DALL-E, Midjourney, and other new generative artificial intelligence (AI) applications for creating images have taken the internet by storm. These algorithms can generate impressive graphics from natural-language prompts. There are many text-to-image tools on the market today that anyone can use to quickly create stunning graphics and design templates, and many people are wondering how this technology will impact the graphic design industry.
The tech space is ripe with doomsday news signaling the massive loss of jobs that AI will cause. And business leaders are always looking for ways to cut costs, which can potentially disrupt knowledge workers in the short term. But AI is more likely to augment graphic designers’ experience and capabilities than replace the need for human ingenuity entirely.
Generative AI is set to play an expanding role in the field of graphic design. In this analysis, I’ll evaluate how graphic designers might utilize AI to enhance their work. I’ll also weigh the limitations of generative AI and view some ways graphic designers can already weave AI into their workflows.
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How AI Is Used In Graphic Design Today
AI is making ripples through many areas, and most leaders realize its importance: In a 2022 Deloitte report on AI in the enterprise, 94% of respondents say AI is critical to success.
Individual designers are also excited about using AI to explore new mediums and produce higher-quality work. According to an Adobe survey, only 19% of creative professionals have not yet used generative AI tools such as ChatGPT, DALL-E, RunwayML, Stable Diffusion, or Midjourney in their professional work.
But it’s not only designers and illustrators taking advantage of these new AI capabilities — AI is also bringing artistic capabilities to non-designers. An O’Reilly report on low-code/no-code adoption reports that 62% of those using low-code or AI to generate art identify as developers, DevOps professionals, or data analysts.
How Generative AI Will Assist Designers
Many AI-powered graphic design tools
- Generating graphics from conversational inputs: Generative AI tools can quickly generate graphics using natural language prompts. Trained on massive datasets of art, neural networks like Midjourney and DALL·E 2 can mimic the style of well-known artists.
- Automating repetitive design tasks: AI can automate less creative, menial tasks in the design process. This includes generating marketing assets; for example, for an event with a panel discussion, AI could select quotes for social media posts, create event speaker profile images, or build presentation graphics. Adobe Sensei GenAI, a generative AI tool trained on Adobe products, can use text-to-image to generate assets and banners.
- Suggesting templates and workflows: Generative AI will become more embedded within design programs. An AI copilot with thorough knowledge of a designer’s tastes and behaviors could suggest subsequent actions and workflows. Furthermore, an AI assistant could interpret natural language prompts to work with a design program in fine-grained ways, like contorting or expanding a shape.
- Boosting creativity and ideation: Generative AI could also assist designers during a project’s brainstorming phase. For example, an AI like Deep Dream Generator can create instant mockups or inform a color scheme. Generative AI can also produce variations on a theme — for example, an algorithm made 7 million different versions of Nutella’s graphic identity.
- Adjusting and enhancing photos: AI is already widely used to make instant adjustments to photos. These include removing backgrounds, altering facial expressions, and doing other basic image editing and enhancements. By utilizing AI, designers can achieve professional-looking editing results without being photography professionals. For example, Cloudinary’s generative AI features enable actions like generative fill, generative remove, generative replace, and AI-powered image captioning.
- Enabling dynamic personalization AI can suggest graphical styles to match specific target demographics. This could generate personalized graphics based on user types and continually refine them according to performance results.
AI Limitations for Visual Design
Generative AI has plenty of limitations, so it’s not a magical solution to every design issue. One, the artistic generations are rarely photo-realistic. AI-generated output is often surrealistic and struggles with numerical accuracy; a common example is producing hands with missing fingers.
Also, because many generative AI applications are trained on real art, the results are simply an amalgamation of what’s come before. It does not produce anything more than a median result. This not only limits uniqueness and creativity but is a contentious gray area for intellectual property law.
Finally, generative AI can hallucinate and carry bias. This could result in misrepresenting gender or race. For example, models trained solely on data sets with white people may not register darker skin tones or might incorrectly label individuals.
Because of these limitations, AI-generated art will require human oversight to ensure it’s used correctly.
AI: Augmenting, Not Replacing Designers
Instead of treating AI as a replacement for human labor, many leaders view it as augmented intelligence that improves human work. This perspective seems appropriate considering the many ways AI can work hand-in-hand with designers. This human-machine partnership will increase agility, reduce cost, expand personalization, and accelerate time to market for new designs.
I believe that companies that augment the employee experience, rather than replace talent with AI, will be better off in the long run. AI is always evolving, but given the current limitations of generative AI, it cannot fully supplant the expertise of professional graphic designers. Along with governing AI outputs, human designers will still be necessary to bring creativity and emotional relatability to design work. In essence, AI will help but not take over completely.