Generative AI heralds the arrival of powerful new algorithms capable of generating text, images, computer code and even modelling new data. Understandably, it’s getting significant airtime in boardrooms, and the Utility sector is certainly no exception.
According to research by the Capgemini Research Institute, almost all – 95% – of utilities and energy companies surveyed globally have had conversations about generative Artificial Intelligence (AI) in the last 12 months. Of those, apparently thirty-three percent have already begun to pilot generative AI for different use cases. Yet while almost 40% of utility and energy companies have established a dedicated team and budget for generative AI, 41% state they are taking a “watch and wait” approach.
In this blog I’ll examine how the sector is beginning to explore the use of AI in areas such as improving customer experience, managing assets, and optimizing people and resources.
Improving and creating a better customer experience with AI
Increasingly, utilities are under pressure to broaden their service offerings and become more responsive to customer demands. But they lack the organization and structure to deliver that customer experience affordably. Whilst consumers have become accustomed to on demand, multi-channel options when dealing with providers and suppliers, for many their utility provider relationship still remains heavily reliant on phone and email communication.
Alongside machine learning, the ability of AI to support automation, for example with AI-driven chatbots, offers organizations the opportunity to differentiate their customer service. They can quickly and cost-effectively automate responses to routine customer queries, offer self-service options to schedule and manage service appointments, or execute customer transactions without adding human headcount and cost. At the same time, by using data analytics, providers can better understand usage trends and needs, and so offer customers energy and water saving tips and value-added services, such as energy audits.
IFS Cloud already provides utility and other sectors with cost effective ways to leverage AI powered bots, natural language processing (NLP), and technical capabilities around remote assistance. By automating routine tasks, and learning and predicting what users might need, the experience is transformed for both customers and staff alike.
Extending operating lifetimes: working smarter with Asset Lifecycle Management
Utility networks are ageing worldwide. A recent US Department of Energy report estimates around 70% of infrastructure in the US is over 50 years old. Amidst increasing climate change pressures, energy providers are spinning several plates: they are trying to update and build the grid back better, embrace electrification from renewables whilst maintaining resilience, and create a two-way grid capable of supporting “Behind–the–Meter” self-generation from microgrids.
The result sees companies competing to procure finite resources, be that transformers, turbines or solar PV panels. Whilst this transition is underway, they must also extend the normal lifetime of existing ageing assets and fleets. This in turn demands robust maintenance and monitoring to ensure equipment remains reliable and safe.
To address these needs, organizations are looking to AI-enabled digital technology such as IFS Cloud to streamline and optimize assets in the field across the entire asset lifecycle. When planning for quarterly or annual inspections, big data analytics and machine learning capabilities embedded in IFS Cloud allow resources and skills to be prioritized to those assets that are expected to become problematic or likely to fail prematurely. Alongside this, AI-driven predictive asset performance management capability puts powerful functionality around both finance and supply chain management. By leveraging AI and predictive algorithms, IFS Cloud can help to answer questions like ‘do we have the right inventory in the right locations to meet predicted demands?’ And ‘Can we execute strategic just-in-time procurement, securing the best possible price in a competitive market?’
Optimizing and planning resources – AI helps us understand what we will need.
We all know global warming is resulting in extreme climatic changes and events. Unlikely weather-driven failures are happening far more frequently. We’ve seen heatwaves causing sweeping wildfires, destroying infrastructure, and sudden exceptional sub-zero temperatures accompanied by heavy snowfall and ice causing failures and outages. Unseasonably heavy rainfall, high winds and storms are creating flash flooding world-wide.
AI-enabled EAM software enables modeling for future ‘what-if’ scenarios based on current and historic sensor data. We can project environmental risks, model predicted load and population growth, and even understand the potential impact of failures in the field. For example, as we shift towards decarbonization and electrification, we can estimate the extra capacity the system will need to meet demand. These projections can inform our ‘what next’ infrastructure planning. Organizations can decide what to retain, replace, or even relocate, or adapt. For instance, extreme hot weather events could place powerlines at risk of wildfires, while more severe winters jeopardize ice buildup and assets freezing. Financial modeling can estimate the Total Cost of Ownership based on maintaining this existing infrastructure in high-risk areas, or replacing it with new, buried linear assets.
What next? AI supporting people and skills.
Planning for the necessary people, skills and resources is equally challenging. Utilities need to be sure they can deploy the labor required to ensure the lights stay on, but they can’t afford to over-hire. Equally, a global skills shortage means balancing out contractor and internal resources efficiently.
Looking forward, I see AI and automation playing an important role in supporting hiring and training across the sector. I know from the Customer Advisory Board IFS facilitates that our utility customers are not only struggling to retain their current grid staff and skills, but also battling to acquire the new technology skills the network will need. Facing a limited resource pool, AI and automation can help organizations optimize resource management, and support recruitment, training and retraining as the landscape transitions and evolves.
Conclusion
In a highly regulated sector, with obvious supply obligations around energy continuity and service to meet demand, it is hardly surprising that the sector’s approach to introducing AI has been cautious to date. With the estimated global use of AI in energy to hit $7.78 billion by 2024, it is clear that utilities need to understand and embrace this opportunity.
Caution is prudent, but utilities need to educate themselves on how AI can help… and how it needs to be managed.
