AI DRIVEN CUSTOMER RELATIONSHIP MANAGEMENT AS A TOOL FOR MANAGERIAL AGILITY UNDER ECONOMIC UNCERTAINTY AND DIGITAL DISRUPTION: A SYSTEMATIC LITERATURE REVIEW
Jul 2026· Business Excellence and Management· Vol 16, pp. 210-229· 0 citations· 21 references
TL;DR
The results suggest that AI driven CRM creates value not only by automating customer management activities, but especially by improving the quality and speed of managerial decisions, but also argues that agility cannot be produced by technology alone.
Abstract
This paper examines how Customer Relationship Management (CRM) is being transformed by Artificial Intelligence (AI) into a managerial instrument for agility in periods marked by economic uncertainty and digital disruption. In unstable markets, customers become more selective, less predictable and more exposed to digital alternatives, which makes customer retention and fast managerial interpretation increasingly important. The objective of the paper is to analyse how AI driven CRM supports managers in understanding customer behaviour, identifying customer related risks and responding more rapidly to market changes. The study is designed as a systematic literature review, structured according to the PRISMA logic. The review is based on academic articles selected from major scientific databases and complemented by one official contextual source on the digital economy. The selected studies are examined through thematic coding, with attention to customer intelligence, predictive decision making, personalization, customer retention, organizational agility and the ethical limits of automation. The results suggest that AI driven CRM creates value not only by automating customer management activities, but especially by improving the quality and speed of managerial decisions. However, the paper also argues that agility cannot be produced by technology alone. Data privacy, customer trust, algorithmic bias and excessive automation remain critical limits. The contribution of the paper consists in developing a structured synthesis that connects AI driven CRM with managerial agility and responsible customer management under challenging business conditions.
This article analyses how Management Information Systems transform managerial decision making and organizational performance, from traditional systems to modern integrated and AI enhanced systems. The paper uses a qualitative conceptual methodology based on secondary sources, including specialized books, scientific articles, systematic literature reviews and institutional reports. The analysis shows that traditional information systems mainly supported recording and reporting activities, while modern systems such as ERP, CRM, BI and DSS improve coordination, customer management, performance evaluation and decision support. The integration of AI adds predictive, automation and recommendation capabilities, but also creates new challenges related to data quality, employee acceptance, ethics, privacy, security and managerial responsibility. The main contribution of the article is the conceptual distinction between three managerial roles: manager as controller of information, manager as coordinator of integrated data and manager as critical evaluator of AI supported decisions. The paper concludes that MIS strengthen management, but do not replace human judgement.
Unknown authors· New Trends in Sustainable Bu...· 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
Artificial Intelligence (AI) is increasingly transforming talent management by enhancing recruitment, employee development, workforce planning, and organisational decision-making. However, empirical evidence on its effectiveness within multinational Fast-Moving Consumer Goods (FMCG) organisations remains limited. This study examined the influence of AI adoption on talent management practices in multinational FMCG companies, focusing on organisational preparedness, recruitment and selection, employee development, performance management, employee retention, and organisational competitiveness. An exploratory mixed-methods research design combined quantitative and qualitative approaches. Quantitative data were analysed using descriptive statistics, Pearson's product-moment correlation, and one-sample t-tests, while qualitative data were analysed thematically. AI effectiveness was rated highly (M = 4.45, SD = 0.86), as were the importance of integrating theoretical viewpoints with practical AI applications (M = 4.53, SD = 0.96) and organisational preparedness for managing AI consequences (M = 4.48, SD = 0.97). AI effectiveness was positively correlated with theoretical integration (r = .630, p < .001) and organisational preparedness (r = .645, p < .001); theoretical integration and preparedness were also strongly correlated (r = .886, p < .001). Qualitative findings indicated that AI improves recruitment efficiency, workforce planning, employee development, and organisational competitiveness, while raising concerns regarding algorithmic bias, transparency, employee privacy, and organisational readiness. The study concludes that AI can enhance talent management and organisational performance when supported by effective leadership, ethical governance, organisational preparedness, and continuous employee development. The findings provide empirical support for the Resource-Based View and the Technology–Organisation–Environment Framework and offer practical guidance for responsible and sustainable AI-enabled talent management.
Igboko Omeaku Princewill· Asian Journal of Economics B...· 0 citations
The article examines the potential of artificial intelligence to optimize management processes and improve decision-making in large organizations and proposes a conceptual AI-based management framework that integrates data sources, analytical models, and decision support systems into a unified adaptive cycle with a feedback mechanism.
Serhii Kubitskyi, Yevhen Kozlovskyi, K. Balabukha et al.· Human Resources Management a...· 0 citations
Purpose: This study examines the relationship between artificial intelligence (AI) integration and organizational financial performance and identifies the conditions that strengthen or weaken this relationship.Methodology: The study uses a qualitative literature-review design. Twenty peer-reviewed journal articles published between 2019 and 2026 were synthesized through thematic content analysis.Results: The literature identifies four recurring mechanisms: operational efficiency and cost control, risk and fraud reduction, revenue and investment optimization, and financial reporting and assurance. AI is generally associated with stronger profitability, cost efficiency, risk-adjusted returns, asset allocation, and firm value when supported by reliable data, adequate infrastructure, skilled personnel, explainable models, and effective governance.Conclusions: AI should be treated as an organizational capability rather than a stand-alone technology because its financial value depends on responsible integration with financial processes and governance systems.Limitations: The review is limited to twenty articles, uses qualitative synthesis rather than meta-analysis, and includes more evidence from banking, auditing, and investment than from nonfinancial sectors.Contributions: The study integrates fragmented evidence into a direct conceptual account of the AI-financial performance relationship and provides a basis for future company-level empirical research.
Industrial firms require timely environmental information to evaluate resource use, compliance exposure, and the long-term consequences of strategic alternatives. This study examines the association between environmental business intelligence and sustainable strategic decision quality and evaluates green strategic agility as a potential transmission mechanism in Jordanian industrial organizations. A cross-sectional survey yielded 312 usable responses from managers, supervisors, administrative employees, and technical specialists. Environmental business intelligence was modeled through environmental data quality, analysis and prediction, reporting and dashboards, and sustainability integration. The primary sustainable decision-quality construct used 14 directly observed items; six planned items absent from the original data file were excluded from all reported models. Composite-based partial least squares structural equation modeling with 5,000 bootstrap samples showed a strong positive association between environmental business intelligence and sustainable strategic decision quality (β = .729, p < .001, f² = 1.083) and a small positive association with green strategic agility (β = .152, p = .007, f² = .024). Green strategic agility was not significantly associated with decision quality (β = −.067, p = .093), and the indirect effect was unsupported. Because the agility scale showed weak reliability and convergent validity, its structural results are interpreted as exploratory diagnostics rather than confirmatory mediation evidence. The findings emphasize environmental information governance while identifying measurement and organizational-routine conditions that future research must address.
Unknown authors· Journal of Project Managemen...· 0 citations
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