AI is taking off at an incredible pace. Landing it, inside an organization built with and by humans, market and business constraints and real stakes, that's a specific discipline. That's what I do, I land AI.
Because no one managed the humans, the governance, the politics, and the messy middle between a shiny strategy and organizational reality. That's where AI initiatives stall, vibe-coded pilots proliferate and llm-generated 33 pages docs stack up without being read.
The technology is incredibly exciting and many ideas can take off in days instead of months. Organizations buy the technology, unlock the token funds, run the pilots, brief the board — and then hand it to an org that was never built to catch it. Landing requires something different: the execution layer, the change management, the people infrastructure, the governance at the crux of innovation and safety. The discipline of actually finishing.
That's where I work. Not on the runway — in the air, making sure it comes down in one piece.
I build the operational layer that turns AI ambition into enterprise reality — from technical foundations and governance to the enablement systems and organizational change that drive responsible adoption at scale. The process is consistent, even when the org isn't.
Understand the real problem before touching the solution. Map organizational readiness, stakeholder dynamics, and the gap between stated objectives and actual constraints. The ask is rarely the problem.
Assess where the org actually is — data quality, process repeatability, decision complexity, human tolerance for change — before defining where it needs to go. Transformation without a baseline is just ambition.
AI adoption doesn't happen in training rooms. It happens when people find AI useful in the work they already do. Build literacy through workflow integration, not abstract capability-building.
Centralized teams can't land AI at scale. Build a decentralized network of practitioners — AI Business Partners, cohort leads, internal champions — who carry the transformation into every function.
I build the infrastructure that turns AI ambition into enterprise reality. That's been true across my career — from Amazon, where I led DesignOps across Search Experience and Prime Wardrobe (two different problems: one ML, one personalization, same discipline of working backwards from business outcomes), to Unity, where I served as Chief of Staff to the CTO before stepping into enterprise AI strategy. The throughline is consistent: translating what's technically possible into what's operationally real.
At Unity, I own the company-wide AI roadmap across third-party tooling, first-party application development, and AI maturity programs — working across Legal, Security, Finance, DevOps, and IT to ensure AI adoption is governed, compliant, and responsible.
Three engagements. Three different problems. The same discipline underneath. Full stories are available — reach out for access.
Newly appointed as Chief of Staff to the CTO of a 500-person org spanning Engineering, AI/ML, IT, and Research. Previous CoS attempts to build a visibility dashboard had failed. The CTO was dropping into IC-level decisions without context, teams were rattled, and middle management was losing credibility — but no one had diagnosed why.
Build the CTO a dashboard. That was the stated mandate. What I actually needed to do was figure out whether a dashboard was even the right answer.
Diagnosis first. I ran discovery across the org before touching any artifact — meeting team leads, individual contributors, and the CTO leadership team to understand what was actually breaking. What emerged: teams weren't surfacing issues early (psychological safety gap), directors didn't have language or structure for managing up, and the CTO — lacking visibility — was filling the information gap by going direct to ICs.
I involved the org in designing both the artifact and the process. Red/yellow signals instead of data density. An ops review rhythm that gave the CTO a weekly heartbeat. Structured escalation paths that made it safe for teams to flag problems early.
The org rhythm aligned to the ops review. Teams started actively using red/yellow signals to surface issues proactively. The CTO stopped drive-by managing because he had the visibility he needed. Middle management rebuilt trust because they had a structure for managing up. The dashboard was the output. Rebuilt org communication was the outcome.
Diagnose before prescribing. The ask is rarely the problem.
Unity had the ambition and the budget for AI transformation but lacked the infrastructure to make it real. Pilots were proliferating without governance. Teams had different tools, different definitions of success, and no shared maturity baseline. Legal, Security, and Finance were nervous. Leadership wanted results.
Design and lead the company-wide AI transformation program — from strategy through execution. Own the roadmap, the operating model, and the outcomes.
Built a maturity framework that gave the org a shared language and a clear baseline. Established a centralized team of 8 AI Solutions Architects alongside a decentralized network of 12+ AI Business Partners embedded across functions. Built cross-functional governance aligned with Legal, Security, Finance, and IT. Designed and deployed a structured training curriculum covering both tools and human skills. Built adoption tracking so we could measure what was actually changing, not just what was being launched.
Moved from Initial to Measured maturity in 12 months. Token management infrastructure live for 2,000+ developers. Governance framework operational across Legal, Security, Finance, and IT. LLM-based applications delivered for Operations, G&A, and Customer Success.
Maturity before transformation. You can't skip the baseline.
This case study is in development. Reach out directly for a conversation about this work.
Not at the scale of what my team ships — these are personal experiments, outside my day job and NDA scope. But staying close to the tools is part of how I stay credible about what AI actually does, and doesn't do.
Senior AI strategy and operations roles.
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