Orchestrating AI First Revenue Systems at Scale (NDA)
I lead AI-first UX research for Salesforce revenue systems, studying how pricing, billing, and sales tools interconnect. I map complex ecosystems product configuration, usage tracking, and billing; across roles, platforms, and geographies to reveal hidden dependencies and friction where human judgment meets AI. I conduct deep implementation research (e.g., Revenue Cloud Analytics) to align predictive systems with real-world decisions. Using AI-supported synthesis, I compress analysis, unify signals across regions, and turn ambiguous workflows into shared understanding, guiding the platform toward a context-aware, agentic enterprise experience focused on systemic reasoning rather than isolated features.
Research Methodologies
Workshop planning: Facilitating human first customer/ product developer engagements
AI-assisted synthesis: Pattern recognition and insight distillation using LLMs and tagging systems
Field studies: On-the-ground research with billing, ops, and finance teams
Mixed-methods: Qual + quant across journeys, usage logs, and system telemetry
Heuristic audits: UX breakdowns, IA flaws, and automation blind spots
Strategic workshops: Cross-team alignment on friction, workflows, and roadmap impact
Ecosystem mapping: Tool interdependencies, human-AI handoffs, and systemic leverage
JTBD modeling: Persona and agent-level responsibilities reframed for AI-first systems
Concept testing: Early validation of AI features before build
Salesforce Tower NYC