This phase of research was supported through the Innosuisse Innovation Cheque and conducted through a collaboration between the Digital Society Initiative at the University of Zurich and Donzé Unlimited. The research focused on validating and refining the broader AILAS transformation analysis framework through empirical research, expert interviews, and structured multi-stakeholder feedback.

The study combined semi-structured interviews, expert consultations, and interdisciplinary workshops involving participants from academia, banking, pharmaceuticals, consulting, and AI product management. Contributors included researchers and professors from UZH and ETH Zurich, alongside industry participants from organisations including UBS, Roche, Stratac AG, and other AI and strategy professionals. The objective was to examine how organisations evaluate AI opportunities, governance requirements, organisational readiness, ethical considerations, and implementation risks across real operational environments.

The research validated strong demand for a more holistic approach to AI transformation analysis. Participants consistently responded positively to a system capable of combining AI use-case discovery with AI Ethics, AI Governance, AI Readiness, and AI Compliance within a single analytical framework. The findings reinforced the view that organisations do not experience these dimensions independently in practice, but as interconnected layers of the same transformation process.

The research also revealed that the primary source of value was not the originally envisioned marketplace and expert-matching layer, but the analytical and decision-support capabilities of the framework itself. Participants placed significantly greater value on structured diagnostics, comparative visibility, transformation monitoring, and strategic oversight than on consultant discovery alone. This insight contributed directly to the evolution of AILAS toward a Decision Intelligence Engine for enterprise AI transformation.

A further structural insight emerged around reporting and monitoring. While static reports were considered informative, participants consistently preferred dynamic dashboards, continuous visibility, and evolving oversight systems capable of supporting longitudinal decision-making across the AI journey. These findings later shaped the architectural direction of AILAS toward living transformation dashboards, portfolio analysis, and continuous governance and monitoring systems.