AI in Software Development 2025: What Works, What Doesn't

AI is fundamentally changing software development - but not in the way many think. Instead of replacing developers, AI tools are becoming valuable assistants. Here's my hands-on experience: What works, what doesn't, and where the real productivity gains are.

The AI Development Landscape in 2025

The major AI tools for developers have become established:

• GitHub Copilot: IDE integration for code completion and chat
• Cursor: AI-native IDE with Claude and GPT integration
• Claude Code: CLI tool for complex code operations
• ChatGPT/Claude: General-purpose assistants for explanations and debugging

Where AI Really Helps

1. Boilerplate Code and Scaffolding

This is where AI shines. Controllers, models, migrations, API endpoints - anything that follows a known pattern:

2. Test Creation

AI can derive meaningful test cases from existing code. Particularly valuable:

• Edge cases you would overlook yourself
• Consistent naming conventions
• Setup/teardown boilerplate

3. Understanding Legacy Code

A game-changer for legacy modernization: AI can:

• Explain complex functions in natural language
• Uncover implicit dependencies
• Provide refactoring suggestions with explanations

4. Documentation

JSDoc, PHPDoc, README generation - AI saves enormous time here and is often more consistent than manually written docs.

Where AI Hits Its Limits

Architecture Decisions

AI doesn't know long-term consequences. It can't answer "Microservices or Monolith?" for your specific team, budget, and business context.

Domain-Specific Knowledge

Industry-specific logic, regulatory requirements, historically grown business rules - AI lacks this context.

Security-Critical Code

AI can introduce security vulnerabilities. Generated code should always undergo security review.

Best Practices for AI-Assisted Development

1. AI as Code Reviewer

Use AI for an initial code review before the team review:

• "Find potential bugs in this code"
• "Which edge cases aren't handled?"
• "How can I make this code more testable?"

2. Pair Programming with AI

Explain your intention to AI before writing code. This forces you to think clearly:

• What is the goal?
• What constraints exist?
• What are the expected edge cases?

3. Iterative Refinement

The first AI suggestion is rarely perfect. Refine iteratively:

1. Have first draft generated
2. Provide specific criticism: "The exception handling is too generic"
3. Request improved version
4. Finally optimize yourself

Productivity Measurement in Practice

From my experience in Fractional Tech Lead engagements:

Task	Without AI	With AI	Savings
CRUD Controller	45 min	15 min	67%
Unit Tests (10 Cases)	60 min	25 min	58%
Legacy Code Review	90 min	45 min	50%
API Documentation	30 min	10 min	67%
Architecture Design	120 min	100 min	17%

Takeaway: The biggest gains are in repetitive tasks, not complex decisions.

AI Tools in Enterprise Use

Data Privacy and Compliance

Important considerations for enterprise deployment:

• Code Privacy: What data is sent to AI providers?
• Self-Hosted Options: Ollama, LocalAI for sensitive projects
• License Questions: What about copyright of generated code?

Team Onboarding

Successful AI integration requires:

1. Clear guidelines: What may be done with AI?
2. Prompt engineering training
3. Quality assurance: AI code must go through the same review process

Conclusion: AI as Force Multiplier

AI makes good developers better but doesn't replace expertise. The greatest value lies in:

• Accelerating routine tasks
• Faster understanding of unfamiliar code
• More consistent documentation

The art is to deploy AI where it has strengths - and continue to rely on human expertise for architectural decisions.