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 cross-functional operating cadence for weekly AI service reviews, monthly value decisions, release gates, and escalation ownership.
An operations kit for managing retrieval quality through source ownership, freshness checks, citation audits, and regression scoring.
A release governance kit for managing prompt, model, policy, retrieval, and tool-authority changes in agentic systems.
An operations kit for AI-assisted support queues covering triage policy, containment metrics, escalation, QA, and customer communications.
An evidence pack for regulated AI rollouts connecting domain constraints, reviewer evidence, release gates, and audit-ready decisions.
A regression suite for AI releases covering task quality, source grounding, safety, tool behavior, latency, and cost movement.
A telemetry kit for model-backed services covering request traces, quality signals, cost, latency, fallback, and incident triggers.
A rollout runbook for moving AI-assisted workflows from pilot to controlled scale with queue gates, training, controls, and adoption metrics.
An enablement kit for driving trusted AI adoption through training, champion networks, feedback loops, and behavior metrics.
A portfolio prioritization kit for ranking AI opportunities by value, feasibility, risk, operating readiness, and learning leverage.
A model risk operations kit for financial services AI systems covering evidence, approvals, monitoring, controls, and audit readiness.
A healthcare AI safety intake kit for triaging clinical-adjacent workflow ideas before pilot, procurement, or production rollout.
A manufacturing AI kit for connecting quality signals, maintenance notes, production exceptions, and operator feedback into governed intelligence loops.
A public sector procurement kit for specifying AI requirements, vendor evidence, data boundaries, accessibility, and operating controls.
A retail service automation kit for scaling AI-assisted support, store operations, exception handling, and customer experience monitoring.
A claims operations kit for using AI across intake, coverage evidence, adjuster review, leakage monitoring, and customer communications with explicit controls.
A logistics operations kit for detecting shipment, inventory, carrier, supplier, and customer-commitment exceptions with evidence-backed recovery paths.
A technology operations kit for preparing AI-assisted incident triage, owner routing, release evidence, and support handoffs before production escalation.
A people-operations kit for policy guidance assistants covering sensitive topics, privacy boundaries, escalation, and manager-ready evidence.
A finance operations kit for AI-assisted reconciliation, variance explanation, close controls, reviewer evidence, and audit-ready reporting.
An operations kit for AI-assisted queue prioritization, capacity sensing, escalation hygiene, exception resolution, and SLA protection.
A program operations kit for AI portfolio governance covering delivery risks, decision logs, executive briefings, funding gates, and value evidence.
A security operations kit for testing, monitoring, and responding to prompt injection across retrieval, tools, memory, and agent workflows.
A data-boundary kit for preventing sensitive data leakage across prompts, retrieval, logs, model providers, tools, and exports.
A tool-governance kit for reviewing AI-callable actions by authority, reversibility, credentials, approval, logs, and rollback.
A model-operations kit for routing policies covering provider selection, fallback, quality gates, latency budgets, data boundaries, and cost controls.
A knowledge-operations kit for governing retrieval sources from intake and ownership through freshness, permission, quality, and retirement.
A vendor-governance kit for evaluating AI providers across model risk, data handling, controls, support, portability, and operating evidence.
An incident communications kit for AI failures covering internal escalation, customer messaging, regulatory notice, and postmortem evidence.
A context-governance kit for deciding what AI systems may remember, retrieve, personalize, retain, forget, and expose to users.
A release-control kit for shipping AI systems with evaluation evidence, rollback paths, owner approvals, launch notes, and monitoring gates.
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.
An operating kit for model routing, runtime incident triage, provider fallback drills, release gates, and remediation ownership.
A finance model for attributing AI runtime cost by workflow, department, customer segment, provider, and outcome.
A benchmark pack for measuring AI value across baseline cost, adoption, unit economics, and value-review decisions.
A control kit for managing AI value through adoption curves, unit economics, operating cost, quality signals, and scale decisions.
A calculator outline for estimating automation value from cycle time, error rate, labor mix, risk reduction, and adoption.
A control kit for classifying workflow exceptions, routing them to the right owner, and measuring automation containment without hiding rework.
A rollout map for adapting AI programs to regulated industries with domain constraints, evidence models, release gates, and operating reviews.
An adoption plan for moving AI services from launch to measurable usage, feedback, training, and continuous improvement.