A comprehensive six-stage model for understanding and implementing AI collaboration across the complete creative production cycle.
Our research framework conceptualises the creative process as a continuous chain of interconnected stages, each offering distinct opportunities for human-AI collaboration.
Unlike traditional models that treat AI as an isolated tool for specific tasks, the Full-Chain approach emphasises the seamless flow of AI assistance throughout the entire creative journey—from the first spark of inspiration to final delivery.
Each stage in the chain builds upon the previous, with AI serving as a persistent collaborative partner that maintains context, learns preferences, and adapts its assistance to the evolving needs of the project.
The ideation stage represents the critical starting point where creative direction is established. Here, AI serves as an expansive thinking partner, helping creators explore possibilities beyond their immediate frame of reference.
Through analysis of vast creative databases, trend recognition, and generative prompting, AI can surface unexpected connections and novel approaches that human creators might not independently consider. The key is maintaining human creative agency while leveraging AI's capacity for comprehensive exploration.
Our research examines how AI can facilitate divergent thinking without overwhelming the creative process, and how the balance between AI-generated suggestions and human curation affects the quality and originality of initial concepts.
Conceptualisation transforms abstract ideas into tangible visual or structural forms. This stage witnesses some of the most dramatic AI integration, with generative models capable of producing sophisticated visual outputs from textual descriptions.
AI image generators, 3D modelling assistants, and layout tools allow creators to rapidly visualise concepts that would traditionally require significant time investment. This acceleration enables broader exploration of the concept space before committing to specific directions.
Our research investigates how AI-generated concept visualisations influence designer decision-making and whether the ease of generation affects the depth of conceptual exploration.
Iteration is where concepts are stress-tested and refined through multiple variations. AI dramatically accelerates this traditionally time-consuming phase, enabling the generation of hundreds of variations in the time previously required for a handful.
The ability to rapidly explore parametric variations, colour schemes, compositional alternatives, and stylistic options fundamentally changes how designers approach the refinement process. Rather than committing early to a direction, designers can maintain optionality longer.
Our research explores optimal human-AI iteration workflows and examines how designers maintain creative coherence across AI-assisted variation generation.
Refinement focuses on polishing selected concepts to professional standards. AI tools in this stage assist with technical perfection—upscaling, detail enhancement, consistency checking, and format optimisation.
This stage also sees AI contributing to quality assurance, identifying potential issues in design execution, accessibility compliance, and technical specifications that might otherwise require extensive manual review.
Our research examines how AI-assisted refinement affects the perceived quality of final outputs and investigates the balance between AI polish and authentic human craft.
Production transforms finalised designs into deliverable formats. AI streamlines this traditionally labour-intensive phase through automated asset generation, format conversion, and batch processing.
For complex projects, AI can generate multiple format variations, handle repetitive production tasks, and ensure consistency across large asset libraries—dramatically reducing production timelines while maintaining quality standards.
Our research investigates how AI production assistance affects project economics and examines the changing role of production specialists in AI-augmented workflows.
Delivery encompasses the final presentation and distribution of creative work. AI assists with contextual adaptation, generating variations optimised for different platforms, audiences, and use cases.
This stage also involves AI-powered analytics and feedback integration, allowing creative outputs to be refined based on performance data and audience response—closing the loop back to the ideation stage for future projects.
Our research examines how AI-facilitated delivery optimisation affects creative impact and investigates the feedback loops between delivery analytics and creative decision-making.
While each stage presents distinct characteristics, the Full-Chain framework emphasises their interconnection. AI's true transformative potential emerges not from excellence at any single stage, but from maintaining coherent assistance across the entire creative journey.
"The future of creative practice lies not in choosing between human creativity and AI capability, but in orchestrating their seamless collaboration across every phase of the creative process."
Our ongoing research continues to refine understanding of how these stages interact and how creative practitioners can optimise their human-AI collaboration strategies for maximum creative impact.