"""
Seed dataset for the Genesys CX Cloud TEI (Forrester, Dec 2025).

"The Total Economic Impact™ Of CX Cloud — Cost Savings And Business
Benefits Enabled By Genesys And Salesforce" (commissioned by Genesys and
Salesforce). Composite: global supply company, $2.5B revenue, 10,000
employees, 600 CX agents (400 concurrent licenses), 80,000 weekly
interactions averaging 12 minutes.

Each row uses the friendly value shape accepted by
``core.tei_client.TEIClient.update_values``. Benefit values are *nominal*
(pre-risk-adjustment); Athena applies the field-level risk adjustment.
Cost values are nominal too — push them pre-multiplied by
``(1 + risk_adjustment)`` per the Palladium convention (Athena never
risk-adjusts costs).

Published headline (3-yr risk-adjusted, 10% discount)::

    Benefits PV   $14,840,638
    Costs PV      $ 4,057,170
    NPV           $10,783,468
    ROI           266%
    Payback       ~4 months (computed; the study does not headline it)

Athena discounts Year-0 "Initial" amounts as Year-1 cashflows (Forrester
leaves Year 0 undiscounted). With this study's large initial cost
($1,309,000 risk-adjusted) that difference is material, so this module
also exports ``ATHENA_EXPECTED`` — the totals Athena *should* produce
under its own discounting. Verification: match ATHENA_EXPECTED tightly
(pipeline correctness), then reconcile to PUBLISHED with the explained
Year-0 delta.

NOTE on the published PDF: the Total Costs table (p.14) prints the
implementation initial as $1,304,600, but the detail table, the cash-flow
analysis, and the math (1,190,000 × 1.10) all give $1,309,000 — the p.14
figure is a typo in the study.
"""

from __future__ import annotations

#: 3-year nominal benefit cashflows. Risk adjustment stored separately.
BENEFITS: list[dict] = [
    {
        "field_key": "legacy_retirement",
        "table": "benefits",
        "label": "Retirement of legacy systems with CX Cloud adoption",
        "category": "Cost Savings",
        "year_values": {"1": 680_000, "2": 930_000, "3": 930_000},
        "risk_adjustment": 0.05,
        "notes": (
            "PDF A1–A4. Telephony $250k Y1 ramping to $500k (legacy "
            "sunset completes mid-Y1) + WFM/recording/transcription apps "
            "$100k + reduced dev effort $230k (2,400 hrs @ $94) + reduced "
            "platform mgmt $100k (1,500 hrs @ $65). Risk adj 5%."
        ),
    },
    {
        "field_key": "self_service_savings",
        "table": "benefits",
        "label": (
            "Cost savings from reallocated workers and avoided seasonal "
            "hires with increased customer self-service"
        ),
        "category": "Productivity",
        "year_values": {"1": 2_329_600, "2": 2_329_600, "3": 2_329_600},
        "risk_adjustment": 0.15,
        "notes": (
            "PDF B1–B8. Self-service completion 15%→25% on 80k weekly "
            "interactions → 8,000 deflected/week → 40 FTEs @ $58,240 "
            "fully burdened. Risk adj 15%. (PDF B7 formula cites B2 where "
            "the 12-min interaction length is meant; 40 FTEs is correct.)"
        ),
    },
    {
        "field_key": "agent_efficiency",
        "table": "benefits",
        "label": "CX agent efficiency gains",
        "category": "Productivity",
        "year_values": {"1": 2_912_000, "2": 2_912_000, "3": 2_912_000},
        "risk_adjustment": 0.10,
        "notes": (
            "PDF C1–C6. MTTR 12→10 min on 60k agent-handled interactions "
            "per week → 104,000 hrs/yr @ $28 fully burdened. Risk adj 10%."
        ),
    },
    {
        "field_key": "agent_assist_sales",
        "table": "benefits",
        "label": "Incremental sales from agent assist capabilities",
        "category": "Revenue",
        "year_values": {"1": 600_000, "2": 600_000, "3": 600_000},
        "risk_adjustment": 0.05,
        "notes": (
            "PDF D1–D3. $500M revenue impacted (20% of $2.5B) × 1.5% lift "
            "× 8% gross margin. Risk adj 5%."
        ),
    },
]


#: Costs are nominal; push × (1 + risk_adjustment). "initial" is the
#: Year-0 component (companion non-annual field in Athena).
COSTS: list[dict] = [
    {
        "field_key": "cx_cloud_licenses",
        "table": "costs",
        "label": "CX Cloud solution costs (licenses)",
        "category": "Subscription",
        "initial": 0,
        "year_values": {"1": 840_000, "2": 840_000, "3": 840_000},
        "risk_adjustment": 0.05,
        "notes": (
            "PDF E1–E3. Genesys Cloud CX 2 $170/user/mo + Salesforce "
            "Voice $25/user/mo + connector $25/user/mo, 400 concurrent "
            "users, 20% contractual discount → $650k + $95k + $95k. "
            "Risk adj +5%. Seat licenses ONLY — AI consumption is a "
            "separate line (genesys_ai_tokens)."
        ),
    },
    {
        "field_key": "implementation",
        "table": "costs",
        "label": "Implementation and deployment cost",
        "category": "Implementation",
        "initial": 1_190_000,
        "year_values": {"1": 0, "2": 0, "3": 0},
        "risk_adjustment": 0.10,
        "notes": (
            "PDF F1–F5. 10-week implementation: 20 FTEs @ $80/hr fully "
            "burdened ($640k) + $550k professional services. Risk adj "
            "+10% → $1,309,000 (the p.14 Total Costs table's $1,304,600 "
            "is a typo in the study)."
        ),
    },
    {
        "field_key": "ongoing_management",
        "table": "costs",
        "label": "Ongoing management costs",
        "category": "Operations",
        "initial": 0,
        "year_values": {"1": 202_800, "2": 202_800, "3": 202_800},
        "risk_adjustment": 0.10,
        "notes": (
            "PDF G1–G3. 5 people @ 30% time (12 hrs/wk) @ $65/hr. "
            "Risk adj +10%."
        ),
    },
    {
        "field_key": "genesys_ai_tokens",
        "table": "costs",
        "label": "Genesys AI Experience token consumption",
        "category": "Subscription",
        "initial": 0,
        "year_values": {"1": 0, "2": 0, "3": 0},
        "risk_adjustment": 0.0,
        "notes": (
            "NOT in the published study — Forrester modeled $0 AI "
            "consumption even though benefits B (self-service uplift), "
            "C (AI coaching/assist), and D (agent assist upsell) all "
            "depend on AI capabilities that Genesys bills via AI "
            "Experience tokens. Seeded at $0 to reproduce the published "
            "totals. For client cases, enter the negotiated annual token "
            "cost from the Genesys quote and document the quote details "
            "(token volume, unit price, tier) in these notes."
        ),
    },
]


#: Composite-organization drivers — for scaling to a specific client.
ASSUMPTIONS: dict = {
    "annual_revenue": 2_500_000_000,
    "employees": 10_000,
    "agents_fte": 600,
    "concurrent_licenses": 400,
    "weekly_interactions": 80_000,
    "interaction_minutes": 12,
    "self_service_rate_before": 0.15,
    "self_service_rate_after": 0.25,
    "mttr_saved_minutes": 2,
    "agent_hourly_rate": 28,
    "agent_annual_salary": 58_240,
    "revenue_impacted": 500_000_000,
    "revenue_lift": 0.015,
    "gross_margin": 0.08,
    "discount_rate": 0.10,
    "analysis_years": 3,
}


# ────────────────────────────────────────────────────────────────────
# Genesys AI Experience tokens
#
# Genesys bills AI consumption in "AI Experience tokens" — pricing is
# tiered, capability-dependent, and deal-specific. Athena stores a
# single annual cost value per line, and so do we: enter the negotiated
# annual figure from the Genesys quote into ``genesys_ai_tokens`` and
# document the quote details (volume, unit price, tier) in the field
# notes. For sizing context, the study's own drivers imply ~1,040,000
# self-service interactions/yr (B5 × 52) and ~3,120,000 agent-assisted
# interactions/yr (C1 × 52) would draw tokens.
# ────────────────────────────────────────────────────────────────────


# ────────────────────────────────────────────────────────────────────
# Verification targets
# ────────────────────────────────────────────────────────────────────

#: Published Forrester totals (3-yr risk-adjusted PV @ 10%).
PUBLISHED: dict = {
    "total_benefits_pv": 14_840_638,
    "total_costs_pv": 4_057_170,
    "net_present_value": 10_783_468,
    "roi_percentage": 266,
}

#: What Athena should produce given its own discounting (Year-0 initial
#: treated as a Year-1 cashflow: implementation PV = 1,309,000 / 1.10 =
#: 1,190,000 instead of 1,309,000). Match these tightly; the difference
#: vs PUBLISHED is methodology, not error.
ATHENA_EXPECTED: dict = {
    "total_benefits_pv": 14_840_640,
    "total_costs_pv": 3_938_170,
    "net_present_value": 10_902_470,
    "roi_percentage": 276.8,
}


def all_values() -> list[dict]:
    """Return BENEFITS + COSTS — single-call payload for update_values."""
    return BENEFITS + COSTS