AI isn’t predictable, it adapts, making embedded engineering even more complex. A model that works in the lab might fail in the real world. So, how do successful teams deploy AI at the edge?

A/B test models in the field—controlled environments aren't enough.

Collect real-world performance data—observability tools are key.
AI deployment isn’t a one-and-done process. It requires constant iteration and real-world validation.

What would make AI adoption easier for your team?

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