PLS job f6831150 frontline AI adoption operating system production pack

acceptance-tests.md

Acceptance Tests

1. Primary artifact URL returns HTTP 200.
2. Required appendix files exist: production brief, data model, acceptance tests, decision record, artifact URL record.
3. D1 / D7 / D14 / D30 path exists.
4. Purpose-to-purpose E2E connects course insight to pilot, adoption evidence, and business metric.
5. At least three frontline workflows are defined for D7 pilot.
6. Each pilot has champion owner, sponsor, metric, due date, and governance boundary.
7. Data model includes schema, API, sync, permissions, and audit.
8. Market maturity includes at least two external sources.
9. People sync includes a LINE-ready ask.
10. Learning memory exists.

E2E Scenario

Given a frontline team submits a weekly pain point, when a champion and sponsor approve a pilot, then the system records baseline, AI-assisted workflow, adoption metric, governance boundary, and D7 decision of scale, continue, pause, or kill.

artifact-url-or-pr.md

Artifact URL or PR

Primary artifact URL: https://gist.github.com/esz135888/93af84216011df495c179c2a0eb3ee6d#file-frontline-ai-adoption-operating-system-html

Artifact type: public Gist production pack.

data-model.md

Data Model

frontline_ai_use_cases

• id: uuid primary key.
• team: text.
• frontline_owner_id: uuid.
• sponsor_id: uuid.
• pain_point: text.
• weekly_frequency: integer.
• current_workflow: text.
• target_outcome: text.
• risk_level: enum low, medium, high.
• status: enum intake, pilot, scaled, paused, killed.

workflow_pilots

• id: uuid primary key.
• use_case_id: uuid.
• before_steps: jsonb.
• after_steps: jsonb.
• ai_tool: text.
• human_review_required: boolean.
• started_at: timestamptz.
• due_at: timestamptz.
• decision: enum continue, scale, pause, kill, unknown.

adoption_metrics

• id: uuid primary key.
• pilot_id: uuid.
• metric_type: enum time_saved, error_rate, customer_response_time, revenue_influence, risk_reduction, employee_satisfaction.
• baseline_value: numeric.
• current_value: numeric.
• unit: text.
• evidence_ref: text.
• measured_at: timestamptz.

champion_reviews

• id: uuid primary key.
• pilot_id: uuid.
• reviewer_id: uuid.
• adoption_blocker: text.
• recommended_change: text.
• created_at: timestamptz.

governance_exceptions

• id: uuid primary key.
• pilot_id: uuid.
• risk: text.
• approval_owner_id: uuid.
• status: enum pending, approved, rejected, expired.
• audit_reason: text.

API / Sync

• POST /ai-adoption/use-cases
• POST /ai-adoption/use-cases/:id/pilot
• POST /ai-adoption/pilots/:id/metrics
• GET /ai-adoption/scorecard
• POST /ai-adoption/governance-exceptions

Permissions / Audit

Frontline champions can submit use cases and metrics. Sponsors approve workflow changes. Governance owners approve risky data/tool use. Louis can decide scale/pause/kill. All score changes and approvals are append-only audit events.

decision-record.md

Decision Record

Decision

Use project / system for this round.

Why

The insight is about production relations and bottom-up frontline adoption. A research memo would preserve the idea but not change behavior. A full system is too early before pilots. The right first production artifact is a project operating system that can become a scorecard/workflow app after D7 pilots.

Options Considered

• Research memo: too passive.
• Communication: too small; cannot measure adoption and value.
• Presentation: useful later for leadership decision, but premature before pilot evidence.
• Project / system: best path; creates use case intake, champion network, metrics, governance, and a D30 system upgrade.

Adoption Status

Recommended. Start with 3 frontline workflow pilots.

Feedback Needed If Rejected

Clarify whether the blocker is lack of frontline owners, unclear AI tool policy, missing metrics, or no sponsor authority.

e2e-verification.md

E2E Verification

Status: pass.

Verified target:

• Primary artifact URL: https://gist.github.com/esz135888/93af84216011df495c179c2a0eb3ee6d#file-frontline-ai-adoption-operating-system-html
• Gist pack URL: https://gist.github.com/esz135888/93af84216011df495c179c2a0eb3ee6d

Checks:

• Primary artifact URL returns HTTP 200.
• Gist includes primary HTML and appendices.
• Upload-files succeeds for the deliverable.
• Complete uses stable shared cloud URLs.

frontline-ai-adoption-operating-system.html

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  <title>大模型企業落地一線採用作戰系統</title>
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<body>
  <header>
    <span class="pill info">PLS production delivery pack</span><span class="pill ok">Solution: project / system</span>
    <h1>大模型企業落地一線採用作戰系統</h1>
    <p class="sub">把外部課程筆記「大模型企業落地的核心是生產關係調整，自下而上調動一線員工積極性」產品化成可執行 operating system：一線場景、champion network、AI 任務改造、採用證據、價值指標、治理邊界與下一輪工具化。</p>
    <section class="grid kpis">
      <div class="card"><div class="metric">D1</div><div class="label">定義一線採用場景與 champion owner</div></div>
      <div class="card"><div class="metric ok">70%</div><div class="label">轉型重心放在人與流程，不只工具</div></div>
      <div class="card"><div class="metric warn">28%</div><div class="label">BCG 指出一線員工 AI 訓練落後領導層</div></div>
      <div class="card"><div class="metric">D30</div><div class="label">形成 AI adoption scorecard 與錢路徑</div></div>
    </section>
  </header>
  <main class="grid">
    <section class="grid two">
      <div class="card">
        <h2>本輪問題</h2>
        <p>這份課程筆記的價值不是觀點本身，而是提醒：AI 導入失敗常因為權力、流程和一線動機沒有改。若只由一把手下令，會得到表面使用率；若讓一線把痛點、流程改造和 AI 工具權限接起來，才會生成可持續的生產關係。</p>
        <span class="pill">Owner: Louis / AI transformation lead</span><span class="pill">Due: D7 first pilot</span><span class="pill">Acceptance: 3 frontline workflows measured</span>
      </div>
      <div class="card">
        <h2>解法選型</h2>
        <p><strong>project / system</strong>。先用 project pack 建立一線 champion、採用節奏、RACI 和指標，再升級成 adoption scorecard / workflow app。不是 research memo，也不是只做培訓簡報。</p>
      </div>
    </section>

    <section class="card">
      <h2>D1 / D7 / D14 / D30 路徑</h2>
      <div class="grid timeline">
        <div class="card day"><h3>D1</h3><p>選 3 個一線高頻痛點，任命 champion，定義 before/after 工作流與資料欄位。</p></div>
        <div class="card day"><h3>D7</h3><p>完成 3 個 AI-assisted workflow pilot，記錄節省時間、錯誤率、採用阻力與權限缺口。</p></div>
        <div class="card day"><h3>D14</h3><p>建立 champion network 與 weekly adoption review，把有效做法變 SOP / prompt / tool。</p></div>
        <div class="card day"><h3>D30</h3><p>形成 adoption scorecard，將一線使用證據、價值、治理與預算加碼決策同表管理。</p></div>
      </div>
    </section>

    <section class="card">
      <h2>Purpose-to-Purpose E2E</h2>
      <div class="grid flow">
        <div class="step"><strong>原始目的</strong>大模型企業落地要改變生產關係，而非上層命令使用工具。</div>
        <div class="step"><strong>產出物</strong>一線採用作戰台、champion network、workflow pilot、scorecard。</div>
        <div class="step"><strong>人採用</strong>一線提出痛點、共同設計 AI 工作流、回報阻力與收益。</div>
        <div class="step"><strong>指標改善</strong>節省時間、錯誤率下降、客戶回覆加速、流程透明、工具選型收斂。</div>
        <div class="step"><strong>錢路徑</strong>釋放人力、降低返工、提高成交/交付速度，將有效 pilot 轉成付費方案或內部標準工具。</div>
      </div>
    </section>

    <section class="grid two">
      <div class="card">
        <h2>Adoption Gates</h2>
        <table>
          <thead><tr><th>Gate</th><th>Pass Rule</th><th>Self-Heal</th></tr></thead>
          <tbody>
            <tr><td>frontline_problem</td><td>痛點由一線提出，且每週發生。</td><td>若只有主管假設，退回訪談。</td></tr>
            <tr><td>workflow_redesign</td><td>有 before/after 流程、責任和輸入輸出。</td><td>缺流程圖則轉為 SOP 任務。</td></tr>
            <tr><td>champion_owner</td><td>每個 pilot 有一線 champion 與主管 sponsor。</td><td>缺任一方不得進 D7 pilot。</td></tr>
            <tr><td>value_measure</td><td>有時間、品質、收入、風險任一量化指標。</td><td>沒有 baseline 則先跑 3 天人工紀錄。</td></tr>
            <tr><td>governance_boundary</td><td>資料、權限、審批、錯誤責任清楚。</td><td>高風險使用場景需 supervisor approval。</td></tr>
          </tbody>
        </table>
      </div>
      <div class="card">
        <h2>資料 / API / 權限</h2>
        <p><strong>Tables:</strong> <code>frontline_ai_use_cases</code>, <code>workflow_pilots</code>, <code>champion_reviews</code>, <code>adoption_metrics</code>, <code>governance_exceptions</code>.</p>
        <p><strong>APIs:</strong> <code>POST /ai-adoption/use-cases</code>, <code>POST /ai-adoption/pilots/:id/metrics</code>, <code>GET /ai-adoption/scorecard</code>.</p>
        <p><strong>Permissions:</strong> frontline champion can submit metrics; sponsor approves workflow change; governance owner approves risky data/tool use.</p>
      </div>
    </section>

    <section class="grid three">
      <div class="card"><h2>價值 / 錢路徑</h2><p>把 AI 從口號變成 workflow ROI：每個 pilot 要能回收時間、降低返工、增加成交或交付速度，D30 決定加碼/停止。</p></div>
      <div class="card"><h2>人的能力提升</h2><p>一線員工學會描述流程、衡量改善、提出工具需求；主管學會用證據支持而非命令推行。</p></div>
      <div class="card"><h2>下一輪升級</h2><p>接 PLS 後台，讓一線提交 use case、AI 自動生成 SOP、週會輸出 adoption scorecard。</p></div>
    </section>

    <section class="card source">
      <h2>Market Maturity Inputs</h2>
      <p>McKinsey 指出 scaling gen AI 需要 operating model，涵蓋 staffing、organization、technology、compliance 和 measurement: <a href="https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/a-data-leaders-operating-guide-to-scaling-gen-ai?sid=soc-POST_ID">McKinsey gen AI operating model</a>.</p>
      <p>BCG AI at Work 2024 調查指出 managers 和 frontline employees 的 AI 訓練率落後 leaders，AI adoption 是管理挑戰: <a href="https://www.bcg.com/publications/2024/ai-at-work-friend-foe">BCG AI at Work 2024</a>.</p>
      <p>BCG factory-floor AI research 強調 70/20/10：70% effort on people and business transformation, 20% data/tech backbone, 10% algorithms: <a href="https://www.bcg.com/publications/2024/shaking-up-the-factory-floor-with-digital-and-ai">BCG Shaking Up the Factory Floor</a>.</p>
    </section>
  </main>
</body>
</html>

learning-memory.json

{
  "project": "AI 自建專案：外部課程筆記：大模型企業落地",
  "job_id": "f6831150-43dd-4a83-9891-01c8d2e2c9fc",
  "selected_solution": "project/system",
  "learned_signal": "Enterprise LLM adoption depends on production relation redesign and bottom-up frontline motivation, not top-down mandate alone.",
  "market_learning": "Mature gen AI adoption uses operating model redesign, frontline training, champions, metrics, governance, and more effort on people/process than algorithms.",
  "next_run_bias": "Prefer frontline use case pilots and adoption scorecards over research summaries.",
  "must_check_next": [
    "Which 3 frontline workflows are selected?",
    "Who is champion and sponsor for each pilot?",
    "What baseline metric exists?",
    "What governance boundary blocks tool use?",
    "What D7 scale/continue/pause/kill decision was made?"
  ]
}

market-maturity.md

Market Maturity

Comparable Practices

• McKinsey gen AI operating model: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/a-data-leaders-operating-guide-to-scaling-gen-ai?sid=soc-POST_ID
• BCG AI at Work 2024: https://www.bcg.com/publications/2024/ai-at-work-friend-foe
• BCG Shaking Up the Factory Floor with Digital and AI: https://www.bcg.com/publications/2024/shaking-up-the-factory-floor-with-digital-and-ai

PLS Gap

The insight exists, but it needs production shape: frontline use case intake, champion network, workflow pilots, baseline metrics, governance boundaries, and scale/kill decision cadence.

This Round Upgrade

This pack turns the note into an operating system with data model, APIs, adoption gates, people sync, and D30 scorecard path.

people-sync.md

People Sync

LINE Draft

Louis，這輪我把「大模型企業落地不是一把手強推，而是生產關係調整、一線自下而上採用」做成一線 AI adoption 作戰台。D1 先選 3 個一線每週高頻痛點，各自指定 champion 和 sponsor；D7 跑 3 個 AI-assisted workflow pilot，量測省時、錯誤率、客戶回覆速度或收入影響；D30 再決定哪些變成 SOP / tool / system。

Ask

請指定第一批 3 個一線 workflow：業務、客服、營運、財務或交付哪三個先跑？

If No Reply

先用「每週重複、可量化、低權限風險」三條件挑候選，不做高風險資料場景。

production-brief.md

Production Brief

場景

專案：AI 自建專案：外部課程筆記：大模型企業落地的核心是生產關係調整，強調自下而上調動一線員工積極性，而非一把手自上而下強制推行。

這不是摘要任務，而是把課程 insight 轉成可落地的 enterprise AI adoption operating system。

本輪產出

建立「大模型企業落地一線採用作戰系統」，包含一線 use case intake、champion network、workflow pilot、adoption gates、資料模型、權限、驗收、people sync 和 learning memory。

D1 / D7 / D14 / D30

• D1: 選 3 個一線高頻痛點，任命 champion，定義 before/after 工作流。
• D7: 完成 3 個 AI-assisted workflow pilot，量測時間、品質、阻力與權限缺口。
• D14: 建立 champion network 和 weekly adoption review。
• D30: 形成 AI adoption scorecard，支援加碼、停止、工具化與治理決策。

Owner / Due / Acceptance

• Owner: Louis / AI transformation lead.
• Field owners: frontline champions.
• Due: D7 first pilot.
• Acceptance: 3 個一線 workflow 有 baseline、AI 改造、採用證據、價值指標、治理邊界和 next action。

production-readiness.md

Production Readiness

Ready Now

• Openable adoption operating console.
• Required production appendix pack.
• D1/D7/D14/D30 path.
• Data model and API/sync spec.
• Acceptance tests, people sync, learning memory.

Integration Required

• Add frontline use case intake in PLS.
• Connect pilot metrics to weekly review.
• Create champion network owner field.
• Add governance approval for high-risk AI use.

Failure / Rollback

If no frontline owner exists, do not run pilot. If no metric baseline exists, run manual baseline collection first. If governance boundary is high-risk and unapproved, block use case until approval.

skill-usage.md

Skill / Tool Usage

Tools Used

• PLS helper: doctor, touch, claim, context, progress, upload-files, complete.
• Web search: checked McKinsey, BCG AI at Work, and BCG factory-floor AI adoption practices.
• GitHub CLI: publishes production pack as a public Gist.
• curl: verifies primary artifact URL returns HTTP 200.

Evidence

The job was claimed and context was read through helper commands. Progress was written before production work. External maturity references were checked. Final artifact is published and verified before completion.

solution-selection.md

Solution Selection

Selected route: project / system.

This is an enterprise AI adoption operating problem. The smallest useful artifact is a project pack with system-ready data model, not a standalone document. It must move from insight to workflow pilot to adoption metrics to scale/kill decisions.

Production stack:

• Framework: frontline problem -> champion -> workflow pilot -> metric -> governance -> scale decision.
• Workflow: D1 intake, D7 pilot, D14 review, D30 scorecard.
• Data model: use cases, pilots, metrics, champion reviews, governance exceptions.
• Tool: openable HTML operating console.
• Acceptance: 3 measurable frontline pilots.
• Upgrade: PLS adoption scorecard and workflow app.