Robert Helewka
64fb83257d
- Add 202512_GenesysCX TEI study (config, seed data, notebooks, README) with NPV $10.8M / ROI 266% including AI-token cost line - Add explicit `key` parameter to all chart wrappers in app/components to prevent StreamlitDuplicateElementId errors when the same figure type renders across Summary/Benefits/Costs tabs - Render benefits bar and cost pie charts on their respective tabs - Add benefits_vs_costs_by_year chart wrapper
376 lines
13 KiB
Plaintext
376 lines
13 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3da9de99",
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"metadata": {},
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"source": [
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"# 01 · Business Case — Genesys CX Cloud TEI\n",
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"\n",
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"Working view of the live Athena tool, plus the **Genesys AI Experience token**\n",
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"cost line the published study omits. Run `00_provision.ipynb` first.\n",
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"\n",
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"The published study models \\$0 AI consumption while three of its four benefits\n",
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"(self-service uplift, agent efficiency, agent-assist sales) depend on AI\n",
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"capabilities that Genesys bills via AI Experience tokens. Token pricing is\n",
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"tiered and deal-specific, so the model keeps it simple — **one annual cost\n",
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"value, entered from the Genesys quote**, exactly as Athena stores it. Quote\n",
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"details (volume, unit price, tier) go in the field notes for the audit trail."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "89deae70",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys, pathlib # path shim: works on a fresh kernel\n",
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"for _p in [pathlib.Path.cwd(), *pathlib.Path.cwd().parents]:\n",
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" if (_p / \"pyproject.toml\").exists():\n",
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" sys.path.insert(0, str(_p)); break\n",
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"\n",
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"import pandas as pd\n",
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"from core.bootstrap import init\n",
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"from core.notebook_helpers import charts\n",
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"\n",
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"pal = init(study=\"202512_GenesysCX\")\n",
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"client, seed, config = pal.client, pal.seed_data, pal.config\n",
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"\n",
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"TOOL_ID = config.TOOL_PUBLIC_ID\n",
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"assert TOOL_ID, \"No PALLADIUM_GENESYSCX_TOOL_PUBLIC_ID in .env — run 00_provision.ipynb first.\"\n",
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"tool = client.get_tool(TOOL_ID)\n",
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"print(f\"Tool: {tool.get('name')} ({TOOL_ID}) status={tool.get('status')}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "09afaf70",
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"metadata": {},
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"source": [
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"## Current financial summary"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3d80c19a",
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"metadata": {},
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"outputs": [],
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"source": [
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"summary = client.calculate(TOOL_ID)\n",
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"client.print_summary(TOOL_ID)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "392df0ba",
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"metadata": {},
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"outputs": [],
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"source": [
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"values = client.get_values(TOOL_ID)\n",
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"benefit_rows = [v for v in values if v.get(\"table\") == \"benefits\"]\n",
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"cost_rows = [v for v in values if v.get(\"table\") == \"costs\"]\n",
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"\n",
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"def value_table(rows, *, initial=False):\n",
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" out = []\n",
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" for v in rows:\n",
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" yv = v.get(\"year_values\") or {}\n",
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" rec = {\"field\": v.get(\"label\") or v[\"field_key\"]}\n",
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" if initial:\n",
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" rec[\"Initial\"] = v.get(\"initial\", 0.0)\n",
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" rec.update({f\"Year {y}\": yv.get(str(y), 0.0) for y in (1, 2, 3)})\n",
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" rec[\"risk_adj\"] = v.get(\"risk_adjustment\")\n",
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" out.append(rec)\n",
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" return pd.DataFrame(out)\n",
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"\n",
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"print(\"Benefits (nominal; Athena risk-adjusts at calculate time):\")\n",
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"display(value_table(benefit_rows))\n",
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"print(\"Costs (stored pre-multiplied by 1 + risk_adj):\")\n",
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"display(value_table(cost_rows, initial=True))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2383f761",
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"metadata": {},
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"source": [
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"## Financial visualizations\n",
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"\n",
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"All figures are risk-adjusted and built from the live Athena values, using\n",
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"the shared `core.notebook_helpers.charts` helpers (same ones the Streamlit\n",
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"app uses)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# ── Visual theme — plain values, edit freely (not confidential) ──\n",
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"THEME = {\n",
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" \"heading_font\": \"Helvetica Neue, Arial, sans-serif\", # chart titles\n",
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" \"body_font\": \"Helvetica, Arial, sans-serif\", # axes, legends, labels\n",
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" \"font_color\": \"#1F2937\", # hex\n",
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"\n",
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" # Circle-chart slice colours, applied in order a..j\n",
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" \"pie_colors\": {\n",
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" \"a\": \"#1565C0\",\n",
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" \"b\": \"#2E7D32\",\n",
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" \"c\": \"#C62828\",\n",
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" \"d\": \"#F9A825\",\n",
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" \"e\": \"#6A1B9A\",\n",
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" \"f\": \"#00838F\",\n",
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" \"g\": \"#EF6C00\",\n",
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" \"h\": \"#5D4037\",\n",
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" \"i\": \"#37474F\",\n",
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" \"j\": \"#AD1457\",\n",
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" },\n",
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"\n",
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" \"bar_green\": \"#2E7D32\", # benefits bars\n",
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" \"bar_red\": \"#C62828\", # costs bars\n",
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"}\n",
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"\n",
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"charts.apply_theme(**THEME)\n",
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"print(\"Theme applied — re-run the chart cells below to restyle.\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "14d5942a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cost breakdown — share of total three-year cost per line item\n",
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"charts.cost_breakdown_pie(\n",
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" cost_rows, title=\"Cost Breakdown — share of 3-year total (risk-adjusted)\"\n",
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").show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d13f342e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Benefits — three-year risk-adjusted total per category\n",
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"charts.benefits_bar(\n",
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" benefit_rows, title=\"Benefits (Three-Year, Risk-Adjusted)\"\n",
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").show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9ef6caee",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Benefits vs costs, year by year (Initial = one-time Year-0 costs)\n",
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"charts.benefits_vs_costs_by_year(benefit_rows, cost_rows).show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6f069df6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cash flow with cumulative net benefits — the Forrester-style exhibit\n",
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"def _yearly_breakdown(benefit_items, cost_items):\n",
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" \"\"\"Risk-adjusted yearly rows + initial, computed from the live values.\"\"\"\n",
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" initial = sum(float(c.get(\"initial\") or 0) for c in cost_items)\n",
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" rows, cumulative = [], -initial\n",
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" for y in (1, 2, 3):\n",
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" b = sum(float((v.get(\"year_values\") or {}).get(str(y), 0) or 0)\n",
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" * (1 - float(v.get(\"risk_adjustment\") or 0))\n",
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" for v in benefit_items)\n",
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" c = sum(float((v.get(\"year_values\") or {}).get(str(y), 0) or 0)\n",
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" for v in cost_items)\n",
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" cumulative += b - c\n",
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" rows.append({\"year\": y, \"benefits\": b, \"costs\": c,\n",
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" \"net\": b - c, \"cumulative_net\": cumulative})\n",
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" return rows, initial\n",
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"\n",
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"yb, initial_cost = _yearly_breakdown(benefit_rows, cost_rows)\n",
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"charts.cashflow_chart(yb, initial_cost=initial_cost).show()\n",
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"pd.DataFrame(yb)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b1ff0315",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Waterfall — Benefits PV down to NPV (from the Athena summary)\n",
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"charts.waterfall([\n",
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" (\"Benefits PV\", float(summary[\"total_benefits_pv\"])),\n",
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" (\"Costs PV\", -float(summary[\"total_costs_pv\"])),\n",
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" (\"NPV\", float(summary[\"net_present_value\"])),\n",
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"], title=\"Benefits PV → Costs PV → NPV\").show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c78f77a9",
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"metadata": {},
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"source": [
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"## Genesys AI Experience tokens — annual cost\n",
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"\n",
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"Enter the negotiated annual token cost from the Genesys quote. For sizing\n",
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"context, the study's own drivers imply roughly **1,040,000** self-service\n",
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"interactions/yr and **3,120,000** agent-assisted interactions/yr would draw\n",
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"tokens — bring the actual figure from the quote, not a derivation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "99b9c665",
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"metadata": {},
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"outputs": [],
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"source": [
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"# ── Deal inputs ──\n",
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"AI_TOKEN_ANNUAL_COST = 0.0 # $/yr from the Genesys quote — 0 reproduces the published study\n",
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"AI_TOKEN_QUOTE_NOTE = \"\" # e.g. \"Quote #1234: 4.2M tokens/yr @ $0.05, tier 2 commit\"\n",
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"\n",
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"print(f\"AI token line: ${AI_TOKEN_ANNUAL_COST:,.0f}/yr\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b7c6c4c7",
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"metadata": {},
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"source": [
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"### Sensitivity — what the AI line does to NPV and ROI\n",
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"\n",
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"Computed locally from the current Athena summary: an annual cost `Δ` raises\n",
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"costs PV by `Δ × 2.4869` (the 3-year, 10% annuity factor) and lowers NPV by\n",
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"the same amount."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "09ceeea2",
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"metadata": {},
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"outputs": [],
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"source": [
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"ANNUITY = sum(1 / 1.10**n for n in (1, 2, 3)) # 2.4869\n",
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"\n",
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"base_benefits_pv = float(summary[\"total_benefits_pv\"])\n",
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"base_costs_pv = float(summary[\"total_costs_pv\"])\n",
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"\n",
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"# Remove any token cost already stored, to get a clean zero-token base.\n",
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"current_tokens = next(\n",
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" (v for v in values if v[\"field_key\"] == \"genesys_ai_tokens\"), {})\n",
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"current_annual = (current_tokens.get(\"year_values\") or {}).get(\"1\", 0.0)\n",
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"base_costs_pv -= current_annual * ANNUITY\n",
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"\n",
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"sweep = [0, 100_000, 250_000, 500_000, 750_000, 1_000_000]\n",
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"if AI_TOKEN_ANNUAL_COST and AI_TOKEN_ANNUAL_COST not in sweep:\n",
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" sweep = sorted(sweep + [AI_TOKEN_ANNUAL_COST])\n",
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"\n",
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"rows = []\n",
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"for ai_annual in sweep:\n",
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" costs_pv = base_costs_pv + ai_annual * ANNUITY\n",
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" npv = base_benefits_pv - costs_pv\n",
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" rows.append({\n",
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" \"AI cost/yr\": f\"${ai_annual:,.0f}\" + (\" ← your input\" if ai_annual == AI_TOKEN_ANNUAL_COST and ai_annual else \"\"),\n",
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" \"Costs PV\": f\"${costs_pv:,.0f}\",\n",
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" \"NPV\": f\"${npv:,.0f}\",\n",
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" \"ROI\": f\"{npv / costs_pv * 100:,.0f}%\",\n",
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" })\n",
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"\n",
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"display(pd.DataFrame(rows))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6516270c",
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"metadata": {},
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"source": [
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"## Push the AI token cost to Athena\n",
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"\n",
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"Writes the annual cost into `genesys_ai_tokens` (quote details in the notes),\n",
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"recalculates server-side, and saves a version."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c6caacea",
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"metadata": {},
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"outputs": [],
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"source": [
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"PUSH = False # ← set True once AI_TOKEN_ANNUAL_COST is final\n",
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"\n",
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"if PUSH:\n",
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" note = (\n",
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" f\"AI Experience tokens: ${AI_TOKEN_ANNUAL_COST:,.0f}/yr. \"\n",
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" + (f\"{AI_TOKEN_QUOTE_NOTE} \" if AI_TOKEN_QUOTE_NOTE else \"\")\n",
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" + \"Line absent from the published Forrester study.\"\n",
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" )\n",
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" client.update_values(TOOL_ID, [{\n",
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" \"field_key\": \"genesys_ai_tokens\",\n",
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" \"year_values\": {\"1\": round(AI_TOKEN_ANNUAL_COST, 2),\n",
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" \"2\": round(AI_TOKEN_ANNUAL_COST, 2),\n",
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" \"3\": round(AI_TOKEN_ANNUAL_COST, 2)},\n",
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" \"notes\": note,\n",
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" }])\n",
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" summary = client.calculate(TOOL_ID)\n",
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" client.print_summary(TOOL_ID)\n",
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" client.save_version(TOOL_ID, note=f\"AI token cost set: {note}\")\n",
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" print(\"✅ Pushed, recalculated, and versioned. Re-run the visualization \"\n",
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" \"cells above to refresh the charts.\")\n",
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"else:\n",
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" print(\"Dry run — set PUSH = True to write to Athena.\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c261ad48",
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"metadata": {},
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"source": [
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"## Next steps\n",
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"\n",
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"- Adjust other drivers for the client (interaction volume, agent count,\n",
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" self-service delta) via `client.update_values` or the Streamlit app\n",
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" (`make app`), saving a version per scenario.\n",
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"- Export charts for a deck: any figure object supports\n",
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" `fig.write_image(\"chart.png\")` (needs `pip install kaleido`) or\n",
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" `fig.write_html(\"chart.html\")`.\n",
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"- Export for the report pipeline:\n",
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" `python -m palladium export $PALLADIUM_GENESYSCX_TOOL_PUBLIC_ID -o exports/export.json`"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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} |