Let’s say you take your work seriously and have been doing digital marketing for over a decade, mainly web dev, SEO, and the copywriting that goes along with it. You’ve seen trends come and go, you know what works and what doesn’t, and you have a solid understanding of your target audience.

You would know a thing or two about AI in marketing, wouldn’t you? Well, I’ve worked with over 100 companies this last decade, and I’m here to tell you yes: AI in digital marketing is distorting reality as you know it.

Before we even get into it, think about what you’ve recently come across in posts and articles about AI and digital marketing in your feed. Maybe you’ve also read posts you thought were written by a human, but much to your surprise, they were created by AI. 

Let’s talk about AI marketing, from its definition to what it means for us all. 

What Kind of AI Tech Is Most Prominent in Marketing?

Marketing is creating relationships with people and finding ways to encourage them to think positively about your business. 

If AI could accurately predict what people would do based on their characteristics and previous behavior, wouldn’t it be able to create a perfect marketing strategy?

That’s the idea behind predictive analytics: using data and algorithms to predict future outcomes. This powerful tool allows marketers to tailor their campaigns and content to specific target audiences, increasing the likelihood of success.

Of all predictive models, generative AI tech is the most prominent. 

Generative AI

Generative AI involves using artificial intelligence (AI) to generate new content or information based on existing data. This tech has made huge strides in the last few years, with applications advancing in fields like:

  • Music composition.
  • Image generation.
  • Text writing.
  • Video and audio production.

Some of the key principles behind generative AI include:

1. Deep Learning 

Deep learning involves training artificial neural networks to recognize patterns within data and accurately predict or decide based on those patterns. It’s achievable via multiple layers of interconnected nodes that learn from the data and adjust their connections accordingly.

Deep learning allows generative AI to produce more complex and realistic outputs. For example, deep learning models help to generate human-like images and even create music that mimics the style of famous composers.

2. Neural Networks

The brains behind neural networks actually borrowed that concept from the human brain, consisting of interconnected nodes that process information and make decisions. Neural networks also power key functions of deep learning. 

In generative AI, neural networks are used to analyze existing data and generate new data based on learned patterns.

3. Natural Language Processing (NLP)

NLP is a branch of AI that focuses on enabling computers to understand, interpret, and manipulate human language. All NLP techniques are based on machine learning algorithms, and they train on large datasets to develop the ability to process human language.

For example, all text-based generative AI systems use NLP techniques. They scour the billions of posts published online for relevant words, phrases, arguments, and data sets. They then string together these pieces of information to generate coherent and human-like text. 

What Are the Shortfalls of Generative AI?

While generative AI is impressive, it also has limitations and shortcomings. Some of the main shortfalls of generative AI include:

  • Lack of control over output: Since generative AI relies on learning from data, its output may not always align with what the user wants or intends.
  • Need for vast amounts of data: Generative AI requires a large amount of training data to achieve high levels of accuracy and quality.
  • Biased and outdated outputs: Remember, outputs are wholly dependent on the type of data available online. Since no primary data is generated via new research, the outputs may be biased and outdated if that’s the quality of data it trains on. 
  • Limited creativity: While generative AI can create new content based on existing patterns, it can’t generate truly original or creative ideas. As more outputs are generated, the system may start to loop and produce similar variations of existing content.
  • Dependence on human input: Generative AI relies heavily on human input and supervision, as it needs humans to provide the training data and evaluate its outputs for quality and accuracy.

The Copyright Concerns Around AI-Generated Content

Since this technology relies on learning from existing content, there’s a constant debate about who owns the rights to the output generated by AI systems.

Some argue that these outputs shouldn’t be protected under copyright law as humans do not create them. Others argue that the training data used belongs to humans and, therefore, the output should be protected.

This debate gets stickier when AI-generated content serves commercial purposes. Who holds the rights to this content? The company that owns the AI system, the individuals who provide the training data, or the user commanding the AI system?

Legal experts are still grappling with these questions, and it’s essential to think about the legal implications of using AI-generated content. 

Search Generative Experience (SGE) and Bing AI Integration

Search Generative Experience (SGE) is a new approach to finding answers when you search online. It combines generative AI overviews with Google’s search engine capabilities.  

SGE leverages the vast amount of data published online and uses it to generate human-like text based on a user’s search query. This integration allows for more easier way to access whatever information you are searching for on Google.

Prior to Google’s SGE, Bing also incorporated AI-generated answers or overviews, powered by OpenAI, on its search results pages.

Additionally, SGE also addresses some of the shortcomings of generative AI by giving users more control over the output. Users can refine their search query to specify the type of answers they want, providing a more personalized and creative experience.

Essentially, it reads all the articles posted by different authors on the topic question and evaluates accuracy of all facts by deriving an average of the data provided by different researchers. That part is powered by Bing and Google’s massive search ranking capabilities. 

The tricky part is this; AI overviews simultaneously deploy generative NLP AI models to generate answers, which are then displayed prominently over the organic search results. To make matters worse, the AI-generated answers have often resulted in critical errors and wrong information due to hallucinations.

Also, SGE still relies on human input for training data and quality control, highlighting the need for ongoing human involvement in AI development. 

Overall, this integration shows the potential for combining different AI technologies to enhance their capabilities and provide more accurate and useful results for users. 

Concerns Around Search Generative Experience (SGE) 

Bloggers, scholars, and established publications aren’t too happy about SGE. Anyone who knows how crucial content is in a digital marketing strategy would find it hard to support AI-generated work without hesitation, especially if it’s used without proper attribution or permission. 

Close your eyes more for a moment and imagine you’ve been publishing online for years. Most people publishing online expect search engines to send user traffic to their websites. 

Many websites use search engine optimization (SEO) techniques, such as keywords and backlinks, to increase their visibility on search engines like Google. The target is ranking favorably on relevant search results to increase your web traffic. For more context, read: ‘SEO for Small Businesses: What You Need to Know.’

It has always been this way since the early days of the internet. Blogs and websites share information, ideas, and creativity with a global audience, hoping to gain financial or personal benefits from their content.

However, with the rise of Search Generative Experience (SGE), there are growing concerns about copyright and intellectual property rights. SGE displays AI-generated answers directly to users without redirecting them to the publisher’s website, potentially reducing traffic and ad revenue for the original content creator. 

So naturally, publishers accuse SGE of cutting them out of the equation and profiting off their content without proper compensation or attribution. 

They intend to use the full force of copyright law to protect their rights. They’re also considering hiding their content from search engines by using robots.txt or meta tags, preventing SGE from crawling and indexing their website. 

However, this could also mean losing out on potential traffic and visibility. Users may also suffer as paywall barriers are implemented to protect content, limiting access to information and knowledge. 

AI in Paid Digital Marketing and Ad

As more businesses turn to digital marketing, AI algorithms are crucial in targeting and personalizing advertisements. These algorithms analyze user behavior, interests, and demographics to determine the most effective ads for each individual.

On one hand, this can lead to more relevant and personalized ads for consumers, potentially improving their overall online experience. On the other hand, it raises concerns about privacy and the use of personal data for targeted advertising.

Companies are also using AI to optimize their ad spend, ensuring they target the right audience and allocate their budget effectively. It can attract better conversion rates and lower business costs, but it could disadvantage smaller companies when large corporations command more advanced AI tech.

My Concerns Regarding AI in Paid Marketing

While AI in paid marketing is one of the top digital marketing trends in 2024, some concerns must be addressed.

For example, AI algorithms could perpetuate societal biases and discrimination. If these algorithms are trained on biased data, they may continue to perpetuate those biases in their targeting and ad placement.

A lack of transparency on how AI algorithms make decisions could also cause problems. As the technology becomes more complex, businesses and users may struggle to understand how and why certain ads target them. It can cause distrust and confusion.

Additionally, using personal data for targeted advertising raises privacy and data protection concerns. With AI algorithms continuously collecting and analyzing user data, there’s a risk that your information could fall into the wrong hands.

Let’s not forget consumer behavior and the potential for AI to manipulate it. That’s a real concern for me. 

AI Is Only a Tool to Better Your Marketing Strategy

As with any technology, AI is only a tool. Businesses and marketers must use it ethically and responsibly.

Businesses should prioritize transparency in their AI algorithms and be mindful of biased data when training them. They should also have clear policies for handling user data and protecting privacy.

As consumers, we can also protect our data and be mindful of how targeted ads may influence our behavior. We should also hold businesses accountable for their use of AI in marketing.

Otherwise, AI tools are here to enhance our marketing strategies and provide valuable insights.

Jarod Thornton

Author Jarod Thornton

I love working on WordPress development!

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