There's a design pattern in vertical AI SaaS that keeps winning: extreme depth on a single critical workflow, with enough adjacent capability to make the core workflow useful. Not broad coverage. Not a platform play. One workflow, done better than anyone else, with AI that's meaningfully more accurate because of domain-specific training.
The examples are instructive:
A legal AI that focuses exclusively on contract review for a specific contract type (M&A asset purchase agreements) with 98% accuracy on the 200 clauses that matter, rather than a general legal AI that handles all document types with 75% accuracy.
A healthcare AI that does clinical documentation for one specialty (cardiology) with real-time EHR integration, using training data from 50,000 cardiology encounters, rather than a general clinical documentation tool that handles all specialties with less accuracy.
A construction AI that predicts schedule risk specifically for commercial ground-up construction projects, using a decade of project cost and timeline data, rather than a general project management AI.
In each case, the narrow AI is materially more accurate and more useful for the specific workflow than the general AI. The narrow AI company is also much smaller, with a smaller team and a smaller market — but it has higher win rates, higher NRR, and a clearer moat.
Why narrow beats wide for AI in vertical markets:
Training data quality. Deep domain training on a specific workflow produces better models than broad training across many workflows. The narrow company has better training data for their use case than any general AI can compile.
Customer trust. Enterprise customers in regulated industries don't trust "pretty good" AI for high-stakes decisions. "Specifically designed for this workflow with this accuracy track record in this industry" is a completely different trust proposition.
Build narrow. Own it completely. Expand from strength.