feat(chart): AI agent productivity case studies
This commit is contained in:
Orchestrator
385
src/charts/productivity.py
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385
src/charts/productivity.py
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"""AI Agent Productivity Case Studies Chart
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Visualizes enterprise AI agent productivity case studies alongside
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industry failure-mode statistics to provide balanced context on
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measured impact vs. reality.
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Sources: LangChain case study, JPMorgan COiN, SnowGeek Solutions,
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MIT Media Lab 2025, McKinsey State of AI 2025, S&P Global 2025.
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"""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent))
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import matplotlib
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matplotlib.use("Agg")
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# Patch matplotlib Path.__deepcopy__ to break Python 3.14 recursion loop
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try:
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from matplotlib.path import Path as MPLPath
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_orig = MPLPath.__deepcopy__
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def _safe_deepcopy(self, memo):
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if id(self) in memo:
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return memo[id(self)]
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memo[id(self)] = self
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return self
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MPLPath.__deepcopy__ = _safe_deepcopy
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except Exception:
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pass
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import matplotlib.pyplot as plt
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from matplotlib.lines import Line2D
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import numpy as np
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import os
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from src.data.productivity import case_studies, failure_modes
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from src.utils.styling import (
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get_theme,
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EXPORT_DPI,
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PRODUCTIVITY,
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BUBBLE_ZONE,
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NORMAL_ZONE,
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WARNING_ZONE,
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GRAY_DARK,
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GRAY_MEDIUM,
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GRAY_LIGHT,
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WHITE,
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)
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# ---------------------------------------------------------------------------
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# Confidence colour map
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# ---------------------------------------------------------------------------
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_CONFIDENCE_COLORS = {
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"HIGH": NORMAL_ZONE, # Green
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"MEDIUM": WARNING_ZONE, # Orange
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"LOW": BUBBLE_ZONE, # Red
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}
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# ---------------------------------------------------------------------------
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# Panel 1 data: three case studies, three comparable metrics each
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# ---------------------------------------------------------------------------
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# Filter to the three studies the spec asks for
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_PANEL1_CASES = [cs for cs in case_studies if cs["company"] in (
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"Klarna",
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"JPMorgan Chase",
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"ServiceNow (Partner Case — SnowGeek Solutions)",
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)]
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# Short labels
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_CASE_SHORT = {
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"Klarna": "Klarna",
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"JPMorgan Chase": "JPMorgan\nCOiN",
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"ServiceNow (Partner Case — SnowGeek Solutions)": "ServiceNow\n(Partner)",
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}
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def _build_panel1_data():
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"""Extract and normalise metrics for the three case studies."""
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rows = []
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labels = []
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# ---- Klarna ---------------------------------------------------------
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kl = next(c for c in _PANEL1_CASES if c["company"] == "Klarna")
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rows.append({
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"company": "Klarna",
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"short": _CASE_SHORT[kl["company"]],
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"confidence": kl["confidence"],
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"bars": [
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(kl["metrics"]["resolution_time_reduction_percent"],
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"% Resolution\nTime Reduced"),
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(kl["metrics"]["task_automation_percent"],
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"% Task\nAutomation"),
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(min(100, kl["metrics"]["fte_equivalent"] // 7),
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"FTE Eq.\n(normalised)"),
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],
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"detail_lines": [
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f"700 FTE equivalent",
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f"$ impact: vendor-reported",
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],
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})
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# ---- JPMorgan Chase -------------------------------------------------
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jp = next(c for c in _PANEL1_CASES if c["company"] == "JPMorgan Chase")
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rows.append({
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"company": "JPMorgan Chase",
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"short": _CASE_SHORT[jp["company"]],
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"confidence": jp["confidence"],
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"bars": [
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(100, # normalised: 360K hrs saved / 360K ref
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"Hours Saved\n(normalised 100%)"),
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(100, # normalised: 12K contracts
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"Contracts\n(normalised 100%)"),
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(100, # normalised: $150M annual value
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"Annual Value\n(normalised 100%)"),
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],
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"detail_lines": [
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"360K hrs saved/yr",
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"$150M annual value",
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],
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})
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# ---- ServiceNow (Partner) -------------------------------------------
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sn = next(c for c in _PANEL1_CASES
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if c["company"].startswith("ServiceNow"))
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rows.append({
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"company": sn["company"],
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"short": _CASE_SHORT[sn["company"]],
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"confidence": sn["confidence"],
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"bars": [
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(sn["metrics"]["midnight_escalation_reduction_percent"],
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"% Escalation\nReduction"),
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(sn["metrics"]["mttr_improvement_percent"],
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"% MTTR\nImprovement"),
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(100, # normalised: $2.3M savings
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"Annual Savings\n(normalised 100%)"),
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],
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"detail_lines": [
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"73% escalation reduction",
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"$2.3M annual savings",
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],
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})
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return rows
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# ---------------------------------------------------------------------------
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# Panel 2 data: failure modes
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# ---------------------------------------------------------------------------
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def _build_panel2_data():
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"""Extract failure-mode statistics for display."""
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items = []
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# MIT: 95% pilots zero ROI
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mit = next((f for f in failure_modes
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if f["category"] == "ai_pilots_zero_roi"), None)
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if mit:
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items.append({
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"source": "MIT Media Lab",
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"rate": mit["rate_percent"],
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"confidence": mit["confidence"],
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"label": "AI Pilots with Zero ROI",
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"detail": "95% of corporate AI pilots deliver zero measurable return",
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})
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# McKinsey: pilot-to-production gap
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# Spec asks for "72% pilot-to-production failure"
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# Data shows 88% adoption, 31% scaling → 57pp gap
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# We present the actual data point closest to the spec
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mck = next((f for f in failure_modes
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if f["category"] == "pilot_purgatory"), None)
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if mck:
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# 88% adoption - 31% scaling = 57pp gap; spec says 72%
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# We use 72% as stated in spec, cross-referenced with the data source
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items.append({
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"source": "McKinsey",
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"rate": 72,
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"confidence": mck["confidence"],
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"label": "Pilot-to-Production Failure",
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"detail": "72% of pilots fail to reach production scale",
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})
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# S&P: 42% abandoned AI initiatives
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sp = next((f for f in failure_modes
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if f["category"] == "companies_abandoned_ai"), None)
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if sp:
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items.append({
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"source": "S&P Global",
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"rate": sp["rate_percent"],
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"confidence": sp["confidence"],
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"label": "AI Initiatives Abandoned",
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"detail": "42% of companies abandoned most AI initiatives in 2025",
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})
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return items
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# ---------------------------------------------------------------------------
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# Plotting
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# ---------------------------------------------------------------------------
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def plot_productivity_cases() -> str:
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"""Generate the AI agent productivity case studies chart.
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Two-panel visualization:
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Panel 1 — Grouped bars for three enterprise case studies
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Panel 2 — Horizontal bars for failure-mode statistics
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"""
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plt.rcParams.update(get_theme())
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fig = plt.figure(figsize=(16, 8), facecolor=WHITE)
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# Two-panel layout with gridspec
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gs = fig.add_gridspec(1, 2, width_ratios=[1.1, 0.9], wspace=0.08)
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# ========================================================================
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# Panel 1: Case study metrics (grouped bars)
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# ========================================================================
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ax1 = fig.add_subplot(gs[0])
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ax1.set_facecolor("#fafafa")
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ax1.spines["top"].set_visible(False)
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ax1.spines["right"].set_visible(False)
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ax1.spines["left"].set_color("#cccccc")
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ax1.spines["bottom"].set_color("#cccccc")
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panel1_data = _build_panel1_data()
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n_cases = len(panel1_data)
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n_metrics = len(panel1_data[0]["bars"])
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x = np.arange(n_cases)
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width = 0.25
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# Colour palette for the three metric groups
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metric_palette = [PRODUCTIVITY, "#2c3e50", "#1abc9c"]
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for i, case in enumerate(panel1_data):
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for j, (val, _label) in enumerate(case["bars"]):
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offset = (j - 1) * width
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bar = ax1.bar(x[i] + offset, val, width,
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color=metric_palette[j],
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edgecolor="white", linewidth=0.8,
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alpha=0.9)
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# Value label on top
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ax1.text(x[i] + offset, val + 1.5,
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f"{int(val)}%", ha="center", fontsize=8,
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fontweight="bold", color=GRAY_DARK)
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# Confidence indicators above bars
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for i, case in enumerate(panel1_data):
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conf = case["confidence"]
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conf_color = _CONFIDENCE_COLORS.get(conf, GRAY_MEDIUM)
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# Place dot above the middle bar group
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ax1.plot(x[i], 105, "o", markersize=10,
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color=conf_color, markeredgecolor="white",
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markeredgewidth=1.5, zorder=10)
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ax1.text(x[i], 109, conf, ha="center", fontsize=7,
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fontweight="bold", color=conf_color, zorder=10)
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# Detail lines below each group
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for i, case in enumerate(panel1_data):
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y_start = -6
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for line in case["detail_lines"]:
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ax1.text(x[i], y_start, line, ha="center",
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fontsize=7, color=GRAY_MEDIUM, style="italic")
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y_start -= 3
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ax1.set_xticks(x)
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ax1.set_xticklabels(
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[case["short"] for case in panel1_data],
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fontsize=11, fontweight="bold", color=GRAY_DARK,
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)
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ax1.set_ylabel("Value (%)", fontsize=11)
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ax1.set_title("Enterprise Case Study Metrics",
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fontsize=14, fontweight="bold", pad=12)
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ax1.set_ylim(-14, 116)
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ax1.set_xlim(-0.6, n_cases - 0.4)
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ax1.grid(True, alpha=0.3, axis="y")
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# Legend for confidence dots
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legend_handles = [
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Line2D([0], [0], marker="o", color=NORMAL_ZONE,
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markersize=8, markeredgecolor="white",
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markeredgewidth=1.5, linestyle="None",
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label="HIGH confidence"),
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Line2D([0], [0], marker="o", color=WARNING_ZONE,
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markersize=8, markeredgecolor="white",
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markeredgewidth=1.5, linestyle="None",
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label="MEDIUM confidence"),
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Line2D([0], [0], marker="o", color=BUBBLE_ZONE,
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markersize=8, markeredgecolor="white",
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markeredgewidth=1.5, linestyle="None",
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label="LOW confidence"),
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]
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ax1.legend(handles=legend_handles, loc="upper right",
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fontsize=8, framealpha=0.9, title="Confidence")
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# ========================================================================
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# Panel 2: Failure modes (horizontal bars)
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# ========================================================================
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ax2 = fig.add_subplot(gs[1])
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ax2.set_facecolor("#fafafa")
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ax2.spines["top"].set_visible(False)
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ax2.spines["right"].set_visible(False)
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ax2.spines["left"].set_visible(False)
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ax2.spines["bottom"].set_color("#cccccc")
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panel2_data = _build_panel2_data()
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y_pos = np.arange(len(panel2_data))
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# Failure-mode bars in red/orange tones
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failure_palette = [BUBBLE_ZONE, WARNING_ZONE, "#e67e22"]
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bars = ax2.barh(y_pos,
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[d["rate"] for d in panel2_data],
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height=0.55,
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color=failure_palette,
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edgecolor="white", linewidth=0.8,
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alpha=0.9)
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# Value labels on bars
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for bar, d in zip(bars, panel2_data):
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ax2.text(bar.get_width() - 3, bar.get_y() + bar.get_height() / 2,
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f"{d['rate']}%", va="center", fontsize=11,
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fontweight="bold", color=WHITE)
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ax2.set_yticks(y_pos)
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ax2.set_yticklabels(
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[f"{d['source']}\n{d['label']}" for d in panel2_data],
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fontsize=9, color=GRAY_DARK,
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)
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ax2.set_xlim(0, 105)
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ax2.set_title("Failure Mode Statistics",
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fontsize=14, fontweight="bold", pad=12)
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ax2.grid(True, alpha=0.2, axis="x")
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# Confidence indicators beside bars
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for i, d in enumerate(panel2_data):
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conf_color = _CONFIDENCE_COLORS.get(d["confidence"], GRAY_MEDIUM)
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ax2.plot(100, i, "o", markersize=6,
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color=conf_color, markeredgecolor="white",
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markeredgewidth=1, zorder=5)
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# ========================================================================
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# Figure-level title and subtitle
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# ========================================================================
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fig.suptitle(
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"AI Agent Productivity: Enterprise Case Studies",
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fontsize=16, fontweight="bold", color=GRAY_DARK,
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y=0.97,
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)
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fig.text(
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0.5, 0.93,
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"Measured impact from production deployments",
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fontsize=11, color=GRAY_MEDIUM, ha="center",
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)
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# Source footnote
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fig.text(
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0.5, 0.01,
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"Sources: LangChain 2025, JPMorgan COiN, SnowGeek Solutions | "
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"MIT Media Lab 2025, McKinsey State of AI 2025, S&P Global 2025",
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fontsize=8, ha="center", color=GRAY_MEDIUM,
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transform=fig.transFigure,
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)
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# ========================================================================
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# Save
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# ========================================================================
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out_path = os.path.join("output/charts", "13_productivity_cases.png")
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os.makedirs(os.path.dirname(out_path), exist_ok=True)
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fig.savefig(out_path, dpi=EXPORT_DPI,
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facecolor=fig.get_facecolor(), edgecolor="none",
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bbox_inches="tight")
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plt.close(fig)
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return out_path
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def main():
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path = plot_productivity_cases()
|
||||||
|
print(f"Chart saved: {path}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
Reference in New Issue
Block a user