The Risks, Advantages, and Best Practices of Generative AI

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Let's start with the basics. Generative AI, the biggest 'buzz word' in the Artificial Intelligence (AI) universe, refers to the process of using intelligence to generate new data, as opposed to analyzing and categorizing existing data – which is a more traditional AI practice. Very simply, generative AI is a type of artificial intelligence system or algorithm capable of generating new content, including text, images, and media, in response to a set of prompts or inputs.

As is often the case when an amazing new toy comes out in the world of digital marketing, we all want a piece of the pie, and for good reason. But generative AI also has its risks. Although it is an essential resource for your activity, there are a number of aspects that need to be taken into consideration before rolling the dice.

Let's start with the positive aspects.

The advantages
The biggest advantage of generative AI is undoubtedly the increase in operational efficiency that it generates (or can generate). Since it allows the automation of tasks that could be performed manually, generative AI makes it possible to considerably reduce time and resources by automating day-to-day tasks. For example:

Keywords Brainstorming – Generative AI is a very useful phone number library asset in the early stages of keyword and topic research. An AI algorithm can be used to analyze search data and generate a list of relevant keywords – based on the initial query – that fall into both popular and low-competition categories.
Performing A/B testing – AI can be used for A/B testing, but only for the content creation part, that is, to generate the text for each of the tests. It is worth noting that generative AI tools often fail when asked to perform even the simplest analytical calculations, so do not use them to evaluate test results, test validity, statistical significance, or anything similar.
Image creation – The simplest use of generative AI is the access that companies currently have to an infinite library of images. These images can be used (for now) for commercial purposes, in marketing campaigns, among others, without the risk of copyright infringement.
Creating connected assets – Connecting an ever-updating language model with generative video and speech capabilities will enable an infinite volume of video assets that can be tailored to individual consumers or use cases. Video and other content are not just dynamic, they can adapt to any use case and any business.
Take a look at our whitepaper “ 30 Ways to Use AI in Digital Marketing ” to discover more reasons why you should integrate intelligent solutions into your digital strategy…

The Risks
As we all know, there is no silver lining without a silver lining and, just like the benefits it provides, there are also a series of risks that need to be taken into account when we talk about generative AI, such as:

False or incorrect information – As there is no single, clear source of information, there is a high risk of misinformation and/or synthetic misinformation. Generative AI platforms are known to often generate false responses , known as hallucinations, or are used to produce “ deep fakes ” that are increasingly difficult to identify. (Always double check!).
Privacy – One of the main concerns regarding generative AI is the lack of privacy legislation to protect user data. This can affect users in a number of ways, including: consent violations, inadequate anonymization, and unauthorized storage, processing, and sharing of data. There is currently no governance/protection in place regarding confidential or sensitive information. Users should assume that any prompts entered into a generative AI tool will become public information.
Bias and discrimination – While most tools include prevention measures to mitigate the risk of bias and/or discrimination, they are far from being 100% fool-proof. Even the most advanced generative AI can be, and is, inevitably hampered by the data it is trained on. Be it bias, racism, sexism, etc.
Malicious AI – AI can be manipulated to be used for malicious and/or destructive purposes – namely to overcome cybersecurity measures and commit fraud, theft or money laundering (and these are just a few examples). Cybercriminals seek to target those with weaknesses in their cybersecurity, using malicious AI to bypass “benign” AI – often resorting to reverse engineering functionality for malicious purposes.