The Integration of AI and IoT in Marketing: A Systematic Literature Review

Abstract

This systematic literature review investigates the integration of artificial intelligence (AI) and the Internet of Things (IoT) in marketing, with a focus on their application in enhancing consumer engagement, personalization, and strategic decision-making. Using the Scopus database and a refined keyword search strategy, the study identified 223,671 initial records, which were narrowed down to 121 peer-reviewed academic articles after applying strict inclusion and exclusion criteria. Thematic analysis revealed that foundational technologies—such as machine learning, big data, and deep learning—dominate the field, while marketing strategy, decision systems, and customer experience emerge as central application areas. Co-citation and keyword network analyses indicate a technocentric and interdisciplinary knowledge structure, but also expose significant gaps in research related to ethics, regulation, consumer trust, and small business contexts. The review highlights opportunities for future research in underexplored areas such as sentiment analysis, sustainability, and human–AI interaction. For practitioners, the findings underscore the strategic importance of AI and IoT in driving personalized, data-driven marketing, while emphasizing the need for ethical transparency and regulatory alignment. Limitations include reliance on a single database, potential exclusion of relevant studies due to keyword constraints, and a focus on peer-reviewed journal articles only. This review addresses key gaps in the literature by offering a focused synthesis of current research and proposing directions for more balanced and responsible innovation in AI-enabled marketing.

1. Introduction

Artificial intelligence (AI) and the Internet of Things (IoT) have emerged as transformative technologies in modern marketing, offering unprecedented capabilities in customer personalization, automation, and data-driven decision-making [1,2]. AI facilitates adaptive marketing through machine learning algorithms, predictive analytics, and automation tools that tailor consumer interactions and streamline operations. Concurrently, IoT enhances real-time data acquisition, enabling businesses to track consumer behavior, optimize supply chains, and deliver context-aware marketing interventions [2]. Despite these advances, the integration of AI and IoT into marketing strategies remains fraught with complex challenges.

The central debate in current literature lies between the promise of hyper-personalized, efficient marketing ecosystems and the ethical, technical, and organizational barriers that impede their full-scale deployment [3,4]. Issues such as data privacy, algorithmic bias, and consumer autonomy continue to raise ethical concerns [5], while practical implementation is often stymied by high costs, fragmented infrastructure, lack of skilled professionals, and cultural resistance to automation [6]. Moreover, while several studies highlight the advantages of AI and IoT individually, few offer an integrated perspective on how these technologies can synergize to revolutionize marketing practices [5].

Unanswered questions persist regarding how businesses can achieve a balanced, ethical, and scalable integration of AI and IoT that delivers value without compromising trust. For instance, which frameworks can ensure data transparency and fairness in AI-driven personalization? How can organizations bridge the skills gap and overcome infrastructural limitations to adopt AI and IoT cohesively?

This review addresses a critical gap in the literature by synthesizing interdisciplinary insights from marketing, information systems, and ethics to evaluate the converging impact of AI and IoT on marketing strategies. It aims to contribute a novel perspective by (i) critically examining emerging trends in AI–IoT integration in marketing, (ii) identifying persistent barriers and ethical dilemmas, and (iii) proposing a conceptual roadmap for future research and practice. By doing so, this paper advances the existing knowledge base and sets the foundation for more sustainable and ethically responsible digital marketing frameworks.

The analysis performed reveals that during the examined period, core research themes such as artificial intelligence, the Internet of Things, and big data served as foundational and cross-cutting topics across multiple domains, paving the way for themes like learning systems, indicating high developmental momentum. The integration of AI and IoT is fundamentally transforming marketing by enabling operational efficiencies across industries. These technologies enhance consumer engagement through varying tools while also reshaping entire sectors. However, their adoption brings significant challenges, including ethical concerns around data privacy, as well as technical and financial barriers. Furthermore, important hurdles further hinder effective implementation, and businesses must align innovation with strategic effectiveness to build consumer trust and drive competitive advantage in an increasingly data-driven market landscape. The following paper is organized as follows: a section on materials and methods, results, discussion of key trends, and the conclusion.

2. Materials and Methods

To analyze academic publications on the Internet of Things and artificial intelligence on marketing, the researcher conducted a systematic bibliometric literature review (LRSB). This method was selected for its ability to provide a broad and in-depth assessment of existing studies by identifying key trends, themes, and gaps in the literature. To enhance the transparency and reproducibility of the review, the study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework [7]. PRISMA ensures a structured and systematic approach to selecting, screening, and documenting research studies, making it a crucial tool for generating a rigorous and methodologically sound literature review [8]. This structured approach is particularly useful for dynamic fields like e-commerce, where technological advancements continually reshape global trade practices.

The LRSB method, as discussed by Rosário and Dias [9] and Rosário and Raimundo [10], offers a more structured and detailed analysis of a research area compared to conventional literature reviews. It prioritizes the careful selection of studies that directly address the research question while maintaining a high degree of transparency. This allows for a thorough evaluation of the methodology, findings, and overall quality of the included studies.

Following a systematic process, the LRSB approach is based on a well-defined protocol for screening and selecting sources, ensuring both the reliability and the relevance of the data. The process is divided into three main stages with six specific steps, as detailed in

Table 1. Process of systematic LRSB.

Fase	Step	Description
Exploration	Step 1	Formulating the research problem
	Step 2	Searching for appropriate literature
	Step 3	Critical appraisal of the selected studies
	Step 4	Data synthesis from individual sources
Interpretation	Step 5	Reporting findings and recommendations
Communication	Step 6	Presentation of the LRSB report

Source: own elaboration.

and further elaborated by Rosário and Dias [9] and Rosário and Raimundo [10].

The research utilized the Scopus database to identify and select credible sources, taking advantage of its strong reputation in academic and scientific communities, while assessing the quality of the studies.

Using only the Scopus database for the literature review is justified due to its comprehensive coverage, high-quality indexing, and advanced analytical capabilities. Scopus is one of the largest and most trusted abstract and citation databases of peer-reviewed literature, covering a wide array of disciplines. It includes journals, conference proceedings, and books from reputable publishers, ensuring a high standard of academic rigor. Moreover, Scopus offers robust citation tracking, bibliometric tools, and keyword-mapping features that support in-depth analysis of research trends, author impact, and thematic development—capabilities that are crucial for a structured and data-driven literature review. Its standardized metadata and indexing protocols also enhance the reliability and reproducibility of the search results, making it especially suitable for systematic and thematic reviews. Therefore, focusing on Scopus ensures both the quality and the consistency of the literature review process.

Nonetheless, relying exclusively on the Scopus database still poses a limitation, while offering high-quality and comprehensive coverage can introduce certain biases into the literature review, such as under-representing regional, non-English, or niche publications, leading to a twisted global perspective. In addition, the focus on highly cited works may also limit exposure to emerging ideas and alternative viewpoints, whilst potential delays in indexing recent publications can affect the currency of the review. To summarize, whereas Scopus provides a robust foundation, its limitations should be acknowledged to ensure a balanced and robust understanding of the research landscape.

Additionally, the search was restricted to publications available up to February 2025, which may have limited the inclusion of the most recent research. To maintain a high standard of rigor and credibility, the study focused exclusively on peer-reviewed academic and scientific publications.

The bibliographic search process was carried out in the Scopus database. For the initial research, the researcher used the keywords “Internet of Things”, limited to TI-TLE-ABS-KEY. This resulted in 223,671 documents. Adding the keyword “artificial intelligence” reduced the results to 21,719, and the keyword “marketing” resulted in 259 documents. Finally, it was limited to “artificial intelligence” articles, resulting in 212 scientific or academic documents. This helped ensure that only the most relevant documents were selected for analysis. This filter resulted in 121 documents (N-121), which were then synthesized in the final report.

To ensure the relevance and rigor of the documents analyzed in the final report, the study applied clear inclusion and exclusion criteria (

Table 2. Screening methodology.

Database Scopus	Screening	Publications
Meta-search	Keyword: Internet of Things	223,671
First inclusion criteria	Keyword: Internet of Things; artificial intelligence	21,719
Second inclusion criteria	Keyword: Internet of Things; artificial intelligence; marketing	259
Screening	Keyword: Internet of Things; artificial intelligence; marketing Exact keyword: artificial intelligence Until February 2025	121

Source: own elaboration.

). The focus was strictly on peer-reviewed journal articles that examined the role of artificial intelligence in enhancing marketing within a business context.

To maintain a well-defined dataset, studies that did not specifically address artificial intelligence were excluded. This structured selection process helped ensure that the final body of literature was both high in quality and closely aligned with the study’s objectives. A detailed summary of the search process can be found in Table 2.

The researchers conducted a thorough content and thematic analysis to examine, evaluate, and present the selected documents, following the framework established by Rosário and Dias [9] and Rosário and Raimundo [10]. To ensure that only academically credible and highly relevant sources were included, strict selection criteria were applied. The analysis centered on studies that explored luxury brands and consumer behavior, prioritizing research that aligned closely with the study’s objectives. Each study was assessed based on its relevance to the topic, methodological rigor, and publication in peer-reviewed journals.

The keyword search strategy followed an iterative refinement process using Boolean operators to ensure comprehensive yet focused results. Starting with “Internet of Things”, the search was progressively narrowed by adding “artificial intelligence” and “marketing”, using the AND operator, which reduced the results from 223,671 to 259 documents. A final relevance filter focusing on artificial intelligence refined the selection to 212, from which 121 peer-reviewed academic articles were chosen for analysis. Strict inclusion criteria ensured that only studies directly examining AI’s role in marketing—particularly in business contexts like consumer behavior and luxury branding—were included. Exclusion criteria eliminated non-peer-reviewed sources, peripheral mentions of AI or IoT, technical studies unrelated to marketing, and articles lacking methodological rigor. This structured and selective approach ensured the final dataset was both high in quality and closely aligned with the study’s objectives.

A visual representation of this selection process is provided in Figure 1.

A total of 121 academic and scientific documents from the Scopus database were examined using a combination of narrative and bibliometric analysis, following the guidelines set by Rosário and Dias [9] and Rosário and Raimundo [10]. These methods allowed for a comprehensive review of the content, with a strong focus on identifying recurring themes and ensuing key findings that were directly related to the research questions. The PRISMA flow diagram illustrates a rigorous selection process, beginning with 223,671 records identified from the Scopus database. Following automated filtering, which excluded 201,952 records as ineligible, 21,719 records were screened. Of these, 21,460 were removed based on title and abstract review, leaving 259 reports for full-text retrieval. However, 138 of these were excluded for not focusing on artificial intelligence, and only 121 reports were assessed for eligibility. Of the 121 documents selected, 53 were journals, 36 were conference proceedings, 19 were book series, 11 were books, and 2 were trade journals.

Despite this thorough screening, no studies met all the inclusion criteria, resulting in zero new studies being included in the final review. This process highlights the stringency of the selection criteria and the specificity required for inclusion.

3. Results

This section presents the main themes and keywords that have emerged from a preliminary analysis. In this way, it embraces peer-reviewed articles on the “Internet of Things and artificial intelligence in marketing”, up to February 2025. The year 2024 has the highest number of peer-reviewed publications, reaching 26. Figure 2 summarizes the peer-reviewed literature published until February 2025.

The publications were sorted as follows: Advances in Intelligent Systems And Computing (4) and Studies in Systems Decision and Control (3), with two publications (Technology in Society; Sustainability Switzerland; Scientific Reports; Lecture Notes on Data Engineering and Communications Technologies; Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics; Eai Springer Innovations in Communication and Computing; ACM International Conference Proceeding Series; 6th International Conference on Inventive Computation Technologies Icict 2023 Proceedings; 2021 IEEE 2nd International Conference on Big Data Artificial Intelligence and Internet of Things Engineering Icbaie 2021), and the remaining publications with one document.

The observed rise in publication volume from 2015 to 2024 reflects a growing academic interest in artificial intelligence and related technologies, with a notable surge beginning in 2019. This trend can be attributed to rapid technological advancements, increased funding and institutional focus on digital innovation, and the accelerated adoption of AI solutions during the COVID-19 pandemic. Additionally, the integration of AI across diverse disciplines such as healthcare, marketing, and business has broadened its research appeal, attracting contributions from a wider academic base. These patterns underscore the field’s evolution from emerging interest to a mature, high-impact area of scholarly inquiry. Of note, the apparent drop-off that comes up in 2025 is due to the sparse time span of two months analyzed in 2025–January and February.

Figure 3, on the other hand, highlights the countries with the highest levels of scientific contributions in specific research fields, with India, the USA, China, and Australia standing out as the leading nations in terms of publication volume. These countries demonstrate a strong research presence, reflecting their active role in advancing knowledge within the respective areas of study.

Table 3. Top 10 countries by number of publications.

Country	Number of Publications
India	106
USA	58
China	39
Australia	16
France	11
Turkey	11
Indonesia	10
Ecuador	9
South Korea	9
United Arab Emirates	8

Source: own elaboration.

and Figure 3 provide a visual representation of the top 10 countries that have made significant contributions to research in the examined fields.

The global distribution of research on the integration of AI and IoT in marketing reveals significant contributions from countries such as India, the United States, and China, with notable activity also observed in the UK, Australia, Germany, and others. This trend reflects a growing international interest driven by rapid technological advancements, increased investment in digital innovation, and rising market demand for intelligent marketing solutions. The prominent role of both developed and emerging economies (e.g., Turkey and Indonesia) underscores the strategic importance of this research area, as institutions and industries alike seek to leverage AI and IoT for competitive advantage. Overall, the pattern highlights a dynamic and expanding research landscape shaped by the convergence of technological capability, economic policy, and global business needs.

This study highlights the nations that place a strong emphasis on studying luxury brands and consumer trends, offering insights into their research priorities and academic focus in this area.

The leadership of countries such as India, the United States, and China in AI–IoT marketing research is closely tied to strategic national initiatives, strong funding frameworks, and rapid technological advancements [1]. India’s Digital India initiative and AI strategy by NITI Aayog have fostered digital innovation and industry–academia collaboration. In the U.S., federal programs like the National AI Initiative and significant private-sector investment have driven cutting-edge research. China’s New Generation AI Development Plan and smart retail ecosystem, led by tech giants like Alibaba, have positioned it as a global frontrunner. Similarly, the UK’s AI Sector Deal and Germany’s Industry 4.0, along with support from agencies like Innovate UK, highlight strong governmental support for interdisciplinary research [5]. These initiatives collectively explain the high research output and global leadership in integrating AI and IoT into marketing.

Building upon those most important contributors, the Bradford’s law identifies ten key publications, highlighted in grey in Figure 4, as the core sources in the field, collectively accounting for 40% of the total scientific output. According to this principle, when a new topic begins to gain attention, a small number of journals initially publish the majority of research on the subject. These journals then become dominant contributors for a period, while other publications gradually start introducing articles on the same topic. As the topic gains recognition, a core group of journals gradually emerges as the leading contributors to the field. Among these, 26 journals play a significant role, with the first 15 publications serving as key sources. These journals foster scholarly interactions by providing a foundation for researchers who reference, engage with, and build upon existing studies, further advancing the body of knowledge in the area.

The subject areas represented in the 121 academic documents reflect a strong interdisciplinary foundation, with the most relevant contributions coming from computer science (68 documents) and business, management, and accounting (34 documents)—the two fields most closely aligned with the integration of AI and IoT in marketing. Additional significant input comes from engineering (30) and decision sciences (26), emphasizing the technical and strategic decision-making dimensions of the topic. Contributions from social sciences (21) and economics, econometrics, and finance (17) further underscore the growing interest in the societal and financial implications of AI-driven marketing strategies.

The most cited work within this body of literature is the article titled “Artificial intelligence in marketing: Systematic review and future research direction”, which has received 361 citations. Published in the International Journal of Information Management Data Insights—a Q1-ranked journal with an SJR of 2.14 and H-index of 34—this article serves as a pivotal reference in the field. Its primary aim is to deliver a comprehensive review of AI in marketing, utilizing bibliometric, conceptual, and intellectual network analysis to map existing research from 1982 to 2020, thereby offering valuable insights and future research directions.

Of note, this Bradford’s Law analysis reveals that research on the integration of AI and IoT in marketing is concentrated within a small number of core sources, primarily composed of interdisciplinary conference proceedings and systems-oriented journals such as Advances in Intelligent Systems and Computing, Studies in Systems, Decision and Control, and various IEEE and ACM conferences. This suggests that the field is currently dominated by technical and engineering perspectives rather than traditional marketing scholarship. The limited presence of mainstream marketing journals is notable and highlights the field’s emerging, cross-disciplinary nature. As the research matures and gains broader relevance, a gradual shift toward marketing-specific journals is likely, reflecting deeper integration into the marketing discipline and industry practices.

In Figure 5, we can analyze citation changes for documents published until February 2025. The period ≤2015–2025 shows a positive net growth in citations with an R2 of 46%, reaching 212 in 2024.

The h-index is a measure used to assess both the impact and the productivity of published research. It is calculated by identifying the highest number of publications that have been cited at least the same number of times. In the analysis conducted, 22 documents met this criterion, each having received at least 22 citations. This metric provides insight into the significance and reach of the research within its field. Citations for all scientific and academic documents from the period up to February 2025 totaled 2282, with 36 of the 85 documents without any citation.

Of note, the article “Artificial intelligence in marketing: Systematic review and future research direction” has become a foundational work in its field, as reflected by its high and steadily increasing citation count. Published at a pivotal moment when interest in AI applications within marketing was accelerating, the article’s impact stems from its comprehensive methodological approach—using bibliometric, conceptual, and intellectual network analysis to map developments from 1982 to 2020. It not only synthesizes fragmented research but also identifies key thematic clusters and proposes future research directions, making it a critical reference for scholars. Its publication in a high-ranking, Q1 journal further amplifies its visibility and credibility, explaining why it continues to be extensively cited across disciplines. Moreover, the R² value shown in the figure, representing a regression trend line across citation growth by year, is 0.955, which indicates an extremely strong correlation between the publication year and citation growth. This suggests that citations have increased in a highly predictable, linear fashion over time, reflecting the growing centrality and ongoing relevance of that particular article in the field.

Furthermore, using the main keywords “Internet of Things”, “artificial intelligence”, and “marketing”, the bibliometric analysis identified key trends and indicators reflecting the evolving scope of scientific and academic research, as illustrated in Figure 6.

These findings were generated using VOSviewer software (version 1.6.18), with a particular focus on the primary search terms: “Internet of Things”, “artificial intelligence”, and “marketing”.

The keyword co-occurrence network reveals that research on AI and IoT in marketing is centered on key technological themes such as artificial intelligence, machine learning, deep learning, and blockchain, forming the core of the field. Surrounding clusters highlight applications in commerce, digital marketing, and customer engagement, as well as strategic areas like decision support and information management. Human-centered themes such as user trust and experience are also present, although less prominent. Remarkably, the network displays underexplored areas related to ethics, privacy, regulation, and the societal impacts of AI, suggesting gaps in the literature. This indicates that whereas the field is technologically robust and commercially driven, there is a growing need for more research focused on governance, inclusivity, and human–AI interaction.

This study is grounded in a comprehensive review of academic and scientific literature examining how the Internet of Things and artificial intelligence enhance marketing strategies. The three-field plot focuses on the central domain, in which “AU” represents the author, while highlighting the primary researcher of interest and illustrating connections to “CR” (cited references) and “DE” (author keywords). To analyze the relationships between key terms used by authors in the reviewed studies, a diagram was created using Bibliometrix. This visualization, shown in Figure 7, provides a clear representation of the links and interactions between significant concepts in the field.

The Sankey diagram visually represents the frequency of various themes through the size of each box, while the connecting lines illustrate the relationships and transitions between them. The thickness of these lines indicates the strength of the connection between themes, reflecting how closely they are linked. This approach, as explained by Xiao et al. [11], helps to effectively convey the flow and progression of key topics within the analysis. As depicted in Figure 7, the keywords that appeared most often include “Internet of Things” (11 incoming flows; 0 outgoing flows), “artificial intelligence” (12 incoming flows; 0 outgoing flows), “machine learning” (10 incoming flows; 0 outgoing flows), and “marketing” (9 incoming flows; 0 outgoing flows). These terms are predominantly linked to the most frequently cited references.

To sum up, the three-field plot illustrates the intellectual structure of research on AI and IoT in marketing by mapping connections between cited references, influential authors, and key research themes. It reveals a tightly integrated knowledge base, where prominent authors such as Kumar, Dwivedi, and Rana build upon foundational works to explore emerging topics like artificial intelligence, the Internet of Things, machine learning, and customer experience. The alignment between core references and keywords reflects a maturing field focused on the technological transformation of marketing. However, the limited presence of ethical, regulatory, and theoretical keywords suggests underexplored areas, indicating future research opportunities in broadening the field’s conceptual and societal depth.

Figure 8 presents a detailed visualization of the relationships between key terms found in academic literature, highlighting connections among frequently used keywords. This analysis offers valuable insights into the central themes explored in the studies and helps identify potential avenues for future research. Additionally, the figure illustrates a broad network of co-citations and thematic clusters, enhancing the understanding of citation patterns and strengthening the overall findings of the study.

The thematic map is structured using specific parameters, including a minimum cluster frequency of 50 (per thousand documents), 5 labels per cluster, a label size of 3, and a baseline scaling value of 0.3. A line within the map distinguishes relevance (centrality) from progress (density). As illustrated in Figure 8, the thematic analysis is divided into four quadrants, each represented by circles of varying colors.

The upper-right quadrant features the core themes of the field, which are both well developed and highly relevant. These themes exhibit strong centrality and high density, making them integral to the discipline.

The lower-right quadrant includes foundational and cross-cutting themes that are crucial but not yet fully developed. They are characterized by strong centrality but low density, indicating their significance in shaping future research.

The lower-left quadrant highlights themes that are either emerging or in decline, as reflected by their low density and weak centrality. These areas may represent developing trends or topics losing relevance.

The upper-left quadrant consists of highly specialized themes with strong internal development but limited external connections. These themes have high density but weak centrality, suggesting they are peripheral or niche areas within the broader research landscape.

This categorization provides a structured overview of how different research themes are positioned within the field, offering insights into both well-established topics and potential areas for further exploration.

To summarize, the keyword co-occurrence map reveals a technocentric research structure, with central themes focused on “machine learning”, “artificial intelligence”, “big data”, “deep learning”, and “marketing strategy”, indicating a strong emphasis on data-driven technologies and their strategic marketing applications. In contrast, peripheral keywords such as “brand personality”, “theoretical study”, and “automated driving” highlight niche or emerging topics that remain underexplored. The sparse presence of behavioral, ethical, and regulatory terms points to conceptual gaps in the literature, suggesting that while the field is technologically robust, there is a need for more human-centered, theoretical, and societal perspectives to enrich its development.

When we look at Figure 9, which covers 121 documents, we see that on the lower-right side of the axis, where the basic and cross-cutting research themes of the period in question are concentrated, “artificial intelligence”, “Internet of Things”, and “gig data” appear as the main theme, followed by aspects such as “pharmaceutical industry”, “drug marketing”, and “economic development” as niche themes.

On the upper right side are the driving themes of the period, in which “learning systems”, “marketing strategy”, “decision trees”, “human”, “article”, and “humans” are the central themes. To a lesser extent, “information management”, “human resource management”, and “Internet of Things” appear in the upper quadrant.

On the upper-left side of the axis, we see that as peripheral themes, “customer-service”, “inventory control”, and “inventory management” appear, and it can be understood that these themes are on the way to becoming emerging or declining themes.

On the lower left, which corresponds to emerging or declining themes, “customer” and “theoretical study” appear, which are basic and cross-cutting themes, or emerging or declining themes.

In short, the thematic map reveals that artificial intelligence, the Internet of Things, and big data function as foundational elements within the field, positioned in the “basic themes” quadrant due to their high relevance but relatively lower development. This suggests they are widely integrated across studies but still evolving in depth and application. Conversely, motor themes such as marketing strategy, learning systems, and decision trees are both central and well developed, actively driving the field forward and shaping current research directions. Niche themes like customer service and inventory control are specialized and well developed but less influential, while emerging or declining themes, including smart cities and sentiment analysis, indicate underexplored or fading areas that could represent future research opportunities if revisited with new approaches.

Furthermore, Figure 10 illustrates a substantial network of co-citations and interconnected units, offering a more in-depth examination of cited references. This visualization enhances the understanding of citation patterns and strengthens the overall conclusions of the study.

As displayed, the co-citation network unveils a tightly connected core of influential studies that form the intellectual foundation of AI and IoT research in marketing. Frequently cited together, these works share thematic focus in areas such as digital transformation, big data analytics, and AI-driven consumer engagement, indicating strong conceptual alignment across the field. Their co-citation patterns suggest a mature and coherent research domain, where scholars consistently draw on a common body of literature to build and extend theoretical frameworks. Peripheral nodes hint at emerging or niche topics that may represent future directions, highlighting a field that is both well established and dynamically evolving.

4. Discussion: Key Trends in AI- and IoT-Driven Marketing

As shown above, the adoption of AI and IoT technologies in marketing has led to various emerging trends that are reshaping the industry. The main trends are as follows:

4.1. AI-Enabled Customer Insights and Personalization

Artificial intelligence (AI) significantly transforms customer engagement and behavior analysis through machine learning, natural language processing (NLP), and predictive analytics [1]. Its integration into customer relationship management (CRM), targeted advertising, and sentiment analysis allows businesses to deepen consumer satisfaction and loyalty, and optimize sales performance [12,13,14].

AI enhances customer interactions by leveraging data-driven personalization and predictive insights, crucial for refining customer retention strategies. The primary causes of this trend include the exponential growth of data availability and advancements in computational power, enabling AI systems to deliver highly accurate predictions and personalization at scale. The result is significantly improved conversion rates, customer satisfaction, and engagement. For instance, recommendation engines in e-commerce use real-time analytics, leading to an increase in sales and customer loyalty by providing relevant product suggestions precisely when users are most likely to purchase [15,16]. AI-enhanced CRM platforms leverage predictive analytics and IoT-generated data, optimizing customer loyalty programs and promotions by accurately identifying high-value customers and predicting their preferences [12].

AI-driven sentiment analysis refines consumer understanding by evaluating social media, reviews, and feedback, enabling businesses to anticipate market trends and personalize brand messaging effectively. This trend emerges primarily from consumers’ increased engagement on digital platforms, where significant volumes of unstructured data are generated daily. As a result, businesses can proactively manage brand reputation and customer relationships, achieving higher levels of consumer trust and advocacy [17]. Dynamic pricing models supported by AI leverage historical purchase data and competitive analysis, driven by increasing market competition and the need for real-time responsiveness, and leading to optimized pricing strategies that significantly enhance revenue generation and market share [18].

Beyond marketing, AI boosts predictive capabilities across various sectors. In industrial IoT (IIoT), deep learning optimizes operational efficiency and asset management, driven by the industry’s demand for reduced downtime and increased productivity. Consequently, companies can predict equipment failures more accurately, thus improving reliability and reducing maintenance costs [19]. In healthcare, AI significantly enhances predictive models, notably in improving breast cancer screening accuracy, spurred by the healthcare industry’s critical need for early detection and improved patient outcomes, leading to enhanced diagnostic accuracy and more effective patient care [20,21]. Nonetheless, balancing automation with human interaction remains crucial to maintaining personalized customer experiences, driven by consumers’ continued preference for empathy and personalized human interaction in customer service [22,23,24].

The integration of IoT with behavioral analytics, termed the Internet of Behaviors (IoB), further deepens consumer engagement, fueled by the rapid adoption of IoT devices and wearable technology. This results in highly detailed insights into consumer habits, enabling unprecedented personalization of customer experiences. However, it introduces significant challenges regarding privacy, data security, and ethical AI deployment, necessitating stringent governance to ensure consumer trust and transparency [25,26,27]. AI-driven encryption further strengthens digital marketing security, protecting consumer data through edge computing, in response to increasing cybersecurity threats and consumer privacy concerns [28,29,30].

AI substantially impacts content marketing by using NLP and deep learning to generate high-quality content and support continuous customer interactions via chatbots. This trend is driven by the escalating consumer demand for immediate and engaging interactions and businesses’ need to maintain continuous customer service. Consequently, companies observe improved customer experiences, higher satisfaction levels, and more efficient lead generation [22,31,32]. Predictive analytics in digital marketing leverage AI tools to forecast demand and consumer sentiment, largely driven by the need for competitive advantage and real-time responsiveness in rapidly evolving markets, and significantly enhancing sales performance across e-commerce platforms and digital marketplaces [18,33,34].

In the retail sector, AI drives innovation through improved product discovery and smart mirrors for personalized luxury shopping experiences, driven by consumers’ increasing expectation for highly personalized and interactive shopping experiences [35,36,37]. The result is increased consumer engagement, greater customer satisfaction, and higher conversion rates [38,39,40]. AI-powered smart devices, wearable technology, and IoT-driven fashion elevate purchasing experiences by processing real-time data to adapt user behaviors, improving usability, aesthetics, and product engagement, and directly influencing consumer loyalty and repeat purchases [41,42,43].

Smart technologies leveraging AI are revolutionizing multiple industries:

• Smart vending machines utilize real-time analytics and computer vision for seamless purchasing, driven by consumer demand for convenience and operational efficiency, thereby leading to improved sales and service quality [44,45,46].
• Real estate optimizes property management and energy efficiency through AI-driven analytics, addressing the industry’s increasing focus on sustainability and cost reduction, thereby improving operational performance and environmental impact [47,48,49].
• Smart textiles and clothing integrate AI to dynamically adapt user needs and preferences, driven by consumer expectations for personalized comfort and adaptability, thereby significantly enhancing product usability and satisfaction [50,51,52].
• Smart transportation solutions enhance urban mobility, driven by urbanization and increased vehicle congestion, thereby significantly improving safety, efficiency, and convenience through optimized traffic management and autonomous vehicle technologies [53,54,55,56,57,58].
• Smart tourism and hospitality sectors utilize AI-driven real-time booking systems, addressing the demand for seamless, personalized travel experiences, thereby resulting in increased customer satisfaction and operational efficiency [59,60,61,62,63].
• Smart farming employs IoT and AI, responding to the critical need for sustainable agricultural practices, thereby significantly enhancing yield, resource management, and environmental sustainability [64,65,66].

AI thus continues to profoundly reshape customer engagement, marketing strategies, and operational efficiencies, emphasizing ethical data management to sustain consumer trust and enhance competitive advantage [24,67,68].

4.2. AI and IoT in Fashion and Retail Marketing

AI is significantly transforming trend prediction by enabling the anticipation of emerging industry patterns. Through machine learning (ML), AI-driven systems generate actionable insights that support strategic decision-making processes [69,70,71,72]. In particular, the convergence of AI and the Internet of Things (IoT) is reshaping the fashion and retail industries by enhancing inventory management through the utilization of vast, real-time datasets [1,41].

In the fashion and retail sectors, AI-driven tools analyze consumer search trends and purchasing behaviors to forecast upcoming fashion preferences. Brands now use these AI-powered insights not only to optimize product launches [73] but also to refine advertising strategies through behavioral analytics and real-time customer feedback analysis [17,74]. Consequently, smart retail environments—enabled by AI and IoT—are elevating customer experience and allowing for hyper-targeted marketing campaigns [36,67,75].

In luxury retail, the application of AI has been especially transformative. One of the most impactful innovations is AI-driven personalized customer engagement. Recommendation engines tailor product suggestions based on past purchases and customer preferences, creating a highly curated shopping journey [68]. Moreover, AI-powered virtual assistants offer high-touch, responsive customer service that mimics traditional luxury experiences [22].

Luxury retailers are also leveraging virtual fitting rooms, allowing customers to visualize products in real time before making a purchase—a key innovation in enhancing convenience and personalization [49]. These experiences extend into the semantic metaverse, where AI is redefining how luxury consumers interact with digital environments, thus further deepening brand engagement [76]. E-commerce platforms, powered by AI, enable high-net-worth customers to navigate and acquire exclusive products seamlessly, facilitating efficient digital luxury shopping [1,12].

Another significant advancement lies in AI-enhanced supply chain transparency, particularly in high-value sectors such as luxury watches and jewelry. Here, blockchain-integrated AI systems help combat counterfeiting while also enhancing security in financial transactions [5,77,78,79]. These systems also contribute to more inclusive markets by supporting artisans in reaching elite clientele through AI-enabled sales and marketing tools [80,81]. Furthermore, insurance providers are utilizing AI to develop personalized, data-driven policies tailored to affluent consumers’ specific needs [82].

Operationally, AI is driving innovation within cloud-based ERP systems by optimizing logistics, streamlining global supply chains, and accelerating cross-border transactions—benefits that are increasingly important in luxury commerce and even extend into other industries such as pharmaceuticals [24,25,27,83,84,85,86]. Dynamic pricing models and demand forecasting systems, powered by AI, allow luxury brands to maintain pricing strategies that reflect both market demand and the imperative to preserve brand exclusivity [17].

However, while these innovations offer vast potential, they also pose strategic challenges. Luxury brands must navigate the delicate balance between embracing AI-driven innovation and upholding ethical AI governance. The integration of AI with the Internet of Behaviors (IoB), for instance, raises substantial concerns around consumer privacy, data ownership, and regulatory compliance [23]. Failure to address these concerns may undermine brand trust and authenticity—key pillars of the luxury value proposition.

Critically, while many studies highlight the advantages of AI integration—enhanced personalization, operational efficiency, and expanded market access—less attention has been given to its potential to commodify luxury, diluting the exclusivity that defines high-end brands. As such, a more nuanced approach is necessary: one that not only adopts AI capabilities but also enforces transparent ethical frameworks to ensure that personalization does not infringe upon consumer autonomy, and that data insights do not erode the cultural capital of luxury branding.

4.3. Industry 4.0 and AI-Driven Marketing Transformation

Beyond consumer applications, AI-powered smart manufacturing and Industry 4.0 technologies are radically transforming industrial processes. These innovations notably improve predictive maintenance, supply chain optimization, and automated quality control [55,87]. In particular, AI-enabled IoT sensors continuously monitor equipment conditions, allowing for failure prediction before breakdowns occur, thereby significantly enhancing operational efficiency and reducing downtime [34,88,89].

These advancements are especially pronounced in the energy sector, where AI facilitates efficiency gains, enhances risk assessment, and supports strategic decision-making—all while integrating more sustainable practices [90,91]. The convergence of AI, big data, and IoT also revolutionizes supply chain management, promoting continuous improvement in industrial operations through real-time data analytics and adaptive systems [92].

In contrast, in medical device marketing, these technologies manifest differently. Here, AI-powered IoT and big data reshape marketing strategies—particularly in prosthetics and orthotics—by supporting personalized care and enabling service-oriented business models [93,94]. These shifts reflect a broader transformation in the healthcare industry, where marketing becomes increasingly customer-centric and data-driven, aligning with both commercial objectives and patient needs.

Moreover, creative sectors such as the animation industry also benefit from Industry 4.0 adoption. In these contexts, AI is used not only for automation and content generation [95] but also to improve manufacturing efficiency [96,97] and streamline research workflows [98]. The introduction of IoT-driven AI models, employing neural networks, fuzzy logic, and bio-inspired algorithms, extends these capabilities further, enhancing predictive decision-making across sectors [99,100,101].

From a marketing perspective, the integration of AI with Industry 4.0 technologies signifies a paradigm shift. Businesses increasingly leverage AI-powered tools such as big data analytics to facilitate strategic decision-making [102]. This includes hyper-personalization, where AI algorithms analyze consumer behavior to provide tailored recommendations [1], as well as enhanced customer segmentation, which identifies high-value consumers [12]. In doing so, companies reduce human intervention through marketing automation, while still delivering personalized engagement [22]. Meanwhile, AI’s predictive capabilities support real-time trend analysis in digital marketing and dynamic pricing strategies [18,103,104].

A more nuanced impact is evident in B2B marketing, where digital servitization, driven by Industry 4.0, transforms the customer experience. The use of AI-powered CRM systems and IoT-enabled analytics allows for personalized service offerings and increases operational agility, shaped by audience perception and interaction patterns [17,94,105,106,107]. This transformation reflects a shift from product-centric to value-centric business models.

Despite its transformative potential, this integration is not without challenges. AI bias, data transparency, and cybersecurity risks remain critical concerns, particularly in sensitive areas like healthcare and finance [108,109,110]. Nevertheless, across domains—from marketing to tourism, healthcare, and urban development—the benefits are tangible. In healthcare, AI facilitates patient-specific diagnostics and treatments [43,64,111,112]; in marketing, it enables consumer behavior prediction [113,114,115]; and in tourism, AI optimizes visitor experiences, resource use, and operational efficiencies, while safeguarding the privacy of younger audiences [36,116,117,118].

Additionally, smart cities capitalize on AI and IoT for improved traffic management, energy consumption, and public safety, using real-time data to bolster urban sustainability [68,119]. The medical device industry, in particular, exemplifies how AI supports marketing efficiency, customer targeting, and alignment with the Sustainable Development Goals (SDGs) [48,93]. Here, AI helps businesses understand demand trends, offer customized promotions, and deliver dynamic pricing, all while optimizing logistics and reducing operational costs [12,17,18,23,94,120,121,122].

The causes of these transformations lie in the convergence of data availability, computational power, and increasing market complexity, which drive demand for agile and intelligent systems. The consequences are various: on the positive side, firms achieve enhanced responsiveness, cost reduction, and customer satisfaction; however, they must also navigate ethical concerns, regulatory uncertainty, and the need for digital literacy among stakeholders.

Furthermore, industry-specific contexts influence the pace and nature of adoption. For instance, healthcare and medical device industries require greater scrutiny due to patient safety and compliance issues, whereas sectors like tourism and entertainment have more scope to experiment and deploy AI-driven personalization.

Ultimately, the integration of AI and Industry 4.0 technologies presents a dual-edged evolution in marketing—enabling precision, personalization, and performance, but also demanding careful governance, ethical foresight, and cross-sector collaboration.

4.4. Challenges in AI and IoT Integration in Marketing

4.4.1. Data Privacy and Ethical Concerns

AI- and IoT-driven marketing strategies have revolutionized how businesses interact with consumers, primarily through real-time data collection and behavioral insights. However, this evolution also introduces significant ethical and legal dilemmas, particularly regarding consent, data security, and consumer tracking [123]. The proliferation of the Internet of Behaviors (IoB) intensifies these concerns by promoting granular behavioral analytics. While IoB can facilitate hyper-personalized experiences, studies warn that without clear transparency frameworks, such practices risk breaching privacy laws and infringing on individual autonomy [23]. This tension illustrates a broader dichotomy: the technological capability to predict and influence behavior versus the imperative to protect consumer rights.

The root cause of these challenges lies in the asymmetry of information and power between consumers and marketers. For example, the opaque nature of algorithmic processes often precludes user awareness or understanding of how personal data are used to influence decisions. This lack of transparency, when coupled with algorithmic bias, can lead to unfair targeting and pricing discrimination—a phenomenon that undermines consumer trust and violates principles of fairness [18]. Differential pricing based on perceived willingness to pay, inferred through AI models, may optimize short-term profits but at the cost of long-term brand loyalty and reputational integrity.

Furthermore, while sentiment analysis and emotion recognition tools offer deeper insights into consumer states and preferences, their use raises concerns of emotional manipulation and ethical overreach [17]. The emotional granularity these tools provide, if unchecked by ethical oversight, can be exploited to manipulate consumer decisions, challenging notions of informed choice and autonomy. The core issue here is the ethical threshold: at what point does insight become intrusion?

A similar dynamic exists in AI-driven content creation and chatbot interactions. When users cannot distinguish between human and machine communication, the authenticity of engagement is compromised. Transparency in these interactions is not only a regulatory expectation but also a prerequisite for ethical marketing practice [22]. Misrepresenting AI-generated content as human-authored erodes consumer trust and can have legal implications under emerging digital communication standards.

Comparative studies further reveal differing levels of consumer tolerance and regulatory stringency across jurisdictions. For instance, while the European Union mandates explicit consent and algorithmic transparency, other regions may have looser enforcement, leading to inconsistent standards in global marketing strategies [24]. This fragmented regulatory landscape forces businesses to navigate a complex compliance matrix, often reactive rather than proactive in addressing ethical AI concerns.

In contrast, some research highlights the potential of ethical AI governance frameworks to harmonize innovation with rights-based approaches. When businesses embed transparency, fairness, and accountability into their AI and IoT systems, they are more likely to foster consumer trust and achieve sustainable engagement. Prioritizing such principles not only mitigates legal risks but also differentiates brands in increasingly privacy-conscious markets.

Ultimately, while AI and IoT technologies present unprecedented opportunities for personalized and efficient marketing, the underlying trends underscore the need for robust ethical frameworks. The convergence of behavioral data analytics, algorithmic decision-making, and immersive customer engagement necessitates a recalibration of marketing strategies—one that centers on regulatory compliance, algorithmic accountability, and data ethics. Without these safeguards, the very technologies designed to enhance consumer relationships may end up undermining them.

4.4.2. Skill Gaps and Workforce Adaptation

The integration of artificial intelligence (AI) and the Internet of Things (IoT) into marketing practices has the potential to revolutionize customer engagement, personalization, and operational efficiency. However, the successful deployment of these technologies necessitates a deep understanding of data science, machine learning algorithms, and automation systems [73]. Despite growing awareness of AI’s strategic value, a persistent skills gap among marketing professionals poses a significant barrier to full-scale implementation [12]. This gap is rooted not only in the lack of technical expertise but also in organizational inertia and the limited availability of interdisciplinary training pathways.

Several studies highlight divergent interpretations of the talent gap’s root causes. For instance, while Ref. [12] attributes the gap to insufficient training opportunities for marketers, other scholars emphasize a broader misalignment between educational curricula and the fast-evolving demands of AI-driven industries [1]. Furthermore, the organizational structure of many firms reinforces silos that separate marketing departments from technical teams, inhibiting the exchange of knowledge and collaborative innovation [1].

The consequences of this divide are complex. From a strategic standpoint, the underutilization of AI and IoT tools limits firms’ ability to harness real-time consumer insights and automate personalized content delivery—critical factors in contemporary competitive environments. On an operational level, it results in inefficiencies and reduced agility, particularly as consumer expectations for hyper-personalized experiences continue to rise. Moreover, reliance on third-party AI vendors due to internal skill shortages can increase costs and reduce control over proprietary consumer data [73].

To address these challenges, businesses must invest in targeted upskilling initiatives, including AI literacy programs, data analytics workshops, and ongoing professional development. However, such efforts must be complemented by organizational change, including the creation of interdisciplinary teams that encourage collaboration between marketers, data scientists, and AI developers. As Ref. [1] argues, fostering mutual understanding between these domains not only bridges the skills gap but also leads to more consumer-centric AI applications by integrating technical innovation with deep domain expertise in branding and consumer psychology.

In contrast to top-down training approaches, some studies posit for embedded learning models, where AI and IoT tools are integrated into everyday workflows, enabling marketers to develop skills through experiential learning and iterative experimentation. While such models may take longer to implement, they can produce more sustainable and adaptable expertise over time.

Ultimately, the strategic integration of AI and IoT in marketing requires more than technical training; it demands a cultural shift toward data-driven decision-making and cross-functional collaboration. Without such changes, the risk is that marketing departments remain marginal actors in digital transformation efforts, undermining their potential to drive innovation and sustained competitive advantage.

4.5. Implementation Barriers

The integration of artificial intelligence (AI) and the Internet of Things (IoT) into marketing strategies introduces significant technical complexities. These complexities largely stem from the necessity to synchronize diverse digital systems, such as customer relationship management (CRM) platforms, analytics tools, and IoT sensors, to facilitate seamless data flow and automation [12]. While such integration promises enhanced customer insights and personalized engagement, it remains hindered by the lack of standardized AI frameworks, which hampers interoperability across platforms and limits the scalability of AI solutions [124]. This fragmentation creates bottlenecks in data sharing and reduces the agility with which businesses can adapt marketing strategies based on real-time insights.

One of the primary challenges lies in managing the massive volumes of consumer data generated by IoT devices. Studies highlight that, although AI holds the potential to unlock actionable insights from these data, current systems often lack the scalability required to process them efficiently [103,125]. The consequences of this are twofold: first, organizations may suffer from data sets that fragment customer information, leading to disjointed marketing campaigns, and second, the inability to derive real-time analytics undermines the competitive advantage AI is supposed to offer.

Moreover, the financial burden associated with implementing AI- and IoT-driven marketing infrastructure presents a formidable barrier, particularly for small and medium-sized enterprises (SMEs). Investment is needed not only in sophisticated algorithms and scalable cloud infrastructure but also in cybersecurity measures to protect consumer data and ensure regulatory compliance [94]. The economic implications are severe—while large corporations can amortize these costs across wider operations, SMEs often face prohibitive upfront expenditures with uncertain returns on investment (ROIs), which limits their participation in the AI-driven digital marketing evolution [18,126].

Interestingly, while some studies emphasize the technological and financial challenges of adoption, others argue that a major hurdle is the lack of strategic clarity among marketing leaders regarding AI’s capabilities and use cases, which disengage results in underutilization of available tools or misalignment between AI initiatives and core marketing objectives [126]. Additionally, there is a growing concern that over-reliance on automated systems may erode the human element in customer engagement, potentially diminishing brand loyalty in certain demographics that value personalized, human-centric interactions [18].

In response to these challenges, recent literature suggests several pathways forward. The development of modular and scalable AI systems capable of gradual integration can mitigate cost and complexity issues, particularly for SMEs. Furthermore, fostering industry-wide standards for AI interoperability can reduce integration friction, enhance data fluidity, and ensure consistency in performance across platforms [124]. Government incentives and public–private partnerships may also play a critical role in encouraging AI adoption through subsidies, tax incentives, and shared infrastructure programs [125].

In conclusion, while the convergence of AI and IoT holds transformative potential for marketing, its implementation is burdened by technical, financial, and strategic barriers. Addressing these requires a multifaceted approach: Encouraging the development of scalable and cost-effective AI solutions, promoting industry collaboration on standardization, and ensuring that AI adoption strategies are aligned with both organizational capacities and consumer expectations. Only through such coordinated efforts can businesses—regardless of size—fully harness the power of AI and IoT to drive sustainable marketing innovation.

5. Conclusions

The integration of artificial intelligence (AI) and the Internet of Things (IoT) is transforming marketing by enabling hyper-personalization, automation, predictive analytics, and data-driven decision-making. AI-driven tools boost customer engagement, refine targeted advertising, and improve operational efficiency, while IoT eases real-time data collection, thus optimizing supply chain management and consumer behavior tracking. Altogether, these technologies are reshaping digital marketing strategies, thus enabling businesses to gain a competitive advantage in an increasingly data-driven economy.

However, the widespread adoption of AI and IoT in marketing faces critical challenges: ethical concerns surrounding data privacy, algorithmic bias, and transparency must be addressed to maintain consumer trust; regulatory compliance with global data protection laws is essential; and businesses must also navigate workforce skill gaps and high implementation costs. The complexity of integrating AI into existing marketing workflows, along with the need for robust cybersecurity measures, further complicates its adoption. Despite these challenges, AI and IoT offer immense potential for innovation in marketing.

To sum up, this review provides a comprehensive overview of the integration of artificial intelligence and the Internet of Things in marketing, highlighting key themes, influential studies, and research trends. The findings reveal a technologically mature but conceptually evolving field, with strong foundations in AI, big data, and machine learning, yet notable gaps in ethical, human-centered, and regulatory dimensions.

Research Gaps and Future Directions

This review acknowledges several limitations that may influence its findings. Relying solely on the Scopus database may have excluded region-specific, non-English, or grey literature, limiting the global scope. The keyword search, though systematic, may have missed relevant studies using alternative terms or deeper content not captured in the title, abstract, or keywords. The cut-off date of February 2025 may have excluded the most recent developments. Strict inclusion criteria narrowed the focus to peer-reviewed journal articles on AI in marketing, potentially overlooking interdisciplinary or adjacent insights. Additionally, many of the reviewed studies were technocentric, lacking depth in ethical, regulatory, or SME-related considerations.

On the one hand, these limitations highlight the need for broader future research to enhance inclusivity and contextual richness in the field. For instance, more research to ensure responsible AI governance is needed. Also, studies should explore AI’s long-term impact on consumer trust, engagement, and brand loyalty. Until AI-powered recommendations emphasize marketing effectiveness, understanding how continuous AI-driven interactions influence customer perception, emotional engagement, and purchasing decisions requires deeper analysis. Future research should focus on affordable AI adoption models, scalable AI tools, and AI-driven automation strategies tailored either to smaller businesses with limited resources or, for instance, to other institutions, such as universities.

On the other hand, the findings have key implications for both research and practice. For researchers, they highlight the need to move beyond technical foundations toward more human-centered, ethical, and interdisciplinary studies, with emerging themes like sustainability and smart cities offering future research opportunities. For marketing professionals, the results emphasize the strategic role of AI and IoT in enabling personalized and data-driven marketing. However, success depends on building trust, ensuring transparency, and aligning with evolving consumer expectations and regulatory standards. Together, the insights encourage both innovation and responsibility in the application of AI in marketing.