Generative AI: capturing strategic value in businesses Open Access

The emergence of generative artificial intelligence (GenAI) chatbots in 2022 marked a new era of artificial intelligence (AI) (Feng, Botha, & Pitt, 2024), revolutionizing industries and driving products and business models (Feng et al., 2024). Nevertheless, many companies encounter challenges in implementing these solutions (Feng et al., 2024). GenAI has demonstrated significant advances in areas such as market analysis, content production for marketing campaigns, customer service, and real-time data-driven problem-solving. Nevertheless, issues related to data accuracy, model ethics, and other emerging concerns persist (Brown et al., 2024).

ChatGPT is among the most transformative AI tools developed in recent years. It presents significant opportunities and ethical, legal, and operational challenges with unknown impacts on individuals, organizations, and society (Dwivedi et al., 2023). Studies emphasize that GenAI acts as a catalyst for both exploratory and incremental innovations, allowing the discovery of new opportunities and optimizing operational processes (Gupta & Yang, 2024; Mariani & Dwivedi, 2024; Roberts & Candi, 2024; Bilgram & Laarmann, 2023). For this reason, its adoption has become a strategic priority for business leaders.

Despite the potential benefits, the integration of GenAI faces barriers, such as a lack of technical and strategic knowledge, that hinder its application in organizational processes and compromise the generation of value (Hutzschenreuter & Lämmermann, 2024). In addition, although AI applications can be used to create value, many firms fail to capture at least part of this value, since the implementation of the technology does not guarantee financial return; for value capture through AI, business model innovation is essential (Åström, Reim, & Parida, 2022). A central aspect of this challenge is the need for managers to adapt, as they have to interact with algorithms that play critical roles in organizational decision-making. This interaction requires behavioral changes and the development of new competencies (Hillebrand, Raisch, & Schad, 2025).

The terms value creation and value capture are often used interchangeably in strategic management (Ross, 2024; Minerbo & Brito, 2022), yet they represent distinct phases. Value creation relates to benefits delivered to customers, whereas value capture concerns how much of that value the firm retains as profit or competitive advantage (Ross, 2024). In a value network involving supplier, firm, and customer, total value spans from the supplier’s cost to the customer’s willingness to pay. Each actor may capture part of this value; in particular, the firm captures the margin between selling price and production cost (Ross, 2024; Åström et al., 2022). Value capture theory (VCT), based on cooperative game theory, explains this distribution, focusing on interactions that generate value without requiring actual cooperation (Gans & Ryall, 2017). In AI contexts, greater exposure to technology is linked to increased value creation and capture (Eisfeldt, Schubert, & Zhang, 2023).

With a focus on firms’ performance heterogeneity regarding AI, and drawing on the value capture concept, this article presents a systematic literature review (SLR) to investigate how GenAI is being used by companies in various sectors to capture business value. Scientific productions from January 2022 to November 2024 were analyzed, focusing on the application of GenAI in generating business value. This study identifies, evaluates, and interprets research in corporate management, synthesizing existing knowledge and mapping gaps and trends in literature. It also proposes a future research agenda to deepen GenAI’s impact on business.

Two systematic reviews were identified at screening: Mariani and Dwivedi (2024) explored the implications of GenAI on innovation management, while Khan and Umer (2024) discussed applications of ChatGPT in the financial sector. However, because research has remained focused on specific industries, no study has presented a comprehensive view across multiple business areas of the value capture opportunities that GenAI offers. The present article addresses these gaps by consolidating GenAI value capture opportunities across multiple company areas, identifying limitations, and proposing a research agenda.

According to Chiarello, Giordano, Spada, Barandoni, and Fantoni (2024), GenAI is an emerging technology characterized by uncertainties and high impact. It positions itself as a transformative force capable of reconfiguring organizational performance in various sectors. Its application promotes efficiency, innovation, and competitiveness while expanding the ability to explore and combine new knowledge and skills (Gupta, Nair, Mishra, Ibrahim, & Bhardwaj, 2024). Tools such as ChatGPT are also transforming productivity dynamics (Dwivedi et al., 2023) by freeing professionals from repetitive tasks and creating space for strategic and innovative activities (Mariani & Dwivedi, 2024).

Whereas conventional AI focuses on process optimization and prototyping acceleration, GenAI stands out for driving disruptive innovations and offering deeper strategic analysis (Roberts & Candi, 2024). Mariani and Dwivedi (2024) reinforce that integrating new skills and process automation are the main drivers of GenAI, allowing the analysis and materialization of innovative ideas. Likewise, Singh, Chatterjee, and Mariani (2024) identify that optimizing the use of large volumes of corporate data has generated significant disruption in the field of innovation services, increasing the ability of organizations to extract strategic value from the business.

GenAI is democratizing access to innovation, enabling companies of different sizes and investment capabilities to use AI to explore new horizons more efficiently (Bilgram & Laarmann, 2023). It offers more adaptive approaches than traditional methods, broadening the scope of innovation (Kanbach, Heiduk, Blueher, Schreiter, & Lahmann, 2024; Korzynski et al., 2023). However, its strategic integration faces obstacles in the form of limited governance and the need for more robust oversight (Dwivedi et al., 2023).

Brown et al. (2024) highlight that the inclusion of non-human “team members” challenges traditional concepts of trust and responsibility, requiring more robust governance policies. They also raise a critical point concerning the need to redefine the idea of a team, given that GenAI, despite imitating human behaviors, still lacks feelings, intentions, and responsibilities comparable to those of humans (Brown et al., 2024).

Ethical and regulatory challenges also emerge as central elements. Mariani and Dwivedi (2024) emphasize that collaboration between humans and GenAI can open new frontiers of creativity, provided that well-defined legal frameworks support it. They also address the unauthorized use and spread of misinformation, advocating for regulations that protect consumers and ensure a fair innovation environment. There is a need for governance strategies that promote security and reliability, ensuring the protection of proprietary information and the detection of misinformation (Fui-Hoon Nah, Zheng, Cai, Siau, & Chen, 2023). These aspects are fundamental to the successful implementation of tools such as ChatGPT in companies (Fui-Hoon Nah et al., 2023).

Despite the obstacles, there have been significant advances in the use of GenAI. Feng et al. (2024) propose a framework to optimize GenAI-based chatbots, transforming individual productivity and organizational efficiency. Jackson, Saenz, and Ivanov (2024) warn that validation of results and limitations in simple systems make human collaboration indispensable, especially when dealing with complex problems. Nevertheless, as GenAI’s capabilities evolve, these barriers are expected to be overcome gradually, enabling more robust and sophisticated solutions (Jackson, Saenz, et al., 2024). Effective integration between humans and GenAI requires an ethical approach that complements rather than replaces human creativity (Roberts & Candi, 2024).

Despite AI’s potential to boost efficiency, enhance quality, foster innovation, and support decision-making, firms still struggle to understand how to capture the value it creates Astrom, Reim, & Parida (2022). Ross (2024) deepens VCT by distinguishing value creation from value capture within strategic management, focusing on the roles of suppliers, customers, and firms. Superior value creation does not ensure equivalent capture, as outcomes are shaped by bargaining, market conditions, strategic positioning, and business models (Minerbo & Brito, 2022).

Minerbo and Brito (2022) offer a systematic review unifying fragmented insights on value creation and value capture in buyer–supplier relations. Analyzing 195 articles across marketing, operations, and strategy, they present a general framework with four elements: value creation dimensions, mechanisms (e.g. processes), relationship traits (e.g. trust, power), and capture outcomes. They clarify that although they are linked, value creation (benefit generation through collaboration) and value capture (benefit appropriation) are distinct. Value capture depends not only on created value but also on relational and structural conditions.

In the AI context, Åström et al. (2022) explore how firms offering AI-based solutions align value creation with value capture to build viable business models. Their qualitative study introduces a three-phase framework: enabling value creation, aligning capture mechanisms, and shaping commercial offerings. The key insight is that AI adoption alone does not yield economic value; to both create and capture value, firms must strategically configure models, including technology, contracts, pricing, and perceived customer value. The distinction between value creation and value capture is thus central to capturing business value.

This SLR was conducted in three stages: (1) identification of relevant content based on titles, abstracts, and keywords; (2) grouping and refinement of the main descriptors; (3) an iterative classification process, consolidating similar categories into a final list (Ermel, Lacerda, Morandi, & Gauss, 2021).

Based on clear and replicable criteria, the SLR sought to answer this question: How is GenAI being used by companies in various industries to capture business value? The research strategy, inclusion and exclusion criteria, and process steps were rigorously outlined according to the guidelines proposed by Kraus et al. (2022) and Ermel et al. (2021).

Given the emerging nature of the theme and the limited number of studies, the initial stage involved the selection of the bibliographic portfolio for the literature review, with the definition of the keywords “Generative Artificial Intelligence” and “Generative AI.” The search was carried out in the Scopus and Web of Science databases, well known for their scope and advanced analysis tools (Ermel et al., 2021). According to the methodological criteria established by Kraus et al. (2022), only peer-reviewed articles written in English were included. The publications analyzed, dated between January 1, 2022 and November 15, 2024, were limited to journals with journal citation reports (JIF) above 4.0. The inclusion and exclusion criteria are detailed in Table 1.

In line with the literature-grounded theory proposed by Ermel et al. (2021), the process followed four steps: review, analysis, synthesis of the literature, and presentation of the results. Initially, 169 articles were identified, 79 from Scopus and 90 from Web of Science. After the initial screening, which eliminated 27 duplicates and one restricted access study, 141 articles remained. Then, 100 articles that were out of scope were excluded on the grounds of their irrelevance to the theme of the study; the excluded articles adopted approaches that were individual (23), technical (47), or sectoral, such as education (24), tourism and culture (5), and public sector (1). Of the remaining 41 articles, 15 were discarded because they had a journal impact factor (JIF) lower than 4.0, yielding 26 articles for the final screening. Analysis in greater depth of the 26 articles led to the exclusion of seven articles that were misaligned with the specific objectives of the research, resulting in 19 articles for review. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow summarizing the screening process is presented in Figure 1.

Finally, the selected articles were read in full. Initially, a spreadsheet was created and filled with the following information: title, keywords, objective, methodology, challenges, and opportunities in GenAI applications. The main results of the articles were then summarized in a document to observe whether there were relationships between them. Three categories of analysis emerged from the observation: (1) Applications of AI in the organizational environment, (2) Challenges of generating value from AI tools, and (3) Opportunities in the use of AI.

The 19 articles selected for this review, listed in Table 2, stand out for the diversity of GenAI applications in different sectors and business areas that they consider. The highest-qualifying article in the JIF, published in the United States by Khan and Umer (2024), explores the use of GenAI in the financial sector. The multidisciplinary study by Dwivedi et al. (2023), published in the United Kingdom, presents a comprehensive overview of several sectors, including computer science, marketing, information systems, education, politics, hospitality and tourism, management, and nursing. Both studies, bolded in Table 2, reflect relevant approaches but with specific focuses.

Of the 19 articles analyzed, 17 address the application of GenAI in business. Four of these have a broader approach: Brown et al. (2024), Feng et al. (2024), Singh et al. (2024), and Ye et al. (2024). The practical analysis of Ye et al. (2024), who explore the impact of GenAI on Chinese companies, is of note. Brown et al. (2024) provide a robust theoretical analysis of how GenAI shapes organizational strategies, complemented by practical examples of companies transforming their operations and customer experience.

Brown et al. (2024) mention the companies Zalando and Instacart, which use ChatGPT to customize e-commerce and meal planning, and DHL, which applies computer vision to automate logistics processes, promoting safety and efficiency. Brands such as Coca-Cola, Heinz, and Nestlé leverage GenAI in innovative advertising campaigns. Mastercard also uses GenAI to improve customer service and personalize interactions (Brown et al., 2024).

Singh et al. (2024) suggest that GenAI is an exploratory and incremental innovation engine driven by the combination of new knowledge and process automation. They also highlight the importance of training employees to optimize knowledge absorption, as a company’s innovative capacity depends directly on its ability to apply new knowledge (Singh et al., 2024).

Unlike traditional models, ChatGPT, which is powered by GenAI, goes beyond pre-programmed responses, transforming industries such as customer service by increasing efficiency and transforming interactions (Feng et al., 2024). The integration of chatbots and investments in talent qualification creates substantial competitive advantages (Feng et al., 2024), impacting both individual and company productivity by automating tasks and offering customized solutions in their respective markets (Feng et al., 2024; Cillo & Rubera, 2024).

The study by Ye et al. (2024), focused on China, is the only one that directly relates the use of GenAI to organizational performance. The analysis of 286 companies from various industries notes that management relations moderate the impact of GenAI. The results suggest that managers should prioritize strategic adaptation, aligning implementations with organizational objectives (Ye et al., 2024).

In relation to the key areas of the organizational environment (marketing, human resources, sustainability, customer service and supply chain), eight articles discuss crucial topics, emphasizing how GenAI can transform these sectors, thereby contributing to the strategic evolution of companies.

In the field of marketing, Brüns and Meißner (2024) examine how GenAI, when used to complement human creativity, improves the perception of brand authenticity. This balance strengthens consumer confidence and offers difference in marketing strategies. GenAI drives innovative content creation while preserving authenticity and generating loyalty (Brüns & Meißner, 2024). In the context of marketing innovation, Cillo and Rubera (2024) emphasize how GenAI reshapes business-to-consumer relationships, strengthening sustainable competitive advantage. By generating relevant content, it redefines the role of marketing, providing greater value to the consumer and redefining market dynamics (Cillo & Rubera, 2024). In the field of sales, Gupta et al. (2024) discuss the integration of GenAI with new technologies to create personalized experiences in real-time and immersive marketing, an approach that positions companies to gain substantial competitive advantages (Gupta et al., 2024).

In the human resources sector, the study by Chowdhury et al. (2024) proposes a strategic framework to integrate GenAI into people management practices, highlighting the importance of balancing automation and human intervention. Anwar and Graham (2020) note that balanced interaction between AI and humans is essential for organizational success.

In terms of sustainability, De Villiers et al. (2024) present a conceptual model for using AI in sustainability reporting, balancing efficiency and governance risks. Ghobakhloo et al. (2024) explore the impact of GenAI on manufacturing sustainability, highlighting Industry 5.0, focusing on optimized processes and waste reduction. Both studies emphasize GenAI as an affordable solution that promotes economic efficiency, social responsibility, and environmental sustainability.

Ferraro et al. (2024) discuss how GenAI is revolutionizing customer service by offering constant greater efficiency and personalization. They underline that, to be successful, companies must balance innovation with social responsibility, adopting GenAI ethically and responsibly (Ferraro et al., 2024).

The work of Jackson et al. calls attention to the impact of GenAI in optimizing logistics operations and internal processes. Jackson, Saenz, et al. (2024) present a framework that automates logistical simulations, democratizing the use of advanced solutions and promoting competitiveness. They also highlight how GenAI has the potential to drive sustainable practices by reducing waste and optimizing routes. GenAI, if integrated with sustainable practices in supply chains, aligns economic performance with significant environmental responsibility (Jackson, Ivanov, et al., 2024).

Mariani and Dwivedi (2024), Bilgram and Laarmann (2023), Roberts and Candi (2024), and Joosten et al. (2024) broaden the scope by exploring how GenAI redefines organizational innovation. Joosten et al. (2024) show that GenAI accelerates creativity and prototyping, while Mariani and Dwivedi (2024) discuss how it creates new possibilities for innovation and competitive advantages. Bilgram and Laarmann (2023) emphasize the role of large language models in transforming the early stages of ideation, and Roberts and Candi (2024) emphasize the importance of complementarity between human creativity and AI.

All the studies in the SLR mention value creation mechanisms enabled by GenAI solutions, but only one mentions value captured due to AI adoption (Brown et al., 2024). None of them measures the value captured effectively. Table 3 summarizes the value creation and capture mechanisms mentioned in the studies.

The implementation of GenAI in organizations faces significant challenges, a fact that helps to explain the lack of studies empirically demonstrating how value is captured from such projects. These challenges, shown in Table 4, were identified in the 19 articles analyzed and identified by the authors as obstacles to be overcome. They require careful analysis and, in some cases, regulation.

Effective integration of GenAI in organizations faces ethical, technical, and regulatory challenges that require priority attention. Transparency in the use of tools is crucial, given the difficulty in differentiating content generated by AI from content produced by humans, which compromises trust and authenticity (Dwivedi et al., 2023). In addition, algorithmic biases, which often arise from a lack of diversity in training data, affect the impartiality and quality of prototypes (Bilgram & Laarmann, 2023). These obstacles are even more critical in sensitive sectors such as finance, where biased information can hamper strategic decisions (Khan & Umer, 2024).

Another relevant paradox is the tension between personalization and privacy. Precisely because it can provide personalized experiences, GenAI raises ethical concerns about the collection and use of personal data. Additionally, AI-associated vulnerabilities, such as the misuse of chatbots, require continuous surveillance and human intervention (Ferraro et al., 2024).

The reliability of information generated by GenAI, despite its potential, still depends on human validation to ensure accuracy and functionality, especially in logistics operations in which technical limitations emerge (Jackson, Ivanov, et al., 2024; Jackson, Saenz, et al., 2014). Similarly, the low practical feasibility of some AI-generated ideas requires human intervention to transform them into applicable solutions, evidencing the essential complementarity between human and AI capabilities (Joosten et al., 2024; Bilgram & Laarmann, 2023).

For Ferraro et al. (2024), the lack of balance between quality and empathy is crucial; although AI increases efficiency, it lacks the emotional sensitivity to deal with complex situations. The authors recommend integrating guidelines that preserve empathy in critical interactions, thereby balancing automation and human interaction (Ferraro et al., 2024).

In the field of data privacy and security, exposure to risks such as misuse of sensitive information highlights the urgency of clear regulations and organizational practices to ensure integrity and compliance (Gupta et al., 2024; Feng et al., 2024; Brown et al., 2024). Moreover, the obligation to disclose AI-generated content can compromise authenticity in ways that impact brand image and consumer engagement (Cillo & Rubera, 2024).

Ferraro et al. (2024) recommend robust privacy and data governance policies, coupled with investment in training and oversight to ensure responsible use of GenAI. Khan and Umer (2024) emphasize the need for strong governance, highlighting ethical challenges such as bias, privacy, and impact on human employment, as well as warning of risks that include generation of false information and exposure to sensitive data.

The adoption of GenAI also faces cultural and organizational barriers, not least resistance to change and lack of technological infrastructure, which hinder its integration, especially in traditional companies (Jackson, Saenz, et al., 2024; Mariani & Dwivedi, 2024; Singh et al., 2024; Dwivedi et al., 2023). Strategies for re-skilling teams and technology alignment are essential for overcoming these barriers and ensuring the sustainable use of GenAI.

Ethical and regulatory challenges emerge as central issues in the implementation of GenAI, stressing the absence of robust frameworks to address ethical dilemmas. These frameworks should include practical guidelines to ensure the integrity of AI-based systems (Mariani & Dwivedi, 2024; Singh et al., 2024; Dwivedi et al., 2023). Ghobakhloo et al. (2024) propose a roadmap to integrate technological innovation and sustainability in manufacturing, emphasizing the need for governance and partnerships to overcome technical and ethical challenges, such as compatibility of legacy systems and impact on employment.

Jackson et al. also recognize the importance of integration with legacy systems and the need for specialized training. While Jackson, Saenz, et al. (2024) see the reduction of technical barriers as essential, Jackson, Ivanov, et al. (2024) emphasize the importance of a robust infrastructure to sustain technological benefits. Managers who combine technological innovation with human capacity building can turn these obstacles into opportunities, ensuring the effectiveness and positive impact of GenAI on organizations.

It is also worth noting that high uncertainty in dynamic markets prevents effective application of GenAI, while practices such as greenwashing can mask unsustainable initiatives under a false appearance of responsibility (De Villiers et al., 2024). These challenges reinforce the importance of policies that guide responsible use of GenAI, ensuring its positive impact on organizations.

Governance and AI literacy are pillars of GenAI’s success in organizations. Bilgram and Laarmann (2023) highlight the need to redefine the roles and skills of teams in order to adapt to technology. Brown et al. (2024) stress that the integration of GenAI challenges traditional concepts of collaboration and responsibility, requiring new managerial approaches to align AI with human dynamics. The training of professionals, as Jackson, Ivanov, et al. (2024) argue, is essential to ensure strategic and sustainable use of this technology.

Despite GenAI’s transformative potential, its implementation brings challenges that require strategic management. Joosten et al. (2024) and Bilgram and Laarmann (2023) highlight the ethical bias caused by biased data, compromising equity and making it difficult to accept the solutions generated. Technical complexity, as pointed out by Jackson, Saenz, et al. (2024), limits the alignment of ideas generated by AI to organizational resources, making their practical application difficult. In addition, effective integration of GenAI requires robust organizational processes. Bilgram and Laarmann (2023) emphasize that structured workflows are crucial to ensure the effectiveness of results. Limitations such as the poor visual quality of prototypes, as noted by Brown et al. (2024), can also compromise innovation and applicability. These challenges reinforce the importance of interdisciplinary approaches and strategic planning to capture the full value of GenAI.

Finally, the applicability of GenAI in supply chain risk management, as observed by Baryannis, Validi, Dani, and Antoniou (2018), illustrates its potential. GenAI offers adaptive solutions based on large volumes of data, strengthening risk mitigation and operational efficiency. To explore these opportunities, it is vital to deepen the debate on AI governance and literacy by ensuring responsible, strategic, and sustainable implementation of GenAI.

The increasing exposure to marketing generated by GenAI and greater regulatory transparency highlight the need to investigate how these changes impact consumer perceptions (Peres, Schreier, Schweidel, & Sorescu, 2023). Brüns and Meißner (2024) suggest that collaborative use of GenAI, complementing humans in content creation, mitigates negative reactions and preserves a brand’s sense of authenticity. This approach is crucial to maintaining the connection between consumers and brands in a dynamic digital environment. However, Brüns and Meißner (2024) warn that exclusive use of GenAI in content creation can reduce consumers’ emotional connection with a brand, as the technology’s lack of empathy compromises authentic engagement with followers’ identities, which raises questions about the limits of GenAI for building deep relationships.

In customer service, Ferraro et al. (2024) note the negative impact of the lack of empathy in automated interactions, affecting the quality of service. In addition, algorithmic bias can generate inappropriate responses, which undermine consumer confidence in GenAI systems. The literature also emphasizes strategic questions about the adoption of GenAI for innovation. Cillo and Rubera (2024) suggest that companies should adjust their strategies to exploit GenAI’s potential, taking into account its impact on consumers’ creative behavior. Nevertheless, using proprietary data for personalization raises privacy concerns (Huang & Rust, 2024) and may weaken strategic assets (Cillo & Rubera, 2024). Given consumers’ concerns about their privacy, this issue is of critical importance; a strategic balance between innovation and trust is essential (Thomaz, Salge, Karahanna, & Hulland, 2020). Therefore, use of GenAI in consumer interactions requires a balance between innovation and authentic human relationships. In a rapidly evolving digital landscape, organizations must align their strategies with customer expectations while preserving ethics and privacy.

The social impact of using GenAI in companies, especially in task automation and employee reorganization, is a central theme in human resource management (HRM) (Chowdhury et al., 2024). Developing a strategic HRM framework, as Brown et al. (2024) highlight, is crucial to harnessing GenAI’s innovative capabilities in ways that boost the productivity and creativity of skilled staff. However, the transformation must take into account the risks and potential misuse of technology (Brown et al., 2024; Chowdhury et al., 2024; Khan & Umer, 2024).

Ferraro et al. (2024) highlight the paradoxes of GenAI in customer service. While its use promotes personalization, a lack of empathy can lead to isolation, compromising the customer’s experience. Automation reduces costs; however, it can also cause the displacement of jobs and create social challenges. Despite offering quality solutions in some contexts, chatbots lack the sensitivity to handle complex situations, raising concerns about privacy and security. These paradoxes reflect the social challenges associated with GenAI and the need for balanced strategies.

In addition, Dwivedi et al. (2023) identify that the existence of “data custodian” professionals (who manage the data that feed the GenAI systems) demonstrates the dependence of those systems on qualified human contributions, challenging the idea of an autonomous AI. Chowdhury et al. (2024) argue for the importance of continuous learning in the workforce to align organizational goals and redirect human talent to more valuable activities. This approach fosters a symbiotic relationship between humans and technology, mitigating negative impacts while maximizing organizational benefits. Adoption of GenAI should be supported by HRM strategies that value human contributions and ensure ethical and sustainable implementation.

Despite the challenges to overcome, GenAI offers a broad spectrum of opportunities that can boost organizations’ strategic value. As part of organizational transformation, GenAI is redefining the way companies structure their operations and make decisions. Its ability to analyze large volumes of data in real time allows it to identify trends and generate insights that strengthen adaptability and competitiveness in global markets (Dwivedi et al., 2023; Khan & Umer, 2024).

In the context of collaborative innovation, GenAI-based ideation platforms have enabled new forms of co-creation, connected global teams, and accelerated the development of innovative products and services (Mariani & Dwivedi, 2024; Bilgram & Laarmann, 2023). These tools allow code-free prototyping and agile methodologies such as design thinking, thereby reducing the time between design and delivery of solutions to the market (Joosten et al., 2024).

GenAI is increasing the depth of consumer interactions. AI-powered tools are transforming experiences by personalizing recommendations and offering immersive content that integrates the physical and digital worlds (Ferraro et al., 2024; Gupta et al., 2024). This level of personalization not only increases customer satisfaction but also strengthens brand loyalty.

In operational efficiency, GenAI is enabling automation at levels never before achieved. Supply chain applications, such as those implemented by DHL and Instacart, are demonstrating their effectiveness in monitoring assets, forecasting demands, and optimizing resources, ensuring significant savings and greater accuracy in operations (Jackson, Ivanov, et al., 2024a).

Finally, a notable benefit of GenAI is its potential to democratize access to innovation. Affordable tools enable non-technical experts to explore advanced solutions, broadening a company’s innovation base (Khan & Umer, 2024; Bilgram & Laarmann, 2023). This accessibility is transforming the organizational landscape, encouraging continuous learning and promoting inclusion in the development of strategic solutions.

The themes that emerge from our SLR suggest numerous applications of GenAI for value creation; nevertheless, value capture remains underrepresented in the literature. According to VCT, value creation is necessary for value capture, but not sufficient. As a result, there is heterogeneity in firms’ performance, with some firms able to capture more value than others (Ross, 2024). Figure 2 summarizes the main topics discussed in the papers.

This study makes a significant contribution by consolidating recent advances in the use and impact of GenAI on organizations. The analysis highlights process automation, personalization of experiences, and optimization of operations, promoting efficiency and innovation. Studies such as Ye et al. (2024), on Chinese companies, and Chowdhury et al. (2024), which proposes practical frameworks for integrating GenAI into HR, show how well-structured strategies can generate organizational growth and positive social impact.

Nevertheless, some gaps are evident. The exclusion of sectors such as education and health prevented a comprehensive view of their patterns in technology usage. The lack of studies on governance and literacy in GenAI limited the discussion on the topic. The review also did not address ethical impacts, regulation, or the long-term implications for human capital, such as requalification and adaptation to new technological requirements.

In addition, the adoption of strict criteria (JIF >4.0) may have excluded relevant studies, particularly in emerging research areas where high-impact publications are still developing or are less prevalent. This methodological choice was made to ensure a high level of theoretical and methodological rigor among the selected studies, thus reinforcing the reliability of the evidence analyzed. On the other hand, still considering high-impact journals, it is observed that although the analysis broadly addresses the potential of GenAI, it is theoretical rather than focused on practical applications, making it clear that the topic is still emergent and has room for empirical research.

To advance understanding of GenAI value capture in companies, we propose the following research agenda:

1. Cultural and structural barriers to GenAI adoption: Explore the organizational, cultural, and managerial barriers that hinder effective GenAI adoption, particularly in legacy firms and highly regulated industries. Key questions include: What forms of organizational resistance (e.g. fear of obsolescence, aversion to change, lack of a digital mindset) prevent GenAI integration? How do hierarchical structures, departmental silos, or rigid governance models limit value capture from GenAI? Which changes in management strategies, leadership models, business models and capability-building efforts are most effective in enabling GenAI value capture?

2. Social value creation and competitive advantage: Examine how GenAI can simultaneously drive social value (e.g. inclusion, accessibility, transparency) and business performance. Key questions include: Can GenAI-enabled innovations enhance society and environment well-being without eroding firms’ competitive advantage? How do companies balance commercial imperatives with GenAI’s broader societal impact (e.g. misinformation, job displacement)? What are the reputational, ethical, and long-term strategic benefits of prioritizing social value through GenAI?

3. Responsible GenAI and its links to sustainability and the United Nations Sustainable Development Goals (SDGs): Investigate the intersection of responsible AI governance and corporate sustainability strategies. Key questions include: How can GenAI be aligned with the SDGs? What frameworks exist or need to be developed to ensure that GenAI is designed, implemented, and monitored ethically and responsibly? How do firms operationalize GenAI ethics across the lifecycle, from model development to deployment, in ways that support environmental and social sustainability?

4. Value capture measurement and metrics: Quantification and assessment of value captured through GenAI initiatives. Future studies should develop and validate new key performance indicators that go beyond ROI (return on investment) to include dynamic measures such as customer lifetime value; metrics that capture intangible gains, such as improvements in brand value, organizational learning, and AI-driven intellectual property; and approaches to link GenAI investments with financial outcomes (e.g. revenue growth, margin improvement) and strategic outcomes (e.g. market repositioning, ecosystem influence).

GenAI is transforming industries such as finance, marketing, HR, sustainability, supply chain, and innovation. In finance, it optimizes audits and regulatory data mining; however, it faces ethical challenges related to data transparency and security. In marketing, it enhances personalization and content creation, but it requires human intervention to maintain the authenticity of brands. In HR, strategic frameworks highlight the balance between automation and human interaction. In sustainability, it improves reporting and organizational practices aligned with environmental goals. In the supply chain, it automates logistics operations, such as demand forecasting and inventory control, improving efficiency. In innovation, prototyping tools democratize access, yet human–AI collaboration remains essential to ensure ethics and viability.

Despite GenAI’s advances, its potential has yet to be captured fully. Challenges remain, such as model transparency, regulatory compliance, and risk mitigation related to sensitive data and inaccurate information. The application of GenAI is in its early stages, suggesting room for technological maturation. In addition, areas such as co-creation with consumers (which extends personalization) and human–AI partnerships (which improve the resolution of complex problems) promise to increase the strategic value of the technology.

Overcoming these barriers and developing ethical practices can unlock new possibilities for GenAI and maximize its positive impact. As the technology matures, it can redefine processes, foster innovation, and create sustainable outcomes for business and society.