Intelligent automation and AI's roles in electricity sector
According to the history books, the formal distribution of electricity to power homes and businesses reaches its 140th anniversary in 2022. The electricity industry has evolved and expanded to become one of life’s most important services. But it also faces a unique set of challenges that means it can no longer carry on with business as usual.
These include the very real need for sustainability in power generation, underlined by outcomes from the 26th United Nations Climate Change conference held in November 2021. Pledges by countries to decarbonise energy supplies are now firmly in place, and there will be a renewed emphasis on solar and wind generation moving forward.
As economies rebound from COVID, energy prices - especially for natural gas - are soaring around the world due to increased demand, leading to sharp spikes in energy bills for consumers and providers switching to coal to generate electricity. More coal burnt means more greenhouse gas emissions, leading to a greater urgency to switch to newer, cleaner technologies.
This has huge implications for the highly regulated electricity industry across the globe, which risks both fines and reputational risk when it fails to deliver power according to the requirements of regulatory frameworks. The transition from fossil fuels to sustainable energy production will need to be managed carefully when less predictable methods are used.
The problem with energy legacy IT technologies
This brings us to the problem of legacy IT systems. As with the aging infrastructure that the electricity industry struggles to keep up to date, because of the growing shortage of relevant skills and expertise, it can be difficult to find the investment to upgrade IT platforms.
So, while initiatives such as smart metering should bring benefits through lower costs and greater efficiencies, in practice the vast volumes of data gathered are difficult to manage and analyse in any meaningful way, for example when making predictions for future consumption in real-time.
And while choice has been removed from some consumers because of the energy pricing crisis, providing excellent levels of customer service is a key element in reducing churn and gaining market share. This is the case both for signing up and servicing customers, but also in terms of fixing physical problems with supply.
Legacy IT systems mean that the information needed by contact center staff to support customers is often held in different systems. People are used as the connectors between those systems, creating friction in processes such as change of address, billing or fault repair scheduling.
So given the need to deliver value to shareholders, while meeting regulatory requirements and keeping customers satisfied, how can electricity companies adapt their processes and adopt a more data-driven approach to managing their businesses – without wholesale replacement of legacy IT systems?
Enter intelligent automation and AI
One answer lies in the adoption of intelligent automation (IA) and artificial intelligence (AI), a fusion of technologies set to transform the way in which the electricity industry operates. A global industry is emerging to apply IA and AI to almost every aspect of sustainable electricity production and distribution, and large organizations are adopting automation platforms to deliver real change.
Through our work with electricity companies, we have identified a number of areas where automation and AI are bringing demonstrable benefits.
Utility companies can be massively impacted by their customer experience (CX) scores. This could result in millions of pounds per annum in incentives/penalties imposed by the regulators, which can be painful if not managed effectively. By integrating customer relationship management (CRM) and billing systems, utilities can avoid leaving customer agents with complex systems and multiple data sources. Digital workers can do the heavy lifting of pulling data into a single view of the customer.
The reality for many organisations is that their underlying digital landscape is a mix of old and new. The capability to bring both together is key. Taking information from decades old customer IT systems to blend into modern workforce management systems is still, to a degree, done by human beings cutting and pasting from one system to the other.
This alone provides a rich vein of improvement that would help operational response teams and create efficiencies. It also allows these individuals to spend more time in empathetic discussions with customers, who are often under stress as most calls are to deal with a problem on a lifeline service.
This is aligned with the climate agenda, but also includes metrics such as the reporting of performance around regulatory targets for pollution and efficient energy generation. Such reporting is critical and underpinning automated systems can manage in-day monitoring and responses which in turn enables provision of accurate reporting against targets.
As the US energy grid is modernised, state legislatures, public utility commissions and providers are assessing needs, policies, costs and returns on investments. The American Society for Civil Engineers found that trends in investments in energy grids will lead to funding shortfalls of $42bn for transmission and $94bn for distribution by 2025 all the while needing to incorporate diverse energy supplies, increase resiliency and upgrade the infrastructure.
This is massively complicated, but AI and other technologies that increase knowledge of grid operations can enable fluctuating supply and demand to be balanced better, optimized energy use, increased efficiency, and improved responses to outages.
Optimised utility plant maintenance
Aging energy generation and distribution infrastructures are some of the biggest challenges facing utilities in developed countries. It has a huge impact on their ability to provide a reliable, cost effective and ‘future proof’ provision for end users.
In some cases, these providers are working with 30+ year-old equipment and are looking to maximize its life by implementing IoT, IA and AI around workflows such as predictive maintenance. This is where sensors on large equipment feed data to a SCADA system where IoT/IA/AI/automation platforms can help determine the likelihood of a failure. Depending on their findings, they can automatically schedule a field service request and technician to fix before failure, resulting in extended life, lower costs and greater efficiencies.
Pretty much all providers have goals to achieve net-zero by a certain timeframe. Embracing RPA, advanced analytics and AI is instrumental in meeting climate change goals and the growing demand for clean, cheap, reliable water. For example, San Diego Gas and Electric prevents wildfires by utilizing sensor data – along with satellite weather data. Another great example is to use drones to perform inspections on infrastructure and solar farms and computer vision to detect for anomalies where digital workers collect the data, analyse and perform the next best action.
Barriers to change in the energy industry
Given the benefits IA and AI can deliver to the electricity industry, why is there still a reluctance in some quarters to adopt relevant technologies? For every organization that has taken early mover advantage and seen measurable results – such as a reduction in customer onboarding, automated engineer scheduling and friction-free change of address processes – there are others that have yet to take any meaningful steps to adopt IA and AI.
In our experience, barriers tend to be cultural rather than technological, or even budgetary. There needs to be buy-in not just by the senior leadership team and the business, but by the IT team as well: the best results come from continuous programs of change, not just one-off, ad hoc projects.
Another challenge is that companies operating in a competitive industry can be a little reluctant to share best practices and measurable outcomes from their IA and AI programs. And in a sector where lines of business compete for scarce resources, a joined-up, integrated approach towards digital transformation can be difficult to achieve.
Finally, the industry can have fears about loss of control over what is an essential service if too much work is taken away from human experts and given to digital workers instead. However, as many utilities have now discovered, a digital worker can operate 24 hours a day, 365 days a year with higher levels of productivity, accuracy, security and speed than their human counterparts.
The electricity industry has come a long way since the first electricity generating stations were opened in 1882.
But thanks to climate change, aging infrastructure and legacy systems, it has now reached an inflection point where it needs to start doing things differently, and that will include the adoption of smart technology platforms built around IA and AI.