A STRATEGIC FRAMEWORK FOR AI PRODUCT DEVELOPMENT AND EVALUATION IN ENTERPRISE SOFTWARE

Abstract

This article presents a comprehensive framework for developing and evaluating AI products in enterprise software systems, addressing the critical challenges organizations face during AI transformation initiatives. The article introduces a structured approach to decision-making for AI integration, encompassing ROI evaluation, user value assessment, and business impact analysis. It establishes distinct methodologies for both assistive and autonomous AI systems, providing detailed metrics for measuring success and performance across different implementation scenarios. Across various industries, the framework has shown potential in reducing implementation time, increasing user adoption rates, and enhancing overall project success rates, highlighting its practical applicability. The article methodology combines theoretical analysis with practical case studies, resulting in a flexible yet robust framework that can adapt to various organizational contexts. The framework's primary contribution lies in its practical approach to bridging the gap between theoretical AI capabilities and real-world implementation challenges, offering product leaders a systematic methodology for AI product development and evaluation. By addressing both current implementation challenges and future scalability requirements, this framework provides organizations with a foundational tool for navigating their AI transformation journey while maintaining a focus on measurable business outcomes and user value creation.