Early in my product career, I worked on a tool used by market participants to make daily operational decisions in U.S. electricity markets. One afternoon, a customer called – not because something had crashed, but because the product was behaving slightly differently than expected during a rare market condition. The numbers were technically correct. The logic was sound. But the customer hesitated to act.
That moment stuck with me. In energy systems, correctness is necessary, but it isn’t sufficient. If users don’t trust a product enough to rely on it when stakes are high, the product has failed its most important job.
That lesson matters even more today. Climate tech is no longer a niche category. It is becoming core infrastructure. Electricity demand is rising again in the United States after years of relative stagnation, with the IEA now forecasting US power demand to grow at roughly 2% annually from 2025 to 2027, materially above prior expectations. Globally, data center electricity demand is projected to more than double to around 945 TWh by 2030. This is roughly equivalent to the current annual electricity consumption of Japan.
At the same time, the physical systems underpinning the energy transition remain slow, constrained, and deeply regulated. By the end of 2024, nearly 2300 GW of generation and storage capacity was sitting in US interconnection queues according to Lawrence Berkeley National Lab (Figure 1). This reflects enormous momentum, but also the friction involved in turning climate ambition into operational reality.
Product management advice often assumes a world where experimentation is cheap, feedback is instant, and users can switch tools with minimal consequence. Climate and energy technology do not work that way.
In energy systems, feedback cycles stretch across months or even years. Experiments carry real financial and operational risks. Customers are utilities, grid operators, regulators, and asset owners: organizations that prize stability, predictability, and trust over novelty. When a product decision goes wrong, the consequences aren’t limited to churn or a failed experiment; they can include regulatory exposure, revenue loss, or reliability impacts that affect entire regions.
After more than a decade working across power markets, energy analytics, and climate research, I’ve learned that product leadership in climate tech requires a different mindset. The fundamentals of product management still apply, but they must be adapted to environments that are regulated, capital-intensive, and tightly coupled to physical infrastructure.
This article shares lessons I’ve learned building and scaling products in this space. Lessons that will resonate whether you’re already working in climate tech or considering the transition.
You Don’t Ship Features. You Ship Trust
In consumer software, product success is often measured in engagement metrics and adoption curves. In energy and climate tech, success looks different.
A product succeeds when customers trust it enough to use it in high-stakes decisions:
- battery operator bidding into ERCOT during scarcity conditions,
- utility planner evaluating transmission constraints,
- commercial team pricing a long-term service agreement around availability guarantees,
- or an industrial customer deciding whether to invest in on-site generation or storage.
Trust determines whether a product becomes embedded in daily workflows or remains something users double-check before acting.
That trust is built slowly. It comes from predictable behavior, transparent assumptions, and an obvious understanding of the system the product operates within. Customers notice when a tool behaves consistently during edge cases: market shocks, outages, or rule changes and they remember when it doesn’t.
And the environment is only getting more complex. U.S. utility-scale battery storage has grown rapidly, with EIA reporting a 66% increase in battery capacity in 2024, followed by another record 15 GW added in 2025, and plans for 24 GW more in 2026. In ERCOT alone, EIA expects battery capacity to expand from roughly 15 GW in 2025 to 37 GW by the end of 2027.
That growth creates opportunity, but it also raises the bar for product reliability. When software is increasingly involved in dispatch, market participation, warranty enforcement, service guarantees, and outage response, customers need to understand not just what a tool recommends, but why.
That means the best climate-tech products often win not by offering the most features, but by being:
- predictable under edge cases
- transparent about assumptions
- explicit about uncertainty
- auditable when rules change
In this space, trust compounds. Once a product becomes part of a customer’s operational muscle memory, it becomes difficult to displace (Figure 3). But the reverse is also true: one bad failure during a high-stakes event can set adoption back by months or years.
Problem Definition Is Real Product Work
One of the most common mistakes PMs make when entering climate tech is jumping too quickly to solutions.
In regulated systems, solutions are rarely greenfield. Market rules, physical constraints, and compliance requirements define the solution space long before engineering begins. This makes problem definition the most valuable and most underestimated part of product work.
Customers often articulate symptoms rather than root causes. They might ask for better reporting when the real challenge is understanding risk exposure. They might request automation when the underlying issue is uncertainty about how rules will be interpreted in edge cases. Taking requests at face value can lead to feature accumulation without real impact.
Effective climate tech PMs slow the conversation just enough to ask better questions. What decision is the customer actually trying to make? What are they afraid of getting wrong? What constraints are shaping their behavior?
By reframing problems around decisions and outcomes, PMs create space for engineering, analytics, and domain experts to collaborate on solutions that matter.
Prioritization Changes When Experiments Are Expensive
In many technology companies, experimentation is encouraged because the cost of being wrong is low. In energy and climate systems, experimentation often happens in production with real assets and real money on the line. This reality fundamentally changes prioritization.
Frameworks like RICE or MoSCoW are still useful, but they must be supplemented with explicit discussions of downside risk. A feature that looks compelling on paper may introduce operational complexity or regulatory ambiguity that outweighs its upside.
This is increasingly relevant as the power system becomes more dynamic. NERC’s 2025 Long-Term Reliability Assessment warned that summer peak demand forecasts have surged by 224 GW, a 69% increase over the prior forecast, driven in part by data center growth and electrification.
When the grid itself is becoming more volatile, product decisions need to be more conservative about hidden fragility.
In practice, prioritization in climate tech is less about scoring features and more about making tradeoffs visible. When stakeholders understand not only what you’re building, but what you’re choosing not to build-and why-alignment becomes much easier.
Transparency, in this context, is a form of leadership.
Influence Comes from Translation, Not Authority
Product Managers in climate tech rarely have formal authority over outcomes. Instead, they operate at the intersection of engineers, market experts, policy teams, and commercial stakeholders. Each with distinct incentives and mental models.
Engineers think in constraints and failure modes. Market experts think in rules and incentives. Executives think in risk and capital allocation. Customers think in operational accountability. The PM’s value lies in translation.
This translation often looks unglamorous: writing assumptions down, surfacing implicit decisions, and asking questions that feel inconvenient in the moment but prevent costly misalignment later. Over time, this work builds credibility.
Influence follows naturally when stakeholders trust that you understand both their world and the system as a whole. In regulated industries, that trust is far more powerful than positional authority.
Data Matters but Judgment Matters More
Climate and energy products are inherently data rich. Markets generate massive volumes of operational and price data. Assets stream telemetry continuously. Yet data alone rarely provides clear answers.
Energy systems are noisy. Weather variability, policy changes, fuel prices, and market design updates all introduce uncertainty. Waiting for perfect data is rarely an option, and chasing false precision can be actively misleading.
I saw this firsthand while supporting battery storage optimization during ERCOT’s transition to real-time co-optimization (RTC+B). We incorporated weather forecast data into optimization workflows because even small changes in short-term weather could influence renewable generation patterns, price volatility, ancillary service conditions, and therefore the economics of when a battery should charge, hold, or discharge. But the lesson was not that more data automatically creates better decisions. Forecasts update constantly, signal quality changes by horizon, and the commercial value of a forecast depends on how it interacts with market rules and real-time operating constraints. The product challenge was not simply ingesting weather data. It was helping customers distinguish between signal and noise and giving them enough confidence to act when conditions changed quickly. That is the kind of environment where judgment, explainability, and trust matter just as much as analytics.
Strong PMs learn to ask better questions of imperfect data. They focus on directional insight rather than certainty. They are explicit about assumptions and uncertainty, helping stakeholders understand where confidence is high and where judgment is required.
In climate tech, product leadership is as much about informed judgment as it is about analytics.
Build Products for the Long Arc
Another critical mindset shift for PMs in climate tech is time horizon.
Infrastructure, markets, and policy evolve slowly but when they change, the impact is enormous. Products built for this space must be designed to adapt, not just to today’s rules, but to plausible future states of the system.
This perspective influences everything from architecture decisions to feature sequencing. It favors extensibility over short-term optimization and resilience over speed. It also forces PMs to think beyond quarterly metrics and consider how today’s choices constrain tomorrow’s options. Building for the long arc doesn’t mean avoiding iteration. It means iterating with foresight.
Teaching Is a Force Multiplier
One of the least appreciated parts of climate-tech product leadership is education.
This sector has steep learning curves. Engineers may not fully understand market incentives. Commercial teams may underestimate operational constraints. Software teams can miss regulatory nuance. And customers themselves are often adapting to changing rules, new market structures, or unfamiliar asset behavior.
That is why teaching is not a side activity in climate tech. It is part of the product as can be seen in Figure 5.
I learned this firsthand in power markets, where some of the highest-leverage product work I did was not writing requirements or shipping features, but helping people understand how the system itself was changing. Internally, I regularly ran lunch-and-learns for cross-functional teams to explain evolving market design, operational workflows, and how product decisions mapped to real customer risk. Those sessions helped align engineering, commercial, and support teams around the same mental model especially in environments where small misunderstandings could easily turn into roadmap churn, poor customer communication, or avoidable operational mistakes.
Externally, webinars for users were just as important. During periods of market transition, customers were not only looking for product updates, they were looking for interpretation. They wanted to understand what had changed, how those changes affected their assets and strategies, and how to use the product with confidence under the new rules. In those moments, education became a core part of adoption. A feature can be technically sound, but if users do not understand the context around it, they may not trust it enough to act.
That is why strong PMs in climate tech often act as educators as much as builders. Internal explainers, customer webinars, post-launch debriefs, and simple frameworks can all create leverage far beyond the feature itself. In a field where misunderstandings can become contractual, financial, or reliability problems, teaching is core product work.
Closing Thought
Climate tech is not an easy place to build products. The systems are complex, the constraints are real, and progress rarely follows a clean roadmap. Success often comes not from moving fast, but from making the right tradeoffs under uncertainty.
The Product Managers who thrive in this space aren’t the ones who ship the most features. They are the ones who earn trust, frame problems clearly, and understand how technical, regulatory, and human systems interact. They know when to rely on data and when to rely on judgment. They build for resilience, not just velocity.
If you are willing to adapt your craft, climate tech offers something rare: the chance to work on products where correctness matters, context matters, and decisions have consequences beyond the screen. And in a world navigating the energy transition, that kind of product leadership isn’t just valuable, it’s essential.