Balancing Innovation and Responsibility: AI in Energy Efficiency

FORT WASHINGTON, PA (October 8, 2025) – AI can be a powerful accelerant for energy efficiency programs—helping to identify the right customers, personalize outreach, and increase participation rates in ways that traditional marketing cannot match. When deployed strategically, AI can yield energy savings that far exceed the relatively small amount of electricity consumed by its use, creating a strong net benefit for the grid and the environment.

However, there is a legitimate concern about credibility and optics. Energy efficiency programs must lead by example, ensuring that the tools they use reflect the same sustainability principles they promote. Heavy reliance on energy-intensive computing without a clear plan to offset or minimize its footprint risks undermining customer trust and program integrity.

AI can make program marketing more precise and efficient by analyzing customer data to target those most likely to benefit. This reduces wasted outreach, accelerates adoption of energy-saving measures, and delivers grid benefits far greater than the energy required to run the models. As AI increasingly runs on renewable power and is deployed responsibly, it will align with sustainability goals while minimizing its environmental footprint. AI can help customers take lasting steps toward efficiency, making the small energy cost a powerful investment in a cleaner, more resilient grid.

Conversely, an argument can be made that programs should first minimize their own operational footprint before asking customers to change their behavior, though. If less energy-intensive marketing methods could achieve similar results, then using AI could be seen as an unnecessary and avoidable contradiction. Using AI tools that require substantial computing power to recruit customers into energy efficiency programs can create a perception of hypocrisy. If the tools themselves are energy-intensive—particularly when powered by fossil-fuel-based electricity—it risks undermining the program’s core message of reducing energy demand and environmental impact. Customers or stakeholders may question whether the recruitment benefits justify the environmental cost, especially if the AI models are run repeatedly for large-scale campaigns or rely on cloud computing centers with high carbon footprints.

To responsibly integrate AI, programs should:

1. Prioritize Efficiency in AI Operations – Select AI models and workflows that minimize computational load.
2. Source Clean Energy Where Possible – Use cloud providers or data centers that are using renewable energy, matching electricity use with renewable energy purchases or have commitments to 100% renewables in the future.
3. Apply AI Only Where It Adds Clear Value – Focus on scenarios where AI can significantly increase recruitment effectiveness or program impact, while avoiding unnecessary processing.
4. Test Concepts in the Shallow End – Begin with small-scale pilots to test tactics and generate a proof of concept before expanding. This approach not only reduces risk but also provides measurable insights, builds organizational confidence, and ensures resources are invested in strategies proven to deliver results.
5. Measure and Communicate the Net Benefit – Track the energy use of AI activities against the estimated energy savings from recruited participants and share these results transparently.

By following this framework, AI can be leveraged in a way that upholds the program’s mission, delivers measurable net energy benefits, and maintains the trust of customers and stakeholders. When applied as a tool for human augmentation rather than replacement, AI supports staff in checking data against real-world implementation, minimizing the risk of errors or hallucinations, and ensuring that insights remain accurate, practical, and grounded in reality.