In the dynamic landscape of the modern world, artificial intelligence (AI) has become an omnipresent force in our daily conversations. The abilities of AI have revolutionized the ways we approach tasks, reshaping our routines to become more efficient. Take college students, for instance, using generative AI tools like ChatGPT to come up with ideas on how to structure assignments by simply pasting the prompt into the chat. In the pre-AI era, students relied solely on their own thinking and perspective to brainstorm ideas. With that being said, who is to say that marketers can’t use ChatGPT in the same sense to generate creative processes?
Imagine asking AI to conduct swift and precise market research on the specified target audience to come up with data results that would traditionally demand days, if not weeks, of manual effort. The efficiency gains are evident, as AI possesses the capabilities necessary to reduce the time and energy typically invested by marketing teams in crafting strategies.
As AI continues its evolution, it becomes inevitable for the implementation of AI in the marketing industry. The marketing landscape will undergo a paradigm shift, becoming more reliant on this technology to boost marketing campaigns faster and more effectively to stay ahead of the competition. Yet, in the midst of this transformation, a crucial question arises: How far can marketers rely on AI before encountering performance challenges?
To better understand the potential drawbacks of excessive reliance on AI, let's begin by exploring the advantages of incorporating AI into marketing.
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Pros of Using AI in Marketing
For starters, in digital marketing, AI holds a lot of potential in this realm. Jason Hall (2019) articulates this potential in a Forbes article, emphasizing AI's creative and strategic contributions. As content marketing has recently become a pivotal aspect of businesses to be successful, AI can not only map out an effective content strategy to assist in the buyer’s journey, but also generate content within defined parameters that offers marketers a draft to fortify their digital presence. These capabilities are significant as AI can construct a comprehensive framework for content and a strategy to back it up based on the presented data models.
Speaking of data, AI can also assist in gathering valuable market research and data. Jason Hall (2019) highlights how the adoption of AI in marketing can draw large-scale actionable insights through its ability to conduct big data analysis, alongside predicting customer behavior. A notable example illustrating AI’s potential in analyzing data sets on a grand scale is its application in assessing the food market towards children on digital media (Olstad & Boyland, 2023). In response to the World Health Organization’s announcement to limit the exposure of unhealthy food marketing to children, a methodologic gap has come to light, where researchers - hindered by limited resources for manual data analysis - are unable to fully engage with the enormous amounts of volume of the digital food market. In addressing this challenge, several research teams developed AI systems to counteract this gap.
In the UK, the AI system established employed a deep learning workflow for image recognition to classify advertisements based on still images. Meanwhile, in Canada, their AI system actively monitors media concerning food marketing to children on various platforms. The scalability and reach that these AI systems provide significantly outweigh the results of manual labor from these research teams. This results in providing valuable insights, capturing marketing techniques used by the food industry to target children that would have been challenging to uncover through traditional data-gathering methods (Olstad & Boyland, 2023).
While this example isn't directly related to a marketing team performing research, it underscores the potential of training AI systems to collect extensive data, showcasing its performance in motivating buyers and sparking meaningful change. As AI continues to evolve, its applications in marketing are becoming increasingly diverse and impactful, promising innovative approaches to engage and influence audiences. However, it's crucial to strike a balance between one’s own performance and the use of AI.
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Pitfalls of Over-Reliance in AI
It’s one scenario to utilize AI to strengthen marketing strategies, but it’s another to depend entirely on this technology, especially during time-scarce situations. For one, relying on AI to meet deadlines can impact team communication (Shaikh & Cruz, 2022). Reflect on the last time you were presented with a deadline that seemed impossible to accomplish. Did you feel stressed and compelled to cut corners? If so, it’s a valid response, as navigating time-scarce situations encourages heuristic thinking - quick and efficient judgments (Shaikh & Cruz, 2022).
Now, imagine having an intelligent assistant at your disposal that can act swiftly to complete tasks based on commands given. Instead of meeting with team members to explore other various options, it’s reasonable to assume one might rely more on communicating with an intelligent assistant than scheduling multiple meetings. The research study by Shaikh & Cruz (2022) argues that in time-critical situations, people tend to seek more assistance from machines, transforming traditional communication methods into human-machine interaction. This shift results in the hindering of conveying creative ideas, thoughts, and findings among team members. In short, the more one relies on AI to complete work tasks, the more heuristic thinking is promoted, resulting in a loss in team communication.
It’s not only marketing teams that can be at risk by over-relying on AI technology, but the overall customer experience as well. In the research review from Liu-Thompkins et al. (2022), a misalignment of focus is highlighted in utilizing AI marketing applications to enhance the customer experience. As there’s a current trend toward replacing humans with AI in forms of customer interaction, there has been an overlook of the importance of teaching AI the emotional aspects crucial for effectively engaging with customers. With that being said, present AI systems primarily consider more behavioral factors when approaching customers, leading to potential consequences.
For instance, the introduction of AI humanoid service robots resulted in a higher level of psychological discomfort compared to interactions with human providers. In the case of AI sales agents, customers were found to be more hesitant to make purchases compared to chatting with a human salesperson, highlighting the lack of empathy in AI chatbots. These instances, derived from previous studies identified by Liu-Thompkins et al. (2022), present a significant gap between AI and human agents in terms of communicating with customers due to the absence of emotional understanding.
Hypothetically, if companies were to increasingly prioritize AI production to support customer experience in the future, an over-reliance on AI could potentially impact the customer journey negatively, given the current emotional limitations of these AI systems.
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Guidelines to Use AI Responsibly: Optimizing Collaborative Intelligence
Now that we've explored both the advantages and challenges of over-reliance associated with AI in marketing, let's turn to strategies that marketing teams can employ to maximize AI capabilities without compromising team performance and the overall customer journey.
Recognizing the lack of emotional understanding within existing marketing AI systems (Liu-Thompkins et al., 2022), it's wise not to compel AI systems into this realm. In marketing, distinct tasks demand varied skill sets, and it's suggested to leverage the team's human intelligence for tasks involving contextual tasks and emotional engagement. Conversely, AI excels in quantitative tasks, showcasing its strength in analytical intelligence and non-contextual tasks.
With that being said, emphasizing collaborative intelligence - a balance between AI and human intelligence tasks - will be most beneficial for marketing teams. To successfully collaborate both intelligences, marketers need to (Huang & Rust, 2022):
1. Recognize the strengths of their AI systems vs their own team’s strengths:
To understand the relative strengths of AI and a marketing team, it’s crucial to examine the inherent characteristics that shape their intelligence. In simplified terms, AI is designed to mimic human intelligence essentially. Its strengths, as a machine, lie in data processing, computation, and analytics. These strengths characterize three forms of intelligence levels identified by Haung & Rust (2022) that humans also possess: mechanical, thinking, and feeling intelligence.
The first intelligence is mechanical, which for AI revolves around data gathering, except there are no contextual factors that it receives. Moving onto thinking intelligence, this is where AI learns from models and algorithms, primarily driven by big data being inputted. Finally, the feeling intelligence of AI is based on learning from emotional data, such as analyzing a driver’s emotional state using machine learning (Huang & Rust, 2022).
On the other hand, human strengths stem from contextual, biological, and cultural roots that impact the three intelligence levels. In the mechanical aspect, humans respond to contextual factors within a specified marketing scenario, then intuition plays a critical role in thinking intelligence, and first-hand emotional experiences contribute to the feeling intelligence (Huang & Rust, 2022).
To summarize, considering these characteristics alongside the three intelligence types, the relative strengths of AI are mechanical and analytical, while human intelligence is contextual and biological (Huang & Rust, 2022). Although AI is designed to go above and beyond human intelligence, there are still factors missing that only humans can comprehend.
Marketers must grasp and keep a balance in these strengths, as a common pitfall is misusing AI by relying too much on its strengths to fill in numerous areas that it’s not qualified for by nature. For instance, allowing AI to lead creative product design or negotiate prices would overlook the contextual factors involving creativity, intuition, and the overall essence of feeling intelligence (Huang & Rust, 2022). Considering AI’s limitations and its relative strengths compared to your own, ask yourself this question when an upcoming project requires more than just quantitative tasks: Would you entrust AI to take charge of a creative project when your human intelligence is inherently more qualified for such tasks?
2. Forge partnerships between basic AI tools and your marketing team:
Given AI’s capabilities in non-contextual tasks, alongside a marketer’s inherent understanding of contextual factors, a clear framework is laid out for establishing a collaborative relationship between the marketing team and AI. While it’s acknowledged that AI may lack the creative outlook that marketers possess, innovation often requires the support and insight needed from data and analytics (Huang & Rust, 2022). This presents an opportunity for collaboration, wherein marketers can leverage AI for data analysis, informing and steering strategic decisions.
For example, the fashion company GAP takes advantage of AI by utilizing it to perform predictive analyses of fashion trends that are then used to help GAP fashion designers produce clothing to match customer preferences (Huang & Rust, 2022). By using analytical AI, GAP empowers its fashion designers with insights into consumer preferences, aiding in the creation of clothing that resonates with its target audience. Moreover, it’s important to understand the key here is the term “aiding,” highlighting a partnership where marketers don't merely “follow” analytical AI, but actively engage and use its capabilities to further strengthen the marketing team’s objectives.
To avoid triggering over-reliance on AI, simply establish a collaborative relationship by integrating lower-level AI that shows promise for automating analyses and non-contextual tasks that complement creative marketing team functions (Huang & Rust, 2022). Ensure that AI serves to support marketers rather than the other way around.
3. Acknowledge how your customer base perceives AI:
In the face of customer unrest caused by the limitations of AI’s feeling intelligence (Huang & Rust, 2022), a cloud of uncertainty looms over consumers regarding the true capabilities of AI (Liu-Thompkins et al., 2022). As companies continuously use AI for customer interactions, a significant barrier emerges in which customers doubt whether AI can comprehend the entirety of their engagement (Davenport et al., 2019).
The straightforward solution, considering the evaluation of AI strengths and human strengths, is to integrate human representatives into customer interactions instead of solely relying on AI. Despite this, as time progresses through this digital age, businesses are becoming more eager to join the AI wagon even though customers remain cautious about engaging with AI-driven interactions (Liu-Thompkins et al., 2022).
Following this, the concept of artificial empathy becomes a consideration for marketing teams integrating AI into their customer experience strategies. The infusion of care and understanding, facilitated by artificial empathy, presents an opportunity for creating a more positive image of marketers using AI in customer interactions. However, it’s also essential to recognize that AI’s ability to be empathetic is dependent on the social and emotional cues that AI perceives from customers (Liu-Thompkins et al., 2022).
Therefore, striking a balance between AI and human representatives in customer interactions becomes paramount. Over-reliance on AI alone risks exacerbating customer skepticism and might fall short of capturing the emotional aspects that human representatives bring to the table.
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Conclusion
Through exploring the capabilities of current AI, there are many advantages to utilizing its technological advancements in the world of marketing. The current AI algorithms empower marketing teams to uncover extensive, nuanced data efficiently, a task that would otherwise demand significant manual effort from a marketing team (De Bruyn et al., 2020). Beyond data gathering, AI serves as a valuable framework for marketers to aid in crafting strategies and enhancing the customer journey (Hall, 2019). However, the path to successfully integrating AI in marketing depends on adhering to three critical guidelines: recognizing the strengths of both human and AI intelligence, fostering collaborative relationships, and understanding the consumer perspective on AI.
Going back to college students and generative AI tools, it’s one scenario to use AI as a reference, but it’s another to solely rely on AI tools. Just as students who instruct AI to compose entire essays risk crossing into the territory of plagiarism, marketers must avoid over-reliance to prevent shifts in team dynamics and hindrances to creative tasks.
For marketing teams, excessive dependence on AI affects individual approaches to tasks, favoring heuristic thinking over a comprehensive evaluation of options with the marketing team (Shaikh & Cruz, 2022). Over-reliance on AI also affects the customer experience, as AI is perceived as cold-hearted and lacking empathy by customers (Liu-Thompkins et al., 2022). Consequently, utilizing AI excessively in external communication efforts can damage the reputation of a company to its customers.
So, the question remains: How far can marketers rely on AI before facing performance challenges? The answer lies in maintaining balance. Marketers must recognize that AI is a tool, not a team member. AI and marketers possess different types of intelligence, with AI focusing on analytics, while marketers grasp contextual factors (Huang & Rust, 2022). With that being said, striking a healthy balance involves keeping both AI and marketing teams within their inherent intelligence levels, ensuring there’s consistency in collaborative relationships and consumer interactions with AI.
As we navigate the changing landscape of AI in marketing, let's remember that success lies in a delicate equilibrium. Embrace the power of AI as a valuable tool, not a replacement, and explore its potential collaboratively. Please share your experience on AI and let's continue this conversation to shape a future where AI enhances, rather than hinders, the artistry of marketing.
Sources:
Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2019). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K.-U., & von Wangenheim, F. (2020). Artificial Intelligence and Marketing: Pitfalls and Opportunities. Journal of Interactive Marketing, 51(1), 91–105. https://doi.org/10.1016/j.intmar.2020.04.007
Hall, J. (2019, April 21). How Artificial Intelligence Is Transforming Digital Marketing. Forbes. https://www.forbes.com/sites/forbesagencycouncil/2019/08/21/how-artificial-intelligence-is-transforming-digital-marketing/?sh=4de3ce4a21e1
Huang, M.-H., & Rust, R. T. (2022). A Framework for Collaborative Artificial Intelligence in Marketing. Journal of Retailing, 98(2), 209–223. https://doi.org/10.1016/j.jretai.2021.03.001
Liu‐Thompkins, Y., Okazaki, S., & Li, H. (2022). Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience. Journal of the Academy of Marketing Science, 50(6), 1198–1218. https://doi.org/10.1007/s11747-022-00892-5
Olstad, D. L., & Boyland, E. (2023). Towards effective restriction of unhealthy food marketing to children: unlocking the potential of artificial intelligence. International Journal of Behavioral Nutrition and Physical Activity, 20(1). https://doi.org/10.1186/s12966-023-01458-6
Shaikh, S. J., & Cruz, I. (2022). AI in human teams: effects on technology use, members’ interactions, and creative performance under time scarcity. AI & SOCIETY, 38(4), 1587–1600. https://doi.org/10.1007/s00146-021-01335-5