Unmasking the Magic: Synthetic Data as a Strategic Tool for AI Innovation

Sammanfattning

Background: Big Data models are facing significant limitations due to data scarcity, high costs, and stringent privacy regulations like GDPR. In consequence, Artificial Intelligence (AI) models are currently struggling with a "data hunger" crisis. While AI innovation has historically relied on the passive accumulation of historical information, synthetic data (SD) is emerging as a critical strategic resource to bypass these constraints. Purpose: The purpose of this research is to analyse how synthetic data reshapes the development of AI models by reconfiguring the interaction between Technology Push and Market Pull. Method: Adopting a qualitative and exploratory approach, the research utilizes an inductive multiple-case study design across five cases following Eisenhardt’s framework for cross-case analysis. Primary data was gathered through 15 interview sessions with 17 industry experts (CEOs, CTOs, and Data Scientists). Data processing and thematic coding were systematically conducted using the Gioia methodology. Conclusion: The research demonstrates that synthetic data transforms the linear relationship between push and pull into a recursive cycle, where models generate the data necessary for their own improvement. The study identifies four core pillars: the redefining of learning through recursive loops, the existence of socio-technical barriers related to human expertise, domain contingency and the rise of predictive synthesis. Ultimately, competitive advantage is shifting from the possession of historical databases toward the ability to design "synthetic sandboxes" for simulating future scenarios.