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MOST AI STRATEGIES OPTIMIZE FOR USE CASES. THE WINNERS OPTIMIZE FOR WORKFLOWS. THE FIRST CREATES DEMOS. THE SECOND CREATES LEVERAGE. WE START BY MAPPING WHERE THE WORK ACTUALLY HAPPENS.
// 01 — ABOUT
I lead AI deployment inside organizations where the stakes are high and the standards are higher.
Most of what I build isn't visible publicly. The tools live behind firewalls. The deployments serve internal users. The wins are measured in productivity and quality rather than press releases.
But this is some of the most consequential AI work happening in enterprise right now: making senior expertise scalable, automating the manual work that buries professionals, and building governance that lets organizations move fast without breaking trust.
I'm here to share what I'm learning — about deployment patterns that actually work, the gap between AI hype and AI value, and what it takes to lead AI inside organizations that can't afford to get it wrong.
// 02 — EXPERIENCE
Twenty-one years across consulting, product, and AI.
2003 — Present
10+years
Consulting
Strategy and transformation inside professional services.
Deep expertise in organizational change, technology strategy, and operating model design. Working at the most complex organizations during their most consequential transformations. The discipline that taught me how organizations actually decide, resist, and adopt.
2015 — Present
8+years
Product
Building things people actually rely on.
Product leadership across enterprise software, internal platforms, and client-facing systems. The discipline that taught me the gap between what stakeholders ask for and what users actually need — and how to ship in the space between those two things.
2021 — Present
3+years
AI
Deploying AI inside organizations that can't afford to get it wrong.
Director-level AI deployment in professional services: agents, evaluation frameworks, governance structures. The work where the other two disciplines converge — strategy depth, product instinct, and the technical fluency to know what's actually possible.
"The combination is the differentiator: strategy depth, product instinct, AI fluency. Twenty-one years building toward a moment where all three matter simultaneously."
// 03 — DOMAINS
Where the work lives.
Nine intersecting disciplines that show up in almost every engagement.
AI Deployment
From prototype to production — integration, volume handling, organizational adoption.
Evaluation Design
Frameworks for what "good enough" means across high-stakes professional workflows.
Agent Architecture
Systems that automate and augment expert work without removing human judgment.
Enterprise Strategy
Technology decisions inside complex organizations with competing stakeholders.
Change Management
Why organizational adoption fails and the structural reasons it gets ignored.
Knowledge Work
Making professional expertise scalable without diluting the expertise itself.
Governance & Risk
Moving fast without breaking trust — compliance, accountability, audit trails.
Product Leadership
Building what users need, not what stakeholders ask for, inside enterprise constraints.
AI Literacy
Closing the gap between capability and understanding before it becomes a liability.
// 04 — MANIFESTO
What I believe about AI in enterprise.
The unit of analysis is the deployment, not the model.
What separates AI investments that compound from those that stall is never the model choice. It's the deployment decision: which workflow it integrates with, what judgment it augments, what feedback loop it creates.
Most enterprise AI fails before it reaches the user.
Strategy documents, proof-of-concepts, and pilots are not AI deployment. They're AI theater. Real deployment means production systems that handle volume, edge cases, and organizational resistance — and keep working when the novelty wears off.
The evaluation threshold problem is the most underreported question in enterprise AI.
Every enterprise AI project eventually hits the same wall: how good is good enough? The absence of rigorous evaluation frameworks isn't a research problem. It's a leadership problem — and it's causing billions in misallocated investment.
AI literacy is a leadership responsibility, not an HR initiative.
When AI capability outpaces organizational understanding, you don't get fast adoption. You get fear, workarounds, and poor decisions made confidently. Closing that gap deliberately is the AI leader's actual job.
Internal AI leadership requires a different playbook than AI-native leadership.
The strategies that work at AI-first companies create technical and organizational debt when applied inside traditional enterprise. Different constraints, different stakeholders, different definition of done. The playbook transfer problem is real and almost never discussed.
Strategy without execution infrastructure is a liability.
Strong AI POV without deployment infrastructure doesn't create competitive advantage. It creates technical debt, failed pilots, and organizational cynicism. The execution layer isn't implementation detail. It is the strategy.
// 05 — DEPLOYMENT
How AI actually gets deployed.
Four phases. Each one harder than it looks. The work that separates AI investments that compound from those that stall.
// 06 — PUBLIC PROJECTS
Building in the open.
Documenting what I build outside of work — small AI systems, experiments, and prototypes. Each shipped publicly with the code, the reasoning, and what I'd do differently next time.
01/03 In development
Meeting Intelligence Agent
An AI system that generates structured summaries, action items, and decision logs from meeting transcripts. Built on Whisper + Claude. Ships publicly once it clears firm publishing guidelines.
WhisperClaudeTypeScript
View details →
02/03 In development
AI Evaluation Framework
An open framework for evaluating LLM outputs against domain-specific quality criteria. Designed to address the threshold problem: what does "good enough" mean for high-stakes professional work?
PythonLLM eval
→ 03/03 Planned
Knowledge Worker Augmentation Suite
A collection of AI tools targeting specific workflows where professional expertise creates the most value — and where manual effort creates the most drag.
AgentsRAG
→ // 07 — WRITING
Long-form essays on AI deployment.
Observations, frameworks, and arguments about AI in enterprise. Follow on LinkedIn for shorter takes while essays are in progress.
Essay 01 First draft
The Last Mile Problem in Enterprise AI
Why most enterprise AI investments fail not in model selection or integration, but in the final 20% between working prototype and organizational adoption.
Deployment Enterprise
Essay 02 Outlined
The Evaluation Gap
The absence of rigorous evaluation frameworks is the most underreported problem in enterprise AI deployment — and it's causing billions in misallocated investment.
Evaluation Strategy
Essay 03 Researching
Internal vs. AI-Native: Why the Playbooks Don't Transfer
Why strategies that work at AI-first companies create debt when applied inside traditional enterprise — and what actually works instead.
Leadership Enterprise
// 08 — CONNECT