Human-AI collaboration
What is Human-AI collaboration?
A way of working where AI handles the grunt and the speed while people keep the judgment, the context and the final call. Stanford's HAI calls this augmentation: using AI to extend what people can do, not to automate them out. In practice it means designing the handoff between human and machine on purpose.
Why it matters
Plenty of AI projects skip this idea and pay for it later. Aim to automate a job away completely and you run straight into the long tail of cases the model handles badly, the ones a person used to catch. Miss those and you either ship the errors or quietly reinsert the humans you removed, having spent the budget twice. Planning for collaboration from the start sidesteps that. The useful question stops being “can AI do this job” and becomes “which parts, and what does the person do with the rest”. The hardest spot is the boundary between them, where work is passed across and each side can assume the other already checked it.
In practice
A claims team lets the model take the first read of every case and draft a decision, while adjusters spend their time on the flagged and the unusual ones. The model carries the routine bulk; the people are there for the calls that need context or carry real risk. The design work is deciding exactly which cases cross over to a human, and making sure none slips through unreviewed.
