Client-Centric Strategy and Predictive Intelligence: A Cross-Sector Comparative Study of Leading Global Financial and Consulting Firms

Abstract

This research explores the use of client-focused AI by the top global consulting and financial institutions in terms of personalisation and algorithmic bias reduction, the impact of data protection law across jurisdictions on the quality of predictive capability and the potential value of generative AI in text, audio and video. Research methodology: This research employed a two-stage approach. Firstly, a bibliometric analysis was conducted on the body of work related to client focused AI and algorithmic bias. Following this qualitative comparative research was conducted on global consulting and financial institutions using the Information Systems Success Model and the Technology Acceptance Model as a theoretical basis. Personalisation and sentiment analysis research has been conducted at a higher rate than bias reduction research and as a result algorithmic bias has only made a limited contribution to client focused AI. Institutions which embed data protection and accountability requirements into the design of their systems can achieve innovation in prediction whilst also achieving customer trust. Disconnected accountability results in siloed visibility through marketing and sales and results in disjointed customer trust. The use of generative AI can provide value to customers and enhance the customer experience but only if supported by transparency and human in the loop. Contribution to research: A framework for Client-value based on Predictive, Responsible, Integrated, Sustainable and Measurable is proposed. This framework makes the connection between responsible generative and predictive AI and resilience and trust in volatile environments. It can provide a tool for governance diagnosis for managers and provide a foundation for proportionate oversight by regulators. Novelty: This research is novel in its use of both bibliometric analysis and cross industry case studies to explore generative engagement, compliance and fairness within a client focused AI context.