Open to opportunities Hi, I'm Ben Schippers
Not "launch and hope." 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 surfaced 700+ of them, driving 95+ into shipped feature 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 helped protect $212M in at-risk ARR by treating post-launch like product.
Crisis response for pharma, biotech, manufacturing, finance—I've written the executive briefing and triaged the support queue simultaneously for incidents impacting 433,000 users.
When one product grew 173% in users over 18 months, we didn't hire 173% more people. We built systems—diagnostics, routing, self-service—that dramatically improved efficiency. 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.
Quantified outcomes across a decade of building:
Senior Program Manager, AI Platforms & Enterprise Operations
Across 5 years, spanning 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.
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.
Managed partner programs spanning 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
Led the cross-functional effort to build enterprise AI adoption intelligence across multiple product lines. Created the signal-to-engineering pipeline that identified 76 blockers, unblocked 7,380 users, and contributed to 94,000 seats added.
A major enterprise AI rollout was accelerating across multiple product lines. Each operated independently with no shared visibility into adoption patterns. Enterprise customers were hitting adoption walls that no single team could see. Support cases were accumulating with patterns that spanned organizational boundaries, and there was no systematic way to capture what was actually breaking or why.
Leadership needed someone who could bridge the gap. I was brought in to lead the effort.
I was asked to lead the cross-functional insight collection effort—not because I had formal authority over these teams, but because I'd built the cross-functional trust to make it work.
The real challenge wasn't technical—it was organizational. Each team 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-team challenge:
This was a greenfield initiative—no prior system existed. The framework created a sustainable system for surfacing and resolving adoption blockers across the enterprise AI 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 teams and route it to people who can act. That's not support. That's product intelligence infrastructure.
The 94,000 seats added by customers who received 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
All projects built in collaboration with Claude Code — thought partner, execution layer, quality & lifecycle support. The methodology is in the writing below.
Gamified achievement platform for adults. Badge progression, troop management, photo-verified requirements, and Stripe-powered subscriptions. Full-stack production app with 19 database tables.
Visit Site →AI-powered inventory management. Snap a photo, get structured data. Claude Vision for image analysis, embedding-based duplicate detection, voice scanning, and semantic search across your belongings.
AI-powered physical product design from idea to shop-ready plans. Describe what you want to build, and Claude generates a full spec—cut lists, shopping lists, exploded views, and 3D-printable connectors that replace traditional joinery. Parametric geometry engine, OpenSCAD exports, and a brainstorm mode that suggests builds from your existing material stock.
Visit Site →Mobile road trip companion with three AI-narrated raccoon characters who react to your drive in real time. GPS-triggered storytelling, automatic day-to-spooky mode shift at sunset, snack stop scoring, and Spotify integration. Conversion-focused landing with waitlist capture and password-gated demo showcase.
Visit Site →Prototype mutual benefit platform concept for tech workers. Pooled fund model with simulated risk modeling, employer-cap diversification, and a two-sided marketplace. Built the pricing engine, benefit calculator, and simulated claims workflow. Not a licensed financial product.
Visit Site →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 ScreenshotAI-powered gameplay narrative engine. Desktop app captures screenshots during play, analyzes them with Claude Vision, and generates chronicles in the voice of preset or custom storytellers. Survivor journals, war dispatches, colony epics—built from your actual runs. Local-first with optional cloud hosting, managed AI tiers, and shareable public entries.
Distraction-resistant desktop environment. Full-screen CRT terminal aesthetic with Pomodoro integration, focus-gated media, and embedded productivity tools.
Behind the Screens — thoughts on AI, product, and building in public
Self-sustaining AI infrastructure for global public good. A framework for converting idle compute capacity into verified outcomes through UN outcome-based funding. Whitepaper, DRAFT v2.0.
Read the Paper →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 →Research suggests only 5% of AI pilots deliver measurable 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.
Randonautica-inspired exploration experiment. Quantum entropy from ANU's RNG scattered across a map, statistical anomaly detection to find clusters, and OpenStreetMap verification to confirm you can actually walk there. An excuse to stitch together a dozen APIs and see what breaks.
Visit Site →If you're building enterprise AI infrastructure that has to work at scale—especially when production complexity outpaces the team's ability to respond—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.
