Integrating Artificial Intelligence into Industrial Marketing: Challenges, Benefits and Possible Ways Forward

Written by Russell W.

Chapter 1. Introduction

1.1. Research Rationale

The emergence of artificial intelligence (AI) has already created revolutionary changes in multiple spheres, including web development, design, and content creation (Chintalapati & Pandey, 2022). Based on Labib (2024), AI utilisation by marketers created six main trends, namely: AI-enhanced market dynamic strategies, psychosocial dynamic, AI for decision-making, AI for consumer services, AI for ethical promotion, and AI for value transformation. These effects create both qualitative improvements associated with new and unique solutions based on big data analyses and quantitative advancements stemming from automation and optimisation of business processes. With that being said, the integration of AI in the sphere of industrial marketing is associated with both opportunities and threats (Mikalef et al., 2021). On the one hand, this technology offers unique advantages, such as sensing, seizing, and transforming dynamic capabilities (Mikalef et al., 2021), advanced marketing intelligence and market data analytics, the ability to reduce decision-making uncertainties, and easy conceptualisation of emerging customer needs and new value definitions. On the other hand, the previous empirical evidence suggests that the rates of AI adoption in the industrial marketing context remain relatively limited (Han et al. 2021; Keegan et al., 2022).

This problem is largely associated with the complexity of AI implementation in this sphere, as well as the lack of clear integration roadmaps, market-tested solutions, automation accountability, and controversial quality of such instruments as chatbots forming negative past experiences (Han et al., 2021; Paschen et al., 2019; Prior & Keranen, 2020). As noted by Duan et al. (2019) and Paschen et al. (2019), the advantages of artificial intelligence largely rely on big data availability that may be limited in industrial marketing and the B2B context in general. This complexity creates a dilemma for manufacturing firms. From the Industry 4.0 advancement standpoint, the organisations missing this important trend may lose competition to their peers embracing the first mover advantage in the sphere of AI industrial marketing and making full use of it (Prior & Keranen, 2020). Thus, the body of research on the topic remains limited, which makes it difficult to appraise the risks involved and formulate effective implementation strategies. This study aims to explore how artificial intelligence can be integrated into industrial marketing in the light of associated benefits, challenges, and possible ways forward.

1.2. Research Aim and Objectives

This thesis seeks to investigate the challenges, benefits, and possible ways forward that can be created by the integration of artificial intelligence into industrial marketing. This aim will be realised via the following research objectives:

  1. To critically analyse the artificial intelligence technologies utilised in B2B marketing, including machine learning, predictive analytics, and natural language processing.
  2. To identify key benefits of AI implementation in the context of industrial marketing.
  3. To evaluate key success factors and challenges affecting the process of artificial intelligence integration in industrial marketing.
  4. To propose a strategic framework of AI implementation in this sphere, formulate practical recommendations on how the implementation process can be facilitated, and evaluate possible ways forward that are available to industrial marketing professionals.

1.3. Artificial Intelligence in the Context of Industrial Marketing

Industrial marketing is defined as the promotion of products and services in the business-to-business (B2B) context occurring within the industrial sector (Ledro et al., 2022). This makes it different from similar practices focused on individual customers since the focus on customer relationships is shifted to long-term collaboration (Han et al., 2021). Industrial marketing highlights the role of reliability, trust, and high levels of technical expertise that is more prominent than similar factors in the business-to-consumer (B2C) segment, where individual deals frequently occur only once or do not involve comparable transaction sizes (Akter et al., 2023). This makes the aforementioned AI tools more relevant for monitoring longitudinal trends, processing customer data, and developing unique offerings recognising the needs and preferences of specific customers (Papagiannidis et al., 2022). Popular industrial marketing practices include trade shows, direct sales, loyalty programmes, product presentations, and the provision of recommendations from past clients (Taleghani & Shadpour, 2023). Generally, such promotional tactics are focused on the delivery of technical information rather than advertising messages, since their recipients are knowledgeable industry professionals with many years of expertise who are accustomed to making informed, rather than spontaneous, purchase decisions.

From an AI utilisation standpoint, the existing secondary evidence suggests a number of industry marketing areas that may benefit from these instruments. On the one hand, artificial intelligence can be used for analysing big data in order to produce valuable insights (Papagiannidis et al., 2023). They may include unrealised customer needs, industry trends, implicit supply and demand preferences, or future requirements for maintenance services and/or repair works. These elements contribute to customer relationship management (CRM) and may be especially important for the industrial B2B segment focused on long-term collaboration (Ledro et al., 2022). On the other hand, AI tools can be used for the personalisation of communication with customers and the provision of unique opportunities in this sphere (Keegan et al., 2023). Multi-language chatbots processing new requests on a 24/7 basis and personalised content creation may be seen as some possible examples of the unique opportunities in this sphere.

1.4. Thesis Structure

The thesis is structured as follows. The introduction has outlined the research rationale, set the aim and objectives, and provided a background information on the use of artificial intelligence in the context of industrial marketing. The next chapter is the Literature Review, which critically evaluates existing secondary literature on the topic and synthesises the key debates, gaps, and results reported by authors in this sphere. The following Theoretical Framework chapter presents the theoretical concepts, models, and perspectives informing this research study’s focus and outlining the main research problem. It also provides the theoretical lens underpinning the process of its analysis and interpretation and formulates central hypotheses and research questions. The Methods section presents the techniques used to address these objectives including data collection methods, research design, sampling techniques, and data analysis instruments, as well as a discussion of limitations and ethical considerations.

The Results chapter summarises the study findings in a structured manner and presents the outputs of statistical tests, graphical analysis, and other tools used to process the earlier acquired information. The Discussion section builds upon these results to identify new and unique findings that develop the earlier explored secondary literature on the topic. Finally, the Conclusion chapter reflects on the achievement of initial thesis goals and puts newly obtained results into the context of existing knowledge in the studied sphere. It also includes a discussion of key contributions made by the study, its broader implications, and practical and actionable recommendations emerging from its outcomes.


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