A Generative Ecosystem in Graphic Design

Generative AI is changing the game in graphic design and has attracted the focus of C-suite executives in its versatility across virtually all business processes, from product development to risk management.

Industries relying most heavily on knowledge work, such as telecommunications, are likely to see the most disruption (read: opportunity) from generative AI. Already, generative AI is transforming the telecommunications sector, catalysing advancements in network optimization, predictive maintenance, and dynamic resource allocation for unparalleled efficiency and connectivity breakthroughs.

Much of generative AI’s value derives from its embedment into everyday tools, strengthening its value chain composition to a wide variety of neural networks.

Cloud-based generative AI platforms are lowering the requirement for compute resources and upfront investment, meaning designers can streamline working processes with ease.

Generative AI is a class of artificial intelligence that involves algorithms and models capable of generating new and original content, such as images, text, or other forms of data. Unlike traditional AI, which relies on predefined rules, generative AI uses complex neural networks to learn patterns from existing data and generate novel outputs.

In the form of language, generative transformers work to train neural networks on data and apply attention mechanisms so that AI models understand what to focus on. ChatGPT is arguably the most salient of these transformers. Launched in November of 2022, it was initially trained on 45 terabytes of data, using 175 billion parameters to inform its predictions. The public-facing version of GPT-3.5 reached 100 million users in the span of two months – an unprecedented success. In graphic design, generative AI can be fine-tuned to content domains in several ways, including:

• Automation of Repetitive Tasks
  Generative AI can automate repetitive design tasks, such as resizing, cropping, and formatting, allowing designers to focus more on creative aspects rather than routine operations 
• Content Creation and Variation
  It enables the generation of diverse and customizable design elements. Designers can use generative models to create variations of graphics, reducing the time and effort spent on manual iterations. 
• Conceptualisation and Prototyping
  By quickly generating conceptual designs and prototypes, generative AI expedites the early stages of the design process, allowing designers to explore multiple ideas efficiently. 
• Extended Content Lifecycle
  Generative AI can contribute to the creation of evergreen content by continuously generating fresh variations, preventing aesthetic fatigue and ensuring that designs remain relevant over an extended period. 
• Data-Informed Design Insights
  Utilising data-driven insights, generative AI can analyse trends and user preferences, providing designers with valuable information to inform their creative choices and optimise visual content. 

The latest crop of generative AI tools is demonstrating how AI can be applied in creative workflows. At Soda, we use Midjourney, Gigapixel AI, ChatGPT and the Adobe Suite – to name a few. Every italicised sentence in this article? ChatGPT. The graphic? Midjourney v6.0. 

Looking to how generative AI will shape the career landscape; we can expect to see roles like “Prompt Engineer” arise as the creativity to apply and harness artificial intelligence capabilities becomes a highly sought-after asset. Creative prompts yield creative outputs, after all. 

In an ecosystem that is heavily reliant on generative AI to maximise efficiency and productivity, it’s do or die.