1.1 Background for Review

The application of AI has changed the perspective of marketing due to the opportunity for empowering individual marketing as well as enhancing the relationship with customers. AI tools help these marketers predict customer behavior, deliver up-to-date content, and strategically make complex decisions that reduce time consumption during multichannel communication instances [1]. When AI is inserted into existing structures and processes, issues associated with humanizing the processes, keeping the human touch, and ensuring that AI-focused strategies are in line with organizational growth plans also emerge.

Specifically, this literature review will aim to systematically evaluate how AI is important for the marketing field and organisational success. Cohesively, this research responds to identified research questions that relate to the impact of AI on marketing frameworks, integration difficulties, and best practices.

Artificial intelligence has advanced many fields at a very high level and marketing is one of the sectors that has been impacted most by the technology. AI brought about drastic improvement in traditional forms of marketing whereby the use of big data, the prediction of consumers and their behaviour and the ability to enjoy privacy-led personal communication with customers all impact traditional marketing [1]. It is essential to understand that as organisations continue to incorporate AI into the business environment, opportunities for improving customer interaction, organisational efficiency and general organisational success become apparent [2].

In digital marketing, AI uses are reported to embrace customer profiling and prediction, content customization, and real-time advertising [3]; [4]. These tools help marketers to fulfil consumers’ expectations of relevance and personalisation which become particularly crucial in today’s global context [5]. Outside the B2C (Business to consumer) orientation of the technology, AI is especially valuable in B2B (Business to Business) promotion where data makes it possible to create unique, more effective and long-term customer relations and ensure consistency of the brand message across different markets [6]; [7].

AI brings many benefits and trying to implement the AI system into the marketing game plan is a great move but it has some drawbacks that are associated with the ethical use of AI, consumer trust in AI, AI and brand identity. For example, AI in content creation and brand engagement interactions have to be regulated to prevent loss of brand identity and branding that seems artificial in some ways; that is a sure way to drive away the loyal customer base [8]; [9]. Moreover, the ethical consideration of data privacy and AI transparency becomes an essential factor as the consumers and the regulatory authorities are more concerned with the proper use of AI that ensures privacy and the fairness of action by implementing AI models.

This review aims to understand AI as the opportunity and change tool in marketing strategies’ development and organization’s success, as well as outline the difficulties that appear with its use. Consequently, this paper presents a current state-of-the-art discussion about how consumer behaviour, brand relationships, and ethical concerns are influenced by AI, and thus offers a profound understanding of the breakthrough potential of AI as well as the necessary strategic reflections for its proper implementation. Concerning a synthesis of recent papers in diverse settings, this review aims to cast light on how to embrace AI to advance the marketing discipline while confronting pressing issues to build consumer trust and facilitate organizational buy-in.

Research Objectives

• To identify the role of marketing in organisational success and the influence of artificial intelligence on this success
• To analyse existing marketing frameworks and assess the influence of artificial intelligence on marketing initiatives based on these frameworks
• To investigate the impact of artificial intelligence on an organisation's marketing initiatives and its overall success
• To explore the challenges of incorporating artificial intelligence into marketing initiatives from a human-brand-strategy perspective [8].
• To suggest strategies for marketing teams and board management to adopt artificial intelligence in their marketing initiatives

Research Questions

• How does marketing contribute to organisational success, and what is the role of artificial intelligence in enhancing this contribution?
• How do existing marketing frameworks incorporate artificial intelligence, and what is the impact of AI on marketing initiatives developed through these frameworks?
• What is the effect of artificial intelligence on marketing initiatives, and how does it contribute to an organisation's overall success?
• What challenges arise when incorporating artificial intelligence into marketing initiatives from the perspectives of human involvement, brand perception, and strategic alignment?
• What strategies can marketing teams and board management adopt to successfully integrate artificial intelligence into marketing initiatives?

2.1 Inclusion and Exclusion Criteria

According to the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) this work was carried out systematically and selectively following a clear line of procedures for the identification of the most relevant and high-quality studies for this research. To achieve this primary objective, it was necessary to identify how the application of AI plays a part in the outcome of marketing campaigns and about teams within an organisation. To accomplish this, the following criteria for inclusion and exclusion of research papers and articles were employed; only research papers that addressed these research questions to a reasonable extent were included [5].

Inclusion Criteria

These criteria were designed to select recent, readily available, and higher-quality empirical publications related to the research topic. The specific criteria are as follows:

1. Publication Date: For this reason, only the articles and scientific papers that were published between 2018 and 2024 were included in the analysis. This type of time frame was adopted to cover the recent development and admixture in applying AI in marketing, as the dynamics in AI and machine learning technologies are quite dynamic. Restricting the research to the period of the last five years meant only the most relevant information regarding modern tendencies in marketing was taken into account.
2. Full-Text Accessibility: Interpreting the findings from the studies presented, the requirements of their full openness to the researchers had to be met to provide an opportunity to review their content. Given the ever-growing number of research articles, only those articles were considered for review if they were available either through full-text institutional databases or through open access sources to provide a comprehensive critical analysis of methodologies, results and conclusions drawn by the respective authors. This criterion made it possible to include only studies that provide all the necessary information for critical analysis [10].
3. Peer-Reviewed Journal Articles: For consistency and quality, only articles from peer reviewed journals were considered in the study. Scholarly articles in peer-reviewed journals pass through a process of review that normally accredits the credibility, precision and dependability of the results presented. Selecting only peer-reviewed sources, like journal articles, reduced the dependency of findings on the completeness and reliability of the data source.
4. Relevance to Research Objectives: The question guiding each study had to be dedicated to addressing one or several issues related to AI in marketing, including its implications to key conceptualisations of marketing, customer interactions, brand perceptions, and organisational effectiveness. Research that was in line with the objectives of the study offered focused information which was useful in enriching the findings’ context and validity for the topic [3].

Exclusion Criteria

Certain types of studies were excluded to maintain focus and quality:

Studies Outside the 2018-2024 Range: To reduce sources of ‘dated’ information that would not capture the current or advanced possibilities of AI in marketing, articles from before 2015 were not included. Since the rate of technology growth is tremendous, studies of the period before and after this period can be considered less relevant to the current state of AI.

Inaccessible Articles: Any study that the authors could not obtain in full text for any reason was excluded. This criterion was aimed at solving the problem of having limited information to facilitate a thorough analysis of the information provided. Any articles that could not provide full-text access were excluded to prevent comprehension breaks in the study [11].

Non-Peer-Reviewed Sources: Conference papers, reports, and book chapters were excluded because non-PE-relevant papers from these sources may be of poor quality and have not undergone peer review. Such sources might be useful for this purpose but are generally more questionable because they do not necessarily incorporate ironclad methodologies and critical approaches to the information analysed in peer-reviewed articles.

Irrelevant Studies: Any article not closely related to the study’s objectives was omitted for review. For instance, patents that only included AI related to other fields or with no connection to a marketing campaign were not included. This enhanced the criteria for selection of the articles to the extent that all the studies included could indeed be considered to be directly informed about the use of AI in the marketing context [12].

Summary

Therefore, based on these inclusion and exclusion criteria, the formation of a pool of relevant and high-quality literature was deemed possible for the study. This strict approach to the selection of papers served the purpose of excluding the works that did not have a direct relevance to how AI can be used in marketing and how it impacted organizations, for the analysis to be built upon credible findings. This yielded 38 papers that complied with the criteria and were reflective of the said objectives to provide a systematic review of the effect of AI on marketing plans.

2.2 Search Strategy

The literature search commenced with an extensive review of 40,871 articles sourced from the ScienceDirect database, using specific search terms such as “AI in marketing,” “customer engagement,” and “brand management” to capture relevant studies [1]. This search aimed to identify scholarly works exploring the integration of artificial intelligence within marketing domains.

Research Strings

The search strings developed for this review included targeted keywords to streamline the identification of relevant literature. Three main research strings were used:

1. Artificial Intelligence and Marketing Initiatives
2. Artificial Intelligence and Brand Management
3. Artificial Intelligence and Marketing

Each search string yielded a distinct number of articles in ScienceDirect, with the following initial totals:

• Artificial Intelligence and Marketing Initiatives: 7,230 articles
• Artificial Intelligence and Brand Management: 11,058 articles
• Artificial Intelligence and Marketing: 22,583 articles

This preliminary step produced a combined total of 40,871 references, forming the initial dataset for analysis.

Step 1: Duplicate Removal

The next stage focused on refining the initial dataset by removing duplicate articles to ensure each unique article was evaluated independently. This process eliminated approximately 6,473 duplicates, reducing the dataset to 34,398 studies, which were then prepared for further analysis.

Step 2: Publication Date Filtering

To maintain relevance and timeliness in the review, an additional filter was applied to include only articles published within the recent timeframe of 2018-2024. This refinement led to the exclusion of 5,705 articles, resulting in a refined selection of 28,693 unique journal articles published within the specified period.

Step 3: Accessibility and Content-Type Filtering

Further screening involved removing studies that were not accessible in full, as well as excluding book chapters, conference papers, and abstracts. This process led to the selection of 9,832 articles, focusing solely on full-length journal articles. Exclusion criteria ensured the analysis cantered on peer-reviewed journal studies, directly aligning with the study’s objectives.

Step 4: Relevance Evaluation and Final Selection

Each article in the remaining dataset underwent a detailed review to assess relevance to the research objectives, specifically focusing on the role and impact of AI in marketing initiatives. This final step resulted in the exclusion of 9,794 articles that did not meet relevance criteria. Ultimately, 38 studies met all criteria, forming the final dataset for the study [13].

This systematic selection and appraisal process ensured that only the most relevant and high-quality studies were retained, providing a strong and up-to-date foundation for analysing the influence of AI on marketing strategies and outcomes.

DATA EXTRACTION AND SYNTHESIS

Data were systematically extracted and organized into a table to illustrate key findings, methodologies, and observations across studies. A sample of the data synthesis table is presented below:

Here's a summary table of the 38 references, capturing their main focus, methodology, and key findings:

This table provides a concise overview of each study, covering the year, title, methodology, and main findings, allowing you to quickly grasp the focus and insights of each reference. Let me know if you need additional details for any specific reference [4].

4.1 AI's Role in Marketing and Organizational Success

AI has become an effective tool in marketing that assists companies in delivering customer interactions in far more personalized and accurate manners that enhance organizational performance in a major way. For instance, the use of AI in predicting the conduct of consumers has been helpful, especially in creating tailored marketing strategies to retain consumers, [1]. The dynamic of AI makes it possible to make changes immediately in marketing campaigns so the messages that are being communicated will be current [5]; [7]. Furthermore, as [14] indicate, the use of IVA increases brand loyalty because interactions become lighter, and interactions are made easier by the use of smart voice assistants, but there is an issue with building trust.

It is also increasingly customized to improve the globalization of branding [15], refine customer targeting [11], and address multiple cultural standards worldwide [16]. However, AI tools also enable micro-entrepreneurs and small business enterprises to exhibit the same opportunities in the marketing communication process as the large business enterprises through enhancing the marketing outreach capacity. With the help of tools such as AI-incorporated CRMs, businesses can manage their B2B customers, which in turn enables organizations to maintain their clients and build strong partnerships [7]. In the B2B context, the utilisation of AI is known as an enhancer of strategic selling based on the capability to implement dynamic and data-oriented approaches to marketing [17]; [6]. As [18] found out, new creations should be made jointly with the help of AI, and while [19] proved AI can still impose certain control over artistic authenticity because it was found that creative fields also require some AI control to remain genuine.

Finally, the appreciation of the adoption of artificial intelligence within marketing and its impact on shareholder response highlights how AI remediates organizational effectiveness by enriching corporate brand leverage and business process improvement [20]. Customer segmentation and brand consistency, which AI is capable of solving, prove its importance for meeting current marketing objectives and future business development [13]; [3].

4.2 Integration of AI within Existing Marketing Frameworks

Even though the AI application can be introduced as a stand-alone innovation, for it to complement the pre-existing marketing strategies, it has to fit into several structures to support marketing objectives and meet consumer expectations. In social marketing, AI operates to facilitate the personalization of the marketing campaign by utilizing the customer interaction data in the creation of marketing initiatives that meet the customer’s preferences [8]; [21]. Nevertheless, AI introduction must be performed delicately especially when creating brand personality as each element should be aligned consistently across different digital interfaces [5]. The same level of detail is shown in the case of AI-branded apps that serve to overcome the issue of customer churn through the increased adoption of better value added through personalized experiences based on user behaviour [22].

Integration of AI has extended to B2B marketing techniques where CRM tends to enhance customer relations by making use of data analytics to refine the techniques of interactions. AI is also useful in global brand management because it is difficult to manage a brand identity across different cultures and market standards. This was when [15] and B2B branding strategies [6] focused on the consistency of the brand. More importantly, the implementation of AI to assure content authenticity remains here, with [23] noting that ‘‘To maintain customer trust, specifically in the B2B context, AI-driven content must look natural.’’

Design thinking with the help of artificial intelligence enables user orientation and develops marketing innovations [24]. AI can capture characteristics of consumer behaviour and respond to them in near real-time, as demonstrated by [17]; this explains why scholars and proponents of AI consider the technology as useful in improving the effectiveness of the extant marketing frameworks. Finally, the integration of AI innovation into brands to achieve environmental sustainability is an opportunity to appeal to consumers’ interest in environmentally sustainable products on the globe [13];[4].

4.3 Challenges in AI Integration: Human, Brand, and Strategic Perspectives

Human Involvement

The use of artificial intelligence contributes to a minimal level of human touch in the marketing processes, a drawback that will reduce customer trust [14]. When brands integrate AI into the content creation process, [8] noted that efficiency at the cost of perceived brand authenticity is attainable, particularly in markets that established positive attitudes towards PI. In other words, how to get hybrid models that reflect organizational values, such as honesty and the desire for efficiency on the one hand and empathy and creativity on the other.

Brand Perception

Marketing automation using artificial intelligence can be off-brand and misleading if not integrated well within the company’s ethos. [9] also point out that information sharing through AI may hurt brand trust because customers consider AI technology as an impersonal tool. According to [5], it is followed by this premise; Artificial Intelligence improves the enhanced personalization of brands but requires adequate regulation to avoid the development of a gap between the mentioned brand and the audience.

Strategic Alignment

Another factor that must be considered is that a specific form of AI integration in marketing must begin with a clear strategic alignment between AI projects and the company’s general objectives. The literature indicates that companies find themselves challenged by how to best integrate AI application functions with organizational strategic goals, which results in a disjointed customer experience [25]; [26]. The goal-setting situation challenges marketers to help unify and guide the communications strategy around various AI projects to guarantee they work towards the company’s strategic vision.

However AI AI-based integrated solutions for marketing communications is not an entirely unchallenging affair for any business; it poses human, brand and strategic implications. From the human-relationship viewpoint, the involvement of artificial intelligence poses the problem of emotions, namely, the parties cannot trust each other due to the lack of a guarantee that they will be treated or will treat the other party as friends [9]; [26]. Research has it that perceived authenticity becomes lower when media contents are created through AI since consumers rely on real interaction especially on social media [8] Moreover, anthropomorphic uses of AI, for example, voice assistants, must be humanlike but not too humanoid to sound odd [26].

In fact, from a strategic perspective, the integration of AI in the context of marketing frameworks needs to address organisational goals, as stressed by [20] to gain shareholders’ support and to ensure no resistance by internal stakeholders. Culture also contributes; for instance, AI adoption rates differ per market: the West African nation of Ghana, for example, has specific preferences for AI-forward product experiences – a point that suggests cultural tailoring [27]. On a more general level, various sectors, such as luxury hospitality, experience two threats related to AI: firstly, the use of AI can be perceived as an opportunity that leads to a higher turnover intention.

The use of AI in brand building and maintenance is not straightforward with consistent and clear evidence, especially for a global brand that needs to implement the brand strategy working with AI insights universally [16]; [15]. [12] AI's impact on consumer behaviour including brand switch (see figure below) outweighs the benefits when not used strategically hence calling for more attention in developing strategic decisions on AI. Moreover, the employment of AI for creative content in branding brings concerns about effectiveness and quality because values that are believed to be generated by AI are not deep in the same way as human input [18]; [19].

Last but not least, ethical issues will prevail as data manipulation and privacy issues regarding AI is still a problem that brands should handle appropriately to ensure consumers’ trust [28]; [29]. Therefore, I have positioned that although AI brings enormous advantages, overcoming the identified human, brand, and strategic issues is crucial for effective AI implementation in marketing.

4.4 Recommended Strategies for AI Integration in Marketing

For AI to bring out the desired change in marketing, organizations must ensure operational strategies that seek to capture some of the ethical issues of AI. Organizations are achieving not only operational and customer engagement benefits but, when AI is implemented appropriately and systematically, also their brand integrity, consumer trust, and strategic objectives can be advanced cohesively. To ensure a smooth integration of AI in marketing, these strategies are suggested:

1. One has clear goals and objectives to achieve which AI is to be implemented and ensure that these goals align with marketing goals

The foundation of AI integration starts with setting SMART goals that are in line with the general marketing direction of a firm. Organisations should define particular marketing objectives, including enhanced customer interactions, enhanced brand identifications, or better resource utilisation [1]. By connecting those goals with AI projects, marketers can guarantee that AI-related efforts are not going to become siloed technological undertakings.

AI helps marketers to nurture relationships on a level that, until now, was impossible. Due to the understanding of the customer’s life cycle, behaviour, preferences and buying patterns, AI can produce better content and better products that the customers need, which can further improve the experience of the customers [14]. Nonetheless, corporations must be wary not to overstep the mark about personalisation as that will then breach privacy. Opt-in systems seem to be quite useful in managing customer concerns about privacy since they give clients an option of how their information will be used [9].

It means that AI applications should be evaluated continuously to determine how they help marketing achieve its objectives and align with the expectations of customers. Companies should create specific targets for AI-specific campaigns for example the number of customers that are reached, the number of customers that make a purchase, and the changes in sentiments of customers after an AI campaign among other factors. Also, acquiring customers’ feedback on their talks with AI can help marketers understand which aspects need enhancement and continuously advance AI use cases [30].

AI is set to revolutionalise marketing operations by providing possibilities for personalised customer interactions, data forecasts, as well as more efficient use of resources. However, there are drawbacks to adopting AI in marketing, which are connected to brand consistency, AI strategic positioning, and human interaction. These problems can be solved by adopting new hybrid models, creating ethical standards and having strategic goals. Thus, further qualitative studies should examine the developments of AI in the marketing context between different sectors and regions to refine these approaches [22]. When employing AI for marketing, it is crucially important for brands that are active in different countries to be culturally relevant. AI models should be sensitive to cultural and regional preferences and otherwise risk turning audiences off [27]; [16]. For example, AI applications for personalization introduce the issue of local preferences, which can even dramatically influence consumers’ acceptance and activity levels. Through the customization of AI-based promotional campaigns based on distinct geographical areas, brands can deepen their customer base affiliation worldwide.

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