Reconfiguring competitive advantage: a resource dynamic framework for generative AI adoption in digital content marketing

The advent of Generative Artificial Intelligence (Gen-AI) is establishing itself as a catalyst for the widespread dissemination of Artificial Intelligence (AI) across the business landscape, revolutionizing how textual, visual, audiovisual, and sound content are generated (Dasborough, 2023). The remarkable ability of this technology to produce outputs with a human-like appearance is redefining the boundary between human creativity and computational capabilities (Dwivedi et al., 2023), marking a turning point in the traditional dynamics of numerous sectors, with marketing leading among the areas most benefited by this transformation (Rust, 2020). In this context, tools like ChatGPT, Midjourney and SORA emerge as emblematic examples of Gen-AI's revolutionary impact. ChatGPT, for instance, has redefined how companies approach creative writing and customer service management by providing immediate assistance and generating personalized content on a large scale (Dwivedi et al., 2023). Similarly, Midjourney uses advanced algorithms to create visually stunning artworks that can adapt to various esthetic preferences, democratizing access to high-quality digital art (Yin et al., 2023). SORA, on the other hand, is transforming the landscape of audiovisual content processing and creation, enabling the production of videos and sounds. A 2023 report by McKinsey & Company highlights the significant economic impact that Gen-AI is set to have on marketing and sales functions, forecasting an impact estimated between 760 and 1,200 billion dollars across various industries (Chui et al., 2023). Such projections not only highlight the scale of innovation introduced by Gen-AI but also signal its transformative implications for operational efficiency, cost reduction, and the reinvention of content creation paradigms (Wahid et al., 2023).

In this context, the role of Gen-AI in Digital Content Marketing (DCM) is becoming increasingly prominent. Its capacity to augment the creative and productive capabilities of marketing professionals is fostering new modes of content ideation, production, and distribution (Dwivedi et al., 2023; Wahid et al., 2023). Research from Boston Consulting (2023) further illustrates the strategic relevance of Gen-AI in key marketing activities, particularly in generating data-driven insights, personalizing user experiences, and developing scalable content solutions (Kshetri et al., 2023; Ratajczak et al., 2023).

The integration of Gen-AI into marketing strategies has been shown to enhance consumer engagement and personalization efforts, indicating a future where content strategies are increasingly sophisticated, data-informed, and audience-specific (Kumar et al., 2019; Kshetri et al., 2023). By enabling precise and timely communication through the analysis of large-scale, meaningful datasets, Gen-AI supports the strategic implementation of personalization in marketing practices (Dwivedi et al., 2023; Kshetri et al., 2023). Consequently, its effective adoption is critical for optimizing inbound marketing approaches and delivering tailored content through the most appropriate formats and channels (Vlačić et al., 2021). These capabilities significantly enhance consumer engagement by ensuring that communications are relevant and aligned with the expectations of the audience (Kumar et al., 2019; Huang and Rust, 2021).

Moreover, the internal implications of Gen-AI adoption are profound. By increasing efficiency, creative output, and speed of content generation, Gen-AI strengthens the productive capacities of organizations engaged in content marketing (Wahid et al., 2023).

Despite the growing enthusiasm surrounding the adoption of Gen-AI, significant uncertainties remain regarding its long-term impact on content creation. In particular, the literature raises critical concerns related to the ethical and legal dimensions of deploying such technologies (Du and Xie, 2021; Dwivedi et al., 2023), as well as their potential implications for the professional skillsets required in the marketing domain (Vlačić et al., 2021).

Although some studies have examined the evolution of marketing practices following the introduction of AI (Huang and Rust, 2021; Vlačić et al., 2021), the focus has largely remained on AI in its broadest sense, often overlooking the specificities of its generative branch (Huang and Rust, 2021; Kumar et al., 2019; Vlačić et al., 2021).

To the best of our knowledge, there is a lack of comprehensive academic investigations into the implications of the relationship between Generative AI and content marketing, particularly regarding ethical, legal, and professional competency dimensions. Although there is broad scholarly agreement on the strategic value of digital technologies in facilitating the production of relevant, personalized, and timely content to enhance user experience (Järvinen and Taiminen, 2016; Kumar et al., 2019; Terho et al., 2022), the specific integration of Gen-AI into content marketing remains underexplored. In particular, little is known about how this technology may reshape existing practices and affect strategic and organizational processes within the field.

However, merely adopting Gen-AI in isolation does not automatically ensure a sustainable competitive advantage. DCM primarily relies on intangible resources such as creativity, specialized expertise, and the capacity for innovation. In this context, the Resource-Based View (RBV) approach proves particularly relevant, as it focuses on the identification, enhancement, and strategic exploitation of internal organizational resources that are difficult for competitors to replicate (Barney, 1991). In scenarios marked by the rapid democratization of technologies, such as in the case of Gen-AI, competitive advantage does not stem solely from the availability or adoption of the technology itself, but rather from the firm's ability to integrate it with distinctive assets, such as proprietary data, specialized know-how, and an innovation-oriented organizational culture capable of sustaining a durable competitive edge.

In this perspective, the criteria provided by the VRIO framework: Value, Rarity, Inimitability, and Organization, offer a useful lens through which to assess the strategic potential of a firm's internal resources. Moreover, the VRIO framework helps determine whether a resource can provide a sustained competitive advantage by assessing its strategic importance and difficulty to replicate, thus providing a robust analytical lens to assess how these resources can decisively contribute to value creation, ensuring not only operational efficiency but also strategic differentiation over the long term (Kozlenkova et al., 2014). Indeed, while Gen-AI tools are becoming rapidly accessible and standardized, their availability alone doesn't translate to market leadership. To address the issues raised by the identified research questions, the present study employs the Delphi method, an approach that allows for the aggregation of perceptions from a diverse group of experts (Adler and Ziglio, 1996), in line with the multidisciplinary nature of this research. Accordingly, 14 experts have been selected, who play a significant role in the dynamics between Gen-AI and content marketing, including content marketing practitioners, AI and digital marketing consultants, AI provider as well as experts in the related ethical and legal issues. The goal of this study is to develop a set of recommendations for these stakeholders, in order to outline how Gen-AI interacts with content marketing and what the implications of this relationship are. This will establish a foundation for understanding the current and radical evolution underway, while also providing critical insights into the future prospects of this sector. The structure of the document is articulated and described in detail in the following sections. Section II offers a detailed review of the existing literature on DCM, Gen-AI's theoretical underpinnings, and ongoing debates regarding the ethical, legal, and professional competencies implicated by Gen-AI's application. Section III outlines the methodology adopted in this research, specifically the Delphi method. It details the rationale behind selecting this approach and provides an in-depth explanation of the sequential rounds of consultation conducted. Section IV presents the empirical findings of the study and outlines an emerging scenario from the unanimous consensus of experts on specific assertions regarding the interaction between Gen-AI and DCM. Section V presents the discussion by analyzing them through the lens of the Resource-Based View (RBV) and the VRIO model, assessing the strategic value of Gen-AI in DCM. Furthermore, it introduces an integrated framework that applies the dynamic capabilities as an extension of RBV theory, illustrating how organizations can leverage Gen-AI to build sustainable competitive advantages in an evolving digital landscape. Finally, Section VI outlines the theoretical and practical contributions of the study, while Section VII presents the conclusions, summarizing the key insights and implications.

Digital content marketing (DCM) represents a profound transformation in companies' approach to the market, shifting from merely selling to providing valuable and relevant content to consumers (Davis, 2012; Holliman and Rowley, 2014; Järvinen and Taiminen, 2016; Hollebeek and Macky, 2019). The evolution of communication strategy has shifted from a traditionally unidirectional approach, which can disrupt user engagement, to a more customer-centric approach. This new approach seeks to attract customers by delivering compelling and valuable content, with the aim of establishing and maintaining long-term relationships with consumers (Holliman and Rowley, 2014). Academic literature offers various definitions of content marketing (Pulizzi, 2013; Rancati and Gordini, 2014; Hollebeek and Macky, 2019). Holliman and Rowley (2014, p. 285) describe it as “the marketing and business process for creating and distributing valuable and compelling content to attract, acquire, and engage a clearly defined and understood target audience–with the objective of driving profitable customer action”. A consensus among scholars suggests that content marketing is an inbound marketing approach (Pulizzi and Barrett, 2009; Rancati and Gordini, 2014; Terho et al., 2022) specifically crafted to captivate customers through the creation of pertinent, compelling, and valuable content (Holliman and Rowley, 2014; Järvinen and Taiminen, 2016; Hollebeek and Macky, 2019). According to some authors, valuable content is characterized by its usefulness, engagement, timeliness, educational value, and relevance (Pulizzi and Barrett, 2009; Holliman and Rowley, 2014; Rancati and Gordini, 2014; Wang et al., 2019; Hollebeek and Macky, 2019). Holliman and Rowley (2014) propose adopting a strategic approach to content marketing, beginning with defining brand objectives, such as promoting brand awareness, engagement, and trust, conversion or nurturing sales leads, improving customer service, or contributing to the development of customer loyalty. Rose and Pulizzi (2011) identify specific objectives based on the content marketing funnel, including brand awareness, lead conversion and nurturing, customer conversion, customer service, customer upsell, and the creation of passionate subscribers. To develop an effective content marketing strategy, companies must accurately map their audience to gain a comprehensive understanding of their target (Bruce et al., 2017; Terho et al., 2022). This stage is essential for capturing customer interest at key moments in their purchasing decision-making process. Gaining a thorough understanding of the customer requires identifying buyer personas. In the B2B context, this involves analyzing business challenges, goals, motivations, purchasing criteria, and values (Terho et al., 2022). In the B2C context, it focuses on demographic characteristics and buyer personality (Lehnert et al., 2021). Insights are gathered through interviews, data analysis, and monitoring social media conversations (Terho et al., 2022). In this context, the customer journey for buyer personas is outlined, identifying the touchpoints necessary to gain a deep understanding of the audience's needs (Terho et al., 2022; Holliman and Rowley, 2014). Yaghtin et al. (2020) outline a structured approach to planning a goal-oriented content marketing strategy, divided into four key phases: planning, production, promotion, and content optimization. In the planning phase, it is important to select appropriate content that engages the target audience. It is essential to consider the identified audience's needs to create content that helps customers solve specific problems (Holliman and Rowley, 2014). When creating valuable content, marketers must choose the media that provides the greatest value for the customers (Jefferson and Tanton, 2013; Lehnert et al., 2021). Within this framework, personalization is essential for addressing the interests of audiences, enabling consumers to engage with content that aligns with their preferences (Wall and Spinuzzi, 2018). Once the content has been distributed and promoted, it is vital to establish metrics and measurement techniques aligned with the objectives of the DCM strategy, in order to assess the effectiveness of the strategy executed (Holliman and Rowley, 2014; Lehnert et al., 2021). According to Yaghtin et al. (2020), leveraging user feedback further enhances the content creation and dissemination process.

Current literature discusses the importance of technology in DCM field, emphasizing how the integration of content marketing with IT tools such as marketing automation (Järvinen and Taiminen, 2016) and digital data (Thorleuchter et al., 2012) can facilitate the achievement of desired results. This approach makes it possible to provide personalized and targeted information to customers based on their recent online activities (Järvinen and Taiminen, 2016). In this scenario, artificial intelligence is emerging as a key technology for personalizing content to meet customer needs and increase engagement (Kumar et al., 2019; Huang and Rust, 2021).

In recent decades, artificial intelligence (AI) has experienced rapid development, evolving from pioneering programs such as the General Problem Solver and ELIZA in the 1950 and 1960s (Newell et al., 1959; Weizenbaum, 1966), to more sophisticated forms like Generative AI (Gen-AI). This new form of AI, capable of generating text and multimedia content in a manner similar to human production (Dasborough, 2023), has opened new perspectives across various sectors, including marketing. A notable example of Gen-AI is ChatGPT, launched in 2022, which belongs to the family of large language models (LLM) and is designed for conversational interactions through deep learning methodologies (Cascella et al., 2023). Indeed, as stated by Tafesse and Wien (2024), it has been proved that tools like ChatGPT facilitate the creation and curation of digital content, accelerating the adoption of gen-AI in marketing and simplifying the work of industry professionals. For this reason, ChatGPT represents a revolution for marketing, as it can manage text, images, and videos, analyze multimedia content, and address a wide range of industry needs (Rivas and Zhao, 2023). As AI becomes increasingly integrated into marketing strategies, its effectiveness largely depends on the availability and quality of data used for training and optimization, which directly impact the accuracy and relevance of the output (Uren and Edwards, 2023). Among the various fields impacted by the introduction of AI, content marketing emerges as one of the most transformed (Chintalapati and Pandey, 2022). Indeed, ChatGPT and other AI technologies provide substantial support and significant time and effort savings for marketing professionals, especially in the processes of content ideation and generation (Lund and Wang, 2023). In the sphere of content creation, ChatGPT enables marketers to produce articles for blogs, social media content, newsletters, and product descriptions on a scalable basis, offering particularly advantageous support for small businesses with limited resources (Gupta et al., 2024). This approach has marked a revolution in content creation, ushering in a new era of greater efficiency and productivity (Wahid et al., 2023). One of the most significant impacts of AI in content marketing lies in the improvement of key processes such as automation, personalization, and the generation of insights (Chintalapati and Pandey, 2022; Arango et al., 2023). AI has the potential to augment creativity by automating repetitive tasks, providing data-driven insights, and enhancing ideation processes through advanced algorithms, thus enabling individuals to focus on high-order cognitive functions and strategic problem-solving (Ameen et al., 2022). AI-based tools optimize content production, simplifying tasks such as caption generation and maintaining brand consistency across multiple platforms, allowing marketers to focus on more high-value strategic activities (Roshne et al., 2024). In particular, the use of AI technologies and data-driven tools enables advanced content personalization, tailoring communications to the preferences and behaviors of individual consumers (Sang, 2024). While AI greatly enhances operational efficiency, human creativity remains indispensable for refining and optimizing generated content. Consequently, the synergy between artificial intelligence and human intervention is essential for increasing audience engagement and strengthening brand perception (Roshne et al., 2024). In specific sectors, such as hospitality, AI has proven effective in enhancing content personalization across multiple channels, increasing efficiency, productivity, and guest satisfaction, ultimately leading to increased bookings (Bilgihan and Ricci, 2024). Additionally, integrating AI technologies with social media monitoring allows businesses to respond promptly to consumer inquiries, optimizing their competitive strategies (Chen, 2023). AI and machine learning further advance content marketing through automation and the generation of data-driven insights, enabling greater personalization, budget optimization, and the development of more responsive content strategies tailored to consumer preferences (Kuzyk et al., 2023). While ChatGPT offers numerous benefits, its use should inspire and enrich existing content, preserving the role of human creativity and avoiding excessive reliance that could diminish the creative potential of marketing professionals. Additionally, it remains fundamental for marketers to analyze and validate the generated content to ensure consistency with the brand's image (Gupta et al., 2024).

The rapid evolution of AI is significantly reshaping the global employment landscape, introducing both opportunities for innovation and efficiency and concerns regarding potential job displacement due to technological advancements (Bankins et al., 2023; Du and Xie, 2021). The McKinsey Global Institute predicts that up to 375 million workers worldwide will need to change occupational categories by 2030 due to automation and artificial intelligence (Manyika et al., 2017). This transformation has sparked a multifaceted debate among scholars, with some argue that roles involving repetitive tasks are at increased risk of being replaced by information systems (Kolade and Owoseni, 2022), while others argue that the impact of AI also extends to cognitive tasks (Novakova, 2020). Huang and Rust (2018) propose the AI Job Replacement theory, suggesting that AI's influence occurs fundamentally at the task level, rather than entire job. Some jobs may become more specialized, requiring workers to have a deep understanding of specific technologies or data analysis techniques. Other jobs may require strong interpersonal and communication skills, as well as the ability to collaborate with automated systems (Manyika et al., 2017). Initially, AI replaces simple, repetitive tasks reliant on mechanical intelligence before progressing to tasks requiring analytical intelligence, such as logical reasoning and decision-making based on established rules. This trajectory implies that future professionals will need to develop interdisciplinary skills, with an emphasis on intuitive intelligence, capable of fostering creative thought and understanding complex contexts, and empathic intelligence to effectively handle situations with heightened emotions. Jaiswal et al. (2022) highlight that while AI can perform tasks associated with mechanical intelligence, such as basic data analysis, it faces challenges in replicating tasks demanding analytical, intuitive, and empathic capabilities, which involve complex cognitive processing and continuous learning. In this context, Rahmani and Zohuri (2023) argue that with the automation of repetitive tasks by machines, the importance of purely human skills, such as creativity, critical thinking, emotional intelligence, and adaptability, is growing. Artificial intelligence does not merely replace human beings but becomes an ally in the process of skills development, offering opportunities for updating and retraining that effectively respond to the ever-changing needs of various industrial sectors. According to Su et al. (2021), the indispensability of advanced cognitive skills, including problem-solving, innovation, and persuasive communication, is evident, as these are abilities beyond the current scope of AI algorithms. The literature suggests that to achieve a harmonious coexistence with AI, professionals must possess a blend of technical, interpersonal, and conceptual skills. These range from technological proficiency and computer literacy to advanced digital technologies, to interpersonal skills essential for effective communication and teamwork, and conceptual skills like critical thinking and decision-making (Zirar et al., 2023). The concept of work transformation in the era of automation is fundamental to promoting an evolution of roles that pushes towards higher levels of responsibility, enhancing the distinctive characteristics of human beings. The main challenge is to equip the workforce with the necessary skills to successfully navigate this transition. Companies are recognizing the importance of investing in professional development and retraining initiatives, adopting a proactive approach to addressing the changes in the labor market. The transformation of work, driven by automation, is not limited to responding to technological innovations but embraces a future where people and technology collaborate synergistically. This process will lead to the creation of a flexible, creative workforce that can fully leverage the opportunities offered by automation powered by artificial intelligence (Mossavar-Rahmani and Zohuri, 2024). Kaplan and Haenlein (2020) argue for the necessity of a foundational understanding of programming among individuals to foster a deeper grasp of AI systems. The dynamics of the evolving job market underscore the importance of upskilling and reskilling as foundational strategies to facilitate a symbiotic relationship with AI, focusing on enhancing human skills to complement, rather than be replaced by technological capabilities (Zirar et al., 2023). Through targeted training and skill development, employees can not only improve their current performance but also prepare for emerging roles within their organizations (Novakova, 2020; Li, 2022), highlighting the need for continuous professional development and training initiatives to build an effective collaborative ecosystem between humans and AI (Jaiswal et al., 2022; Kolade and Owoseni, 2022; Li, 2022; Zirar et al., 2023).

In order to expand the adoption of Gen-AI in content marketing, several ethical and legal considerations that academia is currently investigating must be considered (Grewal et al., 2024). From a legal perspective, a critical aspect is the attribution of authorship and intellectual property rights of the generated content (Fontana, 2025). Indeed, when using Gen-AI, it becomes unclear whether the AI model itself or the human user who crafted the prompts and parameters is the rightful copyright holder. This ambiguity can lead to complexities regarding ownership of the generated content and potential infringement of third-party copyrights (Chesterman, 2025). In this context, Formosa et al. (2025) has analyzed civil and penal liabilities, especially concerning content production, suggesting the introduction of new categories such as “hybrid authorship” and “shared responsibility” to adequately recognize the contribution of each actor involved. In any case, the issue is more intricate than it seems, and various academics are addressing this debate from different perspectives. On one hand, some argue that since Gen-AI models lack the requisite level of creativity and independent thought, the human user who provides the prompts and edits the output should be considered the author (Zhou and Nabus, 2023). Others assume that the complex algorithms and training data employed by generative models contribute significantly to the originality of the content, suggesting a form of shared authorship (Bozkurt, 2024). UNESCO (2021) has identified challenges to creators' rights due to the interplay between human and algorithmic creativity within the creative industries. At the same time, some scholars are analyzing the ethical issues underlying the use of Gen-AI highlighting how this technology involves challenges related to data protection, misinformation, and bias. For instance, the ability of generative models to create deepfakes and synthetic media, which can appear indistinguishable from real content, threatens public trust and the very fabric of democratic societies (Al-Kfairy et al., 2024). This capacity to generate misinformation raises significant concerns regarding truthfulness and transparency, necessitating the development of more robust regulatory frameworks to mitigate the associated risks. Farina et al. (2025) further contribute to this argument research by emphasizing how the indiscriminate implementation of these technologies can exacerbate social inequalities, leading to the formation of oligopolies and the centralisation of economic power. Indeed, the large-scale adoption of Gen-AI in the labor market could also lead to a reduction in individual freedom, making political intervention necessary to safeguard social equity and promote the common good. To address these challenges, it is important to adopt a strategy that includes a rigorous risk analysis and the promotion of ethical standards throughout all stages of Gen-AI development (Hermann and Puntoni, 2024). According to the authors, the analysis of ethical implications should not stop at mere legal compliance but should also include a broader reflection on the social and moral impact of the technology, fostering the development of Gen-AI systems that promote fairness, transparency, and human well-being (Hermann and Puntoni, 2024). Research by Karvalko (2024) also adds that without proper regulation, the opportunity to manage the negative effects of Gen-AI could vanish, making it impossible to correct future errors. The author highlights the need for regulatory intervention to ensure that the technology does not compromise fundamental human rights or cause economic and social imbalances. Luckett (2023)further discussed issues such as safety, privacy, and discrimination in the use of Gen-AI. He draws attention to the importance of educating the public on the risks and potential of these technologies to ensure responsible and informed use.

The Resource-Based View (RBV), first introduced by Wernerfelt (1984) and later formalized by Barney (1991), focuses on identifying and managing internal resources that can secure a sustainable competitive advantage. For resources to be considered strategically relevant, they must meet specific criteria: they need to be valuable, rare, difficult to imitate, and effectively exploited by the organization (Barney, 1991; Peteraf, 1993). Directly derived from the RBV, the VRIO model structures this analysis into four dimensions (Barney, 1996). First, Value refers to a resource's ability to help a company seize opportunities or neutralize competitive threats. Second, Rarity indicates that the resource must be possessed by only a few firms, making it a source of differentiation. Third, Imitability highlights that if competitors find it costly or difficult to replicate, the resource ensures sustainability. Finally, Organization emphasizes that the firm must have the necessary structures and processes in place to fully leverage the resource's potential. In today's evolving digital landscape, the widespread accessibility of Gen-AI, with tools such as ChatGPT and Midjourney, has reshaped the DCM domain (Grewal et al., 2024). These technologies are no longer confined to a select few but are available to a broad spectrum of industry players (Feuerriegel et al., 2024). However, this democratization presents a critical challenge: competitive advantage lies in the integration of gen-AI with firm's unique internal assets (Lee et al., 2024). In this new paradigm, success is no longer solely defined by the ability to implement advanced AI tools but by the capacity to orchestrate them in synergy with the company's distinctive resources. The VRIO model provides a systematic approach to assessing how the combination of Gen-AI and internal resources can lead to a sustainable competitive advantage. This analytical framework serves as a theoretical roadmap, distinguishing companies that, despite operating in a landscape where technology is widely available, can create internal synergies that amplify the effectiveness and impact of digital innovations in content marketing.

A Delphi method was undertaken to explore the influence of Gen-AI on content marketing strategies and content creation processes. This method, initially proposed by Bell (1967), Dalkey and Helmer (1963), and further elaborated by others (Rowe and Wright, 1999; Skulmoski et al., 2007) is recognized for its effectiveness in forecasting outcomes reliant on human behavior and technological dynamics, rather than deterministic natural phenomena. Specifically, in the marketing domain, it has been employed to anticipate future trends and events, highlighting its utility in strategic planning (Knutson et al., 2004; Larreche and Montgomery, 1977). The Delphi method operates on the principle that a carefully selected group of experts can effectively represent broader opinions on any given topic. In this process, the identities of these experts are kept anonymous to ensure unbiased contributions. A moderator plays a key role by designing questionnaires and feedback reports, which are circulated among the participants to refine their responses iteratively and to extract “knowledge” from a pool of experts (Keeney et al., 2001; Loo, 2002). This methodological approach ultimately leads to the creation of a detailed result report, encapsulating the collective insights and conclusions drawn from the expert panel's deliberations. This approach is chosen for its proven effectiveness, the necessity to engage a limited number of specialists due to the scarcity in the field, and to preserve the anonymity that prevents any dominance by certain members within the communication process (Adler and Ziglio, 1996). This iterative process, which can be repeated as needed, starts with open-ended questions to grasp the phenomenon broadly. Subsequent rounds aim to refine and align the experts' views towards a consensus, thereby crafting a unified scenario of the future (Tiberius and Hirt, 2019). Given the multidisciplinary nature of the study, it was considered to involve a panel of 14 experts who play a significant role in the dynamics between Gen-AI and content marketing, including content marketing practitioners, AI and digital marketing consultants, AI provider as well as ethical and legal experts. To our knowledge, there are no previous Delphi studies focused on Gen-AI within the domain of content marketing. Consequently, a preference is given to an exploratory (i.e. broad) strategy rather than a specialized (i.e. in-depth) approach. This exploratory approach allows for covering several salient aspects of Gen-AI without excessive detail, while maintaining enough breadth to provide a comprehensive overview of the topic (Tiberius and Hirt, 2019). Subsequent Delphi analyses can adopt a more focused trajectory, investigating specific subdomains or more granular questions. According to the literature, the scenario under consideration is divided into six thematic categories: (1) current benefits of Gen-AI in the content marketing field, (2) quality and creativity of Gen-AI-created content, (3) strategies and practices adopted by marketers and content creators, (4) ethical and legal implications related to the use of Gen-AI in content creation, (5) implications on professional skills in content marketing caused by the rise of Gen-AI, and (6) future prospects for developments in this technology. Each topic was explored with the involvement of the previously mentioned experts. The research required two distinct phases. The initial stage is termed the “exploratory” phase (Adler and Ziglio, 1996). It involved conducting an online qualitative survey with seven open-ended questions covering six previously identified subject areas. Before distributing it to the panel of experts, a preliminary test was conducted with marketing scholars from the authors' respective universities using the Typeform platform. This preliminary examination aimed to assess the clarity and relevance of the questions, ensuring no vagueness that might compromise the integrity and accuracy of the initial findings. Responses from the preliminary survey were analyzed through content analysis, a method designed to identify core themes (Powell, 2003). The research progressed to a second phase, wherein statements formulated from the initial insights were presented to experts, who were then asked to indicate their agreement or disagreement using a 5-point Likert scale. This scale ranged from “Do not agree” to “Agree”, with intermediate options such as “Somewhat disagree”, “I don't know”, and “Somewhat agree”. Respondents were provided with detailed information, both verbally and in writing, to ensure they were well-informed and comfortable with the survey items. This stage, termed the “evaluation phase”, included a statistical analysis to identify the most compelling scenarios (Adler and Ziglio, 1996). In the analysis of the results of the second round, the Interquartile Range (IQR) was calculated for each statement posed to determine which statements gained the most consensus, thereby facilitating the definition of the scenario under investigation. The IQR is defined as the span between the third and first quartiles and is calculated by subtracting the value of the first quartile (q1) from the third quartile (q3) (Whaley, 2005). Given the high level of consensus across the various statements, an IQR range of 0–1 was used to develop the final scenario.

Firstly, this section initially presents the outcomes of the second round of Delphi method, precisely detailing, for each identified theme and each formulated statement, the attained Interquartile Range (IQR), as well as the presence or absence of consensus regarding the statements (Table 1). To explore the first area of investigation, the interaction between Gen-AI and DCM, the analysis has identified the following themes: (1) the use of Gen-AI in content marketing, (2) the relationship between Gen-AI and human creativity in content marketing. Regarding the subsequent objective of investigating the implications of the relationship between Gen-AI and content marketing in the legal, ethical, and professional skills domains, the analysis has highlighted the following areas of interest: (1) implications for professional skills, (2) ethical and legal implications. Secondly, following the analysis of statements that achieved broad consensus (with an IQR between 0 and 1), a series of recommendations was developed (Table 2). These have been crafted with the aim of providing experts with a strategic framework, which outlines the interaction between Gen-AI and DCM strategies, along with the associated implications. Finally, to further validate our analysis, we employed Krippendorff's test. The Krippendorff's Alpha coefficient was calculated to assess the level of agreement among multiple raters evaluating the same questions on a 1 to 5 scale. The analysis was conducted using the online software (https://www.k-alpha.org/) and incorporated numerical differences between the ratings to account for the distance between values. This test is useful in validating the reliability of human assessments, thereby reinforcing the robustness of human-based decisions in contexts where interpretive variability could otherwise undermine the research outcomes (Marzi et al., 2024). The resulting Alpha value of 0.936, with a 95% bootstrap confidence interval, indicates a high level of agreement among the raters. Although not perfect, the observed disagreement (1.98) is significantly lower than the expected disagreement (31.02), confirming that the ratings are highly consistent and reliable. Indeed, an Alpha value close to 1 suggests strong inter-rater reliability (Krippendorff, 2004).

α = 1–31.021.98 = 0.936.

The first theme explores the interaction between Gen-AI and content marketing, emphasizing the various benefits of this relationship as well as the strategies and practices involved. These include substantial improvements in efficiency and speed in content production, as well as the ability to enable advanced personalization through sophisticated data analysis. Gen-AI enhances scalability and adaptability across multiple platforms, facilitating seamless content creation and delivery. Additionally, it plays a significant role in supporting strategic content planning, optimizing content distribution by determining the most effective timing for publication, and ultimately improving consumer engagement through the delivery of highly personalized, relevant content. The improvements in efficiency and productivity are clearly evidenced by the first statement, which achieved expert consensus (IQR of 2): “Gen-AI significantly enhances efficiency and speed in content production within marketing”. To this regard, in the first round of Delphi method, Expert 1 emphasied that “Gen-AI enables the rapid creation of content, significantly surpassing the speed of human labor and substantially increasing the volume of material produced”. Expert 9 added, “It automates content production, reducing the time required for creation”. Expert 12 stated, “The volume and speed at which generative AI models produce content are incomparable to those of a human.”

The consensus among experts confirms the role of Gen-AI in enhancing efficiency and productivity in marketing content creation. The statements presented highlight both the technology's ability to significantly accelerate the production process compared to human effort and the contribution of automation in reducing the time required for content generation.

The increase in efficiency and productivity associated with the use of Gen-AI in content marketing is further evidenced by the statement: “Gen-AI, in the planning phase, serves as an assistant capable of developing comprehensive and detailed editorial plans swiftly”. This highlight Gen-AI's capability to optimize the process of editorial planning, semplyfing and accelerating planning activities for marketing professionals. The expert consensus (IQR of 2) confirms the significant role of Gen-AI in the strategic planning of content marketing, reflecting a convergence of opinions on its ability to enhance this critical phase. In particular, Expert 6 noted that “[…] it is possible to create a comprehensive and profound editorial plan in a very short time”, emphasizing the speed and precision with which Gen-AI can support the development of complex editorial strategies. The agreement (IQR of 2) has been reached for the second statement “Gen-AI streamlines the creation of content, including articles, social media posts, and video scripts”, highlighting a strong alignment from the experts on the impactful role of Gen-AI in enhancing content creation.

Expert 2 stated, “The key advantages are undoubtedly execution speed and the ability to assemble elements that would be highly complex to achieve through traditional graphic work alone.” Expert 6 added, “Starting from an image, it is possible to generate multiple variations in a very short time. Adapting the graphic style to the desired outcome is simple and immediate, something that would not be feasible to the same extent with a human designer.” Expert 9 noted, “In the production phase, it simplifies and accelerates the creation of text, images, and videos, enabling greater scalability of content.”

The consensus reached among experts underscores the contribution of Gen-AI in making content production more efficient and scalable. The automation of the process reduces execution time while expanding creative possibilities, enabling operations that would be significantly more complex using traditional methods.

Additionally, the following statement “Gen-AI enables the customisation of content to meet audience needs through data analysis” emerges a considerable homogenity in the expert's perceptions (IQR of 1) on Gen-AI's role in enhancing content personalization based on data-driven audience insights and behavioral analysis. To illustrate, expert 9 emphasized that “[…] it enhances the personalisation and segmentation of the audience, allowing for more sophisticated targeting strategies and campaigns based on more accurate insights into user behavior”; similarly, expert 1 outlined how Gen-AI supports the “personalisation of content, for example, by basing it on the behavioral analysis of e-commerce visitors, it is possible to tailor the displayed content to better match their specific interests, increasing engagement and the effectiveness of the content”; expert 7 pointed out how this enables “the creation of posts, texts, images, in relation to what a certain group of consumers prefers”.

The broad consensus among experts highlights the role of Gen-AI in content personalization through data analysis. The use of techniques based on behavioral insights enables the refinement of targeting strategies, enhancing the relevance and effectiveness of content in line with audience preferences.

Finally, Gen-AI significantly enhances the generation of data-driven insights and the improvement of consumer engagement. This is particularly evident in the assertion: “In content distribution, Gen-AI is useful for determining optimal timings for social media posting and newsletter dispatch, maximizing visibility and engagement”, the convergence of respondents' opinions (IQR of 2) demonstrates how Gen-AI can optimize the timing of content distribution to enhance social media and newsletter engagement, by insights into audience preferences. Expert 1 observed that Gen-AI “[…] can determine the best times to post on social media or send newsletters to maximise visibility and engagement”; while expert 9 highlighted how the use of Gen-AI can influence “distribution strategies through a better understanding of audience preferences”.

The second theme addresses the influence of Gen-AI on quality and creativity, exploring how the current strenghts, limitations, and synergires between human creativity and AI are shaping the trajectory of content marketing in the near future. The first statement “during the ideation phase, Gen-AI acts as an assistant in brainstorming, providing original insights and content” received a high level of consensus among respondents (IQR of 0), highlighting the synergistic interaction between human creativity and artificial intelligence, with Gen-AI serving as a valuable tool in enhancing the creative process. In this regard, Expert 9 through the quotation “However, Gen-AI can be synergistically integrated with human creativity, providing suggestions and support throughout the content generation process” emphasizes the added value created by the synergy between human creativity and AI. This point is further reinforced by the observation of Expert 13, who noted that “The content generated by these models can provide creative insights, assisting the user in a form of human-machine brainstorming” further highlighting a potential co-starring role of AI in content generation.

However, from a creative perspective, Gen-AI operates by analyzing and recombining pre-existing data and patterns. While this process may facilitate the generation of seemingly innovative ideas, the extent to which such ideas can be considered genuinely new remains a subject of debate. Expert 1 critically reflected on this issue, stating: “In terms of creativity, AI functions by analysing and recombining existing data and models. This process can indeed lead to innovative ideas, but are they truly ‘new’? I often wonder whether AI-generated creativity is, in reality, merely a reworking of what has already been done, lacking that unique spark of originality that defines human creativity–the kind capable of generating truly revolutionary ideas”.

However, the second statement, “The use of Gen-AI in content creation may lead to a decline in linguistic and writing skills”, which reached consensus (IQR of 2), raises concerns about the potential deterioration of human skills due to increasing reliance on AI, as well as the risks of excessive dependence on such technologies, which could inhibit original and innovative thinking.

This concern is emphasized by Expert 5, who stated, “I would describe it as an impoverishment of language, writing, and logical connections.”

This demonstrates a marked tendency among experts to delegate content creation entirely to avoid widespread “atrophy”. Similarly, consistent with previous findings, there is a strong consensus among experts (IQR of 2) for the statement “Excessive utilization of Gen-AI detrimentally affects human creativity”.

Expert 9 stated, “There is a risk of excessive dependence on AI technology for content creation, which could potentially limit human creativity.”

Expert 11 added, “One of the disadvantages is the risk of a decline in creativity among industry professionals.”

Furthermore, the subsequent statement, “In the creation of marketing content with a high emotional impact, Gen-AI lacks empathy and an appropriate tone of voice”, which again achieved significant convergence of respondent opinions (IQR of 1), demonstrates the limitations of AI in producing emotionally engaging content, highlighting the irreplaceability of human intuition and empathy in these areas. In this regard, Expert 1 asserted: “In terms of content requiring empathy, emotional nuances, or a narrative approach, AI is considered distant from being able to match the sensitivity and depth of a human being”. In line with this sentiment, Experts 8 and 9 posit respectively “AI may not be able to offer human intuition, empathy, and critical thinking, which are essential for creating creative and engaging content,” and “While AI can efficiently produce content and aid in creative inspiration, the quality and authenticity of human content remain distinctive and often preferable for tasks requiring empathy, deep persuasion, and emotional connection”. These statements underscore the irreplaceability of human intervention in all matters relating to the empathetic and emotional sphere, aspects that are difficult to replicate by a machine. However, the future of content marketing, enriched by the introduction of Gen-AI, appears to be moving towards an increasing integration between artificial intelligence and human creativity. In light of these considerations, the statement “In the future, Gen-AI will significantly improve content personalization in content marketing” demonstrated significant convergence of opinions among the participating experts (IQR of 2). Specifically, Expert 2 stated: “Considering the current state of the art, which is quite advanced, we expect a further significant step forward in the coming years regarding the improvement of AI-generated content”. In line with this perspective, Experts 8 and 10 expressed similar views: “I foresee deeper integration of Gen-AI in content marketing in the near future, with innovations that improve content personalisation, audience segmentation accuracy, and the ability to generate even more sophisticated and human-like content”, and “A highly interesting context for the evolution of content marketing is personalisation. Creating personalised content based on specific audience data is quite simple with Gen-AI, and it is expected to improve significantly soon”. These comments reflect a shared optimism regarding the potential developments that AI could bring to content marketing and its impact on industry professionals. Finally, the statement “In the future, content marketing software will be fully integrated with Gen-AI”, with an IQR of 1, demonstrates a high degree of agreement among experts regarding the future convergence between Gen-AI and marketing software. Expert 1 stated: “The application of AI will occur in multiple sectors; this will be supported by advanced language models that will increasingly be integrated to function optimally”. Experts 3 and 4 shared the same vision, respectively stating: “I foresee greater integration with other platforms in the near future, which could lead to further advancements, particularly in the field of content marketing”, and “It is likely that increasingly powerful software will be developed, leading to a subsequent increase in real-time interaction through tools similar to WhatsApp within every device (smart glasses, watches, wearables)”. This alignment of views suggests that these insights and needs may lead to significant technological innovations in the short term, reshaping the landscape of content creation and marketing more broadly.

In what concerns the theme “Impact on Professional Skills”, the analysis of responses has highlighted significant and convergent results. The first statement “Gen-AI will require a re-skilling of the skills needed in the content marketing sector, emphasizing analytical, strategic, and creative abilities” recorded an IQR of 2, defining a consensus on the mutation of skills required in the professional landscape. Expert 6 highlights that “the skills required are changing, it becomes fundamental […] to be able to analyze, apply strategy and creativity” and this implies “a reskilling and also an upskilling”. Expert 1 stated: “This is a cultural and professional shift that requires an open mindset, continuous training, and the ability to quickly adapt to new tools and strategies”, highlighting the importance of lifelong learning. In this constantly evolving landscape, they add: “AI competence is becoming essential across all sectors, pushing professionals to update and expand their skill sets in order to maintain a competitive advantage.”. The effective integration of AI tools into workflows will no longer be optional but a key differentiating factor in the job market.

The second statement, “Gen-AI will replace human capital in the content marketing sector”, is distinguished by having the highest rating (IQR of 3) among the three statements considered. This variability index indicates a less unanimous consensus among experts, who agree that Gen-AI cannot fully replace human capital, albeit presenting differing reflections and nuances. On one hand, Expert 14 remains moderately optimistic, stating that “the critical thinking of the content creator cannot yet be completely replaced”, while on the other, Expert 5 predicts “a significant reduction in personnel especially in communication agencies and journalistic outlets”, and Expert 7 expresses cautious optimism, stating “I do not believe it can replace human experience, but maybe I am a bit optimistic”.

Finally, the third statement “Effective use of Gen-AI requires acquiring prompt engineering skills” showed broad consensus (IQR of 1) on the strategic role of acquiring skills in prompt engineering for the effective use of Gen-AI. Expert 8 predicted “a demand for those who excel in creating prompts”, while Expert 6 noted that “it becomes fundamental to know how to use and write the right prompts”.

The expansion of Gen-AI adoption in content marketing entails several legal and ethical considerations that need to be addressed. From a legal perspective, a critical issue concerns the attribution of authorship and intellectual property rights over the generated content. When using Gen-AI, ambiguities arise regarding who the rightful copyright holder is: the AI model itself, the human user providing the prompts, or both. These complexities require a sophisticated legal framework to manage content ownership and originality. The Delphi study results support this need, showing consensus among experts on addressing legal issues related to copyright and data protection (IQR 2). Expert 1 noted that “Navigating the complexities of copyright with Gen-AI demands a nuanced understanding of ownership and originality” emphasizing the importance of a robust legal framework for safeguarding intellectual property rights. Additionally, Expert 2 highlighted the increasing importance of legal safeguards for copyright in AI-generated content. Data protection concerns were further emphasized by Expert 3, who pointed to the broad legal considerations around data protection and the authenticity of AI-generated virtual identities.Another key issue is legal responsibility. The Delphi results show moderate consensus that creators of AI algorithms should be held accountable for any harm caused by the content generated by Gen-AI (IQR 2). Expert 10 stated that “The onus of accountability in the realm of Gen-AI must fall on its creators; they must shoulder the legal consequences for the content their algorithms produce” stressing the need to assign legal responsibility to algorithm developers. This aligns with discussions on introducing new concepts such as “hybrid authorship” and “shared responsibility” to recognize the contribution of each actor involved in AI-generated content. From an ethical standpoint, the use of Gen-AI raises issues related to data protection, misinformation, and bias. Generative models can create deepfakes and synthetic media that threaten public trust and democratic systems. These capabilities raise serious concerns about transparency and truthfulness, highlighting the need for stronger regulatory frameworks to mitigate associated risks.The Delphi results clearly emphasize the importance of transparency in Gen-AI applications (IQR 1). Expert 8 remarked that “The clarity in the lineage of AI-generated content is paramount; transparency is the bedrock of trust in Gen-AI applications” underscoring the need for AI systems that clearly identify the origin of generated content. Expert 9 added that “Transparency is not just a technical requirement but a moral imperative to ensure accountability in AI-generated content” reiterating the central role of transparency in maintaining user trust and content authenticity. Another key topic discussed in the literature is the need for Gen-AI to be programmed to minimize racial, gender, or cultural biases in the content it generates. The widespread adoption of these technologies could potentially amplify social inequalities and lead to the concentration of economic power. Experts in the Delphi study agreed on the importance of an inclusive and proactive approach to addressing these biases (IQR 2). Expert 5 stated that “Ensuring Gen-AI is free from racial, gender, or cultural prejudices is not optional but a foundational aspect of its design” emphasizing the critical importance of bias neutralization in AI development. Expert 6 highlighted that “the neutralisation of ingrained biases is critical to foster a diverse and equitable digital space” reinforcing the need to promote diversity and equality in the digital environment through ethical and responsible AI design.

Furthermore, privacy protection and regulation of Gen-AI use are central issues, with many experts stressing the need for strict regulations to safeguard personal data. The Delphi results show strong agreement on this point (IQR 1). Expert 5 noted that “In an era where digital footprints are omnipresent, the safeguarding of personal data against misuse by Gen-AI cannot be overstated” indicating that data protection must be a priority. Expert 12 also highlighted the importance of balancing technological innovation with respect for fundamental human rights, stating that “Regulation ensures that Gen-AI serves humanity without violating our right to privacy.”

Finally, the findings have highlighted the urgency of regulatory intervention, necessitated by the rapid technological evolution and the increasing accessibility of Gen-AI, to ensure that this technology does not compromise fundamental human rights or lead to economic and social imbalances. Expert 1 stated: “The democratisation of generative AI will make it increasingly accessible and widely adopted across various sectors” and emphasized that “The rapid advancement of AI technologies demands swift and decisive regulatory action at all levels to mitigate potential risks”.

Similarly, Expert 9 warned that “Without universal standards, Gen-AI poses a legal patchwork that could lead to international conflicts and a race to the ethical bottom.” The need for timely and global action to establish effective regulatory frameworks was further emphasized by Expert 14, who noted that “The window for shaping the governance of AI on a global scale is narrowing; we must act with urgency.” In conclusion, all experts underline the importance of adequate ethical and legal training for the use of Gen-AI in companies (IQR 1). Expert 6 stated that “Education is the cornerstone of ethical AI utilisation–adequate training in the ethical and legal aspects of AI is non-negotiable” stressing the critical role of knowledge and awareness in fostering a company culture based on responsibility and integrity. Expert 4 added that “Training employees on the ethical use of AI transcends compliance; it embeds a culture of responsibility and integrity,” further supporting the need for training to ensure responsible AI use within organizations.

In addition, organizations leveraging Gen-AI should establish clear internal policies. Expert 1 stated “This entails defining precise guidelines on how Gen-AI is utilised, with particular attention to compliance with copyright regulations and the ethical use of data.” Table 2 provides an overview of the key statements from the Delphi study, highlighting the areas where the highest level of expert consensus was reached.

This study adopts the Resource-Based View (RBV) as the theoretical framework to interpret the impact of Gen-AI in DCM. The selection of this approach is primarily based on the fact that RBV focuses on a firm's internal resources and capabilities, which are considered the key drivers of sustainable competitive advantage. In a sector where Gen-AI is becoming increasingly accessible and standardized, merely adopting the technology does not ensure differentiation. Therefore, it is essential to understand how firms can integrate this technology with intangible assets to establish a distinctive competitive advantage. Furthermore, the VRIO model, an analytical extension of RBV, provides a clear and systematic structure for assessing the potential of internal resources. According to this model, resources and capabilities must be valuable, as they contribute to creating value for the firm; rare, since they are not readily available to all industry players; inimitable, meaning they are difficult to replicate due to their tacit nature or the complexity of organizational processes; and organized, as the firm must possess the necessary structures and processes to fully exploit these resources. Finally, RBV aligns perfectly with the key themes of DCM, where the quality of output largely depends on a firm's ability to leverage intangible resources and technological innovations. Consequently, adopting RBV as a theoretical framework not only enables a structured interpretation of the Delphi study results but also provides a solid foundation for deriving concrete managerial implications. In summary, competitive advantage in DCM does not stem solely from the implementation of technology but rather from a firm's ability to develop and utilize unique and inimitable internal resources, as demonstrated through the VRIO model. The manifesto emerging from the Delphi study results suggests that the adoption of Gen-AI in DCM can be systematically decoded and assessed through the VRIO model. This framework enables the identification of how the highlighted resources and capabilities can be translated into sustainable competitive advantages.

Below is a detailed analysis of the findings according to the four dimensions of the model:

• (a)

  Value creation through efficiency and personalization

The Delphi study participants acknowledged that Gen-AI significantly enhances process automation (Gupta et al., 2024; Wahid et al., 2023) and enables content personalization (Sang, 2024; Bilgihan and Ricci, 2024; Kuzyk et al., 2023). These capabilities contribute to reducing production times and improving audience engagement, thereby generating economic and operational value for businesses (Chintalapati and Pandey, 2022; Arango et al., 2023).

• (b)

  Enhancement of assisted creativity

The findings reveal that Gen-AI can support brainstorming and idea generation, integrating human creativity with advanced computational capabilities (Lund and Wang, 2023). When effectively orchestrated, this synergy generates added value in terms of innovation and content quality.

• (c)

  Specialized expertise and know-how in prompt engineering

Among the key themes that emerged, the need to develop advanced skills for the effective use of Gen-AI stands out. The mastery of prompt engineering and the ability to interact strategically with AI models represent resources currently held by a limited number of companies, making them elements of competitive differentiation.

• (d)

  Use of proprietary data and unique insights

Access to exclusive datasets and the ability to thoroughly analyze audience behavior are distinctive factors that render Gen-AI adoption particularly “rare.” These assets enable the delivery of targeted and personalized content, which is difficult to replicate without high-quality internal data (Uren and Edwards, 2023).

• (e)

  Synergistic integration between AI and human creativity

Although Gen-AI technology is rapidly spreading, the ability to effectively combine technological innovation with human sensitivity and creative talent remains challenging to imitate (Ameen et al., 2022; Roshne et al., 2024). The specificity of internal processes and organizational know-how constitutes an asset that is difficult for competitors to replicate.

• (f)

  Organizational culture and ethical governance

The manifesto highlights the importance of transparency, compliance with privacy regulations, and continuous training. (Zhou and Nabus, 2023). These aspects, strongly linked to corporate culture and governance processes, represent intangible resources that not only enhance a company's reputation but are also difficult to emulate, requiring years of consolidation and structural investments (Hermann and Puntoni, 2024).

• (g)

  Efficient internal structures and processes

To fully exploit Gen-AI resources, it is essential for companies to establish well-structured workflows and coordination processes with a particular emphasis between marketing, IT, and innovation teams. The Delphi study results emphasize the necessity of integrating technology synergistically, highlighting that success also depends on organizational capability (Zirar et al., 2023; Mossavar-Rahmani and Zohuri, 2024).

• (h)

  Investment in training and continuous learning

The ability to develop and maintain specialized skills, particularly in prompt engineering, requires internal training programmes and ongoing updates. The implementation of organizational processes that support continuous learning and innovation ensures that resources remain effectively utilized in the long term (Jaiswal et al., 2022; Kolade and Owoseni, 2022; Li, 2022).

The adoption of the RBV and the VRIO model has enabled a detailed mapping and interpretation of the resources to be leveraged and the skills to be developed to gain a competitive advantage in DCM, particularly in relation to the adoption of Gen-AI.

The results of the Delphi study indicate that, although VRIO resources, such as proprietary data, specialized expertise, and an innovation-driven organizational culture, are fundamental to developing a competitive advantage, they are not sufficient on their own to ensure sustained success in a rapidly evolving technological environment such as that of Gen-AI-based DCM. The experts involved highlighted the need to integrate the RBV with a dynamic perspective capable of explaining how such resources can be continually renewed and reorganized in response to changes in the market environment and emerging technologies (Eisenhardt, 2000; Teece et al., 1997).

In the context of DCM, propelled by the swift advancement of technologies such as generative AI, the dynamic dimension becomes all the more critical. Possessing hard-to-imitate resources (such as expertise, proprietary data, and an innovation-oriented culture) is no longer enough; firms must also have the organizational and managerial mechanisms necessary to reshape these resources in response to shifting market opportunities. This need aligns with the Dynamic Capabilities approach (Teece et al., 1997), which focuses on a firm's ability to reconfigure and transform its resources in a timely and effective manner.

In this sense, dynamic capabilities represent a necessary extension of the RBV: while the RBV defines which resources can generate a competitive advantage, dynamic capabilities clarify how these resources must be managed and adapted in order to maintain their strategic relevance. The integration of these two paradigms, as highlighted by the findings of the Delphi study, thus provides a more robust theoretical foundation for understanding how to build and sustain competitive advantage, especially in a sector where innovation driven by AI is accelerating at an unprecedented pace.

Dynamic capabilities are presented through three distinct yet interrelated dimensions. First, the sensing dimension challenges organizations to develop sophisticated mechanisms and processes that vigilantly monitor external signals, thereby identifying opportunities and emerging threats linked to Gen-AI at an early stage. Missing these subtle cues in a rapidly transforming market may result in a perilous lag in innovation adoption, with potentially disruptive consequences. The Delphi study results have highlighted that companies must be able to constantly monitor the external environment to identify new opportunities and threats associated with Gen-AI. For instance, the panel emphasized the necessity of staying updated on technological innovations and evolving ethical and legal regulations. Integrating the concept of sensing underscores the importance of developing mechanisms for continuous monitoring and analysis, elements that enhance the traditional RBV approach.

Following this, the seizing dimension encapsulates an organization's ability to swiftly convert identified opportunities into concrete, strategic actions. The Delphi study underscores the critical importance of investments in specialized training, for instance, in prompt engineering, and the development of integrative processes that harness the synergistic potential of AI and human creativity. In this context, possessing the right resources is merely a starting point; it is the timely and proficient utilization of these assets that transforms latent potential into a tangible competitive advantage. The third dimension, transforming, emphasizes a continuous, transformative approach that is essential for sustaining competitive advantage. It calls for the establishment of flexible organizational structures, a culture of ethical governance, and an enduring commitment to lifelong learning. The results highlight the need to structure flexible organizational processes and establish a culture of ethical governance and continuous learning. Figure 1 presents the integrative framework that combines VRIO with Dynamic Capabilities to sustain a competitive advantage.

Figure 1 highlights the integration between the VRIO criteria, inherent to the RBV, and the key dimensions of Dynamic Capabilities, sensing, seizing, and transforming, to provide a more comprehensive understanding of how the adoption of technologies such as Gen-AI can translate into a sustainable competitive advantage in DCM.

Specifically, the VRIO component enables the identification and enhancement of the core resources and capabilities a firm must possess. These include content automation and personalization (Valuable), specialized technical skills such as prompt engineering and proprietary data (Rare), the synergistic integration of AI with human creativity (Inimitable), and an organizational structure equipped with integrated workflows, ethical governance, and continuous training (Organized).

However, the framework also emphasizes that the mere availability of VRIO resources is not sufficient, particularly in today's highly dynamic environment. This is where Dynamic Capabilities become essential: sensing refers to the firm's ability to continuously monitor technological and regulatory developments, promptly identifying new opportunities or potential threats related to the use of Gen-AI in DCM; seizing concerns the firm's capacity to capture and implement these opportunities through targeted investments in training, technological customization, and operational integration; transforming involves the ongoing redefinition and renewal of the internal mix of skills and processes through re-skilling and up-skilling initiatives, ensuring maximum competitive agility.

This integrated framework thus offers a robust model for understanding how firms can effectively leverage Gen-AI to achieve and sustain competitive advantage in the dynamic landscape of DCM.

This study proposes an extension of the Resource-Based View framework (Barney, 1991) by explicitly integrating it with the concept of Dynamic Capabilities, with the aim of offering a broader and more dynamic understanding of competitive advantage in the context of DCM powered by Gen-AI. While the RBV traditionally identifies a firm's strategic resources according to the VRIO criteria (Barney, 1996), it does not always provide the analytical tools necessary to explain how these resources can be developed and renewed in environments marked by rapid technological advancement and shifting market conditions.

By contrast, the Dynamic Capabilities approach emphasizes a firm's ability to create, modify, and reorganize its resource base over time, ensuring ongoing strategic adaptation (Teece et al., 1997). In an era in which access to cutting-edge technologies such as Gen-AI can quickly diffuse among competitors, the key differentiating factor lies not in the mere availability of advanced tools, but in the ability to effectively integrate them into organizational practices and to reshape them in response to emerging market challenges (Lee et al., 2024).

Through the integration of RBV and Dynamic Capabilities, the theoretical framework proposed here enables the assessment of both the nature of the resources that make a firm competitive and its ability to manage and transform those resources in order to sustain long-term advantage (Eisenhardt, 2000). This perspective is particularly relevant in the domain of DCM, where the pace of change and the centrality of innovation require continuous revision of strategies, skills, and organizational structures (Homburg et al., 2022).

In this light, our research provides an interpretive lens that directly links the availability of VRIO resources to the effectiveness with which they are updated and reconfigured. It offers a theoretical contribution aimed at enriching the existing literature and informing future empirical investigations on Gen-AI and DCM.

Given the multidisciplinary nature of Gen-AI, the paper presents practical contributions by providing guidance to a broad spectrum of actors, including content practitioners, digital marketing consultants, ethicists and legal professionals and training providers. As a matter of fact, the rapid rise of Gen-AI as a disruptive force in the domain of DCM has led to diverse and sometimes contrasting positions and approaches among professionals. In this context, reaching a consensus among these figures on certain key statements enabled us to produce a manifesto as one of the research outputs. This outcome aims to highlight best practices that practitioners should adopt for conscious, productive, and sustainable use of these technologies. The study underlines Gen-AI's key role in enhancing the speed and quality of content generation, a finding of paramount importance for content professionals tasked with the large-scale production of premium content. Gen-AI's capability to streamline production timelines without sacrificing creativity presents a strategic asset for organizations seeking a competitive advantage in content strategy and heightened productivity. Moreover, the paper reveals Gen-AI's adeptness at crafting customized content swiftly and intuitively, thereby boosting engagement across various digital mediums. This reinforces the view of AI as a collaborative partner in the creative process, enriching human creativity with innovative insights and recommendations. This cooperative dynamic accentuates AI's role in augmenting rather than replacing human creativity, prompting practitioners to integrate AI-generated ideas while preserving the essential human creative spark. The research also voices concerns over AI's potential to erode language and writing competencies, urging a balanced approach to AI utilization. This approach should ensure AI's application to routine content creation tasks while fostering the development of human linguistic and creative skills. Additionally, the document advises ethical and legal professionals on the cautious application of Gen-AI, given its expanding influence. It calls for adherence to transparency, ethical development, and legal guidelines, suggesting a framework for the responsible deployment of AI that melds innovation with ethical and legal standards. Finally, the paper stresses the necessity for training and human resources leaders within organizations and educational institutions to proactively address the implications of Gen-AI on workforce skills, advocating for comprehensive reskilling initiatives. These initiatives should focus on designing educational programs that equip individuals with the competencies needed for efficacious collaboration with Gen-AI technologies.

This study explored how Gen-AI is redefining DCM and under what conditions it can lead to a sustainable competitive advantage. By integrating the Resource-Based View with the concept of Dynamic Capabilities and validating this connection through a Delphi panel of 14 experts from the fields of marketing, AI consulting, technological development, and ethical-legal domains, we found that the mere adoption of Gen-AI is not sufficient. What truly matters is a firm's ability to orchestrate inimitable resources, such as proprietary data, specialized know-how, and an innovation-oriented culture, with dynamic capabilities that enable the timely identification of new opportunities, targeted investment, and the reconfiguration of processes, roles, and skills. In this light, Gen-AI emerges not as a substitute for routine creative activities, but as a catalyst for new forms of personalization, data-driven storytelling, and real-time iteration, thus expanding the boundaries of strategic innovation. The theoretical framework proposed contributes to the literature by going beyond the static logic of the RBV alone, explaining not only which resources are needed but also how they must be continuously renewed for AI to become a true source of competitive advantage.

The guidelines identified through the Delphi method serve as a beacon for professionals navigating the complexities of integrating AI into their marketing strategies, ensuring that while striving for innovation, they also uphold the highest standards of ethical and legal responsibility. Furthermore, the discourse on the implications of Gen-AI for professional skills in the content marketing sector underscores the urgency of re-skilling and up-skilling the workforce. As the digital marketing landscape evolves, so must the competencies of those within it, ensuring they are equipped to leverage the benefits of Gen-AI while mitigating its potential downsides. Finally, this paper contributes to the burgeoning discourse on Gen-AI in DCM, offering a nuanced understanding of its impact and laying the groundwork for future research in this dynamic field. As we navigate the challenges and opportunities presented by Gen-AI, it is imperative that we remain vigilant, ensuring that the advancement of technology serves to enhance human creativity, foster ethical practices, and ultimately, drive forward the transformative potential of DCM. This study represents an initial exploration of the topic, yet it presents limitations that pave the way for future research. Firstly, the emerging nature of Gen-AI in content marketing entails an evolving understanding of its potential and challenges, suggesting the need for further empirical studies that can provide a more detailed and quantitative perspective on its real impact. Moreover, the primary focus of this work has been on the perceptions of experts, which, although valuable, may not fully capture the final consumers' expectations in terms of quality and creativity. Therefore, exploring the viewpoint of consumers could offer additional insights into the effectiveness of Gen-AI in content creation. From an ethical and legal standpoint, although this article has addressed significant issues concerning transparency, privacy, and data protection, there remains ample space to examine the specific legal implications regulating the use of Gen-AI. Future studies could explore in depth how existing regulations apply to Gen-AI and what new legal frameworks may be necessary to address emerging challenges. Lastly, the implications of Gen-AI on professional skills in the DCM sector highlight the need for a more detailed analysis of training and professional development strategies. Future research could investigate the specific skills required to work effectively with Gen-AI, examining how organizations can better prepare their employees for the era of artificial intelligence in content marketing.

This paper forms part of a special section “Unveiling the Future of Creativity and Innovation Management in the Era of Generative Artificial Intelligence”, guest edited by Dr Antonio Crupi, Dr Alessandra Costa, Dr Asha Thomas and Dr Puja Khatri.