Hi, I'm Ben Schippers
Not "move fast and break things." More like: ship intentionally, measure honestly, and harden what matters—so the system improves over time instead of collapsing under scale.
I specialize in the gap between "it works in demo" and "it works at scale."
Most product problems aren't bugs—they're adoption blockers hiding in support tickets. I built systems that found 700+ of them and converted hundreds into shipped improvements.
Not features—the internal systems that make teams faster. Routing logic. Prioritization frameworks. Launch playbooks. The boring stuff that enables velocity.
18 months post-launch isn't "maintenance"—it's where products either scale or collapse. I've kept $212M ARR from churning by treating post-launch like product.
Crisis response for pharma, biotech, manufacturing, finance—when the VP's phone is ringing and 433,000 users are impacted, you need someone who can write the exec update AND fix the queue.
When one product grew 173% in users, we didn't hire 173% more people. We built systems—diagnostics, routing, self-service—that made the team 80% more efficient. More users, same-ish team, better outcomes.
Most companies treat support as a cost center. I see it as a massive source of product signal that most companies ignore. Every escalation is a failed user journey. Every blocker is a feature gap. You just need systems to capture it.
The framework I rebuilt didn't get adopted across 8 product lines because leadership mandated it. It got adopted because it worked—teams saw results and pulled it into their workflows.
From 3 people to $355M in value—here's the progression:
Senior Program Manager, AI Platforms & Enterprise Operations
Copilot · Graph · Windows 365 · Teams Devices
Owned a portfolio of internal platforms across 8 product lines—signal systems, routing intelligence, self-service tools, and quality measurement. The infrastructure that turned customer friction into engineering action.
Program Manager → Operations Manager
Microsoft Premier Support
Built Premier Engineering from a 3-person pilot to 150 agents handling 60K incidents/year. Full lifecycle ownership—built it, scaled it, responsibly wound it down when business conditions changed.
Engagement Manager
1,000+ Office 365 migrations across 12 global partners. Built partner enablement programs for enterprise cloud adoption.
Recommender systems, LLM integration, A/B testing, telemetry-driven roadmap
Developer ecosystems, diagnostics/reliability, 0-to-1 frameworks, enterprise deployment
SQL/Kusto, Python, Power BI, Azure DevOps, TypeScript, Supabase
Cross-org alignment, executive communication, crisis execution
B.S. Interdisciplinary Science & Technology — University of Arizona
Former dendrochronologist. Yes, tree rings. It's where the domain name comes from.
From Silos to Signal
November 2024. Microsoft's Copilot rollout is accelerating across enterprise. Three clouds—Dynamics, Azure, M365—operating in silos with no shared visibility. Enterprise customers hitting adoption walls that nobody can see from inside any single cloud. Support cases piling up with patterns that span organizational boundaries. No systematic way to capture what's actually breaking or why.
Leadership needed someone who could bridge the gap. I got the call.
I was asked to lead the Copilot for All moment insight collection team—not because I had formal authority over these clouds, but because I'd built the cross-functional trust to make it work.
The real challenge wasn't technical—it was organizational. Each cloud had its own priorities, its own metrics, its own definition of success. I had to build something valuable enough that teams would voluntarily participate.
I took the new-product support methodology I'd refined across other launches and adapted it for the cross-cloud Copilot challenge:
The framework didn't just fix immediate problems—it created a sustainable system for surfacing and resolving adoption blockers across the Copilot ecosystem.
Enterprise AI adoption fails in the gaps between teams. The model works. The demo is impressive. But when real users hit real edge cases, they fall into organizational seams where nobody has visibility.
My job was to build the connective tissue—the systems that capture signal across silos and route it to people who can act. That's not support. That's product intelligence infrastructure.
The 94,000 seats added post-support engagement weren't because we answered tickets faster. They were because we identified what was actually blocking adoption and got it fixed.
Production applications I've shipped end-to-end
Gamified achievement platform for adults. Badge progression, troop management, photo-verified requirements, and Stripe-powered subscriptions. 38K lines of TypeScript, 19 database tables.
Visit Site →AI-powered inventory management. Snap a photo, get structured data. Embedding-based duplicate detection, voice scanning, and semantic search across your belongings.
Collaborative knowledge system where AI doesn't just read—it maintains, labels, and improves the knowledge base. Natural language intent detection, auto-tagging, confidence scoring, and real-time orchestration view.
📷 View ScreenshotDistraction-resistant desktop environment. Full-screen CRT terminal aesthetic with Pomodoro integration, focus-gated media, and embedded productivity tools. 7,700+ lines of GDScript.
Behind the Screens — thoughts on AI, product, and building in public
The $1K Experiment Part 2: What happens when the framework compounds. 5.5 hours to working MVP. 2,031 lines became 106,000. Shipping is addictive—here's the warning label.
Read on LinkedIn →Could a senior PM with product clarity but no coding background actually build and ship real software? 31 days, 215 commits, 38K lines of TypeScript. The 64/33/3 collaboration model that made it work.
Read on LinkedIn →MIT found only 5% of AI pilots deliver impact. The early warning system hiding in your support tickets—and the metrics that predict failure before day 90.
Read on LinkedIn →Experimental projects and works in progress
Interactive exploration of mindfulness and mechanics. Blending hard science with contemplative practice.
Multi-platform simulation game. More details coming soon.
Location-based ASCII survival simulation. Uses real geography for procedural world generation.
If you're building enterprise AI infrastructure that has to work at scale—especially when production gets weird and customers get loud—I've been there. Reach out.
Atlanta, GA · Open to relocation
Read from the center out. Thicker rings = growth years.
Former dendrochronologist. The domain name had to mean something.
