Artificial Intelligence as a B2B marketing tool: A microfoundation dynamic capability approach

- Artificial Intelligence in B2B Marketing

- Dynamic capabilities and microfoundations

- Research approach

- Data analysis

- Conclusions using AI to power dynamic B2B marketing opportunities

The role of AI in facilitating effective B2B marketing is central to this study, and more specifically, to exploring how AI can harness the dynamic capabilities that help create the necessary impact. This sequence of associations is represented by the conceptual framework adopted in this study. The logic behind this study is that AI can enable or enhance the underlying processes that make up the dynamic capabilities of a firm. In turn, the presence of tremendous dynamic opportunities allows firms to modernize their B2B marketing operations.

Artificial Intelligence in B2B marketing

Artificial intelligence has rekindled interest as the next frontier of productivity and innovation. The vast majority of research to date has working towards examining the potential business value that AI can bring within organizational boundaries, with some of the early studies empirically showing such effects. In the broader area of ​​research into the value of IT in business and in terms of new IT-driven organizational capabilities, there is a growing consensus that IT enables firms to increase productivity through intermediate organizational capabilities. The basic premise of this perspective is that leveraging new IT applications is central to organizations, as it helps develop incremental effects with intermediate organizational capabilities that ultimately lead to a competitive advantage.

Currently, there is still limited understanding of the mechanisms by which artificial intelligence applications provide competitive advantage. The main argument in our study is that, depending on the context of use, organizations can realize different kinds of benefits for each of the underlying processes that make up their dynamic capabilities. While empirical research examining the mechanisms by which AI drives business value in B2B marketing is still somewhat limited, some research avenues have offered insight into what AI can provide. Specifically, there is ongoing discussion about how AI can help organizations automate processes, gain insight into data that was previously unavailable, and improve their interactions with key customers. AI has been shown to enable firms to automate several different manual processes, including interacting with customers (for example, using chatbots), or other manual activities.

In their recent work, Coombs, Hislop, Taneva, and Barnard (2020) present a conceptual business value model for intelligent automation, a subset of artificial intelligence technologies. This work demonstrates the synergistic relationship between technological and non-technological investments and the proposed mechanisms through which business value is realized. Building on the field of B2B marketing, it proposes a theoretical model for explaining the impact of AI in B2B marketing by improving rational decision making. This work shows that AI capabilities are not limited to process automation, but are also improving knowledge management practices related to B2B marketing activities. Other empirical work also provides insight into how marketing activities such as pricing and consumer behavior can be improved with artificial intelligence technologies.

Dynamic possibilities and microfoundations

The Dynamic Capability View (DCV) has been one of the most influential theoretical approaches to the study of strategic management over the past decade. Building on the Schumpeterian logic of creative destruction, it proposes the use of dynamic opportunities that enable firms to recognize and exploit emerging business opportunities, as well as change the way they do business in order to adapt to changing market conditions. Despite some differences in the definitions used, there is an increasing consensus that dynamic capabilities are purposefully developed and consist of a set of identifiable and specific processes. These processes are usually understood as internalized and purposeful, directed towards independent corporate action. A key reason for the researchers’ close attention to the concept of dynamic capabilities was their perceived impact on important outcome variables. Dynamic capabilities are in contrast to operational or conventional capabilities, which focus on how the firm currently generates a profit, and are offered to add value through evolutionary suitability. Recent studies have confirmed such claims, and empirical results have shown that they lead to systematic change, allowing for renewed operational capabilities and increased flexibility in response to market changes. They represent key areas in achieving sustainable competitive advantage.

In documentation, dynamic opportunities are broken down into three main processes focused on strategic change. These include identifying new opportunities and threats, seizing new opportunities through the development of business models and strategic investments, and transforming or reconfiguring existing business models and strategies. In his influential paper, Tees (2007) argues that sensing includes analytical scanning, search, and exploration systems across a variety of markets and technologies. On the other hand, capture includes an assessment of existing and new opportunities and possible investments in related developments and technologies that are most likely to gain acceptance in the market. Finally, transformation involves the continuous alignment and reorganization of specific tangible and intangible assets. Past research has focused on the outcomes of dynamic capabilities, with significantly less research on how the underlying processes that make up dynamic capabilities arise. This stream of research looked at prior research at different levels of analysis, including organizational, individual, and environmental levels, so as to highlight factors that either promote or hinder the development of dynamic capabilities. However, to date, as far as we know, there is little research on the impact of AI on the underlying processes that make up dynamic capabilities.

Research approach

Since empirical research on the value of AI and its impact on strategic development, especially in a business context, is still in its early stages, we applied a case study method. The choice of the case study research method was based on the fact that it allows for the collection of rich descriptions of phenomena and detailed explanations of events that are not well understood in documentation from the point of view of several key actors. In our research project, we chose a design with multiple examples because it allows for replication logic that treats a set of cases as a series of experiments, each serving to confirm or disprove a set of observations.

We conducted our research in high-tech firms because these types of firms were shown to be pioneers in AI adoption. In addition, the types of projects initiated by firms in this sector tend to be more complex and more of a major part of their competitive strategies. The buzz of the past few years has prompted a large number of firms to invest in AI pilot projects. Firms are now realizing that AI is not just a means of gaining a distinct competitive advantage, but a necessity in order to stay competitive at all. All three cases that were selected for the purposes of this study were implemented by AI solutions at least 2 years ago. In addition, these firms began their implementations at almost the same time, which makes their maturity levels the same. In their respective industries, each firm holds a national leadership position in revenue, profit, market share, and headcount. All firms also have a significant international presence, with a significant proportion of their revenues from activities outside their national borders. However, while we looked for firms with similarities to be able to compare and reproduce results, we also found it necessary that they exhibit sufficient heterogeneity to help assess potential generalizability.

Data analysis

For data analysis, we followed the recommendations of Miles, Guberman, and Saldana (2013), and opted for a thematic analysis when examining the data. This was done through a systematic and repetitive procedure that used data comparisons, emerging topics, and the latest documentation to facilitate the process. As a first step, we developed separate case studies for each firm. We looked at patterns in participants’ responses and any distinctive aspects in their descriptions of how AI was being used to support B2B marketing operations. In addition, we looked at the underlying mechanisms and underlying conditions that link such decisions to improvements in B2B marketing. Following this process, we have linked the appropriate concepts on a case-by-case basis. As part of this step, we reviewed the findings of the initial coding and established links between our chosen categories and emerging themes.

Although we had a set of theoretically based concepts that partially defined the definition of key concepts, we allowed other concepts and patterns to emerge from the collected raw data. To improve the generalizability of the findings and to deepen the understanding and explanation of these concepts, we conducted a comparative analysis between each category as well as between the same categories in different cases. The goal of this was to be able to compare and contrast how operations in each of the three firms have changed with the introduction of artificial intelligence applications, how this process was carried out, and what problems they faced. All disagreements between the programmers were resolved through discussion. In addition, once we arrived at the initial findings, we shared them with key experts to assess their plausibility and point out any aspects that we misunderstood or overlooked.

In the last step, we combined emerging themes and concepts with theoretical concepts in documentation. Therefore, we took an iterative approach, moving back and forth between new topics and existing literature, to explore the broad possible explanations for our findings and to develop an explanation for the findings.

Conclusions

Leveraging AI to power dynamic B2B marketing opportunities

Consistent with our hypothesis, we first explored how the adoption and use of AI has transformed B2B marketing through three case studies. We found that the use of AI led to improved understanding, faster reaction times, new marketing approaches, and new revenue streams. We’ll discuss performance enhancements in more detail below. In the three cases reviewed, the use of AI was aimed at supporting a wide range of performance aspects in relation to marketing (generating new ideas and targeted dissemination of information), new business models (creating new services based on analytics), customer service (faster response times to customer inquiries and increased customer satisfaction), and quality assurance (product quality assurance and response to defects).