Case Study: Accelerating Energy Rate Evolution with AI
Background
Energy rate design is traditionally a slow, cumbersome process. Stakeholders—including regulators, utilities, and energy platform providers—must navigate complex constraints while ensuring rates drive both customer engagement and sustainability goals. The current approach often results in rigid, one-size-fits-all structures that take years to evolve, limiting their ability to respond to real-world energy trends.
Opportunity
Recognizing these inefficiencies, I initiated a design-led exploration into how AI could shrink feedback loops in rate creation. Rather than replacing human decision-making, AI’s strength lies in enabling faster iteration, better data-driven confidence, and a more adaptive approach to evolving rates. My goal was to reimagine the rate design process—not by automating it entirely, but by making it more agile, responsive, and effective through AI-enhanced modeling and experimentation.
Approach
I framed the process using two key cycles:
•MCE Model—Model, Configure, Explain—to establish rates aligned with energy savings targets.
•CMSE Cycle—Confirm, Modify, Scale, Expand—to continuously refine rates based on real-world performance.
To push this further, I introduced the Larger Experimental Evolution Process (LEEP)—a structured but flexible methodology for testing and evolving rates at market scale. LEEP acknowledges the regulatory realities of different markets, providing a framework where rates can evolve through phased rollouts, opt-in structures, and AI-assisted decision-making.
Impact
This project created a space for future thinking in energy rate design, demonstrating how AI can be a force multiplier rather than a replacement for human expertise. AI doesn’t fundamentally change what’s possible—it simply enables rate evolution to happen faster, with more confidence and precision. By integrating AI into rate modeling, impact analysis, and iterative refinement, the industry can accelerate the transition toward smarter, more sustainable pricing models.
This work serves as a provocation and prototype for what’s possible: a future where rate design is not a bottleneck, but a dynamic tool for energy transformation.
Here are a few images of the exploration
Rate Campaign – The Base Product
Rate Campaign is a foundational energy rate management tool. This project explored a future design vision, though at the time, it remained a traditional web app—without AI-driven augmentation.
Natural Language Campaign Insights
Automatically generated critiques and insights could be seamlessly integrated across the platform, helping a smaller team of rate creators work smarter and more effectively.
The Rate Configuration TaskA closer look at the current-day product—a static, simplistic rate configuration tool that has remained largely unchanged despite periodic UX updates. The notes here reflect a lightweight heuristic review, highlighting opportunities where AI could enhance usability and efficiency.
Rate Explainer - Digital Population Testing
A digital twin approach to A/B testing, allowing rates to be pre-tested against simulated human behaviors before real-world deployment.
Rate Campaign Chat
The dream: a rate curation interface that doesn’t demand constant deep focus. Instead, it enables different mental modes—monitoring, improving, and creatively shaping the future of electricity. AI becomes a collaborator, not just a worker.
