2026· International journal of research and innovation in social science· Vol 10, pp. 5405-5419· 0 citations
TL;DR
A novel conceptual framework is developed that integrates AI-driven personalization with responsible data governance and is proposed as a foundation for future empirical validation, offering organizations practical guidance.
Abstract
Walk into any business meeting today and the same buzzwords often dominate the conversation: data, personalization, and customer experience. Yet behind these terms lies a persistent challenge. Organizations are collecting more customer data than ever before, but many continue to struggle with distinguishing between personalization that enhances customer value and practices that feel intrusive. This paper investigates how organizations implement Artificial Intelligence (AI)-powered Customer Relationship Management (CRM) systems, why many initiatives fail to deliver the expected outcomes despite substantial investments, and what differentiates successful implementations from unsuccessful ones. This study uses a qualitative secondary research design structured as a scoping review, guided by the PRISMA framework, to synthesize evidence from nine peer-reviewed empirical studies published between 2019 and 2024 and retrieved from the Dimensions AI database. The findings identify 21 Critical Success Factors (CSFs), grouped into four interconnected domains: organizational culture and capabilities, data and technological infrastructure, strategic alignment and business processes, and customer and ethical governance. The review also highlights the significant performance benefits of AI-enabled CRM, with effective real-time personalization capable of increasing customer lifetime value by more than 20%. However, the findings reveal that the primary limitation is not technological but psychological. Customers possess a threshold beyond which personalization is perceived as intrusive rather than valuable, resulting in declining trust and reduced business performance. To address this challenge, the paper develops a novel conceptual framework that integrates AI-driven personalization with responsible data governance. This framework is proposed as a foundation for future empirical validation, offering organizations practical guidance.
Artificial intelligence has become embedded in three enterprise decision domains that are usually studied separately: customer experience, marketing intelligence and anti-money-laundering analytics. Their technical foundations overlap, yet their objectives, evidence conditions, error costs and accountability requirements differ sharply. This critical narrative review integrates scholarship on these domains to assess where artificial intelligence creates defensible value, where empirical claims outrun the evidence, and how digital enterprises should govern shared analytical capabilities without collapsing unlike decisions into a single automation logic. Literature was selected from accessible scholarly indexes, repositories and article records for the period 1 January 2015 to 8 June 2026, with earlier methodological work retained where conceptually necessary. The synthesis indicates that artificial intelligence is most credible when the target decision is well specified, feedback is informative, model outputs are connected to an organisational action, and meaningful human escalation remains available. In customer experience, personalisation and conversational systems can improve relevance and interaction efficiency, but privacy concerns, disclosure effects, algorithm aversion and perceived loss of agency make customer response strongly context dependent. In marketing intelligence, predictive gains do not automatically translate into incremental value because data are endogenous to earlier decisions, causal questions remain distinct from prediction, and organisational analytics capability mediates adoption. In anti-money-laundering analytics, supervised, graph, temporal, active-learning and hybrid approaches can improve alert prioritisation and pattern discovery, yet generalisability is constrained by extreme class imbalance, weak or selective ground truth, proprietary data, adaptive adversaries and limited prospective external validation. Across all three domains, the central design problem is not whether to automate, but how to align objectives, data provenance, interpretability, human authority and monitoring with the consequences of the decision. The review therefore argues for portfolio-based enterprise AI governance: shared infrastructure should be combined with domain-specific evidence standards, utility functions and escalation controls.
Temiloluwa Paul Adetola, Kevin Leziga Giami, Obiamaka Okosieme· Journal of Scientific Resear...· 0 citations
It has been found that CRM in combination with BI could lead to deeper customer understanding, forecasting capabilities, and making marketing strategies customer-specific and has practical value for enterprises in that they should consider not only sophisticated CRM equipment but also data management and human resources to be able to fully benefit from BI interfacing.
Satyendra Kumar Vanapalli· International Journal of App...· 0 citations
What conditions does AI need to meet to improve leadership and trust inside a technology firm rather than damage them? That question is the starting point of this paper. We pulled together recent research alongside production case studies from six major firms (Google, Microsoft, IBM, Salesforce, Adobe, LinkedIn) and tried to separate the marketing claims from what these tools deliver in practice. Some numbers hold up well: AI-driven leadership assessment improves prediction accuracy by 80%, and hiring costs drop 30–50% in firms where the technology is deployed well. Other claims are conditional. Returns only show up once the cultural work is done. We trace four maturity stages and find that 91% of what blocks adoption is cultural rather than technical. Firms that get past those blockers report 40–75% less time spent on recruitment, 95% accuracy in spotting employees about to leave, and 14.9% higher leadership-performance scores. Trust sits underneath every one of these outcomes. Where it already exists, AI adoption runs 21 percentage points smoother. Where it does not, AI makes things worse, not better. The remainder of the paper sets out the conditions under which AI strengthens leadership instead of eroding it.
P. Somani, Rupali Khaire· International journal of res...· 0 citations
In today’s digital service landscape, organizations increasingly deploy Artificial Intelligence (AI) technologies in the hope of delivering faster, more personalized, and seamless customer experiences. However, many firms continue to face challenges in translating AI capabilities into measurable improvements in customer satisfaction, largely because AI tools often operate without adequate support from structured Knowledge Management (KM) practices. When the knowledge base behind automated systems is fragmented or inconsistent, customers encounter variability in service quality, reduced trust, and limited personalization depth. This study investigates how AI-enabled service attributes supported by KM-related perceptions shape overall customer experience by analyzing empirical data from 300 customers who regularly engage with AI-driven service platforms. The dataset captures key experiential dimensions, including personalization, service speed, ease of use, trust in AI, and the perceived usefulness of knowledge support embedded in automated service responses. Using descriptive statistics, correlation analysis, regression modelling, and ANOVA, the study finds that trust in AI systems and perceived KM usefulness exert the strongest influence on overall satisfaction, while other attributes such as personalization and ease of use demonstrate weaker predictive power. The results also reveal that customer satisfaction does not significantly differ across varying levels of AI interaction frequency, suggesting that qualitative aspects of AI-based interactions matter more than the volume of usage. Overall, the findings emphasize that optimal customer experience arises not from AI functionality alone, but from the quality, clarity, and reliability of the knowledge that informs AI outputs. The study provides empirical insights to guide organizations in strengthening KM foundations to support AI systems more effectively and enhance customer experience outcomes.
Maram Saleh Miralam· Journal on Innovation and Su...· 0 citations
Scientific Problem: Governments worldwide are deploying AI-enabled services at scale, yet uncertainty persists about whether these systems genuinely contribute to citizen satisfaction and institutional trust and what organisational conditions make them do so. Existing research has examined technical AI capability and organisational data culture in isolation, leaving unresolved whether both exert independent effects when modelled together and whether emotional and relational mechanisms explain how these antecedents reach citizen outcomes. Aim: This study tests whether AI Sophistication (AIS) and Big Data Organisational Culture (BDOC) independently influence Customer Experience (CX) and Public Trust (PT) in UAE government AI services, whether Emotional Intelligence (EI) and the Real-Time Commensality (RTC) scale developed in this study mediate those relationships, and whether Multicultural Competence (MC) moderates them. Method: Survey data were collected between October and December 2024 from N = 500 verified users of UAE government AI services across healthcare, education, transportation, and administrative domains. All seven constructs were validated against standard psychometric thresholds (AVE > 0.70, CR > 0.88, HTMT < 0.85). A Dual-Driver Mediation–Moderation Model was tested using hierarchical regression, bootstrapped mediation (Hayes PROCESS macro, 5000 resamples), and interaction-term moderation. Results: AIS and BDOC showed near-identical associations with CX (B = 0.567 and B = 0.568, p < 0.001), consistent with dual-driver equivalence. In the full model, BDOC substantially outperformed AIS as a predictor of PT (B = 0.294 vs B = 0.108). All eight mediation pathways were supported, with EI showing consistently larger indirect effects (range: 0.070–0.093) than RTC (range: 0.049–0.072). EI recorded the lowest construct mean (M = 1.959) despite being the strongest mediator—the affective gap. MC did not moderate any pathway, indicating no model-level evidence of differential associations across levels of Multicultural Competence; given the configural-only invariance assessment, this should not be interpreted as conclusive proof of cultural convergence. Conclusions: These findings extend the DeLone and McLean IS Success Model by positioning BDOC as a co-equal predictor of citizen satisfaction and the stronger predictor of institutional trust—suggesting that sustainable government AI delivery may depend as much on organisational culture as on technical capability. Future Research: Longitudinal designs, cross-country comparisons, and validation of RTC against established social presence measures are identified as priority future directions.
Nasser Ahmed Alabdouli, S. Alhashmi· Sustainability· 0 citations
The findings indicate that organizations that articulate their governance frameworks clearly, standardize their data management processes, and publicly set up accountability lines, experience a substantial increase in the accuracy of data, operational efficiency, customer trust, and regulatory preparedness.
Srichandra Boosa· International Journal of AI,...· 0 citations
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