Methodological Framework For The Development of Ai-Based Prototypes Under An Organizational Collaborative Innovation Approach

The Diagnosis phase establishes a shared problem definition, examines data availability, and identifies operational, ethical, and organizational constraints. The Research phase reviews relevant computational approaches, synthesizes domain-specific literature, and defines the technical and experimental parameters required for responsible AI development. The Prototyping phase comprises data-processing pipelines, model construction, and iterative refinement supported by traceability and reproducibility practices. The Validation phase evaluates the prototype using predefined technical metrics, robustness analyzes, and user-centered operational assessments conducted jointly with organizational stakeholders. The Transfer phase addresses integration requirements, documentation standards, capacity-building processes, and knowledge-transfer mechanisms to support adoption.

By combining methodological, organizational, and governance-oriented components, the framework defines a structured reference model that can be used to guide university–organization initiatives aimed at designing, evaluating, and transitioning AI-based prototypes to operational environments.