In the advertising sector, Artificial Intelligence has become a game-changer, powering the creation of highly targeted campaigns and automated decision-making processes (Farooq et al., 2025). The adoption of programmatic advertising has accelerated the replacement of traditional channels, enabling the optimization of precision and operational efficiency with the aid of machine learning. These systems sift through extensive data like browsing history and buying patterns to provide content that aligns with consumer preferences. With the growing presence of AI in digital platforms, researchers have increasingly been interested in examining how it affects buying intention, depending on the specific application (Dhar and Dhar, 2022; Khuong & An, 2025). 

AI's hyper-personalization capabilities have been demonstrated to minimize customer fatigue by filtering down product selections to items most relevant to them (Chandra et al., 2022). Moreover, the use of AI-powered chatbots and recommendation engines has revolutionized the way consumers discover and research products (Bello et al., 2025; Lafi, 2025). While these developments have come with significant progress, the swift adoption of AI has also sparked serious concerns about fairness, transparency, and consumer rights (Farooq et al., 2025; Kumar & Suthar, 2024). Today, marketing is done at the cusp of technological prowess and ethical responsibility, requiring extensive integration of the available literature (Jain & Pandey 2025; Kumar 2025). This systematic review seeks to map the current knowledge on AI-driven personalization and its subsequent impact on consumer behavior (Raj, 2026).

Research GAP

While there has been a significant increase in the number of studies related to AI in marketing, there still remain a few key gaps in the existing knowledge (Jain et al., 2023). First, the ethical frameworks guiding AI-driven consumer targeting are not all the same, causing a “regulatory lag” behind technology development (Kumar, 2025). Second, literature is highly fragmented and the lack of a unified theoretical model results in a fragmented intellectual structure of personalized marketing (Chandra et al., 2022). Third, existing studies are mostly confined to developed markets with few investigations into the cultural and regional factors in emerging e-commerce markets, such as Malaysia or Vietnam (Abdullah et al., 2024; Khuong & An, 2025). Fourth, the current knowledge of the long-term psychological effects of algorithmic surveillance on consumer trust is scarce (Neves & Pereira, 2025; Uwayezu et al., 2026), although there is already awareness of its short-term impacts. Lastly, the relationship between predictive analytics and actual consumer behavior, such as impulse buying and decision fatigue, is still largely unexplored (Bhojwani et al., 2026; S, 2026).

Objectives

Main Objective

The main objective of this study is to systematically review and synthesize contemporary academic literature to develop a comprehensive understanding of how AI-powered personalized advertising influences consumer buying behavior.

Secondary Objectives

• To identify the key AI mechanisms (e.g., recommendation engines, chatbots, dynamic pricing) those are related to personalization in the digital advertising.
• To examine the mediating role of perceived relevance, trust and usefulness between the relationship of AI personalization and purchase.
• To evaluate the ethical and legal challenges associated with AI-driven advertising, specifically focusing on privacy violations and algorithmic bias.

Neves & Pereira use PRISMA to illustrate how AI & ML has transformed programmatic advertising, leading to improved real-time optimization and precision targeting (Neves & Pereira, 2025). Khuong & An explore the Vietnam digital landscape and discover that perceived personalization has a strong positive effect on purchase intention through its effect on perceived relevance and usefulness (Khuong & An, 2025).

Dai & Liu conduct a quantitative study of 760 respondents, demonstrating that AI personalization has the greatest impact on purchase intention, with the second greatest impact coming from the effectiveness of the chatbot (Dai & Liu, 2024). Bhojwani et al. create a single framework to demonstrate that AI capabilities such as recommendation engines boost consumer confidence, but are constrained by perceived privacy concerns (Bhojwani et al., 2026). Vineesh et al. emphasize the role of AI-powered recommendation systems in mitigating information overload and fostering impulse buying, as they are tailored to the needs of each consumer (S, 2026). Araf et al. find in their systematic review on AI-based pricing that in some cases, disclosing that the price is being determined by AI may actually have the opposite effect on price-sensitive consumers (Araf et al., 2026).

Vomberg et al. investigate algorithmic dynamic pricing and conclude that this decreases the trust of consumers but they "grow accustomed to it when it becomes a norm in the market" (Vomberg et al., 2024). Nguyen et al. delve into the explainable AI and social media space and discovers that positive attitudes towards AI-powered ads directly impact purchase decisions (Nguyễn et al., 2025). Lafi explores how Chat GPT is affecting things, emphasizing that, with its advanced conversational abilities, it can greatly boost engagement and conversion rates, creating a sense of empathy in the interaction (Lafi, 2025). Raj reviews 186 studies on this topic from 2010 to 2025 and puts forward the “integrative model” in which algorithmic trust is a mediator between the effects of personalization and behavior (Raj, 2026).

Chandra et al. conduct a bibliometric review of 383 publications, and suggest six themes: personalization-privacy paradox, curating experiences with the use of big data, the notion of “you” in context of personalization, the visibility of personalization, personalization as a tool to strengthen relationships, and the ethical and legal issues associated with personalization (Chandra et al., 2022). Kumar & Suthar discuss the ethical concerns of AI in marketing, including issues of manipulation, discrimination, and a lack of social interaction (Kumar & Suthar, 2024). Farooq et al. discuss the ethical dangers of biased algorithms about advertising can be biased with manipulative strategies, which may destroy consumer trust in the long run (Farooq et al., 2025). Chaudhary explores how Amazon is employing AI in dynamic pricing and the importance of ethical frameworks and transparency in sustaining trust in e-commerce (Chaudhary, 2025).

This study follows PRISMA guidelines to rigorously select the study population by maintaining transparency in the selection process (Neves & Pereira, 2025; Raj, 2026). Key terms like "AI advertising," "consumer behavior," and "machine learning" were used to conduct a systematic search across key academic databases such as Google Scholar, Science Direct, and Emerald Insight (Araf et al., 2026; Raj, 2026). Inclusion criteria emphasized peer-reviewed articles published from 2020 to 2026, to reflect the latest technological developments (Bello et al., 2025). A total of 532 records were initially identified, of which 153 were deemed potentially relevant following title and abstract screening for exclusion of duplicates (Araf et al., 2026). Lastly, 14 core studies were chosen to be detailed in the literature review related to AI mechanisms and consumer outcomes (Araf et al., 2026; Raj, 2026). The data extraction was targeted towards significant findings, methodology (such as the PLS-SEM, regression analysis), and the specific AI characteristics studied (Dai & Liu, 2024; Khuong & An, 2025).

Conceptual Framework

The proposed conceptual framework is a combination of technological and psychological constructs that explain the consumer journey in AI driven environments (Bhojwani et al., 2026; Raj, 2026). AI systems, such as recommender systems and predictive analytics, contribute to what is known as "Perceived Personalization" at the antecedent level (Dai & Liu, 2024; Khuong & An, 2025). It is believed that this personalization will positively impact "Perceived Relevance" and "Perceived Usefulness" that are considered to be the key elements affecting "Consumer Satisfaction" (Khuong & An, 2025; S, 2026). However, "Algorithmic Trust" (Khuong & An, 2025; Raj, 2026) plays an important role as a mediator between personalization and "Purchase Intention". Moreover, ‘Privacy Concerns' and ‘Ethical Transparency' serve as moderating factors that can both reinforce and mitigate the route to purchase (Bhojwani et al., 2026; Dai & Liu, 2024). The framework indicates that trust can be fostered not only through personalization but also through perceived value and XAI (Khuong & An, 2025; Nguyễn et al., 2025).

PRISMA-Style Systematic Selection Phases:

The systematic selection process was done in four phases:

•  Identification: 532 records identified through database searching and citation mining (Araf et al., 2026).
•  Screening: After removing duplicates, 385 records were screened according to the relevance of the titles and abstracts regarding AI and consumer behavior (Araf et al., 2026).
•  Eligibility: 153 full text articles were evaluated for their eligibility according to their empirical rigor and focus on personalized advertising (Araf et al., 2026; Raj, 2026).
•  Included: 56 studies were included in the broader synthesis, with 14 primary works included in the literature review section to focus on specific mechanisms and outcomes (Araf et al., 2026; Poola et al., 2026).

AI Personalization Mechanisms

• Recommendation Systems: These systems use behavioral profiling and collaborative filtering to provide hyper-personalization, as seen with Amazon and Instagram (Chaudhary, 2025). They turn out to be the most influential factor in the reduction of effort required in making decision and in satisfaction (Bhojwani et al., 2026; S, 2026).
• Dynamic Pricing: AI algorithms dynamically optimize pricing based on demand and consumer profiles (Araf et al., 2026). They can be effective in maximizing revenue, but they may also lead to perceptions of unfairness if not executed transparently (Ali, 2026; Vomberg et al., 2024).
• Conversational Agents: Successful chatbots such as ChatGPT can improve engagement by providing real-time communication and a sense of empathy to the consumer (Dai & Liu, 2024; Lafi, 2025).

Consumer Outcomes

• Purchase Intention: When the ethical issue is taken care of, the AI personalization is able to account for a considerable amount of variance in purchase intention (to $0.55$%) (Dai & Liu, 2024).
• Trust and Privacy: A "privacy paradox" exists where consumers continue to use AI platforms despite a lack of trust in data practices (Uwayezu et al., 2026). Trust can be established by "Explainable AI" and GDPR-compliant governance (Nguyễn et al., 2025; Thakur & Gupta, 2025).
• Decision-Making Efficiency: AI helps to minimize the information overload and enables faster product assessments and impulse buying (Bhojwani et al., 2026; S, 2026).

The synthesis of findings paints a picture of a double-edged sword in AI-powered advertising: its undeniable ability to boost efficiency and relevance brings with it deep ethical hazards (Farooq et al., 2025). The success of personalization relates to its capability of reducing cognitive load, but it typically sacrifices consumer privacy and autonomy (Chandra et al., 2022; Uwayezu et al., 2026). One of the most significant findings in the literature is that trust does not necessarily come from personalization, but rather from the perceived usefulness of the data and transparent data practices (Bhojwani et al., 2026; Khuong & An, 2025). Furthermore, the "transparency-trust paradox" implies that transparency is not enough, and that even if people know that an AI is being used, they may retaliate against the brand if the motivation behind the use is deemed to be for exploitation (Araf et al., 2026). Marketers need to overcome this "profit at any cost" mindset and adopt a stakeholder equilibrium approach that takes into account the firm's revenue, customer fairness, and regulatory compliance (Araf et al., 2026; Kumar & Suthar, 2024).

Future Research Agenda

Future research should focus on longitudinal studies to explore how AI surveillance affects brand loyalty and psychological well-being over the long term (Dai & Liu, 2024; Neves & Pereira, 2025). Additionally, there is a need for more cross-cultural studies to gain deeper insights into the impact of regional regulations and cultural norms on the adoption of AI personalization (Abdullah et al., 2024; Neves & Pereira, 2025). Additionally, the use of new technologies like Augmented Reality, IoT, and blockchain technology for secure data management should also be considered (Chandra et al., 2022; Dai & Liu, 2024; Kingsley, 2022). The exploration of AI's potential in "integrated creativity" and its ability to blend automation with human creativity continues to be a critical area for the advertising sector, as referenced by Neves and Pereira, (2025).

Ethical and Legal Considerations

Algorithmic discrimination, manipulative tactics, and the absence of accountability are all ethical issues in the use of AI for advertising (A. Kumar, 2025; D. Kumar & Suthar, 2024). Prejudices or biases in algorithms can lead to the disproportionate exclusion of certain consumer groups, jeopardizing trust and fairness (Farooq et al., 2025). To address these risks, organizations should prioritize investing in bias detection tools and implementing privacy-enhancing technologies (Kumar & Suthar, 2024). From a legal standpoint, complying with regulations such as GDPR and creating explainable AI are essential for consumer protection and data security (Nguyễn et al., 2025; Thakur & Gupta, 2025). To prevent public backlash and legal challenges, transparency in data collection and the rationale behind algorithmic decisions is crucial (Farooq et al., 2025; Kumar & Suthar, 2024).

This systematic review ends that AI-powered individualized promoting is a transformative power that can considerably influence consumer purchase patterns by way of elevated relevance and ease of decision-making (Bello et al., 2025; Bhojwani et al., 2026). However, it is only successful if it can overcome the systemic risk of privacy breaches and algorithm bias (Farooq et al., 2025). From a practitioner standpoint, the study suggests that transparency and trust-building go hand in hand with technical optimization (Khuong & An, 2025; Kumar & Suthar, 2024). To ensure the sustainable relationship with consumers, ethical guidelines should be adopted and “explainability” should be emphasized (Kumar & Suthar, 2024; Thakur & Gupta, 2025). In conclusion, the sustainable development of AI in the advertising sector relies on the harmony between technological advancement and human dignity and ethical responsibility (Araf et al., 2026; Farooq et al., 2025).

The author acknowledges & thanks the scholars and institutions that have contributed publications, policy reports, and datasets on AI-powered personalized advertising and consumer buying behavior.

The author declares no conflict of interest in the conduct and publication of this study.