Built For CBRE Conversation · April 2026
Head of AI Products
Trusted & Measurable AI in Production at Enterprise Scale
Enterprise AI product leadership turns ambiguous opportunities into safe, measurable capabilities: crisp success criteria from discovery through adoption, with product, data, engineering, and governance aligned end-to-end. That is how GenAI, predictive insight, and agentic automation anchor in real workflows, earn sustained adoption, and compound value for operators and the enterprise over time.
How I Map To The Role
Head of AI Products scope in brief: where my track record shows up.
- Roadmap, priorities, funding Sequenced agentic platform work with leadership: $14M+ opportunity context, 150+ teams in motion.
- Discovery, experiments, success criteria 50+ production AI workflows; wins surfaced in metrics (~40% decision lift where measured).
- GenAI, predictive, agentic 2,000+ nodes, 1,500+ MCP tools, Ludex AI patterns (RAG, vectors, MCP).
- Evaluation, monitoring, production readiness Evaluation-friendly agent flows; 21 C# connectors, 2,500+ endpoints, discipline before broad rollout.
- Governance, privacy, HITL, audit Traceable tool use, access-aware patterns, human review when outcomes matter. Same bar I use before recommending scale.
- KPIs: quality, safety, adoption, cost $4M ARR influenced; ~25% fewer errors via 15+ guides; adoption signals across 150+ teams.
- Playbooks, reusable patterns 15+ guides and templates for faster, safer onboarding.
- Mentorship, cross-functional leadership Mentored on AI workflow design; 200+ stakeholders; 5,000+ conference attendees.
Example angles to explore: document-heavy GenAI; governed agents with tools; orchestrated operations with audit trails.
Selected Videos
Short clips from builds and demos: integrations, agent tooling, orchestration, MCP, telemetry. I walk through architecture choices, how tools fire, and tradeoffs for enterprise audiences.
GitHub
I am a hands-on builder. Below is public work I have developed and shipped, synced live from GitHub so you can review recent delivery in one place instead of clicking through a profile.
Repositories could not be loaded. Open GitHub.
Live Proof-Of-Concept Demos
Two references you can open today: LibreChat for conversational AI and agents, n8n for workflow orchestration. These are intentional POCs, not CBRE production. They give us something concrete to react to on integration, governance, and rollout, instead of debating decks.
That pairing is how most enterprises pilot this space: a conversational or agent layer with orchestration behind it. I stay hands-on with delivery so what we discuss maps to what actually ships, not the trend of the quarter.
Demo Access
- LibreChat
- Create your own account on the sign-in screen to explore the POC. No shared password required.
- n8n
This instance uses a shared login (not open self-signup). Use:
- Email:
charley.s.forey@gmail.com - Password:
abc123XYZ$
POC demo login only, not a secured environment.
- Email:
What This POC Shows
- LibreChat: Agents and MCP hooks framed the way I pilot grounded GenAI: traceable tool calls and room to judge product sense, not slides.
- n8n: Cross-system flows with clear ownership between steps. A fast way to show orchestration before we fund platform-wide rollout.
Proof Points
- Trimble · Principal AI Solutions Architect (Agentic AI Platform)
2,000+ API nodes · 1,500+ MCP tools · 50+ production workflows · $14M+ opportunity scope · 150+ teams · ~40% decision lift (program metrics). Outcomes first, then platform scale: adoption and measurable decision improvement across internal and external teams; pipeline figures are opportunity context, not booked revenue. - Revenue And Enablement
$4M ARR influenced · 200+ stakeholders · 15+ guides and templates · ~25% fewer implementation errors · 4 conferences · 5,000+ attendees. Alignment work that closes revenue, plus assets that scale adoption and reduce rework. - Integrations And GenAI Engineering
21 production C# connectors · 2,500+ endpoints · $10M+ pipeline · $4M+ proposed dev scope · Ludex AI (fine-tuning, RAG, vectors, MCP). Production plumbing plus an end-to-end GenAI product pattern I reuse for enterprise delivery.
About Charley
I am at my best when leaders are ready to sharpen the mandate: turn ambition into priorities, success measures, and a release rhythm that risk, security, and operations can champion with you. I carry one narrative from the executive room to the delivery room so strategy and execution reinforce each other and keep compounding.
Head of AI Products is an owner role: choosing which AI investments to fund, how to prove value, and how to scale with resilience as velocity grows. My career has been that blend at enterprise scale: sound judgment, hands-on delivery, and the cross-functional trust that lets global organizations advance together with confidence.
Partnership runs through everything I do. I am direct, collaborative, and accountable. I surface choices early, keep teams aligned on what matters most, and invest deeply in the success of every leader and builder beside me. Lasting AI capability grows from shared momentum, clarity, and mutual respect.
I thrive alongside executives who elevate both the business story and the engineering bar. I bring focused energy to your priorities, your operating reality, and the excellence we can define together. I would be honored to advance this work with you and help shape what comes next.