The origin of automation and artificial intelligence (AI) dates back to the 1940s, to the emergence of neural networks and the Turing test. Business professionals and researchers have invested in intelligent automation (IA) and AI technologies such as service robots, robotic process automation, generative AI, machine learning, and affective or cognitive computing, to name a few. With recent developments in sensors and smart technologies, and large language models (LLMs) now with enhanced capability to learn and to process information at large scale and emulate human capabilities, the importance of automation is accelerating. Companies already employ marketing automation for tasks such as communicating with customers (e.g., using conversational chatbots), generating and personalizing content (e.g., Reisenbichler et al. 2022), and buying media. While such automation offers tremendous opportunities, it comes with significant up-front investment, complexity in design and implementation, and several other challenges (Krafft et al. 2019; Skiera 2022).

Increasingly, AI is improving how automation can bring about performance improvements for marketing operations and innovations in how marketers engage with their various stakeholders. IA refers to a practice that harnesses methods and technologies, business process automation, robotic process automation (RPA), natural language processing (NLP) and generation (NLG), entity detection, and computer vision, enabling computing applications to undertake tasks and processes that were previously carried out by humans (Bornet et al. 2020). This promises to improve business performance (e.g., see Libai et al. 2020), empowering marketing workers to improve both their effectiveness and efficiency.

AI as a core enabler of IA has the potential to dramatically increase the scope and capability of automated marketing operations. The world’s largest technology companies, including Alphabet, Meta, Amazon, and Microsoft are rapidly innovating in this space. A testimony to the tremendous level of interest in IA is Microsoft’s multibillion-dollar investment in Open AI (the developer of ChatGPT). Microsoft’s “Dynamics 365 Copilot” promises to bring the power of AI and automation across the marketing organization, including sales teams, customer service agents (via conversation boosters in Power Virtual Agents; Le et al. 2023; Sajtos et al. 2020), product listing in online commerce, customer insights, supply chain coordination, and marketing planning tasks such as segmentation and targeting.

This special issue therefore aims to focus on understanding the IA of higher-order marketing tasks and processes, including evaluative thinking, agile marketing (e.g., Kalaignaman et al. 2022), design thinking (e.g., Beverland et al. 2015; Allen et al. 2018), causal reasoning, brainstorming, and idea generation (e.g., Girotra et al. 2023)

When companies invest in and deploy IA for marketing operations, they need to consider multiple factors, since marketing automations can vary widely in terms of what they do and how they work. Marketing automations differ based on the technologies they employ, the type and modality of data (structured or unstructured; text, images, speech) that they use (see Du et al. 2021), and the outcome (rule- or inference-based) they produce. For instance, validating customers’ physical or email addresses requires an automation process different from one to classifying customer queries into the correct category. These different marketing automations often focus on a single task, but companies increasingly connect these automations to form end-to-end automation chains or automation journeys, which can have an adverse effect on IA transparency and employee autonomy. However, as with employees, automation applications are not necessarily specialized, and can be employed to complete different tasks in the business depending on where the needs arise. Automation capabilities promise benefits in efficiency and effectiveness but present challenges for marketing practitioners who must redesign their internal operations.

Besides organizational workflows, marketing automations are likely to affect company brands and their communication. Marketing automations can deliver seamless experiences with brands when, for instance, a customer books an appointment, and first receives a confirmation, which is followed by timely reminders of the event or the need to make a change. These streamlined and personalized processes have the power to result in faster and personally relevant experiences and contribute to creating a stronger brand experience. At the other extreme, of course, these very same automations can be too rigid, can demand or capture too much or inappropriate information, and become frustrating for customers, which can increase the likelihood of the customer taking their business elsewhere. More importantly, as these automations become more widely available, companies need to consider whether and how their marketing automations are aligned with and support their brand values and their ethical standards prior to deploying them (Wirtz et al. 2023). Finally, companies develop automations for customers to help them in their daily routines, and for automation developers it is critical to understand the tasks and processes people are keen to automate, and how best to automate them.

Relevant Topics and Questions for Research

Considering numerous differences in how automations are built and work and the areas in which they can be employed, companies have a number of decisions to make when it comes to developing and investing in IA. Based on the issues outlined, this special issue aims to focus on particular aspects of IA by addressing some of the following questions.

1. What tasks or processes are most suitable for marketing automation? How should companies decide on or prioritize which tasks and processes to automate?
2. How do IA and marketing automation tools influence employee and business performance?
3. What are the competitive implications of AI-based automation? Topics addressing this question may consider how the potential for greater personalization through automation could potentially alter competitive dynamics and outcomes.  
4. How will IA and AI-enabled marketing activities change consumer behavior?
5. What are the future domains of marketing automation? How will IA transform areas that are already heavily automated, such as data analysis, content generation, real-time personalization, media buying, market research, and so on?
6. How will IA contribute to creating more effective and efficient business processes? Are consumers willing to pay more for more effective and efficient processes?
7. What tasks and processes do customers automate? What IA and marketing automation tools are available for customers to use? How does automation of marketers interact with that of customers?
8. What changes in employee skill levels or loyalty to the firm are required or can be observed as a result of introducing marketing automations in their jobs? Under what conditions does IA lead to either skill erosion (or, at the extreme, human replacement) or to skill development (human enhancement)?
9. What new roles will marketers of the future be required to undertake, and what skills would they need to be effective in these roles?
10. How should we train the next generation of marketers? Will IA and marketing automation tools change (shrink or enhance) marketers’ roles in the future—and if so, how? Will IA and marketing automation tools be likely to create new roles and responsibilities for marketing professionals (e.g., Chief Ethics Officers)?
11. How helpful is IA in building brands? Or will IA erode companies’ efforts to build humanized brands?
12. What criteria should companies use to assess the effectiveness of IA and marketing automation tools? How can ROI on automations be calculated? How is employee time saved in jobs (partial task automation) incorporated into these calculations?
13. How should companies develop their automation strategies? Do companies always first automate the processes that account for the largest share of their transactions?
14. Will the need to automate accelerate companies’ efforts to integrate customer data stored across multiple isolated databases?
15. What ethical, regulatory or moral issues are important for brands and marketers to consider in the future of hyper automated marketing organisations? What is the role of AI alignment strategies and AI safety for AI-powered and IA marketing operations? 

The special issue invites original manuscripts on these, or related topics. We especially welcome empirical work and articles focused on technological aspects of intelligent automation relevant to marketing practitioners. 

Editorial Timeline

Planned publication date: Mid to late 2025

Online session about the special issue at the Virtual AMA Winter Conference: February 20, 2024. While presenting or attendance of the conference is not required to submit to the special issue, the special issue editors and guests will be discussing the future of AI and automation to help attract and motivate research contributions in this area. Register for the conference here.

Submission deadline: August 31, 2024

Submitting Your Manuscript to the Special Issue

All submissions will be considered for publication in the Journal of Interactive Marketing, via a double-anonymous peer review, drawing on prominent scholars with interest and expertise in the area. Each manuscript may also be considered by prominent marketing practitioners and thought leaders.

Submissions must be made via the journal’s ScholarOne site, with author guidelines available here. 

Please contact André Bonfrer with questions (andre.bonfrer@deakin.edu.au).

Special Issue Editors

André Bonfrer is Professor of Marketing at Deakin Business School, Deakin University. He has published extensively in various leading journals in marketing on topics related to marketing technology, advertising, and branding, and has served on editorial boards for several leading journals in marketing.

Laszlo Sajtos is Associate Professor, University of Auckland Business School. He researches the impact of emergent technologies on service management. His work is published in top journals including the International Journal of Research in Marketing, Journal of Service Research, Journal of Interactive Marketing, among others.

Jochen Wirtz is Professor of Marketing and Vice Dean of MBA programs, National University of Singapore. His research focuses on services marketing and management and has been published in over 200 academic articles and books, including Services Marketing: People, Technology, Strategy (9^th edition) and Essentials of Services Marketing (4^th edition).

Erik Hermann is Permanent Affiliate Professor of Marketing, ESCP Business School Berlin. His research focuses on consumer behaviour and psychology as well as the ethical and sociotechnical issues and implications of the development and deployment of artificial intelligence in marketing. His research has led to publications in leading marketing journals such as Journal of Marketing, International Journal of Research in Marketing, Journal of the Academy of Marketing Science, Journal of Business Ethics, and Journal of Advertising.

References

Allen, B.J., Deepa Chandrasekaran, and Suman Basuroy (2018), “Design Crowdsourcing: The Impact on New Product Performance of Sourcing Design Solutions from the ‘Crowd,’” Journal of Marketing, 82 (2), 106–23.

Beverland, Michael B., Sarah  J.S. Wilner, and Pietro Micheli (2015), “Reconciling the Tension Between Consistency and Relevance: Design Thinking as a Mechanism for Brand Ambidexterity,” Journal of the Academy of Marketing Science, 43 (5), 589–609.

Bornet, Pascal, Ian Barkin, and Jochen Wirtz (2020), Intelligent Automation: Welcome to The World of Hyperautomation: Learn How to Harness Artificial Intelligence to Boost Business & Make Our World More Human. World Scientific.

Du, Rex Y., Oded Netzer,  David A. Schweidel, and Debanjan Mitra (2021), “Capturing Marketing Information to Fuel Growth,” Journal of Marketing, 85 (1), 163–83.

Girotra, Karan, Lennart Meincke, Christian Terwiesch, and Karl T. Ulrich (2023), “Ideas Are Dimes a Dozen: Large Language Models for Idea Generation in Innovation,” SSRN (August 2), http://dx.doi.org/10.2139/ssrn.4526071.

Kalaignanam, Kartik, Kapil R. Tuli, Tarun Kushwaha, Leonard Lee, and David Gal (2021), “Marketing Agility: The Concept, Antecedents, and a Research Agenda,” Journal of Marketing, 85 (1), 35–58.

Krafft, Manfred, Laszlo Sajtos, and Michael Haenlein (2020), “Challenges and Opportunities for Marketing Scholars in Times of the Fourth Industrial Revolution,” Journal of Interactive Marketing, 51 (1), 1–8.

Le, Khan Bao Quang, Laszlo Sajtos, and Karen Veronica Fernandez (2023), “Employee-(Ro)Bot Collaboration in Service: An Interdependence Perspective,” Journal of Service Management, 34 (2), 176–207.

Libai, Barak, Yakov Bart, Sonja Gensler, Charles F. Hofacker, Andreas Kaplan, Kim  Kötterheinrich, and Eike Benjamin Kroll (2020), “Brave New World? On AI and the Management of Customer Relationships,” Journal of Interactive Marketing, 51, 44–56.

Reisenbichler Martin, Thomas Reutterer, David A. Schweidel, and Daniel Dan (2022), “Frontiers: Supporting Content Marketing with Natural Language Generation,” Marketing Science, 41 (3), 441–52.

Sajtos, L., B.G. Voyer, M. Sangle-Ferriere, and B. Sung (2020), “Algorithmic Decision-Making, Agency and Autonomy in a Financial Decision Making Context: An Experiment,” ACR North American Advances.

Skiera, Bernd (2022), “MarTech and SalesTech,” NIM Marketing Intelligence Review, 14 (2), https://www.nim.org/en/publications/nim-marketing-intelligence-review/detail-issue/martech-and-salestech.

Wirtz, Jochen, Werner Kunz, Nicole Hartley, and James Tarbit (2023), “Corporate Digital Responsibility in Service Firms and their Ecosystems,” Journal of Service Research, 26 (2), 173–90.


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