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
