Field guide / primary domain
31
sources in this field
Updated September 4, 2026
Current thesis
The shortest path to orientation.
This synthesis records claims and practices from the cited sources; reported outcomes and product capabilities have not been independently verified.
Leadership in the AI era hinges on structured question-selection and disciplined problem framing. Most adoption stalls from weak imagination and poor problem definition rather than tooling gaps; teams optimize workflows superficially instead of reconsidering them from first principles. Real blockers are change-management costs—attrition, data-leak risk, token expense, quality degradation—requiring ~5 senior people to jointly commit to bearing that burden. Structured questioning prevents silent misalignment by forcing explicit, testable hypotheses. Three problem-tree types serve distinct purposes: Why-tree uncovers root causes, What-tree sequences workplan and outputs, How-tree ranks options when cause is known. Each requires MECE (mutually exclusive, collectively exhaustive) structure; mixing types is a common failure mode. Inquiry modes—contextual, appreciative, eigenquestion—combined with open-and-close rewrites unlock insights after ~25 questions. Effective analytical prompts explicitly instruct avoidance of filler ('every graphic and word should matter') and let the model choose freely between text and visuals. The formula remains: structured questioning plus disciplined action equals innovation.
Evidence board
Claims worth carrying forward01Aggregating full business context (email, Slack, texts, Notion, meeting notes across 20+ projects/teams) into GPT-6 Astra produced the single most useful AI output the author has experienced—more valuable than the model's visual generation features.
02The high-value prompt was not carefully engineered—it was an unstructured, dictated (Wispr Flow) ramble. This suggests prompt polish matters less than context completeness when the model has full access to a person's work history.
03Effective prompt pattern for high-context analytical requests: explicitly instruct the model to avoid filler ('never fill space for the sake of it... every graphic and word should matter') and let it choose freely between text and charts/visuals based on need.
04Cross-tool context ingestion (previously siloed email, Slack, texts, Notion, meetings) is what surfaces cross-cutting business insights—wasted-time activities, team dependability gaps, skill priorities—that no single tool's data could reveal in isolation.
05Product/business problems often fail early because teams never agree on what problem they're solving — a single ambiguous question can make people simultaneously think about cause, plan, and solution, leading to mismatched answers and weak decisions.
06Three distinct problem-tree types serve different jobs: Why-tree (root: why is X happening; leaves: candidate causes; output: testable hypotheses), What-tree (root: what work does a deliverable require; leaves: analyses/decisions/commitments/artifacts; output: sequenced workplan), How-tree (root: how might we reach a goal; leaves: concrete actions; output: ranked options).
07Choose tree type by what answer is needed: Why-tree when cause is unknown, What-tree when you need to produce a plan, How-tree when the cause is already known and you need options — mixing these up is a common failure mode.
08All three tree types share one structural rule: MECE (branches shouldn't overlap, and together should cover all important areas). This check reveals gaps and redundancies but cannot prove the tree's content is actually true.
Adjacent fields
Latest evidence
Riley Brown
Riley Brown found that feeding GPT-6 Astra complete context across email, Slack, texts, Notion, and meeting notes for 20+ projects—then asking an unstructured, dictated prompt—produced the most useful AI output he's received, outperforming Astra's visual generation capabilities.
George from 🕹prodmgmt.world
A PM skill (mckinsey-issue-tree, part of AI PM OS) applies Minto's Pyramid Principle and consulting-style problem trees (Why/What/How) so teams agree on what problem they're solving before jumping to causes, plans, or solutions, using AI to draft branches and run MECE checks repeatably.
claire vo 🖤
Claire Vo argues AI adoption inside companies stalls due to two human-systems problems: employees lacking the product-management creativity/skills to reimagine workflows, and leaders unwilling to force organizational change despite knowing it's needed. Tools and model intelligence are rarely the actual blocker.
George from 🕹prodmgmt.world
A framework (packaged as an AI PM OS skill) argues product teams fail by answering the first question they encounter rather than treating question-selection as its own discrete task, using wide-exploration techniques and Warren Berger's rewrite methods to surface higher-value questions before ranking and committing to an action.
George from 🕹prodmgmt.world
A framework distinguishing three problem-solving question types—Why (causes), What (work breakdown), and How (actions)—to prevent teams from conflating diagnosis, planning, and solutioning, packaged as an AI-assisted skill in AI PM OS.
George from 🕹prodmgmt.world
A framework for writing decision memos that combines top-down (principles-to-specifics) and bottom-up (evidence-to-conclusions) analysis, comparing them on one page to surface agreement, conflict, and gaps before stating a conclusion-first recommendation.
Dave Kline
Dave Kline describes a shift from ad-hoc 1:1 meetings to a structured 5-minute AI prep routine using five specific prompts, claiming it materially improves meeting quality and outcome clarity.
Aakash Gupta
Hannah Stulberg (DoorDash PM) built a shared git repo where every team function checks in context—call summaries, decision logs, analytics queries—queryable in natural language via Claude Code. Four independent implementations (DoorDash, Pendo, Google, solo) converged on the same 3-layer architecture, suggesting a generalizable pattern for solving institutional knowledge loss.
Dave Kline
Dave Kline describes replacing a perpetually skipped manual Friday review with an AI-assisted workflow that completes in 12 minutes across 5 steps. The post teases the framework but the actual steps are not included in the captured content.
Vijay Iyengar
Vijay Iyengar argues the default weekly 1:1 is structurally flawed because its fixed cadence decouples meetings from actual need. He proposes replacing most 1:1s with public-channel work, real-time escalation, written decision communication, ambient connection, and work-oriented feedback—while preserving 1:1s for onboarding (daily for 2-3 weeks), early-career reports, remote teams, on-demand requests, and peer-manager alignment.