AI Product & Transformation Leadership.
Majid Asghari Tabrizi — operator who restructured companies from the inside; architect who built the machinery an AI-native organization runs on.
Two careers, one thesis: the hard part of AI transformation is not the models — it is the operating model around them. Majid has led both sides: product and marketplace operations where decisions had P&L consequences, and production AI systems where autonomy had to be earned by structure. He leads the work that turns AI ambition into an organization that actually runs.
Where executives get stuck — and what I own end-to-end.
Not advisory positions. Each of these is a leadership mandate: diagnosis, operating-model design, cross-functional execution, and measurable discipline.
AI transformation stuck at pilots
Proof-of-concepts succeed; the company does not change. The gap is authority, workflow and incentives — not model quality.
Owns: the operating-model change, the function-by-function rebuild, the evidence gates that decide scale-up.
The organization is the bottleneck
Decisions queue for weeks, knowledge lives in heads, coordination cost grows faster than headcount. Adding intelligence to that structure amplifies the jam.
Owns: decision latency redesign, knowledge as a governed system, the human-authority layer.
Products without a nervous system
AI features shipped bolt-on to legacy workflows — brittle, unmeasured, unowned past the demo.
Owns: product architecture for AI-native workflows, evaluation discipline, the company brain that remembers.
Evidence, classified the way an operator would.
No claimed client outcomes, no invented metrics. Every class below is labeled for what it is — and verifiable.
- VERIFIED
Publicly verifiable system
The AI Decision Intelligence Laboratory: a real decision system, 18,600 logged decisions, disciplined abstention by design, zero real orders — published with its full evidence matrix.
Visit the laboratory → - VERIFIED
Open-source work
Production agent kernel, MIT-licensed LLM router, engineering harness — public repositories, public history.
See the repositories → - OPERATOR VERIFIED
Career across eight layers
From how software works to how human + AI organizations should operate — a career documented as increasing scopes of ownership, lived rather than claimed.
Walk the journey → - ILLUSTRATIVE
Organization OS simulator
The interactive operating-model demonstration on the homepage — explicitly illustrative, never presented as a client result.
See the model →
Eight layers of scope.
Not a sequence of titles — a sequence of increasingly harder questions about reality, decisions, autonomy and learning.
- 2008–2014TECHNOLOGYHow software works — and how it survives contact with production.
- 2014–2018PRODUCTHow products create value — for whom, and what changes if you build it.
- 2018–2020ORGANIZATIONHow teams create products — the system around the code.
- 2020–2023MARKETPLACE SYSTEMSHow organizations operate — every decision propagates.
- 2023–2026DECISION SYSTEMSHow complex systems make decisions — under uncertainty, with consequences.
- 2025AI INFRASTRUCTUREIntelligence became infrastructure — routed, ranked, budgeted.
- 2025–2026AGENT RUNTIMEFrom model to worker — governed execution, recovery, verification.
- 2026LEARNING SYSTEMSWhat if the system didn’t start from zero every time?
- NOWLEARNING SYSTEMSAI-native operating systems — the convergence of every layer.
How impact will be reported.
This is the exact structure every engagement case follows. Shown here as a template — deliberately without numbers. Real cases are published only with evidence the client and the record can support.
- 01Situation
- 02My ownership
- 03Decisions I made
- 04Cross-functional leadership
- 05Measured outcome
Numbers appear in a case only when they are evidenced. Until then, this template stays honest — and empty.
Hiring? Talk directly.
For executive and product leadership roles: one email, one reply — questions answered directly by Majid, no intermediaries.
Looking for transformation consulting instead? That path starts here →