Author(s)

Kamal Khan, Anjon Rathore, Amit Kumar^*

Affiliation(s)

Department of Computer Application, National University, Gazipur, Bangladesh.

Corresponding Author

Amit Kumar

ABSTRACT

The COVID-19 pandemic acted as a pivotal driver of digital transformation across U.S. industries, forcing organizations to adopt Artificial Intelligence (AI) and Business Analytics (BA) at unprecedented speed to manage severe disruptions. This study examines how AI and BA work in synergy to boost IT project success and strengthen organizational agility in the post-pandemic era. The rapid transition to remote work, digital operations, and real-time data usage highlighted the need to embed intelligent technologies into core business strategies. AI solutions such as machine learning, natural language processing, and robotic process automation have enabled predictive decision-making, process automation, and adaptive project management. In parallel, BA tools have delivered real-time forecasting, performance monitoring, and actionable insights. Together, these technologies are reshaping traditional IT project frameworks, helping businesses remain competitive, agile, and resilient in volatile markets. Drawing on an extensive review of literature and industry case studies, this paper analyzes the transformative effects of AI and BA on operational efficiency, talent management, and strategic governance. It also addresses ethical concerns, including data privacy and digital equity. The findings offer a strategic roadmap for U.S. organizations to transition from reactive problem-solving to proactive innovation leveraging AI and BA to achieve long-term adaptability, resilience, and digital maturity in a rapidly evolving global environment.

KEYWORDS

AI; Business analytics; COVID-19; Digital transformation

CITE THIS PAPER

Kamal Khan, Anjon Rathore, Amit Kumar. AI and business analytics in post-pandemic U.S. digital transformation: driving it project success and organizational agility. Journal of Trends in Financial and Economics. 2024, 1(2): 40-47. DOI: https://doi.org/10.61784/jtfe3055.

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