Team Training & Adoption (TTA) Principles

Enterprise Architecture principles for building team capability in AI-assisted development.

Category Overview

flowchart TB
    subgraph Training["TRAINING PROGRESSION"]
        L1["Level 1: Foundation<br/><i>All devs</i><br/>Basic usage, Security"]
        L2["Level 2: Advanced<br/><i>Senior devs</i><br/>Advanced prompts, Architecture"]
        L3["Level 3: Leadership<br/><i>Tech leads</i><br/>Governance, Risk mgmt"]

        L1 --> L2 --> L3
    end

    subgraph Principles["TTA PRINCIPLES"]
        TTA001["TTA-001: Skills Development<br/><i>Structured training programs</i>"]
        TTA002["TTA-002: Adoption Governance<br/><i>Responsible adoption mgmt</i>"]
        TTA003["TTA-003: Knowledge Sharing<br/><i>Practice sharing mechanisms</i>"]
    end

Key Concerns:

• Training curriculum by role and level
• AI-assisted coding vs. “vibe coding” distinction
• Gradual adoption with success criteria
• Knowledge sharing and community building

Principles in This Category

Relationship to Other Categories

flowchart TB
    PS["PS: Planning &<br/>Strategy<br/><i>Strategy informs training needs</i>"]

    PS --> TTA

    TSI["TSI: Tool Selection<br/><i>New tools require training</i>"]
    TTA["TTA: Training &<br/>Adoption<br/><i>Enables effective AI adoption</i>"]
    DC["DC: Development<br/>& Coding<br/><i>Training enables dev practices</i>"]

    TSI --> TTA
    TTA --> DC
    TTA --> GSC

    GSC["GSC: Governance<br/>Security<br/><i>Training includes compliance</i>"]

TTA-001: Skills Development

Statement

Implement structured training programs that build AI-assisted development skills progressively by role and experience level.

Rationale

Implications

Training Level Framework

LEVEL 1: FOUNDATIONAL (All Developers)

Duration: 4-8 hours

Format: Online + hands-on

LEVEL 2: ADVANCED (Senior Developers, Architects)

Duration: 8-16 hours

Format: Workshop + projects

LEVEL 3: LEADERSHIP (Tech Leads, Managers)

Duration: 8 hours

Format: Seminar + case studies

Maturity Alignment

Governance

Compliance Measures

• Training curriculum documented and approved
• Completion tracking system in place
• Level 1 completion required before AI tool access
• Annual refresher training required
• Training effectiveness measured

Exception Process

Related Principles

• TSI-001: Evaluation Framework (training for new tools)
• DC-002: Prompt Engineering (prompting skills training)
• PS-004: Structured Prompting (prompt governance training)

TTA-002: Adoption Governance

Statement

Manage AI tool adoption through defined governance stages with success criteria and responsible scaling.

Rationale

Implications

flowchart LR
    subgraph Stages["ADOPTION STAGES"]
        Pilot["PILOT<br/><i>Small team, low-risk projects</i>"]
        Validate["VALIDATE<br/><i>Measure success, document learnings</i>"]
        Expand["EXPAND<br/><i>Broader teams, refine guidance</i>"]
        Standardize["STANDARDIZE<br/><i>Org-wide rollout, standard practices</i>"]

        Pilot --> Validate --> Expand --> Standardize
    end

Success Criteria by Stage

Maturity Alignment

Governance

Compliance Measures

• Adoption stages documented with success criteria
• Stage progression requires approval
• Metrics collected at each stage
• Learnings documented and shared
• Rollback procedures defined

Exception Process

Related Principles

• PS-002: Strategic Integration (strategy guides adoption)
• TSI-001: Evaluation Framework (tools evaluated before pilot)
• GSC-001: Governance Framework (adoption within governance)

TTA-003: Knowledge Sharing

Statement

Establish mechanisms for sharing AI-assisted development practices, patterns, and lessons learned across teams.

Rationale

Implications

flowchart TB
    subgraph Sources["KNOWLEDGE SOURCES"]
        Individual["Individual Discovery"]
        Team["Team Patterns"]
        Project["Project Learnings"]
        Org["Organization Standards"]
    end

    subgraph Repository["KNOWLEDGE REPOSITORY"]
        Prompts["Prompt library (categorized, tested)"]
        Patterns["Pattern catalog (what works, what doesn't)"]
        Cases["Case studies (success stories, failures)"]
        FAQ["FAQ and troubleshooting guides"]
    end

    subgraph Sharing["SHARING MECHANISMS"]
        direction LR
        CoP["Communities of Practice"]
        KB["Knowledge Base & Wiki"]
        Training["Training Updates"]

        CoP <--> KB <--> Training
    end

    Sources --> Repository
    Repository --> Sharing

Maturity Alignment

Governance

Compliance Measures

• Knowledge repository established and accessible
• Contribution guidelines documented
• Content review process defined
• Sharing events scheduled regularly
• Knowledge usage metrics tracked

Exception Process

Related Principles

• TTA-001: Skills Development (training integrates shared knowledge)
• PS-004: Structured Prompting (prompt library maintenance)
• DC-002: Prompt Engineering (prompt patterns shared)

Category Summary

Principle Matrix

Legend: Requirements increase with maturity level

Critical Distinction: AI-Assisted vs. “Vibe Coding”

⚠️ Training MUST distinguish these approaches

Key Takeaways

1. Training is prerequisite - No AI tool access without appropriate training
2. Levels match roles - Different training for developers, seniors, leaders
3. Adopt gradually - Phased rollout with success criteria at each stage
4. Share actively - Knowledge multiplies when shared across organization
5. Distinguish coding approaches - AI-assisted ≠ “vibe coding”

Next Steps

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