If I Had These Capabilities Then: Starting With the Renewable Energy Customer, Not the Model

Decision Architecture Pathway

Separating contextual adaptation from deterministic logic in the customer journey:

Current Journey Baseline onboarding

Points of Uncertainty Where customers pause

Contextual Support Layer Smallest useful explanation

Deterministic Calculation Guaranteed logic & numbers

Customer Decision Approachable commitment

There is something slightly uncomfortable about looking backwards at an old product problem with today's technology. It becomes very easy to see everything that could have been added. That isn't necessarily useful.

When I think back to building a consumer-facing renewable-energy experience, the most interesting question for me isn't, "Where could I have inserted a language model?"

It is whether these newer capabilities would have helped us understand and support the customer without making an already unfamiliar subject feel even more complicated. That distinction matters.

Renewable Energy and the Burden of the Unfamiliar

Renewable energy can require customers to make decisions about concepts they do not encounter every day. Electricity consumption, emissions, renewable-energy certificates and the relationship between individual behavior and a wider energy market are not necessarily difficult because the customer lacks the intelligence to make decisions. They are difficult because the customer has other things to do.

Our job was to make the experience approachable enough that someone could understand what we were asking, make a reasonable decision and continue.

That led us toward a lightweight experience that valued clarity and accessibility rather than insisting that every customer arrive with perfect information. The result was a product that could get people moving rather than leaving them stranded at the starting line.

Contextual Synthesis: The Smallest Useful Explanation

Looking at that problem now, contextual synthesis is the capability that interests me first. Not because I would want a system producing elaborate explanations of renewable-energy markets. Quite the opposite.

I would want to know whether it could take the information relevant to that particular customer and help present the smallest useful explanation needed for the next decision.

• Someone who already understands their electricity bill does not need the same explanation as someone encountering the subject for the first time.
• Someone who has reliable usage data available does not need the same flow as someone estimating from memory.

The product problem therefore becomes less about generating information and more about controlling how much information enters the experience. That is where I think newer capabilities could have been helpful:

Probabilistic Explanation vs. Deterministic Calculation

I would still be cautious about where the system stopped. The underlying calculation that determined quantities or financial values would be a place where I would prefer deterministic logic. If an input means the calculation should produce $X$, I want the system to produce $X$ consistently.

The probabilistic layer could help explain the result. It should not casually reinvent the calculation. That separation feels increasingly important to me.

The Temptation Would Have Been Personalization

I can imagine the roadmap conversation very easily:

Perhaps. But this is where I would want to slow the conversation down. Personalization is not automatically value.

Sometimes consistency creates trust. Sometimes allowing the user to see exactly how an estimate was reached is more valuable than creating an experience that feels magically tailored. And sometimes the right answer is simply better design.

That is one of the reasons I like starting with the problem rather than the capability.

I Would Use the Prototype to Reduce One Uncertainty at a Time

The first experiment I would want probably wouldn't be a complete conversational interface. I would be more interested in a bounded comparison:

• Could we identify two or three points in the onboarding journey where customers historically needed additional explanation?
• Could contextual synthesis generate a short explanation from a tightly controlled body of approved material?
• Could users still see the underlying assumptions and calculation?

Then I would observe. Not simply conversion. I would want to know where people pause, what they ask next, what they ignore, and whether the additional explanation makes the decision easier.

My product background has made me fairly suspicious of assuming behavior from metrics alone. I use data heavily, but I have also learned that people regularly adapt products in ways the original design did not anticipate. A probabilistic capability doesn't make that less true. It probably makes observation more important because now the output itself may vary.

What I Would Preserve

The most important part of the original approach is something I would keep exactly as it was: Make the difficult thing easier to enter.

The technology available today might give us more ways of explaining, retrieving, and adapting information. It would not change the underlying product responsibility. The customer still needs to know enough to make a decision they understand. And if a simpler interface, clearer copy or deterministic calculator does that job better, I would happily leave the model out of the way.