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Generative AI Use Case Library for the Utilities Industry

Identify value-driven generative AI use cases to transform your organization.

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  • Adoption has been slow. The benefits of Gen AI technologies for utilities are understood but the adoption has been slow despite rapid growth in investment and development.
  • Utility leaders are unaware of relevant use cases for the utilities industry and building them can be difficult and time-consuming.
  • Utility leaders lack insights for developing a business-aligned Gen AI strategy as part of a transformation effort, nor do they have an understanding of how AI & ML can impact the business and how it can provide significant value.

Our Advice

Critical Insight

Utilities should approach a Gen AI adoption strategically and methodically within a larger digital strategy, with a clear understanding of the specific use cases and benefits.

Impact and Result

Info-Tech’s use case library provides practical guidance to help utilities accelerate value-driven Gen AI use case adoption.

Generative AI Use Case Library for the Utilities Industry Research & Tools

1. Generative AI Use Case Library for the Utilities Industry Deck – Identify value-driven Generative AI use cases to transform your organization.

Leverage a robust set of Gen AI use cases to identify potential initiatives that can accelerate value creation. Use cases cover a range of technologies and capabilities and include insight into sources of value.

Unlock a Free Sample

Generative AI Use Case Library for the Utilities Industry

Identify value-driven generative AI use cases to transform your organization.

Analyst Perspective

Deploy value-driven artificial intelligence (AI) use cases responsibly.

AI is not new for utilities. In fact, data scientists or engineers in the line of business have been using machine learning (ML) models to assist in system planning for years. One of the unfortunate realities is that they spend weeks and months wrangling data before they can get to the mathematical modeling and AI part of the problem solving. Developing strong fundamental data fabrics can accelerate the deployment of AI use cases at scale.

In the era of generative AI (Gen AI), the adoption of AI technologies has regained the spotlight recently. Utilities are cautiously optimistic about their potential while assessing their limitations and risks. When it comes to emerging technologies, most utilities have always been followers. You certainly won't see a lot of utilities jumping on the bandwagon of implementing quick and cool demos. Unless the disruptions are imminent and the ROIs are clear, utilities will stay patient and reap the benefits of others' proven use cases and adapt from others' lessons learned.

In the spirit of not implementing technology for the sake of technology, we recommend our members adopt a value-driven methodology to examine the potential benefits of AI use cases while balancing the risks and limitations.

Jing Wu

Jing Wu
Principal Research Director
Utilities, Industry Practice
Info-Tech Research Group

Executive Summary

Your Challenge

Adoption has been slow. The benefits of AI technologies for utilities are understood, but the adoption has been slow despite rapid growth in investment and development.

Utilities do not know how to deploy AI technologies at scale rapidly and responsibly, given the limitations and risks.

Utility leaders lack insights for developing a business-aligned AI strategy and governance model to establish alignment between IT and business.

Common Obstacles

Utility leaders have a limited understanding of the potential use cases and how they can get started to support strategic objectives.

Utilities are concerned about the risks of AI and compliance with privacy laws, regulations, and policies.

Utility organizations are challenged with the quality and accessibility of the huge amount of data being generated and they are uncertain about how to effectively address them.

Info-Tech's Approach

Introduce an approach to build your Gen AI roadmap rapidly and responsibly via a six-step practical framework to accelerate the adoption.

Help utility leaders understand and discover AI use cases that can address some of their business challenges as well as support organizational strategic goals.

Guide utility leaders to start their AI journey by identifying and prioritizing AI use cases for their business capabilities through a benefits realization model.

Info-Tech Insight

Utilities should approach AI adoption strategically and responsibly, with a clear understanding of the specific use cases and their benefits, and a plan for addressing the challenges associated with implementation and ongoing use. Utilities can take on a risk-based approach to act on sophisticated AI technology and ready-to-use solutions to balance AI opportunities and risks.

Gen AI is an innovation in ML

Gen AI

A form of ML, whereby, in response to prompts, a Gen AI platform can generate new outputs based on the data it has been trained on. Depending on its foundational model, a Gen AI platform will provide different modalities and thereby use case applications.

Audio – Converts text to sound

Visual – Enables text to image, video, or web design conversions

Code – Creates code in various programming languages based on human
language prompts

Text – Creates text-based outputs such as articles, blog posts, emails, and
information summaries


An approach to implementing AI, whereby the AI system is instructed to search for patterns in a dataset and then make predictions based on that set. In this way, the system "learns" to provide accurate content over time (think of Google's search recommendations).


A field of computer science that focuses on building systems to imitate human behavior. Not all AI systems have learning behavior; many systems operate on preset rules, such as customer service chatbots.

Info-Tech Insight

Many vendors have jumped on "Gen AI" as the latest marketing buzzword. When vendors proclaim to offer Gen AI functionality, pin down what exactly is "generative" about it. The solution must be able to induce new outputs from inputted data via self-supervision – not trained to produce certain outputs based on certain inputs.

Gen AI is accelerating AI adoption

AI and ML usage is not new for utilities. Data scientists in the line of business have been leveraging different machine learning algorithms to assist in data analysis. Smart grid technologies back in the early 2010s started to offer AI capabilities to help analyze vast amounts of data about energy consumption and grid performance. Utilities have been gradually and steadily gaining momentum over the past few years introducing new possibilities to the risk-averse industry.

Gen AI technology has gained its massive appeal due to its easy accessibility to the general public. Besides the exponential increase in computing power and the explosion of data, the recent advancement in the sophistication of the Gen AI algorithm has opened all kinds of possibilities. For those who do not have a degree in AI or the talent of data scientists, you can now reap the benefits of easy entry points of Gen AI foundation models to accelerate your adoption. Vendors are now looking into ways to integrate the new capabilities of Gen AI to enhance its existing AI offerings or adding the capabilities to stay relevant.


of European utilities executives had a defined AI strategy in 2018, and 83% considered it a high to medium priority for their business.
Source: Roland Berger, 2018


of global organizations have created their first AI strategy or accelerated their existing AI strategies in 2023.
Source: Scale, 2023

About Info-Tech

Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.

We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.

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Jing Wu


  • Mike Flaxman, VP of Product, Heavy.AI
  • Tony Duarte, Director of International Business Department, Heavy.AI
  • Aron Calfas, Head of Digital Risk and Assurance, Sydney Water
  • Vipin Thapa, Enterprise Information Architect, Sydney Water

Search Code: 102810
Last Revised: September 27, 2023

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