Your documentation problem isn't as simple to fix as you think it is.
Most organizations' software applications documentation are outdated, incomplete, or locked in the heads of developers who are about to retire.
Unfortunately, GenAI by itself is not the solution.
The Regulatory Wake-Up Call
Here's how this typically plays out:
Your compliance team gets notice of a new regulatory requirement affecting how customer data is processed. They need to document exactly how your systems currently handle this data—every transformation, every decision point, every storage location.
You spin up your shiny new GenAI documentation tool. It analyzes your codebase, reads your existing (outdated) documentation, and produces a beautiful, comprehensive document in hours instead of weeks. Your compliance officer is thrilled.
Then the auditors arrive.
They spot three discrepancies between what your documentation claims and what your systems actually do. Two are minor. The third? It reveals that customer data has been flowing to an unapproved third-party system for the past eighteen months.
The fine is significant. The remediation cost is worse. But the real damage? Leadership's trust in your AI initiative just evaporated.
Why GenAI Documentation Fails the Accuracy Test
Google's FACTS research shows that even the best AI models achieve only 74% accuracy when grounding responses in provided documents. That number drops when the AI needs to:
- Synthesize information across multiple source files
- Distinguish between current code and commented-out legacy logic
- Trace data flows across program boundaries
- Identify what code is actually executed versus what's simply present
For casual documentation—onboarding materials, architecture overviews, general system descriptions—74% might be acceptable. You can afford some imprecision when you're just trying to help a new developer get oriented.
But for regulatory documentation, modernization or development specifications, or incident post-mortems? 74% accurate means 26% wrong. And you often can't tell which 26%.
The Compound Error Problem
Here's where it gets worse. Documentation isn't a one-time event—it's a chain of dependencies:
- Your GenAI documents the customer data flow for compliance
- Your architects use that documentation to plan a cloud migration
- Your developers use the migration plan to write new services
- Your QA team uses the specifications to write test cases
- Your security team uses the architecture docs to configure access controls
If the original documentation was wrong, every downstream decision compounds the error. By the time you discover the problem, you've built months of work on a faulty foundation.
This is why so many modernization projects fail spectacularly. Not because the GenAI produced gibberish—it produced plausible, well-formatted documentation that was almost correct. Just inaccurate enough to be dangerous.
What Deterministic AI Changes
Now imagine a different approach:
Before GenAI writes a single word of documentation, deterministic AI analyzes your software applications:
- Code slicing traces every line that influences each business function
- Cross-program analysis follows data flows regardless of system boundaries
- Runtime analysis identifies what code actually executes, not just what exists
- Dependency mapping captures every relationship between components
Mapped into a Knowledge Graph, our product, Cobol Colleague, creates a verified knowledge foundation—a complete, accurate map of what your systems actually do. And it’s queryable, and accessible across your enterprise, ready to answer humans and agentic systems.
Cobol Colleague generates and passes context to GenAI to translate to documentation, it's grounded in facts, not probabilities. When it describes a data flow, it's traceable to specific code paths. When it documents a business rule, it can cite every location where that rule is implemented.
What This Looks Like in Practice
For Regulatory Compliance: Your compliance officer asks: "How do we currently implement GDPR's right-to-deletion for customer records?"
- Traditional GenAI: Analyzes documentation and code, produces plausible explanation (74% reliable)
- Deterministic + GenAI: Traces actual execution paths, identifies every storage location, maps complete deletion flow (100% verifiable)
The difference? When regulators audit, you can show them the exact code, the complete data lineage, and proof that no storage locations were missed.
For Modernization Planning: Your enterprise architect needs specifications for rewriting a critical batch process.
- Traditional GenAI: Summarizes code functionality, suggests modern equivalents (good starting point, requires extensive verification)
- Deterministic + GenAI: Extracts complete business function logic, identifies all edge cases, documents every dependency (architects make decisions with confidence, not hope)
The difference? Your $5M modernization project doesn't discover "missed requirements" six months in.
For Production Support: A critical overnight batch job failed. Your team needs to understand what happened and document the root cause.
- Traditional GenAI: Analyzes logs and code, proposes likely causes (helpful for brainstorming, requires validation)
- Deterministic + GenAI: Traces exact execution path, identifies precise failure point, documents complete business impact (MTTR reduced by 60%, post-mortem is audit-ready)
The difference? Your post-mortem documentation actually reflects what happened, not what probably happened.
The Audit Trail That Saves You
Here's the part that keeps CFOs up at night: when something goes wrong, you need to prove exactly what your systems did and why.
GenAI alone can't give you that. It can tell you what probably happened based on available information. But "probably" doesn't satisfy regulators, auditors, or the executives explaining a breach to the board.
Deterministic AI creates a complete audit trail. Every documented fact traces back to specific source code. Every business rule links to actual implementation. Every data flow maps to verified execution paths.
When the auditors ask "How do you know this documentation is accurate?"—you can show them.
The Real Cost of Documentation Guesswork
Think about what inaccurate documentation costs your organization:
- Compliance risk: Fines, remediation, reputational damage when audits reveal discrepancies
- Failed modernization projects: When specifications miss critical business logic, migrations fail catastrophically
- Extended incidents: When troubleshooting relies on outdated documentation, MTTR explodes
- Knowledge loss: When retiring developers leave, their institutional knowledge goes with them—unless you've captured it accurately
- Strategic paralysis: When leadership can't trust documentation, they can't make confident modernization decisions
These aren't theoretical risks. According to industry research, 74% of modernization projects fail or significantly underperform—often because documentation of existing systems was incomplete or inaccurate.
You Can't Build on Quicksand
Your GenAI documentation initiatives aren't failing because GenAI isn't powerful enough. They're failing because probabilistic models, no matter how sophisticated, can't provide the factual precision that business-critical documentation requires.
But you also can't go back to purely manual documentation. It's too slow, too expensive, and too dependent on scarce expertise.
The answer isn't choosing between speed and accuracy. It's building the precision and context that makes AI-generated documentation trustworthy.
When documentation is grounded in verified, bite-sized facts extracted directly from your systems, GenAI becomes genuinely transformative:
- Precise and auditable compliance documentation
- Specifications that capture every edge case
- Incident post-mortems that actually reflect what happened
- Knowledge preservation that survives developer turnover
All with the audit trail and factual precision your business actually needs.
Let's discuss what deterministic AI could mean for your documentation challenges. Whether you're facing regulatory requirements, planning modernization, or simply trying to preserve institutional knowledge before your COBOL experts retire—we should talk about how to make your documentation both fast and reliable.
Schedule a 30-minute consultation to explore how deterministic AI could provide the missing foundation for your documentation initiatives. Because in a world where regulators, auditors, and executives demand proof, "approximately correct" isn't good enough.