{
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.13.0"
    },
    "blog_metadata": {
      "topic": "How Copilot in PowerPoint for Government Clouds Changes the Playbook for Regulated Productivity",
      "slug": "how-copilot-in-powerpoint-for-government-clouds-changes-the-",
      "generated_by": "LinkedIn Post Generator + Azure OpenAI",
      "generated_at": "2026-06-30T18:46:04.993Z"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# How Copilot in PowerPoint for Government Clouds Changes the Playbook for Regulated Productivity\n",
        "\n",
        "This notebook turns the blog post into a hands-on validation workflow for regulated Microsoft 365 Copilot planning. It focuses on readiness gates, licensing visibility, policy posture, pilot adoption, and executive reporting using Python-based mock data so teams can test governance logic before production rollout. Feature scope and timing can vary by government cloud environment, tenant, and release stage, so treat these exercises as planning patterns rather than proof of service availability."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "%pip install pandas matplotlib"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "import csv\n",
        "from io import StringIO\n",
        "from collections import Counter, defaultdict\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Why this matters beyond one PowerPoint feature\n",
        "\n",
        "The core argument is that government-cloud Copilot is not just a feature milestone; it is a governance milestone. In regulated environments, the practical question is no longer whether AI productivity tools deserve attention, but whether the organization is ready to govern them before users normalize them.\n",
        "\n",
        "A useful rollout sequence is:\n",
        "1. Government cloud tenant readiness\n",
        "2. Inventory prerequisites\n",
        "3. Review licensing visibility\n",
        "4. Validate policy posture\n",
        "5. Pilot Copilot in PowerPoint\n",
        "6. Export usage and exception data\n",
        "7. Governance reporting\n",
        "8. Executive review and scale decision"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "rollout_steps = [\n",
        "    \"Government cloud tenant readiness\",\n",
        "    \"Inventory prerequisites\",\n",
        "    \"Review licensing visibility\",\n",
        "    \"Validate policy posture\",\n",
        "    \"Pilot Copilot in PowerPoint\",\n",
        "    \"Export usage and exception data\",\n",
        "    \"Governance reporting\",\n",
        "    \"Executive review and scale decision\",\n",
        "]\n",
        "\n",
        "for i, step in enumerate(rollout_steps, start=1):\n",
        "    print(f\"{i}. {step}\")\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 2.5))\n",
        "ax.axis(\"off\")\n",
        "for i, step in enumerate(rollout_steps):\n",
        "    x = i\n",
        "    ax.text(x, 0.5, step, ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"round,pad=0.4\", fc=\"#D9EAF7\", ec=\"#4A6FA5\"))\n",
        "    if i < len(rollout_steps) - 1:\n",
        "        ax.annotate(\"\", xy=(x + 0.55, 0.5), xytext=(x + 0.15, 0.5), arrowprops=dict(arrowstyle=\"->\", lw=1.5))\n",
        "ax.set_xlim(-0.5, len(rollout_steps) - 0.5)\n",
        "ax.set_ylim(0, 1)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Readiness inventory for a regulated rollout\n",
        "\n",
        "This example converts the blog's illustrative PowerShell readiness object into Python. The goal is to make core Copilot readiness signals visible to an executive sponsor before any pilot begins."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "readiness = {\n",
        "    \"TenantName\": \"contoso-gov.onmicrosoft.com\",\n",
        "    \"Cloud\": \"GCC High\",\n",
        "    \"PowerPointDesktopManaged\": True,\n",
        "    \"Microsoft365AppsCurrent\": True,\n",
        "    \"SensitivityLabelsPublished\": True,\n",
        "    \"PurviewAuditEnabled\": True,\n",
        "    \"DlpPoliciesEnabled\": True,\n",
        "    \"ConditionalAccessBaseline\": True,\n",
        "    \"ApprovedPilotGroup\": \"M365-Copilot-PowerPoint-Pilot\",\n",
        "}\n",
        "\n",
        "readiness_df = pd.DataFrame(list(readiness.items()), columns=[\"Signal\", \"Value\"])\n",
        "print(readiness_df.to_string(index=False))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Licensing visibility for pilot users\n",
        "\n",
        "The blog recommends validating who is actually ready for pilot participation instead of assuming availability. This Python version summarizes exported assignment data and calculates a simple `ReadyForPilot` flag."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "licenses = [\n",
        "    {\"UserPrincipalName\": \"alex@contoso.gov\", \"CopilotLicense\": True, \"PowerPointApps\": True, \"PilotGroup\": True},\n",
        "    {\"UserPrincipalName\": \"sam@contoso.gov\", \"CopilotLicense\": True, \"PowerPointApps\": True, \"PilotGroup\": False},\n",
        "    {\"UserPrincipalName\": \"lee@contoso.gov\", \"CopilotLicense\": False, \"PowerPointApps\": True, \"PilotGroup\": True},\n",
        "]\n",
        "\n",
        "licenses_df = pd.DataFrame(licenses)\n",
        "licenses_df[\"ReadyForPilot\"] = (\n",
        "    licenses_df[\"CopilotLicense\"]\n",
        "    & licenses_df[\"PowerPointApps\"]\n",
        "    & licenses_df[\"PilotGroup\"]\n",
        ")\n",
        "\n",
        "print(licenses_df[[\"UserPrincipalName\", \"ReadyForPilot\"]].to_string(index=False))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Policy posture exception reporting\n",
        "\n",
        "Safe rollout usually depends on ordinary enterprise controls such as MFA, managed devices, and training completion. This example flags users whose posture would block a compliant Copilot in PowerPoint pilot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "users = [\n",
        "    {\"User\": \"alex@contoso.gov\", \"MFA\": True, \"ManagedDevice\": True, \"SensitivityTraining\": True},\n",
        "    {\"User\": \"sam@contoso.gov\", \"MFA\": True, \"ManagedDevice\": False, \"SensitivityTraining\": True},\n",
        "    {\"User\": \"lee@contoso.gov\", \"MFA\": False, \"ManagedDevice\": True, \"SensitivityTraining\": False},\n",
        "]\n",
        "\n",
        "exceptions = []\n",
        "for u in users:\n",
        "    if not (u[\"MFA\"] and u[\"ManagedDevice\"] and u[\"SensitivityTraining\"]):\n",
        "        issues = []\n",
        "        if not u[\"MFA\"]:\n",
        "            issues.append(\"MFA missing\")\n",
        "        if not u[\"ManagedDevice\"]:\n",
        "            issues.append(\"Device not managed\")\n",
        "        if not u[\"SensitivityTraining\"]:\n",
        "            issues.append(\"Training incomplete\")\n",
        "        exceptions.append({\"User\": u[\"User\"], \"Exception\": \"; \".join(issues)})\n",
        "\n",
        "exceptions_df = pd.DataFrame(exceptions)\n",
        "print(exceptions_df.to_string(index=False))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Governance data flow from export to executive review\n",
        "\n",
        "The blog includes a sequence diagram showing how admins export pilot usage, licensing, and policy exceptions; governance scripts aggregate the data; and executives decide whether to scale, remediate, or extend the pilot. The code below simulates that end-to-end flow with small in-memory datasets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "usage_csv = StringIO(\"\"\"user,department,prompts_used,deck_creations\n",
        "alex@contoso.gov,Operations,18,6\n",
        "sam@contoso.gov,Finance,7,2\n",
        "lee@contoso.gov,Operations,0,0\n",
        "\"\"\")\n",
        "\n",
        "license_csv = StringIO(\"\"\"user,CopilotLicense,PowerPointApps,PilotGroup\n",
        "alex@contoso.gov,True,True,True\n",
        "sam@contoso.gov,True,True,False\n",
        "lee@contoso.gov,False,True,True\n",
        "\"\"\")\n",
        "\n",
        "exception_csv = StringIO(\"\"\"user,exception\n",
        "sam@contoso.gov,Device not managed\n",
        "lee@contoso.gov,MFA missing\n",
        "lee@contoso.gov,Training incomplete\n",
        "\"\"\")\n",
        "\n",
        "usage_df = pd.read_csv(usage_csv)\n",
        "license_df = pd.read_csv(license_csv)\n",
        "exception_df = pd.read_csv(exception_csv)\n",
        "\n",
        "license_df[\"ReadyForPilot\"] = license_df[[\"CopilotLicense\", \"PowerPointApps\", \"PilotGroup\"]].all(axis=1)\n",
        "active_users = int((usage_df[\"prompts_used\"] > 0).sum())\n",
        "summary = {\n",
        "    \"pilot_users\": int(len(usage_df)),\n",
        "    \"active_users\": active_users,\n",
        "    \"adoption_rate\": round(active_users / len(usage_df), 2),\n",
        "    \"ready_for_pilot\": int(license_df[\"ReadyForPilot\"].sum()),\n",
        "    \"policy_exceptions\": int(len(exception_df)),\n",
        "}\n",
        "\n",
        "print(\"Executive-ready summary:\")\n",
        "print(summary)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Pilot adoption executive summary\n",
        "\n",
        "This is the blog's Python example for turning exported Microsoft 365 usage data into a compact executive summary. It helps leaders compare pilot participation with actual usage."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "data = StringIO(\"\"\"user,department,prompts_used,deck_creations\n",
        "alex@contoso.gov,Operations,18,6\n",
        "sam@contoso.gov,Finance,7,2\n",
        "lee@contoso.gov,Operations,0,0\n",
        "\"\"\")\n",
        "\n",
        "rows = list(csv.DictReader(data))\n",
        "active_users = sum(1 for r in rows if int(r[\"prompts_used\"]) > 0)\n",
        "total_prompts = sum(int(r[\"prompts_used\"]) for r in rows)\n",
        "\n",
        "print({\n",
        "    \"pilot_users\": len(rows),\n",
        "    \"active_users\": active_users,\n",
        "    \"adoption_rate\": round(active_users / len(rows), 2),\n",
        "    \"total_prompts\": total_prompts,\n",
        "})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Summarize policy exceptions by category\n",
        "\n",
        "Adoption data alone is not enough in regulated environments. This example counts exception categories so executives can see where control gaps are concentrated."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "exceptions_csv = StringIO(\"\"\"user,exception\n",
        "sam@contoso.gov,Device not managed\n",
        "lee@contoso.gov,MFA missing\n",
        "lee@contoso.gov,Training incomplete\n",
        "\"\"\")\n",
        "\n",
        "counter = Counter()\n",
        "for row in csv.DictReader(exceptions_csv):\n",
        "    counter[row[\"exception\"]] += 1\n",
        "\n",
        "for name, count in counter.most_common():\n",
        "    print(f\"{name}: {count}\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Department-level dashboard view\n",
        "\n",
        "The blog recommends pairing value signals with control gaps. This example creates a compact department-level summary from exported pilot metrics so leaders can compare adoption patterns across teams."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "usage_csv = StringIO(\"\"\"user,department,prompts_used\n",
        "alex@contoso.gov,Operations,18\n",
        "sam@contoso.gov,Finance,7\n",
        "lee@contoso.gov,Operations,0\n",
        "mira@contoso.gov,Finance,11\n",
        "\"\"\")\n",
        "\n",
        "stats = defaultdict(lambda: {\"users\": 0, \"active\": 0, \"prompts\": 0})\n",
        "for row in csv.DictReader(usage_csv):\n",
        "    dept = stats[row[\"department\"]]\n",
        "    prompts = int(row[\"prompts_used\"])\n",
        "    dept[\"users\"] += 1\n",
        "    dept[\"active\"] += int(prompts > 0)\n",
        "    dept[\"prompts\"] += prompts\n",
        "\n",
        "for department, values in stats.items():\n",
        "    print(department, values)\n",
        "\n",
        "stats_df = pd.DataFrame([\n",
        "    {\"department\": department, **values, \"adoption_rate\": round(values[\"active\"] / values[\"users\"], 2)}\n",
        "    for department, values in stats.items()\n",
        "])\n",
        "\n",
        "print(\"\\nTabular view:\")\n",
        "print(stats_df.to_string(index=False))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Export an audit-friendly readiness report\n",
        "\n",
        "The original post includes a PowerShell example that exports a readiness report to CSV. This Python version creates the same kind of audit-friendly artifact for pilot tracking."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "report = [\n",
        "    {\"Control\": \"Licensing visibility\", \"Status\": \"Pass\", \"Owner\": \"M365 Admin\"},\n",
        "    {\"Control\": \"Managed devices\", \"Status\": \"Gap\", \"Owner\": \"Endpoint Team\"},\n",
        "    {\"Control\": \"Sensitivity labels\", \"Status\": \"Pass\", \"Owner\": \"Purview Admin\"},\n",
        "]\n",
        "\n",
        "report_df = pd.DataFrame(report)\n",
        "path = \"copilot_ppt_gov_readiness.csv\"\n",
        "report_df.to_csv(path, index=False)\n",
        "print(f\"Saved readiness report to {path}\")\n",
        "print(report_df.to_string(index=False))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Approved and restricted PowerPoint-generation scenarios\n",
        "\n",
        "The blog argues that regulated organizations should document approved and prohibited scenarios before broad enablement. A compact framework is often more useful than a long policy memo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "scenario_matrix = pd.DataFrame({\n",
        "    \"Approved first\": [\n",
        "        \"First-draft internal briefings from already-permitted source documents\",\n",
        "        \"Executive summary slides for existing policy papers\",\n",
        "        \"Meeting recap decks built from internal notes and files with aligned permissions\",\n",
        "    ],\n",
        "    \"Restricted or prohibited\": [\n",
        "        \"Decks intended for public release without human review\",\n",
        "        \"Synthesis across highly sensitive or compartmented content unless explicitly authorized\",\n",
        "        \"Audience-tailored briefings where classification, export control, or legal privilege is unclear\",\n",
        "    ],\n",
        "})\n",
        "\n",
        "print(scenario_matrix.to_string(index=False))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Source references for validation planning\n",
        "\n",
        "Use the following Microsoft documentation to validate assumptions in your own tenant and government cloud environment:\n",
        "\n",
        "1. Microsoft 365 Copilot hub: https://learn.microsoft.com/en-us/microsoft-365/copilot/\n",
        "2. Microsoft 365 Copilot Chat Privacy and Protections: https://learn.microsoft.com/en-us/copilot/privacy-and-protections\n",
        "3. Microsoft 365 Copilot APIs Overview: https://learn.microsoft.com/en-us/microsoft-365/copilot/extensibility/copilot-apis-overview\n",
        "4. Microsoft 365 Copilot architecture: https://learn.microsoft.com/en-us/microsoft-365/copilot/microsoft-365-copilot-architecture\n",
        "5. Web search and public web access posture: https://learn.microsoft.com/en-us/microsoft-365/copilot/manage-public-web-access\n",
        "6. Copilot Chat FAQ: https://learn.microsoft.com/en-us/copilot/faq\n",
        "7. Purview DLP for Microsoft 365 Copilot and Copilot Chat: https://learn.microsoft.com/en-us/purview/dlp-microsoft365-copilot-location-learn-about\n",
        "8. Copilot Dashboard in Viva Insights: https://learn.microsoft.com/en-us/viva/insights/org-team-insights/copilot-dashboard"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Next Steps\n",
        "\n",
        "This notebook demonstrates the blog's main point: regulated Copilot rollout is primarily a governance and operating-model challenge, not just a feature launch. The most practical path is to validate tenant scope, inventory prerequisites, confirm licensing and policy posture, run a narrow pilot, and pair adoption metrics with compliance exception reporting.\n",
        "\n",
        "Suggested next steps:\n",
        "- Replace mock datasets with your exported tenant data\n",
        "- Validate current support in your specific government cloud\n",
        "- Confirm web grounding, DLP, audit, and sensitivity label posture\n",
        "- Define approved and prohibited PowerPoint-generation scenarios\n",
        "- Build an executive dashboard that shows value signals next to control gaps"
      ]
    }
  ]
}