Evaluating and optimizing productivity of Generative Artificial Intelligence in organizational projects

Abstract

As Generative Artificial Intelligence (GenAI) continuous to evolve, its integration into organizational workflows present both significant opportunities and complex challenges. While GenAI has demonstrated potential to enhance productivity through automation, decision-making and content generation, organizations struggle to reliably assess its impact. This research investigates how the productivity of GenAI can be accurately assessed and optimized within service-oriented projects. The research employs a qualitative design, combining a Systematic Literature Review, semi-structured interviews, and multiple case studies. The findings reveal that although GenAI can significantly reduce task completion time and improve output quality, its productivity gains are often inconsistently measured. Metrics are used sporadically and lack standardization, moreover the effectiveness of GenAI varies across business context and organizational maturity. To address these challenges, the study introduces the Generative Productivity & Impact Model (G-PIM), a multidimensional framework consisting of five dimensions: strategic impact, operational performance, human-centric outcomes, governance and risk and contextual adaptability. This model provides a holistic view of GenAI’s contribution to business value, emphasizing both direct and indirect productivity indicators. This paper emphasizes the importance of establishing standardized productivity metrics, aligning KPIs with strategic objectives and adopting outcome-based pricing models to accurately assess and optimize the impact of GenAI. Furthermore, fostering organizational readiness and embedding robust governance structures are essential to ensure responsible, scalable, and value-driven GenAI deployment. This research contributes to academic literature by bridging theoretical insights with practical implications, offering actionable recommendations for organizations seeking to leverage GenAI effectively. It also lays the foundation for future research on long-term impacts, human-AI collaboration, and the development of adaptive performance metrics in rapidly evolving technological landscapes.