The potential of AI-generated content has journalists and artists concerned. But as of now, marketers have expressed little anxiety about the advent of AI-generated content. In fact, many brands have embraced AI-generated ad copy and creative to power their ad campaigns.
The potential of AI-generated content has journalists and artists concerned. But as of now, marketers have expressed little anxiety about the advent of AI-generated content. In fact, many brands have embraced AI-generated ad copy and creative to power their ad campaigns.
Heinz used the image generator known as DALLE-E 2 to create a clever marketing campaign titled "Heinz AI Ketchup" to demonstrate that AI-generated images of ketchup bottles tend to resemble Heinz branding. The cloud-computing company VMWare, for example, has writers use Jasper, a focused model of GPT-3, to ramp up their content creation efforts. Popular toy manufacturer Mattel uses the technology to generate images for toy designs and marketing. This clearly illustrates the creative potential of this emerging technology.
However, as transformative as generative AI appears to be, it comes with caveats. The ethical and responsible use of generative AI is one of the major challenges of implementing its capabilities at scale.
Still, marketers seem to be less concerned about whether a machine or robot created a piece of content as long as it resonates with their audiences.
If a piece of AI-generated content resonates with readers and passes standard verification and viewability protocols, most marketers will transact against it.
Kate Scott-Dawkins
Global Director of Business Intelligence, GroupM
That means audiences at large play a primary role in the adoption of AI-generated content. Thus, in the spirit of transparency, it would be ideal to understand when AI is used on any piece of content with the addition of a clear label or watermark.
Likewise, brand suitability and safety are other major concerns. As AI-generated content inundates the market with low-quality ad inventory, it may pose a threat for ad publishing networks and marketers alike. Hence, brands should be vigilant about content adjacency.
Even as AI evolves beyond its current scope, the technology will remain susceptible to errors and quality-related issues. Many industry experts believe the web has no shortage of mediocre content, and so the temptation to use AI is imminent.
Understanding the Good, Bad, and Ugly of Generative AI for Marketing
While there is no dearth of mediocre content across the Internet, should marketers continue to leverage AI-generated content at the risk of compromising brand safety and their digital presence?
There are numerous exciting and positive applications that these tools offer. But we would be remiss as a society not to recognize the potential gray areas this technology may expose. Some of the hot button issues raised by AI-generated content include:
- Lack of Authenticity: There is no way AI can supersede original and authentic content. It can only be used as an instrument and not as a means to an end for content generation.
- Quantity over Quality: While generative AI can be used to quickly and accurately create highly targeted content, it may not always produce the best results. In some cases, AI algorithms may make decisions based on incomplete or inaccurate information —which could lead to poor-quality content or even offensive content being generated.
- Deepfakes and the Protection of Intellectual Property: Generative AI can possibly gobble up private and confidential data to create content without the original author’s permission. The current situation, where researchers and companies building AI tools freely use massive datasets to "train" their tools, violates the spirit of ownership and the protection of IPR as these tools are also being used for commercial benefit.
- Lack of Differentiation: The use of generative AI tools can possibly put an end to ‘out of the box’ or divergent thinking to come up with novel thoughts or ideas. It will simply ensure a level playing field for all, with innovation constrained by its training data and algorithmic models. That means companies using these tools may reach productivity and cost-efficiency goals but struggle with differentiation and retention.
Every story has two sides to it. On one side, the proficiency and scale that generative AI can ignite for businesses of all sizes is truly exciting. But on the other side, businesses should also be acutely aware of the inherent risks before investing in these technologies. It pays to adopt a strategic long-term outlook to understand the good, bad, and ugly of generative AI without hastily jumping into the short-term applications of these tools.
This post first appeared on B2C Marketing Insights To Help You Stay Ahead, please read the originial post: here
