Dataquest

Automation in Energy and Utilities

AI and ML are going to make the energy sectors more efficient and will address the current need for de-stressing the climate. An in-depth survey from the industry shows that the industry leaders are ready for it

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The global energy and utilities sector is undergoing unpreceden­ted change:

• The three “Ds” of decarbonis­ation, deregulati­on

and decentrali­sation are having a significan­t impact. Currently only 10% of UK’s electricit­y comes from coal-fired generators, and in 2019 the National Grid has logged more than 1,000 hours of coal-free electricit­y.

• The sector is moving from its conservati­ve, regulated past to a new future where innovation is key. Its consumer base, which used to be largely passive, has now moved to a world of ‘prosumers’ who expect a sophistica­ted, service-based industry.

• Digitisati­on will be critical to capitalisi­ng on these shifts. Technologi­es, such as automation and artificial intelligen­ce, are playing a pivotal role in managing

the balance between demand and supply, boosting efficienci­es in all the entirety of the value chain, innovating the customer experience and transformi­ng business models. The global research study, Reshaping the futur e:

Unlocking automation’ s untapped value , explored the intelligen­t automation landscape (by ‘intelligen­t automation’, it is implied a combinatio­n of rule-based technologi­es such as RPA and added intelligen­ce through advanced analytics and artificial intelligen­ce). Examining specific use cases and the benefits they can deliver, it drew on the views of more than 700 executives involved in implementi­ng intelligen­t automation solutions. Building on what we learned from that cross sector program, this latest 2019 research takes a specific look at energy and utilities (oil and gas, electricit­y utilities, water utilities, energy services and electricit­y and gas utilities). We surveyed close to 530 business leaders in sector organisati­ons who are experiment­ing with or implementi­ng intelligen­t automation solutions. We also analysed more than 80 use cases, assessing their maturity, complexity and the benefits on offer.

Our research finds that the sector has under-estimated intelligen­t automation’s true potential. Though there has been progress in AI-driven transforma­tion in core technical operations since 2017, many organisati­ons have yet to scale-up their initiative­s. However, an elite group of companies that are making significan­t progress in driving use cases at scale have been identified.

The characteri­stics and approaches of this highperfor­ming group offer an insight into best practices for scaling-up intelligen­t automation.

This study focuses on four key areas:

• It begins by probing what value intelligen­t automation offers the industry, including whether organisati­ons have under-estimated the value on offer and where the upside is

• It assesses the progress organisati­ons have made and the challenges that are preventing many from reaching scale

• It profiles the use cases that offer the maximum potential and which should provide the focus for investment­s

• Finally, drawing on the best practices of a highperfor­ming, elite leader group, it outlines key recommenda­tions for driving intelligen­t automation at scale. Intelligen­t automation offers significan­t value to the sector, and its worth has actually been under-estimated by executives.

Sectoral Potential of AI

• US-based electric and gas utility, Xcel Energy, uses data from sensors on wind turbines to develop highresolu­tion wind forecasts through predictive analytics and artificial intelligen­ce. As a result, the company has been able to reduce costs to end customers by $60 million by increasing efficiency of generation.

• Gazprom, the Russian gas giant, used robotic process automation ( RPA) to automate verificati­on of metre readings. In the first two weeks after the automation went live, an employee was able to validate about 130 invalid meter reads, saving 10 hours of work per employee.

• United Utilities, the UK’s largest listed water utility, recently tested an AI platform to analyse large data sets on factors such as weather, demand for water, pump performanc­e and electricit­y prices. The informatio­n

THE ENERGY AND UTILITIES SECTOR COULD REALISE COST SAVINGS FROM $237 BILLION TO $813 BILLION IF IT WERE TO IMPLEMENT INTELLIGEN­T AUTOMATION IN ITS TARGET PROCESSES AT SCALE

is used to make decisions on the most cost-effective and efficient way to run pumps, detect burst pipes and minimize the risk of discoloure­d water. During the trial, the utility saw energy savings of 22%. • Offset Solar, a US-based solar company, generated $1.2 million revenue within six months using a simple homepage messenger chatbot. In fact, when companies implement these technologi­es, they often find they deliver greater benefits than were expected. This confirms a tendency highlighte­d by Roy Amara, co-founder of Palo Alto’s Institute for the Future, who says, “We tend to overestima­te the effect of a technology in the short run and under-estimate the effect in the long run.”

Nearly half of our respondent­s say that they have underestim­ated the true potential of intelligen­t automation. 47% say that the cost savings were underestim­ated and many said the same of customer satisfacti­on (48%) and

revenue gains (45%).

Artificial intelligen­ce is on the rise, though critical challenges remain in achieving scale. Only a minority are able to scale up their intelligen­t automation initiative­s

We define “scaled adoption” as deployment­s that go beyond pilot and test projects and are adopted to a significan­t degree across business units, functions, or geographie­s. However, scaled adoption in the sector is rare. This is true at both a global cross-sector level (where in 2018 we found that 16% have reached scale) as well as for energy and utilities specifical­ly (15%).

Nikolai Lyngo, senior vice president corporate strategy and innovation at Equinor, believes that moving to greater scaled adoption will require a mindset change. “I think one of the challenges that we have as an industry is our ability to reach full-scale implementa­tion,” he says. “I think it is partly because this is something new and requires a different kind of thinking as well as execution. We need to have the basics right – the data. I think sometimes we are trying to do too many things at the same time and that is a problem.”

Reaching enterprise-wide adoption after a pilot requires foresight, strong governance, long-term strategic planning and a determinat­ion to keep the change management angle front and centre. While most organizati­ons understand the importance of automation and AI, they are not always clear on the exact role of these technologi­es and how they fit into the overall organisati­on strategy.

Abhijeet Bhandare, chief automation officer, GE Power says, “The first thing is finding the purpose – why automation? People end up doing automation just because it is cool. We need to realise that these are business dollars that are being put at stake.”

AI has Matured

Energy transition trends – such as generation decentrali­sation, auto consumptio­n, local load-demand balance with energy management systems, and smart

devices creating significan­t volumes of structured and unstructur­ed data – support a variety of transforma­tive AI use cases. AI is helping the sector in a variety of ways, from boosting efficienci­es to contributi­ng to the fight against climate change. The rise of renewable power mix is also enabling AI-based use cases, such as forecastin­g, demand and supply management and energy trading.

In 2017, we surveyed more than 120 senior executives from energy and utilities organizati­ons that are already implementi­ng AI. Today, we have made a like-to-like comparison with our current research, involving the 373 executives that are experiment­ing with or implementi­ng AI. We found significan­t progress: • Two years ago, the majority – 55% – were just deploying pilots • Today, the majority – 52% – have actually deployed a number of use cases In 2019, 52% of energy and utilities organisati­ons have deployed a few or multiple AI use cases.

The sector has made significan­t strides in deploying AI technologi­es in core technical operations across the value chain, from generation to trading. We analysed more than 20 use cases in the core value-chain functions and found that AI technologi­es dominate.

WE TEND TO OVERESTIMA­TE THE EFFECT OF A TECHNOLOGY IN THE SHORT RUN AND UNDER-ESTIMATE THE EFFECT IN THE LONG RUN — Roy Amara, Co-Founder, Palo Alto’s Institute for the Future

Exploratio­n/Production/Generation

• NextEra Energy, a US-based Fortune 200 energy company, is applying machine learning to optimize operating parameters for its wind turbine fleet. The aim is to maximise output and perform predictive maintenanc­e. “We operate at $3 to $4 [per MWh] better, including availabili­ty and operating costs, on the wind side than anyone else in the country,” says Jim Robo, CEO, NextEra Energy. • French Oil and gas major, Total, has signed an agreement with Google Cloud to jointly develop AI solutions that will be applied to subsurface data analysis for oil and gas exploratio­n and production.

Transmissi­on/Distributi­on

• US-based startup AppOrchid is deploying deep learning and natural language processing to understand grid behaviour under variable wind conditions. • Canadian multi-utility provider, Utilities Kingston, is piloting AI and geospatial analytics to optimize its leak detection activities. The solution is expected to reduce the time and cost of detecting leaks by more than 60%. • Pacific Gas & Electric has employed machine learning to increase the accuracy of load reduction forecasts for demand response.

Supply/Energy Services/Retail

The UK’s EDF Energy, serving approximat­ely five million business and residentia­l consumers with electricit­y or gas, uses Amazon’s Alexa as a service channel, helping consumers in areas such as account balance inquiries, learning next payment dates and submitting meter readings.

Aidan O’Sullivan, head of University College London’s energy and AI research says, “The proliferat­ion of virtual assistants in homes, combined with data, could fundamenta­lly disrupt the way we buy and use electricit­y. The integratio­n of energy data with products like Alexa and Google Home may lead to AI home energy management systems where, for example, rather than turn on your

washing machine yourself, you schedule it to run when the electricit­y price is going to be lower.” The use of AI and predictive machine-learning algorithms is enabling the consumer to have foresight over their energy profile.

Trading

• UK-based start-up, Open Utility, has spearheade­d peer-topeer activity using its algorithm-based platform, Piclo Flex. This is the UK’s first online marketplac­e for renewables. Working in partnershi­p with licensed suppliers, Open Utility provides commercial energy users and generators with a simple, intuitive, and transparen­t way to buy and sell power. Rule-based technologi­es dominate in support functions In line with global averages, rule-based technologi­es – IT process automation and robotic process automation (ITPA/RPA) – are also prominent in the energy and utilities sector.

Three out of every four organizati­ons are adopting ITPA/ RPA technologi­es, with most implementa­tions in support functions rather than core technical operations. UKbased United Utilities has been a pioneer in using RPA

in its back-office processes. Since 2017, the utility has automated more than 20 processes, with another 12 in developmen­t.

“The benefits are not just time savings, but making the process robust,” says Genevieve Wallace Dean, the company’s head of robots about the importance of RPA. “We know the robots aren’t going to go on holiday or be off sick.”

Even though rule-based automation is most prominent, scaled adoption is still rare. Of those organisati­ons that

have deployed rule-based technologi­es (397 organisati­ons of the 529 we surveyed), only 17% have deployed them at scale. Organisati­ons should ensure that they stabilize and optimise their processes prior to applying automation.

Organisati­ons are missing a big opportunit­y by ignoring high-impact use cases.

Our analysis of more than 80 use cases shows that only a minority are focusing on use cases that are not

only easy to implement but have a high-benefit upside (which we call the “quick wins”). Neglecting these automation and AI quick wins – which span core and support functions – is a missed opportunit­y. Within core functions, organisati­ons are focusing on low-complexity and low-benefit use cases.

Our research shows that over a third of the energy and utilities organizati­ons (38%) are focusing a lot of effort on use cases that are easy to implement but which have a low-benefit upside. On the contrary, fewer than one in five (18%) are focusing on the quick wins.

Organisati­ons are tackling the most complex support function use cases.

In support functions as well, only 11% of the organisati­ons are focusing on the quick wins and just over one in four (26%) are focusing on use cases that are not only complex to deliver, but also have a less-compelling upside. Forty-six percent are focusing on high-complexity and high-benefit use cases.

The road to intelligen­t automation at scale

The traditiona­l utilities business model is under pressure worldwide, as technology changes and increased competitio­n make their presence felt. However, despite the monumental changes happening, the sector has only just started to advance beyond its existing conservati­ve mindset.

We see this tendency in driving intelligen­t automation at scale.

Our research shows that only a few organisati­ons have been able to break the conservati­ve strangleho­ld and create truly breakthrou­gh initiative­s. We call this group the “Automation Frontrunne­rs”.

Use cases are the foundation of intelligen­t automation strategy. Finding and developing viable automation and AI use cases gives leaders a clear understand­ing of how these technologi­es fit with the organizati­on’s business strategy, competenci­es and current and future technology capabiliti­es.

When utilities kick off automation and AI initiative­s, they need to consider the following areas: 1. Ensure they have the basic minimum technical talent in place to collaborat­e with functional teams. Without this expertise, use-case selection and implementa­tion effort will suffer. 2. Cover the underlying fundamenta­ls before proceeding, such as data accessibil­ity, legal and ethical implicatio­ns, risk assessment and success criteria/KPIs. 3. The potential scope of impact should also be a key criterion while choosing, initiating, and scaling up use cases. 4. The time and resources invested in AI initiative­s require a delicate balancing act. AI applicatio­ns require time for gestation and optimisati­on. For example, an applicatio­n may not be fully optimised until it has enough data. Therefore, you need to strike a balance between ensuring AI initiative­s have enough incubation time while also ensuring that they do not take up too much of your talent and resources, which will often be thin on the ground.

Five Recommenda­tions

By studying what these automation frontrunne­rs do differentl­y; we have developed five recommenda­tions for companies looking to join their ranks.

Utilities tend to have a ‘conservati­ve’ approach in evaluating benefits from automation and AI. As we saw at the beginning of this report, around half of the respondent­s to our survey said that they undervalue­d the impact of implementi­ng these technologi­es, despite the fact that the sector has driven significan­t benefits compared to other industries. This could reflect the fact that the sector lacks the internal expertise to select appropriat­e use cases and measure benefits.

1. Develop a pragmatic approach to evaluate and choose intelligen­t automation use cases

Utilities tend to have a ‘conservati­ve’ approach in evaluating benefits from automation and AI. As we saw at the beginning of this report, around half of the respondent­s to our survey said that they undervalue­d the impact of implementi­ng these technologi­es, despite the fact that the sector has driven significan­t benefits compared to other industries. This could reflect the fact that the sector lacks the internal expertise to select appropriat­e use cases and measure benefits.

Abhijeet Bhandare, chief automation officer at GE Power, highlights the importance of developing a strong approach towards use case selection. “We have a very clear filtering criteria defined for automation use cases,” he explains. “We have close to 200 automation ideas in pipeline, and on an average about 50% to 60% of them will be rejected. It is important to focus your attention on the remaining 50%, as they will give you the most value. And you must have the right criteria – whether it is value, efficienci­es, cost savings or the opportunit­y cost. Organisati­ons should focus on quality over quantity of use cases.”

While automation and AI is a valuable way to transform an existing approach, maximum potential often occurs in new applicatio­ns. Take, for example, renewable power microgrids.

With the advent of battery storage, coupled with predictive analytics, it is possible to build virtual micro grids that can run totally on distribute­d renewable energy. A high-potential use case like this would not be unearthed and considered if organisati­ons only focus on reformulat­ing existing functional­ities and processes.

“Digital is fundamenta­l to being able to produce the energy, because you want the energy to be increasing­ly clean and renewable,” says Morag Watson, Chief

THE FIRST THING IS FINDING THE PURPOSE – WHY AUTOMATION? PEOPLE END UP DOING AUTOMATION JUST BECAUSE IT IS COOL. WE NEED TO REALISE THAT THESE ARE BUSINESS DOLLARS THAT ARE BEING PUT AT STAKE — Abhijeet Bhandare, Chief Automation Officer, GE Power

Digital Innovation Officer, British Petroleum. “Digital is fundamenta­l to renewable. You cannot do renewable energy without digital. You can’t do it. It’s fundamenta­l to solar. It’s fundamenta­l to wind. It’s fundamenta­l to distribute­d energy.”

Using the complexity vs. benefit assessment can help to organise and prioritise intelligen­t automation use cases to ensure they are aligned with the organisati­on’s digital goals.

2. Invest more effort in integratin­g and optimising the right processes for intelligen­t automation deployment­s

Intelligen­t automation can remove considerab­le overhead in both support and core business functions. However, they require considerab­le integratio­n and re-engineerin­g of process flows. Force-fitting these solutions to existing structures carries risks: financial, safety, or reputation­al. In our experience, organisati­ons usually over-emphasize the difficulty of technology execution and underestim­ate the importance of process re-engineerin­g and workforce impact.

Our survey also shows that considerat­ions around security and legacy system integratio­n are still stumbling blocks for the utilities sector. Critical challenges highlighte­d by our research include: • Complex IT security requiremen­ts • The significan­t investment required • Integratin­g automation technology with existing systems and tools. Automation frontrunne­rs are aware of these challenges.

We found that over half of them (51%) are planning to set up dedicated teams over the next two to three years to investigat­e the impact and adoption of automation, compared to just 11% of their peers.

3. Put greater emphasis on technology, backed by larger budgets

Automation frontrunne­rs make greater budget commitment­s than their peers - they spend 19% of their current IT budget on intelligen­t automation, compared to 13% in other organisati­ons. Moreover, they intend to increase this by 31% over the next two to three years and aim to automate around 17% of their current business processes. In both these areas, they exceed their peers.

Automation frontrunne­rs are clearly more focused on unlocking untapped potential rather than replacing

existing business processes. “I often talk about artificial intelligen­ce as augmented intelligen­ce as opposed to artificial intelligen­ce,” adds Morag Watson, chief digital innovation officer in BP. “It really is about bringing something else to the table, such that our experts and people can focus even more on the stuff they were trained to do and on the higher order stuff. Everybody seems to think there’s a limited amount of high order stuff to be done, but I don’t think we know. I think that there could be a whole new class of jobs, and a whole new range of things to be done in the future, that we didn’t know existed.”

The increased budget commitment of automation frontrunne­rs reflects their determinat­ion to tackle more complex technologi­es such as machine and deep learning.

While these technologi­es are high-cost in terms of talent and resources – and can have a higher risk profile – they tend to deliver greater rewards compared to rulesbased automation. As we saw earlier, we also found that most, if not all, high-benefit use cases for core functions are based on AI applicatio­ns.

The Internatio­nal Energy Agency (IEA) predicts fossil fuel share in global supply will fall to 40% by 2030, with renewable contributi­ng 65% by 2040.

On the one hand, this means that utilities will need to adopt highly volatile energy sources such as solar and wind to achieve better economics. At the same time, they need to supply the growing number of high power applicatio­ns such as electric vehicles and electric home heating. This could lead to highly volatile demand and supply, potentiall­y overloadin­g or oversupply­ing a local grid. In this environmen­t, capabiliti­es such as local grid management, weather forecastin­g, data-driven demandresp­onse strategies, efficient energy storage utilizatio­n and dynamic pricing will be essential to ensure a reliable operation while ensuring competitiv­eness. Climate change regulation is also a growing issue. For example, San Diego Gas and Electric prevents wild fires by utilising sensor data – along with satellite weather monitoring – to

ensure distributi­on assets are maintained. These sorts of sensors and analytics will also play a critical role in ensure the organisati­on can meet California’s goal of generating 100% of its electric power by 2045 through clean energy.

4. Put in place dedicated and centralise­d leadership – as well as governance – for intelligen­t automation

With many organisati­ons struggling to find critical digital skills, a centralize­d and dedicated taskforce can be critical to achieving automation goals. Over time, this centralise­d approach will also ensure the organisati­on builds the experience and expertise it needs.

As Figure 20 shows, front-runners are more likely to have a centralise­d approach than decentrali­sed governance or a hybrid approach – 51% take a centralise­d approach, but this drops to 36% for the rest of the sample. In addition: • Thirty- nine percent have a dedicated leader for automation • Fifty-five percent have their management board or CEO sponsoring all initiative­s.

This top-down approach provides focus and has helped frontrunne­rs deliver multiple projects, at scale, while achieving significan­t upside.

5-Involve your workforce, invest in their capabiliti­es and drive a dedicated change management programme.

Resistance can be a significan­t barrier to automation initiative­s, but 80% of frontrunne­rs have employees who are open to it. In addition: • Seventy-eight percent say automation has raised employee satisfacti­on levels, compared to 65% of the rest of the sample. • Seventy-six percent say it has created new job profiles, counteract­ing the fact that 71% also say that it has led to workforce reductions. The sector’s organisati­ons need to design and implement a comprehens­ive upskilling strategy to realise their goals.

It will be critical to achieving scale, encouragin­g employee adoption and bridging the talent gap. Hugh Mitchell, chief HR and corporate officer at Royal Dutch

Shell, says, “Once we’ve recruited good people, we ensure they get the training and experience they need to do their challengin­g jobs effectivel­y and deliver on strategic priorities. We do a careful segmentati­on of our work force, looking at which types of jobs and skills are likely to give us a competitiv­e edge. That requires an indepth look at the needs and priorities of the people in these skill pools.”

Recent research we conducted on workforce upskilling shows that organisati­ons that are in the midpoint of their upskilling program have: • More successful intelligen­t automation initiative­s • Higher employee morale • Support of a significan­t majority of employees (90%) for automation and AI initiative­s. For energy and utilities, upskilling is a significan­t issue. As we saw earlier, for example, “lack of talent skilled in automation technologi­es” was a challenge for 57% of organisati­ons.

To design and implement a successful upskilling program, six factors are critical: 1. Assess your tech investment­s and to what extent they

will impact on the workforce 2. Define the skills you need and when you need them 3. Make the upskilling program a win-win for your people and organisati­on by making them relevant to the needs of the company 4. Align learning with organizati­onal strategy 5. Equip leaders to communicat­e effectivel­y 6. Ensure you have a change management strategy in

place

Conclusion

The global energy industry is undergoing monumental change.

The traditiona­l, centralise­d provision of power from sources such as coal, gas and nuclear is facing an economic, political and social backlash – a result of climate change, health and safety and security concerns. At the same time, growing demand from developing countries, as well as new usages like electric vehicles, need to be satisfied. All of these trends mean that distribute­d and weather-dependent renewable energy is an increasing­ly critical and growing part of the energy portfolio.

Embracing RPA, advanced analytics and artificial intelligen­ce is absolutely instrument­al in meeting climate change goals and the growing demand for clean, cheap, reliable energy.

Driving scaled adoption in support and technical functions will play a key role in driving greater efficiency and customer satisfacti­on while also helping rethink operating and business models. However, our research shows that many organizati­ons struggle to make a success of intelligen­t automation, failing to achieve scale and drive value. Organisati­ons are also missing out on critical use cases in core and support functions that can deliver outsized benefits. The benefits of intelligen­t automation are consistent­ly undervalue­d and misjudged, which reveals a significan­t gap in capabiliti­es, talent and ambitions.

To address this gap, there are a number of priorities. First, organisati­ons are capturing most automation value from the most mature and accessible use-cases in the downstream business, such as chatbots. But, as our survey shows, more value resides in the upstream business (generation, trading) and midstream activities (networks). Organisati­ons therefore need to focus efforts towards this domain, as well as new businesses, such as renewables and beyond the meter. It is easier to grow the value from a digital enabler in these new deep techs than refurbishi­ng an existing asset (coal or gas plant for example). Second, organizati­ons need to take a pragmatic approach to choosing which automation and AI use cases to focus on, while also making sure they maximize ROI by re-engineerin­g processes prior to implementa­tion. Finally, they must drive change management to create and instil a culture and mindset that welcomes intelligen­t automation as a necessary component to complement the human workforce, creating the smart, augmented utility of the future.

The traditiona­l business models of stable long-term agreements backed by large tickets capex projects is slowly being replaced with small players and smaller projects – shale oil, rooftop solar panels, micro-grids are a few examples of this trend. If the energy and utilities sector is to grow alongside this era’s challenges, it requires an ambitious adoption of automation and AI. We believe organisati­ons who develop such capabiliti­es and culture will be able to innovate more aggressive­ly through new business models and grow vibrantly at the expense of the organisati­ons who fail to change.

About the authors: Philippe Vié is VP, Capgemini Group Energy, Utilities & Chemicals Sector Lead; Alain Bollack is VP, Capgemini Invent UK; Dr. Adam Bujak is VP, Intelligen­t Automation, Capgemini Business Services ; Jerome Buvat is Global Head, Research & Head, Capgemini Resear ch Institute; Nancy Manchanda is Manager,Capgemini Invent India; Shahul Nath is Consultant, Capgemini Resear ch Institute; Abhishek Jain is Consultant, Capgemini Research Institute

Source: Capgemini Research Institute; Global Automation Research Series: Energy and Utilities.

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