AI readiness scorecard
A scoring worksheet for deciding whether a workflow is ready for autonomous or semi-autonomous execution.
תחום שירות Baciu.com
A finance model for attributing AI runtime cost by workflow, department, customer segment, provider, and outcome.
מתחילים בתהליך, במשתמשים ובמצבי כשל לפני בחירת ארכיטקטורה מדידה.
פתח עמודמערכת טובה שומרת מקורות, הערכות, טלמטריה וכללי הסלמה.
פתח עמודהרחבת נושא
A scoring worksheet for deciding whether a workflow is ready for autonomous or semi-autonomous execution.
פתח עמודA control matrix that maps AI capability scope to data access, tool authority, approvals, logging, and incident response.
פתח עמודA starter evaluation set for testing source grounding, citation behavior, permission boundaries, and answer quality.
פתח עמודA production runbook for model routing, fallback, cost controls, latency, tracing, degraded mode, and release review.
פתח עמודA board-ready outline for connecting AI initiatives to outcomes, risk gates, build sequence, and decision cadence.
פתח עמודA tabletop exercise for AI services that can produce wrong answers, unsafe actions, policy violations, or outage cascades.
פתח עמודA practical operating model for assigning ownership across AI product, platform, risk, operations, and business teams.
פתח עמודA structured intake template for deciding whether a process should become an assistant workflow, agent workflow, or deterministic automation.
פתח עמודUse 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.
אטלס מסירה
סננו, השוו ופתחו עמודים מפורטים לארכיטקטורה, ביצוע וממשל של AI.
ספריית יישום
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 decision tree for routing between models, cached answers, degraded mode, escalation, and temporary shutdown.
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.
מעבדת ביצוע
כוונן קצב, אוטונומיה ופרופיל סיכון כדי לראות שלבים מומלצים, תלותים ושערי בקרה.
שלבים מומלצים
אין שליפה ללא משמעת מקור
אמון הוא תכונת מוצר
פעולה עם אחריות
כל שחרור מרוויח אמון
שליטה איפה העבודה מתרחשת
צוותי לקוחות יכולים לפעול באופן עצמאי
רדאר יכולות
בחרו פרספקטיבה ואופק זמן כדי לראות מסלולים, אותות ודפי החלטה רלוונטיים.
תוכנית ביצוע
כל תחום נמסר עם הגדרה מפורשת, ולידציה מדידה וממשל תפעולי שהצוות של הלקוח יכול לאמץ.
צ'קליסט תפעולי
A clear system map covering models, tools, data, workflows, users, and failure modes.
פתח עמודTask sets, regression checks, and release criteria for measurable AI behavior.
פתח עמודHuman approval, access, logging, data-boundary, and incident-response rules.
פתח עמודDocumentation and ownership so the client can operate the system after launch.
פתח עמודהתחל עם זרימות עבודה חוזרות והפיכות שבהן ניתן למדוד תוצאות וגבולות כישלון.
השתמש בערכות eval, תרחישים יריבים וקריטריונים מפורשים של go/no-go הקשורים להשפעה העסקית.
עם גבולות סמכות, ספי ביטחון, מנות הסלמה ועקבות ביצוע מלאות.
התייחסו לשינויים במודל ובבקשות כעל מהדורות: בדיקה, בדיקה, אישור והפצה עם נתיבים לחזרה.
מפת כיסוי
A scoring worksheet for deciding whether a workflow is ready for autonomous or semi-autonomous execution.
פתח עמודA control matrix that maps AI capability scope to data access, tool authority, approvals, logging, and incident response.
פתח עמודA starter evaluation set for testing source grounding, citation behavior, permission boundaries, and answer quality.
פתח עמודA production runbook for model routing, fallback, cost controls, latency, tracing, degraded mode, and release review.
פתח עמודעמודים קשורים
Downloadable implementation outlines for teams planning, evaluating, governing, and operating production AI systems.
פתח עמודA scoring worksheet for deciding whether a workflow is ready for autonomous or semi-autonomous execution.
פתח עמודA control matrix that maps AI capability scope to data access, tool authority, approvals, logging, and incident response.
פתח עמוד