deanpeters

Map evidence-backed growth options across the Ansoff Matrix with risk-rated sequencing. Use when the question is where the next tranche of growth comes from, and at what risk.

The protocol behind every investigation skill. Use when AI research must proceed without you: search-plan gate, Fact/Inference/Assumption labels, confidence stacking, diffable outputs.

Research and draft a competitive battle card from public evidence — every claim labeled and sourced. Use when a rep needs a field-action card, not a research report.

Research a company, industry, or competitor set using web search and seven analytical lenses. Use when you need structured intel that feeds downstream PM skills.

Orchestrate a complete competitive analysis across six steps, from landscape to strategic direction. Use when you need the full picture, not a single scan or card.

Scheduled delta monitoring against a prior competitive snapshot. Use when tracking competitors on a cadence: material shifts only, cited evidence, battle-card update flags, runs unattended.

Research a competitive landscape with cited snapshots, a comparison matrix, and so-what implications. Use when a product decision needs competitive grounding, not a market report.

Triage a competitive or market question into the right intelligence disciplines, cadence, and executing skill. Use when you know something needs researching but not which channel to run.

Diagnose missing, stale, conflicting, inaccessible, excessive, sensitive, or untrusted context and recommend the next source, package, data, or lifecycle intervention.

Assess whether data is fit for a specific AI product decision across provenance, quality, access, representativeness, consent, privacy, freshness, and operations.

Recommend the product, model, workflow, human, and production evaluations needed for an AI decision, based on behavior, consequences, evidence gaps, and lifecycle stage.

Diagnose AI portfolio imbalance, premature scaling, weak evidence, vendor exposure, and zombie pilots; recommend where to explore, validate, scale, pause, or stop.

Design preventive controls, detection, triage, containment, rollback, communication, investigation, remediation, learning, and reporting for AI incidents.

Turn scattered AI ambition into an evidence-aware product strategy thesis with choices, boundaries, outcomes, assumptions, and next bets. Use when direction or non-goals are unclear.

Assign human decision rights, accountability, review, contribution, escalation, and evidence duties for a material AI product or recurring operating decision.

Measure whether AI operating practices change behavior, decisions, workflows, reuse, outcomes, burden, and risk rather than merely increasing activity.

In this physical variant on brainstorming, participants use their body and inexpensive materials or objects to role-play and mimic interaction with a system, product, or experience.

This hands-on activity allows participants to communicate and document their mental model and how they think about a set of information, creating a logical structure (e.g., relationships, sequences, timing).

A fast way to capture and prioritize stakeholders.

A framework based on “influence channels” that shape and define an organization’s culture.

Visually synthesize and communicate a user’s end-to-end experience through actions, pain points, wins, and opportunities in a phased process.

Focus the power of brainstorming using a structured, visual representation of ideas—pushing you to fill out every box with new ideas.

Generate insights into relationships between concepts in a flexible and engaging way to visualize information.

Develop clear statements about your organization’s future aspirations and present activities.

Generate creature acronyms that name PM dysfunctions, blockers, or winning strategies. Use when you want to give your team shared vocabulary for the animals hiding in your product work.