3 posts tagged with "org-design"

Two years ago, standing up an AI center of excellence was the responsible move. Nobody knew how to evaluate a model, procurement had no idea what an inference contract should look like, and legal wanted one throat to choke. Concentrating the ten people who understood any of it into a central team was obviously correct.

Today that same team is the reason your product engineers wait six weeks to change a prompt. The CoE reviews every system-message edit, owns the only eval harness in the company, and gates model upgrades behind a committee that meets biweekly. Teams have noticed. They ship on personal API keys, run evals in notebooks the CoE never sees, and paste customer data into whatever tool answers fastest. You did not prevent shadow AI — you created it, and gave it a governance body to hide from.

In 1964, thirty-eight people watched Kitty Genovese being attacked outside their apartment building in Queens. None of them called the police until it was too late. Latané and Darley spent the next decade explaining why: the more people who can see a problem, the less likely any single one of them is to act. They called it diffusion of responsibility. In their famous seizure experiment, 85% of participants intervened when they thought they were alone with the victim. When they believed four others could also hear the seizure, only 31% did.

Now picture your last AI feature launch. Product wrote the prompt. Engineering picked the model and wired the gateway. The data team curated the retrieval corpus. Safety bolted on the input and output filters. Customer support drafted the escalation playbook. Five teams in the room. Each one shipped its piece on time. Three months in, the feature's accuracy has quietly slid from 89% to 71%, the eval suite has not been run since launch week, and when you ask who owns the regression, every team can name three other teams that own it more.

Most AI features die in the gap between "works in notebook" and "works in production." Not because the model is bad, but because the team that built the model and the team that owns the product have never sat in the same room. The AI team topology problem — where AI engineers sit in your org chart — is quietly the biggest predictor of whether your AI investments ship or stall.

The numbers bear this out. Only about half of ML projects make it from prototype to production — at less mature organizations, the failure rate reaches 90%. Meanwhile, CircleCI's 2026 State of Software Delivery report found that AI-assisted code generation boosted feature branch throughput by 59%, yet production branch output actually declined 7% for median teams. Code is being written faster than ever. It's just not shipping.