Most AI SaaS products are evaluated the wrong way.

Teams compare features, pricing, and UI—but overlook the factors that actually determine long-term success.

When evaluating AI SaaS platforms, I've found six criteria matter far more:

• Intelligence level (rule-based, ML, generative AI, or agentic AI)  
• Autonomy level (assistive vs. autonomous)  
• Vertical specialization (horizontal vs. industry-specific)  
• Integration depth with existing systems  
• Data ownership and model control  
• Governance, security, and customization options

Two products can look nearly identical in a demo and perform completely differently once connected to real enterprise data and workflows.

One area that doesn't get enough attention is data ownership. In regulated industries like finance and healthcare, shared-model architectures can become a major compliance challenge.

Curious how others evaluate AI SaaS products today.

What criteria have been most important in your buying decisions or implementation experiences?

For anyone interested, I recently put together a detailed framework on AI SaaS product classification criteria: [https://www.intellectyx.com/ai-saas-product-classification-criteria/](https://www.intellectyx.com/ai-saas-product-classification-criteria/)

AI SaaS Product Classification Criteria: Complete Framework 2026