ACAI for SBOs: AI Co-creation for Advertising and Inspiration for Small Business Owners

TL;DR

This paper presents ACAI, a GenAI-powered multimodal tool designed to help small business owners co-create brand-aligned advertisements. Through a London-based user study with 16 SBOs, it demonstrates that structured, panel-based inputs and multimodal prompting reduce prompting difficulty, boost user agency, and yield outputs closer to brand identities. The authors propose a three-part design framework—contextual intelligence, adaptive interfaces, and data management—to improve AI-mediated design for novices, along with practical recommendations and a roadmap for future work. The work advances AI-assisted creativity by centering novice users and outlining concrete interface-level strategies to close the gap between business branding needs and GenAI capabilities, enabling more inclusive, efficient advertising workflows for SBOs.

Abstract

Small business owners (SBOs) often lack the resources and design experience needed to produce high-quality advertisements. To address this, we developed ACAI (AI Co-Creation for Advertising and Inspiration), an GenAI-powered multimodal advertisement creation tool, and conducted a user study with 16 SBOs in London to explore their perceptions of and interactions with ACAI in advertisement creation. Our findings reveal that structured inputs enhance user agency and control while improving AI outputs by facilitating better brand alignment, enhancing AI transparency, and offering scaffolding that assists novice designers, such as SBOs, in formulating prompts. We also found that ACAI's multimodal interface bridges the design skill gap for SBOs with a clear advertisement vision, but who lack the design jargon necessary for effective prompting. Building on our findings, we propose three capabilities: contextual intelligence, adaptive interactions, and data management, with corresponding design recommendations to advance the co-creative attributes of AI-mediated design tools.

Figures (6)

• Figure 1: ACAI Prototype Design 1) User input submitted prior to the study 2) Interface design of ACAI incorporating user input and AI content into 3 panels 3) Backend "super prompt" concatenating user input 4) Call to Gemini to generate Ad Brief 5) Ad brief template 6) Visual ad designed by human designer according to Ad brief
• Figure 2: Before Study: 1) User Input 2) Data processing with Gemini 3) Concatenate Super Prompt 4) Ad Brief 5) Wizard of Oz design of Ad brief
• Figure 3: Study Flow Diagram: 6) Interview about using AI for business branding 7) Prototype onboarding 8) Review of Ad brief and Ad design 9) User driven iteration 10) Interview about overall experience and AI co-creation
• Figure 4: Contextual Intelligence - Design recommendations for less generic output 1) Provide baseline data for user preference learning (UPL) 2) User Preference Learning updates Contextual Memory (CM) over time 3) UPL inform interactions in Bi-Directional Feedback (BDF) 4) BDF learns preferences and feeds back to UPL 5) BDF provides new information to CM 6) CM informs responses in BDF. This operates as a dynamic, cyclical process where each component informs and is informed by the others, creating an adaptive and responsive AI-mediated design process within the framework of Contextual Intelligence.
• Figure 5: Detailed view of ACAI inspiration panel interface: 1) Image gallery for inspiration selection comprised of existing business images and additional stylistic images 2) Extraction of style preferences to add to super prompt 3) Sliders for stylistic preferences to add to super prompt 4) Text box to refine stylistic preferences