2 posts tagged with "team-organization"

When Air Canada's support chatbot promised customers a discount fare for recently bereaved travelers, the policy it described didn't exist. A court later ordered Air Canada to honor the hallucinated refund anyway. When a Chevrolet dealership chatbot negotiated away a 2024 Tahoe for $1, no mechanism stopped it. In both cases, the immediate question was about model quality. The real question — the one that matters operationally — was simpler: who was supposed to catch that?

The answer, in most organizations, is nobody specific. AI quality sits at the intersection of ML engineering, product management, data teams, and operations. Each function has a partial view. None claims full ownership. The result is a vacuum where things that should be caught aren't, and when something breaks, the postmortem produces a list of teams that each assumed someone else was responsible.

Most engineering organizations discover the problem the same way: AI demos go well, leadership pushes for broader adoption, and someone decides the right answer is a dedicated team to own "AI infrastructure." The team gets headcount, a roadmap, and a mandate to accelerate AI across the organization.

Eighteen months later, product teams are filing tickets to get their prompts deployed. The platform team is overwhelmed. Features that took days to demo are taking quarters to ship. And the team originally created to speed up AI adoption has become its primary bottleneck.

This is the centralized AI platform trap — and it's surprisingly easy to fall into.