Generative AI for energy and utilities: PwC

2024-07-23 · Source: Curated for you: AI: PwC · Field: Energy & Utilities — Utilities & Infrastructure, Energy Markets & Policy, Renewable Energy Systems · Depth: Intermediate, medium

Summary

PwC highlights five key facts about Generative AI (GenAI) for energy and utility companies, asserting its immediate value and transformative potential. GenAI can significantly lower operating expenses and unlock data value, offering "quick wins" by utilizing foundation models rather than requiring new model builds. Crucially, companies do not need complete data modernization to start, as GenAI can use existing clean data and even accelerate data organization. The technology is highly scalable, with a single GenAI model adaptable to multiple use cases, potentially boosting back-office productivity by 20% to 40%. Furthermore, GenAI helps reduce risks by analyzing data to anticipate supply interruptions, prevent accidents, and combat fraud, while also minimizing the need for extensive new AI talent hires, making projects more feasible and affordable.

Key takeaway

For technology or operations leads at energy and utility companies facing digital transformation pressures, you should prioritize GenAI pilots. Focus on use cases utilizing existing clean data to achieve quick wins and fund further initiatives. This approach can significantly reduce operating expenses, enhance risk management, and boost workforce productivity by 20% to 50% without requiring extensive new AI talent. Implement an "AI factory" model to scale safely.

Key insights

GenAI offers energy and utility companies immediate value, scalability, and risk reduction without full data modernization or extensive new hires.

Principles

• Utilize foundation models for quick wins.
• Existing clean data enables GenAI adoption.
• Single GenAI model scales across use cases.

Method

Implement an "AI factory" operating model to prioritize, allocate resources, and ensure rigorous oversight and governance for safe GenAI scaling.

In practice

• Automate call center responses and insights.
• Track asset health using IoT sensor data.
• Analyze contracts for troublesome clauses.

Topics

• Generative AI
• Energy Utilities
• Digital Transformation
• Risk Management
• Data Modernization
• AI Governance

Best for: CTO, VP of Engineering/Data, AI Product Manager, Director of AI/ML, Operations Professional, Executive

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Editorial summary, takeaway, and curation by AIssential. Original article published by Curated for you: AI: PwC.