AI readiness scorecard
A scoring worksheet for deciding whether a workflow is ready for autonomous or semi-autonomous execution.
Área Baciu.com
A decision tree for routing between models, cached answers, degraded mode, escalation, and temporary shutdown.
Começamos pelo processo, usuários e falhas antes de escolher a arquitetura mensurável mais simples.
Ver páginaUm bom sistema registra fontes, avaliações, telemetria e regras claras de escalonamento.
Ver páginaExpansão do tema
A scoring worksheet for deciding whether a workflow is ready for autonomous or semi-autonomous execution.
Ver páginaA control matrix that maps AI capability scope to data access, tool authority, approvals, logging, and incident response.
Ver páginaA starter evaluation set for testing source grounding, citation behavior, permission boundaries, and answer quality.
Ver páginaA production runbook for model routing, fallback, cost controls, latency, tracing, degraded mode, and release review.
Ver páginaA board-ready outline for connecting AI initiatives to outcomes, risk gates, build sequence, and decision cadence.
Ver páginaA tabletop exercise for AI services that can produce wrong answers, unsafe actions, policy violations, or outage cascades.
Ver páginaA practical operating model for assigning ownership across AI product, platform, risk, operations, and business teams.
Ver páginaA structured intake template for deciding whether a process should become an assistant workflow, agent workflow, or deterministic automation.
Ver páginaUse this document as the starting point for a workshop, operating review, or delivery handoff.
Resource library
Use these outlines as starting points for assessments, runbooks, governance reviews, and executive planning.
A scoring worksheet for deciding whether a workflow is ready for autonomous or semi-autonomous execution.
A control matrix that maps AI capability scope to data access, tool authority, approvals, logging, and incident response.
A starter evaluation set for testing source grounding, citation behavior, permission boundaries, and answer quality.
Atlas de entrega
Filtre, compare e abra páginas detalhadas de arquitetura, execução e governança de IA.
Biblioteca de implementação
A finance model for attributing AI runtime cost by workflow, department, customer segment, provider, and outcome.
A communications plan for AI incidents covering internal escalation, customer updates, regulatory notice, and postmortems.
A practical operating model for assigning ownership across AI product, platform, risk, operations, and business teams.
A review outline for documenting AI data handling, retention, subprocessors, residency, and customer control requirements.
A tabletop exercise for AI services that can produce wrong answers, unsafe actions, policy violations, or outage cascades.
A scoring worksheet for deciding whether a workflow is ready for autonomous or semi-autonomous execution.
A service-level objective template for AI latency, quality, cost, availability, escalation, and degraded-mode behavior.
A risk register for tracking AI authority, reversibility, sensitive data exposure, failure modes, mitigations, and owners.
A dashboard outline for monitoring provider mix, cost drift, latency budgets, fallback rates, and quality regressions.
A source inventory for mapping owners, freshness, permissions, quality issues, retention rules, and ingestion priority.
A release-gate template that connects evaluation results, known regressions, approval decisions, rollback, and launch notes.
A board-ready outline for connecting AI initiatives to outcomes, risk gates, build sequence, and decision cadence.
A steering-committee packet for connecting AI portfolio decisions to milestones, risks, spend, and operating outcomes.
A control matrix that maps AI capability scope to data access, tool authority, approvals, logging, and incident response.
A policy template for defining which AI decisions require approval, who approves them, and what evidence is required.
A production runbook for model routing, fallback, cost controls, latency, tracing, degraded mode, and release review.
A workbook for translating organizational roles into retrieval, tool-use, approval, logging, and audit permissions.
An adoption plan for moving AI services from launch to measurable usage, feedback, training, and continuous improvement.
A handoff checklist for moving AI systems from delivery into operated services with owners, runbooks, controls, and evidence.
A release review checklist for prompt, policy, model, and tool changes before they reach production users.
A scenario catalog for testing prompt injection, unsafe tool use, data leakage, policy bypass, and recovery behavior.
An audit worksheet for checking cited answers against source text, permissions, freshness, and reviewer corrections.
A starter evaluation set for testing source grounding, citation behavior, permission boundaries, and answer quality.
An ownership map for knowledge sources, refresh cadence, permission rules, source quality, and escalation contacts.
A technical specification for AI-callable tools covering schema, permissions, idempotency, retries, and audit trails.
A review worksheet for validating AI-callable tool scopes, sensitive actions, audit trails, and approval thresholds.
A scorecard for comparing model and platform vendors across quality, latency, cost, security, support, and lock-in risk.
A calculator outline for estimating automation value from cycle time, error rate, labor mix, risk reduction, and adoption.
A structured intake template for deciding whether a process should become an assistant workflow, agent workflow, or deterministic automation.
Downloadable implementation outlines for teams planning, evaluating, governing, and operating production AI systems.
A services practice for organizations that need AI systems designed, evaluated, shipped, and operated with accountability.
Use-case patterns for access requests, entitlement review, policy checks, approval packets, and identity-workflow support.
Permission models for deciding what agents may read, draft, recommend, approve, execute, and escalate.
Release patterns for moving agents from prototype to monitored, supported, measurable production services.
Design and enablement solutions for defining agent behavior, permissions, tests, release controls, and handoff workflows.
Sandbox environments for validating agent behavior against realistic data, tools, edge cases, and failure modes.
Laboratório de execução
Ajuste ritmo, autonomia e perfil de risco para ver fases, dependências e controles recomendados.
Fases recomendadas
Não há recuperação sem disciplina de origem
A confiança é uma característica do produto
Ação com responsabilidade
Cada lançamento ganha confiança
Controle onde o trabalho acontece
As equipes do cliente podem operar de forma independente
Radar de capacidades
Escolha uma perspetiva operacional e um horizonte para visualizar trilhas, sinais e páginas de decisão relacionadas.
Trilhas prioritárias
AI spend tied to operating value
Abrir páginaEstratégia com caminho de implementação
Abrir páginaGovernança no ciclo de entrega
Abrir páginaEntrega projetada para propriedade durável
Abrir páginaControle onde o trabalho acontece
Abrir páginaPlano de execução
Cada área é entregue com definição explícita, validação mensurável e governança operacional herdável pela equipa do cliente.
Checklist operacional
A clear system map covering models, tools, data, workflows, users, and failure modes.
Ver páginaTask sets, regression checks, and release criteria for measurable AI behavior.
Ver páginaHuman approval, access, logging, data-boundary, and incident-response rules.
Ver páginaDocumentation and ownership so the client can operate the system after launch.
Ver páginaComece com fluxos de trabalho repetitivos e reversíveis onde os resultados e os limites das falhas podem ser medidos.
Use conjuntos de avaliações, cenários adversários e critérios explícitos de aprovação/rejeição vinculados ao impacto nos negócios.
Com limites de autoridade, limites de confiança, pacotes de escalonamento e rastreamentos de execução completos.
Trate o modelo e solicite alterações como versões: teste, revise, aprove e implemente com caminhos de reversão.
Mapa de cobertura
A scoring worksheet for deciding whether a workflow is ready for autonomous or semi-autonomous execution.
Ver páginaA control matrix that maps AI capability scope to data access, tool authority, approvals, logging, and incident response.
Ver páginaA starter evaluation set for testing source grounding, citation behavior, permission boundaries, and answer quality.
Ver páginaA production runbook for model routing, fallback, cost controls, latency, tracing, degraded mode, and release review.
Ver páginaPáginas relevantes
Downloadable implementation outlines for teams planning, evaluating, governing, and operating production AI systems.
Ver páginaA scoring worksheet for deciding whether a workflow is ready for autonomous or semi-autonomous execution.
Ver páginaA control matrix that maps AI capability scope to data access, tool authority, approvals, logging, and incident response.
Ver página