GenAI seems to be all the rage now. What is GenAI, GAI, or generative AI you may ask? Generative AI is a type of artificial intelligence that has the capability to generate original content in the form of text, imagery, music, and video, derived from unique query prompts. The mention and sudden popularity of “GenAI” took rise in the early 2020s. Interesting enough, the concept of GenAI is not brand-new, it originated from the 1960s with the early development of chatbots. Currently, the traction with GenAI is about the advanced level it has reached with producing high-quality, original content instantly.

GANs Make GenAI Possible 

2014 was a pivotal year with the introduction of generative adversarial networks (GANs), which is how generative AI operates and can produce vast content including: written poems or essays, realistic images, art, designs, animations, sound effects, music compositions and data augmentation. Generative Adversarial Networks is a type of machine learning algorithm based on two neural networks that compete against each other: the generator and the discriminator. The generator creates original, artificial images, text, or audio. Whereas the discriminator evaluates the generated data and determines if it’s real or fake. Like a game, the two models compete against each other. After many attempts, if the generator tricks the discriminator, where the discriminator cannot determine if the content is real or fake, the generator knows it did a good job at producing an original piece of content as output.

Consider this analogy, the input is like deciphering the difference between a genuine or counterfeit designer bag, you see the same emblem, logo, brand colors, resemblance of the same materials, but you don’t know which one is the real deal, so the output could in fact be a really good copy to pass as the real deal. There are ways to render the generator output by customizing either the style, color scheme or fabrics to outdo the original design. 

Neural networks have been designed to imitate how the human brain works, in the sense that the neural networks “learn” the rules from identifying patterns in existing datasets. In the 1950s and 1960s, neural networks were developed, but lacked computing power, and had smaller data sets to work with. As computer hardware became more established in the mid-2000s with big data to work with, neural networks became more reasonable and realistic to generate content.

How Marketers Can Use GenAI 

Across companies and varied industries, the investment in GenAI is becoming more apparent and advantageous to gain a competitive edge in the market. For marketing specifically, there is an expansive list of marketing initiatives to execute, whether it is writing content, conducting market research, designing a marketing campaign, analyzing web traffic, optimizing SEO, analyzing data, or devising a marketing strategy. Digital Marketers can adopt GenAI skills to become more efficient, while generating effective, creative content, empowered by real-time data trends. It is helpful to experiment with different GenAI tools that may help you strengthen a weakness, or where you can foster critical thinking. The use of GenAI should be in moderation and not solely relied upon or to get a job done. You want to be ethical, and intentional that you are staying true to your brand, your customers, and your mission. The beauty of marketing is keeping it authentic, consistent with a human touch that has people in mind.

GenAI can help make your vision come to fruition based on your marketing ideas. If you cannot seem to find an open-source image from Pexels, Pixabay or Google Images, you can create the one you would like to generate by searching specific details of the visual image you need displayed. Instead of asking a designer to mock-up a photoshopped image, you can have the image at your fingertips. GenAI can help marketers with the skill gap of graphic design and coding. Canva rolled out Magic Studio to help streamline AI-powered designs and to assist with layouts. As a marketer, I tend to become fixated on the design, whereas now, I can focus more on strategy and data to make effective business decisions. Coding can be cumbersome, and marketers typically collaborate with programmers to either embed analytics tracking on the backend, or work on webpage optimization. Coding is a great skill for marketers to utilize for leading data analysis and data visualization.

ChatGPT, AlphaCode, GitHubCopilot or GPT-4 are several GenAI platforms to use for coding assistance. Overall, utilizing GenAI platforms in your marketing operations can make you a well-rounded, resourceful, and impactful marketer.

Drawbacks of GenAI

Consumption of too much of a good thing exists for a reason. With the prevalence of GenAI, there are staggering drawbacks around bias and inaccuracy, misinformation, ethical concerns, and rising computational costs. When it comes to the future of work, there is an imminent threat that AI may steal jobs. AI needs to serve as a catalyst for positive change in how people do their jobs to enhance their performance, optimize better results and to create new jobs. AI should not replace humans. There needs to be a healthy balance in utilizing GenAI, but not allowing the extent of GenAI tools to diminish who we are and what we do. GenAI can enhance our capabilities to help us learn quicker and adapt to using modern technologies in our everyday lives. Besides job replacement, there are serious impending security issues of GenAI ranging from manipulated content, phishing attacks, intellectual property theft, and data privacy risks. It is essential for companies to be aware of security threats and to have best practices, and response action plans in place to preserve, protect and guard people and their assets.

Experiment with GenAI 

Have fun chatting with ChatGPT today through OpenAI. Try to work on building thoughtful, complex, and detailed search queries to see what the results could be. More creative the query the better to help you think freely.