Instead of forcing yourself to complete every planned feature, treat decision fatigue as a signal. If you consistently let a feature "die on the vine" because you lack the energy for it, it's likely not a priority for you or the market. This reframes a negative feeling into a useful prioritization tool.

Even with AI accelerating development, a PM's core role is managing what *isn't* being built. The ability to calculate Total Cost of Ownership (TCO) and strategically say "no" is more critical than ever, as even quickly-built features have long-term costs that displace other opportunities.

The cost of keeping a feature isn't just server time or bug fixes; it's the innovation you're sacrificing. By forcing the team to name the specific, valuable project they cannot pursue because of this maintenance burden, you make the opportunity cost real and immediate.

Avoid the paralysis of a "kill list" by managing features through a defined lifecycle. Every feature should be in an active phase: 'Exploring' (testing), 'Exploiting' (scaling), or 'Sunsetting' (managed decline). A feature you can't place in a phase is in limbo and draining resources.

Indecision is more damaging than a bad decision because it doesn't just waste time; it dramatically reduces the team's available options. Delaying a hard choice (e.g., on a compliance issue) eats up the time needed to develop creative workarounds, forcing last-minute cuts to essential elements.

Instead of creating an intermediate, 'half-step' product, commit to the harder but optimal solution if a plausible path exists. This avoids the wasted effort and sunk cost fallacy of a circuitous development path, even though it requires more upfront investment and conviction.

Entrepreneurs often get stuck at crossroads, fetishizing keeping their options open. This is more dangerous than making a wrong decision. A bad choice provides quick feedback and a chance to learn, whereas an unmade decision can lead to indefinite paralysis, consuming time and energy without any progress.

Averages lie. A feature with low overall adoption might be critical for a valuable niche of power users. Killing it based on a surface-level '3% usage' metric, without understanding its importance to that cohort, can alienate your most dedicated customers and create unforeseen negative effects.

The question itself reveals a systemic failure. The real problem isn't the feature, but a lack of upfront validation and product discipline before the feature was ever built. The focus should be on pre-build evidence, not post-build justification.