AI-Native Speed on a Legacy Codebase: How to Outpace Competitors with AI-Driven SDLC
Can AI speed up delivery on a 10- or 15-year-old codebase, or does it just pile up tech debt faster? Watch the full recording of MobiDev’s fireside chat (September 30, 2026).
120+ legacy projects modernized · 65+ AI & ML products delivered · 9.1 NPS
Watch the Full Recording
Speakers: Serhii Ivanov and Ievgen Zadorozhnyi. Moderator: Helen Khailova-Horash. Full session with audience Q&A.
Key Answers from the Webinar
AI SDLC means AI in all five stages of the software development lifecycle: business analysis and specs, architecture, development, QA, and deployment.
Often, yes, it’s a buzzword, if a development company talks about the share of code written by AI or licenses bought, that’s marketing. If it can show written specs before generation, context files in the repository, and reviewers who own them, it’s real.
Developers got faster, but delivery didn’t, because specs, QA, and code review became the bottleneck. Find the bottleneck stage first. When AI speeds up coding alone, QA can’t keep up, specs arrive late, and code review grows when AI writes 500 lines instead of 100. Competitors building from scratch also move faster at first than teams on legacy code.
New features built on top of unrefactored legacy code create bugs, tangled dependencies, and slow reviews. Plan and budget AI-assisted refactoring first; feature work with AI gets much faster after that.
Yes, but only with the right setup. Out of the box, AI guesses convincingly. Four steps make it safe:
- Give the agent the whole repository, not a pasted file.
- Add rules files such as AGENTS.md (for example, “PHP 5, no Composer, global variables are intentional”).
- Record current behavior with characterization tests before changing code.
- Migrate one endpoint, agree on the pattern, then repeat in small reviewable batches.
AI coding adoption follows a curve: initial hype with falling quality, a temporary slowdown while the team adds structure such as spec-driven development, then real speed gains. Applied to coding alone, AI speeds up code generation by 30–40% but can slow review and QA by up to 50%.
Reverse the order: let AI read the codebase and document roles, data flows, and business rules first. On one MobiDev project, this showed that about 50% of the code was dead and the “simple” service had 15 user roles.
Speakers
Serhii Ivanov
Head of Resource Management at MobiDev
Speaker Expertise
Ievgen Zadorozhnyi
Product Manager & Delivery Expert at MobiDev
Speaker Expertise
13+ years in delivery and product consulting; integrates AI-driven development into delivery practice.
This session is for you if you’re a
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FAQ
Not if the vendor works safely. Check four things: enterprise AI environments where your code isn’t used for model training, AI security training and NDAs for engineers, senior engineers reviewing every AI output, and AI security scanning in the CI/CD pipeline.
Match the model to the task: cheap, fast models for simple edits and top-tier models for architecture and complex debugging. Keep sessions short and start a new one for each task. Track usage in provider dashboards, and batch API calls at the architecture level.
Yes. Feathers defines legacy code as code without tests, and AI can generate characterization and unit tests. But AI tends to write tests that pass. Use human review and mutation testing to check that the tests actually catch failures.
A temporary slowdown while processes are set up, then real gains. Clean up legacy code first, apply AI across all five stages, budget for tooling and infrastructure, and expect AI model pricing to change.
Yes, when AI covers the whole SDLC. Applied to coding alone, it speeds up code generation by 30-40%, while review and QA can slow down by 50%, so time-to-market stays flat or gets worse.