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AI-driven Predictive Marketing System
Marketers today navigate overwhelming demands—83% report burnout, balancing short-term objectives while filtering through 6,000+ ads and 30,000+ new products daily. To streamline decision-making, we designed an AI-driven predictive UX model that automates workflows, reduces cognitive load, and enhances customer retention through LLM-powered adaptive messaging.
CLIENT
BERO Brewing
TYPE
•  Predictive UX model
•  AI-assisted workflow
•  B2B Product
•  Freelance Work
(Freelance Product Designer at Living Brands AI)
DURATION
2024 Nov - 2025 April
(Release scheduled, April)
TEAM
Living Brands AI
• 1 Director
• 2 Developers
• 2 Designers
WHAT I DID
• Designed UX system
• Created dev-ready adaptive UI design
• Prototyped AI-integrated workflows
• Refined predictive UX interactions
• Produced developer handoff docs
IMPACT  (Pre-release test)
01. Predictive Content Optimization  (+10% Engagement Boost)

LLM-powered adaptive UX system dynamically optimized AI-generated content, reducing cognitive load for marketers and increasing campaign efficiency by 10% through contextual content refinement.
02. Personalized AI Interaction  (+12% Retention Growth)

AI-driven behavioral analysis and a personalized messaging system improved customer engagement, leading to a 12% increase in retention and a 15% boost in repeat purchases through adaptive AI recommendations.
03. Automated AI Workflow  (+30% Operational Efficiency)
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By integrating predictive automation into the UX flow, the AI-assisted campaign management system reduced manual workload by 30%, saving marketers 20+ hours per month and accelerating go-to-market execution.
OVERVIEW
AI-powered adaptive UX for intelligent marketing

💡 How it Works?
By integrating LLM-powered real-time content generation, brand consistency scoring, and automated workflow optimization, the system eliminates cognitive friction, accelerates execution, and enhances decision intelligence.

✔️  Adaptive AI Assistance: Predicts and automates repetitive tasks, allowing marketers to focus on strategic creativity.
✔️  Context-Aware UX: AI dynamically refines messaging and campaign flow based on real-time data insights.
✔️  Generative AI-Driven Content: Ensures on-brand consistency with AI-enhanced quality control and optimization.
✔️  Predictive Workflow Intelligence: Enables emerging brands to execute at scale with minimal manual intervention.
PROBLEM STATEMENT
Marketers today operate under immense pressure,
caught in a cycle of overload, inefficiency, and short-term reactivity

✔️ Overwhelming Workloads → 83% of marketers report burnout, with constant execution leaving little time for strategic creativity.
✔️ Short-Term Focus → 65% struggle to invest in long-term growth, as they are locked into immediate performance metrics.
✔️ Market Saturation → Consumers face 6,000+ ads daily and 30,000+ product launches yearly, making brand differentiation difficult.
GOAL
1.  Predictive AI for intelligent decision-making
​2. Generative AI for dynamic content & brand consistency
​3. Adaptive UX for workflow acceleration & precision execution
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GOLDEN PATH
​❎ As-Is workflow (Current manual process):
"Execution mode"
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✅ To-Be workflow (Optimized AI-powered process):
"Strategic decision-making"
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KEY FEATURES
1. AI-generated adaptive copy

It dynamically crafts brand-aligned messaging, adapting tone and structure based on audience insights. By automating high-quality content creation, it reduces manual workload while maintaining consistency across campaigns.
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2. Brand consistency & quality control

A real-time AI scoring engine evaluates content against brand guidelines, offering actionable insights to refine messaging. The system continuously learns and optimizes, ensuring every campaign aligns with brand identity and resonates with the target audience.
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3. Predictive AI-powered campaign planning

This intelligent scheduling system seamlessly integrates AI-assisted ideation, structured planning, and dynamic filtering. By anticipating workflow needs, it simplifies execution, allowing marketers to focus on strategy rather than logistics.
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USER FLOW
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FINAL DESIGN SOLUTION
Designing for predictive & adaptive user experience
💡 To create a seamless AI-human collaboration, I designed an interaction model that balances automation with human control.


1. AI-driven copy generating

✔️ Users define key message & tone. AI generates multiple adaptive copy variations tailored to audience context.

✔️ Predictive UX minimizes decision fatigue by presenting optimized recommendations instead of requiring manual setup.


2. Generative AI
campaign setup

✔️ Users input copy objectives, and AI suggests audience segments & campaign types based on historical insights.

✔️ AI auto-executes ad placements & dynamically adjusts audience segmentation based on real-time feedback.


3. Automated deployment & intelligent monitoring

✔️ AI dynamically scores copy quality based on engagement potential and offers confidence-based brief recommendations.

✔️ A lightweight UI provides strategic control, reducing operational complexity while maintaining user oversight.
REFLECTION
📌 Key UX Takeaways:

✅ Designed an adaptive AI UX system that streamlines decision-making without overwhelming users.
✅ Created an intuitive interaction flow that minimizes friction while leveraging AI-driven insights.
✅ Balanced automation with flexibility by allowing users to override AI suggestions when needed.
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📌 Next Steps as a UX Designer:

✅ Improve AI transparency by making decision-making processes clearer within the UI.
✅ Refine adaptability to ensure AI-generated recommendations feel more personalized and context-aware.
✅ Optimize interaction patterns based on real-world user behavior and engagement data.
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💡 Final Thought:

​This project made me think a lot about the balance between automation and user control. Designing AI-powered UX isn’t just about making things faster—it’s about making sure AI actually helps people rather than adding more complexity. I realized how easy it is for AI to feel intrusive or overwhelming if it’s not designed with the user’s needs in mind. The biggest takeaway for me was learning how to create a system where AI feels like a helpful assistant, not a decision-maker taking over.