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It doesn’t matter what industry you’re in, everyone is talking (and debating) about AI. While we’ve seen technology come a long way over the last couple of decades, these tools are arguably the most transformative developments of our time. But like every other new wave of technology, it’s bringing a whole new language we have to learn with it — and important differentiations that need to be cleared up.
Part of my job as Studio & Web Development Manager at Assemble is exactly that. I’m constantly in study mode, researching the latest tools and technology and understanding how they each can/will impact our production processes and outcomes for our clients.
And one of the main things I’ve been noticing is the confusion between AI and automation. They’re not the same, yet the two terms continue to be used interchangeably by business owners and leaders. Because as far as they know — and really care about — they both serve a similar purpose of greater efficiency and better ROIs.
But for marketers, knowing the difference does matter. Because not only are their approaches and capabilities totally unique, but they also do actually drive different results depending on how they’re used. Don’t worry — I’m going to cover all of that in this article. Like I said, this is what I do.
How is Automation Different from AI?
Both of these technologies have a common denominator: they run on data. And while both of these technologies are intended to execute tasks and solve regular problems more efficiently, they both do that in very different ways.
While automated tools collate data; AI systems interpret it.
Think of automation as a means of eliminating repetitive and tedious tasks. The main concept here is taking a process that a human is already doing and making it automatic. Think of it as “If A occurs, then B should follow.”
For example, when deploying a digital campaign, automation tools enable you to create criteria that dictate how and where the ads will be displayed. You can set specific audience indicators to determine placements, set parameters in terms of spending, and prioritize certain ads that are performing better than others. All of these actions are done automatically based on a user input, or a “rule.” Instead of testing and tracking these ads yourself, the technology does it for you based on your requests.
Reversely, while AI also deserves credit in terms of automation, it takes this all a step further, learning from its actions and making adjustments to improve. Instead of merely taking orders, it evaluates your input, applies its own solutions, and continuously learns from the results. We refer to a lot of AI as “generative” as it is capable of producing new content and outputs based on knowledge it collects and stores.
AI technology has the ability to not only curate information, but also process it, which can lend to a new level of immersive experiences and scaling for personalization in digital advertising.
Why Understanding the Difference Matters
To put it simply, each of these systems help achieve different goals. While automation is designed to remove the human element, AI very much still relies on human behavior to not only learn, but also fill the creative void that is missing from the robotic brain. The human touch is undeniably important in taking these “mimicked” human behaviors and personalizing them to the nuances, voice, and goals of each unique brand.
As these technologies continue to evolve and digital platforms flourish, the role of Creative has become more important than ever. While the demand for personalization is now the foundation for impactful and relevant design, effective content delivery has been met with the challenge of executing faster and at a larger scale.
These technologies are also making each other better too. Think of some of your favorite platforms — Facebook, YouTube, Linkedin. Each of these channels utilizes its own unique targeting criteria to help you target your specific audiences. Given that information, algorithms (or pre-set rules) automatically put these ads in front of people who check the boxes (automation).
But now, we’re seeing AI add another layer to that. Machine learning is now analyzing and studying the effectiveness of these placements, as well as the people that may fit your ideal audience. It’s learning from people’s online behaviors to anticipate future actions, and choosing more effective placements and audiences for you.
The Latest Developments in Creative Automation
With AI getting most of the attention right now, we haven’t let automation slip through the cracks. In fact, there have been some awesome advancements recently in this area as creative teams felt it was time to reinvent the process of designing banner campaigns across all formats to meet growing demands. Here’s a little preview.
- Automated Text Treatments for Digital Banners
The campaign headline makes potentially the biggest impression on a user. But until now, handling this text has been a real painful process for designers — different formats require different layouts, certain words need to be on the same line to make sense, different alphabets are more visually appealing than others.
Now, tools like Celtra’s Automated Text Treatment allows designers to tap into a built-in library of standard line break rules that efficiently adapt layouts across multiple languages, which is particularly beneficial in the transcreation process. Designers no longer need to manually adjust layouts or text for discrepancies in localization, but instead can automatically manage text rules across all assets, while maintaining more control over their different content iterations. - Auto Layout for Optimizing Space on Banners
In the past, designers had to manually adjust the layout of various components in a banner to make the most of the design space across various sizes and formats. With auto-layout, they can now set rules for a single template that will automatically resize and reposition elements for the best fit. For high-volume campaigns, this is huge.
Even further, rules can also be applied to optimize ads for the very best performing layouts. Let’s say you want to A/B test the placement of a specific CTA. You can apply a rule that changes this placement based on the number of clicks it receives from users, ensuring your ad utilizes the layout that will get the best results. - Auto Sizes for Bulk Production
Resizing ad variants across platforms and placements is as tedious a process as it gets in design. This can mean manual adjustments across hundreds of ads for global brands. New automation tools can now handle the task for designers, leaving them only with the task of creating one template that is automatically applied to other sizes. As the technology analyzes the minimum layouts needed from the design source, designers can easily tweak and make adjustments where needed, making it much faster and more efficient to scale and mass produce complex media plans. - Auto Cropping for Bulk Design
During production, designers are tasked with all the manual prep of raw assets — from background images to product photos, etc. They’ll spend hours cropping source images for all the sizes they need in all the ads they’re creating. Celtra’s new automation only requires designers to upload the source image, or Master image, and input the sizes they need, and it will generate the cropped images for them. It will also allow them to preview all the crops across digital banners and make adjustments if needed.
This is also super helpful when it comes to packaging. In a single click or command, you consolidate all new images into a folder to expedite the approvals process. This may not be that helpful when you have a couple of images, but when you have multiple ads with multiple concepts, this is a game changer.
Both AI and automation are reshaping the world of production for generations to come. If you’re not taking advantage of these tools, it’s time to start. To learn more about how to expedite and improve your creative processes or employ a team who is ready to do that for you, contact me and our team at Assemble today.