Attributing AI traffic & influence

AI affects your pipeline in two ways that need different attribution. Sometimes a buyer clicks through from an AI surface — traceable, if you know how to spot it. More often, AI influences the buyer (shaping their shortlist, their framing, their preference) without a direct click, so the impact is real but leaves no clean path. Attribution has to handle both, not just the convenient first case.

Where AI does send traceable traffic, the job is to recognize and label it correctly rather than letting it fall into “direct.” That recovers a chunk of AI’s impact from the catch-all bucket and starts to size the channel. It won’t capture everything — but it’s the part you can measure directly, and it’s a meaningful start.

The larger, harder part is AI’s influence where there’s no referral to trace. This calls for modeling — connecting your measured AI visibility (how AI represents and recommends you) to downstream outcomes, so you can estimate AI’s influence even when you can’t follow a single click-path. It’s inference done carefully, not a clean count — but ignoring it would mean ignoring most of AI’s impact.