Friend or Foe: AI could fuel the Utility of Future

Home » Friend or Foe

By Carol Johnston, VP Energy, Utilities and Resources at IFS

The rise of Generative AI introduces potent algorithms capable of crafting text, images, computer code, and even generating novel data models. Naturally, it has become a focal point in boardrooms, and the Utility sector is no exception.

Recent research from the Capgemini Research Institute unveils that a substantial 95% of utilities and energy companies surveyed globally have engaged in discussions about Generative Artificial Intelligence (AI) within the last 12 months. Among them, thirty-three percent have already embarked on pilot programs for various use cases. Despite nearly 40% of utility and energy companies forming dedicated teams and budgets for Generative AI, 41% indicate they are adopting a prudent “watch and wait” stance.

This blog will intricately explore how the sector is at the forefront of leveraging AI in diverse areas, including enhancing customer experience, managing assets, and optimizing personnel and resources.

Transforming Customer Experiences with AI

Utilities face mounting pressure to enhance service offerings and meet evolving customer expectations. Traditional communication methods, such as phone and email, still dominate the utility-customer relationship, lacking the agility needed for today’s demands. In conjunction with machine learning, the utilization of AI for automation, such as AI-driven chatbots, presents an opportunity for organizations to stand out in customer service. Automation allows quick and cost-effective handling of routine customer queries, enables self-service options for scheduling and managing service appointments, and facilitates customer transactions without the need for additional human resources and expenses. Simultaneously, leveraging data analytics enables providers to gain insights into usage patterns, allowing them to offer customers energy and water-saving tips and value-added services like energy audits.

IFS Cloud is already at the forefront, providing utility and other sectors with cost-effective means to leverage AI-powered bots, natural language processing (NLP), and technical capabilities related to remote assistance. By automating routine tasks and employing learning algorithms to predict user needs, the experience is transformed for both customers and staff.
Expanding Operational Longevity: Optimizing Efficiency with Asset Lifecycle Management

Expanding Operational Longevity: Optimizing Efficiency with Asset Lifecycle Management

Global utility networks are confronting the challenge of aging infrastructure, with approximately 70% of the US energy grid exceeding 50 years, as per a recent US Department of Energy report. In the face of climate change, energy providers strive to modernize the grid, accommodate renewable energy electrification, ensure resilience, and facilitate a two-way grid supporting “Behind-the-Meter” self-generation from microgrids.

In this complex scenario, companies contend for finite resources, such as transformers, turbines, and solar PV panels, while simultaneously extending the operational life of existing assets. This requires robust maintenance and monitoring for reliability and safety. To meet these challenges, organizations turn to AI-enabled digital solutions like IFS Cloud for streamlined and optimized asset management throughout the asset lifecycle.

Leveraging big data analytics and machine learning in IFS Cloud, resources and skills are prioritized for assets expected to face issues or premature failure during quarterly or annual inspections. The AI-driven predictive asset performance management capability in IFS Cloud enhances both finance and supply chain management. Utilizing AI and predictive algorithms, IFS Cloud addresses 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?’

Streamlining and Strategizing Resources – AI Guides Future Planning

The impacts of global warming are evident in the increasing frequency of extreme weather events, leading to unexpected failures and disruptions. From heatwaves triggering wildfires to sudden sub-zero temperatures causing outages, and unprecedented rainfall causing flooding, these events pose challenges to infrastructure resilience.

AI-driven Enterprise Asset Management (EAM) software offers the ability to model potential future scenarios through ‘what-if’ analyses based on current and historical sensor data. This includes projecting environmental risks, predicting load and population growth, and assessing the potential consequences of field failures. For instance, with the transition to decarbonization and electrification, AI can estimate the additional capacity required to meet growing demand, informing infrastructure planning decisions. Organizations can then determine whether to maintain, replace, relocate, or adapt existing assets, considering factors like wildfire risks during extreme heat or ice buildup in harsh winters. Financial modeling further aids in estimating the Total Cost of Ownership, aiding decisions on retaining or replacing infrastructure in high-risk areas.

AI-driven Enterprise Asset Management (EAM) software offers the ability to model potential future scenarios through ‘what-if’ analyses based on current and historical sensor data. This includes projecting environmental risks, predicting load and population growth, and assessing the potential consequences of field failures. For instance, with the transition to decarbonization and electrification, AI can estimate the additional capacity required to meet growing demand, informing infrastructure planning decisions. Organizations can then determine whether to maintain, replace, relocate, or adapt existing assets, considering factors like wildfire risks during extreme heat or ice buildup in harsh winters. Financial modeling further aids in estimating the Total Cost of Ownership, aiding decisions on retaining or replacing infrastructure in high-risk areas.

Navigating the Future: AI’s Contribution to Workforce Management

Planning for the necessary workforce, skills, and resources presents considerable challenges for utilities. Ensuring adequate labor to maintain operational stability while avoiding over-hiring is crucial. Additionally, addressing the global skills shortage involves efficiently balancing the use of contractors and internal resources.

Looking ahead, I anticipate a significant role for AI and automation in supporting workforce planning and skill development across the sector. Insights from the Customer Advisory Board facilitated by IFS highlight the dual challenge faced by utility customers—retaining existing grid staff and skills, and acquiring the new technology skills demanded by the evolving network. Given the constraints of a limited resource pool, leveraging AI and automation can optimize resource management and provide crucial support for recruitment, training, and retraining initiatives as the industry landscape undergoes transition.

Conclusion

In a heavily regulated industry with clear responsibilities for ensuring energy continuity and meeting demand, it’s unsurprising that the adoption of AI has been cautious so far. However, with the projected global use of AI in the energy sector reaching $7.78 billion by 2024, utilities must recognize and seize this opportunity. While caution is wise, it is crucial for utilities to educate themselves on how AI can be beneficial and the necessary measures for its effective management.

We have the support you need
We are here for you!

Solutions