The hidden decision criteria killing your best ideas - and the methodology built to expose it.
Every large organization has plenty of people who can say no. Very few who can say yes.
When accountability is diffuse and failure is visible, the rational move is to block rather than champion. Good ideas don't die from undisclosed judgment.
Rachel Kuhr Conn, Founder & CEO at Productable, spent two decades uncovering this pattern across radically different contexts: Caterpillar factory floors, tribal villages in Papua New Guinea, Shark Tank boardrooms, and Air Force innovation programs.
The surface details changed every time, but the underlying problem didn't.
Hidden decision criteria, the unspoken requirements that kill projects nobody knew were deal-breakers, showed up everywhere.
There's a counterintuitive principle in research: the best methodology comes from extreme environments. The military. Rural developing-world communities. High-stakes manufacturing. These contexts aren't representative, but they are useful because the problems are undeniable.
Stress-testing in extremes exposes root causes that lighter environments let you ignore.
For example, when Conn took a women's health product to tribal Papua New Guinea, a handmade pad solution designed to solve an access problem, the extreme environment immediately invalidated the core assumption. The villages were surrounded by donated secondhand clothing. Access wasn't the issue - privacy was. Everyone could see everything.
The solution that emerged, called Secret Moon, disguised products as ordinary household items, wrapped together with a compact umbrella and a baby diaper. They looked like something else entirely.
A few years later, a major consumer brand ran an ad where a tampon disguised as a sugar packet accidentally hit the table. The latent need for discreetness had finally surfaced in a developed market. Conn had found it years earlier, in a place where it couldn't hide.
From Caterpillar to Papua New Guinea to Shark Tank, the same pattern kept reappearing:
Most organizations treat these as separate problems. Conn argues they're the same problem at different scales.
When Conn joined Cuban's team, she found a system built around one discipline: progress toward the next stage of work.
Three questions drove everything:
This outcome-based accountability was applied across a wildly different portfolio - AI firms, cookie brands, bike companies - and it worked because the underlying stages of development were universal, even when the industries weren't.
Most organizations do the opposite: commit large, commit early, and defend the commitment. Defense acquisitions pick billion-dollar programs before validating basic assumptions.
But a portfolio approach works differently. Multiple solutions compete against the same outcome. You test small, early, and cheap. You filter fast. You double down on what shows traction and redirect what doesn't.
Most things don't work at the beginning. Most things that eventually work looked unpromising at first. Conn says that the job is to create conditions where winners can emerge, without overinvesting in or eliminating them too soon.
This is the problem Productable was built to solve. Born from academic research in predictive analytics for innovation success, stress-tested across extreme environments, and refined through partnerships with hundreds of companies, Productable turns what most innovation programs treat as philosophy into an operational system.
Three learning loops drive the Productable system:
Expected results:
The ask, from Shark Tank entrepreneurs, Air Force airmen, and innovation leaders at large corporations, turns out to be identical:
Tell me what my stakeholders actually want, make it clear, and hold them accountable to it.
That's a methodology problem. And methodology is exactly what Productable provides.

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