Is a Failed Product Still a Product? Integrating Systematic Management Theory and Empirical Evidence on Organizational Learning in AI-Driven Enterprises

Uttiya Basu; Arnesh Samanta; Sayan Basuli; Ayan Basuli

doi:10.36948/ijfmr.2026.v08i02.74634

Is a Failed Product Still a Product? Integrating Systematic Management Theory and Empirical Evidence on Organizational Learning in AI-Driven Enterprises

Author(s)	Dr. Uttiya Basu, Mr. Arnesh Samanta, Mr. Sayan Basuli, Mr. Ayan Basuli
Country	India
Abstract	Introduction Product failure has traditionally been interpreted as a negative market outcome signifying consumer rejection, financial underperformance, or strategic miscalculation. However, within the framework of the Systematic Theory of Management, organizations are understood as open systems characterized by continuous interactions among inputs, transformation processes, outputs, and feedback mechanisms (Katz & Kahn, 1978; von Bertalanffy, 1968). From this systemic perspective, a failed product is not merely an unsuccessful output but a functional element within a dynamic feedback loop that influences future organizational behavior. Organizational learning theory further suggests that errors and failures provide critical opportunities for adaptive improvement and double-loop learning (Argyris & Schön, 1978). In AI-driven enterprises, where predictive analytics, machine learning algorithms, and automated decision-support systems are embedded in business operations, product development is increasingly data-intensive. Yet, despite algorithmic precision, product failures continue to occur, indicating that technological sophistication does not eliminate uncertainty. Contemporary research on dynamic capabilities and knowledge creation argues that learning from disruption and failure strengthens long-term adaptability (Teece, 2007; Nonaka & Takeuchi, 1995). This raises a foundational question: does a failed product cease to be a product, or does it transform into a systemic input within the organizational learning architecture? Integrating systems theory with AI-enabled governance, this study reconceptualizes product failure as a feedback-generating mechanism within human–AI collaborative enterprises (Raisch & Krakowski, 2021). Research Gap Existing literature on product failure largely treats it as a market-performance issue rather than as a systemic organizational phenomenon. While organizational learning and dynamic capability theories acknowledge the value of failure, limited research integrates these perspectives with the Systematic Theory of Management in AI-driven contexts. Moreover, empirical studies rarely examine how human–AI synergy transforms product failure into structured feedback mechanisms. Thus, a gap exists in theoretically and empirically reconceptualizing failed products as recursive inputs within adaptive, AI-enabled organizational systems. Objectives of the Study The study aims to examine product failure through the lens of Systematic Management Theory and empirically validate its systemic function in AI-driven enterprises. Specifically, it seeks to analyze product failure as an organizational output that generates recursive feedback within an open-system structure (Katz & Kahn, 1978), to investigate the relationship between product failure and organizational learning capability, and to assess the mediating role of systematic managerial response—such as structured evaluation mechanisms and AI-assisted analytics—in transforming failure into adaptive knowledge. By synthesizing theoretical constructs with empirical evidence, the study bridges classical systems thinking with contemporary AI-integrated organizational contexts. Methodology The research adopts a theoretical–empirical design grounded in systems theory and supported by primary data analysis. Conceptually, the study builds on open-systems thinking (von Bertalanffy, 1968) and organizational learning frameworks (Argyris & Schön, 1978). Empirically, primary data were collected from 83 professionals employed in AI-driven enterprises, including technology startups, fintech firms, and analytics-based organizations. Respondents comprised product managers, AI analysts, and strategic decision-makers directly involved in AI-supported product evaluation processes. A structured questionnaire using a five-point Likert scale measured four constructs: Product Failure Perception, Systematic Managerial Response, Human–AI Synergy, and Organizational Learning Capability. The construct of organizational learning draws from knowledge-creation and dynamic capability perspectives (Nonaka & Takeuchi, 1995; Teece, 2007). Reliability testing indicated acceptable internal consistency, with Cronbach’s alpha values exceeding the 0.70 threshold (Hair et al., 2019). Descriptive statistics, correlation analysis, and mediation regression analysis were applied to test the proposed systemic relationships. Analysis & Discussions The empirical findings support the systemic reinterpretation of product failure. Descriptive analysis indicates that a substantial proportion of respondents perceive product failure as a source of actionable insight rather than as a terminal organizational event. Correlation results reveal a positive and statistically significant association between product failure perception and organizational learning capability, consistent with theoretical propositions on adaptive systems (Katz & Kahn, 1978). Mediation analysis demonstrates that systematic managerial response significantly strengthens the transformation of failure into learning. When structured feedback mechanisms—such as AI-generated diagnostics and cross-functional evaluation—are institutionalized, the relationship between product failure and learning capability becomes more robust. Furthermore, enterprises characterized by higher degrees of human–AI collaboration exhibit stronger adaptive recalibration following product underperformance, aligning with contemporary arguments that AI augments rather than replaces managerial judgment (Raisch & Krakowski, 2021). These findings empirically substantiate the theoretical claim that within an open-system paradigm, product failure operates as recursive feedback rather than systemic breakdown. Future Scope of Research While the present study provides empirical validation using 83 primary responses, future research may extend the framework through larger samples and structural equation modeling to enhance generalizability (Hair et al., 2019). Longitudinal studies could examine how repeated cycles of failure contribute to dynamic capability development over time (Teece, 2007). Comparative analyses across industries or cross-national contexts may further reveal cultural variations in interpreting failure within systemic organizational structures. Additionally, future inquiry may investigate how varying levels of AI autonomy influence feedback absorption and learning intensity in human–AI collaborative systems (Raisch & Krakowski, 2021).
Keywords	Artificial Intelligence; Human–AI Synergy; Organizational Learning; Product Failure; Systematic Management Theory; Systems Thinking.
Field	Business Administration
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-14
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.74634

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Is a Failed Product Still a Product? Integrating Systematic Management Theory and Empirical Evidence on Organizational Learning in AI-Driven Enterprises

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