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Enterprise AI lessons learned from autonomous mobility
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Teradata taps open source frameworks to offer agent-building capabilities | InfoWorld
For years, AI progress was measured by scale: more data, larger models, and more compute.
That formula produced real breakthroughs, but autonomous mobility was one of the first industries to discover its limits.
On the road, AI does not fail quietly.
That pressure forced autonomous mobility teams to confront a reality the rest of enterprise AI is now beginning to face: the hardest problem is not access to models.
As AI moves from pilots into production systems, organizations are discovering that performance depends not only on model capability but on the quality, consistency, and defensibility of the data used to train, evaluate, and improve those systems.