Forward-deployed AI starts with the process

AI LABS frames forward-deployed engineering as observing a real business workflow, surfacing its hidden rules, and routing each step to deterministic software, model judgment, or a human owner. Why it matters: A capable model cannot infer the exception that protects a key account or decide whether an expensive mistake is acceptable. AI adoption succeeds or fails in those routing, review, and trust decisions around the model.

Try this: Run 20 historical cases through the proposed agent, compare its output with the recorded review decisions, and save mismatch traces plus the routing policy before building.