What Are the Challenges and Limitations for AI Marketing?

Tianhui Ou (Tina)
Marketing in the Age of Digital
4 min readNov 21, 2021

In my opinion, Artificial intelligence (AI) marketing makes automated choices based on data gathering, analysis, and further observations of audience or economic patterns that may influence marketing efforts. AI is frequently utilized in marketing campaigns when speed is critical. AI tools learn how to best interact with customers based on their data and profiles, then serve them personalized messages at the proper time without the need for marketing team intervention, ensuring maximum efficiency. AI is being utilized by many marketers today to supplement marketing teams or to execute more tactical activities that require less human finesse.

There are a few challenges and limitations that I believe one should be aware of when implementing AI in marketing, such as training time and data quality, privacy, getting buy-in, deployment best practices, and adapting to a changing marketing landscape. In terms of training and quality, AI tools do not automatically know which activities to take to achieve marketing objectives. To study business goals, consumer preferences, and historical patterns and to grasp the whole context and build competence, they will need time and training. This not only takes time but also needs data quality assurances. If AI technologies are not educated on high-quality data that is reliable, timely, and representative, the tool will make suboptimal judgments that do not reflect user preferences, lowering the instrument’s value.

Privacy is also another challenge that I believe exists in AI marketing as consumers and government regulators are both pushing down on how businesses utilize personal data. Marketing teams must guarantee that they are handling customer data responsibly and in accordance with regulations, such as GDPR, or face severe penalties and harm to their reputation. When it comes to AI, this is a challenge. Unless the tools are expressly developed to follow particular legal requirements, they may go beyond what is regarded allowed in terms of customization utilizing customer data.

Insufficient IT infrastructure is another challenge. A solid IT infrastructure is required for a successful AI-driven marketing approach. AI is capable of processing massive amounts of data. This necessitates the use of high-performance hardware. The cost of setting up and running these computer systems might be rather high. They will most certainly need periodic upgrades and maintenance to stay running smoothly. This may be a major stumbling barrier, especially for smaller businesses with limited IT expenses.

In my opinion, getting buy-in is another issue as it may be challenging for marketing teams to convince business stakeholders of the benefits of AI investments. While KPIs such as ROI and efficiency are straightforward to measure, demonstrating how AI has enhanced customer experience or brand reputation is more difficult. With this in mind, marketing teams must ensure that they have the tools necessary to credit these qualitative advantages to AI investments. Deployment of the best practices is also another challenge that I think exists because AI is relatively new marketing technology. Therefore, concrete best practices have yet to emerge to help marketing teams through their early implementations.

Adapting to a changing marketing landscape is also another limitation to AI marketing that I think exists. With the advent of AI, marketing activities will be disrupted on a daily basis. Marketers must choose which employment will be eliminated and which will be created. According to one estimate, marketing technology will eventually replace almost six out of ten existing marketing expert and analyst employment.

Insufficient investment for implementation is another challenge facing AI marketing that I believe exists. Despite the fact that AI solutions often have a high return on investment, a business case must be built to justify investing in these new technologies. This is especially tough in smaller businesses with already tight expenditures. Complex software and high-performance hardware are required for AI technology, which is costly to implement and maintain. Smaller enterprises may have been unable to take advantage of AI technology due to the high expenditure required.

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