Designing Trustworthy Data Products for Scalable Enterprise Solutions Through GenAI Engineering Integrated with Financial Modeling and Intelligent Product Development

Abstract

In the era of intelligent automation and data-driven decision-making, enterprises face growing pressure to develop trustworthy and scalable data products that can deliver actionable insights while maintaining transparency, security, and economic viability. This study proposes a strategic framework for designing such data products by integrating Generative AI (GenAI) engineering with financial modeling and intelligent product development. The methodology combines advanced AI architectures (e.g., GPT-3.5, BERT, TabTransformer) with scenario-based financial simulations and user-centered design practices to evaluate model performance, economic feasibility, scalability, and ethical compliance. Results demonstrate that GenAI models can achieve high accuracy and explainability while financial modeling ensures economic sustainability across market conditions. System testing confirms architectural resilience under enterprise-scale workloads, while user feedback highlights the success of intelligent feature adoption. Moreover, robust governance protocols reinforce trust through data privacy, auditability, and regulatory alignment. The study concludes that a unified approach merging AI innovation with economic rigor and ethical design enables the creation of enterprise-grade data products that are reliable, scalable, and trusted by stakeholders.