AI readiness scorecard
A scoring worksheet for deciding whether a workflow is ready for autonomous or semi-autonomous execution.
Practical delivery artifacts
Downloadable implementation outlines for teams planning, evaluating, governing, and operating production AI systems.
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.
A production runbook for model routing, fallback, cost controls, latency, tracing, degraded mode, and release review.
A board-ready outline for connecting AI initiatives to outcomes, risk gates, build sequence, and decision cadence.
A tabletop exercise for AI services that can produce wrong answers, unsafe actions, policy violations, or outage cascades.
A practical operating model for assigning ownership across AI product, platform, risk, operations, and business teams.
A structured intake template for deciding whether a process should become an assistant workflow, agent workflow, or deterministic automation.
A risk register for tracking AI authority, reversibility, sensitive data exposure, failure modes, mitigations, and owners.
A release review checklist for prompt, policy, model, and tool changes before they reach production users.
A source inventory for mapping owners, freshness, permissions, quality issues, retention rules, and ingestion priority.
A workbook for translating organizational roles into retrieval, tool-use, approval, logging, and audit permissions.
A release-gate template that connects evaluation results, known regressions, approval decisions, rollback, and launch notes.
An audit worksheet for checking cited answers against source text, permissions, freshness, and reviewer corrections.
A technical specification for AI-callable tools covering schema, permissions, idempotency, retries, and audit trails.
A service-level objective template for AI latency, quality, cost, availability, escalation, and degraded-mode behavior.
A dashboard outline for monitoring provider mix, cost drift, latency budgets, fallback rates, and quality regressions.
A handoff checklist for moving AI systems from delivery into operated services with owners, runbooks, controls, and evidence.
A steering-committee packet for connecting AI portfolio decisions to milestones, risks, spend, and operating outcomes.
A scorecard for comparing model and platform vendors across quality, latency, cost, security, support, and lock-in risk.
A review outline for documenting AI data handling, retention, subprocessors, residency, and customer control requirements.
A scenario catalog for testing prompt injection, unsafe tool use, data leakage, policy bypass, and recovery behavior.
A review worksheet for validating AI-callable tool scopes, sensitive actions, audit trails, and approval thresholds.
A communications plan for AI incidents covering internal escalation, customer updates, regulatory notice, and postmortems.
An ownership map for knowledge sources, refresh cadence, permission rules, source quality, and escalation contacts.
A policy template for defining which AI decisions require approval, who approves them, and what evidence is required.
A decision tree for routing between models, cached answers, degraded mode, escalation, and temporary shutdown.
A finance model for attributing AI runtime cost by workflow, department, customer segment, provider, and outcome.
A calculator outline for estimating automation value from cycle time, error rate, labor mix, risk reduction, and adoption.
An adoption plan for moving AI services from launch to measurable usage, feedback, training, and continuous improvement.