Salesforce
Orchestrating AI First Revenue Systems at Scale (NDA)
Context
At Salesforce, I lead AI-first UX research across revenue systems, investigating how pricing logic, billing operations, and sales tools interconnect. My work spans complex ecosystems where product configuration, usage tracking, and billing outcomes must cohere across roles, platforms, and geographies. I uncover invisible dependencies and latent friction in how teams create, price, and deliver products, often where human judgment intersects with AI reasoning. This includes deep, implementation-level research for initiatives like Revenue Cloud Analytics (RCA), ensuring predictive systems align with real-world decision-making, not just dashboards.
I use AI-supported synthesis methods to compress analysis cycles, unify signals across regions, and translate ambiguous workflows into shared understanding. My research informs the evolution of Salesforce’s platform into a more agentic, context-aware system, where every user action contributes to a responsive, intelligent experience. This isn’t about individual features. It’s about reshaping how enterprise systems think, reason, and evolve, with design and insight at the core.
AI Boosted Research Methodologies
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
