Transforming Organizational Development with AI: Navigating Change and Innovation for Success
Lalithendra Chowdari Mandava
Lalithendra Chowdari Mandava, Department of Human Resource Development , The University of Texas at Tyler, Tyler, TX, USA.
Manuscript received on 15 August 2023 | Revised Manuscript received on 28 August 2023 | Manuscript Accepted on 15 October 2023 | Manuscript published on 30 October 2023 | PP: 13-28 | Volume-13 Issue-1, October 2023 | Retrieval Number: 100.1/ijeat.A42821013123 | DOI: 10.35940/ijeat.A4282.1013123
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Effective change management emerges as a deciding element for an organization’s survival and success in the changing terrain of today’s fiercely competitive business climate. The variety of change management theories and approaches currently available, however, paints a complicated picture plagued by inconsistencies, a lack of strong empirical support, and unproven assumptions about contemporary organisational dynamics. This essay aims to lay the groundwork for a new paradigm in effective change management by critically examining popular theories of change management. The paper addresses the gap between theory and practice, concluding with suggestions for further research. In parallel, artificial intelligence (AI) has made remarkable progress, enabling computers to mimic human autonomy and cognition. Industry-wide excitement has been sparked by the enthusiasm among academics, executives, and the general public, resulting in significant investments in leveraging AI’s potential through innovative business models. However, the lack of thorough academic guidance forces managers to struggle with AI integration issues, increasing the risk of project failure. This article provides an in-depth analysis of AI’s complexities and its role as a catalyst for revolutionary business model innovation. A thorough literature assessment, which involves sifting through a sizable library of published works, combines up-to-date information on how AI is affecting the development of new business models. The findings come together to form a roadmap for seamless AI integration, which includes four key steps: understanding the fundamentals of AI and the skills required for digital transformation, assessing current business models and their innovation potential, developing key proficiencies for AI assimilation, and gaining organisational acceptance while building internal competencies. This article seamlessly combines the fields of organisational change management and AI-driven business model innovation, providing a thorough explanation to help businesses undergo a successful transformation and innovation. The convergence of these disciplines offers a practical vantage point for successfully adapting to, thriving in, and profiting from a dynamic business environment. Artificial intelligence (AI), a massively disruptive force that is altering international businesses, is at the vanguard of this revolution. The ability of AI to make decisions automatically, based on data analysis and observation, opens up hitherto untapped possibilities for value creation and competitive dominance, with broad consequences spanning several industries. With its rapid scaling, ongoing improvement, and self-learning capabilities, this evolutionary invention functions as an agile hybrid of capital and labour. Notably, AI’s architecture serves as the cornerstone for data-driven decision support by efficiently sifting through large and complex datasets to extract valuable insights. Thus, the symbiotic marriage of organisational change management and AI-driven business model innovation provides a comprehensive narrative, guiding businesses not only to survive but also to thrive in an ever-evolving business environment. It is emphasised how business models (BMs) interact with technology to affect how well businesses function, underscoring the need to take BMs into account when using AI. Business model innovation (BMI) that AI unlocks can improve products, streamline processes, and reduce costs. However, there is a void between technological improvements and their operationalization via BMs. Successful AI integration depends on a well-structured BM, which promotes agility and makes the most of technological resources. BMI is accelerated by AI, which reshapes sectors via innovation. Although interest in AI is high, strategic, cultural, and technological constraints sometimes prevent significant investments from producing positive economic results. To fully utilize AI’s capabilities, structured BMs are required. Despite an increase in research, a lack of cohesive information persists regarding the business applications of AI. To address this gap, we investigate implementation-related challenges in AI. Analyzing AI-driven BM transformation and risk management is aided by a study on BMI and digital transformation at the same time. The purpose of this study is to further our understanding of AI-driven business model innovation and to provide a valuable framework to help practitioners navigate the potential and difficulties of AI implementation. The suggested roadmap aims to identify current knowledge gaps and future research initiatives.
Keywords: Transforming, Navigating, Innovation, AI’s Analyzing AI-driven
Scope of the Article: Artificial Intelligence