AI teams lose track of how models influence subsequent versions, making it hard to attribute improvements or regressions. A lineage tracker could solve this.
Build a version control system specifically for AI models that tracks training data, architecture changes, and performance characteristics across generations.
AI research labs and enterprise ML teams would pay for better model governance tools.
Start with simple metadata tagging and visualization of model relationships.
The biggest risk is existing ML platforms adding similar features natively.