The Cons
Lack of accuracy - Errors in your data can lead to inaccuracies in analysis and costly business decisions.
Requires skills - AI tools are more accessible to marketers but th db center uk ey still require knowledge and skills to use them effectively. This makes upskilling and hands-on experience with AI tools important.
Bias - AI is only as effective as the data it uses so you need to be aware of the possibility of gender, racial, cultural, or socioeconomic biases.
Data sensitivity and security - AI systems can use extensive datasets which may include sensitive information that needs to be managed securely.
Ethics - Ethical concerns include consent, the manipulation of user data and behavior, and the ‘stalking factor’ in hyper-targeted ads.
Transparency - Questions about plagiarism, authorship, transparency, and intellectual protection will be more relevant as AI-generated content becomes more commonplace.
There is a wide r
ange of tools, techniques, and methods in AI that enable machines to perform tasks that traditionally require human intelligence. Here are some of the most commonly-used AI technology types and some great examples of AI in marketing.
1) Machine Learning
Machine learning (ML) is a process where machines can figure out how to problem-solve on their own by drawing on previous data sets and making predictions on decisions based on data. This means they “learn” on their own.