Most teams “use AI”; few are rebuilding how they ship software. This post defines AI-native development, explains why it matters for leaders, and introduces aidevscore.com—a short assessment that benchmarks six dimensions of maturity.

Buying a team license for GitHub Copilot is a purchase. Committing to change how you build software—and what you build—is strategy.

Most organizations today are in the “purchase” category. They’ve adopted AI coding assistants, maybe experimented with ChatGPT for documentation, but haven’t fundamentally changed how they approach software development. The difference between using AI tools and becoming AI-native is the difference between incremental improvement and transformational change.

What AI-native actually looks like

In mature AI-native organizations, AI agents are woven throughout the development lifecycle—not as isolated tools, but as integrated participants in every stage of software creation.

Here’s what this looks like in practice:

Product managers create structured specifications in formats that both humans and agents can parse. These specs define requirements, constraints, and acceptance criteria with enough precision for AI agents to act on them.

AI agents use these specifications to generate:

• Application scaffolding and boilerplate code
• Initial test suites
• Technical documentation
• Data models and API contracts

Engineers review and refine these outputs, focusing on architecture, business logic, and edge cases rather than writing boilerplate from scratch.

Code generation happens in cloud-based containers, with agents producing pull requests that enter the same review process as human-authored code.

Code review shifts its focus to intent, correctness, and safety rather than line-by-line edits. Reviewers ask: “Does this solve the right problem? Are we handling edge cases? What are the security implications?”

The benefits are tangible:

• Faster delivery cycles
• Fewer late-stage defects
• More time spent on requirements definition and system design
• Less time on repetitive coding tasks
• Better documentation (because it’s generated as a byproduct)

Teams report shipping features in days that previously took weeks, with higher quality because testing and documentation happen in parallel with development rather than as afterthoughts.

How well are you doing?

To help teams assess their current state, I’ve created aidevscore.com—a short assessment that evaluates your readiness across six critical dimensions:

1. Literacy and Skills

Does your team understand AI-native practices? Can they write effective prompts? Do they know how to review AI-generated code? This dimension measures the foundational knowledge required to work effectively with AI agents.

2. SDLC Integration

How deeply is AI integrated into your development processes? Is it ad-hoc (developers use Copilot when they feel like it) or systematic (agents are part of your CI/CD pipeline)? This dimension evaluates how AI fits into your existing workflows.

3. Tooling Coherence

Do you have a unified toolchain, or is it a patchwork of disconnected AI tools? Can your agents access the right context at the right time? This dimension assesses whether your tools work together or against each other.

4. Collaboration Patterns

Have your team workflows adapted to AI-native practices? Do product managers write specs that agents can consume? Do engineers know how to collaborate with AI agents as teammates? This dimension measures how well your people and processes have evolved.

5. Trust and Safety

How do you manage risk when agents generate code? What guardrails are in place? How do you handle security, privacy, and compliance? This dimension evaluates your governance and risk management practices.

6. Business Impact

Are you measuring outcomes? Can you demonstrate ROI? Do stakeholders understand the value? This dimension assesses whether AI adoption is producing measurable business results.

The assessment takes about 10-15 minutes and provides:

• An overall maturity label (Emerging, Developing, Advancing, or Leading)
• A radar visualization showing strengths and gaps across the six dimensions
• Concrete recommendations for your next steps

Getting started into AI Native Development

The biggest mistake I see organizations make is trying to “tool shop” their way to maturity. They buy licenses, set up dashboards, and expect transformation to happen automatically. It doesn’t work that way.

Start small and focused:

• Choose one team to experiment
• Pick one product or feature
• Ideally something greenfield, or a frontend slice over existing APIs
• This minimizes risk while you learn what works

Learn the fundamentals:

• How to write specs that agents can consume
• How to structure prompts effectively
• How to review AI-generated code
• How to integrate agents into your CI/CD pipeline
• What context agents need to be effective

Expand methodically: Once you have one team succeeding:

1. Deepen the practice: Extend AI usage upstream (design, planning) and downstream (QA, operations)
2. Spread horizontally: Bring learnings to adjacent teams
3. Build capabilities: Develop internal expertise and best practices
4. Measure relentlessly: Track both velocity and quality metrics

This isn’t just about adopting new tools—it requires mindset changes throughout your organization:

• Engineers must learn to collaborate with AI agents
• Product managers must write more structured specifications
• QA teams must adapt testing strategies for AI-generated code
• Leadership must understand the strategic implications

The organizations that succeed treat this as a multi-quarter transformation initiative, not a weekend hackathon.

You are not alone

If you’re wondering where to start or how to translate your assessment results into action, I can help. I offer:

• Strategy sessions to help leadership teams understand what AI-native development means for their organization
• Hands-on workshops to train development teams on AI-native practices
• 90-day planning to convert assessment results into concrete action plans

Visit transcode.be to learn more or reach out on Bluesky if you’d like to chat.

Take the assessment

Ready to see where you stand? Visit aidevscore.com and complete the assessment. It takes 10-15 minutes and provides immediate, actionable feedback.

Whether you’re just starting your AI-native journey or already well down the path, understanding your current position is the first step toward meaningful improvement.