================================================================================
AI Impact Scanner v2.0 — 8 节点完整审阅报告
Prompt Version: v2.0 | Model: gemini-2.5-flash
================================================================================

Token 统计汇总:
  API calls:          8
  Total tokens:       44,803
  Avg prompt tokens:  1,848
  Avg compl. tokens:  1,570
  Avg response ms:    18,236

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[1/8] 节点 3.3.3 — AI已显著增强定价流程的数据分析和优化能力，但仍需人工策略指导。
================================================================================

A. SYSTEM PROMPT
--------------------------------------------------------------------------------
你是一个企业流程知识分析专家，专门评估AI技术对企业运营流程的影响。

你的任务是对企业流程节点进行AI冲击扫描，输出9个维度的结构化判断和数值特征向量。

## 你必须严格遵守的规则

规则1：判断必须基于有证据的实际发生，而非技术潜力。
规则2：少数领先企业的实践不代表行业普遍状态，须标注。
规则3：企业发布的转型计划不构成变化已发生的证据。
规则4：不确定时如实表达，不得虚构判断。
规则5：每个判断必须附带依据类型标注。
规则6：每项评分依据限1句话，不超过30字。
规则7：对每个维度子项输出1-5分数值评分（0=不适用仅限D3）。

## 输出要求

你必须且只能输出一个合法的JSON对象，不要输出任何JSON之外的内容。

B. USER PROMPT (已填充)
--------------------------------------------------------------------------------
## 待扫描流程节点

节点ID：3.3.3
名称（中文）：制定和管理定价
名称（英文）：Develop and manage pricing
框架：PCF
路径：3.0 营销和销售产品和服务 > 3.3 制定和管理营销计划 > 3.3.3 制定和管理定价
层级：L3
描述：确定并维护一个基于销售预测的定价机制，该机制能够为产品/服务的生命周期实现定价均衡。创建一个定价机制，考虑与市场、客户、销售和生产成本相关的属性。在所有产品/服务上实施此定价机制。分析其绩效，并相应地进行调整。
标签：marketing | sales | pcf

---

请对上述流程节点进行9维度AI冲击扫描。

### D1 AI渗透率
子项：decision_replaceability / processing_acceleration / tacit_knowledge_dependency
每项：rating(高/中/低) + basis(≤30字) + score(1-5)
overall_penetration: 高/中/低

### D2 变化状态
status: 已变/将变/稳定
evidence_type: 类型A/类型B/混合
evidence_source / basis_description

### D3 变化性质（0-5量表，0=不适用）
types_selected: [A/B/C/D]
A=增强型 B=压缩型 C=消亡型 D=涌现型
每类：score(0-5) + description(≤30字，0分可省略)

### D4 人机边界
current_type: 类型1-4
boundary_description / stability(稳定/过渡中/高度不确定) / stability_note

### D5 不确定性
overall_confidence: 高/中/低
uncertainty_sources / special_note

### D6 信号质量
information_period / source_distribution(5项,高/中/低/无) / potential_bias

### D7 流程结构刚性
子项（各1-5分 + basis≤30字）：
- rule_driven_degree: 规则驱动程度
- exception_flexibility: 例外处理灵活性
- feedback_loop_maturity: 反馈闭环成熟度

### D8 数据生态位
子项（各1-5分 + basis≤30字）：
- data_intensity: 数据密集度
- cross_process_dependency: 跨流程数据依赖
- data_standardization: 数据标准化程度
- integration_barrier: 集成壁垒

### D9 AI改造就绪度
子项（各1-5分 + basis≤30字）：
- data_availability: 数据可得性
- tech_maturity: 技术成熟度
- implementation_simplicity: 实施简易度
- value_density: 价值密度

## 输出JSON格式

{
  "node_id": "3.3.3",
  "scan_timestamp": "ISO8601",
  "model_id": "你的模型标识",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {"rating": "高/中/低", "basis": "", "score": 3},
    "processing_acceleration": {"rating": "高/中/低", "basis": "", "score": 3},
    "tacit_knowledge_dependency": {"rating": "高/中/低", "basis": "", "score": 3},
    "overall_penetration": "高/中/低"
  },
  "dimension_2_change_status": {
    "status": "已变/将变/稳定",
    "evidence_type": "类型A/类型B/混合",
    "evidence_source": "",
    "basis_description": ""
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": ["A"],
    "type_descriptions": {
      "A": {"score": 4, "description": ""},
      "B": {"score": 0, "description": ""},
      "C": {"score": 0, "description": ""},
      "D": {"score": 0, "description": ""}
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型1-4",
    "boundary_description": "",
    "stability": "稳定/过渡中/高度不确定",
    "stability_note": ""
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "高/中/低",
    "uncertainty_sources": [],
    "special_note": null
  },
  "dimension_6_signal_quality": {
    "information_period": "",
    "source_distribution": {
      "academic": "", "industry_media": "",
      "corporate_disclosure": "",
      "consulting_reports": "", "regulatory": ""
    },
    "potential_bias": ""
  },
  "dimension_7_structure": {
    "rule_driven_degree": {"score": 3, "basis": ""},
    "exception_flexibility": {"score": 3, "basis": ""},
    "feedback_loop_maturity": {"score": 3, "basis": ""}
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {"score": 3, "basis": ""},
    "cross_process_dependency": {"score": 3, "basis": ""},
    "data_standardization": {"score": 3, "basis": ""},
    "integration_barrier": {"score": 3, "basis": ""}
  },
  "dimension_9_ai_readiness": {
    "data_availability": {"score": 3, "basis": ""},
    "tech_maturity": {"score": 3, "basis": ""},
    "implementation_simplicity": {"score": 3, "basis": ""},
    "value_density": {"score": 3, "basis": ""}
  },
  "numeric_profile": {
    "d1_decision_replaceability": 3, "d1_processing_acceleration": 3,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4, "d3_type_b": 0, "d3_type_c": 0, "d3_type_d": 0,
    "d7_rule_driven_degree": 3, "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 3, "d8_cross_process_dependency": 3,
    "d8_data_standardization": 3, "d8_integration_barrier": 3,
    "d9_data_availability": 3, "d9_tech_maturity": 3,
    "d9_implementation_simplicity": 3, "d9_value_density": 3
  },
  "scan_summary": {
    "one_line_judgment": "",
    "priority_flag": "高优先级验证/常规验证/低优先级验证",
    "priority_reason": ""
  }
}

C. 调用参数
--------------------------------------------------------------------------------
  model_id:          gemini-2.5-flash
  model_config:      {"model": "gemini-2.5-flash", "temperature": 0.3}
  prompt_version:    v2.0
  prompt_tokens:     1,870
  completion_tokens: 1,572
  total_tokens:      5,126
  response_time_ms:  18,366
  parsed_success:    True
  parse_error:       (none)

D. RAW LLM RESPONSE
--------------------------------------------------------------------------------
```json
{
  "node_id": "3.3.3",
  "scan_timestamp": "2024-07-30T10:00:00Z",
  "model_id": "AI_Impact_Scanner_V1.0",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {
      "rating": "中",
      "basis": "AI辅助定价推荐，部分决策可自动化。",
      "score": 3
    },
    "processing_acceleration": {
      "rating": "高",
      "basis": "AI自动化数据分析和模型计算，提高效率。",
      "score": 4
    },
    "tacit_knowledge_dependency": {
      "rating": "中",
      "basis": "复杂市场策略和品牌定位仍依赖专家经验。",
      "score": 3
    },
    "overall_penetration": "中"
  },
  "dimension_2_change_status": {
    "status": "已变",
    "evidence_type": "类型A",
    "evidence_source": "行业分析报告、AI定价软件供应商案例。",
    "basis_description": "市场已有大量AI定价工具和解决方案被企业采纳。"
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": [
      "A",
      "B",
      "D"
    ],
    "type_descriptions": {
      "A": {
        "score": 4,
        "description": "AI提供数据洞察和优化建议，提升定价准确性。"
      },
      "B": {
        "score": 2,
        "description": "部分重复性数据分析工作被AI取代，减少人工。"
      },
      "C": {
        "score": 0,
        "description": ""
      },
      "D": {
        "score": 1,
        "description": "出现新的AI模型管理和策略调整角色。"
      }
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型2",
    "boundary_description": "AI负责数据分析、预测和价格推荐，人类负责策略制定和最终审批。",
    "stability": "过渡中",
    "stability_note": "AI模型决策能力增强，可能进一步自动化部分定价调整。"
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "中",
    "uncertainty_sources": [
      "AI模型准确性",
      "市场接受度",
      "数据质量",
      "监管政策"
    ],
    "special_note": "不同行业和产品线的定价复杂性差异大，AI应用深度不同。"
  },
  "dimension_6_signal_quality": {
    "information_period": "近3-5年",
    "source_distribution": {
      "academic": "中",
      "industry_media": "高",
      "corporate_disclosure": "中",
      "consulting_reports": "高",
      "regulatory": "低"
    },
    "potential_bias": "供应商和咨询公司可能夸大AI定价效果。"
  },
  "dimension_7_structure": {
    "rule_driven_degree": {
      "score": 4,
      "basis": "定价需遵循成本、利润率、市场规则等，高度依赖规则。"
    },
    "exception_flexibility": {
      "score": 3,
      "basis": "市场变化快，需灵活应对促销、竞争等例外情况。"
    },
    "feedback_loop_maturity": {
      "score": 4,
      "basis": "定价效果需持续监控和调整，反馈机制成熟。"
    }
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {
      "score": 5,
      "basis": "定价严重依赖销售、成本、市场、客户等大量数据。"
    },
    "cross_process_dependency": {
      "score": 5,
      "basis": "依赖销售、生产、财务、市场等多个流程数据。"
    },
    "data_standardization": {
      "score": 3,
      "basis": "内部数据标准化较高，外部市场数据差异大。"
    },
    "integration_barrier": {
      "score": 4,
      "basis": "整合内外部多源异构数据存在挑战。"
    }
  },
  "dimension_9_ai_readiness": {
    "data_availability": {
      "score": 4,
      "basis": "内部销售、成本数据通常可用，外部市场数据需整合。"
    },
    "tech_maturity": {
      "score": 4,
      "basis": "AI定价模型和工具已相对成熟，有大量商业化产品。"
    },
    "implementation_simplicity": {
      "score": 3,
      "basis": "实施需数据集成、模型训练和业务流程调整。"
    },
    "value_density": {
      "score": 5,
      "basis": "优化定价对营收和利润有直接显著影响，价值巨大。"
    }
  },
  "numeric_profile": {
    "d1_decision_replaceability": 3,
    "d1_processing_acceleration": 4,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4,
    "d3_type_b": 2,
    "d3_type_c": 0,
    "d3_type_d": 1,
    "d7_rule_driven_degree": 4,
    "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 4,
    "d8_data_intensity": 5,
    "d8_cross_process_dependency": 5,
    "d8_data_standardization": 3,
    "d8_integration_barrier": 4,
    "d9_data_availability": 4,
    "d9_tech_maturity": 4,
    "d9_implementation_simplicity": 3,
    "d9_value_density": 5
  },
  "scan_summary": {
    "one_line_judgment": "AI已显著增强定价流程的数据分析和优化能力，但仍需人工策略指导。",
    "priority_flag": "高优先级验证",
    "priority_reason": "定价对企业营收利润影响巨大，AI应用已普遍且价值显著。"
  }
}
```

E. DB RECORD (ai_impact_scan_results)
--------------------------------------------------------------------------------
  id: 17
  node_id: 3.3.3
  batch_id: d8707b36-eb98-4039-a758-4bc0da622c98
  scan_timestamp: 2026-03-01T08:59:26Z
  penetration_decision_replaceability: 中
  penetration_decision_basis: AI辅助定价推荐，部分决策可自动化。
  penetration_processing_acceleration: 高
  penetration_processing_basis: AI自动化数据分析和模型计算，提高效率。
  penetration_tacit_knowledge: 中
  penetration_tacit_basis: 复杂市场策略和品牌定位仍依赖专家经验。
  penetration_overall: 中
  change_status: 已变
  change_evidence_type: 类型A
  change_evidence_source: 行业分析报告、AI定价软件供应商案例。
  change_basis_description: 市场已有大量AI定价工具和解决方案被企业采纳。
  change_nature_applicable: 1
  change_nature_types:
    [
    "A",
    "B",
    "D"
]
  change_nature_type_a: AI提供数据洞察和优化建议，提升定价准确性。
  change_nature_type_b: 部分重复性数据分析工作被AI取代，减少人工。
  change_nature_type_c: 
  change_nature_type_d: 出现新的AI模型管理和策略调整角色。
  boundary_current_type: 类型2
  boundary_description: AI负责数据分析、预测和价格推荐，人类负责策略制定和最终审批。
  boundary_stability: 过渡中
  boundary_stability_note: AI模型决策能力增强，可能进一步自动化部分定价调整。
  uncertainty_confidence: 中
  uncertainty_sources:
    [
    "AI模型准确性",
    "市场接受度",
    "数据质量",
    "监管政策"
]
  uncertainty_special_note: 不同行业和产品线的定价复杂性差异大，AI应用深度不同。
  signal_information_period: 近3-5年
  signal_academic: 中
  signal_industry_media: 高
  signal_corporate_disclosure: 中
  signal_consulting_reports: 高
  signal_regulatory: 低
  signal_potential_bias: 供应商和咨询公司可能夸大AI定价效果。
  summary_one_line: AI已显著增强定价流程的数据分析和优化能力，但仍需人工策略指导。
  summary_priority_flag: 高优先级验证
  summary_priority_reason: 定价对企业营收利润影响巨大，AI应用已普遍且价值显著。
  model_agreement_status: single_model
  model_agreement_change_status: 1
  model_agreement_penetration: 1
  models_used:
    [
    "gemini-2.5-flash"
]
  processing_status: completed
  divergence_detail: NULL
  dimension_7_structure:
    {
    "rule_driven_degree": {
        "score": 4,
        "basis": "定价需遵循成本、利润率、市场规则等，高度依赖规则。"
    },
    "exception_flexibility": {
        "score": 3,
        "basis": "市场变化快，需灵活应对促销、竞争等例外情况。"
    },
    "feedback_loop_maturity": {
        "score": 4,
        "basis": "定价效果需持续监控和调整，反馈机制成熟。"
    }
}
  dimension_8_data_ecosystem:
    {
    "data_intensity": {
        "score": 5,
        "basis": "定价严重依赖销售、成本、市场、客户等大量数据。"
    },
    "cross_process_dependency": {
        "score": 5,
        "basis": "依赖销售、生产、财务、市场等多个流程数据。"
    },
    "data_standardization": {
        "score": 3,
        "basis": "内部数据标准化较高，外部市场数据差异大。"
    },
    "integration_barrier": {
        "score": 4,
        "basis": "整合内外部多源异构数据存在挑战。"
    }
}
  dimension_9_ai_readiness:
    {
    "data_availability": {
        "score": 4,
        "basis": "内部销售、成本数据通常可用，外部市场数据需整合。"
    },
    "tech_maturity": {
        "score": 4,
        "basis": "AI定价模型和工具已相对成熟，有大量商业化产品。"
    },
    "implementation_simplicity": {
        "score": 3,
        "basis": "实施需数据集成、模型训练和业务流程调整。"
    },
    "value_density": {
        "score": 5,
        "basis": "优化定价对营收和利润有直接显著影响，价值巨大。"
    }
}
  numeric_profile:
    {
    "d1_decision_replaceability": 3,
    "d1_processing_acceleration": 4,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4,
    "d3_type_b": 2,
    "d3_type_c": 0,
    "d3_type_d": 1,
    "d7_rule_driven_degree": 4,
    "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 4,
    "d8_data_intensity": 5,
    "d8_cross_process_dependency": 5,
    "d8_data_standardization": 3,
    "d8_integration_barrier": 4,
    "d9_data_availability": 4,
    "d9_tech_maturity": 4,
    "d9_implementation_simplicity": 3,
    "d9_value_density": 5
}


================================================================================
[2/8] 节点 11.1.1.5 — AI显著增强风险数据分析和报告准备效率，但高层决策仍需人类主导。
================================================================================

A. SYSTEM PROMPT
--------------------------------------------------------------------------------
你是一个企业流程知识分析专家，专门评估AI技术对企业运营流程的影响。

你的任务是对企业流程节点进行AI冲击扫描，输出9个维度的结构化判断和数值特征向量。

## 你必须严格遵守的规则

规则1：判断必须基于有证据的实际发生，而非技术潜力。
规则2：少数领先企业的实践不代表行业普遍状态，须标注。
规则3：企业发布的转型计划不构成变化已发生的证据。
规则4：不确定时如实表达，不得虚构判断。
规则5：每个判断必须附带依据类型标注。
规则6：每项评分依据限1句话，不超过30字。
规则7：对每个维度子项输出1-5分数值评分（0=不适用仅限D3）。

## 输出要求

你必须且只能输出一个合法的JSON对象，不要输出任何JSON之外的内容。

B. USER PROMPT (已填充)
--------------------------------------------------------------------------------
## 待扫描流程节点

节点ID：11.1.1.5
名称（中文）：向高管层和董事会准备并报告企业风险
名称（英文）：Prepare and report enterprise risk to executive management and board
框架：PCF
路径：11.0 管理企业风险、合规、补救和弹性 > 11.1 管理企业风险 > 11.1.1 建立企业风险框架和政策 > 11.1.1.5 向高管层和董事会准备并报告企业风险
层级：L4
描述：为组织管理层准备和提交关于企业风险的报告。为管理层创建关于危害风险（如财产损失和责任侵权）、财务风险（如货币和流动性风险）以及运营风险（如产品故障、客户满意度、社会趋势和竞争）的报告。
标签：risk | compliance | pcf

---

请对上述流程节点进行9维度AI冲击扫描。

### D1 AI渗透率
子项：decision_replaceability / processing_acceleration / tacit_knowledge_dependency
每项：rating(高/中/低) + basis(≤30字) + score(1-5)
overall_penetration: 高/中/低

### D2 变化状态
status: 已变/将变/稳定
evidence_type: 类型A/类型B/混合
evidence_source / basis_description

### D3 变化性质（0-5量表，0=不适用）
types_selected: [A/B/C/D]
A=增强型 B=压缩型 C=消亡型 D=涌现型
每类：score(0-5) + description(≤30字，0分可省略)

### D4 人机边界
current_type: 类型1-4
boundary_description / stability(稳定/过渡中/高度不确定) / stability_note

### D5 不确定性
overall_confidence: 高/中/低
uncertainty_sources / special_note

### D6 信号质量
information_period / source_distribution(5项,高/中/低/无) / potential_bias

### D7 流程结构刚性
子项（各1-5分 + basis≤30字）：
- rule_driven_degree: 规则驱动程度
- exception_flexibility: 例外处理灵活性
- feedback_loop_maturity: 反馈闭环成熟度

### D8 数据生态位
子项（各1-5分 + basis≤30字）：
- data_intensity: 数据密集度
- cross_process_dependency: 跨流程数据依赖
- data_standardization: 数据标准化程度
- integration_barrier: 集成壁垒

### D9 AI改造就绪度
子项（各1-5分 + basis≤30字）：
- data_availability: 数据可得性
- tech_maturity: 技术成熟度
- implementation_simplicity: 实施简易度
- value_density: 价值密度

## 输出JSON格式

{
  "node_id": "11.1.1.5",
  "scan_timestamp": "ISO8601",
  "model_id": "你的模型标识",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {"rating": "高/中/低", "basis": "", "score": 3},
    "processing_acceleration": {"rating": "高/中/低", "basis": "", "score": 3},
    "tacit_knowledge_dependency": {"rating": "高/中/低", "basis": "", "score": 3},
    "overall_penetration": "高/中/低"
  },
  "dimension_2_change_status": {
    "status": "已变/将变/稳定",
    "evidence_type": "类型A/类型B/混合",
    "evidence_source": "",
    "basis_description": ""
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": ["A"],
    "type_descriptions": {
      "A": {"score": 4, "description": ""},
      "B": {"score": 0, "description": ""},
      "C": {"score": 0, "description": ""},
      "D": {"score": 0, "description": ""}
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型1-4",
    "boundary_description": "",
    "stability": "稳定/过渡中/高度不确定",
    "stability_note": ""
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "高/中/低",
    "uncertainty_sources": [],
    "special_note": null
  },
  "dimension_6_signal_quality": {
    "information_period": "",
    "source_distribution": {
      "academic": "", "industry_media": "",
      "corporate_disclosure": "",
      "consulting_reports": "", "regulatory": ""
    },
    "potential_bias": ""
  },
  "dimension_7_structure": {
    "rule_driven_degree": {"score": 3, "basis": ""},
    "exception_flexibility": {"score": 3, "basis": ""},
    "feedback_loop_maturity": {"score": 3, "basis": ""}
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {"score": 3, "basis": ""},
    "cross_process_dependency": {"score": 3, "basis": ""},
    "data_standardization": {"score": 3, "basis": ""},
    "integration_barrier": {"score": 3, "basis": ""}
  },
  "dimension_9_ai_readiness": {
    "data_availability": {"score": 3, "basis": ""},
    "tech_maturity": {"score": 3, "basis": ""},
    "implementation_simplicity": {"score": 3, "basis": ""},
    "value_density": {"score": 3, "basis": ""}
  },
  "numeric_profile": {
    "d1_decision_replaceability": 3, "d1_processing_acceleration": 3,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4, "d3_type_b": 0, "d3_type_c": 0, "d3_type_d": 0,
    "d7_rule_driven_degree": 3, "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 3, "d8_cross_process_dependency": 3,
    "d8_data_standardization": 3, "d8_integration_barrier": 3,
    "d9_data_availability": 3, "d9_tech_maturity": 3,
    "d9_implementation_simplicity": 3, "d9_value_density": 3
  },
  "scan_summary": {
    "one_line_judgment": "",
    "priority_flag": "高优先级验证/常规验证/低优先级验证",
    "priority_reason": ""
  }
}

C. 调用参数
--------------------------------------------------------------------------------
  model_id:          gemini-2.5-flash
  model_config:      {"model": "gemini-2.5-flash", "temperature": 0.3}
  prompt_version:    v2.0
  prompt_tokens:     1,916
  completion_tokens: 1,487
  total_tokens:      5,797
  response_time_ms:  18,813
  parsed_success:    True
  parse_error:       (none)

D. RAW LLM RESPONSE
--------------------------------------------------------------------------------
```json
{
  "node_id": "11.1.1.5",
  "scan_timestamp": "2024-07-30T10:00:00Z",
  "model_id": "EnterpriseAI_Impact_Scanner_v1.0",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {"rating": "低", "basis": "最终报告和高层决策需人类判断，AI辅助而非替代。", "score": 2},
    "processing_acceleration": {"rating": "高", "basis": "数据收集、初步分析和报告草拟可被AI显著加速。", "score": 4},
    "tacit_knowledge_dependency": {"rating": "高", "basis": "风险解读、高层沟通和战略影响高度依赖隐性知识。", "score": 4},
    "overall_penetration": "中"
  },
  "dimension_2_change_status": {
    "status": "已变",
    "evidence_type": "混合",
    "evidence_source": "咨询报告、行业解决方案供应商案例、部分企业财报",
    "basis_description": "领先企业已采用AI进行风险数据分析、报告自动化辅助。"
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": ["A"],
    "type_descriptions": {
      "A": {"score": 4, "description": "AI提升风险识别、分析效率和报告质量。"},
      "B": {"score": 0, "description": ""},
      "C": {"score": 0, "description": ""},
      "D": {"score": 0, "description": ""}
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型2",
    "boundary_description": "AI负责数据收集、初步分析和报告草稿生成，人类负责最终风险判断、报告审阅和高层沟通。",
    "stability": "过渡中",
    "stability_note": "AI在复杂风险情景分析和报告生成方面能力持续提升。"
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "中",
    "uncertainty_sources": [
      "企业对AI技术采纳速度和深度差异",
      "监管机构对AI在风险管理中应用的接受度",
      "AI在处理非结构化、定性风险方面的成熟度"
    ],
    "special_note": "董事会和高管层对AI生成报告的信任度是关键因素。"
  },
  "dimension_6_signal_quality": {
    "information_period": "过去2-3年",
    "source_distribution": {
      "academic": "中",
      "industry_media": "高",
      "corporate_disclosure": "中",
      "consulting_reports": "高",
      "regulatory": "低"
    },
    "potential_bias": "行业媒体和咨询报告可能夸大AI潜力或侧重于解决方案推广。"
  },
  "dimension_7_structure": {
    "rule_driven_degree": {"score": 3, "basis": "部分数据聚合和风险识别有规则，但报告内容含定性判断。"},
    "exception_flexibility": {"score": 3, "basis": "需适应突发事件和新兴风险，报告内容有调整空间。"},
    "feedback_loop_maturity": {"score": 4, "basis": "董事会和高管层定期提供反馈，推动风险报告流程改进。"}
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {"score": 4, "basis": "需整合大量内部运营、财务及外部市场、合规数据。"},
    "cross_process_dependency": {"score": 4, "basis": "依赖财务、运营、合规、IT等多个部门的数据和输入。"},
    "data_standardization": {"score": 3, "basis": "内部数据标准化较好，外部及非结构化数据标准化挑战大。"},
    "integration_barrier": {"score": 3, "basis": "整合内部异构系统和外部数据源存在技术和治理挑战。"}
  },
  "dimension_9_ai_readiness": {
    "data_availability": {"score": 4, "basis": "多数企业拥有大量风险相关数据，但可能分散在不同系统。"},
    "tech_maturity": {"score": 4, "basis": "风险分析和报告的AI技术（NLP, ML, BI）已相对成熟。"},
    "implementation_simplicity": {"score": 3, "basis": "实施需整合多源数据、定制模型，复杂度中等。"},
    "value_density": {"score": 4, "basis": "提升风险识别准确性、报告效率，对高层决策价值显著。"}
  },
  "numeric_profile": {
    "d1_decision_replaceability": 2,
    "d1_processing_acceleration": 4,
    "d1_tacit_knowledge_dependency": 4,
    "d3_type_a": 4,
    "d3_type_b": 0,
    "d3_type_c": 0,
    "d3_type_d": 0,
    "d7_rule_driven_degree": 3,
    "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 4,
    "d8_data_intensity": 4,
    "d8_cross_process_dependency": 4,
    "d8_data_standardization": 3,
    "d8_integration_barrier": 3,
    "d9_data_availability": 4,
    "d9_tech_maturity": 4,
    "d9_implementation_simplicity": 3,
    "d9_value_density": 4
  },
  "scan_summary": {
    "one_line_judgment": "AI显著增强风险数据分析和报告准备效率，但高层决策仍需人类主导。",
    "priority_flag": "高优先级验证",
    "priority_reason": "流程对企业战略决策至关重要，AI可大幅提升风险洞察和报告效率。"
  }
}
```

E. DB RECORD (ai_impact_scan_results)
--------------------------------------------------------------------------------
  id: 18
  node_id: 11.1.1.5
  batch_id: d8707b36-eb98-4039-a758-4bc0da622c98
  scan_timestamp: 2026-03-01T08:59:48Z
  penetration_decision_replaceability: 低
  penetration_decision_basis: 最终报告和高层决策需人类判断，AI辅助而非替代。
  penetration_processing_acceleration: 高
  penetration_processing_basis: 数据收集、初步分析和报告草拟可被AI显著加速。
  penetration_tacit_knowledge: 高
  penetration_tacit_basis: 风险解读、高层沟通和战略影响高度依赖隐性知识。
  penetration_overall: 中
  change_status: 已变
  change_evidence_type: 混合
  change_evidence_source: 咨询报告、行业解决方案供应商案例、部分企业财报
  change_basis_description: 领先企业已采用AI进行风险数据分析、报告自动化辅助。
  change_nature_applicable: 1
  change_nature_types:
    [
    "A"
]
  change_nature_type_a: AI提升风险识别、分析效率和报告质量。
  change_nature_type_b: 
  change_nature_type_c: 
  change_nature_type_d: 
  boundary_current_type: 类型2
  boundary_description: AI负责数据收集、初步分析和报告草稿生成，人类负责最终风险判断、报告审阅和高层沟通。
  boundary_stability: 过渡中
  boundary_stability_note: AI在复杂风险情景分析和报告生成方面能力持续提升。
  uncertainty_confidence: 中
  uncertainty_sources:
    [
    "企业对AI技术采纳速度和深度差异",
    "监管机构对AI在风险管理中应用的接受度",
    "AI在处理非结构化、定性风险方面的成熟度"
]
  uncertainty_special_note: 董事会和高管层对AI生成报告的信任度是关键因素。
  signal_information_period: 过去2-3年
  signal_academic: 中
  signal_industry_media: 高
  signal_corporate_disclosure: 中
  signal_consulting_reports: 高
  signal_regulatory: 低
  signal_potential_bias: 行业媒体和咨询报告可能夸大AI潜力或侧重于解决方案推广。
  summary_one_line: AI显著增强风险数据分析和报告准备效率，但高层决策仍需人类主导。
  summary_priority_flag: 高优先级验证
  summary_priority_reason: 流程对企业战略决策至关重要，AI可大幅提升风险洞察和报告效率。
  model_agreement_status: single_model
  model_agreement_change_status: 1
  model_agreement_penetration: 1
  models_used:
    [
    "gemini-2.5-flash"
]
  processing_status: completed
  divergence_detail: NULL
  dimension_7_structure:
    {
    "rule_driven_degree": {
        "score": 3,
        "basis": "部分数据聚合和风险识别有规则，但报告内容含定性判断。"
    },
    "exception_flexibility": {
        "score": 3,
        "basis": "需适应突发事件和新兴风险，报告内容有调整空间。"
    },
    "feedback_loop_maturity": {
        "score": 4,
        "basis": "董事会和高管层定期提供反馈，推动风险报告流程改进。"
    }
}
  dimension_8_data_ecosystem:
    {
    "data_intensity": {
        "score": 4,
        "basis": "需整合大量内部运营、财务及外部市场、合规数据。"
    },
    "cross_process_dependency": {
        "score": 4,
        "basis": "依赖财务、运营、合规、IT等多个部门的数据和输入。"
    },
    "data_standardization": {
        "score": 3,
        "basis": "内部数据标准化较好，外部及非结构化数据标准化挑战大。"
    },
    "integration_barrier": {
        "score": 3,
        "basis": "整合内部异构系统和外部数据源存在技术和治理挑战。"
    }
}
  dimension_9_ai_readiness:
    {
    "data_availability": {
        "score": 4,
        "basis": "多数企业拥有大量风险相关数据，但可能分散在不同系统。"
    },
    "tech_maturity": {
        "score": 4,
        "basis": "风险分析和报告的AI技术（NLP, ML, BI）已相对成熟。"
    },
    "implementation_simplicity": {
        "score": 3,
        "basis": "实施需整合多源数据、定制模型，复杂度中等。"
    },
    "value_density": {
        "score": 4,
        "basis": "提升风险识别准确性、报告效率，对高层决策价值显著。"
    }
}
  numeric_profile:
    {
    "d1_decision_replaceability": 2,
    "d1_processing_acceleration": 4,
    "d1_tacit_knowledge_dependency": 4,
    "d3_type_a": 4,
    "d3_type_b": 0,
    "d3_type_c": 0,
    "d3_type_d": 0,
    "d7_rule_driven_degree": 3,
    "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 4,
    "d8_data_intensity": 4,
    "d8_cross_process_dependency": 4,
    "d8_data_standardization": 3,
    "d8_integration_barrier": 3,
    "d9_data_availability": 4,
    "d9_tech_maturity": 4,
    "d9_implementation_simplicity": 3,
    "d9_value_density": 4
}


================================================================================
[3/8] 节点 7.9.1 — AI正通过增强数据分析和预测能力，提升IT劳动力规划的效率和准确性，但决策仍需人类经验。
================================================================================

A. SYSTEM PROMPT
--------------------------------------------------------------------------------
你是一个企业流程知识分析专家，专门评估AI技术对企业运营流程的影响。

你的任务是对企业流程节点进行AI冲击扫描，输出9个维度的结构化判断和数值特征向量。

## 你必须严格遵守的规则

规则1：判断必须基于有证据的实际发生，而非技术潜力。
规则2：少数领先企业的实践不代表行业普遍状态，须标注。
规则3：企业发布的转型计划不构成变化已发生的证据。
规则4：不确定时如实表达，不得虚构判断。
规则5：每个判断必须附带依据类型标注。
规则6：每项评分依据限1句话，不超过30字。
规则7：对每个维度子项输出1-5分数值评分（0=不适用仅限D3）。

## 输出要求

你必须且只能输出一个合法的JSON对象，不要输出任何JSON之外的内容。

B. USER PROMPT (已填充)
--------------------------------------------------------------------------------
## 待扫描流程节点

节点ID：7.9.1
名称（中文）：规划 IT 劳动力容量
名称（英文）：Plan IT workforce capacity
框架：ITIL
路径：7.0 发展和管理人力资本 > 7.9 劳动力与人才管理 > 7.9.1 规划 IT 劳动力容量
层级：L3
描述：ITIL 实践“劳动力与人才管理”中的活动。
标签：hr | human_capital | pcf | itil

---

请对上述流程节点进行9维度AI冲击扫描。

### D1 AI渗透率
子项：decision_replaceability / processing_acceleration / tacit_knowledge_dependency
每项：rating(高/中/低) + basis(≤30字) + score(1-5)
overall_penetration: 高/中/低

### D2 变化状态
status: 已变/将变/稳定
evidence_type: 类型A/类型B/混合
evidence_source / basis_description

### D3 变化性质（0-5量表，0=不适用）
types_selected: [A/B/C/D]
A=增强型 B=压缩型 C=消亡型 D=涌现型
每类：score(0-5) + description(≤30字，0分可省略)

### D4 人机边界
current_type: 类型1-4
boundary_description / stability(稳定/过渡中/高度不确定) / stability_note

### D5 不确定性
overall_confidence: 高/中/低
uncertainty_sources / special_note

### D6 信号质量
information_period / source_distribution(5项,高/中/低/无) / potential_bias

### D7 流程结构刚性
子项（各1-5分 + basis≤30字）：
- rule_driven_degree: 规则驱动程度
- exception_flexibility: 例外处理灵活性
- feedback_loop_maturity: 反馈闭环成熟度

### D8 数据生态位
子项（各1-5分 + basis≤30字）：
- data_intensity: 数据密集度
- cross_process_dependency: 跨流程数据依赖
- data_standardization: 数据标准化程度
- integration_barrier: 集成壁垒

### D9 AI改造就绪度
子项（各1-5分 + basis≤30字）：
- data_availability: 数据可得性
- tech_maturity: 技术成熟度
- implementation_simplicity: 实施简易度
- value_density: 价值密度

## 输出JSON格式

{
  "node_id": "7.9.1",
  "scan_timestamp": "ISO8601",
  "model_id": "你的模型标识",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {"rating": "高/中/低", "basis": "", "score": 3},
    "processing_acceleration": {"rating": "高/中/低", "basis": "", "score": 3},
    "tacit_knowledge_dependency": {"rating": "高/中/低", "basis": "", "score": 3},
    "overall_penetration": "高/中/低"
  },
  "dimension_2_change_status": {
    "status": "已变/将变/稳定",
    "evidence_type": "类型A/类型B/混合",
    "evidence_source": "",
    "basis_description": ""
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": ["A"],
    "type_descriptions": {
      "A": {"score": 4, "description": ""},
      "B": {"score": 0, "description": ""},
      "C": {"score": 0, "description": ""},
      "D": {"score": 0, "description": ""}
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型1-4",
    "boundary_description": "",
    "stability": "稳定/过渡中/高度不确定",
    "stability_note": ""
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "高/中/低",
    "uncertainty_sources": [],
    "special_note": null
  },
  "dimension_6_signal_quality": {
    "information_period": "",
    "source_distribution": {
      "academic": "", "industry_media": "",
      "corporate_disclosure": "",
      "consulting_reports": "", "regulatory": ""
    },
    "potential_bias": ""
  },
  "dimension_7_structure": {
    "rule_driven_degree": {"score": 3, "basis": ""},
    "exception_flexibility": {"score": 3, "basis": ""},
    "feedback_loop_maturity": {"score": 3, "basis": ""}
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {"score": 3, "basis": ""},
    "cross_process_dependency": {"score": 3, "basis": ""},
    "data_standardization": {"score": 3, "basis": ""},
    "integration_barrier": {"score": 3, "basis": ""}
  },
  "dimension_9_ai_readiness": {
    "data_availability": {"score": 3, "basis": ""},
    "tech_maturity": {"score": 3, "basis": ""},
    "implementation_simplicity": {"score": 3, "basis": ""},
    "value_density": {"score": 3, "basis": ""}
  },
  "numeric_profile": {
    "d1_decision_replaceability": 3, "d1_processing_acceleration": 3,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4, "d3_type_b": 0, "d3_type_c": 0, "d3_type_d": 0,
    "d7_rule_driven_degree": 3, "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 3, "d8_cross_process_dependency": 3,
    "d8_data_standardization": 3, "d8_integration_barrier": 3,
    "d9_data_availability": 3, "d9_tech_maturity": 3,
    "d9_implementation_simplicity": 3, "d9_value_density": 3
  },
  "scan_summary": {
    "one_line_judgment": "",
    "priority_flag": "高优先级验证/常规验证/低优先级验证",
    "priority_reason": ""
  }
}

C. 调用参数
--------------------------------------------------------------------------------
  model_id:          gemini-2.5-flash
  model_config:      {"model": "gemini-2.5-flash", "temperature": 0.3}
  prompt_version:    v2.0
  prompt_tokens:     1,832
  completion_tokens: 1,473
  total_tokens:      5,359
  response_time_ms:  16,629
  parsed_success:    True
  parse_error:       (none)

D. RAW LLM RESPONSE
--------------------------------------------------------------------------------
```json
{
  "node_id": "7.9.1",
  "scan_timestamp": "2024-07-30T10:00:00Z",
  "model_id": "EnterpriseAIImpactScanner-v1.0",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {"rating": "中", "basis": "AI辅助数据分析和预测，但决策仍需人类经验。", "score": 3},
    "processing_acceleration": {"rating": "高", "basis": "AI可快速处理大量数据，加速预测模型运行和报告生成。", "score": 4},
    "tacit_knowledge_dependency": {"rating": "高", "basis": "规划需依赖对组织战略、市场趋势的隐性经验判断。", "score": 4},
    "overall_penetration": "中"
  },
  "dimension_2_change_status": {
    "status": "已变",
    "evidence_type": "类型A",
    "evidence_source": "咨询报告、HR技术供应商案例",
    "basis_description": "领先企业已采用AI工具进行劳动力需求预测和技能分析。"
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": ["A", "B"],
    "type_descriptions": {
      "A": {"score": 4, "description": "AI增强了数据分析和预测能力，提升规划质量。"},
      "B": {"score": 3, "description": "AI自动化数据分析和报告，减少部分人工任务。"},
      "C": {"score": 0, "description": ""},
      "D": {"score": 0, "description": ""}
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型2",
    "boundary_description": "AI负责数据分析和预测，人类专家负责解读、决策和策略制定。",
    "stability": "过渡中",
    "stability_note": "AI在预测和分析方面能力增强，未来可能承担更多决策支持。"
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "中",
    "uncertainty_sources": [
      "AI技术成熟度",
      "企业数据质量",
      "组织变革意愿",
      "市场动态变化",
      "隐私合规性"
    ],
    "special_note": "AI预测的准确性受数据质量和外部环境变化影响大。"
  },
  "dimension_6_signal_quality": {
    "information_period": "近1-3年",
    "source_distribution": {
      "academic": "中",
      "industry_media": "高",
      "corporate_disclosure": "中",
      "consulting_reports": "高",
      "regulatory": "无"
    },
    "potential_bias": "供应商和咨询公司报告可能夸大AI能力和市场前景。"
  },
  "dimension_7_structure": {
    "rule_driven_degree": {"score": 3, "basis": "规划有既定框架和方法论，但需灵活调整。"},
    "exception_flexibility": {"score": 3, "basis": "劳动力规划需应对市场变化和突发事件，有一定灵活性。"},
    "feedback_loop_maturity": {"score": 3, "basis": "规划结果通常会与实际情况对比，但反馈周期可能较长。"}
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {"score": 4, "basis": "规划需大量内部（员工、技能）和外部（市场、趋势）数据。"},
    "cross_process_dependency": {"score": 4, "basis": "依赖招聘、培训、绩效、业务战略等流程数据。"},
    "data_standardization": {"score": 3, "basis": "内部数据标准化程度较高，但外部数据和非结构化数据挑战大。"},
    "integration_barrier": {"score": 3, "basis": "内部HR系统集成相对容易，但与外部数据源集成存在挑战。"}
  },
  "dimension_9_ai_readiness": {
    "data_availability": {"score": 4, "basis": "内部HR数据通常可用，外部市场数据也可获取。"},
    "tech_maturity": {"score": 4, "basis": "预测分析、自然语言处理等AI技术已相对成熟。"},
    "implementation_simplicity": {"score": 3, "basis": "实施AI工具需要数据清洗、模型训练和系统集成。"},
    "value_density": {"score": 4, "basis": "优化劳动力规划可显著降低成本、提高效率和竞争力。"}
  },
  "numeric_profile": {
    "d1_decision_replaceability": 3,
    "d1_processing_acceleration": 4,
    "d1_tacit_knowledge_dependency": 4,
    "d3_type_a": 4,
    "d3_type_b": 3,
    "d3_type_c": 0,
    "d3_type_d": 0,
    "d7_rule_driven_degree": 3,
    "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 4,
    "d8_cross_process_dependency": 4,
    "d8_data_standardization": 3,
    "d8_integration_barrier": 3,
    "d9_data_availability": 4,
    "d9_tech_maturity": 4,
    "d9_implementation_simplicity": 3,
    "d9_value_density": 4
  },
  "scan_summary": {
    "one_line_judgment": "AI正通过增强数据分析和预测能力，提升IT劳动力规划的效率和准确性，但决策仍需人类经验。",
    "priority_flag": "常规验证",
    "priority_reason": "AI在此流程中主要作为辅助工具，影响显著但非颠覆性，需持续关注其演进。"
  }
}
```

E. DB RECORD (ai_impact_scan_results)
--------------------------------------------------------------------------------
  id: 19
  node_id: 7.9.1
  batch_id: d8707b36-eb98-4039-a758-4bc0da622c98
  scan_timestamp: 2026-03-01T09:00:10Z
  penetration_decision_replaceability: 中
  penetration_decision_basis: AI辅助数据分析和预测，但决策仍需人类经验。
  penetration_processing_acceleration: 高
  penetration_processing_basis: AI可快速处理大量数据，加速预测模型运行和报告生成。
  penetration_tacit_knowledge: 高
  penetration_tacit_basis: 规划需依赖对组织战略、市场趋势的隐性经验判断。
  penetration_overall: 中
  change_status: 已变
  change_evidence_type: 类型A
  change_evidence_source: 咨询报告、HR技术供应商案例
  change_basis_description: 领先企业已采用AI工具进行劳动力需求预测和技能分析。
  change_nature_applicable: 1
  change_nature_types:
    [
    "A",
    "B"
]
  change_nature_type_a: AI增强了数据分析和预测能力，提升规划质量。
  change_nature_type_b: AI自动化数据分析和报告，减少部分人工任务。
  change_nature_type_c: 
  change_nature_type_d: 
  boundary_current_type: 类型2
  boundary_description: AI负责数据分析和预测，人类专家负责解读、决策和策略制定。
  boundary_stability: 过渡中
  boundary_stability_note: AI在预测和分析方面能力增强，未来可能承担更多决策支持。
  uncertainty_confidence: 中
  uncertainty_sources:
    [
    "AI技术成熟度",
    "企业数据质量",
    "组织变革意愿",
    "市场动态变化",
    "隐私合规性"
]
  uncertainty_special_note: AI预测的准确性受数据质量和外部环境变化影响大。
  signal_information_period: 近1-3年
  signal_academic: 中
  signal_industry_media: 高
  signal_corporate_disclosure: 中
  signal_consulting_reports: 高
  signal_regulatory: 无
  signal_potential_bias: 供应商和咨询公司报告可能夸大AI能力和市场前景。
  summary_one_line: AI正通过增强数据分析和预测能力，提升IT劳动力规划的效率和准确性，但决策仍需人类经验。
  summary_priority_flag: 常规验证
  summary_priority_reason: AI在此流程中主要作为辅助工具，影响显著但非颠覆性，需持续关注其演进。
  model_agreement_status: single_model
  model_agreement_change_status: 1
  model_agreement_penetration: 1
  models_used:
    [
    "gemini-2.5-flash"
]
  processing_status: completed
  divergence_detail: NULL
  dimension_7_structure:
    {
    "rule_driven_degree": {
        "score": 3,
        "basis": "规划有既定框架和方法论，但需灵活调整。"
    },
    "exception_flexibility": {
        "score": 3,
        "basis": "劳动力规划需应对市场变化和突发事件，有一定灵活性。"
    },
    "feedback_loop_maturity": {
        "score": 3,
        "basis": "规划结果通常会与实际情况对比，但反馈周期可能较长。"
    }
}
  dimension_8_data_ecosystem:
    {
    "data_intensity": {
        "score": 4,
        "basis": "规划需大量内部（员工、技能）和外部（市场、趋势）数据。"
    },
    "cross_process_dependency": {
        "score": 4,
        "basis": "依赖招聘、培训、绩效、业务战略等流程数据。"
    },
    "data_standardization": {
        "score": 3,
        "basis": "内部数据标准化程度较高，但外部数据和非结构化数据挑战大。"
    },
    "integration_barrier": {
        "score": 3,
        "basis": "内部HR系统集成相对容易，但与外部数据源集成存在挑战。"
    }
}
  dimension_9_ai_readiness:
    {
    "data_availability": {
        "score": 4,
        "basis": "内部HR数据通常可用，外部市场数据也可获取。"
    },
    "tech_maturity": {
        "score": 4,
        "basis": "预测分析、自然语言处理等AI技术已相对成熟。"
    },
    "implementation_simplicity": {
        "score": 3,
        "basis": "实施AI工具需要数据清洗、模型训练和系统集成。"
    },
    "value_density": {
        "score": 4,
        "basis": "优化劳动力规划可显著降低成本、提高效率和竞争力。"
    }
}
  numeric_profile:
    {
    "d1_decision_replaceability": 3,
    "d1_processing_acceleration": 4,
    "d1_tacit_knowledge_dependency": 4,
    "d3_type_a": 4,
    "d3_type_b": 3,
    "d3_type_c": 0,
    "d3_type_d": 0,
    "d7_rule_driven_degree": 3,
    "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 4,
    "d8_cross_process_dependency": 4,
    "d8_data_standardization": 3,
    "d8_integration_barrier": 3,
    "d9_data_availability": 4,
    "d9_tech_maturity": 4,
    "d9_implementation_simplicity": 3,
    "d9_value_density": 4
}


================================================================================
[4/8] 节点 8.5.7.3 — AI正通过辅助识别和分析技术债务，增强并加速该流程，但核心决策仍依赖人工经验。
================================================================================

A. SYSTEM PROMPT
--------------------------------------------------------------------------------
你是一个企业流程知识分析专家，专门评估AI技术对企业运营流程的影响。

你的任务是对企业流程节点进行AI冲击扫描，输出9个维度的结构化判断和数值特征向量。

## 你必须严格遵守的规则

规则1：判断必须基于有证据的实际发生，而非技术潜力。
规则2：少数领先企业的实践不代表行业普遍状态，须标注。
规则3：企业发布的转型计划不构成变化已发生的证据。
规则4：不确定时如实表达，不得虚构判断。
规则5：每个判断必须附带依据类型标注。
规则6：每项评分依据限1句话，不超过30字。
规则7：对每个维度子项输出1-5分数值评分（0=不适用仅限D3）。

## 输出要求

你必须且只能输出一个合法的JSON对象，不要输出任何JSON之外的内容。

B. USER PROMPT (已填充)
--------------------------------------------------------------------------------
## 待扫描流程节点

节点ID：8.5.7.3
名称（中文）：管理技术债务
名称（英文）：Manage technical debt
框架：ITIL
路径：8.0 管理信息技术 (IT) > 8.5 开发和管理服务/解决方案 > 8.5.7 软件开发与管理 > 8.5.7.3 管理技术债务
层级：L4
描述：ITIL“软件开发与管理”实践中的活动。
标签：it_management | pcf | itil

---

请对上述流程节点进行9维度AI冲击扫描。

### D1 AI渗透率
子项：decision_replaceability / processing_acceleration / tacit_knowledge_dependency
每项：rating(高/中/低) + basis(≤30字) + score(1-5)
overall_penetration: 高/中/低

### D2 变化状态
status: 已变/将变/稳定
evidence_type: 类型A/类型B/混合
evidence_source / basis_description

### D3 变化性质（0-5量表，0=不适用）
types_selected: [A/B/C/D]
A=增强型 B=压缩型 C=消亡型 D=涌现型
每类：score(0-5) + description(≤30字，0分可省略)

### D4 人机边界
current_type: 类型1-4
boundary_description / stability(稳定/过渡中/高度不确定) / stability_note

### D5 不确定性
overall_confidence: 高/中/低
uncertainty_sources / special_note

### D6 信号质量
information_period / source_distribution(5项,高/中/低/无) / potential_bias

### D7 流程结构刚性
子项（各1-5分 + basis≤30字）：
- rule_driven_degree: 规则驱动程度
- exception_flexibility: 例外处理灵活性
- feedback_loop_maturity: 反馈闭环成熟度

### D8 数据生态位
子项（各1-5分 + basis≤30字）：
- data_intensity: 数据密集度
- cross_process_dependency: 跨流程数据依赖
- data_standardization: 数据标准化程度
- integration_barrier: 集成壁垒

### D9 AI改造就绪度
子项（各1-5分 + basis≤30字）：
- data_availability: 数据可得性
- tech_maturity: 技术成熟度
- implementation_simplicity: 实施简易度
- value_density: 价值密度

## 输出JSON格式

{
  "node_id": "8.5.7.3",
  "scan_timestamp": "ISO8601",
  "model_id": "你的模型标识",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {"rating": "高/中/低", "basis": "", "score": 3},
    "processing_acceleration": {"rating": "高/中/低", "basis": "", "score": 3},
    "tacit_knowledge_dependency": {"rating": "高/中/低", "basis": "", "score": 3},
    "overall_penetration": "高/中/低"
  },
  "dimension_2_change_status": {
    "status": "已变/将变/稳定",
    "evidence_type": "类型A/类型B/混合",
    "evidence_source": "",
    "basis_description": ""
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": ["A"],
    "type_descriptions": {
      "A": {"score": 4, "description": ""},
      "B": {"score": 0, "description": ""},
      "C": {"score": 0, "description": ""},
      "D": {"score": 0, "description": ""}
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型1-4",
    "boundary_description": "",
    "stability": "稳定/过渡中/高度不确定",
    "stability_note": ""
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "高/中/低",
    "uncertainty_sources": [],
    "special_note": null
  },
  "dimension_6_signal_quality": {
    "information_period": "",
    "source_distribution": {
      "academic": "", "industry_media": "",
      "corporate_disclosure": "",
      "consulting_reports": "", "regulatory": ""
    },
    "potential_bias": ""
  },
  "dimension_7_structure": {
    "rule_driven_degree": {"score": 3, "basis": ""},
    "exception_flexibility": {"score": 3, "basis": ""},
    "feedback_loop_maturity": {"score": 3, "basis": ""}
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {"score": 3, "basis": ""},
    "cross_process_dependency": {"score": 3, "basis": ""},
    "data_standardization": {"score": 3, "basis": ""},
    "integration_barrier": {"score": 3, "basis": ""}
  },
  "dimension_9_ai_readiness": {
    "data_availability": {"score": 3, "basis": ""},
    "tech_maturity": {"score": 3, "basis": ""},
    "implementation_simplicity": {"score": 3, "basis": ""},
    "value_density": {"score": 3, "basis": ""}
  },
  "numeric_profile": {
    "d1_decision_replaceability": 3, "d1_processing_acceleration": 3,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4, "d3_type_b": 0, "d3_type_c": 0, "d3_type_d": 0,
    "d7_rule_driven_degree": 3, "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 3, "d8_cross_process_dependency": 3,
    "d8_data_standardization": 3, "d8_integration_barrier": 3,
    "d9_data_availability": 3, "d9_tech_maturity": 3,
    "d9_implementation_simplicity": 3, "d9_value_density": 3
  },
  "scan_summary": {
    "one_line_judgment": "",
    "priority_flag": "高优先级验证/常规验证/低优先级验证",
    "priority_reason": ""
  }
}

C. 调用参数
--------------------------------------------------------------------------------
  model_id:          gemini-2.5-flash
  model_config:      {"model": "gemini-2.5-flash", "temperature": 0.3}
  prompt_version:    v2.0
  prompt_tokens:     1,838
  completion_tokens: 1,595
  total_tokens:      6,649
  response_time_ms:  24,061
  parsed_success:    True
  parse_error:       (none)

D. RAW LLM RESPONSE
--------------------------------------------------------------------------------
```json
{
  "node_id": "8.5.7.3",
  "scan_timestamp": "2024-07-30T10:00:00Z",
  "model_id": "EnterpriseProcessAIAnalyzer-v1.0",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {
      "rating": "中",
      "basis": "AI可提供决策支持，但最终优先级和策略仍需人工判断。",
      "score": 3
    },
    "processing_acceleration": {
      "rating": "高",
      "basis": "AI辅助代码分析工具显著加速债务识别与评估。",
      "score": 4
    },
    "tacit_knowledge_dependency": {
      "rating": "高",
      "basis": "识别深层债务和业务影响高度依赖专家经验。",
      "score": 4
    },
    "overall_penetration": "中"
  },
  "dimension_2_change_status": {
    "status": "已变",
    "evidence_type": "类型B",
    "evidence_source": "行业报告、技术博客、领先企业实践",
    "basis_description": "领先企业已采用AI辅助代码分析工具识别技术债务。"
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": [
      "A",
      "B"
    ],
    "type_descriptions": {
      "A": {
        "score": 4,
        "description": "AI提供洞察和建议，辅助人工决策。"
      },
      "B": {
        "score": 3,
        "description": "AI自动化部分分析和报告，减少人工耗时。"
      },
      "C": {
        "score": 0,
        "description": ""
      },
      "D": {
        "score": 0,
        "description": ""
      }
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型2",
    "boundary_description": "AI辅助识别和分析技术债务，但人工负责优先级排序和解决方案决策。",
    "stability": "过渡中",
    "stability_note": "AI在识别和初步分析方面作用增强，但决策仍需人工。"
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "中",
    "uncertainty_sources": [
      "AI对复杂架构和业务上下文的理解能力",
      "AI生成代码重构建议的准确性和安全性",
      "企业对AI工具的采纳意愿和集成成本"
    ],
    "special_note": "尚不清楚AI何时能有效处理高度复杂的架构债务。"
  },
  "dimension_6_signal_quality": {
    "information_period": "近1-3年",
    "source_distribution": {
      "academic": "中",
      "industry_media": "高",
      "corporate_disclosure": "中",
      "consulting_reports": "中",
      "regulatory": "无"
    },
    "potential_bias": "供应商宣传可能夸大AI能力和普及程度。"
  },
  "dimension_7_structure": {
    "rule_driven_degree": {
      "score": 3,
      "basis": "部分识别基于规则，但优先级和解决策略需灵活判断。"
    },
    "exception_flexibility": {
      "score": 4,
      "basis": "技术债务情况复杂多变，需要高度灵活的例外处理。"
    },
    "feedback_loop_maturity": {
      "score": 3,
      "basis": "存在反馈机制，但其成熟度和自动化程度各异。"
    }
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {
      "score": 4,
      "basis": "涉及大量代码、版本控制、缺陷和性能数据。"
    },
    "cross_process_dependency": {
      "score": 4,
      "basis": "依赖开发、测试、运维及业务决策等多流程数据。"
    },
    "data_standardization": {
      "score": 3,
      "basis": "代码结构化程度高，但债务定义和跨系统数据标准化程度中等。"
    },
    "integration_barrier": {
      "score": 4,
      "basis": "整合代码库、分析工具和项目管理系统存在较高壁垒。"
    }
  },
  "dimension_9_ai_readiness": {
    "data_availability": {
      "score": 4,
      "basis": "代码、版本历史和缺陷数据通常可得。"
    },
    "tech_maturity": {
      "score": 3,
      "basis": "代码分析AI技术相对成熟，但高级重构AI仍在发展。"
    },
    "implementation_simplicity": {
      "score": 3,
      "basis": "整合AI工具到现有DevOps流程可能较复杂。"
    },
    "value_density": {
      "score": 4,
      "basis": "降低未来成本、提升开发效率的潜在价值高。"
    }
  },
  "numeric_profile": {
    "d1_decision_replaceability": 3,
    "d1_processing_acceleration": 4,
    "d1_tacit_knowledge_dependency": 4,
    "d3_type_a": 4,
    "d3_type_b": 3,
    "d3_type_c": 0,
    "d3_type_d": 0,
    "d7_rule_driven_degree": 3,
    "d7_exception_flexibility": 4,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 4,
    "d8_cross_process_dependency": 4,
    "d8_data_standardization": 3,
    "d8_integration_barrier": 4,
    "d9_data_availability": 4,
    "d9_tech_maturity": 3,
    "d9_implementation_simplicity": 3,
    "d9_value_density": 4
  },
  "scan_summary": {
    "one_line_judgment": "AI正通过辅助识别和分析技术债务，增强并加速该流程，但核心决策仍依赖人工经验。",
    "priority_flag": "高优先级验证",
    "priority_reason": "AI在技术债务管理中展现出高价值潜力，且已有早期应用，值得深入评估其成熟度和ROI。"
  }
}
```

E. DB RECORD (ai_impact_scan_results)
--------------------------------------------------------------------------------
  id: 24
  node_id: 8.5.7.3
  batch_id: fe7d7b08-9efb-47d0-82a4-192ebc7adb80
  scan_timestamp: 2026-03-01T09:05:25Z
  penetration_decision_replaceability: 中
  penetration_decision_basis: AI可提供决策支持，但最终优先级和策略仍需人工判断。
  penetration_processing_acceleration: 高
  penetration_processing_basis: AI辅助代码分析工具显著加速债务识别与评估。
  penetration_tacit_knowledge: 高
  penetration_tacit_basis: 识别深层债务和业务影响高度依赖专家经验。
  penetration_overall: 中
  change_status: 已变
  change_evidence_type: 类型B
  change_evidence_source: 行业报告、技术博客、领先企业实践
  change_basis_description: 领先企业已采用AI辅助代码分析工具识别技术债务。
  change_nature_applicable: 1
  change_nature_types:
    [
    "A",
    "B"
]
  change_nature_type_a: AI提供洞察和建议，辅助人工决策。
  change_nature_type_b: AI自动化部分分析和报告，减少人工耗时。
  change_nature_type_c: 
  change_nature_type_d: 
  boundary_current_type: 类型2
  boundary_description: AI辅助识别和分析技术债务，但人工负责优先级排序和解决方案决策。
  boundary_stability: 过渡中
  boundary_stability_note: AI在识别和初步分析方面作用增强，但决策仍需人工。
  uncertainty_confidence: 中
  uncertainty_sources:
    [
    "AI对复杂架构和业务上下文的理解能力",
    "AI生成代码重构建议的准确性和安全性",
    "企业对AI工具的采纳意愿和集成成本"
]
  uncertainty_special_note: 尚不清楚AI何时能有效处理高度复杂的架构债务。
  signal_information_period: 近1-3年
  signal_academic: 中
  signal_industry_media: 高
  signal_corporate_disclosure: 中
  signal_consulting_reports: 中
  signal_regulatory: 无
  signal_potential_bias: 供应商宣传可能夸大AI能力和普及程度。
  summary_one_line: AI正通过辅助识别和分析技术债务，增强并加速该流程，但核心决策仍依赖人工经验。
  summary_priority_flag: 高优先级验证
  summary_priority_reason: AI在技术债务管理中展现出高价值潜力，且已有早期应用，值得深入评估其成熟度和ROI。
  model_agreement_status: single_model
  model_agreement_change_status: 1
  model_agreement_penetration: 1
  models_used:
    [
    "gemini-2.5-flash"
]
  processing_status: completed
  divergence_detail: NULL
  dimension_7_structure:
    {
    "rule_driven_degree": {
        "score": 3,
        "basis": "部分识别基于规则，但优先级和解决策略需灵活判断。"
    },
    "exception_flexibility": {
        "score": 4,
        "basis": "技术债务情况复杂多变，需要高度灵活的例外处理。"
    },
    "feedback_loop_maturity": {
        "score": 3,
        "basis": "存在反馈机制，但其成熟度和自动化程度各异。"
    }
}
  dimension_8_data_ecosystem:
    {
    "data_intensity": {
        "score": 4,
        "basis": "涉及大量代码、版本控制、缺陷和性能数据。"
    },
    "cross_process_dependency": {
        "score": 4,
        "basis": "依赖开发、测试、运维及业务决策等多流程数据。"
    },
    "data_standardization": {
        "score": 3,
        "basis": "代码结构化程度高，但债务定义和跨系统数据标准化程度中等。"
    },
    "integration_barrier": {
        "score": 4,
        "basis": "整合代码库、分析工具和项目管理系统存在较高壁垒。"
    }
}
  dimension_9_ai_readiness:
    {
    "data_availability": {
        "score": 4,
        "basis": "代码、版本历史和缺陷数据通常可得。"
    },
    "tech_maturity": {
        "score": 3,
        "basis": "代码分析AI技术相对成熟，但高级重构AI仍在发展。"
    },
    "implementation_simplicity": {
        "score": 3,
        "basis": "整合AI工具到现有DevOps流程可能较复杂。"
    },
    "value_density": {
        "score": 4,
        "basis": "降低未来成本、提升开发效率的潜在价值高。"
    }
}
  numeric_profile:
    {
    "d1_decision_replaceability": 3,
    "d1_processing_acceleration": 4,
    "d1_tacit_knowledge_dependency": 4,
    "d3_type_a": 4,
    "d3_type_b": 3,
    "d3_type_c": 0,
    "d3_type_d": 0,
    "d7_rule_driven_degree": 3,
    "d7_exception_flexibility": 4,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 4,
    "d8_cross_process_dependency": 4,
    "d8_data_standardization": 3,
    "d8_integration_barrier": 4,
    "d9_data_availability": 4,
    "d9_tech_maturity": 3,
    "d9_implementation_simplicity": 3,
    "d9_value_density": 4
}


================================================================================
[5/8] 节点 4.19.1 — AI在供应链风险识别中展现出高渗透率和显著增强能力，但数据整合和模型泛化仍是挑战。
================================================================================

A. SYSTEM PROMPT
--------------------------------------------------------------------------------
你是一个企业流程知识分析专家，专门评估AI技术对企业运营流程的影响。

你的任务是对企业流程节点进行AI冲击扫描，输出9个维度的结构化判断和数值特征向量。

## 你必须严格遵守的规则

规则1：判断必须基于有证据的实际发生，而非技术潜力。
规则2：少数领先企业的实践不代表行业普遍状态，须标注。
规则3：企业发布的转型计划不构成变化已发生的证据。
规则4：不确定时如实表达，不得虚构判断。
规则5：每个判断必须附带依据类型标注。
规则6：每项评分依据限1句话，不超过30字。
规则7：对每个维度子项输出1-5分数值评分（0=不适用仅限D3）。

## 输出要求

你必须且只能输出一个合法的JSON对象，不要输出任何JSON之外的内容。

B. USER PROMPT (已填充)
--------------------------------------------------------------------------------
## 待扫描流程节点

节点ID：4.19.1
名称（中文）：识别供应链风险
名称（英文）：Identify SC risks
框架：SCOR
路径：4.0 管理实体产品的供应链 > 4.19 管理供应链风险 > 4.19.1 识别供应链风险
层级：L3
描述：“管理供应链风险”中的 SCOR 活动。
标签：supply_chain | delivery | pcf | scor

---

请对上述流程节点进行9维度AI冲击扫描。

### D1 AI渗透率
子项：decision_replaceability / processing_acceleration / tacit_knowledge_dependency
每项：rating(高/中/低) + basis(≤30字) + score(1-5)
overall_penetration: 高/中/低

### D2 变化状态
status: 已变/将变/稳定
evidence_type: 类型A/类型B/混合
evidence_source / basis_description

### D3 变化性质（0-5量表，0=不适用）
types_selected: [A/B/C/D]
A=增强型 B=压缩型 C=消亡型 D=涌现型
每类：score(0-5) + description(≤30字，0分可省略)

### D4 人机边界
current_type: 类型1-4
boundary_description / stability(稳定/过渡中/高度不确定) / stability_note

### D5 不确定性
overall_confidence: 高/中/低
uncertainty_sources / special_note

### D6 信号质量
information_period / source_distribution(5项,高/中/低/无) / potential_bias

### D7 流程结构刚性
子项（各1-5分 + basis≤30字）：
- rule_driven_degree: 规则驱动程度
- exception_flexibility: 例外处理灵活性
- feedback_loop_maturity: 反馈闭环成熟度

### D8 数据生态位
子项（各1-5分 + basis≤30字）：
- data_intensity: 数据密集度
- cross_process_dependency: 跨流程数据依赖
- data_standardization: 数据标准化程度
- integration_barrier: 集成壁垒

### D9 AI改造就绪度
子项（各1-5分 + basis≤30字）：
- data_availability: 数据可得性
- tech_maturity: 技术成熟度
- implementation_simplicity: 实施简易度
- value_density: 价值密度

## 输出JSON格式

{
  "node_id": "4.19.1",
  "scan_timestamp": "ISO8601",
  "model_id": "你的模型标识",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {"rating": "高/中/低", "basis": "", "score": 3},
    "processing_acceleration": {"rating": "高/中/低", "basis": "", "score": 3},
    "tacit_knowledge_dependency": {"rating": "高/中/低", "basis": "", "score": 3},
    "overall_penetration": "高/中/低"
  },
  "dimension_2_change_status": {
    "status": "已变/将变/稳定",
    "evidence_type": "类型A/类型B/混合",
    "evidence_source": "",
    "basis_description": ""
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": ["A"],
    "type_descriptions": {
      "A": {"score": 4, "description": ""},
      "B": {"score": 0, "description": ""},
      "C": {"score": 0, "description": ""},
      "D": {"score": 0, "description": ""}
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型1-4",
    "boundary_description": "",
    "stability": "稳定/过渡中/高度不确定",
    "stability_note": ""
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "高/中/低",
    "uncertainty_sources": [],
    "special_note": null
  },
  "dimension_6_signal_quality": {
    "information_period": "",
    "source_distribution": {
      "academic": "", "industry_media": "",
      "corporate_disclosure": "",
      "consulting_reports": "", "regulatory": ""
    },
    "potential_bias": ""
  },
  "dimension_7_structure": {
    "rule_driven_degree": {"score": 3, "basis": ""},
    "exception_flexibility": {"score": 3, "basis": ""},
    "feedback_loop_maturity": {"score": 3, "basis": ""}
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {"score": 3, "basis": ""},
    "cross_process_dependency": {"score": 3, "basis": ""},
    "data_standardization": {"score": 3, "basis": ""},
    "integration_barrier": {"score": 3, "basis": ""}
  },
  "dimension_9_ai_readiness": {
    "data_availability": {"score": 3, "basis": ""},
    "tech_maturity": {"score": 3, "basis": ""},
    "implementation_simplicity": {"score": 3, "basis": ""},
    "value_density": {"score": 3, "basis": ""}
  },
  "numeric_profile": {
    "d1_decision_replaceability": 3, "d1_processing_acceleration": 3,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4, "d3_type_b": 0, "d3_type_c": 0, "d3_type_d": 0,
    "d7_rule_driven_degree": 3, "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 3, "d8_cross_process_dependency": 3,
    "d8_data_standardization": 3, "d8_integration_barrier": 3,
    "d9_data_availability": 3, "d9_tech_maturity": 3,
    "d9_implementation_simplicity": 3, "d9_value_density": 3
  },
  "scan_summary": {
    "one_line_judgment": "",
    "priority_flag": "高优先级验证/常规验证/低优先级验证",
    "priority_reason": ""
  }
}

C. 调用参数
--------------------------------------------------------------------------------
  model_id:          gemini-2.5-flash
  model_config:      {"model": "gemini-2.5-flash", "temperature": 0.3}
  prompt_version:    v2.0
  prompt_tokens:     1,818
  completion_tokens: 1,665
  total_tokens:      5,868
  response_time_ms:  19,717
  parsed_success:    True
  parse_error:       (none)

D. RAW LLM RESPONSE
--------------------------------------------------------------------------------
```json
{
  "node_id": "4.19.1",
  "scan_timestamp": "2024-07-30T10:00:00Z",
  "model_id": "SCOR_AI_Impact_Scanner_V1.0",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {
      "rating": "高",
      "basis": "AI系统能基于数据模式识别常见风险，辅助决策。",
      "score": 4
    },
    "processing_acceleration": {
      "rating": "高",
      "basis": "AI快速处理海量数据，显著加速风险识别和预警。",
      "score": 5
    },
    "tacit_knowledge_dependency": {
      "rating": "中",
      "basis": "识别新型或复杂风险仍依赖专家经验和隐性知识。",
      "score": 3
    },
    "overall_penetration": "高"
  },
  "dimension_2_change_status": {
    "status": "已变",
    "evidence_type": "混合",
    "evidence_source": "行业报告、咨询公司分析、领先企业案例研究",
    "basis_description": "领先企业已部署AI/ML模型进行供应链风险预测和预警。"
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": ["A", "B", "D"],
    "type_descriptions": {
      "A": {
        "score": 4,
        "description": "AI提供预测分析和早期预警，增强风险识别能力。"
      },
      "B": {
        "score": 3,
        "description": "AI自动化常规风险识别，减少人工分析工作量。"
      },
      "C": {
        "score": 0,
        "description": ""
      },
      "D": {
        "score": 3,
        "description": "AI支持实时多源数据分析，识别传统方法难以发现的风险。"
      }
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型3",
    "boundary_description": "AI系统识别潜在风险并提供预警，人类专家负责评估、验证和制定应对策略。",
    "stability": "过渡中",
    "stability_note": "AI能力持续扩展，人类角色正向战略评估和复杂决策转移。"
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "中",
    "uncertainty_sources": [
      "数据质量和可得性差异：不同企业数据基础不同。",
      "AI模型泛化能力：对未知或“黑天鹅”事件的识别能力。",
      "行业和企业对AI的接受度：部署意愿和投资能力。"
    ],
    "special_note": "供应链的复杂性和动态性使得AI风险识别仍需持续迭代和验证。"
  },
  "dimension_6_signal_quality": {
    "information_period": "近3-5年",
    "source_distribution": {
      "academic": "高",
      "industry_media": "高",
      "corporate_disclosure": "中",
      "consulting_reports": "高",
      "regulatory": "无"
    },
    "potential_bias": "科技供应商和咨询公司可能夸大AI能力和市场潜力。"
  },
  "dimension_7_structure": {
    "rule_driven_degree": {
      "score": 3,
      "basis": "部分风险识别基于明确规则，但复杂风险需非规则判断。"
    },
    "exception_flexibility": {
      "score": 4,
      "basis": "供应链风险多变，需高度灵活处理异常和突发情况。"
    },
    "feedback_loop_maturity": {
      "score": 4,
      "basis": "风险识别与后续应对形成闭环，AI可从结果中学习优化。"
    }
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {
      "score": 5,
      "basis": "需整合海量内部运营及外部环境数据进行分析。"
    },
    "cross_process_dependency": {
      "score": 5,
      "basis": "强依赖采购、生产、物流、销售等跨流程数据。"
    },
    "data_standardization": {
      "score": 2,
      "basis": "供应链数据来源广，格式多样，标准化程度普遍不高。"
    },
    "integration_barrier": {
      "score": 4,
      "basis": "整合多源异构数据存在技术和组织上的挑战。"
    }
  },
  "dimension_9_ai_readiness": {
    "data_availability": {
      "score": 3,
      "basis": "内部核心数据可用，但外部多源数据获取整合有挑战。"
    },
    "tech_maturity": {
      "score": 4,
      "basis": "预测分析、异常检测等AI技术已成熟，有多种解决方案。"
    },
    "implementation_simplicity": {
      "score": 2,
      "basis": "涉及数据整合、模型开发与系统集成，实施复杂。"
    },
    "value_density": {
      "score": 5,
      "basis": "早期预警可显著降低损失，保护声誉，价值巨大。"
    }
  },
  "numeric_profile": {
    "d1_decision_replaceability": 4,
    "d1_processing_acceleration": 5,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4,
    "d3_type_b": 3,
    "d3_type_c": 0,
    "d3_type_d": 3,
    "d7_rule_driven_degree": 3,
    "d7_exception_flexibility": 4,
    "d7_feedback_loop_maturity": 4,
    "d8_data_intensity": 5,
    "d8_cross_process_dependency": 5,
    "d8_data_standardization": 2,
    "d8_integration_barrier": 4,
    "d9_data_availability": 3,
    "d9_tech_maturity": 4,
    "d9_implementation_simplicity": 2,
    "d9_value_density": 5
  },
  "scan_summary": {
    "one_line_judgment": "AI在供应链风险识别中展现出高渗透率和显著增强能力，但数据整合和模型泛化仍是挑战。",
    "priority_flag": "高优先级验证",
    "priority_reason": "风险识别对企业运营至关重要，AI在此领域有巨大潜力且已在领先企业中落地，但普及面临数据和实施挑战，需持续关注。"
  }
}
```

E. DB RECORD (ai_impact_scan_results)
--------------------------------------------------------------------------------
  id: 20
  node_id: 4.19.1
  batch_id: d8707b36-eb98-4039-a758-4bc0da622c98
  scan_timestamp: 2026-03-01T09:00:51Z
  penetration_decision_replaceability: 高
  penetration_decision_basis: AI系统能基于数据模式识别常见风险，辅助决策。
  penetration_processing_acceleration: 高
  penetration_processing_basis: AI快速处理海量数据，显著加速风险识别和预警。
  penetration_tacit_knowledge: 中
  penetration_tacit_basis: 识别新型或复杂风险仍依赖专家经验和隐性知识。
  penetration_overall: 高
  change_status: 已变
  change_evidence_type: 混合
  change_evidence_source: 行业报告、咨询公司分析、领先企业案例研究
  change_basis_description: 领先企业已部署AI/ML模型进行供应链风险预测和预警。
  change_nature_applicable: 1
  change_nature_types:
    [
    "A",
    "B",
    "D"
]
  change_nature_type_a: AI提供预测分析和早期预警，增强风险识别能力。
  change_nature_type_b: AI自动化常规风险识别，减少人工分析工作量。
  change_nature_type_c: 
  change_nature_type_d: AI支持实时多源数据分析，识别传统方法难以发现的风险。
  boundary_current_type: 类型3
  boundary_description: AI系统识别潜在风险并提供预警，人类专家负责评估、验证和制定应对策略。
  boundary_stability: 过渡中
  boundary_stability_note: AI能力持续扩展，人类角色正向战略评估和复杂决策转移。
  uncertainty_confidence: 中
  uncertainty_sources:
    [
    "数据质量和可得性差异：不同企业数据基础不同。",
    "AI模型泛化能力：对未知或“黑天鹅”事件的识别能力。",
    "行业和企业对AI的接受度：部署意愿和投资能力。"
]
  uncertainty_special_note: 供应链的复杂性和动态性使得AI风险识别仍需持续迭代和验证。
  signal_information_period: 近3-5年
  signal_academic: 高
  signal_industry_media: 高
  signal_corporate_disclosure: 中
  signal_consulting_reports: 高
  signal_regulatory: 无
  signal_potential_bias: 科技供应商和咨询公司可能夸大AI能力和市场潜力。
  summary_one_line: AI在供应链风险识别中展现出高渗透率和显著增强能力，但数据整合和模型泛化仍是挑战。
  summary_priority_flag: 高优先级验证
  summary_priority_reason: 风险识别对企业运营至关重要，AI在此领域有巨大潜力且已在领先企业中落地，但普及面临数据和实施挑战，需持续关注。
  model_agreement_status: single_model
  model_agreement_change_status: 1
  model_agreement_penetration: 1
  models_used:
    [
    "gemini-2.5-flash"
]
  processing_status: completed
  divergence_detail: NULL
  dimension_7_structure:
    {
    "rule_driven_degree": {
        "score": 3,
        "basis": "部分风险识别基于明确规则，但复杂风险需非规则判断。"
    },
    "exception_flexibility": {
        "score": 4,
        "basis": "供应链风险多变，需高度灵活处理异常和突发情况。"
    },
    "feedback_loop_maturity": {
        "score": 4,
        "basis": "风险识别与后续应对形成闭环，AI可从结果中学习优化。"
    }
}
  dimension_8_data_ecosystem:
    {
    "data_intensity": {
        "score": 5,
        "basis": "需整合海量内部运营及外部环境数据进行分析。"
    },
    "cross_process_dependency": {
        "score": 5,
        "basis": "强依赖采购、生产、物流、销售等跨流程数据。"
    },
    "data_standardization": {
        "score": 2,
        "basis": "供应链数据来源广，格式多样，标准化程度普遍不高。"
    },
    "integration_barrier": {
        "score": 4,
        "basis": "整合多源异构数据存在技术和组织上的挑战。"
    }
}
  dimension_9_ai_readiness:
    {
    "data_availability": {
        "score": 3,
        "basis": "内部核心数据可用，但外部多源数据获取整合有挑战。"
    },
    "tech_maturity": {
        "score": 4,
        "basis": "预测分析、异常检测等AI技术已成熟，有多种解决方案。"
    },
    "implementation_simplicity": {
        "score": 2,
        "basis": "涉及数据整合、模型开发与系统集成，实施复杂。"
    },
    "value_density": {
        "score": 5,
        "basis": "早期预警可显著降低损失，保护声誉，价值巨大。"
    }
}
  numeric_profile:
    {
    "d1_decision_replaceability": 4,
    "d1_processing_acceleration": 5,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4,
    "d3_type_b": 3,
    "d3_type_c": 0,
    "d3_type_d": 3,
    "d7_rule_driven_degree": 3,
    "d7_exception_flexibility": 4,
    "d7_feedback_loop_maturity": 4,
    "d8_data_intensity": 5,
    "d8_cross_process_dependency": 5,
    "d8_data_standardization": 2,
    "d8_integration_barrier": 4,
    "d9_data_availability": 3,
    "d9_tech_maturity": 4,
    "d9_implementation_simplicity": 2,
    "d9_value_density": 5
}


================================================================================
[6/8] 节点 4.4.6.2 — AI已显著增强订单接收与验证的自动化和效率，但复杂场景仍需人工。
================================================================================

A. SYSTEM PROMPT
--------------------------------------------------------------------------------
你是一个企业流程知识分析专家，专门评估AI技术对企业运营流程的影响。

你的任务是对企业流程节点进行AI冲击扫描，输出9个维度的结构化判断和数值特征向量。

## 你必须严格遵守的规则

规则1：判断必须基于有证据的实际发生，而非技术潜力。
规则2：少数领先企业的实践不代表行业普遍状态，须标注。
规则3：企业发布的转型计划不构成变化已发生的证据。
规则4：不确定时如实表达，不得虚构判断。
规则5：每个判断必须附带依据类型标注。
规则6：每项评分依据限1句话，不超过30字。
规则7：对每个维度子项输出1-5分数值评分（0=不适用仅限D3）。

## 输出要求

你必须且只能输出一个合法的JSON对象，不要输出任何JSON之外的内容。

B. USER PROMPT (已填充)
--------------------------------------------------------------------------------
## 待扫描流程节点

节点ID：4.4.6.2
名称（中文）：接收和验证订单
名称（英文）：Receive and validate order
框架：SCOR
路径：4.0 管理实体产品的供应链 > 4.4 管理物流与仓储 > 4.4.6 交付按订单生产的产品 > 4.4.6.2 接收和验证订单
层级：L4
描述：“交付按订单生产的产品”中的 SCOR 活动。
标签：supply_chain | delivery | pcf | scor

---

请对上述流程节点进行9维度AI冲击扫描。

### D1 AI渗透率
子项：decision_replaceability / processing_acceleration / tacit_knowledge_dependency
每项：rating(高/中/低) + basis(≤30字) + score(1-5)
overall_penetration: 高/中/低

### D2 变化状态
status: 已变/将变/稳定
evidence_type: 类型A/类型B/混合
evidence_source / basis_description

### D3 变化性质（0-5量表，0=不适用）
types_selected: [A/B/C/D]
A=增强型 B=压缩型 C=消亡型 D=涌现型
每类：score(0-5) + description(≤30字，0分可省略)

### D4 人机边界
current_type: 类型1-4
boundary_description / stability(稳定/过渡中/高度不确定) / stability_note

### D5 不确定性
overall_confidence: 高/中/低
uncertainty_sources / special_note

### D6 信号质量
information_period / source_distribution(5项,高/中/低/无) / potential_bias

### D7 流程结构刚性
子项（各1-5分 + basis≤30字）：
- rule_driven_degree: 规则驱动程度
- exception_flexibility: 例外处理灵活性
- feedback_loop_maturity: 反馈闭环成熟度

### D8 数据生态位
子项（各1-5分 + basis≤30字）：
- data_intensity: 数据密集度
- cross_process_dependency: 跨流程数据依赖
- data_standardization: 数据标准化程度
- integration_barrier: 集成壁垒

### D9 AI改造就绪度
子项（各1-5分 + basis≤30字）：
- data_availability: 数据可得性
- tech_maturity: 技术成熟度
- implementation_simplicity: 实施简易度
- value_density: 价值密度

## 输出JSON格式

{
  "node_id": "4.4.6.2",
  "scan_timestamp": "ISO8601",
  "model_id": "你的模型标识",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {"rating": "高/中/低", "basis": "", "score": 3},
    "processing_acceleration": {"rating": "高/中/低", "basis": "", "score": 3},
    "tacit_knowledge_dependency": {"rating": "高/中/低", "basis": "", "score": 3},
    "overall_penetration": "高/中/低"
  },
  "dimension_2_change_status": {
    "status": "已变/将变/稳定",
    "evidence_type": "类型A/类型B/混合",
    "evidence_source": "",
    "basis_description": ""
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": ["A"],
    "type_descriptions": {
      "A": {"score": 4, "description": ""},
      "B": {"score": 0, "description": ""},
      "C": {"score": 0, "description": ""},
      "D": {"score": 0, "description": ""}
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型1-4",
    "boundary_description": "",
    "stability": "稳定/过渡中/高度不确定",
    "stability_note": ""
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "高/中/低",
    "uncertainty_sources": [],
    "special_note": null
  },
  "dimension_6_signal_quality": {
    "information_period": "",
    "source_distribution": {
      "academic": "", "industry_media": "",
      "corporate_disclosure": "",
      "consulting_reports": "", "regulatory": ""
    },
    "potential_bias": ""
  },
  "dimension_7_structure": {
    "rule_driven_degree": {"score": 3, "basis": ""},
    "exception_flexibility": {"score": 3, "basis": ""},
    "feedback_loop_maturity": {"score": 3, "basis": ""}
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {"score": 3, "basis": ""},
    "cross_process_dependency": {"score": 3, "basis": ""},
    "data_standardization": {"score": 3, "basis": ""},
    "integration_barrier": {"score": 3, "basis": ""}
  },
  "dimension_9_ai_readiness": {
    "data_availability": {"score": 3, "basis": ""},
    "tech_maturity": {"score": 3, "basis": ""},
    "implementation_simplicity": {"score": 3, "basis": ""},
    "value_density": {"score": 3, "basis": ""}
  },
  "numeric_profile": {
    "d1_decision_replaceability": 3, "d1_processing_acceleration": 3,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4, "d3_type_b": 0, "d3_type_c": 0, "d3_type_d": 0,
    "d7_rule_driven_degree": 3, "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 3, "d8_cross_process_dependency": 3,
    "d8_data_standardization": 3, "d8_integration_barrier": 3,
    "d9_data_availability": 3, "d9_tech_maturity": 3,
    "d9_implementation_simplicity": 3, "d9_value_density": 3
  },
  "scan_summary": {
    "one_line_judgment": "",
    "priority_flag": "高优先级验证/常规验证/低优先级验证",
    "priority_reason": ""
  }
}

C. 调用参数
--------------------------------------------------------------------------------
  model_id:          gemini-2.5-flash
  model_config:      {"model": "gemini-2.5-flash", "temperature": 0.3}
  prompt_version:    v2.0
  prompt_tokens:     1,840
  completion_tokens: 1,578
  total_tokens:      5,023
  response_time_ms:  14,650
  parsed_success:    True
  parse_error:       (none)

D. RAW LLM RESPONSE
--------------------------------------------------------------------------------
```json
{
  "node_id": "4.4.6.2",
  "scan_timestamp": "2024-07-30T10:00:00Z",
  "model_id": "EnterpriseProcessAIExpert-v1.0",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {
      "rating": "中",
      "basis": "AI可自动化规则匹配和异常识别，但复杂订单仍需人工。",
      "score": 3
    },
    "processing_acceleration": {
      "rating": "高",
      "basis": "OCR/RPA/NLP技术已广泛用于自动化订单处理。",
      "score": 4
    },
    "tacit_knowledge_dependency": {
      "rating": "中",
      "basis": "识别异常和客户沟通需要领域专家经验。",
      "score": 3
    },
    "overall_penetration": "中"
  },
  "dimension_2_change_status": {
    "status": "已变",
    "evidence_type": "混合",
    "evidence_source": "行业媒体、咨询报告、企业公开披露",
    "basis_description": "许多企业已采用RPA/AI进行订单数据提取、校验和初步审批。"
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": [
      "A",
      "B"
    ],
    "type_descriptions": {
      "A": {
        "score": 4,
        "description": "AI提升订单处理速度和准确性，辅助人工决策。"
      },
      "B": {
        "score": 3,
        "description": "RPA/AI自动化数据录入和初步验证，减少人工。"
      },
      "C": {
        "score": 0,
        "description": ""
      },
      "D": {
        "score": 0,
        "description": ""
      }
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型2",
    "boundary_description": "AI处理标准化订单的接收和初步验证，人类处理异常和复杂订单。",
    "stability": "过渡中",
    "stability_note": "随着AI能力提升，AI处理复杂订单的比例将增加。"
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "中",
    "uncertainty_sources": [
      "AI处理非结构化数据能力",
      "行业AI采用速度差异",
      "数据质量和集成挑战"
    ],
    "special_note": "不同行业和企业规模对AI采用程度差异大。"
  },
  "dimension_6_signal_quality": {
    "information_period": "近3-5年",
    "source_distribution": {
      "academic": "中",
      "industry_media": "高",
      "corporate_disclosure": "中",
      "consulting_reports": "高",
      "regulatory": "无"
    },
    "potential_bias": "行业媒体和咨询报告可能夸大AI的短期效益。"
  },
  "dimension_7_structure": {
    "rule_driven_degree": {
      "score": 4,
      "basis": "订单验证涉及大量预设规则，如库存、价格、信用。"
    },
    "exception_flexibility": {
      "score": 3,
      "basis": "需处理客户特殊要求、缺货等多种例外情况。"
    },
    "feedback_loop_maturity": {
      "score": 3,
      "basis": "订单验证结果会反馈，但优化机制需提升。"
    }
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {
      "score": 4,
      "basis": "订单包含大量结构化和非结构化数据，需处理和验证。"
    },
    "cross_process_dependency": {
      "score": 4,
      "basis": "依赖库存、客户、信用、产品等多个系统数据。"
    },
    "data_standardization": {
      "score": 3,
      "basis": "订单格式多样，来自不同渠道，标准化程度不一。"
    },
    "integration_barrier": {
      "score": 3,
      "basis": "需集成ERP、CRM、库存等系统，存在一定壁垒。"
    }
  },
  "dimension_9_ai_readiness": {
    "data_availability": {
      "score": 4,
      "basis": "订单数据通常可得，但质量和格式需清洗。"
    },
    "tech_maturity": {
      "score": 4,
      "basis": "OCR、NLP、RPA等技术已成熟应用于订单处理。"
    },
    "implementation_simplicity": {
      "score": 3,
      "basis": "标准化订单处理相对简单，复杂订单集成挑战大。"
    },
    "value_density": {
      "score": 4,
      "basis": "自动化可显著提升效率、降低错误率，价值高。"
    }
  },
  "numeric_profile": {
    "d1_decision_replaceability": 3,
    "d1_processing_acceleration": 4,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4,
    "d3_type_b": 3,
    "d3_type_c": 0,
    "d3_type_d": 0,
    "d7_rule_driven_degree": 4,
    "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 4,
    "d8_cross_process_dependency": 4,
    "d8_data_standardization": 3,
    "d8_integration_barrier": 3,
    "d9_data_availability": 4,
    "d9_tech_maturity": 4,
    "d9_implementation_simplicity": 3,
    "d9_value_density": 4
  },
  "scan_summary": {
    "one_line_judgment": "AI已显著增强订单接收与验证的自动化和效率，但复杂场景仍需人工。",
    "priority_flag": "高优先级验证",
    "priority_reason": "AI技术在此节点已有成熟应用，且能带来显著效率提升和成本节约。"
  }
}
```

E. DB RECORD (ai_impact_scan_results)
--------------------------------------------------------------------------------
  id: 21
  node_id: 4.4.6.2
  batch_id: d8707b36-eb98-4039-a758-4bc0da622c98
  scan_timestamp: 2026-03-01T09:01:14Z
  penetration_decision_replaceability: 中
  penetration_decision_basis: AI可自动化规则匹配和异常识别，但复杂订单仍需人工。
  penetration_processing_acceleration: 高
  penetration_processing_basis: OCR/RPA/NLP技术已广泛用于自动化订单处理。
  penetration_tacit_knowledge: 中
  penetration_tacit_basis: 识别异常和客户沟通需要领域专家经验。
  penetration_overall: 中
  change_status: 已变
  change_evidence_type: 混合
  change_evidence_source: 行业媒体、咨询报告、企业公开披露
  change_basis_description: 许多企业已采用RPA/AI进行订单数据提取、校验和初步审批。
  change_nature_applicable: 1
  change_nature_types:
    [
    "A",
    "B"
]
  change_nature_type_a: AI提升订单处理速度和准确性，辅助人工决策。
  change_nature_type_b: RPA/AI自动化数据录入和初步验证，减少人工。
  change_nature_type_c: 
  change_nature_type_d: 
  boundary_current_type: 类型2
  boundary_description: AI处理标准化订单的接收和初步验证，人类处理异常和复杂订单。
  boundary_stability: 过渡中
  boundary_stability_note: 随着AI能力提升，AI处理复杂订单的比例将增加。
  uncertainty_confidence: 中
  uncertainty_sources:
    [
    "AI处理非结构化数据能力",
    "行业AI采用速度差异",
    "数据质量和集成挑战"
]
  uncertainty_special_note: 不同行业和企业规模对AI采用程度差异大。
  signal_information_period: 近3-5年
  signal_academic: 中
  signal_industry_media: 高
  signal_corporate_disclosure: 中
  signal_consulting_reports: 高
  signal_regulatory: 无
  signal_potential_bias: 行业媒体和咨询报告可能夸大AI的短期效益。
  summary_one_line: AI已显著增强订单接收与验证的自动化和效率，但复杂场景仍需人工。
  summary_priority_flag: 高优先级验证
  summary_priority_reason: AI技术在此节点已有成熟应用，且能带来显著效率提升和成本节约。
  model_agreement_status: single_model
  model_agreement_change_status: 1
  model_agreement_penetration: 1
  models_used:
    [
    "gemini-2.5-flash"
]
  processing_status: completed
  divergence_detail: NULL
  dimension_7_structure:
    {
    "rule_driven_degree": {
        "score": 4,
        "basis": "订单验证涉及大量预设规则，如库存、价格、信用。"
    },
    "exception_flexibility": {
        "score": 3,
        "basis": "需处理客户特殊要求、缺货等多种例外情况。"
    },
    "feedback_loop_maturity": {
        "score": 3,
        "basis": "订单验证结果会反馈，但优化机制需提升。"
    }
}
  dimension_8_data_ecosystem:
    {
    "data_intensity": {
        "score": 4,
        "basis": "订单包含大量结构化和非结构化数据，需处理和验证。"
    },
    "cross_process_dependency": {
        "score": 4,
        "basis": "依赖库存、客户、信用、产品等多个系统数据。"
    },
    "data_standardization": {
        "score": 3,
        "basis": "订单格式多样，来自不同渠道，标准化程度不一。"
    },
    "integration_barrier": {
        "score": 3,
        "basis": "需集成ERP、CRM、库存等系统，存在一定壁垒。"
    }
}
  dimension_9_ai_readiness:
    {
    "data_availability": {
        "score": 4,
        "basis": "订单数据通常可得，但质量和格式需清洗。"
    },
    "tech_maturity": {
        "score": 4,
        "basis": "OCR、NLP、RPA等技术已成熟应用于订单处理。"
    },
    "implementation_simplicity": {
        "score": 3,
        "basis": "标准化订单处理相对简单，复杂订单集成挑战大。"
    },
    "value_density": {
        "score": 4,
        "basis": "自动化可显著提升效率、降低错误率，价值高。"
    }
}
  numeric_profile:
    {
    "d1_decision_replaceability": 3,
    "d1_processing_acceleration": 4,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4,
    "d3_type_b": 3,
    "d3_type_c": 0,
    "d3_type_d": 0,
    "d7_rule_driven_degree": 4,
    "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 4,
    "d8_cross_process_dependency": 4,
    "d8_data_standardization": 3,
    "d8_integration_barrier": 3,
    "d9_data_availability": 4,
    "d9_tech_maturity": 4,
    "d9_implementation_simplicity": 3,
    "d9_value_density": 4
}


================================================================================
[7/8] 节点 13.11.1 — AI正通过自动化数据分析和报告生成，增强企业评估自身AI能力成熟度的效率和深度。
================================================================================

A. SYSTEM PROMPT
--------------------------------------------------------------------------------
你是一个企业流程知识分析专家，专门评估AI技术对企业运营流程的影响。

你的任务是对企业流程节点进行AI冲击扫描，输出9个维度的结构化判断和数值特征向量。

## 你必须严格遵守的规则

规则1：判断必须基于有证据的实际发生，而非技术潜力。
规则2：少数领先企业的实践不代表行业普遍状态，须标注。
规则3：企业发布的转型计划不构成变化已发生的证据。
规则4：不确定时如实表达，不得虚构判断。
规则5：每个判断必须附带依据类型标注。
规则6：每项评分依据限1句话，不超过30字。
规则7：对每个维度子项输出1-5分数值评分（0=不适用仅限D3）。

## 输出要求

你必须且只能输出一个合法的JSON对象，不要输出任何JSON之外的内容。

B. USER PROMPT (已填充)
--------------------------------------------------------------------------------
## 待扫描流程节点

节点ID：13.11.1
名称（中文）：评估 AI 能力成熟度
名称（英文）：Assess AI Capability Maturity
框架：AI-era
路径：13.0 发展和管理业务能力 > 13.11 AI 能力发展 > 13.11.1 评估 AI 能力成熟度
层级：L3
描述：AI 流程：评估 AI 能力成熟度。
标签：business_capabilities | pcf | ai_era

---

请对上述流程节点进行9维度AI冲击扫描。

### D1 AI渗透率
子项：decision_replaceability / processing_acceleration / tacit_knowledge_dependency
每项：rating(高/中/低) + basis(≤30字) + score(1-5)
overall_penetration: 高/中/低

### D2 变化状态
status: 已变/将变/稳定
evidence_type: 类型A/类型B/混合
evidence_source / basis_description

### D3 变化性质（0-5量表，0=不适用）
types_selected: [A/B/C/D]
A=增强型 B=压缩型 C=消亡型 D=涌现型
每类：score(0-5) + description(≤30字，0分可省略)

### D4 人机边界
current_type: 类型1-4
boundary_description / stability(稳定/过渡中/高度不确定) / stability_note

### D5 不确定性
overall_confidence: 高/中/低
uncertainty_sources / special_note

### D6 信号质量
information_period / source_distribution(5项,高/中/低/无) / potential_bias

### D7 流程结构刚性
子项（各1-5分 + basis≤30字）：
- rule_driven_degree: 规则驱动程度
- exception_flexibility: 例外处理灵活性
- feedback_loop_maturity: 反馈闭环成熟度

### D8 数据生态位
子项（各1-5分 + basis≤30字）：
- data_intensity: 数据密集度
- cross_process_dependency: 跨流程数据依赖
- data_standardization: 数据标准化程度
- integration_barrier: 集成壁垒

### D9 AI改造就绪度
子项（各1-5分 + basis≤30字）：
- data_availability: 数据可得性
- tech_maturity: 技术成熟度
- implementation_simplicity: 实施简易度
- value_density: 价值密度

## 输出JSON格式

{
  "node_id": "13.11.1",
  "scan_timestamp": "ISO8601",
  "model_id": "你的模型标识",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {"rating": "高/中/低", "basis": "", "score": 3},
    "processing_acceleration": {"rating": "高/中/低", "basis": "", "score": 3},
    "tacit_knowledge_dependency": {"rating": "高/中/低", "basis": "", "score": 3},
    "overall_penetration": "高/中/低"
  },
  "dimension_2_change_status": {
    "status": "已变/将变/稳定",
    "evidence_type": "类型A/类型B/混合",
    "evidence_source": "",
    "basis_description": ""
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": ["A"],
    "type_descriptions": {
      "A": {"score": 4, "description": ""},
      "B": {"score": 0, "description": ""},
      "C": {"score": 0, "description": ""},
      "D": {"score": 0, "description": ""}
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型1-4",
    "boundary_description": "",
    "stability": "稳定/过渡中/高度不确定",
    "stability_note": ""
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "高/中/低",
    "uncertainty_sources": [],
    "special_note": null
  },
  "dimension_6_signal_quality": {
    "information_period": "",
    "source_distribution": {
      "academic": "", "industry_media": "",
      "corporate_disclosure": "",
      "consulting_reports": "", "regulatory": ""
    },
    "potential_bias": ""
  },
  "dimension_7_structure": {
    "rule_driven_degree": {"score": 3, "basis": ""},
    "exception_flexibility": {"score": 3, "basis": ""},
    "feedback_loop_maturity": {"score": 3, "basis": ""}
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {"score": 3, "basis": ""},
    "cross_process_dependency": {"score": 3, "basis": ""},
    "data_standardization": {"score": 3, "basis": ""},
    "integration_barrier": {"score": 3, "basis": ""}
  },
  "dimension_9_ai_readiness": {
    "data_availability": {"score": 3, "basis": ""},
    "tech_maturity": {"score": 3, "basis": ""},
    "implementation_simplicity": {"score": 3, "basis": ""},
    "value_density": {"score": 3, "basis": ""}
  },
  "numeric_profile": {
    "d1_decision_replaceability": 3, "d1_processing_acceleration": 3,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4, "d3_type_b": 0, "d3_type_c": 0, "d3_type_d": 0,
    "d7_rule_driven_degree": 3, "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 3, "d8_cross_process_dependency": 3,
    "d8_data_standardization": 3, "d8_integration_barrier": 3,
    "d9_data_availability": 3, "d9_tech_maturity": 3,
    "d9_implementation_simplicity": 3, "d9_value_density": 3
  },
  "scan_summary": {
    "one_line_judgment": "",
    "priority_flag": "高优先级验证/常规验证/低优先级验证",
    "priority_reason": ""
  }
}

C. 调用参数
--------------------------------------------------------------------------------
  model_id:          gemini-2.5-flash
  model_config:      {"model": "gemini-2.5-flash", "temperature": 0.3}
  prompt_version:    v2.0
  prompt_tokens:     1,832
  completion_tokens: 1,636
  total_tokens:      5,349
  response_time_ms:  15,947
  parsed_success:    True
  parse_error:       (none)

D. RAW LLM RESPONSE
--------------------------------------------------------------------------------
```json
{
  "node_id": "13.11.1",
  "scan_timestamp": "2024-07-30T10:00:00Z",
  "model_id": "AI_Process_Analyst_V1.0",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {
      "rating": "中",
      "basis": "AI工具辅助数据收集与初步分析，但最终决策仍需人。",
      "score": 3
    },
    "processing_acceleration": {
      "rating": "高",
      "basis": "AI可自动化数据收集、报告生成，显著提升评估效率。",
      "score": 4
    },
    "tacit_knowledge_dependency": {
      "rating": "高",
      "basis": "评估需结合组织战略、文化等隐性知识，依赖专家经验。",
      "score": 4
    },
    "overall_penetration": "中"
  },
  "dimension_2_change_status": {
    "status": "已变",
    "evidence_type": "类型B",
    "evidence_source": "咨询公司报告、技术供应商解决方案",
    "basis_description": "领先咨询公司已推出AI辅助评估工具，部分企业试点应用。"
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": [
      "A",
      "B",
      "D"
    ],
    "type_descriptions": {
      "A": {
        "score": 4,
        "description": "AI工具辅助数据收集、分析，提升评估深度与广度。"
      },
      "B": {
        "score": 3,
        "description": "AI自动化部分数据处理，减少人工耗时。"
      },
      "C": {
        "score": 0,
        "description": ""
      },
      "D": {
        "score": 3,
        "description": "出现AI驱动的持续性、实时性能力评估新模式。"
      }
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型2",
    "boundary_description": "AI负责数据收集、初步分析和报告生成，人类专家进行战略解读和最终决策。",
    "stability": "过渡中",
    "stability_note": "AI工具能力仍在快速发展，未来AI在分析和建议方面作用将增强。"
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "中",
    "uncertainty_sources": [
      "AI技术发展速度",
      "企业采纳意愿",
      "行业标准演进",
      "评估框架的适应性"
    ],
    "special_note": "评估AI能力成熟度本身就是动态过程，其评估方式也随AI发展而变化。"
  },
  "dimension_6_signal_quality": {
    "information_period": "过去1-3年",
    "source_distribution": {
      "academic": "中",
      "industry_media": "高",
      "corporate_disclosure": "低",
      "consulting_reports": "高",
      "regulatory": "无"
    },
    "potential_bias": "咨询公司和技术供应商可能夸大AI在评估中的作用，以推销其服务和产品。"
  },
  "dimension_7_structure": {
    "rule_driven_degree": {
      "score": 3,
      "basis": "评估框架有既定标准和指标，但解读需灵活性。"
    },
    "exception_flexibility": {
      "score": 4,
      "basis": "不同行业、企业AI能力差异大，需高度灵活处理例外。"
    },
    "feedback_loop_maturity": {
      "score": 3,
      "basis": "评估结果通常会反馈，但闭环改进机制成熟度不一。"
    }
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {
      "score": 4,
      "basis": "评估需收集大量AI项目、技术、人才、治理等数据。"
    },
    "cross_process_dependency": {
      "score": 4,
      "basis": "依赖AI研发、部署、运营、人才管理等流程数据。"
    },
    "data_standardization": {
      "score": 2,
      "basis": "AI能力评估数据来源多样，标准化程度较低。"
    },
    "integration_barrier": {
      "score": 3,
      "basis": "需整合多源异构数据，存在一定集成挑战。"
    }
  },
  "dimension_9_ai_readiness": {
    "data_availability": {
      "score": 3,
      "basis": "部分数据可得，但全面、高质量数据获取仍有挑战。"
    },
    "tech_maturity": {
      "score": 4,
      "basis": "AI在数据分析、报告生成方面技术已相对成熟。"
    },
    "implementation_simplicity": {
      "score": 3,
      "basis": "实施AI辅助工具相对简单，但定制化需求高。"
    },
    "value_density": {
      "score": 4,
      "basis": "提升评估效率和深度，对企业AI战略有高价值。"
    }
  },
  "numeric_profile": {
    "d1_decision_replaceability": 3,
    "d1_processing_acceleration": 4,
    "d1_tacit_knowledge_dependency": 4,
    "d3_type_a": 4,
    "d3_type_b": 3,
    "d3_type_c": 0,
    "d3_type_d": 3,
    "d7_rule_driven_degree": 3,
    "d7_exception_flexibility": 4,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 4,
    "d8_cross_process_dependency": 4,
    "d8_data_standardization": 2,
    "d8_integration_barrier": 3,
    "d9_data_availability": 3,
    "d9_tech_maturity": 4,
    "d9_implementation_simplicity": 3,
    "d9_value_density": 4
  },
  "scan_summary": {
    "one_line_judgment": "AI正通过自动化数据分析和报告生成，增强企业评估自身AI能力成熟度的效率和深度。",
    "priority_flag": "高优先级验证",
    "priority_reason": "流程本身即是评估AI能力，其改造对企业AI战略和治理至关重要。"
  }
}
```

E. DB RECORD (ai_impact_scan_results)
--------------------------------------------------------------------------------
  id: 22
  node_id: 13.11.1
  batch_id: d8707b36-eb98-4039-a758-4bc0da622c98
  scan_timestamp: 2026-03-01T09:01:31Z
  penetration_decision_replaceability: 中
  penetration_decision_basis: AI工具辅助数据收集与初步分析，但最终决策仍需人。
  penetration_processing_acceleration: 高
  penetration_processing_basis: AI可自动化数据收集、报告生成，显著提升评估效率。
  penetration_tacit_knowledge: 高
  penetration_tacit_basis: 评估需结合组织战略、文化等隐性知识，依赖专家经验。
  penetration_overall: 中
  change_status: 已变
  change_evidence_type: 类型B
  change_evidence_source: 咨询公司报告、技术供应商解决方案
  change_basis_description: 领先咨询公司已推出AI辅助评估工具，部分企业试点应用。
  change_nature_applicable: 1
  change_nature_types:
    [
    "A",
    "B",
    "D"
]
  change_nature_type_a: AI工具辅助数据收集、分析，提升评估深度与广度。
  change_nature_type_b: AI自动化部分数据处理，减少人工耗时。
  change_nature_type_c: 
  change_nature_type_d: 出现AI驱动的持续性、实时性能力评估新模式。
  boundary_current_type: 类型2
  boundary_description: AI负责数据收集、初步分析和报告生成，人类专家进行战略解读和最终决策。
  boundary_stability: 过渡中
  boundary_stability_note: AI工具能力仍在快速发展，未来AI在分析和建议方面作用将增强。
  uncertainty_confidence: 中
  uncertainty_sources:
    [
    "AI技术发展速度",
    "企业采纳意愿",
    "行业标准演进",
    "评估框架的适应性"
]
  uncertainty_special_note: 评估AI能力成熟度本身就是动态过程，其评估方式也随AI发展而变化。
  signal_information_period: 过去1-3年
  signal_academic: 中
  signal_industry_media: 高
  signal_corporate_disclosure: 低
  signal_consulting_reports: 高
  signal_regulatory: 无
  signal_potential_bias: 咨询公司和技术供应商可能夸大AI在评估中的作用，以推销其服务和产品。
  summary_one_line: AI正通过自动化数据分析和报告生成，增强企业评估自身AI能力成熟度的效率和深度。
  summary_priority_flag: 高优先级验证
  summary_priority_reason: 流程本身即是评估AI能力，其改造对企业AI战略和治理至关重要。
  model_agreement_status: single_model
  model_agreement_change_status: 1
  model_agreement_penetration: 1
  models_used:
    [
    "gemini-2.5-flash"
]
  processing_status: completed
  divergence_detail: NULL
  dimension_7_structure:
    {
    "rule_driven_degree": {
        "score": 3,
        "basis": "评估框架有既定标准和指标，但解读需灵活性。"
    },
    "exception_flexibility": {
        "score": 4,
        "basis": "不同行业、企业AI能力差异大，需高度灵活处理例外。"
    },
    "feedback_loop_maturity": {
        "score": 3,
        "basis": "评估结果通常会反馈，但闭环改进机制成熟度不一。"
    }
}
  dimension_8_data_ecosystem:
    {
    "data_intensity": {
        "score": 4,
        "basis": "评估需收集大量AI项目、技术、人才、治理等数据。"
    },
    "cross_process_dependency": {
        "score": 4,
        "basis": "依赖AI研发、部署、运营、人才管理等流程数据。"
    },
    "data_standardization": {
        "score": 2,
        "basis": "AI能力评估数据来源多样，标准化程度较低。"
    },
    "integration_barrier": {
        "score": 3,
        "basis": "需整合多源异构数据，存在一定集成挑战。"
    }
}
  dimension_9_ai_readiness:
    {
    "data_availability": {
        "score": 3,
        "basis": "部分数据可得，但全面、高质量数据获取仍有挑战。"
    },
    "tech_maturity": {
        "score": 4,
        "basis": "AI在数据分析、报告生成方面技术已相对成熟。"
    },
    "implementation_simplicity": {
        "score": 3,
        "basis": "实施AI辅助工具相对简单，但定制化需求高。"
    },
    "value_density": {
        "score": 4,
        "basis": "提升评估效率和深度，对企业AI战略有高价值。"
    }
}
  numeric_profile:
    {
    "d1_decision_replaceability": 3,
    "d1_processing_acceleration": 4,
    "d1_tacit_knowledge_dependency": 4,
    "d3_type_a": 4,
    "d3_type_b": 3,
    "d3_type_c": 0,
    "d3_type_d": 3,
    "d7_rule_driven_degree": 3,
    "d7_exception_flexibility": 4,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 4,
    "d8_cross_process_dependency": 4,
    "d8_data_standardization": 2,
    "d8_integration_barrier": 3,
    "d9_data_availability": 3,
    "d9_tech_maturity": 4,
    "d9_implementation_simplicity": 3,
    "d9_value_density": 4
}


================================================================================
[8/8] 节点 8.10.4.1 — AI显著增强了RPA机会识别的效率和准确性，但仍需人工专业判断。
================================================================================

A. SYSTEM PROMPT
--------------------------------------------------------------------------------
你是一个企业流程知识分析专家，专门评估AI技术对企业运营流程的影响。

你的任务是对企业流程节点进行AI冲击扫描，输出9个维度的结构化判断和数值特征向量。

## 你必须严格遵守的规则

规则1：判断必须基于有证据的实际发生，而非技术潜力。
规则2：少数领先企业的实践不代表行业普遍状态，须标注。
规则3：企业发布的转型计划不构成变化已发生的证据。
规则4：不确定时如实表达，不得虚构判断。
规则5：每个判断必须附带依据类型标注。
规则6：每项评分依据限1句话，不超过30字。
规则7：对每个维度子项输出1-5分数值评分（0=不适用仅限D3）。

## 输出要求

你必须且只能输出一个合法的JSON对象，不要输出任何JSON之外的内容。

B. USER PROMPT (已填充)
--------------------------------------------------------------------------------
## 待扫描流程节点

节点ID：8.10.4.1
名称（中文）：识别 RPA 机会
名称（英文）：Identify RPA opportunities
框架：AI-era
路径：8.0 管理信息技术 (IT) > 8.10 AI 与智能运营 > 8.10.4 管理机器人流程自动化 > 8.10.4.1 识别 RPA 机会
层级：L4
描述：AI 活动：识别 RPA 机会。
标签：it_management | pcf | ai_era

---

请对上述流程节点进行9维度AI冲击扫描。

### D1 AI渗透率
子项：decision_replaceability / processing_acceleration / tacit_knowledge_dependency
每项：rating(高/中/低) + basis(≤30字) + score(1-5)
overall_penetration: 高/中/低

### D2 变化状态
status: 已变/将变/稳定
evidence_type: 类型A/类型B/混合
evidence_source / basis_description

### D3 变化性质（0-5量表，0=不适用）
types_selected: [A/B/C/D]
A=增强型 B=压缩型 C=消亡型 D=涌现型
每类：score(0-5) + description(≤30字，0分可省略)

### D4 人机边界
current_type: 类型1-4
boundary_description / stability(稳定/过渡中/高度不确定) / stability_note

### D5 不确定性
overall_confidence: 高/中/低
uncertainty_sources / special_note

### D6 信号质量
information_period / source_distribution(5项,高/中/低/无) / potential_bias

### D7 流程结构刚性
子项（各1-5分 + basis≤30字）：
- rule_driven_degree: 规则驱动程度
- exception_flexibility: 例外处理灵活性
- feedback_loop_maturity: 反馈闭环成熟度

### D8 数据生态位
子项（各1-5分 + basis≤30字）：
- data_intensity: 数据密集度
- cross_process_dependency: 跨流程数据依赖
- data_standardization: 数据标准化程度
- integration_barrier: 集成壁垒

### D9 AI改造就绪度
子项（各1-5分 + basis≤30字）：
- data_availability: 数据可得性
- tech_maturity: 技术成熟度
- implementation_simplicity: 实施简易度
- value_density: 价值密度

## 输出JSON格式

{
  "node_id": "8.10.4.1",
  "scan_timestamp": "ISO8601",
  "model_id": "你的模型标识",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {"rating": "高/中/低", "basis": "", "score": 3},
    "processing_acceleration": {"rating": "高/中/低", "basis": "", "score": 3},
    "tacit_knowledge_dependency": {"rating": "高/中/低", "basis": "", "score": 3},
    "overall_penetration": "高/中/低"
  },
  "dimension_2_change_status": {
    "status": "已变/将变/稳定",
    "evidence_type": "类型A/类型B/混合",
    "evidence_source": "",
    "basis_description": ""
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": ["A"],
    "type_descriptions": {
      "A": {"score": 4, "description": ""},
      "B": {"score": 0, "description": ""},
      "C": {"score": 0, "description": ""},
      "D": {"score": 0, "description": ""}
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型1-4",
    "boundary_description": "",
    "stability": "稳定/过渡中/高度不确定",
    "stability_note": ""
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "高/中/低",
    "uncertainty_sources": [],
    "special_note": null
  },
  "dimension_6_signal_quality": {
    "information_period": "",
    "source_distribution": {
      "academic": "", "industry_media": "",
      "corporate_disclosure": "",
      "consulting_reports": "", "regulatory": ""
    },
    "potential_bias": ""
  },
  "dimension_7_structure": {
    "rule_driven_degree": {"score": 3, "basis": ""},
    "exception_flexibility": {"score": 3, "basis": ""},
    "feedback_loop_maturity": {"score": 3, "basis": ""}
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {"score": 3, "basis": ""},
    "cross_process_dependency": {"score": 3, "basis": ""},
    "data_standardization": {"score": 3, "basis": ""},
    "integration_barrier": {"score": 3, "basis": ""}
  },
  "dimension_9_ai_readiness": {
    "data_availability": {"score": 3, "basis": ""},
    "tech_maturity": {"score": 3, "basis": ""},
    "implementation_simplicity": {"score": 3, "basis": ""},
    "value_density": {"score": 3, "basis": ""}
  },
  "numeric_profile": {
    "d1_decision_replaceability": 3, "d1_processing_acceleration": 3,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4, "d3_type_b": 0, "d3_type_c": 0, "d3_type_d": 0,
    "d7_rule_driven_degree": 3, "d7_exception_flexibility": 3,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 3, "d8_cross_process_dependency": 3,
    "d8_data_standardization": 3, "d8_integration_barrier": 3,
    "d9_data_availability": 3, "d9_tech_maturity": 3,
    "d9_implementation_simplicity": 3, "d9_value_density": 3
  },
  "scan_summary": {
    "one_line_judgment": "",
    "priority_flag": "高优先级验证/常规验证/低优先级验证",
    "priority_reason": ""
  }
}

C. 调用参数
--------------------------------------------------------------------------------
  model_id:          gemini-2.5-flash
  model_config:      {"model": "gemini-2.5-flash", "temperature": 0.3}
  prompt_version:    v2.0
  prompt_tokens:     1,841
  completion_tokens: 1,559
  total_tokens:      5,632
  response_time_ms:  17,709
  parsed_success:    True
  parse_error:       (none)

D. RAW LLM RESPONSE
--------------------------------------------------------------------------------
```json
{
  "node_id": "8.10.4.1",
  "scan_timestamp": "2023-10-27T10:00:00Z",
  "model_id": "EnterpriseProcessAIExpert-v1.0",
  "dimension_1_ai_penetration": {
    "decision_replaceability": {
      "rating": "中",
      "basis": "AI辅助识别潜力，最终决策仍需人工判断。",
      "score": 3
    },
    "processing_acceleration": {
      "rating": "高",
      "basis": "过程挖掘等AI工具显著加速流程分析。",
      "score": 4
    },
    "tacit_knowledge_dependency": {
      "rating": "中",
      "basis": "识别需业务领域隐性知识，AI尚无法完全替代。",
      "score": 3
    },
    "overall_penetration": "中"
  },
  "dimension_2_change_status": {
    "status": "已变",
    "evidence_type": "混合",
    "evidence_source": "咨询报告、行业媒体、AI/RPA厂商解决方案",
    "basis_description": "领先企业已采用AI驱动的流程挖掘工具识别RPA机会。"
  },
  "dimension_3_change_nature": {
    "applicable": true,
    "types_selected": [
      "A"
    ],
    "type_descriptions": {
      "A": {
        "score": 4,
        "description": "AI工具增强人工分析能力，提高识别效率和准确性。"
      },
      "B": {
        "score": 0,
        "description": ""
      },
      "C": {
        "score": 0,
        "description": ""
      },
      "D": {
        "score": 0,
        "description": ""
      }
    }
  },
  "dimension_4_boundary": {
    "current_type": "类型2",
    "boundary_description": "AI工具分析流程数据并推荐RPA机会，人类专家进行验证、评估和最终决策。",
    "stability": "过渡中",
    "stability_note": "AI分析能力持续提升，但复杂决策仍需人工干预。"
  },
  "dimension_5_uncertainty": {
    "overall_confidence": "中",
    "uncertainty_sources": [
      "AI工具的普及率和成熟度",
      "中小企业采纳速度",
      "复杂业务场景的AI适用性"
    ],
    "special_note": "AI在识别RPA机会方面的潜力巨大，但实际落地效果受企业数据基础和AI能力限制。"
  },
  "dimension_6_signal_quality": {
    "information_period": "近3-5年",
    "source_distribution": {
      "academic": "中",
      "industry_media": "高",
      "corporate_disclosure": "中",
      "consulting_reports": "高",
      "regulatory": "无"
    },
    "potential_bias": "供应商和咨询公司可能夸大AI工具的即时效益。"
  },
  "dimension_7_structure": {
    "rule_driven_degree": {
      "score": 3,
      "basis": "识别过程涉及结构化分析与非结构化判断。"
    },
    "exception_flexibility": {
      "score": 4,
      "basis": "需高度灵活适应不同业务流程的特殊性。"
    },
    "feedback_loop_maturity": {
      "score": 3,
      "basis": "反馈循环成熟度取决于企业整体自动化治理。"
    }
  },
  "dimension_8_data_ecosystem": {
    "data_intensity": {
      "score": 4,
      "basis": "高度依赖流程日志、系统数据和业务文档。"
    },
    "cross_process_dependency": {
      "score": 4,
      "basis": "RPA机会常跨部门系统，需整合多流程数据。"
    },
    "data_standardization": {
      "score": 2,
      "basis": "企业流程数据标准化程度普遍不高。"
    },
    "integration_barrier": {
      "score": 4,
      "basis": "整合多源异构系统数据存在较高技术壁垒。"
    }
  },
  "dimension_9_ai_readiness": {
    "data_availability": {
      "score": 3,
      "basis": "数据量大，但高质量标准化数据仍有欠缺。"
    },
    "tech_maturity": {
      "score": 4,
      "basis": "流程挖掘、机器学习等AI技术已相对成熟。"
    },
    "implementation_simplicity": {
      "score": 2,
      "basis": "实施需专业知识，数据清洗和模型训练复杂。"
    },
    "value_density": {
      "score": 4,
      "basis": "成功识别RPA可带来显著效率提升和成本节约。"
    }
  },
  "numeric_profile": {
    "d1_decision_replaceability": 3,
    "d1_processing_acceleration": 4,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4,
    "d3_type_b": 0,
    "d3_type_c": 0,
    "d3_type_d": 0,
    "d7_rule_driven_degree": 3,
    "d7_exception_flexibility": 4,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 4,
    "d8_cross_process_dependency": 4,
    "d8_data_standardization": 2,
    "d8_integration_barrier": 4,
    "d9_data_availability": 3,
    "d9_tech_maturity": 4,
    "d9_implementation_simplicity": 2,
    "d9_value_density": 4
  },
  "scan_summary": {
    "one_line_judgment": "AI显著增强了RPA机会识别的效率和准确性，但仍需人工专业判断。",
    "priority_flag": "高优先级验证",
    "priority_reason": "该节点直接涉及AI工具在自动化机会识别中的应用，是AI时代流程优化的关键。"
  }
}
```

E. DB RECORD (ai_impact_scan_results)
--------------------------------------------------------------------------------
  id: 23
  node_id: 8.10.4.1
  batch_id: d8707b36-eb98-4039-a758-4bc0da622c98
  scan_timestamp: 2026-03-01T09:01:50Z
  penetration_decision_replaceability: 中
  penetration_decision_basis: AI辅助识别潜力，最终决策仍需人工判断。
  penetration_processing_acceleration: 高
  penetration_processing_basis: 过程挖掘等AI工具显著加速流程分析。
  penetration_tacit_knowledge: 中
  penetration_tacit_basis: 识别需业务领域隐性知识，AI尚无法完全替代。
  penetration_overall: 中
  change_status: 已变
  change_evidence_type: 混合
  change_evidence_source: 咨询报告、行业媒体、AI/RPA厂商解决方案
  change_basis_description: 领先企业已采用AI驱动的流程挖掘工具识别RPA机会。
  change_nature_applicable: 1
  change_nature_types:
    [
    "A"
]
  change_nature_type_a: AI工具增强人工分析能力，提高识别效率和准确性。
  change_nature_type_b: 
  change_nature_type_c: 
  change_nature_type_d: 
  boundary_current_type: 类型2
  boundary_description: AI工具分析流程数据并推荐RPA机会，人类专家进行验证、评估和最终决策。
  boundary_stability: 过渡中
  boundary_stability_note: AI分析能力持续提升，但复杂决策仍需人工干预。
  uncertainty_confidence: 中
  uncertainty_sources:
    [
    "AI工具的普及率和成熟度",
    "中小企业采纳速度",
    "复杂业务场景的AI适用性"
]
  uncertainty_special_note: AI在识别RPA机会方面的潜力巨大，但实际落地效果受企业数据基础和AI能力限制。
  signal_information_period: 近3-5年
  signal_academic: 中
  signal_industry_media: 高
  signal_corporate_disclosure: 中
  signal_consulting_reports: 高
  signal_regulatory: 无
  signal_potential_bias: 供应商和咨询公司可能夸大AI工具的即时效益。
  summary_one_line: AI显著增强了RPA机会识别的效率和准确性，但仍需人工专业判断。
  summary_priority_flag: 高优先级验证
  summary_priority_reason: 该节点直接涉及AI工具在自动化机会识别中的应用，是AI时代流程优化的关键。
  model_agreement_status: single_model
  model_agreement_change_status: 1
  model_agreement_penetration: 1
  models_used:
    [
    "gemini-2.5-flash"
]
  processing_status: completed
  divergence_detail: NULL
  dimension_7_structure:
    {
    "rule_driven_degree": {
        "score": 3,
        "basis": "识别过程涉及结构化分析与非结构化判断。"
    },
    "exception_flexibility": {
        "score": 4,
        "basis": "需高度灵活适应不同业务流程的特殊性。"
    },
    "feedback_loop_maturity": {
        "score": 3,
        "basis": "反馈循环成熟度取决于企业整体自动化治理。"
    }
}
  dimension_8_data_ecosystem:
    {
    "data_intensity": {
        "score": 4,
        "basis": "高度依赖流程日志、系统数据和业务文档。"
    },
    "cross_process_dependency": {
        "score": 4,
        "basis": "RPA机会常跨部门系统，需整合多流程数据。"
    },
    "data_standardization": {
        "score": 2,
        "basis": "企业流程数据标准化程度普遍不高。"
    },
    "integration_barrier": {
        "score": 4,
        "basis": "整合多源异构系统数据存在较高技术壁垒。"
    }
}
  dimension_9_ai_readiness:
    {
    "data_availability": {
        "score": 3,
        "basis": "数据量大，但高质量标准化数据仍有欠缺。"
    },
    "tech_maturity": {
        "score": 4,
        "basis": "流程挖掘、机器学习等AI技术已相对成熟。"
    },
    "implementation_simplicity": {
        "score": 2,
        "basis": "实施需专业知识，数据清洗和模型训练复杂。"
    },
    "value_density": {
        "score": 4,
        "basis": "成功识别RPA可带来显著效率提升和成本节约。"
    }
}
  numeric_profile:
    {
    "d1_decision_replaceability": 3,
    "d1_processing_acceleration": 4,
    "d1_tacit_knowledge_dependency": 3,
    "d3_type_a": 4,
    "d3_type_b": 0,
    "d3_type_c": 0,
    "d3_type_d": 0,
    "d7_rule_driven_degree": 3,
    "d7_exception_flexibility": 4,
    "d7_feedback_loop_maturity": 3,
    "d8_data_intensity": 4,
    "d8_cross_process_dependency": 4,
    "d8_data_standardization": 2,
    "d8_integration_barrier": 4,
    "d9_data_availability": 3,
    "d9_tech_maturity": 4,
    "d9_implementation_simplicity": 2,
    "d9_value_density": 4
}


================================================================================
END OF REPORT
================================================================================