CFO ROI Playbook: Quantify AI Wins, Audit-Ready

```yaml
ledger_id: ROI-2025-001
program: Finance+Support_AuditReady_ROI
owner:
  exec_sponsor: CFO
  dri: Director_Fin_Ops
  partners: [VP_Support, GC, CISO]
regions: [us-east-1]
models:
  provider: azure-openai
  deployment: gpt-4o-mini
  pii_handling: masked_in_prompt
  training_policy: never_train_on_client_data
use_cases:
  - id: UC-DEFLECT-01
    name: L1 Support Deflection Copilot
    systems: [Zendesk, Slack]
    hypothesis: Reduce L1 escalations and rework by 10-15%.
    baseline:
      window_days: 90
      metrics:
        - name: l1_escalation_rate
          value: 0.32
        - name: rework_rate
          value: 0.21
        - name: avg_handle_time_min
          value: 11.4
    experiment:
      split: {control: 0.7, treatment: 0.3}
      start_date: 2025-01-08
      end_date: 2025-02-02
      success_thresholds:
        - metric: l1_escalation_rate
          delta_target: -0.12
          confidence: 0.9
        - metric: analyst_hours_returned
          target_hours_monthly: 250
    guardrails:
      hitl_required: true
      forbidden_actions: [auto_close_high_severity]
      prompt_logging: enabled
      rbac_groups: [Support_L1, Support_Leads, Audit_Readonly]
      data_residency: us-only
  - id: UC-COLLECT-01
    name: Collections Email Assistant
    systems: [Salesforce, Outreach]
    hypothesis: Improve DSO by 1-2 days on treatment cohort with compliant outreach.
    baseline:
      window_days: 90
      metrics:
        - name: dso
          value: 42.7
        - name: promise_to_pay_rate
          value: 0.18
    experiment:
      split: {control: 0.7, treatment: 0.3}
      start_date: 2025-01-10
      end_date: 2025-02-02
      success_thresholds:
        - metric: dso
          delta_target_days: -1.2
          confidence: 0.85
        - metric: outreach_acceptance_rate
          target: 0.25
    guardrails:
      legal_template_version: v3.4
      allow_segments: [US_Commercial_Active]
      deny_segments: [Open_Dispute, Legal_Hold]
      hitl_required: true
      prompt_logging: enabled
      rbac_groups: [AR_Team, Finance_Leads, Audit_Readonly]
      data_residency: us-only
cost_model:
  engineering_hours: 140
  vendor_cost_monthly_usd: 5800
  change_mgmt_hours: 24
  payback_target_months: 3
approvals:
  legal: {owner: GC, status: approved, date: 2025-01-05}
  security: {owner: CISO, status: approved, date: 2025-01-05}
  finance: {owner: CFO, status: approved, date: 2025-01-06}
data_sources:
  snowflake:
    database: FINOPS
    schemas: [ZENDESK_TELEMETRY, SALESFORCE_AR, GOVERNANCE_LOGS, COST_MODEL]
    lineage:
      - source: ZENDESK_TELEMETRY.TICKETS
        target: FINOPS_MART.SUPPORT_KPIS_DAILY
      - source: SALESFORCE_AR.INVOICES
        target: FINOPS_MART.COLLECTIONS_KPIS_DAILY
    evidence_retention_days: 90
reporting:
  weekly_brief_owner: FPnA_Manager
  charts: [hours_returned_ci, payback_curve, risk_register]
  next_step_workflow: [scale, adjust, kill]
```

{
  "title": "CFO ROI Playbook: Quantify AI Wins, Audit-Ready",
  "published_date": "2025-11-21",
  "author": {
    "name": "Lisa Patel",
    "role": "Industry Solutions Lead",
    "entity": "DeepSpeed AI"
  },
  "core_concept": "Industry Transformations and Case Studies",
  "key_takeaways": [
    "Establish a decision ledger that tracks hypotheses, baselines, control groups, and approvals to make ROI auditable.",
    "Run sub-30-day pilots tied to 1–2 finance-critical workflows (e.g., reconciliations, collections) for fast, credible wins.",
    "Implement governance upfront: prompt logging, RBAC, data residency, and human-in-the-loop to satisfy Legal and Audit.",
    "Report a single business outcome that matters to the board (e.g., close faster, hours returned) and link to unit economics."
  ],
  "faq": [
    {
      "question": "How do you prevent ROI inflation from seasonality or mix shift?",
      "answer": "We lock a 90-day baseline, use control/treatment splits, and calculate confidence intervals. We also run sensitivity checks for seasonality and customer mix, and publish them in the ledger."
    },
    {
      "question": "Will this slow down our close?",
      "answer": "No. The 30-day pilot is structured around two measurable workflows with minimal finance disruption. Most work is in telemetry wiring and governance setup, typically completed in week one."
    },
    {
      "question": "What if Legal is not comfortable with AI-generated outreach?",
      "answer": "We implement approved templates, human-in-the-loop review, deny lists, and prompt logging. Legal signs off via the ledger approval step before any treatment traffic flows."
    }
  ],
  "business_impact_evidence": {
    "organization_profile": "Public B2B SaaS with 1,200 employees on AWS; Snowflake, Salesforce, Zendesk, Workday; SOC 2 Type II; SOX 404 in scope.",
    "before_state": "Pilots running in silos; savings were anecdotal; CFO could not tie claims to close speed or DSO; Legal blocked scale pending controls.",
    "after_state": "Decision ledger wired to Snowflake with weekly CFO brief; prompt logs + RBAC + US residency enforced; close faster with measurable hours returned and DSO gains.",
    "metrics": [
      "Close cycle reduced by 1.8 days.",
      "420 analyst hours/month returned across Support Ops + Finance.",
      "2.3-month payback from pilot costs.",
      "DSO improvement of 1.6 days on treatment cohort."
    ],
    "governance": "Legal and Security approved because prompts/outputs were logged, RBAC limited access by role, data processed in-region, and models were never trained on client data; human-in-the-loop enforced for outreach."
  },
  "summary": "CFOs: turn AI pilots into board-ready ROI in 30 days with a decision ledger, control groups, and governance—so finance can defend budgets and accelerate close."
}