When you add up the "priority lists" from across an organization, demand routinely hits 20x to 50x what delivery teams can complete — and no scoring framework changes that arithmetic. Product coach Rich Mironov laid out that math bluntly in "My Next Word to Retire is 'Prioritization'": you have to say "not now, not later, not ever" to most requests. A ProdPad essay argues that teams who reach for RICE spreadsheets and ranking models are treating a decision-confidence problem as if it were a sorting problem — and hiding uncertainty instead of surfacing it.

The essay's core claim: when PMs, Heads of Product and CPOs say "we need to get better at prioritization," they usually mean some combination of:

• We keep changing our minds
• We can't get stakeholders aligned
• Everything feels important
• We don't trust the decisions we're making
• We don't know how to justify saying no

That is not a prioritization problem. It is a decision-confidence problem, defined here as the ability to make trade-offs explicit and commit to a direction long enough to learn something.

Why do teams trust frameworks they shouldn't?

Frameworks feel safe. A spreadsheet feels calm, numbers feel neutral, scores feel defensible. The essay cites the Honest PM critique "RICE Ain't So Nice," which argues scoring models feel rigorous even when they multiply guesses with wide margins of error.

Two failure modes follow. Teams treat a prioritization score as fact even when instinct screams something is off. Or they adjust scores until the spreadsheet agrees with what they already believed. At worst, they build the wrong thing because the spreadsheet said so. The framework didn't remove bias — it hid it.

Gaming is predictable. When a score determines what gets built, stakeholders learn which levers to pull, PMs learn which numbers get funded, and reach estimates inflate while effort estimates conveniently collapse. The result is "prioritization theater": a backlog full of high-impact items with no evidence. Some teams even refactor the scoring algorithm itself when it disagrees with what they already know is right.

What happens when 'fair' prioritization still kills the product?

The essay's sharpest case comes from Michael Goitein's piece "The One Reason Why Prioritization Frameworks Will Never Work, and What to Do Instead." A team runs fair prioritization across stakeholder requests, gradually packs in more features, and ends up with a bloated product that stops making sense — then collapses and has to be rebuilt. The framework decided between feature requests but never forced the higher-level decision: what kind of product are we building, and what do we refuse to become? Prioritize at the wrong level and you can be very efficient at killing your product.

Saeed Khan makes the adjacent point in "Why You Should Avoid Prioritization Frameworks": if you have so many features that you need a spreadsheet to survive, there is a strategy and clarity problem underneath. Decide which problem you're solving, and most feature debates get easier. Skip that, and the backlog becomes an argument you never finish.

The pattern also shows up in categorical methods. When organizations avoid trade-offs, everything in MoSCoW becomes a "Must." That is not a MoSCoW problem; it is, in the essay's words, "an organizational courage problem."

Is prioritization actually a leadership problem?

If demand genuinely runs 20x ahead of capacity, prioritization stops being a product-team problem and becomes a leadership problem. Mironov's related piece, "Permission to Stay Focused," reframes focus — not ideas — as the scarce resource. Decision confidence means the organization backs a decision and accepts what it costs. If leadership reopens decisions whenever someone complains loudly enough, product teams learn to keep options open and keep everyone vaguely happy. That is motion, not progress.

Mironov has also been explicit about the politics: product leaders treat prioritization as analytical, while other executives experience it as negotiation and power — a tension he lays out in a guest piece published by Product Focus.

What actually builds decision confidence?

The essay prescribes unglamorous inputs: clear strategy, shared understanding of the problem, customer evidence, alignment on what success looks like, and leaders who back decisions long enough to learn. Estimation still helps — as a stake in the ground that triggers questions like "what's driving that impact number?" and "what would have to be true for this to pay off?" — not as a replacement for judgment.

Two practical mechanisms stand out. Decision logs preserve context, show reasoning, and distinguish learning from thrashing, cutting the churn that masquerades as prioritization pain. And Now-Next-Later roadmapping — the framework the author created — forces commitment without faking certainty: Now is what you're confident enough to build today, Next is what you believe comes after, Later stays deliberately vague because pretending certainty that far out is dishonest.

Annie Duke's writing on communicating uncertainty adds the final nuance: you can admit what you don't know without undermining trust, as long as you're clear about your reasoning and what you're doing to reduce unknowns. The test the essay leaves PMs with: if you can't explain why you're choosing something without pointing to a number, that's a smell. As product teams face more bets and less predictability, the differentiator is shifting from who runs the best scoring ritual to who can name a trade-off, own it, and hold it long enough to learn.