I'm putting together a proposal for an initiative focused on using AI to analyze legacy enterprise systems and uncover decades of embedded business logic.
The idea is to use AI to analyze things like:
- Database schemas
- Stored procedures
- Legacy application code
- Historical transaction data
- Existing documentation
The goal isn't to automate decisions immediately. It's to first create a documented knowledge base of the rules, dependencies, decision paths, and data relationships that currently drive business operations.
Potential outputs would include:
- Business rule catalog
- Knowledge graph of relationships and dependencies
- Decision trees explaining how outcomes are determined
- Recommendations for future-state data models and modernization opportunities
Before I finalize the proposal, I'd love feedback from anyone who has attempted something similar.
Questions:
- Has anyone successfully used AI to discover and document business rules from legacy systems?
- What worked better: analyzing source code, database logic, transaction history, or a combination of all three?
- How accurate were the AI-generated rules compared to SME validation?
- Did you use knowledge graphs, vector databases, graph databases, or another approach?
- What were the biggest challenges: data quality, context gaps, undocumented exceptions, or something else?
- How did you measure success?
- Rule coverage?
- SME time saved?
- Modernization acceleration?
- Reduced operational risk?
- Were there any tools, platforms, or architectures that performed particularly well?
- If you were starting over, what would you do differently?
- What scope would you recommend for a pilot to demonstrate value in 60-90 days?
- Is there a realistic path from business rule discovery to explainable AI recommendations and decision support, or are those separate initiatives?
My hypothesis is that many organizations are trying to modernize systems without fully understanding the business logic currently embedded in them. It seems like AI could act as a "business rule archaeologist" and create the foundation needed for future modernization, automation, and AI-driven capabilities.
Interested in hearing both success stories and cautionary tales.
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