How We Collected the Data
This analysis is based on a combination of structured data collection and real-world conversations gathered across multiple industry settings, including SaaS-focused conferences, founder meetups, and professional webinars.
We combined quantitative inputs with qualitative context. This allowed us to capture not just what teams are doing with AI, but why they’re making those decisions.
Structured Survey Responses.
We collected responses through a standardized questionnaire designed to surface how teams think about AI adoption, feature prioritization, scaling, and risk. The survey was completed by founders, executives, product leaders, and operators actively building or scaling SaaS products.
The structured format ensured consistency across responses, making it easier to identify patterns, recurring concerns, and points of alignment across different company types and stages.
Direct Conversations and Contextual Insights.
Alongside the survey, we engaged in real conversations with participants. These discussions helped clarify the intent behind answers and validate whether written responses reflected real-world decision-making. These conversations revealed the trade-offs teams were actively weighing speed versus stability, experimentation versus cost, and innovation versus user clarity.
