Menu
O SkillsMP coletou 41 skills de deanpeters/ai-product-operating-model-skills. Abra uma skill para revisar a origem e os detalhes.
Mostrando 40 de 41 skills coletadas.
Diagnose missing, stale, conflicting, inaccessible, excessive, sensitive, or untrusted context and recommend the next source, package, data, or lifecycle intervention.
Idioma do texto original: inglês
Assess whether data is fit for a specific AI product decision across provenance, quality, access, representativeness, consent, privacy, freshness, and operations.
Idioma do texto original: inglês
Recommend the product, model, workflow, human, and production evaluations needed for an AI decision, based on behavior, consequences, evidence gaps, and lifecycle stage.
Idioma do texto original: inglês
Diagnose AI portfolio imbalance, premature scaling, weak evidence, vendor exposure, and zombie pilots; recommend where to explore, validate, scale, pause, or stop.
Idioma do texto original: inglês
Design preventive controls, detection, triage, containment, rollback, communication, investigation, remediation, learning, and reporting for AI incidents.
Idioma do texto original: inglês
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.
Idioma do texto original: inglês
Assign human decision rights, accountability, review, contribution, escalation, and evidence duties for a material AI product or recurring operating decision.
Idioma do texto original: inglês
Measure whether AI operating practices change behavior, decisions, workflows, reuse, outcomes, burden, and risk rather than merely increasing activity.
Idioma do texto original: inglês
Define evidence-based boundaries for what an AI system may do independently, with human approval, or never. Use before launch, scaling, or increasing AI authority.
Idioma do texto original: inglês
Define expected, acceptable, and prohibited AI behavior with representative cases, thresholds, escalation, and consequences. Use before evaluation, launch, or autonomy decisions.
Idioma do texto original: inglês
Turn an AI idea into an owned investment hypothesis with outcomes, economics, constraints, evidence, and a next learning test. Use before funding or expanding an initiative.
Idioma do texto original: inglês
Diagnose role capability, applied learning, workflow adoption, stewardship, and outcome evidence; recommend the next capability-building motion rather than more generic training.
Idioma do texto original: inglês
Design how AI context is created, retrieved, refreshed, versioned, reconciled, retained, expired, excluded, and retired for a recurring purpose.
Idioma do texto original: inglês
Assemble a bounded, reusable context package with purpose, authoritative sources, constraints, decisions, examples, exclusions, and refresh rules. Use for recurring AI-assisted work.
Idioma do texto original: inglês
Redesign a recurring product decision cycle around evidence, context, human judgment, AI assistance, authority, feedback, and measurable learning.
Idioma do texto original: inglês
Build an evidence-aware economic case for an AI investment across value, full lifecycle cost, uncertainty, alternatives, risk, and decision thresholds.
Idioma do texto original: inglês
Define calibrated AI evaluation metrics, rubrics, judges, thresholds, sampling, uncertainty, ownership, and decision rules tied to behavior and consequences.
Idioma do texto original: inglês
Build a governed, representative AI evaluation set with provenance, expected behavior, edge cases, affected groups, adjudication, versioning, and limits.
Idioma do texto original: inglês
Review one AI initiative across value, economics, dependencies, workflow, context, evaluation, governance, controls, capability, and recovery before a material decision.
Idioma do texto original: inglês
Define evidence-based entry, continuation, pivot, scale, pause, and stop decisions for AI investments without turning gates into document approval theater.
Idioma do texto original: inglês
Design a role-based applied learning system with real work, practice, coaching, peer feedback, progression, reinforcement, stewardship, and outcome evidence.
Idioma do texto original: inglês
Assess AI product operating-model maturity across seven categories using evidence, disagreement, and critical-gap logic. Use to identify consequential gaps and next interventions.
Idioma do texto original: inglês
Design and test a bounded AI product operating-model change across decisions, workflows, context, evidence, governance, capability, ownership, and adoption.
Idioma do texto original: inglês
Review whether the AI product operating model improves decisions and outcomes, identify systemic friction and performative activity, and choose the next changes.
Idioma do texto original: inglês
Convert evidence-based operating-model findings into an owned 30-, 90-, 180-, and 365-day sequence of interventions, learning milestones, dependencies, and decisions.
Idioma do texto original: inglês
Frame an AI product opportunity around an evidenced condition, affected actors, consequences, alternatives, and the uncertainty that should be tested next.
Idioma do texto original: inglês
Map how AI behavior may change user behavior, product outcomes, economic value, and risk while exposing causal assumptions and countermeasures.
Idioma do texto original: inglês
Run a recurring AI portfolio review that reallocates capital and capacity using strategy, outcomes, economics, readiness, production evidence, dependencies, and learning.
Idioma do texto original: inglês
Run a recurring review of AI behavior, workflow, human, outcome, control, incident, and affected-party evidence to continue, change, constrain, roll back, or retire.
Idioma do texto original: inglês
Map a recurring product-team motion through decisions, actors, inputs, handoffs, delays, rework, and failure before assigning AI and human responsibilities.
Idioma do texto original: inglês
Assemble current, audience-appropriate evidence about an AI product's purpose, behavior, limits, evaluations, controls, ownership, incidents, and change history.
Idioma do texto original: inglês
Compare AI opportunities across outcome value, evidence, feasibility, responsibility, readiness, and reversibility to recommend explore, validate, defer, or reject.
Idioma do texto original: inglês
Identify which product-team decision or productive workflow should be redesigned with AI first, based on outcome value, friction, evidence, consequence, and readiness.
Idioma do texto original: inglês
Turn a tested human-AI workflow into an inspectable playbook with context, roles, decisions, examples, controls, measures, fallback, and improvement ownership.
Idioma do texto original: inglês
Define which sources are authoritative for a purpose, who owns them, who may use them, how conflicts resolve, and when trust expires.
Idioma do texto original: inglês
Define AI responsibilities, human judgment, review, decision authority, escalation, and learning in a recurring workflow. Use when human-AI collaboration is vague or unreliable.
Idioma do texto original: inglês
Diagnose the most consequential missing AI governance condition across ownership, authority, controls, escalation, oversight, and trust evidence; recommend the next motion.
Idioma do texto original: inglês
Define observable AI product competencies by role and proficiency level, tied to real decisions, evidence, practice, and progression rather than generic tool fluency.
Idioma do texto original: inglês
Convert a proven, improved workflow into a governed, reusable skill with context, decisions, examples, guardrails, evaluations, ownership, and maintenance rules.
Idioma do texto original: inglês
Turn an evidence-based AI product thesis, portfolio choices, outcomes, boundaries, and learning into a clear narrative for aligned organizational action.
Idioma do texto original: inglês