AI-powered discovery tools can now analyze millions of lines of legacy code in hours.
Microsoft’s own Teams group upgraded multiple .NET projects to version 8 in a single day, a process that once took months.
And yet, most modernization programs still spend 8–12 weeks in discovery before writing a line of new code.
If AI accelerates analysis by 40–50%, why are modernization timelines still measured in months of understanding?
Because the hardest part is reconstructing meaning.
Teams are searching for the buried business logic, dependencies, and decisions that shape how the system actually runs.
In this edition, I will share what those months uncover, what happens when you skip them, and why even AI-powered discovery still demands human patience.
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What months of discovery actually uncover
Legacy systems carry decades of decisions that live only in code. AI can scan the technical structure in hours. But understanding the business intent behind that structure takes months.
What AI finds in the scan
Microsoft’s code analyzers in Visual Studio and Azure trace dependencies semi-automatically, visualizing architecture and flagging technical debt.
GitHub’s Copilot-powered upgrade assistant identifies breaking changes and suggests rewrites for deprecated APIs, with developers validating the fixes.
Ford’s China IT team used these tools for .NET modernization, reducing refactoring effort by 70%. The acceleration is real; what used to require weeks of manual mapping now takes hours.
What teams must interpret from the scan
A database field with 12 dependencies might be critical or represent technical debt from a feature nobody uses. An integration layer might handle regulatory reporting or a workaround for a vendor API that changed in 2011.
The tools show structure, but they don’t explain purpose.
Idaho launched a $121 million ERP system that went live with widespread data errors. Transactions were posted twice, funds were misallocated, and basic workflows broke.
Many of those failures traced back to mapping gaps.
Assumptions about how data moved between systems that nobody validated before go-live. The state’s Speaker of the House called it “a joke” and suggested scrapping the system entirely within months of launch.
What happens when you skip discovery
Teams that rush past the discovery stage face problems that weren’t documented during build, after commitments are locked.
Dependencies surface that nobody knew existed
A database field gets deprecated, and four downstream systems break. An API that seemed simple turns out to have 17 exception-handling rules buried across three modules.
The integration layer you planned for two weeks now requires six, since nobody mapped the actual data flow.
The U.S. Department of Defense manages an $11 billion modernization portfolio. Twenty-four major programs are in flight. Only one has met its targets. The rest are bleeding budget and time, some running $815 million over, others delayed by 4 years, because the underlying system’s complexity wasn’t understood before work began.
Why even AI-powered discovery take time
AI accelerates analysis; prioritization is still human
Tools can map dependencies quickly and flag issues such as data clumps, duplicate logic, cyclic dependencies, and hidden contracts.
What they can’t do is rank consequences. That takes judgment. Which flows are regulatory? Which seams inflate the blast radius if they move? Which “do-not-break” invariants keep revenue flowing?
The productive way to move is to do a risk-ranked discovery. Instead of documenting everything, we can probe the riskiest domains first (high coupling, many integrations, unclear ownership).
Keep it time-boxed so momentum doesn’t die. Roughly four to five weeks to dissect what you have; three to four weeks to shape the approach you’ll actually execute.
Why can’t you automate validation?
AI won’t confirm organizational reality. Policy owners still need to bless a “temporary” exception that became permanent. Finance must say whether a duplicate field is audit-critical or dead wood. Ops explains why a fragile job runs at quarter-end.
Mature programs build a validation module. Weekly reviews where business owners confirm AI findings and maintain a decision log that links every recommendation to evidence (files, commits, metrics).
AI Agents make discovery faster and “less of a guessing game,” but win only when humans verify intent and constraints.
Pair that with continuous discovery in each iteration so new findings adjust the plan early, not after you’ve committed.
So, what exactly do you need to do?
- Speed comes from focus and proof; so, make discovery accountable.
- Set an MTC (Mean Time to Comprehension) SLO per subsystem, run risk-first spikes, and require business sign-off on the few choices that define blast radius.
AI has finally made codebases transparent. What it hasn’t achieved is shared clarity about how those systems earn or risk money. That’s the next modernization edge: the ability to explain a system as fast as you can analyze it.
Simform’s NeuVantage accelerator was built around the principle of using AI to reconstruct intent. It helps organizations to migrate and modernize faster with confidence.
