palladium/studies/202602_AmazonConnect/seed_data.py

"""
Seed dataset for the Amazon Connect TEI (Forrester, Feb 2026).

Each row matches the wire shape produced by
``core.tei_client.TEIClient._normalize_value`` so it can be passed
straight to ``client.update_values(public_id, BENEFITS + COSTS)``.

Numbers are the *nominal* (pre-risk-adjustment) values from the PDF —
risk adjustment is stored as a factor and applied by Athena's
calculator (or, locally, by ``core.calculations.risk_adjust_*``).

References for the totals (from the PDF):

    Benefits (3-yr risk-adjusted PV @ 10%):  $101,696,791
    Costs    (3-yr risk-adjusted PV @ 10%):  $ 22,983,076
    NPV                                       $ 78,713,715
    ROI                                       342%
    Payback                                   <6 months
"""

from __future__ import annotations

#: 3-year nominal benefit cashflows. Risk adjustment factor is stored
#: separately; calculator applies it.
BENEFITS: list[dict] = [
    {
        "field_key": "ai_contact_resolution",
        "table": "benefits",
        "label": "AI-driven contact resolution efficiency",
        "category": "Productivity",
        "year_values": {"1": 13_911_040, "2": 23_932_480, "3": 37_797_760},
        "risk_adjustment": 0.15,
        "notes": (
            "PDF Section At/Atr. Composite: 20M annual contacts, 30% YoY "
            "growth, 75% calls, 10-min AHT with legacy. Connect drops AHT "
            "12% Y1 and shifts traffic to chat/self-service. 80% "
            "productivity recapture. Risk adj 15% (legacy performance, "
            "implementation depth, integration scope, growth)."
        ),
    },
    {
        "field_key": "ai_content_sentiment",
        "table": "benefits",
        "label": "AI-powered content and sentiment analysis savings",
        "category": "Productivity",
        "year_values": {"1": 4_586_620, "2": 5_358_412, "3": 6_291_680},
        "risk_adjustment": 0.15,
        "notes": (
            "PDF Section Bt/Btr. Auto post-contact summaries reclaim ~60s "
            "per call; QA scaled from 1–3% to 100%; supervisors freed from "
            "manual review. Risk adj 15%."
        ),
    },
    {
        "field_key": "ai_forecasting_supervision",
        "table": "benefits",
        "label": "AI-enabled forecasting, agent scheduling, and supervision",
        "category": "Productivity",
        "year_values": {"1": 6_651_680, "2": 9_133_760, "3": 12_391_712},
        "risk_adjustment": 0.15,
        "notes": (
            "PDF Section Ct/Ctr. ML-WFM yields 5% agent FTE optimization "
            "and supervisors managing 20% more agents (10→12). 80% "
            "productivity recapture. Risk adj 15%."
        ),
    },
    {
        "field_key": "data_driven_profit_lift",
        "table": "benefits",
        "label": "Data-driven profit lift with increased conversion",
        "category": "Revenue",
        "year_values": {"1": 1_200_000, "2": 1_560_000, "3": 2_028_000},
        "risk_adjustment": 0.20,
        "notes": (
            "PDF Section Dt/Dtr. Composite revenue $10B Y1 (+30% YoY); "
            "5% from outbound contact-center marketing; conversion lifts "
            "from 10% to 12% (+20% relative); 12% operating margin. "
            "Risk adj 20%."
        ),
    },
    {
        "field_key": "legacy_solution_savings",
        "table": "benefits",
        "label": "Legacy solution cost savings",
        "category": "Cost Savings",
        "year_values": {"1": 6_177_600, "2": 8_030_880, "3": 10_440_144},
        "risk_adjustment": 0.20,
        "notes": (
            "PDF Section Et/Etr. Avg legacy license $180/agent-month × "
            "(agents+supervisors) × 12, plus 30% overhead for infra & "
            "third-party tools. Risk adj 20%."
        ),
    },
]


#: Costs include an "initial" (year-0, undiscounted) component for
#: implementation. Cost risk adjustments are applied *upward*.
COSTS: list[dict] = [
    {
        "field_key": "amazon_connect_usage",
        "table": "costs",
        "label": "Amazon Connect usage cost",
        "category": "Subscription",
        "initial": 0,
        "year_values": {"1": 6_456_448, "2": 7_951_164, "3": 9_832_961},
        "risk_adjustment": 0.05,
        "notes": (
            "PDF Section Ft/Ftr. Telephony $0.0106/min + Unlimited AI "
            "$0.0380/min on minutes that reach an agent, plus chat at "
            "$0.0100/message (10 messages/chat). Risk adj 5%."
        ),
    },
    {
        "field_key": "implementation_migration",
        "table": "costs",
        "label": "Implementation and migration cost",
        "category": "Implementation",
        "initial": 1_087_500,
        "year_values": {"1": 188_333, "2": 188_333, "3": 0},
        "risk_adjustment": 0.10,
        "notes": (
            "PDF Section Gt/Gtr. 6-month initial migration: 5 internal "
            "FTE @ $115k + $800k pro-services. Y1/Y2 M&A integrations: 2 "
            "months × 2 FTE + $150k pro-services. Risk adj 10%."
        ),
    },
    {
        "field_key": "ongoing_management",
        "table": "costs",
        "label": "Ongoing management",
        "category": "Operations",
        "initial": 0,
        "year_values": {"1": 256_200, "2": 187_200, "3": 187_200},
        "risk_adjustment": 0.15,
        "notes": (
            "PDF Section Ht/Htr. Y1: 5 IT/PM @ 30% × $115k + 5 business "
            "users @ 30% × $55,800. Y2/Y3: 3 IT/PM @ 30% + 5 business "
            "users @ 30%. Risk adj 15%."
        ),
    },
]


#: Top-line composite assumptions — for the 03_business_case narrative.
ASSUMPTIONS: dict = {
    "agents_fte": 2_000,
    "supervisors_fte": 200,
    "annual_contacts_y1": 20_000_000,
    "growth_rate": 0.30,
    "call_share": 0.75,
    "aht_legacy_minutes": 10,
    "agent_salary": 45_760,
    "supervisor_salary": 55_800,
    "discount_rate": 0.10,
    "analysis_years": 3,
}


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