AI and Graphic Design: Should Designers Be Worried?
When it comes to AI replacing humans, the public seems to think graphic designers will be the first to go.
Image generators like DALL-E 2 create the most visible— and therefore shareable—proof of what AI can do. How can you look at a photorealistic depiction of dogs playing poker, generated on the fly, and not say “whoa”?
To people who believe a graphic designer’s job is making pretty pictures in Photoshop, it looks like AI is on the cusp of total industry domination.
To actual graphic designers—most of whom know better—the promises and perils of AI and graphic design are less clean cut. It’s true that AI can effectively generate images, and even handle more complex tasks, like creating graphic layouts or editing photos. But it’s also true that current AI falls far short of the entire package of skills a human designer offers. What can you do to keep up with AI and compete with it, while still using it to your advantage? How will AI affect the graphic design field in the future? More on that in a moment.
But first: a look at how AI graphic design works— plus what it does well, and what it doesn’t.
The dawn of graphic AI
The casual observer might be forgiven for believing AI started creating images in Summer or Fall of 2022. That’s when AI image art really hit its stride: DALL-E 2 made its services widely available, and the results went viral on social media.
Before then, folks were turning their photos into Renaissance portraits, which was about as advanced as AI image generation got. Seems quaint now, doesn’t it?
But the history of AI and graphic design goes back much further than 2022. It’s beyond the capacity of this article to take a really deep dive into how AI and AI art evolved, but its history stretches back further than you might expect.
On Medium, designer Keith Tam shares how, in 1966, Ken Garland suggested in his Graphics Handbook that computers might come for designers’ jobs:
Even in 1966, when a typical computer occupied a small room, Garland recognized that, thanks to the advances of technology, the tasks with which a graphic designer “expects to be confronted” would change.
He was right. In Garland’s time, before home computers, the tools of the trade included rubber cement, technical pens, and scale rulers. Nobody was confronted with the task of using Photoshop. The day-to-day, hands-on process of graphic design changed drastically in the next 25 years thanks to computers. With the advent of effective AI graphic design tools, what will the next 25 look like? And how can you prepare?
At this point, it’s a good idea to stop and look at how AI does—and doesn’t—work.
How does AI art work?
Even if it isn’t always perfect (see: the hands and teeth problem), it’s hard to look at artwork AI has created and not feel at least a faint tickle of awe.
It’s the work of a computer, after all. Computers: Sometimes they freeze, and you have to turn them off and then on again to make them work. If you spill coffee on the keyboard, it’s a disaster. And, every 4 or 5 years, each of them seems to need replacing.
And yet, with the input of a few words, a computer can dream up a seemingly original piece of art. How?
Artificial narrow intelligence (ANI) vs. artificial general intelligence (AGI)
AI comes in two flavors: Artificial General Intelligence (AGI), and Artificial Narrow Intelligence (ANI.)
AGI is a computer—or a network of computers—that thinks and learns like a human. It broadly mimics intelligence as we understand it, asking questions like, “What is… love?” and calling the scientist who invented it “Father.” AGI is still science fiction.
Turns out, there’s a certain je ne sais quoi to the human mind machines currently can’t capture. A lot of very clever people are trying to change that—they’d love to have their own C3PO or Data to hang out with. But true AGI is a long way out.
ANI does exist. ANI is trained to do one thing well, like write responses in a chat window, or generate images. ANI is what we talk about when we talk about AI graphic design.
How does an AI image generator work?
The ANI that powers an image generator is built using neural networks. A neural network is structured like a very rough approximation of a brain, with many nodes able to connect and disconnect from one another.
Neural networks need training in order to learn how to complete tasks. This training is overseen and managed by humans, and usually involves a lot of tweaking along the way. For most AI—including tools like ChatGPT, as well as image generators— the training consists of looking at huge amounts of data and inferring patterns from it.
For the task of image generation, this involves feeding a neural network millions of images from the internet, along with their accompanying metadata (file names and captions.) The neural network finds correlations between words in the metadata and the contents of the image files, and creates rules from them.
For instance, the neural network might scan 10,000 files named “hippo.jpg.” As it does, it looks at the contents of the image files, finds commonalities between them, and uses that information to create rules about how a hippo looks.
This approach isn’t perfect. For instance, when researchers first started training neural networks on images from the internal, lolcats were in their Golden Age. Many pictures of cats the neural network was trained on featured blocky, white, overlaid text.
After being trained, when researchers tried getting the AI to generate images of cats, a lot of the images it created included vrandom bits of text. The neural network believed that, in addition to whiskers, pointy ears, and cute little paws, cats were made of letters.
Gains made with GAN
You may have heard the term GAN, which stands for Generative Adversarial Network. It was a model for AI dreamed up in 2014 by doctoral student Ian Goodfellow, allegedly while drinking in a Montreal pub.
Once AI projects started implementing and fine-tuning GAN, the quality of AI image generation rapidly improved.
The GAN approach pits two neural networks against one another. One neural network, the generative network, is trained with a set of data—typically, images from the internet, as explained above—and uses it to generate images based on prompts. The other network—the discriminative network—evaluates the results, determining which images are most likely to find human approval.
How diffusion cuts through the noise
By now (2023), GAN is old news. Diffusion is the hot new generative model on the scene. In many cases, diffusion has outcompeted GAN in terms of accuracy, and it’s the technology currently powering DALL-E 2.
Fundamentally, diffusion models learn how to generate images by taking training images, converting those images to visual noise, and then—through repetition and observation—learning how to do the same thing backwards. That is, they learn how to take noise, and convert it into original images.
With a lot of practice, an AI using the diffusion model learns to recognize patterns in how certain types of images convert to certain types of noise, and uses that information to generate original images based on prompts.
The power of prompting
Image generators take text prompts from humans and use them to generate images. Vague or confusing prompts create vague or confusing outputs. On the other hand, the more specific and concrete you’re able to make a prompt, the closer the output will be to what you’re looking for. Prompting is important not only because it steers AI’s output, but be cause graphic designers who know how to do it are better able to use AI to their advantage.
Graphic design work AI does well
By now, you probably recognize that the quality of an image generator’s output depends on how it’s prompted, and the skill of the person doing the prompting. You may already have some creative ideas about how you can start using image generators to boost your own productivity as a graphic designer.
But the role of AI in the future of graphic design doesn’t start and end with image generators. Numerous AI as a service (AIaaS) companies have popped up offering tools that handle specific design tasks.
Understanding how AI affects graphic design now—which tasks AI can currently handle, and which ones it can’t—will give you an idea of what you’re up against in terms of competition. It should also give you new ideas for how you can use AI to your advantage.
Sketching
AI is pretty good at taking rough sketches made by humans and guessing what they’re trying to illustrate. A number of AI tools will take your hand drawn input and turn it into something more polished.
That type of tool can be useful if your illustration skills are weak but you need sketches to communicate with clients or take notes. For instance, check out AutoDraw. As you sketch, it will quickly suggest images to replace your input.
Basic logos
For a while, freelance designers who were willing to churn out logos en masse on platforms like Fiverr could earn themselves a decent revenue stream.
Those days are fading. There’s a plethora of AIaaS tools offering to quickly produce original logos for businesses. You’re unlikely to find big ticket clients going that route; but for your average local landscaper, hair stylist, or vehicle detailer who just wants something to put on their business cards, AI-generated logos are a great deal.
Web design
Even if you web design doesn’t fall in your wheelhouse, it’s smart to keep an eye on how AI is changing the game. Business owners who may once have hired a web designer to create a site for them are increasingly turning to tools like Durable’s AI website designer, which is able to generate a custom tailored site in seconds.
Palettes
When it comes to brainstorming palettes for a project, AI has the market cornered. Tools like Khroma help you generate, modify, and save palettes based on a few simple inputs. (In Khroma’s case, you select your 50 “favorite” colors, and the AI puts together a selection of two-color combinations for you
It’s hard to see how a tool like this would put graphic designers out of their jobs. When was the last time a client came to you looking for a color palette? But it can definitely help get the juices flowing when you’re just getting a project off the ground.
Image enlargement/ enhancement
Companies like VanceAI claim their tools can upgrade image resolution and improve quality using advanced AI. The results aren’t too shabby: Whether you could manage something comparable in a few minutes using Photoshop is beside the point: AI enlargers and enhancers are fast and, in the case of VanceAI, handle batch processing. Next time a client dumps a pile of photos from their flip phone on you and asks you to turn them into a print brochure, it could be AI that saves the day.
Product shots
If you’ve spent any amount of time editing the background out of product photos, you know what an onerous task it can be. Luckily, neural networks don’t get bored. (As far as we know so far.)
Tools like Remove.bg will take product photos, remove the background, replace it, and deliver the final package to you—like a Magic Wand tool that’s actually magic. If you work with a lot of ecommerce clients, it has the potential to be a massive time saver.
Stock models
AI makes fake humans. That is, AI image generators are being used to create photos of people who don’t exist. Whether you find it awe-inspiring or downright creepy, there’s no denying the usefulness of be ing able to generate a totally random headshot on a whim—with no need for stock photo licenses or signed release forms.
Like AI-generated colour palettes, face generators aren’t likely to poach your clientele. But they could help you with a variety of tasks, from rough mock-ups to finished products for clients.
Graphic design work AI doesn’t do well (yet)
The future of graphic design depends upon which jobs AI will be able to handle on its own, and which will require lots of human input. Recent improvements in AI output are impressive, but neural networks still fall short when it comes to a lot of the day-to-day work handled by graphic designers. Here are some areas where AI has yet to make serious inroads.
Packaging
Some experiments have yielded passable packaging designs created by AI, but there’s no AIaaS that’s really crushing it at the moment. There are so many variables at play when it comes to packaging—like materials, or display and shipping needs—that AI is not yet equipped to handle it from start to finish.
You may be able to use AIaaS like Designs.ai to put together a label for a standard sized package or container; anything beyond that takes human skill.
Visual identity / brand books
Some AIaaS tools promise to take your palette, typefaces, visual elements, and copy, throw them in a blender, and create a brand book or a complete set of assets for a marketing campaign. But creating a comprehensive package like that takes a lot of consultation with clients, a lot of back-and-forth, and fine-grained attention to detail. For a mom-and-pop restaurant looking for new menus and a prettier website to match, AI might do the trick. But bigger clients expect more.
Design: Should Designers Be Worried?
Design: Should Designers Be Worried?
Example of AI-generated model
TOUCH HUMAN S
Despite the advantages of Artificial Intelligence graphic design, a traditional agency still holds a unique position in the graphic design industry. A traditional agency, also known as an integrated agency, has the advantage of human creativity and expertise, which is irreplaceable by AI. These agencies excel in delivering custom and personalized designs that reflect a company’s brand values and identity.
Working with a traditional agency also provides opportunities for direct communication and collaboration. As the design progresses, businesses can provide feedback, ensuring that the final output is aligned with their expectations.
Can You Use AI for Graphic Design?
Absolutely. AI is being used to automate several graphic design tasks, particularly those that are tedious or repetitive. A variety of Artificial Intelligence graphic design tools are available, capable of a range of tasks from creating logos to generating high-quality graphics. Take the Artificial Intelligence graphic design tool «Deep Art Effects,» for instance. It uses deep learning technology to convert images into art styles that would otherwise take hours to accomplish manually. Similarly, the AI design tool «Adobe Sensei» leverages AI to automate tasks like cropping images or adjusting lighting, enabling designers to focus on the more creative aspects of their work.
The use of these AI tools in graphic design has been successful, with many businesses and designers leveraging them to increase efficiency and create stunning designs.
Artificial Intelligence Graphic Design Tools: Empowering Creativity
There are various Artificial Intelligence graphic design tools available in the market, each offering unique features and functionalities. Tools such as Adobe Sensei and Deep Art Effects use AI and machine learning technologies to aid in the graphic design process. They are powerful AI tools, enabling users to create stunning designs in just a few clicks.
Additionally, there are other Artificial Intelligence graphic design tools that allow designers to create stunning visuals without the need for extensive HTML CSS code knowledge. A no-code editor allows you to create high-quality designs and fully edited videos without the need to write any code.
AI Graphic Design VS. Tranditional Agency: Pros and Cons
While Artificial Intelligence graphic design tools provide speed, cost-effectiveness, and the ability to generate high-quality designs quickly, they may lack the human creativity that traditional agencies offer. In contrast, a traditional agency can provide customdesigns that truly capture the brand’s essence, but they may be slower and more expensive than their AI counterparts.
In essence, choosing between an Artificial Intelligence graphic design tool and a traditional agency depends on your specific needs and considerations.
FREQUENTLY ASKED QUESTIONS
What is the Meaning of AI in Graphic Design?
In the context of graphic design, AI refers to the use of artificial intelligence and machine learning to automate or enhance design tasks. AI can generate images, make design alternatives, and even assist in the creative process.
Is Artificial Intelligence Graphic Design Free?
While some Artificial Intelligence graphic design tools offer free versions, they often have limitations. Comprehensive AI graphic design software and services typically come at a cost, but this cost is often offset by the speed and efficiency they provide.
Comparatively, the cost of hiring a traditional agency for graphic design services is usually higher, but it provides the benefit of customized, human-made color palettes and designs.
The Future of Graphic Design and AI
The future of graphic design seems to be heavily intertwined with AI. Advancements in Artificial Intelligence graphic design tools are expected to continue, and we may see tools that can mimic human creativity even more accurately.
Furthermore, a potential future trend is the collaboration between human designers and AI. By handling the more repetitive and tedious tasks, AI can free up designers to focus on innovation and creativity.
Conclusion
In the battle for creative dominance between AI graphic design and traditional agencies, there’s no clear winner. Both have their unique strengths and weaknesses. While Artificial Intelligence graphic design tools offer speed and efficiency, a traditional agency offers the irreplaceable human touch.
At Clickable, we believe the future lies in a harmonious blend of AI and human creativity. AI can be a powerful tool in the hands of graphic designers, not a replacement. The key is finding the right balance to maximize the strengths of both Artificial Intelligence graphic design and traditional agencies.
For CEOs, CMOs, business owners, and marketers, understanding this dynamic can help you choose the right path for your graphic design needs. And remember, no matter which path you pick, the ultimate goal is to create high-quality designs that resonate with your audience and boost engagement.
until our next AI venture.
