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name: china-ai-readiness description: Assess an A-share portfolio company's AI readiness for China-focused private equity investments. Evaluates data infrastructure, technology stack, talent, and AI adoption opportunities in the Chinese market context. Triggers on "A股公司AI评估", "AI readiness China", "AI assessment portfolio company", "AI转型评估", or "artificial intelligence readiness [company]".

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