Support AI Pilot Retrospectives: 30‑Day Playbook

```yaml
ai_pilot_retro:
  pilot_name: "Support Copilot - Billing Queue (EN)"
  cadence: weekly
  duration_minutes: 45
  owners:
    - role: Head of Support
      name: "Primary Owner"
      responsibilities: [decision_maker, unblock_resources]
    - role: Ops Analyst
      responsibilities: [metrics_delta, sample_selection, anomaly_report]
    - role: Billing SME
      responsibilities: [correctness_review, kb_patches]
    - role: Compliance Observer
      responsibilities: [evidence_check, residency_attest]
  inputs:
    systems:
      tickets: Zendesk
      knowledge: Salesforce_Knowledge
      logs: s3://support-ai/prompt_logs/billing/
      metrics: snowflake.db.support.pilot_metrics
      comms: Slack #channel: #pilot-billing-retro
    samples:
      interaction_count: 50
      selection: [top_latency, top_edit_distance, dsat_cases, policy_sensitive]
  kpis:
    csat_delta_target: +2.0   # points vs control
    aht_delta_target: -10%    # vs 4-week pre-pilot baseline
    deflection_rate_target: 25%
    escalation_rate_ceiling: 8%
  safety_thresholds:
    hallucination_rate_max: 1.0%   # SME-labeled incorrect answers
    pii_incidents_allowed: 0
    confidence_low_bin_ceiling: 20% # % of suggestions with conf <0.4
  slo:
    suggestion_latency_p95_ms: 800
    retrieval_hit_rate_min: 92%
  gates:
    expand_if_all_true:
      - csat_delta >= +2.0
      - aht_delta <= -10%
      - escalation_rate <= 8%
      - hallucination_rate <= 1.0%
      - suggestion_latency_p95_ms <= 800
    hold_if_any_true:
      - pii_incidents > 0
      - policy_article_stale: true
      - confidence_low_bin > 20%
    rollback_if_any_true:
      - incorrect_refund_actions_detected: true
      - escalation_rate_week_over_week_increase >= 3%
  approval_steps:
    - step: SME_signoff
      owner: Billing_SME
      due_hours: 24
    - step: Compliance_evidence_check
      owner: Compliance_Observer
      due_hours: 24
    - step: Scale_decision
      owner: Head_of_Support
      outcome: [go, hold, rollback]
  knowledge_actions:
    kb_patch_required: true
    articles_to_update:
      - id: KB-1021
        reason: "policy_deprecation"
        approver: Billing_SME
  audit:
    prompt_logging: enabled
    prompt_redaction: pii_masking_v2
    rbac_roles: [agent, team_lead, ops_analyst, compliance]
    data_residency: US
    evidence_links:
      - type: metrics
        href: "snowflake://support/pilot/billing_wk3"
      - type: logs
        href: "s3://support-ai/prompt_logs/billing/2025-01/"
      - type: decisions
        href: "confluence://ai-pilot/decision-ledger"
```

{
  "title": "Support AI Pilot Retrospectives: 30‑Day Playbook",
  "published_date": "2025-12-02",
  "author": {
    "name": "David Kim",
    "role": "Enablement Director",
    "entity": "DeepSpeed AI"
  },
  "core_concept": "AI Adoption and Enablement",
  "key_takeaways": [
    "Retros for AI pilots must be time-boxed, evidence-led, and tied to scale gates (not a free-form post‑mortem).",
    "Log prompts, agent actions, and outcomes to quantify gains and regressions; decide scale on data, not anecdotes.",
    "A standard retro artifact creates repeatability across queues and regions and reduces rework.",
    "Governance signals (prompt logs, RBAC, residency) make Legal comfortable and accelerate expansion approvals.",
    "You can ship a governed retro practice in 30 days using DeepSpeed AI’s audit → pilot → scale motion."
  ],
  "faq": [
    {
      "question": "How do we avoid retros turning into long post‑mortems?",
      "answer": "Time‑box to 45 minutes with a fixed agenda and pre‑read. Decisions must be go/hold/rollback with owner + due date. Anything else goes to the backlog."
    },
    {
      "question": "What if the model is good but the knowledge is stale?",
      "answer": "Gate expansion on KB patches. Track top churn articles, require SME approval in the retro, and re‑sample those intents next week before scaling."
    },
    {
      "question": "Can we run this without Snowflake or BigQuery?",
      "answer": "Yes. We can log to your helpdesk + S3/GCS and visualize in Looker Studio or Power BI. The key is consistent telemetry and a stable template."
    },
    {
      "question": "How do we include multilingual regions?",
      "answer": "Add region/language to your gates and expand per locale once thresholds are met. Enforce data residency and run a 1‑week shadow pilot to gauge tone and policy adherence."
    }
  ],
  "business_impact_evidence": {
    "organization_profile": "Fintech consumer support org with 120 agents across US/EU; Zendesk + Salesforce Knowledge; Slack for ops.",
    "before_state": "Ad hoc pilot reviews, no standard artifact; CSAT 86.3, AHT 8m 40s, escalation 11.2%, frequent knowledge drift.",
    "after_state": "Weekly governed retros with decision ledger and scale gates; CSAT 88.9, AHT 7m 32s, escalation 7.4%, zero PII incidents.",
    "metrics": [
      "AHT reduced 13% in 3 weeks on pilot-tagged tickets.",
      "CSAT improved by +2.6 points; 5-point lift on high‑volume intents after KB patches.",
      "Escalations down 3.8 points; agent edit distance down 28%."
    ],
    "governance": "Security and Legal approved expansion due to immutable prompt logs, RBAC by queue, data residency pinned to US, and human‑in‑the‑loop evidence with rollback steps."
  },
  "summary": "Run tight retros on support AI pilots to codify learnings, raise CSAT, and avoid repeat mistakes—governed scale in 30 days."
}