Transitioning into NatureTech has introduced me to a range of new and complex challenges, but I find an interesting symmetry between my past and present. Our team at Revalue is deeply multidisciplinary, spanning software engineering, geospatial analysis, ecology, social science, and economics. Our team is charting new territory at the intersection of all these fields. Amidst the myriad complexities, one common thread has emerged - the deep need among domain experts to do things “the right way”.
This drive comes from a good place. Mistakes, maintenance burdens, and professional criticism all stem from cutting corners. Many of our domain experts, often from top-tier professional backgrounds, have cultivated a culture of craftsmanship and rigour. Doing things the wrong way leads to real pain, and they have all felt it in the past, so they optimise to avoid it.
But as any engineering leader with some experience under their belt will tell you - the obsessive pursuit of perfection often spirals into waste and inaction. Companies miss their window for impact, spending precious runway chasing an elusive ideal. Engineering teams polish products that don’t solve real needs. Scientists accustomed to academia often struggle to adapt to the pace of the industry.
Entire books have been written about why it’s better to build the right thing the wrong way than the wrong thing the right way. As engineering managers, we’ve seen teams spend months gold-plating features no one uses. Similarly, moving from academia to industry, many scientists are dismayed to find their role has changed. Unlike in a research post, invalidating a method is no longer considered a success - either it works, or it doesn’t, and if it doesn’t, your job is to pivot away from this line of research and find one that does.
Optimising for Lean Sufficiency
Our vision At Revalue, to see nature regenerated at a planetary scale, is predicated on successfully preventing deforestation where it happens and restoring as many natural ecosystems as possible. Achieving this requires efficiently validating project ideas, killing the unrealistic ones to focus on those with real potential. How many dead ends we hit doesn’t matter - what counts is how many viable projects we steward into being.
Lean Sufficiency in Engineering
This is where the power of sufficiency comes in. As leaders, our role is to help domain experts identify the minimum viable level of effort necessary to validate an idea and reach the next phase. To achieve this in product engineering, we often preach to software developers about lean principles, encouraging them to find the bare minimum level of effort necessary to deliver value. The mistake many leaders make, myself included, is neglecting to clarify the difference between “lean code” and the more important concept of a “lean solution.”
Lean code eschews documentation and testing - and inevitably creates technical debt. But a lean solution tightly scopes our work to what’s needed to validate an idea and guide the next iteration. This requires engineers to collaborate closely with stakeholders to identify the key decision points where the team determines whether to stay the course or pivot.
Lean Sufficiency in Science
Scientists face a similar challenge when pushed to streamline their assessments. The pressure to be leaner can make them feel like they’re compromising their standards, leading to lower integrity output. In truth, we ask of them the same as we do of our product engineers, to ask themselves - at what point does additional rigour become over-analysis? - and adjust accordingly.
The key has been, it seems, in asking them define sufficiency differently at each project stage - what’s adequate for validating the fundamental potential value of a project? what’s adequate when validating viability? or feasibility? Each phase requires incrementally deeper analysis and higher costs, but lower-stage assessments aren’t less rigorous - they just provide a lower confidence level in the project’s ultimate success. By mapping out these tiers of sufficiency, scientists can optimise their efforts while maintaining integrity.
Lean Thinking is a Transferrable Multidisciplinary Skill
Over the past year, our multidisciplinary team has focused on developing a robust methodology and supporting technology to efficiently assess carbon projects’ viability, feasibility, and path to commercialisation. By defining clear decision points and analyses for each validation phase, we can quickly filter out low-potential projects and strategically allocate resources.
To support our cross-functional assessment team, our engineering team has developed a platform that not only facilitates the necessary analyses but also provides a level of transparency and interactivity that traditional tools and practices in our industry often lack. This platform allows stakeholders to explore project data and insights more flexibly, enabling them to ask probing questions and gain valuable perspectives without requiring scientists to repeatedly redo analyses.
Achieving this while maintaining scientific and commercial integrity required a rare feat of multidisciplinary collaboration. Crafted along agile and lean principles, we run an iterative multidiscipline product discovery process, bringing together engineering, product, landscape analytics, ecological assessment, scientific, and commercial stakeholders. Each week, these stakeholders gather to realign on the current state of our project assessment technology, discuss the next set of product requirements and agree on the level of sufficiency necessary to keep things as lean but as high integrity as possible. The product engineering team then spends the week learning through delivery of the next slice, and the process repeats.
As a tech leader, I’m struck by the parallels in how scientists and product engineers approach their work. Even more exciting is seeing how many hard-won lessons in managing engineering teams translate powerfully to other domains. While the details may differ, the core challenge is the same - harnessing expertise to rapidly deliver real-world impact through structured cycles of validation.
By relentlessly asking ourselves, “What is sufficient?”, we can channel deep knowledge to solve huge problems.