The rapid growth of omni-commerce applications has led to increased reliance on artificial intelligence systems for personalised services, demand forecasting, and customer behaviour analysis. However, a huge amount of user information that is gathered and processed during this process leads to serious privacy and security issues, especially in the context of logging systems where sensitive data is stored. The proposed paper is a Privacy-Aware AI Logging Framework that is aimed at providing secure, compliant and efficient management of log data within omni-commerce settings. A huge amount of user information that is gathered and processed during this process leads to serious privacy and security issues, especially in the context of logging systems where sensitive data is stored. The proposed system adopts a modular architecture that supports real-time data processing while minimising privacy risks without compromising system performance. Experimental analysis shows that the framework has a good balance between data utility and privacy protection, as it has a high level of accuracy in data analytics and minimal exposure to sensitive data. This approach enhances trust in AI-driven omni-commerce platforms and provides a scalable solution for privacy-aware data management in modern digital ecosystems.
Multinational organizations invest enormous resources in their employees, and now artificial intelligence (AI) is impacting how they invest in them. One common misconception is that AI will soon be making financial workforce decisions without any human assistance. This paper presents a contrary argument. Since these decisions are influenced by tax and labor regulations, ethics, budget constraints, and executive decision-making, the realistic future outlook is AI-assisted decision support, where AI generates forecasts, scenarios, and recommendations, while human leaders make final decisions. The research uses a descriptive approach, based on an integrative literature review of academic and institutional sources spanning 2019 to 2026, and a practitioner perspective from financial planning and analysis (FP&A) and equity compensation. It builds a capability maturity grid for AI and decision-making classes, representing different levels of AI deployment and the class of Workforce financial decisions, along with their associated levels of impact and irrevocability. The model is illustrated through three worked examples: forecasting payroll-tax basis, planning headcount, and designing equity compensation. This study offers two significant contributions. It links two streams of research, typically disjointed, financial AI and Workforce AI, and pushes the spectrum of the question AI can address from what will happen to what the organization will do.
Albin Joseph, P. Mahajan, P. Agarwal et al.· International journal of com...· 0 citations