OVERCOMING BARRIERS TO ARTIFICIAL INTELLIGENCE ADOPTION
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Abstract
The purpose of this study is to explore the barriers to the successful implementation of Artificial Intelligence (AI) in organizations, focusing on psychological, organizational, and ethical challenges. The aim is to identify strategies to overcome resistance and foster trust, ensuring a seamless integration of AI technologies into business operations. Methodology. The research is based on a comprehensive review of existing literature and real-world examples. It employs a qualitative approach to analyze the root causes of resistance to AI adoption, emphasizing psychological fears, organizational misalignments, and ethical concerns. Strategic frameworks and best practices are proposed to address these challenges effectively. Results. The findings reveal that psychological resistance arises from fears of job displacement and mistrust in AI systems, while misaligned strategies and cultural inertia drive organizational resistance. Ethical concerns such as bias, accountability, and privacy violations exacerbate resistance. Strategies such as fostering transparency, aligning AI initiatives with business goals, implementing robust governance, and addressing ethical challenges can significantly reduce resistance and enhance AI adoption. Practical Implications. The study provides actionable insights for business leaders and policymakers to mitigate resistance to AI implementation. By fostering transparency, offering training programs, and ensuring ethical compliance, organizations can build trust among stakeholders. Legal measures and stakeholder engagement are highlighted as critical components for long-term success in AI integration. Value / Originality. This research offers a holistic framework for addressing resistance to AI adoption, integrating psychological, organizational, and ethical dimensions. By bridging gaps between theory and practice, it provides unique insights to support organizations in leveraging AI’s transformative potential while ensuring alignment with societal and ethical values.
How to Cite
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artificial intelligence, decision-making processes, resistance, deep learning, business management
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