Designing a model for the transformation of B2B marketing strategies based on artificial intelligence in small and medium-sized companies

Khaleghi Zanjani, Parna; Tizfahm Fard, Gholamreza; Mola Zeinali, Sahar; SAMADI, MAHMOUD

doi:10.22034/jnamm.2026.579440.1273

Designing a model for the transformation of B2B marketing strategies based on artificial intelligence in small and medium-sized companies

Document Type : Original Article (Qualitative)

Authors

parna khaleghi zanjani ¹

Gholamreza Tizfahm Fard ²

Sahar Mola zeinali ³

MAHMOUD SAMADI ⁴

¹ Master, Department of Management, Znjan Branch , Islamic Azad University, Znjan, Iran.

² PhD in Business Administration, Department of Business Administration, Science and Research Branch, Islamic Azad University, Tehran, Iran.

³ PhD student, Department of Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

⁴ Assistant Professor of Management Department, Takestan Institute of Higher Education, Qazvin, Iran.

https://doi.org/10.22034/jnamm.2026.579440.1273

Abstract

Abstract
The present study aimed to design a transformation model for AI-based B2B marketing strategies in small and medium-sized enterprises. In terms of purpose, this research is applicable, and was conducted through a qualitative approach based on the grounded theory strategy. The research population consisted of marketing managers and artificial intelligence experts. Sampling was carried out by a purposive method, and the data collection process continued until theoretical saturation was achieved, which was ultimately reached after 15 in-depth interviews. Data analysis was conducted in three stages—open, axial, and selective coding—through MAXQDA18 software. The findings indicated that causal factors, including the necessity of analyzing competitive big data, changes in the decision-making patterns of organizational buyers, and pressure to reduce marketing costs, play a key role in initiating this transformation. Contextual factors, including the organization’s digital maturity and the quality of data infrastructure, provide the necessary foundation for the successful implementation of intelligent marketing, while limitations in financial resources, technical complexities, and resistance from traditional sales experts act as intervening factors. According to the results, adopting strategies such as the implementation of intelligent account management systems, predictive analytics, and content automation leads to improved outcomes such as higher return on investment, increased loyalty among key customers, and reduced sales cycles. This study provides a systematic framework that helps managers of small enterprises gain a sustainable competitive advantage in industrial markets by understanding the complex interactions between artificial intelligence tools and marketing strategies.
Introduction
Today, artificial intelligence has emerged as a significant driving force across various industries (Soleymanpoor et al., 2025). The flood of data, coupled with the availability of processing power and storage in digital devices, has generated a renewed interest in artificial intelligence (AI) across various fields in recent years (Sharma et al., 2022). Intense competition among organizations worldwide has also accelerated the need for AI deployment to gain an edge over competitors (Saadisalih et al., 2025). AI is often not perceived by most managers as a core competency that organizations must cultivate to remain competitive in the long run (Hanandeh et al., 2024). One of the key areas of AI application in organizational operations has been B2B marketing (Mikalef et al., 2021). Intelligent solutions are essential for enhancing B2B marketing capabilities in a complex business environment, as B2B operations often involve extensive informational complexity and the need for rapid decision-making. In this regard, due to its ability to process increasing volumes of data and provide rich insights into business partners and key customers, AI has the potential to transform conventional activities (Karamipour et al., 2025). Furthermore, AI applications have been proposed to enable the automation of many manual processes, which can help reduce bottlenecks and increase operational efficiency in B2B operations (Emami et al., 2025).
Nevertheless, despite the aforementioned benefits for large organizations, the implementation of AI in B2B marketing for small and medium-sized enterprises (SMEs) faces different complexities and obstacles. These companies, which are considered the backbone of the economy, unlike large corporations, often struggle with severe marketing budget constraints, a lack of massive datasets for training algorithms, and a shortage of AI specialists (Chatterjee et al., 2021). In the B2B world, where relationships are built on trust and long decision-making cycles, small companies need to know how AI can make their marketing processes intelligent and personalized without damaging human relationships (Zholghadr et al., 2025).
Therefore, the primary issue is that despite the inevitable necessity of digital transformation, there is still no methodical model for integrating AI into B2B marketing strategies in small and medium-sized enterprises. There are many unknown aspects regarding how the managers of these companies adopt this technology and its actual impact on redefining their sales and marketing strategies. Hence, this research seeks to answer the following question: What is the role of AI in the transformation of marketing strategies?

Theoretical foundations
Artificial intelligence refers to a set of technologies that allow machines to simulate functions similar to human intelligence, including learning, reasoning, problem solving, and language comprehension. In fact, artificial intelligence is a system that, by complex algorithms, has the ability to process huge volumes of unstructured data to identify hidden patterns and make predictions or decisions based on them (saadisalih et al., 2025). This ability to analyze patterns distinguishes artificial intelligence from traditional data analysis tools, because artificial intelligence-based systems have the characteristic of “self-correction” and can continuously improve the accuracy of their predictions by receiving new data (seyed javadin et al., 2025).
(Soleymanpoor et al., 2025) in a study entitled "Providing a model of effective factors in the socialization of artificial intelligence technologies", the results of the analysis showed that both independent and dependent variables have a direct positive and significant effect.
(Rostam zadeh ganji et al., 2025) in a study entitled "Providing a model of developing employee cognitive trust in artificial intelligence", the results show that causal factors include transparency, training and awareness, ethical compliance and defining common roles and goals, contextual factors such as organizational culture and resources; intervening factors such as employee resistance and system complexity, and strategies including employee training and empowerment are known as key tools in improving human-machine interactions.
Research Methodology
The present study is applicable in terms of purpose, and qualitative in nature with an exploratory-explanatory approach, designed and implemented based on the grounded theory strategy. The main objective of this study is to identify key components and explain the relationships among artificial intelligence technologies, the transformation of B2B marketing strategies, and their outcomes at the level of small and medium-sized enterprises (SMEs). The statistical population of the study included marketing managers, sales strategists in the business-to-business sector, AI implementation specialists, and senior managers of small and medium-sized enterprises participated in the digitalization process of their commercial activities. Sampling was conducted by a purposive (judgmental) method to ensure that the participants possessed lived experience and a deep understanding of the challenges and opportunities of AI in the B2B environment. The data collection process continued until the stage of theoretical saturation, which was ultimately achieved through 15 in-depth interviews. Data analysis was carried out in three stages—open, axial, and selective coding—by MAXQDA10 software.
Research Findings
The research findings indicated that the transformation of B2B marketing strategies is a pivotal phenomenon shaped by causal factors such as the necessity of big data analysis and competitive pressure. This process, within the context of the organization’s digital maturity and through the management of intervening factors such as employee resistance and financial constraints, is realized through strategies like intelligent personalization and predictive analytics. Ultimately, the implementation of this paradigm model leads to prominent strategic outcomes, including the enhancement of competitive performance, reduction of the sales cycle, and improvement of the organizational customer experience.
Discussion and Conclusion
The research findings indicate that competitive pressure acts as one of the primary drivers steering small and medium-sized enterprises (SMEs) toward artificial intelligence. In today’s industrial markets, acquiring strategic customers is no longer feasible through traditional methods, and the necessity of analyzing competitive big data has forced organizations to adopt intelligent tools. This finding is consistent with the results of Mikale et al. (2021).
Profound transformations in the structure of industrial markets, including supply chain complexity and the urgent need to minimize human error in sales forecasting, act as external drivers. The findings of the current study suggest that due to high volatility in B2B markets, traditional forecasting models have lost their effectiveness. This aligns with the research by Seyedjavadin et al. (2025), which emphasizes the role of business culture in formulating AI-driven strategies.
According to the extracted model, value creation through intelligent insights is the defining milestone that distinguishes leading firms. This finding is closely related to the results of Labin et al. (2024), who identified six emerging clusters in smart marketing (including dynamic market strategies), demonstrating that AI shifts the nature of “strategy” from a static state to a dynamic, data-driven one.
Data analysis revealed that success in marketing transformation is highly dependent on digital maturity and the culture of innovation adoption within managerial echelons. The quality of customer data and the availability of cloud platforms provide the necessary foundation for executing the model. This outcome aligns with the research of Bagheri et al. (2024), who investigated the level of digitalization in knowledge-based companies. Indeed, without structured data infrastructures and specialized human resources, even the most advanced AI tools cannot bring about strategic transformation.
The research findings showed that at the operational level, the resistance of traditional sales staff and the lack of technical expertise constitute the most serious operational challenges. These barriers, identified as intervening factors in the model, indicate that strategic transformation extends beyond technology acquisition and requires a shift in the organization’s human capital. This finding is consistent with the results of Rostamzadeh Ganji et al. (2025) regarding the importance of employees’ cognitive trust; until employees trust the accuracy and functionality of AI, they will resist the implementation of algorithm-based strategies. Furthermore, this is closely related to the study by Soleymanpour et al. (2025) on the necessity of “technology socialization,” meaning that small firms must establish the necessary cultural and educational foundations for AI adoption within the organization prior to model deployment to prevent early-stage project failure.
The results demonstrated that factors such as financial constraints, regulatory gaps, and particularly the unique challenges of the Iranian business environment—such as “sanctions and lack of access to international tools”—affect the model’s outcomes as critical and intervening limitations. The findings of the present study indicate that due to weak financial resources, SMEs lack the capability to compete with large organizations in acquiring licensed technologies. This finding matches the results of Ahmad (2025) regarding ethical and infrastructural challenges, as well as the research by Zolghadr et al. (2025), which emphasized the role of export and technological capabilities in electronic industries.
The research findings revealed that one of the primary strategies for transformation is the re-engineering of traditional processes and aligning them with the requirements of the digital age. This re-engineering encompasses a shift in managerial attitudes and redesigning decision-making structures from intuitive to data-driven modes. This finding is consistent with the research of Seyedjavadin et al. (2025), which highlights the role of business culture in formulating AI-driven strategies.
At the operational level, the findings suggest that firms must move toward intelligent tactics such as strategic personalization at scale and the utilization of predictive analytics in B2B interactions. These tactics allow the organization to focus on opportunities with higher success probabilities by accurately predicting the needs of strategic customers. This outcome is closely related to the research of Emami et al. (2025) regarding the design of an AI-based Customer Relationship Management (CRM) model. It is also aligned with the findings of Saadisalih et al. (2025) regarding innovative models for technology implementation in target markets.
Ultimately, the implementation of these strategies leads to the key outcome of enhancing organizational performance and establishing a competitive advantage. The results showed that the synergy between process re-engineering and intelligent tactics not only increases sales force productivity but also places the firm in a superior position relative to competitors by creating value through intelligent insights. This finding is fully consistent with the results of Karampour (2025), which emphasized the impact of AI capabilities on organizational performance in B2B environments.

Keywords

Artificial intelligence

B2B marketing

small and medium-sized enterprises (SMEs)

strategic transformation

predictive analytics

Subjects

Marketing Management

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