Team formation and role definition
Clarifying team roles, responsibilities, and skill requirements is the foundation for building an efficient AI team. From core roles to cross-functional collaboration, build a complete team structure.
Core role definitions
Core roles in an AI team, along with their responsibilities, required skills, and deliverables.
AI architect
Responsibilities
- •Tool selection
- •Architecture Design
- •Technical Roadmap Planning
Skill requirements
- •Deeply understand AI tools
- •Architectural trade-off thinking
- •Technological foresight
Deliverables
- •Technology selection report
- •Architecture design document
- •Tool evaluation
AI Engineer
Responsibilities
- •Develop using AI tools
- •Create Skill/Pattern
- •Optimize workflow
Skill requirements
- •Proficient use of Cursor/Windsurf
- •Prompt engineering
- •Code review
Deliverables
- •Code
- •Skill library
- •Patterns library
- •Best Practices Document
AI Product Manager
Responsibilities
- •Requirements clarification
- •Spec writing
- •AI tool application scenario design
Skill requirements
- •Spec-driven development
- •Reverse Interview
- •MVP thinking
Deliverables
- •PRD
- •Spec document
- •Requirements clarification record
AI trainer
Responsibilities
- •Team training
- •Knowledge accumulation
- •Promotion of best practices
Skill requirements
- •Training capability
- •Documentation writing
- •Case summary
Deliverables
- •Training materials
- •Learning path
- •Case library
Cross-department collaboration roles
AI tools are not only suitable for technical teams; HR, finance, legal, and other departments can also improve efficiency with AI tools.
HR + AI
Responsibilities
- •Recruitment process optimization
- •Employee training
- •Performance analysis
- •AI Tool Training
AI application scenarios
- •Use Fabric to generate job descriptions and interview questions
- •Use Cursor to create training materials and employee handbooks
- •Use AI tools to analyze employee feedback and generate reports
Skill requirements
- •Using basic AI tools
- •Prompt engineering
- •Data analysis
Deliverables
- •Recruitment document
- •Training materials
- •Analysis Report
Privacy protection:Employee personal information uses a local model and is not uploaded to the cloud
Finance + AI
Responsibilities
- •Cost analysis
- •Budget planning
- •Financial reports
- •Compliance check
AI application scenarios
- •Generate financial report templates using Fabric
- •Use AI tools to analyze cost data and generate insights
- •Use Cursor to write financial process documentation
Skill requirements
- •Using basic AI tools
- •Data analysis
- •Privacy protection awareness
Deliverables
- •Financial reports
- •Cost analysis
- •Process Documentation
Privacy protection:Financial data is processed entirely with local models (Ollama) and is not uploaded to the cloud
Legal + AI
Responsibilities
- •Contract review
- •Compliance check
- •Legal document generation
AI application scenarios
- •Use AI tools to assist contract review (pay attention to privacy protection)
- •Use Fabric to generate legal document templates
- •Use AI tools for compliance checks
Skill requirements
- •Using basic AI tools
- •Legal knowledge
- •Privacy protection
Deliverables
- •Contract review report
- •Legal documents
- •Compliance report
Privacy protection:Use local models for sensitive legal documents
Team size and configuration
Configure roles appropriately according to the team size to ensure efficient team operations.
Small team (3–5 people)
1 AI architect + 2-3 AI engineers + 1 AI product manager
Features
- •Flattening
- •Rapid iteration
- •Knowledge sharing
Medium-sized team (6-15 people)
1 AI architect + 5-10 AI engineers + 1-2 AI product managers + 1 AI trainer
Features
- •Specialization of labor
- •Knowledge base building
- •Process standardization
Large team (16+ people)
AI architecture group + AI engineering group + AI product group + AI training group
Features
- •Organized
- •Systematization
- •Knowledge management platform
Skills requirements matrix
Proficiency requirements for different roles in AI tools and skills (1-5 stars).
| Role | Cursor | Windsurf | Fabric | MCP | Skill | Agent | Spec |
|---|---|---|---|---|---|---|---|
| AI architect | |||||||
| AI Engineer | |||||||
| AI Product Manager | |||||||
| AI trainer | |||||||
| HR + AI | |||||||
| Finance + AI | |||||||
| Legal + AI |
Note:⭐⭐⭐⭐⭐ Expert | ⭐⭐⭐⭐ Proficient | ⭐⭐⭐ Competent | ⭐⭐ Familiar | ⭐ Basic
Hands-on practice
Practice suggestion:
- 1Design a staffing plan for a 10-person AI team (roles, responsibilities, skill requirements)
- 2Write a role responsibility specification (choose one core role and describe it in detail)
- 3Establish skill assessment criteria (how to evaluate team members’ proficiency with AI tools)
Learning outcomes
After completing this chapter, you will:
- 1Understand the core role definitions of an AI team (AI Architect, AI Engineer, AI Product Manager, AI Trainer)
- 2Understand cross-functional collaboration roles (HR + AI, Finance + AI, Legal + AI) and their use cases
- 3Able to configure staffing according to team size (plans for small, medium, and large teams)
- 4Understand the skill requirement matrix for different roles (Cursor, Windsurf, Fabric, MCP, Skill, Agent, Spec)