Set Freshness Expectations and Define What “Late Data” Means

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1The Boundary: AI Drafts BI Artifacts, You Ship Verified Decisions 2Map the BI Artifact Lifecycle and Assign AI Roles 3Non-Delegable BI Judgments for KPI Work 4Recognize AI-in-BI Failure Symptoms Before They Ship 5Verification Rituals, Stop-Conditions, and the BI AI Guardrails One-Pager

6Classify Data for AI Use: A Privacy-Risk Lens for BI Work 7Minimum Access Controls for AI-Assisted BI: Least Privilege and Separation of Duties 8Prompt and Output Handling Rules: Redaction, Storage, and Internal-Only Boundaries 9Human-in-the-Loop Review Gates for KPI, SQL, Semantic, and Narrative Artifacts 10Incident Response for AI Mishaps: Contain, Document, Notify, and Adjust Controls 11The BI-Team AI Governance Mini-Policy: A Reusable Template You Can Adopt

12From “Looks Right” to “Verified”: The BI Prompt→Output→Verify→Revise Loop 13Constraint-First Prompts That Prevent BI Ambiguity 14Few-Shot Conventions for Metric Definitions That Stay Consistent 15A Lightweight Evaluation Loop: Two Drafts, One Decision, Written Evidence 16The Evaluation Log: Making AI Assistance Auditable in BI Delivery 17Red-Teaming BI Artifacts: Drift, Leakage, and Narrative Overreach 18Provider Guidance to BI Checks: Turning Eval Advice Into Your Team Rubric

19From Vague Ask to Decision Statement and Requirements Brief 20Draft KPI Candidates With AI, Then De-Duplicate and Assign Ownership 21Apply the KPI Definition Rubric: Grain, Logic, Dimensions, and Freshness 22Tie KPIs to Likely Systems of Record and Document Assumptions 23Design KPI Acceptance Tests Before Writing SQL 24Red-Team KPI Definitions to Eliminate Ambiguity and Prevent Drift

25Turn KPI Language Into a Verified Glossary Map 26Find Candidate Tables and Join Keys Without Falling for Plausible AI Guesses 27Write a Lineage Narrative That Flags Uncertainty Instead of Hiding It

28Set Freshness Expectations and Define What “Late Data” Means

29Document Caveats and Package the Discovery for Future Governed Q&A

30From KPI Card to SQL Prompt: Constraining the Draft 31Join-Risk Audits: Catching Duplication Before It Ships 32Time Logic Proofs: Time Zones, Windows, and Boundary Dates 33Reconciliation Queries: Proving the KPI With Tie-Outs 34Edge-Case Tests: Late Data, Null Keys, Refunds, and Negatives 35The Validation Log: Turning Checks Into an Audit Trail 36Performance Tweaks Without Semantic Drift: Optimize, Then Re-Prove

37Why a Semantic Layer Exists (and Why Ad-Hoc Measures Drift)38From KPI Card to Certified Metric Spec 39AI-Drafted Documentation and Change Notes That Reduce Ambiguity 40Proving Semantic Equivalence to the Validated SQL 41Change Control for Metrics: Propose, Review, Test, Certify, Communicate, Roll Back 42Drift-Prevention Checks and Ownership of Failures

43Reproducible Pipelines: The Difference Between “It Ran” and “It Matches”44Make the Environment Verifiable: Dependencies, Credentials, and Run Context 45Draft a DAG Skeleton With AI: Tasks, Dependencies, and Publish Gates 46State and Late Data: Idempotency, Partitioning, and Backfill Rules 47AI-Assisted Refactors: What Can Change Semantics Without Anyone Noticing 48Equivalence Checks: Proving a Refactor Preserved the KPI 49Tolerances and Escalation: When a Diff Is a Bug vs an Intended Change 50Tests as Pipeline Steps: Designing the “Test Before Publish” Spine

51Turn a KPI Into Data Quality Contracts You Can Enforce 52Implement KPI Tests With Great Expectations or dbt Patterns 53Specify Anomaly Monitoring That Distinguishes Data Issues From Real Movement 54Enforce the Evidence Rule in AI-Assisted Narratives 55Red-Team Narratives for Overreach, Drift, and Privacy Leakage 56Design Governed Self-Service and AI Q&A Boundaries 57Operate AI-Assisted BI: Review Gates, Usage Monitoring, and Cost Controls 58Build a Phased AI Adoption Roadmap for Your BI Team

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Set Freshness Expectations and Define What “Late Data” Means