01

The ROI calculation has changed. Most teams haven't noticed.

Software transformations earned their bad reputation. Multi-year timelines. Budget overruns. New systems that ended up no better than what they replaced. The rational response was to avoid them unless absolutely necessary.

AI changes this. What took 18 months now takes 3. What required armies of consultants now requires a small team with the right tooling. But there's another variable most teams ignore: the cost of staying put.

The real ROI question isn't "can we afford to transform?" It's two questions: Do you need to innovate in this space? And what's the actual risk profile of your existing code?

If you're in a static market with a stable, well-audited system, transformation probably isn't urgent. But if you need to move fast, integrate AI capabilities, or you haven't done a serious security audit in years? The cost of inaction is higher than you think.

02

Migrations fail by protecting everything equally.

The instinct in any migration is to preserve everything. Every edge case. Every quirk. Every undocumented behavior some team might depend on. This instinct is wrong.

No transformation is perfect. Anyone promising zero regressions hasn't done this at scale. Or they're lying. The question isn't "will we miss something?" You will. The question is: what absolutely cannot break?

Security. Data integrity. Compliance. Core business logic. These are your invariants. Define them explicitly. Protect them with automated verification. Everything else? Catch it early. Fix it fast.

03

Ship to an agent-ready stack. Verify with defense in depth.

Where you land matters. Most migrations target "modern": cleaner code, better frameworks, cloud-native. That's table stakes. The real target is an architecture that AI agents can work with.

An agent-ready codebase has clear module boundaries. Consistent patterns. Well-typed interfaces. Comprehensive test coverage. Not because these are "best practices." Because they're what allows AI coding assistants to understand context, make safe changes, and validate their own work. The same properties that make code maintainable by humans make it navigable by agents.

But shipping AI-generated code, or any code, requires verification layers. The debate about whether to trust AI misses the point. Code quality has never been about who wrote it. It's about what catches errors before production.

This is how Google, Stripe, and Netflix ship thousands of changes daily. The guardrails are the product. They're what make an agent-ready architecture safe to operate.

04

The bottleneck is never the code. It's the coordination.

The technical work isn't the bottleneck anymore. Pattern matching, boilerplate translation, dependency mapping: AI handles this now. The constraint has moved.

What stalls migrations now: decisions waiting for reviewers. Context scattered across twelve tools. Stakeholders misaligned on priorities. Engineers blocked while approvals sit in someone's inbox. The code is ready; the humans aren't.

Most migration tools optimize for the wrong thing. More automation, faster code generation. The actual bottleneck is human coordination: getting the right information to the right person at the right time so they can make a decision and move on.

05

The best migration is the code you don't write.

The instinct in any migration is to translate everything. Take the old code, convert it to the new stack, ship it. This misses the point entirely.

Transformation is the rare opportunity to question requirements that haven't been questioned in years. That billing edge case from 2014? Maybe it serves three customers. That complex workflow? Maybe a managed service handles it now. That custom infrastructure? Maybe it's table stakes in modern cloud.

Every line of code you don't write is a line you don't maintain, don't debug, don't secure, and don't explain to the next engineer. Use managed services. Let someone else carry that complexity.

Some open-source projects we've migrated. All repos are public.

06

Months to hours. By knowing what to skip.

Every migration moves through the same phases: discovery, execution, deployment. Traditionally, each phase takes weeks or months. Not because the work is hard, but because nobody knows what's safe to skip.

"Review every line of code" sounds rigorous. It's actually a failure of prioritization. When you don't know what matters, everything looks important. When you know exactly which patterns are risky and which are mechanical, you review the 5% that matters and validate the rest automatically.

Speed doesn't come from doing the same things faster. It comes from skipping the work that was never necessary. AI handles the mechanical translation. Humans focus on decisions that require judgment. The phases compress because you stop wasting time on the wrong things.