Artificial intelligence focus and firm performance

The literature that seeks to understand the impact of marketing and sales activities on firm performance is vast, highlighting the complexities associated with measuring marketing´s return on investment (Edeling et al., In Press; Feng et al., 2015; Mintz et al., 2021). Further contributing to this challenge, the emergence of new technologies is creating a revolution in marketing—rendering much of the discipline’s extant thinking and practice increasingly obsolete (Rust, 2020). Artificial intelligence (AI, hereafter) is one such technology, poised to transform all facets of marketing through the augmentation and amplification of human intelligence (Davenport et al., 2020; Rust, 2020). Forecasts suggest AI will deliver an economic output of approximately $13 trillion by 2030 and create between $1.4 and $2.6 trillion of value in marketing (Chui et al., 2018). This will facilitate the processing of large scale and unstructured data in real-time, and by generating predictive insights that enhance marketing decision making (Albrecht et al., 2021).

Albeit of unparalleled significance to the competitiveness of firms in the future, AI adoption represents a substantial and costly challenge for firms, one where the impact of efforts is equivocal (Haenlein & Kaplan, 2021; Lui et al., In Press). Indeed, overall adoption of AI among businesses remains low, due in part to a lack of expertise, inadequate technological infrastructure, high levels of perceived complexity and dubious outcomes (Balakrishnan & Dwivedi, 2021; Lui et al., In Press). It is unclear whether the benefits of AI investment outweigh the costs (Bock et al., 2020; Makarius et al., 2020). Firm efficiency is central to determining AI cost–benefit, as efficiency today will influence performance tomorrow. Therefore, the current study examines how firms´ AI focus is related to their gross and net operating efficiency, since firms’ operating efficiency ultimately determines their survival and growth. Thus, building on prior marketing literature and economic theory, we develop a guiding framework to demonstrate how AI could affect firms’ operating efficiency. We then use a system of simultaneous equations to test the empirical relationship between AI focus and operating efficiency. Our study provides timely and nuanced empirical insight into the implications of firms´ strategic focus on AI.

AI refers to the ability of `machines to mimic intelligent human behaviour´ (Syam & Sharma, 2018, p. 136), encompassing the subdomains of machine learning, neural networks and deep learning (Shrestha et al., 2021; Sze et al., 2017). Internet Appendix I provides a comprehensive synopsis of developments in the AI domain, between 2016 and 2022. AI technology is advancing rapidly, and marketing applications, accordingly, are expanding. These include social media mining (Kietzmann et al., 2018), real-time price optimization (Davenport et al., 2020), sentiment analysis (Ma & Sun, 2020), customer churn analysis (Loureiro et al., 2020) and talent acquisition (Makarius et al., 2020; Raisch & Krakowski, 2021). The potential implications of AI applications in marketing are considerable. They span the areas of merchandising (Pillai et al., 2020), supply chain management (Toorajipour et al., 2021), customer management (Ma & Sun, 2020; Pillai et al., 2020) and market research (Huang & Rust, 2021a, b; Lee et al. 2020).

Meanwhile, AI’s touted benefits include greater efficiency in lead generation (Syam & Sharma, 2018), enhanced resilience (Bag et al., 2021), swifter decision making (Duan et al., 2019; Shrestha et al., 2021), greater creativity (Mikalef & Gupta, 2021) and superior performance (Lin et al., 2020; Ma & Sun, 2020). As such AI has become increasingly important for businesses and its impact on marketing will, undisputedly, be profound (Davenport et al., 2020). However, studies that explicitly examine the impact of AI focus on either firm efficiency or performance for a large sample are absent. Prior literature has indicated that AI has a positive effect on firms’ performance (Ma & Sun, 2020). However, our focus is firms’ operating efficiency because firms achieve higher performance only if they are efficient. In other words, firms’ operating efficiency is the channel via which firms achieve higher stock returns. Our study empirically tests the mechanism by which AI is positively related to stock returns.

Our guiding framework draws on both economic and marketing theory. In economics, Zeira (1998) and Mundlak (1988) show that the production of new technology is determined using a set of existing technologies that firms utilize to produce goods and services. The existing technology helps firms produce output and sell it profitably, allowing them to re-invest in new technology. For example, a firm might use technology to collect and analyze customer buying patterns and then try to sell new products to these customers. Further, firms could invest these profits in acquiring better analytical capabilities (including AI), which would enable them to predict customers’ future needs/purchases better and thereby realize additional profits.

Similarly, Acemoglu and Autor (2011) and Acemoglu and Restrepo (2018), show how AI improves firm productivity and creates jobs. We draw on Acemoglu and Restrepo’s model (2018) to detail precisely how AI can increase productivity and employment growth. Zeira (1998) and Mundlak (1988) have implicitly shown that technology is a function of firms’ output among other intermediate technologies. Thus, we argue that AI focus will depend on firm output – and the corollary: AI will improve firm output and employee growth. Since employee growth can increase costs, it is crucial to test whether AI focus improves efficiency, which considers outputs and inputs.

We also draw on the literature in marketing linking AI to sales and marketing expenditure. Singh et al. (2019) and Syam and Sharma (2018) expect AI to alter all sales process stages: from prospecting, pre-approach and presentation, to follow-up. AI can provide real-time feedback to salespeople, monitor tele-conversations in real-time and even infer from a customer’s tone that an unmentioned issue remains (Barro & Davenport, 2019). Bock et al. (2020) find that AI can create value in services by identifying service failures before customers are aware of their existence and aiding in service recovery by identifying alternative solutions that resolve failures. Moreover, AI generates and assesses the promise of sales leads (Davenport et al., 2020), augmenting and elevating firm sales capabilities, to positively impact future sales (Grewal et al., 2017; Huang & Rust, 2018; Syam & Sharma, 2018). Albrecht et al. (2021) establish that machine learning methods yield the most accurate forecasts, while Ma and Sun (2020) find that AI generates accurate predictions that assist marketing decisions and in turn improve all aspects of business performance. Thus, we expect that AI focus will improve marketing inputs and outputs.

We show the simultaneous relationships between AI focus and firm efficiency using a guiding framework. This framework outlines how we expect AI focus to influence firms’ operating efficiency. It is the foundation of our research design and reflects the empirical analysis, which employs a system of ten equations. The framework demonstrates the mechanism via which AI influences firms' operating efficiency. Since the customer base is the most important asset of a firm (Gupta & Lehmann, 2003; Pitt et al., 2000), we expect firms that adopt AI to enhance target marketing. Hence AI will directly impact adspend. As an emerging technology, firms require labor to use and manage AI (Acemoglu & Restrepo, 2018; Gries & Naudé, 2021). Thus, the first two variables in our guiding framework are adspend and number of employees. The next variable in our framework is sales, because AI will affect sales via adspend and labor among other firm-specific characteristics. Subsequently, we use sales per employee to show how AI is related to gross operating efficiency via sales. Finally, we have three net efficiency variables to show how we expect AI will relate to net operating efficiency via gross operating efficiency; return on sales, return on investment, and net operating efficiency.

The framework shows how AI focus is negatively related to adspend per $ of asset (Davenport et al., 2020; Kietzmann et al., 2018) and positively related to number of employees (Acemoglu & Restrepo, 2018). Next, we show how AI focus, adspend per $ of asset and number of employees are related to sales per $ of asset. We do not conject the sign of the relationship between AI focus and sales per $ of asset because the sign of the relationship will depend on the relative strength of the correlation between AI focus and adspend per $ of asset; AI focus and number of employees. Next, we show how sales per $ of asset is related to sales per employee.

Sales per employee is our measure of gross operating efficiency. Prior literature shows a positive relationship between AI focus and sales and AI focus and number of employees. Yet, reskilling employees and reconfiguring marketing positions to support the automation of marketing and selling capabilities is costly (Barro & Davenport, 2019). Therefore, it is important to know if AI focus is positively or negatively related to sales per employee. We hypothesize that AI focus is negatively related to sales per employee because our guiding framework does not predict the sign of the relationship between AI focus and sales. Further, our guiding framework posits a positive relationship between AI focus and number of employees. Hiring more employees comes with additional costs, thus we expect the relationship between AI focus and sales per employee to be negative.

Next, we show how AI focus and sales per employee is related to net income¹ per $ of sales, net income per $ of marketing expenditure² and net income per employee. Vast and ongoing improvements in machine learning translation have amplified the reach and volume of trade for firms (Agrawal et al., 2019; Brynjolfsson & Mcafee, 2017). AI will enable improvements in sales management tasks including scheduling, planning and matching stock with segmentation (Grewal et al., 2017; Huang & Rust, 2018; Syam & Sharma, 2018). Furthermore, AI enables the forecasting of future purchases with a high degree of accuracy and specificity, designing optimal presentation approaches and audio-visual analysis of sales-person-customer communications to enhance sales (Grewal et al., 2017; Huang & Rust, 2018; Syam & Sharma, 2018). Thus we posit:

The last three propositions measure net operating efficiency. We expect that AI will be positively related to these three measures by reducing production cost significantly. However, we do not conject the sign of the relationship between AI focus and return on marketing investment, because our AI Focus is a general measure. Rather, we have a positive sign in the main analysis and a negative sign in the weighted industry analysis, since general AI focus may not be positively related with return on marketing investment. Prior literature shows AI focus could reduce costs (Huang & Rust, 2021a; Stahl et al., 2021). AI enables a superior approach to target marketing and could enhance marketing communication through tailoring of personalized information based on customer insights and analyses (Kaplan & Haenlein, 2019). In this way marketing communications can potentially provide more relevant messages to the appropriate audience at a time and in a place where they are most receptive. These insights are not restricted to consumers, they also can enhance a firm´s approach to potential employees, ensuring that the firm attracts superior talent that will build firm value. Machine learning, one facet of AI, identifies probabilistic matches within data sets. According to Davenport and Ronanki (2018: 111), the application of this technology by GE to integrate supplier data enabled it to save `$80 million in its first year by eliminating redundancies and negotiating contracts that were previously managed at the business unit level´. Applications of AI will optimize internal business operations, improving firm revenue and ultimately profitability. However, whether AI focus will reduce or increase firm costs divides the extant literature (Huang & Rust, 2021a; Stahl et al., 2021). Aside from the costs involved in acquiring AI technologies, an AI focus requires reskilling employees and reconfiguring marketing positions to support the automation of marketing and selling capabilities. Indeed, AI focus could come at a considerable cost. Thus:

The digital revolution has significantly lowered the cost and accessibility of information. Intelligent systems have the potential to process and derive valuable information from complex data sets otherwise unfeasible or impossible to compute with human intellect (Kietzmann et al., 2018; Martínez-López & Casillas, 2013). Automating marketing tasks including segmentation, targeted marketing communications and aspects of marketing planning will optimize external processes, reducing marketing costs and concurrently the number of employees required (Davenport & Ronanki, 2018; Kaplan & Haenlein, 2019). Managerial capabilities that will be of the greatest value are those that involve creativity, determining how best to apply AI and discerning which predictions to appropriate (Agrawal et al., 2019; Syam & Sharma, 2018). Li et al. (2007), for example, present a system that assists in marketing planning in a B2B context. In facilitating the decision making, the intelligent agent technology enhances the negotiation and supplier evaluation efficiency `by saving time and cost´(Li et al., 2007, p. 877). Automating prediction enhances decision making effectiveness in critical situations (Agrawal et al., 2019). Improvements in the accuracy of information inputs will enhance marketing decisions. AI facilitates the automation of repetitive mental tasks which will enable marketing employees to focus on identifying creative opportunities that enhance firms’ advertising or broader marketing strategy (Davenport & Ronanki, 2018; Huang & Rust, 2020; Makridakis, 2017). Automation extends to other expert systems (Davenport & Ronanki, 2018), for instance, Du Pont utilized such systems to correct errors in equipment and save $50 million (Winters, 1991) and more recently Google´s DeepMind unit optimized the use of air conditioners in its data centres, reducing energy consumption by 40% (Agrawal et al., 2019; Evans & Gao, 2019; Shead, 2018). It is reasonable that such gains can be extended to other facets of business operations and transferred specifically to the marketing department. Through real time analysis, that draws on evolving data, AI supports adaptive marketing. It allows for continuous revisions to product information and price offers in `real-time´ (Agrawal et al., 2016; Agrawal et al., 2019; Kumar et al., 2016, p. 25). Therefore, we expect:

Higher operating efficiencies will encourage firms to implement more AI–as the framework depicts (Fig. 1).

Our findings confirm that US-listed firms are in a state of transition/impending transformation (Davenport et al., 2020) with regard to their focus on AI. We show how AI focus in business is associated with improvements in net profitability, net operating efficiency and return on marketing-related investment, while reducing adspend and creating jobs. Our findings are consistent with prior economic (Acemoglu & Restrepo, 2019) and marketing theory (Albrecht et al., 2021; Davenport et al., 2020; Ma & Sun, 2020), which contend that AI can increase productivity, whether through automation and/or job creation. The first implication is that revenues can increase through improved marketing (e.g., dynamic pricing, tailored promotions, product recommendations, enhanced customer engagement). The second implication is that if the use of AI leads to increased automation, there may be cost savings. Costs may decline due to automation, improved customer service or more structured market transactions (Perez-Vega et al., 2021). However, if AI leads to job creation, then there may be short-term increases in costs.

We find that a focus on AI reduces adspend, suggesting more automated promotions and more targeted marketing (Columbus, 2019; Parekh, 2018). The relationship between ADV/Asset and Sales/Asset is consistent with prior literature (Kietzmann et al., 2018; Perez-Vega et al., 2021; Pillai et al., 2020). These results suggest higher AI focus has a negative correlation with adspend because an increase in the use of AI in marketing reduces future advertising spending due to better target marketing, segmentation, analytics (related to marketing strategy), messaging and personalization (Colombus, 2019; Tong et al., 2020). AI can also further reduce advertising expenditure through programmatic buying of digital ads (Parekh, 2018), thus decreasing adspend. Higher AI focus is positively correlated with the number of employees in firms. This result confirms our theoretical framework, in that AI creates jobs and increases employee numbers (Huang & Rust, 2018; Tschang & Almirall, 2021).

We find a reduction in gross operating efficiency and an increase in net operating efficiency, suggesting that costs are increasing at a slower rate than revenue due to automation. We also find that AI focus can boost employment. This can potentially increase costs for the firms in terms of wages and salaries. This is consistent with (Acemoglu & Restrepo, 2018) theoretical results. For example, it is possible that the firm can use chatbots (automation) to reduce adspend and increase personalization but will need to hire skilled employees who can design and manage the chatbots (i.e., creation of new jobs), and hence their headcount will increase. Our findings are also consistent with Vlačić et al. (2021) and Guha et al. (2021). Indeed, IBM CEO Ginni Rometty contends that AI is not a case of man “versus” machine, but rather man “plus” machines (Carpenter, 2015; Davenport et al., 2020).

The findings are also consistent with Davenport et al. (2020), Syam and Sharma (2018) and Pillai et al. (2020), who contend that AI will have a positive impact on sales processes across various industries. Similarly, Tschang and Almirall (2021) contend that AI will not eliminate jobs—but will help workers perform better. They predict that new jobs will be created and demand for uniquely human skills will continue to grow. It is important to mention that our findings do not contradict Huang and Rust (2018), who develop a theory of AI job replacement in the context of services. This is because Huang and Rust (2018) only consider job replacement in services, whereas our employment measure is much broader.

Our study makes several theoretical contributions. While many posit that AI will improve firm performance (Lin et al., 2020; Ma & Sun, 2020; Mishra & Pani, 2020), enhancing firm efficiencies and transforming marketing strategies (Albrecht et al., 2021; Davenport et al., 2020; Haenlein & Kaplan, 2021; Rust, 2020), as far as we know, this is the first attempt to map a guiding framework of AI with empirical analyses for a large sample. We developed and employed a novel quantitative measure of AI focus from listed US firms’ annual 10-K reports. Our study is also the first to empirically examine the role of AI focus on firm performance using 15 years of 10-K data for some 4,052 firms. We find that a focus on AI has a positive correlation with sales, return on sales, return on marketing-related investment and net operating efficiency.

Similarly, we find that AI reduces adspend and creates jobs. This is a significant contribution because as more and more firms embrace AI in different aspects of their operations, it is essential to empirically determine whether (and how) the use of AI affects firm performance. Our research compliments Acemoglu and Restrepo (2018) theoretical framework. Our findings are sufficiently robust and underscore the importance of the AI-productivity link and how the relationship with costs can be either positive or negative depending on the scenario. Our research complements the theoretical relationship between AI and firm performance (Davenport et al., 2020). Demonstrating how the findings are consistent when implemented for a matched sample of firms represents another distinct contribution. The firms are similar in all other characteristics, differing only in their use of AI.

Our results should be interpreted cautiously as our conclusions are based on firms’ use of 122 AI related keywords in their 10-K reports. First, although we have a large sample, it is possible we have ‘missed’ some firms who have an AI focus but have not explicitly mentioned it in the 10-Ks. Alternatively, they may have mentioned terms not on our list of 122 (Internet Appendix III). Second, by focusing on 10-K reports exclusively, we did not include any other verbal or written articulations of AI by the firms concerned. We did not access any promotional materials of these firms, nor any internal documents that might express or expound views on, or stances towards AI.

Third, although we use an industry adjusted measure of AI focus to address time-varying changes across industries, we did not conduct an industry-specific analysis or make comparisons between firms in different industries. Fourth, although we track the use of 122 AI terms, we have not differentiated between terms or categorized terms into broader themes (e.g., automation, customization, job creation). Finally, our ‘dictionary’ of 122 AI terms may not be exhaustive—we may have overlooked some salient terminology given the rapid increase in AI methods and processes over the years. We chose 10-K filings to search for these terms because this is the most common and comprehensive reporting format for US public firms. Prior literature has highlighted the importance of the management and discussion section of the annual 10-K report and its link to firm value. Further, since 10-K filings are a mandatory requirement by the Securities Exchange Commission, the information provided in 10-K is more likely to be truthful and reliable (with less PR ‘spin’ and/or hyperbole).

Multiple directions for future research flow from this work. First, future research could develop a more comprehensive construct of AI focus. Our AI focus measure is quite general. Prior literature has used qualitative measures of AI to examine its impact. Our measure could be further strengthened by expanding and enriching the dictionary of terms. Second, future research could develop a marketing specific construct of AI focus. Focusing on fewer, exclusively marketing-oriented keywords and then examining their relationship with adspend and marketing investment would be valuable.

Third, future research could test theoretically meaningful moderators. For example, differentiating between automation and innovative AI terms and examining their respective effects on performance. Fourth, future research could broaden the scope of variables and test their interactions. For example, examining the impact of AI focus on firm performance in small to medium enterprises or testing the process models in comparable and disparate countries may prove fruitful. The prevalence of articulation of AI might be higher for some industries than in others, as we evidence from the descriptive statistics in the case of business equipment and financial services industries. Indeed, Davenport et al. (2020) contend that the impact of AI on marketing is highest in industries such as consumer packaged goods, retail, banking and travel. Therefore, future research could also pursue industry-level comparisons in the form of case studies.

Fifth, examining novel and untested outcomes represents another potential avenue for future research. We did not find a significant negative relationship between AI and adspend when we used the weighted AI measure. Therefore, future research could investigate this result further by examining the role of marketing related AI on marketing related expenditure. Our results are inconclusive for return on marketing investment. For general AI focus, we find there is a positive correlation between AI and return on marketing investment but for an industry weighted measure of AI focus we find the opposite. Future research can investigate the relationship between AI focus and return on marketing investment by focusing on marketing related AI keywords.

We find that AI focus is negatively related to gross operating efficiency and positively related to net operating efficiency. This suggests costs reduced in other areas of the production process, although cost in terms of employee salary and wages have gone up. Thus, future research that investigates the specific areas where AI has reduced costs is also important. Not all firms have a positive view with regards to AI. Some firms consider AI to be a major risk or even a threat. In such cases, it would be valuable to examine the positive and negative role of AI by analyzing the text surrounding AI terms in the 10-K reports.

Finally, we believe that this study has further demonstrated the richness of 10-K reports and what they offer to finance scholars and marketers and their colleagues in strategy, management and operations. While 10-K reports are essentially technical in nature, they communicate salient messages to stakeholders and the investment community. Automated text analysis tools such as LIWC (e.g., Chung & Pennebaker, 2012; Cohn et al., 2004) would be helpful in future (longitudinal) 10-K analyses.