Short Essay 2_NG Hio Tong (1155220394)

AI in Digital Marketing: Building Comprehensive Guidelines for Future

Over the past decade, it should come as no surprise that Artificial Intelligence (hereafter, “AI”), which has highlighted innovation and integration of advanced technologies, has been prioritized in facilitating a competitive market landscape. The proliferation of AI-driven marketing strategies has revolutionized the nature of media and subsequently altered the way business stakeholders deliver products and services, interact with consumers, and cooperate with potential partners. More specifically, the use of Generative AI has marked a significant shift in the distribution of capitals in customizing media campaigns.  

Previous studies have categorized four kinds of marketing AI, including (1) stand-alone machine-learning apps, (2) integrated machine-learning apps, (3) stand-alone task-automation apps, and (4) integrated task-automation apps. These potential applications vary in levels of intelligence and integration; altogether, they could be tailored-made for a wide range of complicated tasks thereby improving the efficiency and effectiveness of business processes. Consequently, enhanced customer experiences embedded in a more engaging and personalized customer journey bring out profitable results in the product-disclosed-driven loop.

 

Just as the guest speaker shared, “Generative AI adoption is a journey and not a sprint.” Given AI’s enormous capability to automate and optimize various marketing tasks, the vision for adopting AI in favor of the set objectives is associated with criticality. However, one question raised in the Q&A section pointed out that barriers often occur when persuading co-workers and, in several situations, even superiors to utilize AI. Innovations are approached with a skeptical and cautious air, addressing personal doubts that may be boundaries in embracing new technologies. For instance, outdated AI perceptions and skills, restricted investment in AI tools, and prohibited use of AI under monitoring, in turn, suppress the development of AI in digital marketing. Besides individual-level assumptions and criticisms about AI applications, some system-level issues are core challenges that plague AI adoption in the marketing industry. By far, few supportive fundings encourage the adoption of AI in small business companies, which are confronted with challenges even in the early stages of access. Moreover, a lack of regulatory guidelines regarding the extent to which AI adoption is regarded as “appropriate” inhibits the future determination in constructing the field. Companies already equipped with AI knowledge would then struggle to take a step forward while being aware of possible risks underlying the use of data-generated content.

 

While some companies opt for more advanced applications, others still lack a coordinated, strategy-focused approach to implementing bigger projects. To this extent, how companies and other players with AI tools foster an AI-friendly, data-first culture, as well as developing competencies and upskilling has shed light on the future roadmap. Firstly, before constructing relationships with and within audiences externally, it is essential to build up an internal consensus that is tied to the adoption of AI. Some cues of the strategic framework can be individual-focused, such as recruiting an AI council, educating acquired AI skills, and launching access permission to AI tools, to name a few. While guiding practices for maximizing AI solutions in marketing, it is crucial to clarify and define the goals to be achieved and thus select the right applications to use, rather than applying massive chatbots and/or text-to-image generators that might act in a diametrically opposite way. Companies should be honing in on their targeted achievement with AI to avoid putting the cart before the horse.

 

Secondly, companies are in charge of creating a culture of responsible innovation. Ethical considerations surrounding the use of AI in marketing often highlight the boost of plagiarism and copyright infringement, where the initial creativity and ambition have been controversial. With this premise, regulatory systems and/or bodies have to be set internally, with campaign managers conducting “peer review” spontaneously and simultaneously.

 

Finally, yet most importantly, raising concerns about data privacy and security ought to be tackled with practical actions. For marketing campaigns that call out the need for consumers’ data or in-house users’ portraits, maintaining transparency in data collection practices and implementing robust security measures to safeguard sensitive information is paramount. By doing so, it guarantees data use and reinforces trust between companies and consumers, which then broadens the dual acceptance of AI.

 

Just as it is difficult and cost-consuming to go from zero to one, the adoption of AI takes not only time to diffuse but also creativity and openness to evaluate AI options. By starting with one AI transformation, nevertheless, companies may build up confidence with AI , and so can expand more easily later on.