10 practical Generative AI use cases for Energy & Utilities

1. Generated self-service repair guides

Homeowners frequently face minor issues with their WiFi, fuse boxes, central heating systems, etc., often resulting in inconvenient and costly service visits for small problems. By giving Generative AI access to product manuals, you can create a natural language interface allowing customers to resolve these issues on their own. There tools not only enhance customer satisfaction but also fosters loyalty by simplifying customer repairs.

2. Partly-automated replies for resolving disputes and complaints

Customer service agents and legal teams spend a considerable portion of their time addressing frequently asked questions and resolving disputes. This workload can be significantly reduced by adding your company's data sources and legal documents to Generative AI tooling. Such integration facilitates draft responses to common inquiries, including complaints and payment discussions. However, generating accurate responses for specialized questions, such as those related to specific customer contract agreements, remains a challenge. To ensure the quality of responses, it is crucial to keep agents in the loop, allowing them to review and approve AI-generated messages before sending. Implementing this approach has shown the potential to dramatically increase response speeds and decrease the time spent on communications by up to 90%.

3. Automated call logging and summarization

Customer service & tele sales agents need to log and summarize many conversations. This can easily be automated - typically within just a few days of development time – by combining speech-to-text tooling like Deepgram, to generates the text transcript, and GenAI tooling like OpenAI, to summarize the transcript into any desired format. This use case reduces time spent on summarizing calls by up to 100%; proven to result in 10-15% efficiency gain at many clients. Furthermore, Gen AI call summarization also improves quality and consistency of summaries across agents, generating valuable data for further analysis.

4. Automated market, competitor, and pricing reports

Many businesses struggle with accessing accurate market, competitor, and pricing data, often resorting to purchasing expensive reports. Generative AI can assist by scraping and analyzing data from websites, automating the reporting process, and creating interactive reports. To illustrate this, you can distribute a weekly pricing update to your pricing team by automatically analyzing competitor prices. Additionally, you can ensure your innovation team is promptly updated whenever a competitor launches a new feature or product. This approach not only cuts external costs but also keeps the company informed and up-to-date with market trends.

5. Automated payment or churn risk alerting  

Retaining customers and ensuring timely payments are key worries for businesses. Generative AI can help assess and alert to these risks by analyzing conversations and identifying elements that may indicate potential churn or payment issues. This enables proactive alert triggering and advise on e.g., targeted discounts or budgeting for at-risk customers. This proactive approach not only enhances customer retention, but also improves payment rates, significantly impacting bottom lines.

6. Assisted data field entry

Customer service and back office agents are required to fill data fields with high accuracy, as errors like entering the wrong address can have potentially serious implications. Generative AI can enhance this process by verifying the accuracy of filled fields and offering suggestions during data entry. This can be based on live call transcriptions or existing customer information. Implementing this use case not only improves the quality of customer service and back office, but also ensures overall data quality, enabling many other (AI and non-AI) use cases.

7. Localized marketing content  

Marketing departments need to create a substantial amount of content, and local teams need to produce targeted local advertisements. Generative AI can facilitate the creation of marketing copy, visuals, and audio, as well as assist in analyses. Off-the-shelf tools can offer much of the functionality for basic tasks. As a next step, employing techniques such as few-shot prompting or fine-tuning your own models allows for customization with local insights, including language, tone, and imagery. This strategy not only boosts the efficiency of marketing departments but also empowers global teams to develop localized campaigns, significantly reducing the workload on local teams.

8. Maintenance & technician assistance

Technicians often face complex and diverse problems. With Gen AI, you can develop a conversational interface that functions as a virtual technician or maintenance assistant. By integrating Gen AI tooling with Retrieval-Augmented Generation (RAG) alongside your internal databases and product guides, you can create a bot designed to help troubleshoot and resolve issues, manage maintenance, identify the correct parts, and assist with administrative details (e.g., billing). As a next step, you can even bring this proposition to your customers. This solution significantly boosts customer satisfaction and loyalty, while reducing the workload on customer service and technician departments.

9. Automated chatbot improvements

Customers often struggle with using chatbots and frequently escalate to live chat. While developing a successful generative AI-enabled chatbot is challenging, generative AI can significantly enhance existing systems. It excels at identifying frequently asked questions that are either unanswered or incorrectly answered by analyzing vast amounts of chat data. These identified FAQs can then be integrated into the conversation flow. As a next step, you can use generative AI to automatically generate specific content for the conversation flow. This use case improves chatbot quality, leading to fewer escalations to the customer service department.

10. Personalized training for customer service & tele sales agents

Customer service managers and agents often lack the time to review all agent calls to learn how to improve conversations. Generative AI can automatically analyze conversations to pinpoint improvement areas for customer service agents, using speech-to-text and tailored AI tools with best practices, such as guides for handling upset customers.

Automatically highlighting areas for improvement and strengths of agent then enables managers to provide more precise and personalized coaching and training programs. Ultimately, this approach could lead to the development of an AI-powered training program. This use case not only enhances the quality of customer service with each interaction, but also reduces onboarding time, addressing high employee turnover in the customer service industry and enabling personalized trainings for customer service agents.

Ready to unlock the potential of Generative AI within your Energy & Utilities organization?

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