If you're leading technology teams at telecom or ad tech companies, you know the pain of legacy applications. They're the backbone of your business, powering critical services while accumulating technical debt year after year. The question isn't whether to modernize—it's how to do it efficiently without breaking what's already working.

I recently witnessed something that changed my perspective on legacy modernization entirely. One senior developer / architect completed a comprehensive modernization foundation in one day that would traditionally take ten days. This wasn't theoretical—it was real work on a production Django application, adding 6,400+ lines of test code and establishing complete testing infrastructure.

The twist? This was his first time using AI-assisted development.

The Legacy Challenge We All FaceLink to The Legacy Challenge We All Face

The application in question represents a typical scenario many of us deal with. Built in the mid-2010s, it's a Django-based system managing member registrations and course enrollments. Sound familiar?

Here's what made it a perfect case study for legacy modernization:

1. Outdated stack: Django 1.9 (end-of-life since 2017)
2. Aging frontend: Angular 1.3.4 with fundamental architectural differences from modern versions
3. Deprecated APIs: TastyPie framework with no updates since 2016
4. Testing gaps: Minimal automated test coverage—the Achilles heel of legacy systems
5. Technical debt: Years of feature additions creating complex, hard-to-maintain code

If this sounds like applications in your portfolio, you're not alone. These systems are everywhere in telecom infrastructure and ad tech platforms, often running mission-critical workloads.

"The key insight? AI didn't replace human expertise—it amplified it."Link to "The key insight? AI didn't replace human expertise—it amplified it."

Why Testing Comes First in Any ModernizationLink to Why Testing Comes First in Any Modernization

Before touching any legacy code, you need safety nets. This is where most modernization efforts either succeed or fail spectacularly. Without comprehensive test coverage, every change becomes a gamble with production stability.

The traditional approach? Weeks of manual test writing, often incomplete, sometimes abandoned due to time constraints. The AI-assisted approach? A completely different story.

The AI-Powered Modernization ProcessLink to The AI-Powered Modernization Process

Working with Claude Code, the developer followed a structured approach that any senior leader should consider for their modernization efforts:

Phase 1: Infrastructure Setup (Day 1, Morning)Link to Phase 1: Infrastructure Setup (Day 1, Morning)

• Challenge: Outdated development environment using Vagrant
• Solution: Modernized to pyenv/virtualenv with comprehensive documentation
• AI Impact: Rapid environment configuration and documentation generation

Phase 2: Test Foundation (Day 1, Afternoon)Link to Phase 2: Test Foundation (Day 1, Afternoon)

• Challenge: No existing test framework or patterns
• Solution: Complete testing infrastructure with custom TestRunner and SQLite configuration
• AI Impact: Expert-level Django testing patterns implemented instantly

Phase 3: Comprehensive Coverage (Day 1, Evening)Link to Phase 3: Comprehensive Coverage (Day 1, Evening)

• Challenge: Complex business logic embedded in views and models
• Solution: Strategic test coverage across core classes (98%), and views (64%)
• AI Impact: 6,400+ lines of production-quality test code generated and reviewed

The Business Impact That MattersLink to The Business Impact That Matters

Let's talk numbers that resonate in the C-suite:

Cost Efficiency: $20 in AI processing costs versus weeks of senior developer time. For context, if a senior developer costs $150/hour, ten days of work represents $12,000 in labor costs—a 600x return on investment.

Risk Reduction: Moving from 0% to 66% test coverage means future changes can be made with confidence. In telecom and ad tech, where uptime is everything, this risk mitigation is invaluable.

Speed to Market: With proper testing infrastructure, new features can be developed and deployed faster. The foundation enables velocity rather than hindering it.

Technical Debt Paydown: Instead of accumulating more debt, the team actually reduced it while adding functionality—a rare win-win.

What This Means for Your Technology StrategyLink to What This Means for Your Technology Strategy

1. AI as Force Multiplier, Not Replacement

The developer provided strategic oversight, architectural decisions, and quality control. Claude Code handled implementation details. This partnership model preserves institutional knowledge while accelerating execution.

2. Incremental Beats Revolutionary

Rather than attempting a complete rewrite, the team focused on foundational improvements. This approach maintains business continuity while reducing risk—critical for telecom infrastructure and ad tech platforms serving live traffic.

3. Testing Infrastructure as Strategic Investment

The time spent building proper testing tools paid immediate dividends. Every subsequent change became safer and faster to implement.

4. Documentation Amplifies AI Effectiveness

Creating comprehensive project documentation (in this case, a CLAUDE.md file) helped the AI understand context and generate more relevant solutions.

Practical Recommendations for Your TeamsLink to Practical Recommendations for Your Teams

Based on this experience, here's what I recommend for technology leaders considering AI-assisted modernization:

• Start with Assessment: Use AI to analyze your existing codebase and identify the highest-impact, lowest-risk improvements.

• Invest in Testing First: Before any modernization, establish comprehensive test coverage. AI can accelerate this dramatically.

• Choose the Right Battles: Focus on security vulnerabilities and maintenance pain points rather than cosmetic updates.

• Document Everything: AI tools work better with context. Invest time in creating clear project documentation.

• Measure Relentlessly: Track time, cost, and quality metrics to demonstrate ROI and refine your approach.

The Bigger Picture for Telecom and Ad TechLink to The Bigger Picture for Telecom and Ad Tech

In our industries, legacy systems often handle massive scale—millions of users, billions of requests, real-time bidding, network orchestration. The risk of changes is proportionally higher, but so is the potential impact of successful modernization.

AI-assisted development offers a path to modernize these systems incrementally, with proper safety nets, at speeds previously impossible. For organizations managing complex infrastructure or real-time advertising platforms, this approach could be transformational.

Looking ForwardLink to Looking Forward

This case study represents just the beginning. The team used AI to establish testing infrastructure—the foundation for all future modernization work. Next phases will include Django framework upgrades, API modernization, and frontend strategy.

The key insight? AI didn't replace human expertise—it amplified it. The developer's architectural knowledge and strategic thinking remained essential. But the speed of implementation, the comprehensiveness of test coverage, and the quality of generated code exceeded what would have been possible through traditional development approaches.

For technology leaders, the question isn't whether to explore AI-assisted development—it's how quickly you can start experimenting with it on your own legacy modernization challenges.

What's your experience with legacy modernization in telecom or ad tech environments? Have you experimented with AI-assisted development? I'd love to hear about your successes and challenges in the comments.