Project Overview
The AI Engagement Platform revolutionizes how B2B SaaS companies interact with their customers by creating truly personalized experiences at scale. As Lead Product Strategist, I was tasked with bridging the gap between powerful machine learning capabilities and meaningful user experiences.
This platform solves the critical challenge many enterprise businesses face: how to deliver individualized engagement when dealing with thousands of users, each with unique needs and behaviors. The solution leverages AI not just as a technical feature, but as the foundation of a thoughtful experience design approach.
Design Process
- Discovery Immersion: Conducted ethnographic research with 25+ customer success teams to uncover how personalization currently happens manually and identify opportunities for AI enhancement.
- Intent Matrix Creation: Developed a sophisticated framework for mapping user behaviors to likely intents, laying the foundation for the AI system's decision engine.
- Prototype Iteration: Built and tested progressive fidelity prototypes, from basic wireframes to fully functional AI workflows, refining the platform's ability to predict and respond to user needs.
- Fallback UX Design: Created elegant degradation paths for when AI confidence falls below thresholds, ensuring users never feel stranded by algorithmic uncertainty.
- Cross-functional Alignment: Led collaborative sessions with engineering, data science, and customer success teams to ensure seamless integration of AI capabilities with human touchpoints.
AI System Architecture
The heart of the platform is the Intent Matrix – a dynamic system that continuously learns from user interactions to build increasingly accurate behavior models. Rather than treating AI as a black box, we designed for transparency and control.
Key innovations in the AI architecture include:
- Multi-dimensional intent classification that goes beyond binary predictions
- Self-optimizing feedback loops that improve accuracy over time without requiring explicit retraining
- Confidence threshold system that gracefully transitions between AI-driven and human-guided experiences
- Explainable AI modules that help customer success teams understand and trust the platform's recommendations
UX/UI Highlights
The interface design challenge was significant: create a system powerful enough for AI specialists yet accessible enough for customer success teams. The solution balances sophisticated capabilities with intuitive interactions.
Key design principles that guided the UI development:
- Progressive disclosure: Complex AI capabilities unfold only as users need them
- Contextual confidence: Visual indicators show AI certainty levels directly in the workflow
- Human-in-the-loop: Seamless transitions between automated and manual modes
- Outcome visualization: Clear previews of how AI decisions affect the end-user experience
Results & Impact
84%
Increase in engagement for personalized user journeys compared to generic approaches
62%
Reduction in time customer success teams spend creating personalized experiences
3.2x
Improvement in conversion rates for key actions when using AI-suggested user paths
Beyond the metrics, the platform transformed how client companies think about personalization. What was once considered impossible at scale became not just possible but expected, setting a new standard for customer engagement in the B2B space.
Reflections & Lessons
This project reinforced my belief that successful AI products aren't just about advanced algorithms—they're about thoughtful integration with human workflows and expectations. The most valuable insight was that transparency in AI systems builds trust exponentially faster than perfect but unexplainable performance.
The challenges of designing for both AI complexity and human simplicity pushed our team to develop new patterns and approaches that now inform all our platform work. Most importantly, we found that when AI is properly deployed as an experience enhancer rather than a replacement for human connection, the results transcend typical engagement metrics.