When research stalls, the bottleneck is often not the methodology or recruiting but a lack of internal consensus on the target audience. The first step should always be audience definition. If the team can't agree, then the initial research project must be to define and validate the audience itself.

When facing ambiguity, the best strategy is not to wait for perfect information but to engage in "sense-making." This involves taking small, strategic actions, gathering data from them, and progressively building an understanding of the situation, rather than being paralyzed by analysis.

To effectively influence partners, you must understand their priorities. A scrappy research method is to watch their executives' public interviews or internal all-hands meetings. This reveals their strategic goals and allows you to frame your proposal in their language, increasing its resonance.

To avoid stakeholders undermining research results later ('you only talked to 38 people'), proactively collaborate with them before the study to define the minimum standard of rigor they will accept. This alignment shifts the conversation from a post-mortem critique to a pre-launch agreement, disarming future objections.

Instead of complex prioritization frameworks like RICE, designers can use a more intuitive model based on Value, Cost, and Risk. This mirrors the mental calculation humans use for everyday decisions, allowing for a more holistic and natural conversation about project trade-offs.

In AI, low prototyping costs and customer uncertainty make the traditional research-first PM model obsolete. The new approach is to build a prototype quickly, show it to customers to discover possibilities, and then iterate based on their reactions, effectively building the solution before the problem is fully defined.

While research is vital, there's a point of diminishing returns. Over-researching can lead to 'analysis paralysis' by revealing too many edge cases and divergent needs, ultimately stalling the momentum required to build and launch a new product.

Before starting a project, ask the team to imagine it has failed and write a story explaining why. This exercise in 'time travel' bypasses optimism bias and surfaces critical operational risks, resource gaps, and flawed assumptions that would otherwise be missed until it's too late.

Instead of arguing for more time, product leaders should get stakeholder buy-in on a standardized decision-making process. The depth and rigor of each step can then be adjusted based on available time, from a two-day workshop to an eight-month study, without skipping agreed-upon stages.