Factors Affecting in Developing AI-driven Analytics Culture for Achieving Equilibrated Sustainability in B2B Firms: A Morphological Analysis

1. Introduction

The integration of artificial intelligence analytics is revolutionizing B2B operations especially in issues relating to sustainability (). With the help of machine learning, analytics and IoT, B2B firms are opening up opportunities to optimize resource utilization, minimize waste and increase operational visibility that influence circular economy capabilities (Bag and Pretorius, 2022; Brynjolfsson and  McAfee, 2017; Rahman et al.,2021; Behera et al., 2022). For example, intelligent supply chain management systems for demand forecasting, monitoring, and reduction of carbon emissions are crucial for sustainable goals in ethical sourcing and circular economy (Jankovic and Curović, 2023). These developments not only contribute to environmental and economic development but also to the development of customer relations and marketing, making business communication more effective and individual (Chen et al., 2021).

However, the practical application of AI-driven analytics in B2B sustainability encounters the following challenges. The challenges that organizations face include high implementation costs, data privacy issues, low compatibility, and lack of skilled workers in the workforce. Solving these challenges may involve the provision of significant amounts of funding for AI-ready infrastructure, as well as changes in organizational culture from a reliance on intuition to a reliance on data, and improvements to the collaboration between internal and external actors (Dinmohammadi, 2023). However, the challenges do not deter the potential benefits which can be obtained from the strategy. AI can be integrated into supply chain management to enhance efficiency, increase adherence to environmental standards and reporting, and improve relations with stakeholders through and through reporting. These capabilities make AI the foundation of sustainable innovation and a strategic asset for companies exploring the shift in markets caused by the sustainable development agenda (Pal, 2023).

Therefore, this paper seeks to analyze the B2B sustainability implications and prospects of AI analytics culture through a morphological analysis of the existing literature on the topic. Finally, this paper seeks to offer a synthesis of AI adoption and sustainable business future by identifying the key conclusions from the current literature.

2. Problem Statement

The lack of readiness for AI analytics in B2B organizations is making it difficult to manage both environmental and social sustainability practices, particularly in both manufacturing and service industries (Baabdullah  et al.,2021; Bag and Pretorius, 2022; Akther and Tariq, 2024). Although technologies like predictive analytics, IoT, and machine learning offer a great potential for improving resource use efficiency, decreasing waste, and increasing transparency, their deployment is still happening in an unbalanced manner across sectors. In Bangladesh, business-to-business (B2B) firms face significant challenges in achieving sustainability, particularly in maintaining a balanced approach where both environmental and social goals are not treated equally. This imbalance often leads to limited or unproductive efforts, such as fragmented sustainability initiatives (e.g., reducing carbon emissions in one area but not addressing waste management in another), inconsistent implementation of green practices (e.g., some departments recycling materials while others continue to dispose of them improperly), and failure to align business strategies with sustainability goals (e.g., focusing on profits without ensuring safe working conditions or fair wages for employees). As a direct result, firms experience environmental damage, poor employee health and safety, and a lack of trust from workers and communities, which undermines B2B firms’ long-term growth and reputation.

Advanced technological support can facilitate to address such imbalance in social and environmental sustainability management (SESM) in B2B relationships. Often, B2B firms are discouraged from balancing their sustainability efforts due to more dependence on manual processes, high implementation costs of data-driven technology, lack of infrastructure, lack of employee AI literacy, and insufficient organizational readiness for AI-driven analytics (Arntz et al.,2017; Rahman et al.,2023). These factors lead to inefficient usage and understanding of analytics, making it difficult for firms to adopt AI-driven solutions and fully integrate sustainability practices in B2B firms Bangladesh (Table 1.1).

Table 1.1: Overview of Sector wise B2B Firms in Bangladesh

*Note: The estimated numbers are compiled by the author based on sector estimates and may exclude informal or emerging firms.

Also, there is no comprehensive framework to counteract organizational resistance to change and to integrate AI strategies with sustainability objectives, including decarburization and responsible sourcing (Bag and Pretorius, 2022). This is further exacerbated by issues to do with data privacy, compliance to the law, and the ability to scale for the AI solutions, making it impossible for many organizations to harness the full potential of AI. Therefore, it is imperative for researchers to investigate the factors that may hinder or facilitate the use of AI-driven analytics by B2B firms, in order to achieve both strategic advantage and sustainable development goals.

3. Research Objectives

●       To explore the role of AI-powered culture in enhancing sustainability practices within B2B business models.

●       To examine the barriers to adopting AI-driven analytics in the context of B2B sustainability.

●      To develop a framework for leveraging AI-driven analytics to align B2B business strategies with sustainability objectives.

4. Methodology

This study employs a secondary data-based research methodology to explore the adoption of AI-driven analytics and its implications for B2B sustainability. We collected secondary data from credible sources such as peer-reviewed journals, industry reports, case studies, and scholarly articles retrieved from platforms like Google Scholar, JSTOR, Academia, and Sci-Hub. We have carefully selected these sources to ensure a comprehensive review of current trends, challenges, and opportunities in applying AI-driven analytics to sustainability initiatives in B2B models.

To analyze the data, the study used a combination of thematic analysis (TA) and morphological analysis (MA). We employed thematic analysis to identify and categories recurring patterns and themes in the literature, including the role of AI technologies, adoption barriers, and key sustainability outcomes. We used morphological analysis in parallel to systematically explore the interrelationships between critical dimensions, including AI technologies, business applications, sustainability goals, organizational challenges, and cultural shifts. This analytical framework enabled the study to assess potential scenarios and their implications for B2B sustainability. By integrating thematic and morphological analysis, the research provides a robust, multidimensional understanding of how AI-driven analytics can transform sustainable practices in the B2B sector.

Figure 1.1: Systematic Literature Review following PRISMA

Prior to conducting MA, this study follows SLR by following four-step PRISMA process: identification, screening, eligibility assessment, and inclusion, to select relevant literature on the implications of AI-driven analytics for sustainability across diverse sectors (Fig.1.1). Morphological analysis operates as a structured qualitative research method which identifies fundamental variables (Fig.1.2) and their connections when tackling complex multi-dimensional problems (Ritchey 2011). The study uses morphological analysis to study AI driven analytics adoption in B2B firms by creating categories that explain vital dimensions affecting technological advancement and thus, this advancement aids to ensure B2B equitable sustainability. The decomposition of subjects into basic elements allows this method to create a systematic evaluation framework which analyzes AI's sustainability effects and organizational readiness and adoption obstacles (Zwicky, 1969). The analysis method known as morphological analysis finds extensive use in technology forecasting and sustainability research because it effectively evaluates interconnected variables (Cagnin et al., 2013). The method proves efficient for understanding the complicated relationship between AI implementation in business sustainability by organizing various elements that influence AI-driven decision processes and sustainable business model implications.

The initial step of morphological analysis requires establishing key factors that affect the use of AI-driven analytics within B2B sustainability. The required dimensions emerged from a systematic review of peer-reviewed literature and case studies and industry reports. This research applied established morphological analysis techniques described by Rosenhead and Mingers (2001) to identify five main dimensions which included (1) Industry context of B2B sustainability, (2) B2B sustainability crises, (3) Consequences of sustainability crises, (4) Encounter sustainability crises, and (5) Adaptive strategy for AI adoption. The multiple variables within each dimension cover a wide range of possible AI system-business sustainability objective interactions (Geum et al., 2012).

The initial perspective examines different B2B sectors that have the potential to utilize AI technologies for sustainability programs. The AI-driven analytics system implements machine learning and deep learning along with Internet of Things (IoT) and natural language processing (NLP) tools as per Brynjolfsson and McAfee (2017). For example, the manufacturing sector, particularly in industries like ready-made garments (RMG) and pharmaceuticals, uses these technologies more extensively to optimize production, improve supply chain efficiency, and enhance sustainability practices.

Machine learning and deep learning are applied to predictive maintenance, quality control, and energy consumption analysis, while IoT monitors real-time resource usage. NLP tools are also leveraged for analyzing customer feedback and regulatory compliance, ensuring sustainability goals are met in a dynamic market environment. Businesses use machine learning algorithms to analyze extensive datasets for pattern recognition and predictive modeling which leads to better sustainability-related decisions regarding energy efficiency and waste reduction (Ghobakhloo, 2018). The IoT facilitates real-time monitoring of resource consumption, supply chain logistics, and environmental impact assessments (Jankovic and Curović, 2023). In addition, NLP technology is more popular that enabling B2B firms to analyze unstructured data from both customer and industrial buyer feedback and regulatory policies in order to track corporate sustainability commitments (Gupta, 2021). AI ecosystem development through these technologies enables both operational efficiency and sustainability targets (Pal, 2023).

Figure 1.2: Morphological Analysis

The second dimension evaluates how B2B sustainability crises are occurred. Literature found it might be categorized into two perspectives such as supplier end i.e; supplier faces the challenges due to failure in achieving sustainability in both environmental and social.  AI analytics applications function across different business operations that support sustainability goals. AI technologies provide supply chain transparency while optimizing resource utilization and predictive maintenance which results in decreased operational inefficiencies (Chen et al., 2021). Through supply chain management AI-driven analytics helps businesses track compliance of suppliers regarding environmental and ethical standards to maintain sustainable sourcing practices (Jouini et al., 2020). The implementation of predictive maintenance systems driven by artificial intelligence technology reduces equipment breakdowns to minimize both energy consumption loss and operational stoppages (Ghosh and Prakash, 2020). The automation of data collection through AI ensures compliance with environmental regulations including Global Reporting Initiative (GRI) and European Union’s Corporate Sustainability Reporting Directive (CSRD) according to Sutherland et al. (2020). Through these applications AI enables organizations to make data-based choices that support sustainable business strategies.

The third dimension addresses the consequences of sustainability crises that B2B firms face after encountering issues in the buyer-supplier relationship. These firms often suffer reputational damage or long-term consequences for not emphasizing equal eye on social and environmental sustainability failure such as ecosystem degradation for not maintaining the sustainability practices across the all department (Bardgett et al.,2021; de Vasconcelos Gomes et al.,2022), legal and regulatory consequences for human rights violations (Sharma, et al.,2022; Cao et al.,2024), and green governance fails while B2B firms are not able to address the sustainability issues in real-time, this may result in green washing (Li et al., 2018). Additionally, weak green governance hinders the achievement of sustainability goals in the long run. However, these challenges can be mitigated through the data-driven analytics capabilities of B2B firms, which are enhanced by AI technologies. By leveraging AI-driven analytics, firms can better manage risks, track sustainability performance, and implement corrective measures, thereby reducing potential harm to their reputation and long-term sustainability outcomes. AI adoption by B2B firms leads to the achievement of two sustainability targets which consist of lowering carbon emissions while establishing circular economies and managing resources responsibly (Geum et al., 2012). In an AI-driven culture, B2B firms systematically evaluate their carbon footprints by analyzing energy consumption patterns and implementing optimized production schedules across the various areas, thereby contributing to reduced emissions and supporting broader sustainability goals (Vercellis, 2020). Through the use of AI analytics can predict material reuse possibilities and decrease waste production while implementing circular economy strategies (Bag et al.2023; Brynjolfsson and  McAfee 2017). Thus, AI analytics capability of B2B firm allows businesses to monitor their SDG compliance as well as global sustainability frameworks through United Nations Sustainable Development Goals tracking which ensures their operations support environmental and social health (Jankovic and  Curović, 2023). Artificial Intelligence integration into sustainability strategies enables B2B firms to improve their social responsibility standards while simultaneously promoting green innovative sustainable business methods.

AI-driven analytics capability encounters multiple obstacles when B2B firms strive to adopt it for achieving B2B sustainability. The fourth dimension of morphological analysis examines implementation barriers such as expensive deployment costs and privacy issues with data as well as platform compatibility problems and human resource limitations (Rosenhead and Mingers, 2001). Many businesses face substantial financial challenges when investing in AI infrastructure because they need cloud computing as well as advanced analytics platforms alongside specialized AI talent (Ghosh and Prakash, 2020). The processing of significant amounts of business and customer sensitive information by AI models creates data privacy problems (Sutherland et al., 2020). Implementing artificial intelligence becomes more complex because businesses must follow data security standards and the General Data Protection Regulation (GDPR) (Vercellis, 2020). The integration of AI technologies with legacy systems in B2B operations poses a challenge because these systems were not designed for interoperability thus requiring major system updates (Jouini et al., 2020). AI adoption faces challenges because businesses lack sufficient employees with expertise in data science and machine learning (Pal, 2023) as well as because of a lack of professionals trained in AI. The widespread adoption of AI within sustainability-driven business models requires successful resolution of these barriers.

The final dimension analyzes the role of adaptive strategies - proactive, reactive, and transformational in guiding B2B firms toward the successful adoption of AI to support their environmental and social sustainability goals. These strategic responses determine not only the speed and scope of AI adoption but also the extent to which sustainability outcomes are achieved (Birkinshaw et al., 2016; Ahn et al., 2022). Successful AI implementation necessitates a cultural shift from intuition-based decision-making to data-driven strategies (Geum et al., 2012). Leadership commitment serves as a fundamental factor in creating an analytics-based corporate culture because executives need to support AI initiatives while funding digital transformation initiatives (Ritchey, 2011). AI-driven sustainability strategies demand effective collaboration between teams from IT, operations and sustainability since these departments must work together (Brynjolfsson and McAfee, 2017). Companies need to create ongoing learning systems that provide employees with AI capabilities to achieve successful data-based business transformations (Jankovic and Curović, 2023). B2B firms can maximize AI sustainability benefits through change management approaches which combine stakeholder involvement along with stepwise AI implementation systems to reduce employee resistance (Ghobakhloo, 2018).

Existing literature predominantly focuses on firms in developed economies, leaving a population gap in terms of insights from B2B firms in developing countries (Hoque et al., 2016; Arakpogun et al.,2021). Most existing research in Bangladesh primarily focuses on how technology can enhance operational efficiency and competitiveness, without addressing its broader role in social and environmental sustainability such as readiness of AI for achieving sustainability (Arafath,2022; Babu,2021, Faruk et al.,2022).

In addition, there is also an empirical gap in studies that employ robust, mixed-method approaches to examine these dynamics in real B2B settings, particularly where green governance, infrastructural, and human capability challenges are prominent. This gap is significant because sustainability goals (SDG 9 and SDG 12) are increasingly central to B2B operations, yet firms, particularly in developing countries, struggle to integrate AI into sustainability strategies. The lack of a clear theoretical model linking AI culture with specific sustainability outcomes leaves a gap in both theoretical understanding and practical application, limiting the ability of B2B firms to leverage AI effectively for sustainability. There is also no clear evidence on sector-specific impact of AI-driven analytics culture on sustainability performance, particularly within Bangladesh’s manufacturing and service sectors, such as RMG pharmaceuticals, IT services, and banking.

5. Results and Findings

5.1. Opportunities of AI-Driven Analytics in achieving B2B Sustainability in Bangladesh

There is a significant potential for the use of AI-based analytics in B2B processes, especially for handling sustainability issues. The strengths of AI technologies, including predictive analytics, machine learning, and the Internet of Things (IoT), can help B2B firms to increase resource productivity, minimize waste, and enhance internal cross functional analytics capability, all of which are vital for furthering sustainability – social and environmental sustainability both.

5.2. Improving Resource Utilization

AI analytics assist B2B firms to improve resource utilization by integrating large amounts of business data to prevent wastages at inventory or waste management points. Brynjolfsson and McAfee (2017) explain that automation decision-making increases resource utilisation and reduce waste within a business. In the sphere of B2B sustainability, this can mean that energy is used optimally, raw materials are conserved, and production processes are well timed. For instance, the IBM Watson IoT platform offers B2B firm’s real-time analytics on the performance of their assets and structures. Employing smart sensors in machines or production lines, AI will enable B2B firms to determine when machinery is due for maintenance, hence avoiding energy-intensive breakdowns and increasing the durability of the assets (Ghobakhloo, 2018). Such solutions enabled through AI driven analytics are helpful in reducing the time and materials wasted, thus enhancing efficiency in the production line.

5.3. Optimizing Green Supply Chains and Reducing Carbon Footprints in B2B Firms

Green supply chain management is one of the most significant sectors that are revolutionized by artificial intelligence through analytics for sustainability. The authors also explain that incorporating machine learning algorithms as well as predictive analytics can help organizations gain insights into their supply chain networks in real-time, they can use them to predict demand, manage their stocks, and minimize unnecessary transportation. This leads to decreased levels of carbon emissions, less fuel consumption and therefore a decreased carbon footprint.

AI solutions in green supply chain management can also help B2B firms to meet sustainable requirements on ethical buying and circular economy. According to Jankovic and Curović (2023), in the context of using AI technologies in the supply chain, it is possible to ensure the identification of a company’s suppliers and check whether they use ethical labor conditions and obey environmental regulation. With the help of AI technologies in identifying and current carbon emissions profile of products and materials through their lifecycle, firms and organizations can be aware of their sustainability status and CSR requirements.

For example, Walmart uses a sophisticated AI system to monitor the levels of sustainability of the firm’s suppliers. The system incorporates machine learning techniques to process data about the transportation activity, product acquisition, and energy consumption to understand the ways in which suppliers support Walmart towards the achievement of sustainability goals (Gupta, 2021). This level of transparency helps Walmart to work towards its policy of having zero emission in its operations by the year 2040.

5.4. Innovations in Customer Relationship Management (CRM) and Marketing Analytics

 Interactions between business to business (B2B) and business to consumers (B2C), customer relationship management (CRM), and marketing strategies are also being enhanced through the help of AI-driven analytics. When it comes to B2B marketing, it is important to note that every person is a client and a partner that is an important aspect of the concept of sustainable marketing practices. Most importantly, Chen et al. (2021) reckoned that, through the use of AI driven CRM systems can analyze buyer trends for green supplier selection, green purchase behaviors, and understanding patterns to green market appropriately. In a nutshell, the application of predictive analytics means that in order to satisfy a given market demand for green marketing and equally important for green HRM practices, B2B firms shall be able to estimate the chances of demand for green products or services and aim at that instead of developing goods and services to be left without buyers. Moreover, AI can help in selling either cross-selling and/or an upsell of products and services that are eco-friendly, which will be an added income. For example, Salesforce, one of the most popular CRM platforms, integrates artificial intelligence through the Einstein tool, which help a business to understand what the customers are doing in real-time. When it comes to environmental and social sustainability, with the help of this tool, B2B firms can recommend a specific product or service that is sustainable to a certain industrial customer, thereby making the customer more interested in the firms’s sustainability.

5.5. Fostering AI-Driven Analytics Capability in achieving B2B Equilibrated Sustainability

In this study, Equitable Sustainability (EqS) is conceptualized as the integrated pursuit of social and environmental objectives with equal strategic commitment. It represents a shift from fragmented, compliance-oriented practices toward a holistic, embedded framework that advances fairness, inclusivity, and environmental stewardship simultaneously. This concept provides a new lens for understanding sustainable development challenges in B2B firms, particularly in developing economies. The organizational benefits that can be derived from the use of AI-driven analytics for sustainability purposes are well known; however, companies are faced with a number of difficulties when implementing these technologies. To foster AI-driven analytics capability (AIAC) B2B firms in developing country faces the AI infrastructural challenges as embedded with high costs of implementation, data privacy, compatibility of the system, and skill deficiencies in the workforce.

5.6. Costs and Sustainability

The cost implication for the implementation of AI analytics is one of the top factors that can hinder implementation, especially for firms that are small and medium in size. Ghosh and  Prakash (2020) show that the implementation of AI technologies involves costs of procuring hardware such as HPCs, software applications, and professional staff. These initial costs could possibly prevent businesses, especially those in low cash flow industries or industries that do not have a direct high payoff for AI investments. The use of AI analytics for sustainability initiatives is best done with long-term strategic planning in mind because the payback period for such investment is relatively long or obscure. For example, an AI-based solution for green supply chain management could, over time, lower costs through minimizing inefficiencies or resource utilization; it could, however, take a big investment to integrate AI into a B2B firm’s operations (Jouini et al., 2020). Of course, B2B firms should measure these costs against the potential gains that result from enhanced sustainability initiatives. In this regards scholars also emphasize on urgent need to assess the impacts of the Sustainability Paradox and develop strategies that align AI development with sustainability principles to maximize profit and reduce risks (Frimpong, 2025). The paradox suggests that businesses are caught in a dilemma where sustainability investments are essential for future survival and growth, but cost barriers make it difficult to prioritize them in the short term.

5.7. Data Privacy Concerns

Another major concern that hinders B2B sustainability’s embrace of AI-driven analytics is data privacy and security issues. AI systems require vast amounts of data, which may be derived from even sensitive business activities and supply chain processes. Sutherland et al. (2020) noted that firms have to adhere to higher levels of measures in data privacy laws, for example, the GDPR in the EU, whereby the collection of personal data is regulated. Leakage of information or the loss of sensitive information will cause adverse impacts to any firms, particularly in the health, financial, and manufacturing sectors. Some B2B firms in Bangladesh are reluctant to implement artificial intelligence and analytics solutions to support their business because they can lead to non-compliance with the regulations regarding privacy and data protection. As we already mentioned, data leakage and non-compliance to the regulations force many B2B to be cautious when selecting AI-driven analytics solutions and tools because they have to control the whole green supply chain process of data collecting and processing (Vercellis, 2020; Rahman et al.2023).

5.8. Interoperability Issues

Another major challenge facing AI-driven analytics is the compatibility of AI systems with current business models and patterns. According to Jouini et al. (2020), many organizations struggle with applying AI technologies because they either have outdated systems or didn't design them with AI integration in mind. When AI is still in its infancy, B2B firms may face strategic challenges in ensuring that the AI applications they develop can effectively integrate with other software applications, data repositories, and physical systems used throughout the firm. Furthermore, the literature clearly shows that the AI technologies lack standardization, further hindering integration. They come in the form of closed-source systems from different AI vendors, and this is in conducive for integration across different departments or different regions/countries, as this becomes very expensive for anyone who wants to scale up the use of AI across his enterprise. This interoperability problem is especially prevalent in B2B supply chain management, as various players, such as suppliers, logistics providers, manufacturers, etc., have to use the same data systems and technological platforms to work.

Thus, the following conceptual model is suggested based on the SLR and Morphological Analysis of this study (Figure 1.3) for further study based on SLR and MA in this study.

Figure 1.3: A conceptual framework of AI-driven Organizational Culture

In the context of sustainable business practices in B2B context, an AI-driven organizational culture, supported by analytics, collaboration, and learning, enables firms to effectively address both environmental and social sustainability goals. This culture fosters environmental goals such as emissions reduction, green innovation, and circular economy practices, while also promoting social goals including fair labor practices, diversity, and employee well-being.

6. Discussion

AI-driven organizational culture is characterized by the integration of AI across various functions, fostering a culture where employees are encouraged to experiment with AI tools, collaborate across departments, and embrace data-driven decision-making. According to Westerman et al. (2011), organizations that cultivate a strong digital culture are better positioned to implement digital technologies like AI and leverage them to drive innovation and competitive advantage. In the context of sustainability, this organizational culture enables B2B firms to adopt AI tools that optimize resources, minimize waste, and improve supply chain efficiency, as discussed in the earlier statement.

An AI-driven organizational culture encourages the use of AI tools to optimize resource utilization and minimize waste. Kusiak (2018) highlights that organizations that adopt an AI-driven culture can more effectively utilize data to identify inefficiencies in resource allocation. The predictive capabilities of AI tools can help businesses reduce energy consumption, lower material waste, and enhance overall resource efficiency. For B2B firms, this leads to significant environmental benefits, as AI models identify opportunities for reducing carbon footprints and minimizing waste in production processes. This findings support the study of Tabaku et al.,(2025), Nurhaeni et al (2024) who agreed that AI can identify inefficiencies in energy usage and operational processes approx 30% or more, which are critical for reducing carbon emissions. A synchronized organizational cultural and AI adoption helps B2B firms to track and take proactive decision to reduce carbon emissions while improving the overall environmental impact of their operations.

An AI-driven culture facilitates the use of AI tools to enhance green supply chain operations, reducing inefficiencies and improving the predictability of demand and supply. As Choi et al. (2018) point out, organizations with a strong AI culture are more likely to integrate AI into their supply chain management systems, improving inventory control, reducing delays and save resources. AI solutions help B2B managers make more informed decisions based on real-time data, contributing to greater operational efficiency and understanding buyer’s sustainability demand. However, in the absence of an AI-driven culture, however, cross-departmental functions may experience delays or be overlooked altogether, resulting in system inefficiencies. This system loss can discourage or demotivate employees, making them less likely to embrace AI-driven solutions and diminishing the overall effectiveness of supply chain operations. This finding aligns with the study of Rožman et al., (2023).

Our SLR findings suggest that AI-driven analytics, particularly predictive analytics, enable B2B firms to identify not only buyer preferences for eco-friendly products and services, but also expectations related to the firm's internal sustainability practices, such as employee working conditions and ethical internal marketing strategies. By understanding these nuanced expectations, B2B firms can tailor both their environmental y and social sustainability, thereby enhancing industrial customer satisfaction and reinforcing their sustainable brand reputation. A cultural shift towards data-driven decision-making allows B2B firms to tailor these green marketing strategies. However, these findings align with Behera et al. (2022), who addressed ‘congenial organizational culture’ foster cognitive computing system that helps to overcome the B2B marketing challenges (i.e advertising, pricing, outbound marketing, anticompetitive practices and privacy). Hence, B2B firms must address data privacy concerns and ensure ethical handling of data as an integral part of cultivating an AI-driven organizational culture (Chaffey, 2019; Kantar, 2020). This strategic orientation includes promoting greater transparency and strengthening governance mechanisms in B2B firms.

The challenge of heterogeneous sustainability demands is particularly significant in B2B firms like packaging, where different buyers may prioritize different aspects of sustainability (environmental vs. social). Some buyers focus on reducing carbon emissions, while others may emphasize improving social conditions for workers in the supply chain. AI can address these diverse demands, but only if organizations integrate AI analytics tools capable of understanding and responding to these differences. Porter and Kramer (2011) discuss the concept of shared value, where businesses are expected to align their sustainability strategies with societal needs. However, this alignment becomes more complex when industrial clients have diverse and sometimes conflicting sustainability priorities. The challenge lies in creating AI culture and analytics capability that can tailor sustainable solutions to these varied demands while maintaining operational efficiency.

Data security is another crucial challenge when integrating AI. As AI tools often require access to vast amounts of organizational data, there is a significant risk of exposing sensitive business information to external parties or cybercriminals. This is particularly concerning in industries that handle confidential or proprietary data in AI cultural readiness in B2B firms. Greene and Burton, (2025) warn that AI models can be vulnerable to cyber-attacks if the necessary security measures are not in place and employee is not aware its severity. While AI has the potential to enhance data security through predictive analytics, organizations must simultaneously invest in robust security infrastructure to protect against breaches. This requirement for increased cyber security can further raise the costs and complexity of AI adoption, particularly for B2B firms that operate in sectors with high data sensitivity.

The Dynamic Capabilities View (DCV) highlights that firms must continuously adapt their capabilities to sense new opportunities and transform their operations to remain competitive in a rapidly changing environment (Teece, 2014). However, without adequate technical infrastructure and skilled human capital, B2B firms may struggle to harness AI's potential. This AI infrastructural issue also ties into shifting organizational culture towards AI driven culture. An AI-driven organizational culture (AIOC) that encourages cross-functional collaboration and continuous learning is essential to overcoming these challenges.

As the SLR demonstrate (Table 1.1), an AI-driven organizational culture is essential for B2B firms looking to optimize resource utilization, reduce inefficiencies, and achieve sustainability goals.  Again, one of the main challenges for B2B firms is the high initial investment required to implement AI. This includes expenditures on infrastructure, technology, and training programs. However, several studies suggest that while the upfront costs are high, the long-term benefits can far outweigh these initial investments. For instance, Brynjolfsson and McAfee (2017) argue that AI can deliver substantial returns over time through efficiencies in operations, green supply chain management, and resource optimization. Nevertheless, critics such as Arntz et al. (2017) caution that smaller firms with limited resources may find the upfront investment a prohibitive barrier; potentially make worse the digital divide in industries.

To navigate the challenges associated with AI adoption, B2B firms must consider both the technological and cultural aspects of integration. The role of an AI-driven culture becomes central to enabling firms to respond to the diverse and evolving demands of their stakeholders, including sustainability concerns. The integration of AI into business operations has the potential to drive long-term efficiencies, but B2B firms must invest in the right infrastructure, technology, and workforce capabilities to overcome the initial barriers for developing AI-powered culture with an aim of assessing equilibrated sustainability (EqS). By fostering a culture that values continuous learning and cross-functional collaboration, B2B firms can successfully leverage AI to drive sustainable growth, operational excellence, and competitive advantage in the face of complex challenges.

Table 1.1: Key Summary of Systematic Literature Review

Source: Authors’ Own,2025

7. Conclusion

The study emphasizes the critical role of AI-powered culture and B2B analytics capabilities in navigating the complex relationship between industry type, sustainability crises, and adaptive strategies for promoting a balanced sustainability, conceptualized as ‘Equilibrated Sustainability’. The five dimensions of industry type ranging from manufacturing to service sectors determine the specific challenges and opportunities each sector faces in addressing sustainability crises. These crises, driven by environmental degradation, social imbalances, and resource scarcity, can result in severe consequences, including reputational damage, financial loss, increased regulatory pressure and sustainability paradox. As B2B firms encounter these crises, leveraging AI-powered culture becomes increasingly vital. AI can help B2B firms adopt predictive analytics, optimize resource management, and drive operational efficiencies, ultimately allowing firms to respond more effectively to sustainability challenges in real time. Coupled with strong B2B analytics capabilities, particularly in green marketing, green innovation, and green human resources, B2B firms can enhance their transparent decision-making processes, identify sustainable practices social or environmental, and measure performance against sustainability goals. The consequences of failing to adapt sustainability analytics  are significant, not only in terms of financial instability but also in the lost opportunities for green innovation, competitive advantage, and long-term growth. The adaptive strategies employed by B2B firms whether through technological innovation, process optimization, or deeper collaboration with stakeholders are essential for navigating these sustainability crises and driving sustainable growth. Eventually, promoting equilibrated sustainability (EqS) requires B2B firms to integrate AI-driven analytics into their core strategies, balancing environmental and social align with economic considerations. This approach not only enables firms to respond proactively to crises but also fosters resilience and long-term value creation in B2B-SMEs also. By leveraging AI-driven organizational culture (AIOC) and enhanced analytics capabilities, B2B firms can promote green innovation, achieve sustainability goals, and position themselves as leaders in a rapidly evolving market, contributing to the broader goal of sustainable development.

[Note: This is the partial work of PhD thesis and this paper is funded by Matin Construction Ltd, Bangladesh]

Funding: Not applicable.

Conflict of Interest: The authors declare no conflict of interest.

Informed Consent Statement/Ethics Approval: Not applicable.

Declaration of Generative AI and AI-assisted Technologies: This study has not used any generative AI tools or technologies in the preparation of this manuscript.