Why Most Enterprise AI Architecture Fails in Year One
In 2026, enterprise AI isn't failing because models are bad. It is failing because organizations are building brittle demos instead of bounded, operable systems.
Law Zava
AI Operating Model and Technical Leadership
I write about how serious companies organize for AI: decision latency, leadership interfaces, platform bottlenecks, and the failure boundaries that keep ambition from turning into theater.
What I Focus On
Decision Latency
The true throughput limit in AI organizations is how fast leaders can orient, decide, and reroute work under uncertainty.
Leadership Interfaces
Serious AI execution requires explicit role boundaries between CEO, CTO, product, platform, and operators.
Platform Bottlenecks
Central AI enablement teams often become queue managers. The winning shape is controlled decentralization with hard interfaces.
Reality-Tested Roadmaps
Roadmaps matter only when they survive production latency, ownership conflicts, and degraded model behavior.
What I Believe
Reliability and cost discipline aren't at odds — they're the same engineering problem. Teams that understand their hardware, shrink their runtime dependencies, and make failure modes explicit end up with systems that are both cheaper and more reliable.
The best engineering organizations run on clear intent and fast feedback, not process overhead. When ownership is explicit and decision loops stay short, teams move faster without adding organizational drag.
Latest Writing
AI Capital Allocation: What Great CTOs Stop Funding First
Strong AI strategy starts with a kill list. If a project cannot defend margin, risk, or speed, it should not survive the next budget meeting.
AI Strategy: The CTO Perspective (It's Just Data Infrastructure)
A CTO's AI strategy in mid-2026 is brutally simple: It is not about chasing models. It is about building resilient data infrastructure, setting operational boundaries, and measuring throughput.