Research / Findings

Research Findings

Preliminary insights from our ongoing investigation into AI full-chain creative collaboration.

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Research In Progress

We are currently in Phase II of our investigation. The findings presented here represent preliminary data and emerging patterns. Full results will be published upon study completion in Q4 2025.

Key Metrics (Preliminary)

78%
Practitioners report AI integration
↑ 23% from 2023
3.2x
Average iteration speed increase
Consistent across disciplines
61%
Use AI in ideation phase
Highest adoption stage
42%
Report full-chain usage
↑ Growing segment

Emerging Findings

Key patterns identified from practitioner interviews and observational studies.

1

Stage-Specific Adoption Patterns

AI adoption is not uniform across the creative chain. Ideation and iteration stages show highest integration (61% and 58% respectively), while production and delivery stages lag significantly (34% and 28%). This suggests current AI tools are perceived as more valuable for exploratory than executional tasks.

Adoption Workflow Tool Usage
2

The "Curation Shift" Phenomenon

Practitioners consistently describe a fundamental shift in their role from "creator" to "curator" when working with AI. Rather than generating from scratch, designers report spending more time evaluating, selecting, and refining AI-generated options. This shift has significant implications for creative skill development and education.

Role Evolution Skills Education
3

Quality Paradox in AI Collaboration

Initial data suggests a non-linear relationship between AI involvement and perceived output quality. Moderate AI integration (2-3 stages) correlates with highest quality ratings, while both minimal and full-chain integration show lower scores. This "quality paradox" requires further investigation.

Quality Integration Level Paradox
4

Context Continuity Challenges

A major barrier to effective full-chain collaboration is maintaining creative context across different AI tools. Practitioners report significant friction when transitioning between stages, with creative intent often "lost in translation" between tools. This points to a need for better tool integration and context-passing mechanisms.

Workflow Tool Integration Friction
5

Discipline-Specific Variations

AI collaboration patterns vary significantly across creative disciplines. Graphic designers show highest overall adoption (82%), while product/industrial designers report more selective, stage-specific usage (54%). These variations appear linked to output tangibility and iteration cost factors.

Disciplines Variation Adoption

Data Visualisations

AI Adoption by Creative Stage

Percentage of practitioners using AI at each workflow stage

Quality Perception vs AI Integration

Expert panel ratings correlated with AI involvement level

Interactive visualisation available in full report

Preliminary Implications

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For Education

Design curricula may need to emphasise curation and evaluation skills alongside traditional creative techniques.

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For Industry

Studios should consider stage-specific AI strategies rather than blanket adoption across all workflow phases.

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For Tool Developers

Priority should be given to improving context continuity and cross-tool integration for seamless workflows.