Menú
SkillsMP ha recopilado 41 skills de deanpeters/ai-product-operating-model-skills. Abre una skill para revisar su origen y sus detalles.
Mostrando 40 de 41 skills recopiladas.
Diagnose missing, stale, conflicting, inaccessible, excessive, sensitive, or untrusted context and recommend the next source, package, data, or lifecycle intervention.
Idioma del texto original: inglés
Assess whether data is fit for a specific AI product decision across provenance, quality, access, representativeness, consent, privacy, freshness, and operations.
Idioma del texto original: inglés
Recommend the product, model, workflow, human, and production evaluations needed for an AI decision, based on behavior, consequences, evidence gaps, and lifecycle stage.
Idioma del texto original: inglés
Diagnose AI portfolio imbalance, premature scaling, weak evidence, vendor exposure, and zombie pilots; recommend where to explore, validate, scale, pause, or stop.
Idioma del texto original: inglés
Design preventive controls, detection, triage, containment, rollback, communication, investigation, remediation, learning, and reporting for AI incidents.
Idioma del texto original: inglés
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 del texto original: inglés
Assign human decision rights, accountability, review, contribution, escalation, and evidence duties for a material AI product or recurring operating decision.
Idioma del texto original: inglés
Measure whether AI operating practices change behavior, decisions, workflows, reuse, outcomes, burden, and risk rather than merely increasing activity.
Idioma del texto original: inglés
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 del texto original: inglés
Define expected, acceptable, and prohibited AI behavior with representative cases, thresholds, escalation, and consequences. Use before evaluation, launch, or autonomy decisions.
Idioma del texto original: inglés
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 del texto original: inglés
Diagnose role capability, applied learning, workflow adoption, stewardship, and outcome evidence; recommend the next capability-building motion rather than more generic training.
Idioma del texto original: inglés
Design how AI context is created, retrieved, refreshed, versioned, reconciled, retained, expired, excluded, and retired for a recurring purpose.
Idioma del texto original: inglés
Assemble a bounded, reusable context package with purpose, authoritative sources, constraints, decisions, examples, exclusions, and refresh rules. Use for recurring AI-assisted work.
Idioma del texto original: inglés
Redesign a recurring product decision cycle around evidence, context, human judgment, AI assistance, authority, feedback, and measurable learning.
Idioma del texto original: inglés
Build an evidence-aware economic case for an AI investment across value, full lifecycle cost, uncertainty, alternatives, risk, and decision thresholds.
Idioma del texto original: inglés
Define calibrated AI evaluation metrics, rubrics, judges, thresholds, sampling, uncertainty, ownership, and decision rules tied to behavior and consequences.
Idioma del texto original: inglés
Build a governed, representative AI evaluation set with provenance, expected behavior, edge cases, affected groups, adjudication, versioning, and limits.
Idioma del texto original: inglés
Review one AI initiative across value, economics, dependencies, workflow, context, evaluation, governance, controls, capability, and recovery before a material decision.
Idioma del texto original: inglés
Define evidence-based entry, continuation, pivot, scale, pause, and stop decisions for AI investments without turning gates into document approval theater.
Idioma del texto original: inglés
Design a role-based applied learning system with real work, practice, coaching, peer feedback, progression, reinforcement, stewardship, and outcome evidence.
Idioma del texto original: inglés
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 del texto original: inglés
Design and test a bounded AI product operating-model change across decisions, workflows, context, evidence, governance, capability, ownership, and adoption.
Idioma del texto original: inglés
Review whether the AI product operating model improves decisions and outcomes, identify systemic friction and performative activity, and choose the next changes.
Idioma del texto original: inglés
Convert evidence-based operating-model findings into an owned 30-, 90-, 180-, and 365-day sequence of interventions, learning milestones, dependencies, and decisions.
Idioma del texto original: inglés
Frame an AI product opportunity around an evidenced condition, affected actors, consequences, alternatives, and the uncertainty that should be tested next.
Idioma del texto original: inglés
Map how AI behavior may change user behavior, product outcomes, economic value, and risk while exposing causal assumptions and countermeasures.
Idioma del texto original: inglés
Run a recurring AI portfolio review that reallocates capital and capacity using strategy, outcomes, economics, readiness, production evidence, dependencies, and learning.
Idioma del texto original: inglés
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 del texto original: inglés
Map a recurring product-team motion through decisions, actors, inputs, handoffs, delays, rework, and failure before assigning AI and human responsibilities.
Idioma del texto original: inglés
Assemble current, audience-appropriate evidence about an AI product's purpose, behavior, limits, evaluations, controls, ownership, incidents, and change history.
Idioma del texto original: inglés
Compare AI opportunities across outcome value, evidence, feasibility, responsibility, readiness, and reversibility to recommend explore, validate, defer, or reject.
Idioma del texto original: inglés
Identify which product-team decision or productive workflow should be redesigned with AI first, based on outcome value, friction, evidence, consequence, and readiness.
Idioma del texto original: inglés
Turn a tested human-AI workflow into an inspectable playbook with context, roles, decisions, examples, controls, measures, fallback, and improvement ownership.
Idioma del texto original: inglés
Define which sources are authoritative for a purpose, who owns them, who may use them, how conflicts resolve, and when trust expires.
Idioma del texto original: inglés
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 del texto original: inglés
Diagnose the most consequential missing AI governance condition across ownership, authority, controls, escalation, oversight, and trust evidence; recommend the next motion.
Idioma del texto original: inglés
Define observable AI product competencies by role and proficiency level, tied to real decisions, evidence, practice, and progression rather than generic tool fluency.
Idioma del texto original: inglés
Convert a proven, improved workflow into a governed, reusable skill with context, decisions, examples, guardrails, evaluations, ownership, and maintenance rules.
Idioma del texto original: inglés
Turn an evidence-based AI product thesis, portfolio choices, outcomes, boundaries, and learning into a clear narrative for aligned organizational action.
Idioma del texto original: inglés