Valuing new-to-world investments
Building a shared framework for deciding what's worth investing in — when the thing has never existed.
This case study has been deliberately masked. Specific metrics, thresholds, figures, names, and organizational details have been removed to protect confidential information. What remains is the shape of a problem I worked on and how I thought about it.
The problem
An organization making new-to-world product bets kept hitting the same friction: there was no shared, written definition of what made a new investment "worth it." Proposals bounced — reworked again and again against a bar nobody had actually articulated. Underneath the process pain sat a genuinely hard question: how do you value something that has never existed? There are no comparables, adoption is a guess, and every figure in the model is an assumption stacked on another assumption.
How I framed it
I treated it as a trade-off, not a formula — return against risk, and the near term against the long term. A single return metric (an IRR, say) is precise math on invented inputs; on its own it flatters short-term thinking and punishes the slow-then-steep curve most new categories actually follow. So the question I kept asking wasn't "what's the number?" but "is the upside big enough, and are we willing to fund the uncertainty long enough to find out?"
What I did
I evaluated several candidate approaches, weighed what each one captured and what it quietly ignored, and recommended a small, combined view rather than any single "golden arrow." Two design constraints mattered as much as the math: it had to be simple enough for non-finance partners to actually use, and honest enough to say what it couldn't tell you. A framework only creates value if people adopt it and trust it. The real deliverable wasn't a spreadsheet — it was a shared language and an upfront definition of "good," so a hard call could be made faster and with less subjectivity.
The honest caveat
This isn't the "right" answer. It's one approach, at one point in time, meant to be argued with and revised. What I keep coming back to is building the mental models that let a team reason together about something genuinely uncertain — and then holding them loosely enough to change when the evidence does.
