Stage 1: Awareness & Adoption

This stage represents the foundation of our transformation. It combines AI Awareness (understanding what is possible) with AI Adoption (starting to use tools in daily work). The focus is on standardization, governance, and enablement.

Why This Stage Matters

Without a solid foundation of standardized tools and usage guidelines, ad-hoc AI adoption creates risks around security, IP leakage, and fragmented workflows. Stage 1 ensures everyone is moving in the same direction safely.

Risk Area

Unregulated use of public AI tools (e.g., ChatGPT free tier) can lead to data privacy violations. Standardization is key to mitigation.

Key Activities & Deliverables

Activity	Description	Deliverable
Tool Standardization	Roll out enterprise licenses for approved tools.	Enterprise seats for GitHub Copilot & ChatGPT Team/Enterprise.
Governance Setup	Define acceptable use policies and data handling rules.	AI Acceptable Use Policy (AUP) document.
Training & Enablement	Basic prompt engineering and tool usage workshops.	”AI Fundamentals” internal workshop series.
CoE Formation	Establish the AI Center of Excellence.	Core AI CoE Team (Head of AI, Lead Architects).

Tools & Platforms

• GitHub Copilot: For code completion and explanation.
• ChatGPT / Azure OpenAI: For documentation, emails, and brainstorming.
• Cursor IDE: For an AI-native coding experience (pilot group).

Transformation Flow

sequenceDiagram
    participant Dev as Individual Developer
    participant Lead as Team Lead
    participant CoE as AI Center of Excellence
    
    CoE->>Lead: Define AI Policy & Toolset
    Lead->>Dev: Onboard to GitHub Copilot
    Dev->>Dev: Daily usage (Code, Docs)
    Dev->>CoE: Feedback & Use Cases
    CoE->>CoE: Refine Best Practices

Success Metrics

• Adoption Rate: 100% of engineering staff with active Copilot licenses.
• Productivity: 10-40% gain in individual task completion speed (coding, email, docs).
• Compliance: 0 security incidents related to Shadow AI usage.

Real Example: “The 30% Efficiency Win”

Scenario: A senior developer needs to write unit tests for a legacy payment module.

Traditional Way: Manually write boilerplate code for 10+ test cases. Time: 4 hours.

Stage 1 Way: Developer uses GitHub Copilot to generate test skeletons and edge cases, then reviews and refines. Time: 2.5 hours.

Result: 37% Time Savings on a single task.