I would embrace them, I think.

What technology couldn’t you do without?

My noise cancelling headphones. I’m up and down quite a bit on the train, and I find it really helpful being able to concentrate.

Business hero?

When I worked for GE, Jack Welch was there. And what I really admired about him was his consistency of message, and his laser focus on business improvement. Today, I can’t point to someone and say, ‘that’s my business hero’ because I think the business heroes are the ‘servant leaders’ who are quietly getting on with things and enabling their people to do great things. That’s the style I’m trying to emulate.

What did you want to be when you grew up?

I wanted to be a footballer, and then when I realised that I wasn’t fast enough for that, I wanted to be a PE teacher. Tottenham is my team.

What do you do to switch off?

I go for long runs. I’ve just run the London Marathon. I had done one before, the Stockholm Marathon, and I’ve also done a couple of triathlons.

Do you have a prized possession?

Family photos. I have four kids aged 22, twins aged 18, and 17. Three girls and a boy.

Jason Sharpe, director, Social Energy

In the third in our series, Jason Sharpe, director of Social Energy, is excited by the potential of AI to put an ‘energy stockbroker’ in everyone’s home – and by his state-of-the-art music system

What’s the most exciting bit of technology you are working on at the moment and why?

At Social Energy, we’re using AI trading to flip the energy industry on its head. We let every home with our technology trade their electrical energy on the wholesale markets, with complete automation. It’s kind of like putting a little energy stockbroker in the homes of our customers.

What’s exciting about this tech is how it facilitates the future of a decentralised, digitised grid. For years there have been operators pressing buttons, manually switching on power stations to meet grid frequency requirements. With Social Energy AI, our network of homes automatically sense frequency fluctuations and supply or store energy to meet the needs of the grid.

Right now, we’re the first and only company globally to be able to meet the requirements for National Grid domestic frequency balancing services.

What technology, piece of kit or process advancement has excited you most in your working life and why?

It has to be EVs; the consumer vehicle market is so exciting right now! Cars haven’t changed much in how they’re powered since the first internal combustion engine cars, so it feels like we’re at the beginning of a whole new era of driving.

Now with vehicle-to-grid tech developing, something that our team at Social Energy are testing, there’s so much potential beyond just driving. The thought that you’d be able to drive to work, charge your car on cheap daytime energy and then come home and use that excess energy to power your home or trade with the grid, well that’s undeniably an exciting shift in what a car is.

How do you communicate?

I like the old-school methods of picking up the phone and speaking face to face with someone. When I was at First Direct, I had my desk right in the middle of the call centre. There is nothing more valuable than having direct access to each other, effectively removing barriers and avoiding miscommunication.

What bit of tech couldn’t you be without at work?

Slack works well for me. As I said, face-to-face communication is my preference, and I really don’t like email, but sometimes when you are not in the same location, technology can come into its own – but only if it’s simple and intuitive to use.

We use Slack at Vallum, because some of the team are down in London and I am in Yorkshire; Slack allows us to communicate on a business and personal level in real time, so we can avoid email ping-pong and a large inbox.

At home?

My Sonos multi-room music system is my love. I was a very early adopter of Sonos and have influenced many friends to purchase one. Why? They have a quality product, it’s simple and easy to use, they are innovators who don’t stand still, and they have amazing customer service as well.

I am lucky enough to be an official beta tester for Sonos, so I get to influence their new product development and play with them before they come to market.

If money was no object?

A flight on Virgin Galactic to see the earth from space. Failing that, a Porsche 911 EV whenever they come to market.

Most admired entrepreneur or business leader?

Richard Branson, for his simplicity and passion to screw it, just do it; Sam Walton, for his belief that to achieve great things you should swim upstream, go the other way and ignore conventional wisdom; and Steve Jobs for his relentless pursuit of perfection.

Is there a book that has inspired you?

I love reading, and I have a whole list of books I could recommend. The Maverick by Ricardo Semler was one of my first inspirations. Lately it has been Shoe Dog by Phil Knight and Legacy by James Kerr. What all these books have in common, like any great story, is the struggle they face by challenging the status quo, not giving up, believing it was the right path and having the grit to push through it all to achieve success.

Technology can never replace…

Emotion, empathy, passion and wisdom. I am sure artificial intelligence will have a go at replicating this in the future, but to build relationships in business or at home requires human interaction.

Jason Sharpe is a director at Social Energy, a new disrupter in the energy market, and a founding partner at the utility recruitment business Vallum.

He was previously managing director of Ovo Energy, growing the business from 80,000 customers to 450,000 in 14 months. Before that, he was a director at First Direct and Vodafone. 

Tom Guy, global product director at Centrica Hive

It’s the turn of Tom Guy, global product director at Centrica Hive, to talk us through the important tech in his life – and Newcastle United


Most exciting tech you are working on at the moment?

To be honest we don’t look at it that way. We think about what’s the most exciting product or design or even customer problem we have to solve. With that in mind, we’re doing two things at the moment. Firstly, in the Connected Care category and on our Hive Link service. With some simple sensors and some pretty magical data learning we can help elderly loved ones stay in their home longer.

Secondly, the data science work in the area of the business we call Healthy Home. Hive has been developing technology in this area almost from the start of the business, connecting devices that allow the Hive system to constantly check everything is running okay, or flagging issues as they arise – for example, providing more detail for an engineer to point at the most likely issue. We also have a leak sensor that connects to your pipe by your stop tap that can monitor any small to large leaks you may have in your home.

What tech, piece of kit or process most excites you in your working life and why?

There are two pieces of tech we are investigating at the moment to see whether we can create a seamless product experience. Firstly, adding facial recognition to our Hive View Camera. We know customers only want to be notified about things that are important to them. So, either we can mute any notifications of people they recognise, or alternatively we can let you know your teenage daughter has arrived back from school or a friend’s house with a simple notification to your app. Secondly, building out our current Hive Link service, we’re working with a number of tech companies on technologies that would allow us to detect if someone has had a fall.

How do you like to communicate?

There is nothing better than face-to-face conversations and always out of the office, so I enjoy catching up with colleagues in the coffee bars near our HQ in London. I also prefer face to face with our suppliers and designers – however that means getting on a plane and sometimes that’s just not convenient – so Google Hangouts and Slack are widely used by our teams.

What bit of tech couldn’t you be without at work?

It’s an obvious one but probably my iPhone and iPad. It means you can work anywhere, and with the new iPad Pro I haven’t used a Mac for a number of months now. However, I am not sure I would chance doing a keynote without the Mac.

What bit of tech couldn’t you be without at home?

I absolutely love the Sonos range (right) with built-in Alexa. It’s changed how we listen to music in the home. With two teenage girls I am not always overly impressed with the noise coming out of the speakers, but the sound is always superb. The simplicity of the industrial design and digital experience through the app really make it a joy to use.

If money was no object?

Fix the gender pay gap.

And a personal one – buy Newcastle United, spend money on new players and start to enjoy football again!

Most admired entrepreneur or business leader?

I have a wide-ranging respect for a number of leaders, people who I have worked with or for, such as Charles Dunstone of Carphone Warehouse, Talk Talk and now Five Guys – he is so understated and hardly ever in the press, but probably one of our leading entrepreneurs.

Tom Guy describes himself as a seasoned technology and digital executive, leader and strategist, with more than 20 years’ experience in the hardware, music and video, games and software, and retail sectors.

He joined Hive as part of the original founding team in 2013, and leads the global product, design and proposition teams.

Through his own business, Bubblestorm, he is an adviser to the board at cloud technology company Catch Media, and a board member and adviser at the leisure technology start-up Yachtsie.com. Catch Media was the first scan and match service in the industry and is breaking new ground in further developing a smart cloud platform – Play Anywhere. Yachtsie is revolutionising the world of yacht chartering.

Neil Pennington: Utilities and the fourth industrial revolution

Energy trading, retail and resilience will all be boosted by artificial intelligence, explains Neil Pennington

It is the nature of the human condition that we constantly seek out new ways to make our lives easier and to do things more efficiently. Whether it be the discovery of the wheel or electricity, people across the globe discover, develop and use technology to find new ways of doing things; such things are mostly for good, but can have unintended consequences.

Take for instance the industrial revolution. Delivering rapid advances in efficiency, bringing electricity and heating to common use, and creating new sources of economic value, it also helped widen the gap between rich and poor as well as accelerating our journey to the cliff edge of climate change.

Even now, with absolute knowledge that we need radical change, our inability and inertia to give up fossil fuels and fundamentally redesign the energy system is not helping the fight against runaway climate change.

As an industry, we stand at the edge of another revolution. Sometimes known as the fourth industrial revolution, the advent of technologies such as the Internet of Things (IoT), blockchain, nanotechnology, machine learning and artificial intelligence (AI) are already changing the relationship between human and machine, changing the nature of work and bringing entirely new possibilities that could be as radical as eradicating disease and decarbonising the planet; it is in our hands to make this a force for good.

What do we mean by AI?

Arguably, within the utility industry, the technology creating the most excitement and offering the biggest potential for change is AI.

Artificial intelligence today is properly known as narrow AI (or weak AI), in that it is designed to perform a narrow task (for example, predicting behavioural patterns, facial recognition, internet searches or driving a car).

However, the long-term goal of many researchers is to create general AI (AGI, or strong AI).

While narrow AI may outperform humans at whatever its specific task is, such as playing chess or solving equations, AGI would outperform humans at nearly every cognitive task. Although not yet invented, its advent is hailed as a defining moment for the human race.

What’s in it for utilities?

When we look at the utility sector, there are a number of areas that are being transformed through the application of AI.

Retail: the increasing use of chatbots utilising natural language processing is transforming customer service; machine learning is also being used to understand patterns of customer behaviour, to attract and retain customers and even to predict bill (non)-payment.

At the end of 2018, South Staffs Water announced its intention to use AI in its collections system. AI solutions are also gaining traction within the connected home space with devices such as Amazon’s Alexa, which enable the customer to seamlessly interact with their thermostat and control systems (such as Centrica’s Hive and Google’s Nest).

Energy trading: aggregating platforms such as Origami Energy use machine learning to predict asset availability and market prices in near real time, enabling them to successfully bid into markets such as frequency response.

Operational optimisation: fault prediction and dynamic maintenance is one of the clearest uses of AI, enabling operators to predict equipment failures. It does this by using sensor data from various units, and significantly reduces their costs of downtime and maintenance. Shell (as set out at its site Shell.ai) has developed models for searching and identifying common geological features to help find oil and gas resources quicker than traditional methods.

At the consumer household end of the market, Verv is offering a service through demand disaggregation that identifies individual home appliances to predict faults and create alerts when devices are accidentally left on.

Where AI is perhaps set to make the biggest impact is in the area of mobility and the transactive, decentralised grid of the future.

Google’s self-driving car combines lidar, radar and wheel-based sensors, along with data from cameras and Google street view, to combine real-time data with advanced notice of potential events. Further applied to solving the issue of charging, settlement and interoperability between currently proprietary charging infrastructures, and the management of the decentralised grid of the future, AI combined with other technologies such as blockchain will have game-changing consequences.

Dr Neil Pennington is working with a number of organisations, including Rivetz, DISC, Grid Singularity and the Energy Web Foundation, to develop blockchain and digital identity for use in micropayments, messaging and decentralised energy. He is a former smart programme director and UK innovation director at RWE.

Download the full issue of Flex, May, 2019


Water resilience

HARVI saves electricity for United Utilities

By Denise Chevin

“We don’t want machines to take over everything. But we do want them to do all the heavy lifting.”
Kieran Brocklebank, head of innovation at United Utilities

In October 2018, United Utilities became the first water company in the UK to introduce large-scale artificial intelligence (AI) into its operational systems.

It signed a framework agreement with Canadian start-up Emagin to roll out AI across its entire water network in the North West of England, after initial trials demonstrated energy savings of 22 per cent in pumping clean water from service reservoirs in the Oldham area.

Emagin was initially brought on board using a procurement process called Innovation Lab, earlier in the year. The process is run on competition lines with applicants set themes or problems to put forward solutions for. It can be difficult for utilities governed by EU procurement rules to bring in companies that have little track record and are sometimes not quite ready for market.

The Canadian firm applied under the Future of Water category. “That’s where, rather than describe one of our problems, we just say to the universe, ‘hey, what should a future water company look like? How should it work? What services should it offer?’”, explains Kieran Brocklebank, head of innovation at United Utilities.

“Emagin came to us with an offer that basically said, ‘we know all about artificial intelligence and water, what problems have you got?’”

Brocklebank says Emagin was put to work on coming up with a way for the company to move water around its network that balanced supply and demand while minimising energy use. “The task was how to predict demand and make sure we’ve always got enough water. How to move it around the region safely, so that it’s not dislodging and discolouring water, while at the same time, minimising our energy bill. It costs a lot of money to pump water round.”

Data crunching

Emagin’s AI platform, HARVI, can assess vast amounts of data on a wide range of factors such as weather, demand for water, pump performance and electricity prices. Data is collected from a wide range of sources, sensors, customer information, and even Twitter. It uses the information to produce a range of options for humans to select from to produce an accurate pumping schedule every 15 minutes, explains Brocklebank.

“We don’t want machines to take over everything. But we do want them to do all the heavy lifting really, the analysis. We want HARVI to recommend three or four different options that we can choose. And those options are based on how many risks you want to take. Do you want to make sure you’ve always got the service reservoirs fully stocked? Or do you want some to be just at the right level?”

Brocklebank explains: “In most water companies, this pump scheduling happens very infrequently. We probably do one schedule a day, for one area, which means it doesn’t really take into account what is happening throughout the day. So, we either plan a schedule that can’t really cope with unforeseen events, or we over cope by having far too much water.”

Brocklebank says the recommendations made by HARVI are monitored from head office. “The next step is that it automatically makes the choice itself and executes it, with humans just nudging it gently, or overseeing it once a week, or something like that.”

He says the company has saved 22 per cent on energy costs by optimising the amount of energy and time of use.

“We’ll make even more savings if we can get the system to automatically switch the pumps on and off. And as we trust the system, we can go for the ‘riskier’ options.”

The application of HARVI is the latest stage in UU’s “systems thinking”, which has seen the organisation invest millions of pounds over the years in getting the capability for remote monitoring and control and being able to run systems from the centre, which was praised by Ofwat in its PR19 review.

Bigger rollout

Brocklebank says UU is now finalising plans with Emagin to capitalise on the technology in the whole of the North West water network.

UU selected Oldham first because many of its assets could be remotely controlled.

“In terms of the ambitions we have for HARVI, we think this system is going to help us on our electricity consumption – it’s going to save us £10 million in direct cost savings from electricity,” he says.

Eventually, he muses, applications of the AI technology could be much wider: “Can it help us predict leakage, or manage systems to avoid discolouration? What about wastewater, or flooding events? What if we used the HARVI to take all these different inputs?

“We might find there are several AI systems running for us in the background, with one super AI controlling all of them. It’s not just the world of science fiction any more, it’s fact, and it’s with us right now and we’re taking advantage of it.”

Still a place for people

However, he is quick to add that this is not about substituting people with machines. “Often when you talk about AI, people’s minds jump to the negative aspects of over-reliance on machines, or thinking that all humans are going to be replaced. That isn’t where we’re wanting to take this obviously. We want to use AI like we use every tool that humans get access to.

“It’s brand new, it’s very disruptive, it’s changing our business processes, not just what IT systems we rely on. And so, we’re going at this in an assertive, but measured way. We’re trying to find the transactional tasks to give to the robots, but the smart decision-making is with humans.”

The 3 billion litre question: can AI plug the gap?

The latest figures show 3,183 million litres of water are lost each day in England and Wales. How can water firms tackle one of the biggest challenges facing the sector: reducing leakage?

by Jamie Hailstone

The issue of how best to tackle water leakage might be literally as old as the hills itself, but water companies are increasingly taking a high-tech approach, by utilising a wide range of artificial intelligence (AI) and machine learning tools to reduce wastage.

The listening sticks of old have been replaced by acoustic microphones or loggers, which can be placed in pipelines and set to listen for any signs of leakage, with the latest software being used to analyse the recordings and spot warning signs.

This might all sound like science fiction, but today it is science fact and could be an important weapon in the water sector’s battle to improve its track record.

In October last year, the environment, food and rural affairs committee warned three billion litres of water are leaked every day and Ofwat’s target to reduce leakage by 15 per cent by 2025, as part of PR19, was not ambitious enough.

The cross-party group of MPs also called on the water industry to collectively aim to reduce leakage by 50 per cent by 2040, rather than 2050.

In April this year, trade body Water UK hit back with a pledge to triple leakage reduction rates by 2030, as part of a new sector-wide initiative to work in the public interest.

With all eyes on the water sector and its efforts to improve leakage rates, a number of utilities are now rolling out AI solutions to help crunch these all important data patterns.

Leakage projects

Southern Water recently launched a major initiative to tackle leakage in south Hampshire, the Isle of Wight and Southampton.

As part of the project it will be spending £250,000 on installing an extra 1,600 acoustic loggers, to add to the 3,760 already attached to its water pipes.

The loggers will use digital technology to monitor the noise water is making and trained technicians will feed better data from an entirely new leakage reporting system to the find and fix teams.

Phil Tapping, water demand manager, says the programme will help it reach a target of cutting leaks by 15 per cent by 2025. “We’ve had a good record historically on leakage and are near the top of the league table in England, but we have to do more,” he says.

The extra funding in Hampshire is part of an overall additional £4 million investment in acoustic logging by September and a £2.4 million extra investment in leak fixing in the coming year.

In October last year, United Utilities became the first water company in the UK to introduce large-scale AI into its operational systems. The Warrington-based company has signed a framework agreement with the Canadian technology firm Emagin after a successful trial earlier in the year.

Emagin’s artificial intelligence platform, called HARVI, can assess vast amounts of data on a wide range of factors such as weather, demand for water, pump performance and electricity prices.

This is used to help make decisions on the most cost-effective and efficient way to run pumps, detect burst pipes and minimise the risk of discoloured water.

During the 12-week trial, which took place across Oldham in Greater Manchester, HARVI demonstrated energy savings of 22 per cent. United Utilities now plans to deploy the artificial intelligence platform in phases across the whole North West region by the end of 2019.

Meanwhile, Yorkshire Water started trialling smart analytics in January to find and fix leaks in its network around Hebden Bridge and west Sheffield with the aim of avoiding interruptions to services for customers.

As part of the trial, it will provide data to both remote monitoring specialists Servelec Technologies and water consultants Artesia Consulting, who look for disruptions to normal patterns. Any discrepancies will then be flagged and passed back to Yorkshire Water to investigate.

Finally, after teaming up with Capgemini, Severn Trent announced in December last year that it has started to use machine learning – which is a form of AI that allows systems to automatically learn and improve – to help transform the way it approaches leaks.

“There are a plethora of solutions to detect leakages, such as smart balls, which can put be put in to the main pipes, which can detect leaks, or acoustic loggers, which can listen to the sounds of leakage like a traditional hydrophone and log them,” explains Capgemini’s head of insights and data for UK energy and utilities, Raj Malayathil.

“One area water companies should be exploring is the power of data and AI in detecting leakages,” he adds.

“Water companies hold a lot of data such as pressure and flow from the network and also information on historic leakage incidents such as the location of a leak, interventions done, weather data, soil conditions, etc. Using AI and machine-learning techniques on these data sets, water companies can easily detect and locate leaks faster than traditional methodologies.

“By taking these techniques one step further, water companies can use these AI and machine learning solutions to plan their mains replacement activities by identifying pipes and assets that have a higher propensity to fail and replace them quickly to improve the network resilience.

“These systems can also provide a higher level of situational awareness in planning incidents in the network due to extreme weather conditions, like the Beast from the East in 2018, and plan mitigations much faster and improve operational resilience.”

Predicting bursts

Mott MacDonald’s head of asset performance optimisation, Tom Joseph, says AI is increasingly being used to predict where the next burst will happen.

“You can predict where pipes will fail,” explains Joseph. “You can do that by using data on previous bursts and other parameters, like if a pipe is under a road, what the traffic is like on that road and what the temperature is on the ground.

“We are also doing a lot of AI work on the wastewater side of things,” he adds. “We’ve been working on correlation work on blockages, and rain-induced spillages, and when a sewer might spill and the effect it might have on the environment.”

In terms of other benefits, Graeme Wright, chief technology officer for manufacturing, Utilities and Services at Fujitsu UK, also sees a big role for AI in helping workers out in the field.

“Through the use of AI, utility companies will be able to harvest their knowledge and skills and transfer information onto a platform that is readily available to advise engineers on-site, changing the way they work significantly,” says Wright.

“For instance, engineers in the field will be able to receive information on their next task in real time, with a complete view of its status and maintenance history. Engineers can also gain access to further information that may help their task, such as manuals or information on how the system ought to be performing. This access will help them assess what needs to be done more expediently and enable them to progress the task in a more efficient manner.

“With the more data that the AI can collect, we will also see engineers start to use and be guided by ‘co-bots’ and ‘chat-bots’, which will provide guidance based on previous tasks and history, sharing knowledge captured from previous works and past expert interventions.

“As a result, utility companies will be able to share knowledge more easily and work more effectively as tasks will be resolved more often in the first instance, without the need to call for another engineer as they will have all the knowledge and capabilities required,” adds Wright.

Capgemini’s Malayathil adds: “In the utilities world, with the advances of AI, we could potentially see self-healing networks in water and electricity.

“As an example, in water networks with an eco-system of intelligent sensors and edge computing, they will be able to automatically detect a potential leak based on sensor data and automatically adjust the network attributes such as pressure and flow to avoid a leakage incident.”

And Kevan Mossman, transformation director, part of the Odgers Interim network, says AI will also help utilities build profiles of their customer base.

“For example, an AI system could monitor search trends around problems in a specific postcode, such as leaks or outages. The AI could then assess that problem and distribute relevant communication to customers who may be affected almost immediately, without the customer needing to contact them,” says Mossman.

“However, AI cannot work without being fed ‘big data’; something that utilities have historically been poor at making available. As a result, the sector as a whole has been a late adopter and it’s only in the past couple of years that companies have begun implementing large-scale digital transformation programmes to ‘crunch’ the data that they need.”

While the sentient AI systems of science fiction are still some way off, there is no doubt that the utilities sector, like many other sectors, is starting to explore the opportunities the technology can offer, especially as it comes under renewed pressure from politicians and regulators alike.

Interestingly, a study by PwC published this year revealed 85 per cent of international chief executives agree that AI will significantly change the way they do business in the next five years. Two-thirds even said it would be bigger than the internet, which itself has turned out to be fairly revolutionary.

Pinpointing a pipe among 600,000 drawings

Northumbrian Water has developed an innovative approach to managing records by using a cognitive service approach to build a data system with quicker response times.

The utility’s asset master data manager, Colin Richardson, says the “sheer volume of data and information” held by the company prompted the development of the new system.

Northumbrian Water has more than 20 million electronic documents, including more than 600,000 technical drawings. Richardson says there were issues with some drawings being lost or stored on different drives, which made them hard to find.

The company has now built a new single system, which is currently in a beta stage of development, before being officially launched in July. It uses cognitive services to differentiate between different types of drawing and build up a rich set of metadata, which can be searched against a wide variety of keywords.

“It’s an engine rather than a database,” explains Richardson. “In the past, you would have systems where you shared documents, but people have problems with folder structures or the documents themselves were not named very well. It does store data, but only what is needed to allow intelligent mapping between the files and their metadata, such as location, asset, drawing type, etc.

“This new system can scan all the network drives, pull all the information together and enriches the records with metadata, so they are easier to search and maintain,” he says.

“Last month, we had a site in the North East which could not identify the source of a heavy water leak. After digging many holes, the manager contacted us to see if our solution could help. Within a few minutes, we had managed to locate the long-lost site drawings from 1999. This led the workforce directly to where they needed to be and was an early indication of its value.”

Seeing double

Digital twins have been used by the likes of NASA and Formula 1 and, thanks to recent advances in cloud computing and sensors, utilities are exploring their potential to improve asset monitoring and maintenance and more accurately simulate different effects on the network

by Stephen Cousins

Heavy rain in the sewers of Auckland causes frequent overflows that flush raw effluent into local rivers and harbours, posing a serious health risk for the thousands of swimmers who frequent local beaches.

In an effort to better track and mitigate the problem, Auckland Council worked with Mott MacDonald’s Smart Infrastructure business to create a powerful, so-called digital twin of the city’s wastewater infrastructure.

The award-winning tool, hosted on the cloud-based platform Moata, combines up-to-the-minute data from the wastewater and stormwater network with weather and tidal data and various predictive analytic models.

This digital simulation is able to generate a real-time forecast of water quality at 84 beaches and eight freshwater locations around the city and serves up live advice on swimming conditions via the website Safeswim.org.nz.

Digital twins stand at the forefront of innovation and, thanks to the increasing penetration of Internet of Things (IoT)-enabled devices, machine learning techniques and building information modelling (BIM), are expected to play a vital role in the digital transformation of infrastructure.

Digital twins make it possible to accurately map the location and condition of assets, minimising costs associated with maintenance and repairs. They can provide a virtual ‘safe space’ to test out adverse network scenarios and innovations without having to interfere with the operation of real infrastructure. The benefits are increased when multiple models from different sources and sectors are linked together to leverage each other’s data to improve municipal or national planning and co-ordination.

Sarah Hayes is the senior policy adviser at the National Infrastructure Commission (NIC), which is spearheading the development of a National Digital Twin of UK infrastructure. She says: “Utilities are developing digital twins, which is great for them and for their customers, but we will see an even greater public benefit if we bring those and other models together to improve the way infrastructure works in a local area, in a city, or nationally. It’s a long-term vision, but the building blocks are being put in place.”

A digital twin is defined as a digital representation of a physical asset or system, which provides information about its current design, state, condition and its history. A twin can be used to improve decision making around what future infrastructure to build, or how to manage current and future infrastructure. A prerequisite is the inclusion of some element of ‘live’ data and a connection between the physical entity and the twin.

The 2018 Gartner Hype Cycle, which assesses the maturity and adoption of emerging technologies, lists digital twins as among the technologies likely to achieve mainstream business adoption in the next five to ten years. Major software vendors, such as Microsoft and Bentley, recently launched tools that enable infrastructure owners to capture, simulate and interrogate their assets as digital twins. Bentley’s iTwin Services cloud platform enables the creation of both civil infrastructure projects and operational infrastructure assets.

The technology has the potential to provide utility companies with greater visibility of network performance, and quickly crunch the numbers when planning complex scenarios such as an outage or the impact of adding in new infrastructure.

Decentralised energy generation

Engineering consultancy Accenture has developed digital twins for electricity transmission companies to help plan for complications related to decentralised energy generation and the increasing penetration of renewables.

Rohit Banerji, global lead on the development of big data analytics platforms at Accenture, observes: “Our clients are beginning to ask for twins to dynamically plan generation, operational routing, and to run scenarios to understand how the congestion will play out in the bi-directional grid. For example, if solar was to grow in a particular part of the country, or a new wind farm was introduced, how will it impact on congestion?”

Power distribution companies have used twins to simulate the future impact of electric vehicles and related high-volume power storage and fast charging on a network.

Digital twins are very efficient engineering problem solvers. Where a traditional desk study might require extensive manual data gathering, engineering calculations and analysis using software or spreadsheets, twins can combine real-time data streams from various sources and run them through sophisticated algorithms and machine learning to produce rapid results.

Mott MacDonald’s Smart Infrastructure business used this type of approach when developing the digital twins for Auckland Council and a “number of water and sewage companies in the UK”, which cannot currently be named for non-disclosure reasons.

Oli Hawes, head of smart infrastructure, says: “We’re trying to tap into real-time data from across their businesses, whether that’s from rain gauges, their GIS [geographic information system] or their customer relationship management system. Many data sources are themselves already digital twins, for example a digital representation of a hydraulic model built in software. We connect all that together, then layer in other whole system data sources, such as the national weather, river level data from the Environment Agency or tidal information, to create a digitised desk study that runs in real time.”

The Moata platform is able to run around 30 different scenarios in three seconds, where previously it would have taken 14 days using conventional techniques, says Hawes: “The power goes exponential when you start to move past data collection and visualisation on its own to link your models to the real system and unlock value from those additional insights.”

Digital twins are still a nascent technology and various technical, economic and behavioural challenges mean it may be many years before utilities have fully functional models of their entire assets.

Bird’s eye view

The most advanced twins today typically focus on small-scale high criticality problems, such as a specific issue with an energy substation, or long-term planning scenarios that are more tolerant to a lack of real-time data sources and rigorously accurate engineering models.

Building an all-encompassing ‘bird’s eye’ view of a network, including live operational data, would require major investment to retrofit IoT sensors into existing infrastructure and upgrade IT systems. Some networks are constrained by older infrastructure that’s been in place for 40-plus years.

However, that level of forensic detail is not necessary to benefit from the technology, says Samuel Chorlton, project lead at the Data and Analytics Facility for National Infrastructure (DAFNI): “There isn’t one digital twin that solves all things, there are multiple types that a business might look to develop to answer different business questions and the associated data required to drive that is going to vary too.”

When working with clients, Smart Infrastructure tries to break down the need for sensors into a problem-solving exercise around ‘data management, sense making and decision making’. “When clients follow that process they often find they have some of what they need already, and where there are gaps data science takes care of it and we can build the insight piece from what we have,” says Hawes.

The success of digital twins is largely reliant on data interoperability and the ability to share data in different formats, but historic siloed thinking in the infrastructure sector threatens to limit the available insights.

Heavy rain in the sewers of Auckland causes frequent overflows that flush raw effluent into local rivers and harbours, posing a serious health risk for the thousands of swimmers who frequent local beaches

NIC sets the agenda

The NIC recognised the critical importance of data sharing to improve infrastructure performance in its groundbreaking report “Data for the Public Good”, published in 2017.

The government’s response was to ask the Centre for Digital Built Britain (CDBB) to lead the development of the Information Management Framework that will lay the foundation for the development of digital twins and ultimately the creation of a National Digital Twin (an interconnected ecosystem of twins for infrastructure including utilities, roads, rail, schools and hospitals, etc.)

The CDBB has published Gemini principles, which set out a high-level picture of what digital twins should look like, and a basic roadmap for the Information Management Framework.

BIM is expected to lay much of the groundwork for the Framework, explains the NIC’s Hayes: “BIM is all about having data for building infrastructure in a common format and the Framework will build upon that and apply it to existing infrastructure so we can label what we have already got so we can use it integrally and interoperatively with new infrastructure.”

A digital twin hub (DT Hub), a collaborative learning community for those who own or develop twins – including government, asset owners, standards organisations and academia – was launched in April in an effort to translate many of these ideas into reality.

DAFNI’s Chorlton was appointed as chair of the Steering Group for the DT Hub. He told Flex: “We are going to look at questions like: how do we standardise our approach? What does the development of digital twins look like? What does a simple digital twin look like, versus a complex digital twin? How can we provide a common ontology and a common taxonomy so that when, for example, an energy digital twin is trying to talk to a water digital twin, there is a common language they can use to interact? We want to steer everyone in a similar direction so we are contributing to the same outcomes.”

According to Chorlton, technology is the easiest piece in the digital twin puzzle, it is industry culture and a reluctance to open up its data that will be hardest to overcome. “We’ve got to provide reassurance that this can be done in a responsible manner and that it is not going to make your company vulnerable to commercial losses or make us vulnerable from a national security perspective. We’re confident in our approach and how to leverage the technology, it’s now a matter of bringing industry along for the journey,” he concludes.

Many data sources are themselves already digital twins, for example a digital representation of a hydraulic model built in software

Sensors, resilience and strategic decisions

Northumbrian Water is working with researchers at Newcastle University to develop three digital twins designed to improve its operational and strategic decision making.

The water utility invested around £120,000 in the project, which will see four PhD researchers from the University develop models using data from an open source network of sensors installed across the city, plus other sources.

The first twin will aim to capture the biogas upgrading process, whereby sewage sludge is processed to create biogas. Biogas can have propane added to increase its calorific value for supply to the gas network, which adds costs, or it can be fed into in-house combined heat and power engines to make electricity.

Chris Jones, research and development manager at Northumbrian Water, explains: “The idea is we capture those processes as a series of mathematical models, then bring in sensor readings from the processes themselves, plus live information on things like the market value of gas, and the digital twin will use analytics to advise on the best use to make of the gas. The results will be fed as some kind of decision support to the operational team.”

The second twin will aim to predict the various impacts of an operational incident on the network, such as a burst water main, which can traditionally prevent engineers from intervening on the network because access is blocked, either by water or by traffic disruption.

The first prototype combined a surface model of the city with hydraulic modelling software to show where water from a burst pipe, at any location and with a given flow rate, would move over 90 minutes. That is now being expanded to create a browser-based tool to allow any authorised user to quickly understand where the water will flow and manage the response, including sharing the results with the emergency services and city authorities to help them understand the scale of the likely disruption.

The final twin is more strategic and long term and will aim to understand future demand on wastewater and water services. It will capture in detail how customers currently use their services and then identify trends such as the efficiency of white goods, or the effect of people paving over their gardens, which changes the water use and the drainage characteristics of a catchment.

“Capturing those individual choices at property level, then aggregating the data to understand what might be happening at a catchment level, can help us understand whether the current service position will be sufficient,” says Jones. “It’s about understanding what technology and changes to behaviour might mean for us in the future so we can adapt our services to ensure they remain resilient and relevant.”




Alexa, can you help me engage with my customers?

What the rise of voice assistants, chatbots and other AI technology means to improving service and reducing bills

by Nadine Buddoo

Using AI technology to ensure interactions with customers are simple, intuitive and engaging is a hot topic for water and wastewater company Welsh Water.

The not-for-profit water utility is placing customer involvement and innovative communication at the heart of its business strategy. Morgan Lloyd, head of marketing at the company, says: “Welsh Water, like many utilities, are looking at how we can develop bots and AI to give our customers a better and quicker service.

“In the short to medium term, this is likely to focus on using automation to allow customers to do the simple transactional tasks – from submitting a meter reading through a Facebook Messenger chatbot to finding answers to the more ‘transactional’ queries like checking your bill balance or getting information on water supply interruptions more quickly.”

“Welsh Water is looking at how we can develop bots and AI to give our customers a better and quicker service”
Morgan Lloyd, head of marketing, Welsh Water

Like many firms in the sector, Welsh Water is beginning to harness so-called machine learning technology, software that can crunch through vast amounts of data to ‘learn’ more human responses based on patterns of the data.

While observers say AI has been slow to enter the utilities world, it is now waking up to the potential for AI technologies, including predictive analytics, chatbots and voice assistants.

Welsh Water’s foray into AI has seen it develop a Facebook Messenger chatbot to engage with customers. In 2017, the company launched a consultation, called Have Your Say, which gathered the views of thousands of people to help the business plan for the future and improve services for customers. The campaign’s key focus was to build customer trust.

Encouraging customer involvement in utility consultations can be notoriously challenging, so the company knew it had to take a different approach. The Facebook Messenger chatbot offered an opportunity to deliver the consultation in a quick and interesting way.

Alongside a bespoke website and various events during Wales’ summer festivals, the chatbot was one of the new ways the company hoped to reach out to customers. The bot, developed with digital agency Coup Media, allowed Welsh Water to engage with a potentially larger audience and reach demographic groups that are traditionally less likely to engage. Customer engagement surpassed the company’s expectations, with more than 2,500 people taking part through the chatbot and a further 12,500 through the website within just the first few weeks of the campaign.

“There are FMCG [fast moving consumer goods] companies that are using artificial intelligence to predict consumer trends and accelerate the development to market of successful products. Utilities should be looking at the same techniques to identify the consumer disposition for new bundled products and services,” says Toby Siddall, managing director and UKI utilities lead at Accenture.

“This will require best-in-class capabilities in customer analytics, customer insight and service design.”

As well as helping companies identify new services and predict customer trends, AI can help utilities drive behavioural change as they seek to help their customers better engage with their energy consumption and increasingly help them find the right tariffs and services.

Sandra Schroeter, senior international product marketing manager, customer engagement and support at LogMeIn, explains: “We see companies using AI chatbots in customer service a lot. In the utility space, a chatbot can evolve further by integrating with data from smart meters, for example, and would allow customers to ask the chatbot questions about their usage in real time.

“AI can also help agents to be more efficient by monitoring live chats and suggesting answers to the agent, eliminating or minimising the time required for agents to search for answers.

“That said, there will still be a need for human agents, whether that is to deal with more complex customer enquiries; serving customers who don’t want to interact with AI; or handling emotional or high-value interactions that companies don’t want to leave to the AI.” This is a theme we will return to later.

Voice automation

Linking with voice assistants is seen as another avenue for optimising energy consumption.

“Our mission is to make buying energy as simple as buying cornflakes, in an energy market riddled with complexity and customer confusion”
Greg Jackson, founder and chief executive, Octopus Energy

Octopus Energy, which recently acquired AI and machine learning capabilities from failed supplier Usio, has partnered with Amazon Alexa to allow customers to benefit from real-time energy pricing using voice automation.

“Our mission is to make buying energy as simple as buying cornflakes, in an energy market riddled with complexity and customer confusion,” says Greg Jackson, founder and chief executive of Octopus Energy.

“The rise of voice-enabled technology is fascinating, and we wanted to be an early adopter in understanding how that technology can support in the drive to engage consumers in their energy.”

The partnership allows consumers to use Alexa to adjust energy usage based on half-hourly price changes offered by Agile Octopus, a smart time-of-use tariff. Agile Octopus users can directly engage with their energy by asking Alexa a range of questions about their energy use, such as when electricity is cheapest or more expensive, and plan accordingly – saving money and reducing carbon emissions.

Alexa is enabled in more than 100 million devices globally that manage all facets of the smart home, including lights, door locks, heaters and more, allowing customers to seamlessly marry Octopus-provided rates with a range of smart home capabilities.

“The Alexa relationship also builds on a prior integration we built with smart device pioneers If This Then That (IFTTT), where any IFTTT-enabled hardware can be linked to Agile Octopus to turn on when cheapest,” adds Jackson.

Allowing customers to use voice controls to conduct a range of actions, from optimising home heating to charging an electric vehicle, is just the start of where the technology could develop. Octopus is currently collaborating with a number of start-ups and technology companies taking different approaches to driving a smarter energy future. “Fresh innovation that shifts demand away from peak times and maximises flexibility is central to building a smarter grid, and the businesses that can unleash that will build the market of the future,” says Jackson.

Chatbots versus real people

For utilities, it will be essential to manage customer expectations while the sector’s use of the technology remains in its infancy. Susannah Richardson, director for field service and contact centre solutions at IFS, admits that a major barrier to AI technology at this stage is its self-learning capability. Current forms of AI need large quantities of data for algorithms to learn, with an average 200 variations of data needed for AI to seamlessly answer a customer’s request without review. Consequently, there is a bottleneck around the amount of data needed for each specific use case.

“We recommend that you don’t expect the bots to answer every question or resolve all the requests, instead analyse your most common use cases or frequently asked questions and train your bot to resolve these, while seamlessly handing over more unusual or specific questions to a human agent,” says Richardson.

The real evolution of the chatbot will be its ability to employ improved self-learning capability to optimise itself. When the bot needs to transfer to an agent, it copies and learns next time how to recognise, respond and process this request automatically. “This is possible today, but with improved machine learning this will become more and more powerful,” says Richardson.

As an enterprise software business, IFS’s solutions aim to help companies offer more connected services for their customers. By incorporating data from different front and back office systems, the IFS Customer Engagement (CE) platform operates as a centralised interface to manage requests from utilities customers. The platform uses AI chatbots and natural language processing (NLP) to offer self-service options and automatically retrieve the necessary information to address the customer’s need.

Welsh Water’s Lloyd agrees; the company hopes that its fresh approach to customer engagement and innovations such as the chatbot will continue to increase customer trust. But despite successfully using AI to facilitate the consultation, the technology is not without its limitations, he says: “The challenge is that customers expect these tools to provide better service than a human agent.”

“Utilities should be looking at [artificial intelligence techniques] to identify the consumer disposition for
new bundled products
and services”
Toby Siddall, managing director and UKI utilities lead, Accenture

Customers increasingly expect AI-enabled utilities services to match and even exceed their experiences with other sectors that are early adopters of the technology, whether it is chat boxes on retail websites or smart recommendations on social media platforms.

AI can undoubtedly help customer service centres reduce incoming enquiries as people increasingly resolve simple requests for themselves. But simply reducing the number of customer service staff should not be the ultimate goal for businesses. “We might be able to employ AI to reduce the number of agents processing address and subscription changes, but we still need to invest in customer service staff to deal with complex, emotional issues and differentiate our brand,” says Richardson.

“Today’s smartphone empowered customer is more intelligent, they are more informed as they research issues, they are more complex, emotional and social. As a business we need to respond by ensuring that we have humans to provide empathy and have the time to resolve complex issues and go that extra mile to ensure that the customer is not just satisfied but a social advocate.”

Field marshals

Susannah Richardson, director for field service and contact centre solutions, IFS

Beyond using AI to improve engagement with customers, there is scope for the technology to help utilities deliver more responsive repairs in the field.

While the adoption of AI in the field is in the early stages of development, there are already a few common use cases. “The simplest is to help customers help themselves: before they make that call to the support centre, a chatbot or online triage employing NLP can be used to diagnose and frequently resolve the issue,” says Susannah Richardson, director for field service and contact centre solutions at IFS (pictured). “Research shows that customers would much rather use self-service to resolve most issues if given the option.”

If an issue remains unresolved, or the customer chooses to go straight to the support centre, the agent is empowered with the knowledge base and tools to diagnose and remotely resolve the issue, potentially negating the need for an engineer. “In the background, AI can be employed to increase the accuracy and probability of resolution, analysing the likely failure modes for a specific asset,” says Richardson.

If the fault requires a field technician to repair the issue, then the data from the triage, plus data on the in-field asset, can be analysed to ensure that the technician dispatched has the appropriate boot stock to provide a first-time fix, she adds.

Download the full issue of Flex, May, 2019


Smart grid

Striking the right balance of power

Machine learning is at the heart of a smart grid and future localised energy system

by Jamie Hailstone

The art of balancing supply and demand on the grid used to be a relatively straightforward affair.

In days of old, the grid was awash with fixed assets, such as coal-fired power stations, which could be relied upon to generate a constant stream of power, night and day.

But now the UK has a more complicated energy ecosystem, with more assets to manage and a greater share of renewable energy.

And the advent of electric vehicles (EVs), localised grids such as Moixa’s recently announced virtual power plant in Sussex, and home energy trading platforms such as the one developed by Social Energy, all point to a world in which the manual balancing of the grid will become increasingly difficult, if not impossible.

For many in the energy sector, the answer lies in artificial intelligence (AI) and other machine learning systems to predict what energy will be needed and how best to supply it.

Speaking to Flex, the National Grid Electricity System Operator’s (ESO) energy intelligence manager, James Kelloway, says machine learning will be integrated into “many aspects” of the power grid and its control systems “in the not-so-distant future”.

“Machine learning is exceptional at spotting patterns that are not always obvious to even the most talented people,” explains Kelloway.

“It can predict ahead quickly, accurately and adjust automatically to changes in the grid and the external conditions that influence its operation.

“Decentralisation results in a great increase in data and consequently there needs to be a much larger capacity to deal with that data and associated actions and patterns. For context, Germany went from 1,000 generators in 2000 to an estimated 1.5 million not long ago.”

Kelloway adds that the core machine learning work at the ESO is currently all based around forecasting.

“Solar forecasting has been machine learning driven since last year and we are in the process of standing up enhanced forecasting capability for all other aspects of demand to enable a more efficient integration of renewables on the system to minimise consumer cost and carbon dioxide emissions from the generation fleet.”

Making AI while the sun shines

Algorithms and AI systems developed by Upside Energy will be used to analyse all aspects of the revenues available to to Anesco’s Clayhill solar farm. Photo credit: Anesco

Another example of how the sector is working to create a smarter grid can be seen in the recent announcement that EDF Energy and its technology partner Upside Energy have signed a deal to optimise a combined 16MW of solar and battery assets at Clayhill solar farm in Bedfordshire.

Under the agreement, algorithms and artificial intelligence systems developed by Upside Energy will be used to analyse all aspects of the revenues available to the site and understand how to precisely optimise the assets to their full capability.
“We see in the UK that there is an appetite for this kind of solution,” explains EDF Energy’s director of energy solutions, Vincent de Rul.

“It is necessary from a strategic point of view, particularly when you want to push low-carbon solutions,” he adds. “It also has the capability to bring back the stability of the grid and integrate more renewables on the grid.”

EVs and grid management

The other major factor in the shift to a smarter grid is the transition to EVs. Although opinions are divided about when the shift from petrol to EVs will occur, there is no doubt that it will have a huge effect on grid management.

In particular, Kelloway says machine learning could also help in predicting the demand for charging.

“If the machine learning is aware of bus timetables and traffic conditions, this may assist in being super-efficient at predicting the associated charging demand,” he adds.

Using real-time weather data to forecast electricity with AI-driven systems will also play an increasingly large role in the grid of tomorrow, says Kevan Mossman, transformation director, part of the Odgers Interim network.

“It will also enable them to incorporate decentralised energy generation and battery technology into the grid and anticipate how this will impact demand,” says Mossman.

“At the same time, AI can run real-time analysis of a company’s grid and provide a working model of whereand when parts of it are likelyto be impacted by natural disasters or peaks in consumer behaviour.

“For example, it could tell you when a storm is due, the catchments it will hit, which pumps will be affected and whether there are any other parts of the grid that can be made available. This will enable companies to manage energy costs through spot price management.”

The concept of microgrids and virtual power plants is still in its infancy, but this is another area in which AI will play a leading part in managing supply and demand, as well as empowering local residents to trade their unwanted electricity.

In April, Moixa announced plans to create a virtual power plant in Sussex, as part of a scheme that could potentially save the country up to £32 billion.

Moixa’s GridShare platform will aggregate more than 1MW of spare capacity from batteries in homes, schools and council offices, providing a range of services to National Grid, energy companies and energy distribution networks.

The platform will use machine learning and artificial intelligence to tailor its performance to customers’ needs and maximise their savings, and this is expected to cut home energy bills by up to 40 per cent.

AI has its limitations

But Mossman warns that these systems will require large data sets to be fed through machine learning systems – something he adds that utility companies have historically been hesitant about introducing for fear of “losing control”.

Rob Richardson, who heads up the data science work at Habitat Energy, which has developed an optimisation and trading platform for grid-scale battery storage, adds that data inefficiency is a big limiting factor in how AI will be used to develop a smart grid.

“In principle, AI can solve any problem, provided it’s fed enough data and given enough time to train and interact with a real-world environment. However, these are major caveats,” adds Richardson.

The chief executive of Upside Energy, Devrim Celal, believes the availability of more “granular” – or detailed – data is necessary in order to prevent “issues before they happen”.

“You can understand how better to invest money into the right places, so you are building resilience in the right places. And when things fail, you can recover from those failures much quicker,” he explains.

“One of the projects we are looking at at the moment is collecting data from lots of different distributed energy resources – batteries, solar PV [photovoltaics], etc,” he adds.

“Each one of these assets would have a different way of behaving. If you start developing deep knowledge about their behaviour, you can monitor them and pick up any change in behaviour, as a means of identifying cyber threats early on.”

A question of cyber security

There is no question that a more automated and smarter grid raises important questions around cyber security. The Boston Consulting Group and the World Economic Forum recently published a report on cyber resilience in the electricity ecosystem, which called on utility companies to take more preventative action to protect energy supplies.

According to the Boston Consulting Group’s head of cyber security practice, Walter Bohmayr, there are now “constant attacks” on power grids everywhere around the world.

“There are computer systems out there that do nothing else but frequently check the security of utilities and try to find vulnerabilities,” Bohmayr explains.

“Attackers try to send false signals to the AI-supported defence systems and lure them into the wrong directions.

“They can falsely train the defence mechanisms and then get an advantage, and then launch an attack which gets overlooked by the defence mechanism.”

But the chief executive and founder of smart hub firm Verv, Peter Davies, believes energy systems are no more at risk than other sectors.

“If I was a hacker I would far rather go after the banks than the energy companies,” says Davies.

“The key thing with the grid is always security of supply. The consumer wants to know that electricity will constantly come to their home. They want to know that no one is doing anything risky with the grid that might possibly stop that. And on the flip side, if they believe that the use of data will lead to a better service and keep it more secure, then I think they will be in favour of it.”

The direction of travel does seem weighted in favour of AI. In July last year, predictive field service management company Oneserve commissioned research that found a quarter of utility companies in the UK have integrated AI into their systems. A further 37 per cent have plans to follow suit in the next five years.

And as smart and interconnected technology plays an even bigger part in our everyday lives, and the grid becomes ever more complicated, AI will become the norm. Many grid systems already run on a semi-automated basis with manual overrides – so perhaps a fully automated grid is not that far away. In the grid of tomorrow, data could be just as important a commodity as energy itself.

Power to the people

Chris Wright, chief technology officer, Moixa Technology

An automated and smart grid is good news for consumers looking to reduce their bills, according to Moixa Technology’s chief technology officer, Chris Wright.

The British smart energy firm has been working with the Japanese trading house ITOCHU to provide the AI ‘brains’ behind its 10kWh SmartStar Energy Storage System (ESS).

The ITOCHU system uses Moixa’s GridShare Client machine learning service to understand how the storage system is operating and provide customers with live data and regular feedback on their energy flow and savings.

Wright says 1,300 to 1,500 of these storage systems are now being rolled out in Japanese homes every month.

“We are seeing the cost of energy decrease by significant amounts,” he explains. “On average, we are showing that we can achieve a reduction of between 40 and 50 per cent compared to the standard behaviour of the ESS as our Japanese customers come out of the feed-in tariff.

“By predicting how those homes will consume energy for the next day, you can plan ahead. For example, you could make that home available for a flexibility service in the afternoon, when usage is low.

“We use machine learning to learn about customers one by one,” adds Wright. “So, while some energy companies might use average statistics to predict activity, we collect consumption data from homes every
30 seconds. We understand how they use energy and we understand how they generate energy.

“Then we can take that information and combine it with weather forecasting data. We then apply other algorithms and generate a prediction of what that household will do over the next 36 hours. We can then generate an optimum plan for that household over that time period.

“The AI is standing in the place of the consumer. This technology is working for the consumer, without them having to make any interventions.”

Faraday Grid promises paradigm shift in energy distribution

With claims of boosting the capacity of the grid by 25 per cent, Faraday Grid’s ambitions are certainly audacious. Are the days of the conventional transformer numbered?

by Greg Jones

The changing way we generate energy will create problems for our ageing infrastructure. As the system becomes increasingly decentralised and populated by renewable generators, today’s grid will need to adapt to facilitate a more dynamic, multidirectional flow of electricity, to avoid becoming fragile and unbalanced.

How to modify the way we use and generate energy going forward is a difficult question. The Faraday Grid’s founder and chief technology officer, Matthew Williams, believes his company has developed the best, and most sustainable, answer.

“I saw the way that engineering was going to try and resolve the problem was to come up with these new technologies, apply IoT, or blockchain, or whatever, to the energy sector, and use it as expensive add-ons that makes the system more complex, more fragile, more costly, just to address the symptoms. But I felt, as more of a systems designer, that fundamentally the system wasn’t really fit for purpose any more,” says Williams, an Australian systems architect and mechatronic engineer.

Williams and his colleagues at the Edinburgh-based company claim to have developed a transformer replacement that can balance power flow better than devices such as transformers, converters, rectifiers and inverters.

The Faraday Grid’s founders say its technology allows 80 per cent plus integration of non-synchronous renewable energy generation, 25 per cent greater grid carrying capacity and 7 per cent less network losses. To achieve this, it needs only to remove transformers according to the existing schedules of their replacement.

Established in 2016, Faraday was spun out of Australian systems integration firm Exigen, where Williams served as managing director, with current Faraday Grid chief executive Andrew Scobie as chairman. The company has already caught the attention of WeWork chief executive Adam Neumann, who invested £25 million in January. Mark Corben, who spent five years as the chief financial officer of Tideway, has recently been appointed as CFO at Faraday.

Some in the industry are sceptical of Faraday’s ability to create such significant improvement. The wariness, Williams believes, has been justified: “I always take [scepticism] as a positive thing, because electricity is so important you don’t want to be just letting anyone touch it.”

Precise details have been sketchy from The Faraday Grid, pending a patent application, and today’s meeting is also light on technical amplification. Williams is keen to stress the bigger picture on the company’s invention. “It was always about the system, our whole approach was not let’s invent a new widget and try to sell that, it was ‘what are we actually trying to solve’ and how can we enable it? This kind of incremental, iterative approach that people are taking makes the system better but ultimately it’s going to be limited on how good we can make it.

“If we’re going to have our reliable, affordable, decarbonised energy system, we need something that’s going to be resilient and flexible.”

What is the technology?

The company aims to usher in the next evolution of the energy grid on the back of its three-layered technology; the Faraday Exchanger, the Faraday Grid, and Emergent.

The Exchanger is the hardware, an autonomous device able to “control the voltage, the RMS voltage, completely remove all the harmonics, control power factor, and balance across the phases”.

Made up of an electromagnetic core, the Exchanger also has a power electronics control system. The Exchanger’s core differs from a traditional transformer’s in the metals used, as well as the geometry and arrangement of the core and windings.

The power electronics are what provide certain balancing features, with Williams saying that should they fail, the Exchanger will revert to a passive mode, still able to control voltage, power factor, harmonics, and phase balance until the electronics are replaced.

Williams says the Exchanger is more flexible and affordable than devices such as inverters or solid-state transformers because it controls power flow in the magnetic domain, which offers natural filtering and is easily scalable. These others work well at low power, but prices rise greatly as the power level they can deal with increases, which limits them in ways that the Exchangers are not.

He expects the upgrade to Exchangers to be like our vehicular transition. “No one buys horses and carts because cars do a better job.”

Next comes the Faraday Grid, described as the architecture for the future electricity system. It operates with autonomous decentralised control, likened to the internet, with “no master controller sitting over the top of the grid” – with Faraday “the grid is able to dynamically balance itself”.

The Faraday Grid is the cumulation of many Exchangers, each autonomously providing local benefits, which then stack into system-wide improvements. Through the Exchangers’ greater stabilisation, theoretically the grid should be able to handle greater percentages of renewable energy and operate with better efficiency.

And the business model?

Faraday, however, will not sell Exchangers outright. As a service provider, it will be contracted by energy stakeholders to install Faraday technology. Then they will work together to provide cost balancing and stability services at, hopefully, a cheaper price than is currently available.

“The business model is not just about selling product, because the value is really around what it delivers to the system overall. In one sense you could give the devices away for free and generate revenue from the energy as a service model.”

Williams says “in the UK, National Grid is spending over £1 billion a year balancing the system” and “that cost is only projected to go up, in 10 to 15 years that cost is projected to be over £10 billion a year”.

He sees Faraday Grid as the answer, believing that an Exchanger’s improved ability to balance and stabilise will diminish the need for balancing on the generation side. “Faraday provides a service in this balancing market to the overall grid, at a cost saving to the consumer, because it’s at a lower cost, and so the utility gets a benefit out of it, Faraday Grid gets a benefit.”

The third layer, Emergent, jointly owned with Amp, is a platform for “transactive energy” integrated directly into Faraday’s hardware and software. The platform will allow more players to buy and sell energy services, with Faraday earning some remuneration for facilitating transactions.

With Emergent, Faraday is aiming to improve accessibility to the energy market and balance supply and demand across the system by using the fluctuating price as an operational mechanism. It claims it “enables the network to cope with high and low demand periods by using pricing that alters” and believes that by being built into a core grid component, its marketplace will be best positioned to provide the most effective service.

In 2018, Glasgow’s Power Network Demonstration Centre performed a series of comparisons between a prototype Exchanger and a normal transformer, concluding that the Exchanger performed better in each of the 16 tests performed.

From this, Faraday has been eager to push the idea that in validating the effectiveness of a single Exchanger, the system-wide results have also been proven to be accurate. UK Power Networks is set to begin trials of a Faraday Grid soon, the results of which should provide a clearer picture of its abilities on a network level and if they’re good, to convince sceptics.

The need for improving capacity on the network is almost certain. “With the rise of EVs, the electrification of heat potentially, we’ve got some really big fundamental changes coming on the consumption side, and we’ve already started big changes on generation,” observes Williams.

“It really comes down to this old way we’ve managed the electricity grid for so long just isn’t fit for purpose any more, so for us it’s all about providing that platform so we can have more renewables, we can have electric vehicles, that’s what we’re trying to achieve.”

Here’s an idea: safe-entry doorbell

A safe-entry digital doorbell intended to safeguard vulnerable customers, which was first proposed at last year’s Utility Week Live (UWL), is now at the advanced feasibility stage.

The product is intended to reassure water and energy customers that utility callers are genuine. It was one of the ideas to come out of the UWL Hackathon, which took place during the event at the NEC in Birmingham.

The brain-storming event was supported by Microsoft
and United Utilities, who assessed the best ideas afterwards and asked app developer Apadmi to see how it could be developed.

After considering a number of technologies, Apadmi is basing the product on technology that would only allow it to be activated by authorised callers, from their mobiles. Each customer’s device would have a unique code that would be integrated into a field service officer’s job management system.“It’s essentially a cryptographic solution, not dissimilar to the way car keys unlock cars,” explains Adam Fleming, chief technology officer at Apadmi.

The next stage is to look at the commercial feasibility and see where the costs would be borne. “Something like this is highly needed and useful for vulnerable people to give them reassurance and peace of mind,” he adds.

The Hackathon process is being repeated at this year’s event and is focused on tackling two big industry challenges – electric vehicles and smart workforce.

Apadmi and Microsoft will be demonstrating the prototype doorbell on the first day of UWL. For more details, go to www.utilityweeklive.co.uk.

AI could cut global carbon dioxide emissions, says report

The application of artificial intelligence to energy systems could cut global carbon dioxide emissions by 2.2 per cent between now and 2030, says a new report by PwC. The savings will be driven by higher efficiency in the energy sector through intelligent grid systems that use deep predictive capabilities to manage demand and supply and optimise renewable energy solutions.

How AI can enable a Sustainable Future examines the potential opportunities of AI for economic growth and emissions reduction potential and was commissioned by Microsoft.

The research models scenarios for AI’s use across four sectors – agriculture, transport, energy and water. The application of AI levers could reduce worldwide greenhouse gas (GHG) emissions by 4 per cent in 2030, an amount equivalent to 2.4Gt CO2e – equivalent to the 2030 annual emissions of Australia, Canada and Japan combined.

AI combined with the adoption of a complementary technology infrastructure such as AI-enabled distributed energy grids, distributed generation, distributed storage, industrial IoT, electric vehicle charging, dynamic pricing and smart meters in the energy sector, would have the biggest impact on GHG emissions, it concludes.

PwC estimated the energy sector could reduce GHG emissions by up to 2.2 per cent, while in transport, AI could result in a 1.7 per cent cut in GHG emissions.

“By applying AI to improve efficiencies in the energy sector across all fuels and regions, technology can help develop a cleaner and less fossil-fuel dependent energy sector that can lead to a world with a more prosperous economy and less climate change,” say the report’s authors.

The report looked at number of areas within energy to which AI could be applied to reduce carbon dioxide emissions:

• Smart monitoring and management of energy consumption;

• Energy supply and demand prediction;

• Co-ordination of decentralised energy networks;

• Predictive maintenance;

• Increased operational efficiency of renewable assets;

• Increased operational efficiency of fossil fuel assets.

“By allowing energy prices to respond to market signals in real time, smart monitoring has the potential to optimise electricity consumption by not just key sectors but also households and governments. Lower energy costs can result in output expansion by businesses and higher demand by consumers and boost economic activity,” says the report.

Similarly, decentralised energy networks can significantly improve the process of electricity transmission and distribution, resulting in higher productivity for the sector, and boost overall electricity production by enabling faster uptake of renewables, the report says.

The authors observe that automatic pricing of electricity reduces electricity wastage across the economy, lowering emissions, while the greater use of renewables, enabled by localised grids and AI technologies that improve the effectiveness of renewable assets, reduces fossil fuels’ share in energy production and shifts the energy mix towards less carbon-intensive energy sources.

Celine Herweijer, global sustainability leader at PwC UK, commented: “Technology firms and industry alike will need to champion responsible technology practices, considering social, environmental impact and long-term value creation. What is clear is that the companies and countries that fare best will be those that embrace the simultaneous changes and reinforcing opportunities of the AI era and the transition to sustainable economies.”


Download the full issue of Flex, May, 2019


Expert views

“If only we knew _____”  How machine learning is revolutionising utility sector debt collection

sponsored post

by Jon Hickman, chief executive of Flexys Solutions,
explains the benefits of new technology

As consumer debt increases, the utility sector is looking to emerging technologies to help serve customers better and maximise operational efficiency. In debt collection, applying machine learning to extend the value of data and using it to innovate strategy and process is proving to be a game-changer.

The right journey for each customer at a reduced cost

Machine learning provides the best results where there is a business question complex enough to warrant its use. In early collections, that question might be: “How likely is this customer to pay and what is the next best action to make it happen?” At Flexys we have developed a machine learning solution that significantly reduces unnecessary or inappropriate engagements (and their associated costs) and helps to create an elastic capacity so that resources can be targeted more effectively. Meanwhile, customers only experience as much of the collections process as is appropriate, with a lower cost, ‘light touch’ for self-resolving individuals. The objective is to create a ‘segment of one’ decision-making capability that treats each customer as a three-dimensional human being while operating effectively within the resource constraints of the business.


Richard Vennard, Dŵr Cymru Welsh Water, Flexys Refreshing Collections Podcast

There are huge opportunities around technology, we’re trying to do better segmentation, understand customers a little bit better


Augmenting decision-making with context

As collections move into the digital age, machine learning can be used to help fill the gaps in emotional understanding missing from remote contact. Along with existing data, using machine learning to understand and categorise responses and signals in an online journey means that anomalies, difficulties or even potential vulnerability that would otherwise be hidden can be flagged for attention. A strategic decision on the most appropriate way to proceed can then be made based on the organisation’s own policies and practices. This enhanced segmentation makes it easier to treat customers fairly according to their circumstances and avoids creating additional detriment via systems that are not designed with non-average customers in mind.


Richard Vennard, Dŵr Cymru Welsh Water, Flexys Refreshing Collections Podcast

Welsh Water are looking at artificial intelligence and the machine learning pieces to provide a range of different abilities for the customer to interact with us


It’s not the future, it’s now

At Flexys, our specialist R&D team is dedicated to collections-specific applications of machine learning through our modular cognition solution. Our cutting-edge research includes a knowledge transfer partnership with Professor Jim Smith, head of interactive AI, and his team at the University of the West of England.

Our results are impressive and we are keen for more energy and water providers to join our current customers in this exciting and game-changing enterprise.


The Flexys Refreshing Collections podcast episode 3: Utilities is live and available here

Creating a sustainable customer experience

by Sandra Schroeter, International Head, Customer Engagement Technologies, LogMeIn

Sometimes, a proven solution from one area of the business can help solve a problem in another area of the business. Just like focusing on sustainability is helping energy companies compete in our environmentally conscious world, focusing on customer sustainability can help energy companies compete in the business world.

The hard truth is that the current customer engagement model is unsustainable. In a market where ten suppliers have gone out of business since January 2018, where the regulator has introduced the price cap, and where price aggregator sites encourage a race to the bottom, change is needed.

Customer expectations and increasing pressure on margins have not made a happy mix for energy suppliers in the past few years. Affecting both established players and fast-growing independent challengers alike, unhappy customers are voting with their wallets with a record of 5.9 million households switching energy supplier in 2018. Companies that fail to deliver exceptional experiences consistently, struggle to compete on value and have to revert to competing on price.

Sustainability is about doing more with less.

Understanding the customer journey

Companies first need to understand pain points throughout the various customer journeys. Where is the digital experience failing customers during key journeys such as billing enquiries, refunds, forgotten passwords, balance checks and moving home?

A recent study on customer experience revealed that 61 per cent of consumers felt it took too long for enquiries to be resolved. The survey found 35 per cent were frustrated at repeating themselves to different advisers, while 26 per cent experienced difficulty using a self-service option.

The next step is to improve the customer journey employing technology where speed or scale is required. One lever to improve customer journeys is to optimise touchpoints. While some consumers prefer to call or email, others prefer chat or self-service.

The survey found 74 per cent of consumers appreciate the benefits of interacting with an AI chatbot, including quicker resolution and easier access to support.

Increasing loyalty with CX

In “Predictions 2019: Customer Experience” analyst firm Forrester suggest a correlation between price and customer experience: “Companies that despair of differentiating on the basis of CX will resort to price cuts to attract new customers.” This implies that differentiation through CX may prevent destructive price wars.

This is why companies would be wise to adopt a customer-centric approach to increase customer satisfaction and loyalty. A recent McKinsey report suggests that a 30 per cent increase in customer satisfaction (CSAT) and a 50 per cent reduction in cost to serve (CTS) is achievable by analysing various customer journeys and providing digital assistance to make them effortless.

AI for customer engagement

Conversational chatbots and intelligent FAQs provide immediate answers to repetitive questions and perform automated tasks, freeing up agents to be available to help customers in the moments that matter, improving CSAT and net promoter score (NPS) in key engagements.

Use cases beyond a simple knowledge base can be covered by integration with operations systems, systems of record, or data analytics. Customers can easily check their tariff or balance, change their direct debit or submit a claim for a refund without the need to navigate the website.

Sustainability is also about harmony. Achieving the right balance between automated and AI-assisted agent interactions is important. Customers should have the option to get in touch with a human agent, for example when the enquiry is more complex. AI can’t do everything, but it is very good at some things. Harmony between agents and bots is a crucial element in delivering great CX while reducing cost to serve.

Intelligent and consistent CX

CX is the battleground that will decide winners and losers among energy suppliers over the next decade. Improving CSAT, while simultaneously reducing CTS, is key. Companies will have to find the right balance between automation and humanity to develop a sustainable customer engagement strategy. This requires a deep understanding of the customer journey as well as the right technology.

For more information, contact:

Download the full issue of Flex, May, 2019


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