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Introduction: Artificial intelligence (AI) and digitisation offer substantial human potential and profit margins, making them promising retail solutions. Retail leaders have successfully integrated comprehensive uses into their daily operations, while competitors heavily invest in new projects. The Indian retail sector is undergoing a significant transformation, which can be attributed to factors such as growing income, demographic characteristics, and enhanced consumerism, as well as the rapid development of new technologies such as digitisation and AI, which is changing both consumers’ and retailers’ buying behaviour.

Purpose: This study aims to determine the influence of AI on elements that drive digitisation in the retailing sector, as well as the factors that lead to organised retailers adopting digitisation and its impact on their business.

Methodology: The study employs a standardised questionnaire distributed to organised stores via an online link, and the data are analysed with SmartPLS software 3.0.

Finding: The retail sector is driven by elements that promote digitalisation in food and groceries retailing, such as simplicity of operation, adoption of digital payment, quicker internet connection, retailer consumer interface, and the involvement of AI.

Research implication: AI has significant consequences for retailing, which serves as the interface between marketers and customers.

Theoretical implication: The study’s findings reflect the perspectives of retailers, store managers, and entrepreneurs on how digitalisation and AI are crucial for the creation and growth of long-term competitive advantages in retail.

Need of the Study: Digitalisation, machine learning, and artificial intelligence (AI) is changing at a swift pace, significantly uplifting the role of information technology. The present human resource (HR) aspect transpires AI-based resolution, are gradually more effective with HR process, time-consumption and a complex tasks surrounded by the HRM functionalities.

Purpose: This study attempts to investigate the adoption and diffusion of human resource management (HRM) with the phenomenon of AI-based applications. Hence, this study has emphasised the predictors of AI adoption like competitive pressure, performance expectancy, top management support, strategic partner, employee champion, etc. Moreover, how the AI predictors are connected with HR practices. The research sample focused on 207 HR managers and senior managers from various industries.

Methodology: This study is based on a quantitative research technique encompassing mean, standard deviation, exploratory factor analysis (EFA), Confirmatory Factor Analysis (CFA), Average Variance Extracted (AVE), and Dependent Variable (DV).

Findings: The study’s empirical findings show that higher performance expectations and higher management support are both major predictors of AI adoption. In contrast, competitive pressure did not show a significant relationship with such an intention, and the ‘employee champion’ role has a negative impact on AI adoption.

Implications: AI diffusion and implementation show a significant research gap. In previous studies, adoption in HRM was overlooked. The study’s results provide a comprehensive picture of the situation. The framework and a major contribution to the study of the phenomenon in relation to its possible role in AI’s effectiveness and quality in HRM. The research inspires a debate among service providers, policy-makers, and stakeholders, and builds an efficient workplace.