How AI Powered Moodboard Generators Are Revolutionizing Creative Workflows

Introduction

In the last decade, the use of Artificial Intelligence tools has transformed creative production. As AI becomes part of creative workflows, concerns about its impact on work in these fields increase. This paper explores the rapid rise of AI, with generative technologies enabling easier creation of images, text, videos, and sound. 

AI will most likely continue to perform mediating roles in creative work, generating unexpected new possibilities through increased access to otherwise complex and labor-intensive knowledge and practical systems. Rather than prompting an unfettered replacement of humans by machines, the future of these relationships hinges on how one shapes and inserts them into concrete creative processes, products, and ecosystems.

Overview of AI-Driven Tools

The use of artificial intelligence as a tool to assist with creative tasks began in the mid-1950s. Some of the earliest projects included work on generative poetry, a computer program that produced simple Hallelujah songs, and algorithmic composition of short melodies. For several reasons, none of these projects were particularly controversial at the time. 

Despite the rapid pace of innovation leading to stunning collaboration algorithms, AI-assisted creation of videos and photos, and a general realization that human ingenuity had for some time already been using algorithms to guide and transform creative inspiration, most algorithmic efforts to produce stand-alone works were still largely parlor tricks, producing little work of appreciable merit. 

AI tools come in all sizes and shapes, addressing an astounding variety of creative tasks, and possibly enabling a creative act to be performed on the spot. From generative art moodboard generator tools, drawing apps, and avatar generators, to chatbots that can generate conversations, short stories, screenplays, and academic essays, to tools that can produce music and synthesize music videos, to software that generate entirely new video games and allow anyone to create them, AI tools can cater to different people and let them engage in different creative acts. The development and diffusion of these applications is creating new creative possibilities, while at the same time raising challenges and uncertainties for creators. We categorize AI-driven tools into three broad areas: 

• generative models that create new content when given simple user prompts; 
• Creative and collaborative tools that augment and assist human creativity and input with AI, and 
• decision support tools that help content evaluation, curation, and optimization with the support of AI. 

New generations of AI are driving disruptive changes in creativity, content creation, and the creative industries. Such tools are capable of generating content in multiple formats and modalities. A single prompt, input in natural language, is typically capable of generating diverse content outputs in seconds. Users can rapidly create content without the need for technological expertise or creativity skills. Generative AI can combine and remix existing content in novel ways.

Streamlining the Design Process

In creative fields, many tasks involved in the initial ideation and design, as well as support activities in the revision and finalization processes, can involve providing information, manipulating, and adjusting shapes and properties of the design to reach the desired final outcome. In many areas of design, such as in the advertising and entertainment industries, designers spend an inordinate amount of time creating mockup variations for selection by the client or between team members. Process steps in the selection and revision process, such as presentations, sharing documents, and collecting feedback, can take multiple rounds, involving tedious back-and-forth exchanges without a clear idea of how any proposals can fit into the visual objective. AI-assisted generative tools can quickly populate variations upon demand that can meet specific criteria, allowing the designer and the relevant stakeholders to arrive at preferred options that can help expedite the approval process and streamline the workflow. This allows the designer to focus their efforts on innovating instead of executing, and ultimately keeps the human-centric status of the design industry. The result has been a growing demand for design across many business sectors, making growth in creative industries one of the sources driving the increase in nations’ GDP.

Enhancing Collaboration

Collaboration needs to not just happen between artists and businesses, it also needs to happen between different creative disciplines. There are user experience innovators working at the forefront of blending and unifying the creativity of designers, sound engineers, VFX artists, and movement and visual directors to deliver rich, seamless experiences with film, television, music videos, and gaming. And generative AI tools are being built into these engines, allowing users to create photorealistic assets and environments, animatics, motion capture performances, and in-game scripts, bringing the convergence of storytelling and technology to a new level.

Fostering Innovation

Artificial intelligence replaces traditional craftsmanship skills and collective trial-and-error learning, at least in the short run, which implies that a wide span of variation might allow talented artists to take advantage of the tools, even push a lot of eclectic creation to a good enough, unique level. Within the large experimentation space of “what happens when you replace this inpainting masked canvas setting with that one?”, the optimally creative outcome might come as an outlier, even tend to be extreme outside the mold. The same principle that ensures the final product is good leads to extreme variation in the early stages, at least when the respect for craftsmanship does not stifle creativity, or when the dominant supervisors are neither overly demanding nor overly biased.

Forcing local extremes is how human feedback modifies the functionality of AI engines, allowing for the product design stage, which is where the interaction is most vivid and compelling. It is at that crossroads of collaboration that we need to think up new tools and tricks, and figure out how those tricks work in the background, so as to navigate the turbulence of collective creative exploration.

Impact on Marketing

The most prominent impact of AI tools on marketing is related to developing campaigns, such as developing ideas, developing the strategy, conceptualizing imagery, creating copies, selecting or creating music, and analyzing results. AI can help marketers either speed up processes or get out of the way when AI can do it faster at lower costs. Building empathy maps for target audiences and belief-shifting maps, which visualize specific phases of consumer belief systems that marketers will have key strategy decisions to shift, are typical marketing tasks where tools will help speed up the process. Speeding up creative tasks such as copy development will become essential simply because text and content development for individualized use cases, such as emails or website content to deliver personalized messages and conversations for each target segment with enough sizes, will help marketers to increase effectiveness.

Impact on Fashion Design

The effect of AI-driven tools on fashion design may be best understood relative to specific areas where they are being employed. An AI-based trend forecasting tool employs computer vision algorithms to analyze fashion item images on social media and across web fashion retail for different time spans and different locations. It provides customers with its service product themes and color palettes that are currently trending, emerging, or fading based on analysis of this data. Another such tool is based on data-driven image analysis to parse and categorize product images in user-curated collages posted in its online community. On the other hand, a platform offers its users a search option to find fashion-related content tags that are trending upward by analyzing previous search queries. These tags include boards, images based on user data, and recommendation algorithms.

A growing number of AI-based tools are available for fashion designers to implement in various phases of collection development. Models that are trained to predict style can be used as moodboard generator tools; a neural network trained on data from trends is optimized to predict +/- 12 months of global girlswear trend data based on previously popular search terms. AI generative design tools can greatly assist fashion designers as they move on from the trend stage into initial concept-crystallization; a generative pre-trained transformer model can produce language-based outputs based on a natural-language prompt; a platform designed specifically for the fashion designer uses similar technology, enabling users to input descriptive text as a prompt and generate images of new trends, styles, brands, and models.

Impact on Interior Design

While still in the early innings, interior designers can get a sense of the impending disruption caused by generative AI tools by looking at other creative fields. Architects have felt its impact sooner; after all, the words ‘interior design’ rarely result in stunning renderings. Landscape architecture is being helped, with firms using AI tools for concept generation, and cities using it to create new parks and work on stormwater management. Graphic designers and architects are using them for brainstorms at the ideation phase of design. Concept artists for movies are furious about the ability to generate realistic stills, even deriding those who use generated briefings to make films. The term in this scenario refers to artists turning to text-to-image generation tools to quicken their brainstorming processes.

These tools are exciting as concept boards for interior designers because, let’s face it, our sketches look awful. Concept boards are a way to communicate implied color, material, pattern, depth, and light. But are boards the best way to communicate our vision to them? Architects are the masters of scales. They can give their clients architectural models that perfectly show spatial relationships, barometric flow, and massing. A 3D model blows up a concept board, turning it from a 2D to a low-fidelity 3D narrative. Presenting a custom model instead of a simple board ups the ante. It prompts the right client questions when you are just beginning to zero in on an idea. If we were to parallel task some of the client discovery steps involved in writing a space brief by hand with a generative tool-assisted methodology, AI tools could streamline pieces of it easily.

Conclusion

People believe that art needs a soul to “speak.” The question arises as to who possesses the “soul” that can embed artworks with intelligence and significance—only the artist. 

Tasks of the imaginative realm, such as telling a story, developing a gaming level, creating an original image or video, or writing a song, cannot be simply outsourced to AI, no matter how smart the tool is. These activities, nonetheless, can be aided by generative AI apps that are directly used by creators. In other words, the use of AI as a co-creator that takes direct creative actions on behalf of the user adds a whole new dimension, not just to each type of tool but to creative work itself.

Working with AI can be fun, creative, and inspiring if the tools are used responsibly. It is time to change curricula in art and design disciplines, invest wisely in infrastructure, and build carefully crafted models for the benefit of humankind.