AI & RoboticsNews

How generative AI’s impact on digital advertising methodology is evolving

generative AI

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More


The tidal wave of new generative AI tools is causing industries to reassess how they function and identify ways of up-leveling their processes. The current iteration of AI tools offers users unprecedented speed at creating text and visual assets — obviously an interesting proposition for brands and advertisers. But in the near term, the tools’ real benefits are less associated with brand-visibility efforts, and more on paving the way for innovative solutions and quick campaign ideations.

However, today’s generative AI comes with a trove of potential issues around content “ownership” and brand safety. While the digital marketing industry is poised to adopt the technology, it’s important to consider the most impactful ways generative AI can move our industry forward in the near term.

Realities for ad creative today

One thing brands and advertisers need to consider is the potential for generative AI-created content to closely resemble existing artwork. Because content can be generated and implemented into campaigns so quickly, it’s become very easy for brands and advertisers to unknowingly use imagery and messaging that infringes on intellectual property or copyrighted assets. We’ve also found that generative AI often suggests terms, mottos and slogans that are copyrighted unless asked specifically to remove any copyrighted text.

Another consideration is around brand safety; there’s a risk of generative AI creating assets that do not fit brand guidelines or are offensive to certain audiences. This obviously has brand reputation implications. That said, advertisers need to constantly ensure AI-generated content aligns with their brand values and will resonate with target audiences.

Event

Transform 2023

Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.

Register Now

Despite these hurdles, the generative AI market is forecast to reach $188.62 billion by 2032, up from $8.65 billion in 2022. From where we sit, this makes sense. We’re all seeing the surge of interest in AI, and quickly realizing how the current tools represent an amazing “jumping off point” for advancing workflows.

Platforms like Midjourney allow users to develop images simply by typing in basic text. The initial assets it creates, based on your prompt, could turn out to be very close to an image you are thinking of, or could be nothing like you imagined — in a good way. It enables teams to essentially have a very fast, and interesting, brainstorming partner. It opens the door to accidental creativity and inspires fresh perspectives on what branded collateral can be for a campaign.

From there, it’s up to the creative team to carry those assets across the finish line in a way that meets all brand guidelines.

Still a ways to go for code development

Similarly, we’re starting to see generative AI used in developing first-draft code for new digital advertising products or solution updates. When it comes to developing new solutions or evolving existing ones, it can take a few weeks to several months to write and test code. Solutions like ChatGPT deliver first drafts in seconds.

While the speed is very impressive, it’s important to review it for a few critical reasons.

We’ve found that generative AI produces code that is often not optimized for performance or security. Additionally, the code might not be scalable. These issues result in products that miss the mark in regards to reliability standards.

It’s also difficult to maintain, modify and incorporate the code into existing products — and that’s the most impactful drawback at this point. If every digital solution was initially developed by AI, things would likely function properly, and could be easily innovated and updated. But humans developed the initial code, and there is too much variability in how we build solutions. It’s that variability that makes current AI-generated code unable to seamlessly integrate with what we’ve previously made. So, just as with using AI tools for plug-and-play creative assets, we still need a fact-checker or goalkeeper.

Nonetheless, these tools are absolutely here to stay. The quicker we learn their use cases and hindrances, the faster we can optimize our workflows for the better. Only by adopting generative AI tools can brands, advertisers and solution providers understand what’s coming in the new frontier.

Ken Harlan is founder and CEO of MobileFuse.

DataDecisionMakers

Welcome to the VentureBeat community!

DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.

If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.

You might even consider contributing an article of your own!

Read More From DataDecisionMakers


Author: Ken Harlan, MobileFuse
Source: Venturebeat

Related posts
DefenseNews

Air Force official’s musings on rogue drone targeting humans go viral

DefenseNews

Defense Innovation Unit eyes first flight of hypersonic testbed

Cleantech & EV'sNews

As the US heats up, here's how you can help prevent blackouts

Cleantech & EV'sNews

This new solar electric yacht with its own spa pool and theater awaits you

Sign up for our Newsletter and
stay informed!

Share Your Thoughts!

This site uses Akismet to reduce spam. Learn how your comment data is processed.