1、The next digital wave Intelligent Automation in Energy and Utilities Global Automation Research Series: Energy and Utilities Introduction The global energy and utilities sector is undergoing unprecedented change: The three “Ds” of decarbonization, deregulation and decentralization are having a signi
2、ficant impact. Currently only 10% of UKs electricity comes from coal-fired generators, and in 2019 the National Grid has logged more than 1,000 hours of coal-free electricity.1 The sector is moving from its conservative, regulated past to a new future where innovation is key. Its consumer base, whic
3、h used to be largely passive, has now moved to a world of prosumers who expect a sophisticated, service-based industry. Digitalization will be critical to capitalizing on these shifts. Technologies, such as automation and artificial intelligence (AI), are playing a pivotal role in managing the balan
4、ce between demand and supply, boosting efficiencies in all the entirety of the value chain, innovating the customer experience and transforming business models. Our October 2018 multi-sector, global research study, Reshaping the future: Unlocking automations untapped value, explored the intelligent
5、automation landscape (by intelligent automation, we mean 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 executiv
6、es 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).
7、 We surveyed close to 530 business leaders in sector organizations who are experimenting with or implementing intelligent automation solutions. We also analyzed more than 80 use cases, assessing their maturity, complexity, and the benefits on offer. Our research finds that the sector has underestima
8、ted intelligent automations true potential. Though there has been progress in AI-driven transformation in core technical operations since 2017, many organizations have yet to scale-up their initiatives. We did, however, find an elite group of companies that are making significant progress in driving
9、 use cases at scale. The characteristics and approaches of this high-performing group offer an insight into best practices for scaling-up intelligent automation. This report focuses on four key areas: We begin by probing what value intelligent automation offers the industry, including whether organi
10、zations have under-estimated the value on offer and where the upside is We assess the progress organizations have made and the challenges that are preventing many from reaching scale We profile the use cases that offer the maximum potential, and which should provide the focus for investments Finally
11、, drawing on the best practices of a high-performing, elite leader group, we outline key recommendations for driving intelligent automation at scale. 2Intelligent Automation in Energy and Utilities: The next digital wave Executive summary key takeaways The sector has under-estimated intelligent auto
12、mations true potential Nearly half of the respondents have under-estimated the benefits they derived from their intelligent automation initiatives. We estimate that the sector can save between $237 billion to $813 billion from intelligent automation at scale. Scaling-up initiatives is still a critic
13、al issue, though significant progress has been made in the AI solutions Like their peers from other sectors, the energy and utilities industry is facing considerable headwinds when trying to scale their automation initiatives. Currently only 15% have been able to deploy multiple use cases at scale.
14、The sector has made considerable headway in adopting AI solutions. In 2017, only 28% had a few or multiple AI use cases, but today this number stands at 52%. These use cases are primarily aimed at the core competencies. Organizations are missing out on critical use cases that can deliver outsized be
15、nefits In core functions, only 18% of organizations are deploying quick-win use cases (by which we mean they are low on delivery complexity but high in terms of benefits achieved). Use cases such as forecasting, energy trading, yield optimization, grid behavior interfaces and complaints management f
16、all under quick wins. Support functions tend to utilize more robotic process automation (RPA) use cases, with quick wins emerging in order management, contract management, employee data management, and defect detection etc. Only 11% of the organizations are focusing on the quick wins in support func
17、tions. The road to scaling intelligent automation Learning from the best practices followed by high-performing Automation Frontrunners, we have developed five recommendations for scaling intelligent automation: Take a pragmatic approach when evaluating and choosing use cases: Finding and developing
18、viable intelligent automation use cases gives energy and utilities leadership a clear understanding of how they fit in with business strategy, competencies and capabilities. Optimize the right processes before trying for scale: It is essential that organizations have a strong grasp of the process re
19、-engineering and workforce impact before proceeding to try and scale. Force-fitting solutions to existing structures will lead to undesirable consequences and/or suboptimal gains. Put emphasis on breakthrough technology and ensure sufficient resources in place: By focusing on technologies such as ad
20、vanced analytics and deep learning in core functions, you can deliver outsized benefits. Centralize execution, governance and leadership: Using a dedicated team, along with staff rotated from application areas, can allow you to create and sustain “lighthouse projects”. Upskill the existing workforce
21、 ensuring change management: A comprehensive upskilling program will not only give you the viable talent pool you need for execution, it will also help with one of the most challenging areas for any digital transformation culture. The change management practices will help individuals, teams and over
22、all organizations to scale up and benefit from the intelligent automation. Only 15% of energy and utilities organizations have been able to deploy multiple use cases at scale. 3 Intelligent automation offers significant value to the sector, and its worth has actually been under-estimated by executiv
23、es Intelligent automation benefi ts achieved as expected Overestimated intelligent automation benefi ts Underestimated intelligent automation benefi ts 47% 14% 39% 48% 16% 36% 45% 17% 37% Cost savings benefi ts New/incremental revenue benefi ts Customer satisfaction benefi ts Figure 1: Intelligent a
24、utomation benefits expected against actual achieved Source: Capgemini Research Institute Intelligent Automation in Energy and Utilities Survey, February 2019, N=529 executives from energy and utilities organizations that are experimenting with or implementing intelligent automation initiatives. As a
25、 number of organizations are demonstrating, intelligent automation offers significant potential: US-based electric and gas utility, Xcel Energy, uses data from sensors on wind turbines to develop high-resolution wind forecasts through predictive analytics and artificial intelligence. As a result, th
26、e company has been able to reduce costs to end customers by $60 million by increasing efficiency of generation.2 Gazprom, the Russian gas giant, used robotic process automation (RPA) to automate verification of meter readings. In the first two weeks after the automation went live, an employee was ab
27、le to validate about 130 invalid meter reads, saving 10 hours of work per employee.3 United Utilities, the UKs largest listed water utility, recently tested an AI platform to analyze large data sets on factors such as weather, demand for water, pump performance and electricity prices. The informatio
28、n is used to make decisions on the most cost-effective and efficient way to run pumps, detect burst pipes and minimize the risk of discolored water. During the trial, the utility saw energy savings of 22%.4 Offset Solar, a US-based solar company, generated $1.2 million revenue within six months usin
29、g a simple homepage messenger chatbot.5 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 Altos Institute for the Future, who says, “We tend to overestimate the
30、 effect of a technology in the short run and under-estimate the effect in the long run.”6 Nearly half of our respondents say that they have under- estimated the true potential of intelligent automation. As Figure 1 shows, 47% say that the cost savings were under- estimated, and many said the same of
31、 customer satisfaction (48%) and revenue gains (45%). 4Intelligent Automation in Energy and Utilities: The next digital wave The sector is driving significant value from intelligent automation compared to other industries Boosting operations Topline growth Increase in operations quality Improved dat
32、a accuracy Improved workforce agility Increase in staff productivity Faster turnaround time for service requests Improved data consistency Fewer resources required to complete a process Better and faster compliance with legal/ regulatory Actionable operational insights Energy and UtilitiesAll Sector
33、s 40% 30% 37% 30% 33% 20% 32% 26% 32% 21% 32% 20% 28% 18% 14% 21% 21% 27% Improved customer satisfaction /NPS Quicker access to customer insights/data Increase in inbound customer leads Quicker breakeven Faster time to market to launch new products Improved cross-selling Reduced customer churn Energ
34、y and UtilitiesAll Sectors 50% 34% 47% 32% 45% 27% 41% 27% 41% 31% 40% 25% 36% 24% Figure 2: Percentage of executives saying that they achieved operational benefits from their intelligent automation initiatives (top three benefits ranked) Figure 3: Percentage of executives saying that they achieved
35、revenue growth benefits from their intelligent automation initiatives (top three benefits ranked) 47% executives say that the cost savings of intelligent automation were under-estimated. 5 Intelligent automation can drive significant cost savings across the energy and utilities sector Figure 5: Cost
36、 savings that could be realized across energy and utilities by implementing intelligent automation The energy and utilities sector could realize cost savings from $237 billion to $813 billion if it were to implement intelligent automation in its target processes at scale. To demonstrate the cost eff
37、iciencies that intelligent automation can deliver in the sector, we built a model using industry benchmarks and our survey data. (see Figure 5). A. Projected market size (in $ billion)* B. Operating expenses as a % of revenue* (in %) C. Projected operating expenses for the sector (in $ billion) (A*B
38、) Oil and gas11,564.254%6,243.5 Electricity utilities2,840.453%1,493.9 Water networks181.960%108.6 Electricity and gas utilities1,471.155%815.5 Energy services705.254%379.5 Engaging customers Improved customer experience through faster response Reduced number of processes and steps for queries and p
39、urchase Increased availability for customers by being open longer hours Personalized service/products for customers Energy and UtilitiesAll Sectors 81% 60% 78% 61% 74% 30% 67% 48% Figure 4: Percentage of executives saying that they achieved customer satisfaction benefits from their intelligent autom
40、ation initiatives (top three benefits ranked) Source: Capgemini Research Institute Intelligent Automation Use Case Survey, July 2018, N=705 executives from global organizations that are experimenting with or implementing intelligent automation initiatives; Capgemini Research Institute Intelligent Au
41、tomation in Energy and Utilities Survey, February 2019, N=529 executives from energy and utilities organizations that are experimenting with or implementing intelligent automation initiatives. 6Intelligent Automation in Energy and Utilities: The next digital wave Conservative benefits estimate D1. T
42、arget processes to be automated (in %)* E1. Average cost savings from intelligent automation (in %)* F1. Potential cost savings from intelligent automation (in $ billion) (C*D1*E1) Oil and gas16%15%149.8 Electricity utilities14%20%41.8 Water networks15%20%3.3 Electricity and gas utilities 14%30%34.3
43、 Energy services14%15%8.0 G1. Total projected cost savings from intelligent automation (in $ billion)237.2 Optimistic benefits estimate D2. Target processes to be automated (in %)* B. Operating expenses as a % of revenue* (in %) E2. Average cost savings from intelligent automation (in %)* Oil and ga
44、s22%42%576.9 Electricity utilities20%43%128.5 Water networks21%48%10.9 Electricity and gas utilities 19%44%68.2 Energy services20%38%28.8 G2. Total projected cost savings from intelligent automation (in $ billion)813.3 Source: Capgemini Research Institute Intelligent Automation in Energy and Utiliti
45、es Survey, February 2019, N=529 executives from energy and utilities organizations that are experimenting with or implementing intelligent automation initiatives; Capgemini Research Institute analysis; Bloomberg; MarketLine. *Source: Bloomberg and MarketLine analysis. * Source: Bloomberg analysis. N
46、ote: Operating expenses exclude the cost of goods sold; we have assumed a negative correlation between operating expenses and target processes to be automated. *Source: Survey data. The energy and utilities sector could realize cost savings from $237 billion to $813 billion if it were to implement i
47、ntelligent automation in its target processes at scale 7 Testing use casesDeveloped proofs of concept for some automation use cases Deployed pilots for some use casesDeployed a few use cases at scale* Deployed multiple use cases at scale* All Sectors 14%15%17%16%39% Energy and Utilities 14%18%14%15%
48、38% 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 acr
49、oss business units, functions, or geographies. However, scaled adoption in the sector is rare. As Figure 6 shows, 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%). Figure 6: Current level of intelligent automation deployment among organizations experimenting with or implementing intelligent automation, 2019 Source: Capgemini Research Institute Intelligent Automation Use Case Survey, July 2018, N=705 executives
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