China Ai Readiness

china-ai-readiness

Purpose

Evaluate A股被投企业AI就绪度 — assessing portfolio companies' preparedness for AI adoption and transformation in the Chinese market context.

Data Sources

Primary: iFind MCP (Tier-1 付费) / AkShare MCP (Tier-2 免费备选)

get_quote(ticker)                        → Company valuation context
get_financials(ticker, "income")         → Revenue scale, R&D spend
get_stock_info(ticker)                   → Business description

Secondary Sources

• 巨潮 — company filings, R&D disclosure
• 券商研报 — technology assessments
• 行业报告 — AI adoption benchmarks

Workflow

Step 1: Assess Data Infrastructure

Data readiness dimensions:

| Dimension | Assessment | China Context | |-----------|-----------|---------------| | 数据积累 (Data accumulation) | Years of data, volume | Chinese companies often have rich transaction data | | 数据质量 (Data quality) | Completeness, accuracy | Legacy systems may have gaps | | 数据打通 (Data integration) | Siloed vs unified | Common challenge: ERP/WMS/CRM not integrated | | 数据治理 (Data governance) | Policies, standards | Often underdeveloped | | 数字化基础 (Digital foundation) | ERP, cloud adoption | Varies widely by industry/company age |

Step 2: Evaluate Technology Stack

Technology assessment:

| Layer | Questions | Typical China Status | |-------|-----------|---------------------| | 基础设施 | Cloud? On-premise? | Mix of on-premise and hybrid | | 数据平台 | Data warehouse? BI tools? | Often Excel-heavy | | 应用系统 | ERP, CRM, WMS, MES? | ERP common (用友, 金蝶, SAP) | | 开发能力 | Internal IT team? | Varies; often outsourced | | 技术投入 | IT spend as % revenue? | Typically 1-3% |

Step 3: Talent Assessment

AI/tech talent:

| Role | Availability in China | Typical Company Status | |------|----------------------|----------------------| | 数据科学家 | Scarce, expensive | Usually not in-house | | 算法工程师 | Scarce | Outsourced or absent | | 数据工程师 | Available | Often basic level | | 业务分析师 | Available | Excel-based mostly | | 数字化领导 | Rare | Gap at leadership level |

Step 4: Business Process Readiness

Process digitization level:

| Process | Assessment | AI Potential | |---------|-----------|-------------| | 客户管理 | CRM adoption | Customer analytics, personalization | | 供应链 | ERP, WMS | Demand forecasting, optimization | | 生产制造 | MES, IoT | Predictive maintenance, quality | | 财务管理 | ERP, Excel | Automated reporting, anomaly detection | | 营销销售 | WeChat, 抖音 | Targeted marketing, conversion | | 人力资源 | Basic HR system | Workforce analytics |

Step 5: Identify AI Opportunities

Opportunity mapping:

| Business Function | AI Application | Expected Impact | Effort | |------------------|---------------|-----------------|--------| | 销售预测 | Demand forecasting | 10-20% accuracy improvement | Medium | | 客户洞察 | Customer segmentation | 15-25% marketing ROI | Medium | | 供应链优化 | Inventory optimization | 10-30% inventory reduction | High | | 质量控制 | Defect detection | 30-50% defect reduction | High | | 财务自动化 | Invoice processing | 50-80% time savings | Low | | 客服 | Chatbot | 30-50% cost reduction | Medium |

Step 6: Competitive Benchmarking

Peer comparison:

| Company | Digital Investment (% rev) | Key AI Initiatives | Maturity | |---------|---------------------------|-------------------|----------| | Target | [X%] | [Description] | Level 1-5 | | Peer 1 | [X%] | [Description] | Level | | Peer 2 | [X%] | [Description] | Level | | Industry avg | [X%] | | Level |

Step 7: Develop AI Roadmap

Phased approach:

Phase 1: Foundation (0-6 months)

• Data audit and cleanup
• Identify quick-win AI applications
• Hire/develop data team
• Cloud migration planning

Phase 2: Pilot (6-18 months)

• 1-2 AI pilots
• Data platform build-out
• Training and change management
• Measure and iterate

Phase 3: Scale (18-36 months)

• Expand AI across functions
• Advanced analytics capabilities
• AI-driven decision making
• Competitive advantage establishment

Step 8: Investment Implications

For PE investors:

| Scenario | Implication | |----------|-------------| | High readiness | Accelerate with AI investment; value creation potential | | Medium readiness | 1-2 year improvement path; build data foundation | | Low readiness | Significant gap; may limit exit multiple expansion | | No readiness | Strategic question: can this company compete long-term? |

Value creation through AI:

• Margin expansion (automation)
• Revenue growth (better targeting, personalization)
• Valuation multiple expansion (tech premium)
• Competitive positioning

China-Specific AI Context

China AI Landscape

| Layer | Key Players / Technologies | |-------|---------------------------| | Foundation models | 百度文心, 阿里通义, 讯飞星火, 智谱 | | Computer vision | 商汤, 旷视, 依图 | | NLP | 百度, 科大讯飞 | | Industry AI | 海康, 大华 (vision), 格灵深瞳 | | Cloud AI | 阿里云, 腾讯云, 华为云, 百度智能云 |

China AI Adoption Patterns

| Industry | AI Readiness | Key Applications | |----------|-------------|-------------------| | 制造业 | Medium-High | Quality control, predictive maintenance | | 零售 | Medium | Customer analytics, recommendation | | 金融 | High | Risk scoring, fraud detection | | 医疗 | Medium | Imaging, drug discovery | | 物流 | Medium-High | Route optimization, warehouse | | 农业 | Low-Medium | Precision agriculture |

Data Considerations (China)

• 数据安全法 (Data Security Law) — data localization requirements
• 个人信息保护法 (PIPL) — personal data restrictions
• 数据跨境 — cross-border data transfer restrictions
• 政府数据 — access to government data sources

Quality Checks

Before delivering:

• [ ] Data infrastructure assessed
• [ ] Technology stack documented
• [ ] Talent gap identified
• [ ] AI opportunities mapped
• [ ] Roadmap realistic and phased
• [ ] Investment implications clear
• [ ] China regulatory context included

> Data Source Mode Switch: Set env var IFIND_DATA_SOURCE_MODE to control data source preference. > - ifind-only (strict): Use iFind only, error if unavailable > - ifind-fallback (default): iFind preferred, fallback to AkShare > - akshare-only, wind-only (Wind only), wind-fallback (Wind first, fallback to iFind → AkShare): Skip iFind, use AkShare only

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: eitazhou10
• Source: eitazhou10/opencode-for-financial-services-cn
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.