Abstract

Most organizations still make CX, marketing, and AI investment decisions using static dashboards and backward-looking metrics. In this session, I’ll show how we transformed a traditional ROI model into an agentic AI “ROI strategist” embedded directly into a startup’s MarTech stack. This system is capable of predicting revenue, net profit, CAC, churn, and customer lifetime value over a three-year horizon in real time. Starting from a Salesforce + CDP + data warehouse environment, we used n8n as the orchestration layer and Model Context Protocol (MCP) servers as the tool interface to build an LLM-powered agent that:

• Ingests live data from CRM, CDP, and financial systems - Runs forward-looking simulations across best, expected, and worst-case scenarios - Benchmarks performance against industry standards and top competitors - Generates executive-ready financial outputs including ROI, margin, CLV, and churn - Pushes predictions back into the MarTech stack for activation I’ll walk through the actual architecture, including how MCP servers expose data and tools, how the agent loop is designed, and how we made the system reliable through guardrails, evaluation, and cost control. This is a practical, production-focused session for builders and product leaders who want to move beyond LLM demos and design agentic systems that drive real business outcomes.

Bio

Franck Ardourel is a Chief AI & CX Officer, Enterprise Growth Architect, and Professor at Santa Clara University. He specializes in building AI-powered growth systems that connect CX, MarTech, and go-to-market strategy directly to revenue, margin, and enterprise value through his proprietary CX ROI Compass™. Franck has led large-scale transformations—from growing 24 Hour Fitness from $500M to $2.5B to building AI-driven revenue engines generating millions in measurable ROI. His work focuses on moving agentic AI from experimentation into real-world production systems.

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