# CX AI Advisory Diagnostic — contact-center industry overlay.
#
# Value-driver ranges are 🟡 placeholder benchmarks pending citation work
# (see docs/build_spec_v1.md §12): before running with a real client, back
# each range with public sources or "based on N engagements" framing.
# `value_formula` strings are documentation of the math implemented in
# diaglib/value_math.py (dispatched on `kind`) — they are carried into
# exports verbatim, not evaluated.

extends: base
industry: contact_center
display_name: "Contact Center"

value_drivers:
  - id: deflection_lift
    name: "Deflection / containment improvement"
    kind: containment_lift
    baseline_field: current_containment_rate
    lift_range_pts_low: 0.15
    lift_range_pts_high: 0.35
    value_formula: "annual_contact_volume * lift_pts * blended_cost_per_contact"
    source: "Public benchmarks; virtual agent maturity studies"   # 🟡 placeholder

  - id: aht_reduction
    name: "Average handle time reduction"
    kind: aht_reduction
    baseline_field: average_handle_time_seconds
    reduction_pct_low: 0.15
    reduction_pct_high: 0.25
    value_formula: "annual_contact_volume * (baseline_seconds * reduction_pct) * (blended_cost_per_contact / baseline_seconds)"
    source: "Agent assist / copilot case data"                    # 🟡 placeholder

  - id: attrition_reduction
    name: "Attrition reduction"
    kind: attrition_reduction
    baseline_field: annual_attrition_rate
    reduction_pct_low: 0.10
    reduction_pct_high: 0.20
    cost_per_replacement_default: 15000    # 🟡 industry-tunable per engagement
    value_formula: "agent_headcount * (annual_attrition_rate * reduction_pct) * cost_per_replacement"
    source: "Job quality / copilot studies"                       # 🟡 placeholder

# ── Unlock costs ───────────────────────────────────────────────────────
# Rough cost/time ranges per one-level lift, per foundational competency.
# 🟡 estimates — sparse is OK for MVP; a missing lift renders as
# "cost not configured" in the unlock sequence rather than a guess.
unlock_costs:
  data_readiness:
    lift_1_to_2: {cost_low: 150000, cost_high: 300000, weeks: 8}
    lift_2_to_3: {cost_low: 300000, cost_high: 600000, weeks: 12}
    lift_3_to_4: {cost_low: 400000, cost_high: 800000, weeks: 16}
    lift_4_to_5: {cost_low: 500000, cost_high: 1000000, weeks: 20}
  process_discovery:
    lift_1_to_2: {cost_low: 80000, cost_high: 160000, weeks: 6}
    lift_2_to_3: {cost_low: 120000, cost_high: 250000, weeks: 8}
    lift_3_to_4: {cost_low: 150000, cost_high: 300000, weeks: 10}
    lift_4_to_5: {cost_low: 200000, cost_high: 400000, weeks: 12}
  technical_architecture:
    lift_1_to_2: {cost_low: 100000, cost_high: 200000, weeks: 8}
    lift_2_to_3: {cost_low: 200000, cost_high: 400000, weeks: 10}
    lift_3_to_4: {cost_low: 250000, cost_high: 500000, weeks: 12}
    lift_4_to_5: {cost_low: 350000, cost_high: 700000, weeks: 16}