The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) ha... more The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has revolutionized business strategies by enhancing decision-making, optimizing operations, and providing competitive advantages across industries. This systematic review, based on 115 peer-reviewed articles analyzed following PRISMA guidelines, explores the transformative role of these technologies in reshaping business practices, particularly in marketing, supply chain management, financial analytics, and human resource management, while addressing complex challenges. It also highlights emerging trends such as Explainable AI, AI integration with IoT and blockchain, and AI-powered sustainability initiatives, emphasizing their potential to redefine traditional approaches. However, significant barriers like algorithmic bias, data quality issues, implementation costs, and regulatory gaps persist, alongside research gaps in AI adoption within SMEs and developing economies. By synthesizing insights, this review provides a comprehensive understanding of how AI, ML, and DL are shaping modern business strategies and offers valuable guidance for future research and practical applications.
The integration of Artificial Intelligence (AI), Machine Learning (ML), and
Deep Learning (DL) h... more The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has revolutionized business strategies, enabling organizations to enhance decision-making, optimize operations, and achieve competitive advantages. This systematic review examines the transformative role of these technologies in reshaping business strategies across various industries. A total of 115 peer-reviewed articles were systematically analyzed following the PRISMA guidelines to ensure transparency, rigor, and reliability. The study identifies key applications of AI, ML, and DL in marketing, supply chain management, financial analytics, and human resource management, showcasing their ability to address complex business challenges. Additionally, emerging trends such as Explainable AI, AI integration with IoT and blockchain, and AI-powered sustainability initiatives are discussed, highlighting their potential to redefine traditional business practices. Despite these advancements, challenges such as algorithmic bias, data quality issues, implementation costs, and the lack of regulatory frameworks remain significant barriers to adoption. The review also identifies critical research gaps, including limited studies on AI adoption in small and medium-sized enterprises (SMEs) and developing economies. By synthesizing insights from these articles, this study provides a comprehensive understanding of how AI, ML, and DL are shaping modern business strategies, offering valuable directions for future research and practical implementation.
The integration of Artificial Intelligence (AI), Machine Learning (ML), and
Deep Learning (DL) ha... more The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has revolutionized business strategies, enabling organizations to enhance decision-making, optimize operations, and achieve competitive advantages. This systematic review examines the transformative role of these technologies in reshaping business strategies across various industries. A total of 115 peer-reviewed articles were systematically analyzed following the PRISMA guidelines to ensure transparency, rigor, and reliability. The study identifies key applications of AI, ML, and DL in marketing, supply chain management, financial analytics, and human resource management, showcasing their ability to address complex business challenges. Additionally, emerging trends such as Explainable AI, AI integration with IoT and blockchain, and AI-powered sustainability initiatives are discussed, highlighting their potential to redefine traditional business practices. Despite these advancements, challenges such as algorithmic bias, data quality issues, implementation costs, and the lack of regulatory frameworks remain significant barriers to adoption. The review also identifies critical research gaps, including limited studies on AI adoption in small and medium-sized enterprises (SMEs) and developing economies. By synthesizing insights from these articles, this study provides a comprehensive understanding of how AI, ML, and DL are shaping modern business strategies, offering valuable directions for future research and practical implementation.
The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) ha... more The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has revolutionized business strategies by enhancing decision-making, optimizing operations, and providing competitive advantages across industries. This systematic review, based on 115 peer-reviewed articles analyzed following PRISMA guidelines, explores the transformative role of these technologies in reshaping business practices, particularly in marketing, supply chain management, financial analytics, and human resource management, while addressing complex challenges. It also highlights emerging trends such as Explainable AI, AI integration with IoT and blockchain, and AI-powered sustainability initiatives, emphasizing their potential to redefine traditional approaches. However, significant barriers like algorithmic bias, data quality issues, implementation costs, and regulatory gaps persist, alongside research gaps in AI adoption within SMEs and developing economies. By synthesizing insights, this review provides a comprehensive understanding of how AI, ML, and DL are shaping modern business strategies and offers valuable guidance for future research and practical applications.
The integration of Artificial Intelligence (AI), Machine Learning (ML), and
Deep Learning (DL) h... more The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has revolutionized business strategies, enabling organizations to enhance decision-making, optimize operations, and achieve competitive advantages. This systematic review examines the transformative role of these technologies in reshaping business strategies across various industries. A total of 115 peer-reviewed articles were systematically analyzed following the PRISMA guidelines to ensure transparency, rigor, and reliability. The study identifies key applications of AI, ML, and DL in marketing, supply chain management, financial analytics, and human resource management, showcasing their ability to address complex business challenges. Additionally, emerging trends such as Explainable AI, AI integration with IoT and blockchain, and AI-powered sustainability initiatives are discussed, highlighting their potential to redefine traditional business practices. Despite these advancements, challenges such as algorithmic bias, data quality issues, implementation costs, and the lack of regulatory frameworks remain significant barriers to adoption. The review also identifies critical research gaps, including limited studies on AI adoption in small and medium-sized enterprises (SMEs) and developing economies. By synthesizing insights from these articles, this study provides a comprehensive understanding of how AI, ML, and DL are shaping modern business strategies, offering valuable directions for future research and practical implementation.
The integration of Artificial Intelligence (AI), Machine Learning (ML), and
Deep Learning (DL) ha... more The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has revolutionized business strategies, enabling organizations to enhance decision-making, optimize operations, and achieve competitive advantages. This systematic review examines the transformative role of these technologies in reshaping business strategies across various industries. A total of 115 peer-reviewed articles were systematically analyzed following the PRISMA guidelines to ensure transparency, rigor, and reliability. The study identifies key applications of AI, ML, and DL in marketing, supply chain management, financial analytics, and human resource management, showcasing their ability to address complex business challenges. Additionally, emerging trends such as Explainable AI, AI integration with IoT and blockchain, and AI-powered sustainability initiatives are discussed, highlighting their potential to redefine traditional business practices. Despite these advancements, challenges such as algorithmic bias, data quality issues, implementation costs, and the lack of regulatory frameworks remain significant barriers to adoption. The review also identifies critical research gaps, including limited studies on AI adoption in small and medium-sized enterprises (SMEs) and developing economies. By synthesizing insights from these articles, this study provides a comprehensive understanding of how AI, ML, and DL are shaping modern business strategies, offering valuable directions for future research and practical implementation.
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Papers by Sumyta Haque
Deep Learning (DL) has revolutionized business strategies, enabling
organizations to enhance decision-making, optimize operations, and achieve
competitive advantages. This systematic review examines the transformative role
of these technologies in reshaping business strategies across various industries.
A total of 115 peer-reviewed articles were systematically analyzed following the
PRISMA guidelines to ensure transparency, rigor, and reliability. The study
identifies key applications of AI, ML, and DL in marketing, supply chain
management, financial analytics, and human resource management,
showcasing their ability to address complex business challenges. Additionally,
emerging trends such as Explainable AI, AI integration with IoT and blockchain,
and AI-powered sustainability initiatives are discussed, highlighting their
potential to redefine traditional business practices. Despite these advancements,
challenges such as algorithmic bias, data quality issues, implementation costs,
and the lack of regulatory frameworks remain significant barriers to adoption.
The review also identifies critical research gaps, including limited studies on AI
adoption in small and medium-sized enterprises (SMEs) and developing
economies. By synthesizing insights from these articles, this study provides a
comprehensive understanding of how AI, ML, and DL are shaping modern
business strategies, offering valuable directions for future research and practical
implementation.
Deep Learning (DL) has revolutionized business strategies, enabling
organizations to enhance decision-making, optimize operations, and achieve competitive advantages. This systematic review examines the transformative role of these technologies in reshaping business strategies across various industries. A total of 115 peer-reviewed articles were systematically analyzed following the PRISMA guidelines to ensure transparency, rigor, and reliability. The study identifies key applications of AI, ML, and DL in marketing, supply chain management, financial analytics, and human resource management, showcasing their ability to address complex business challenges. Additionally, emerging trends such as Explainable AI, AI integration with IoT and blockchain, and AI-powered sustainability initiatives are discussed, highlighting their potential to redefine traditional business practices. Despite these advancements,
challenges such as algorithmic bias, data quality issues, implementation costs, and the lack of regulatory frameworks remain significant barriers to adoption. The review also identifies critical research gaps, including limited studies on AI adoption in small and medium-sized enterprises (SMEs) and developing economies. By synthesizing insights from these articles, this study provides a comprehensive understanding of how AI, ML, and DL are shaping modern business strategies, offering valuable directions for future research and practical implementation.
Deep Learning (DL) has revolutionized business strategies, enabling
organizations to enhance decision-making, optimize operations, and achieve
competitive advantages. This systematic review examines the transformative role
of these technologies in reshaping business strategies across various industries.
A total of 115 peer-reviewed articles were systematically analyzed following the
PRISMA guidelines to ensure transparency, rigor, and reliability. The study
identifies key applications of AI, ML, and DL in marketing, supply chain
management, financial analytics, and human resource management,
showcasing their ability to address complex business challenges. Additionally,
emerging trends such as Explainable AI, AI integration with IoT and blockchain,
and AI-powered sustainability initiatives are discussed, highlighting their
potential to redefine traditional business practices. Despite these advancements,
challenges such as algorithmic bias, data quality issues, implementation costs,
and the lack of regulatory frameworks remain significant barriers to adoption.
The review also identifies critical research gaps, including limited studies on AI
adoption in small and medium-sized enterprises (SMEs) and developing
economies. By synthesizing insights from these articles, this study provides a
comprehensive understanding of how AI, ML, and DL are shaping modern
business strategies, offering valuable directions for future research and practical
implementation.
Deep Learning (DL) has revolutionized business strategies, enabling
organizations to enhance decision-making, optimize operations, and achieve competitive advantages. This systematic review examines the transformative role of these technologies in reshaping business strategies across various industries. A total of 115 peer-reviewed articles were systematically analyzed following the PRISMA guidelines to ensure transparency, rigor, and reliability. The study identifies key applications of AI, ML, and DL in marketing, supply chain management, financial analytics, and human resource management, showcasing their ability to address complex business challenges. Additionally, emerging trends such as Explainable AI, AI integration with IoT and blockchain, and AI-powered sustainability initiatives are discussed, highlighting their potential to redefine traditional business practices. Despite these advancements,
challenges such as algorithmic bias, data quality issues, implementation costs, and the lack of regulatory frameworks remain significant barriers to adoption. The review also identifies critical research gaps, including limited studies on AI adoption in small and medium-sized enterprises (SMEs) and developing economies. By synthesizing insights from these articles, this study provides a comprehensive understanding of how AI, ML, and DL are shaping modern business strategies, offering valuable directions for future research and practical implementation.