In enterprise product led growth, free to paid funnel breaks in a place most teams don't measure. Adoption spreads bottom up, individual users hit the aha moment on schedule and then conversion stalls the moment the product has to clear procurement, security and legal. Teams read this as a pricing problem or a packaging problem. Far more often, it's a trust problem: the buyer can't get past SSO, data residency or a DPA and there's no self-serve path through any of it. AI is raising the wall, not lowering it. The moment a product ships AI features, a new layer of buyer scrutiny shows up: how is my data used, is it training a model, who's accountable when the output is wrong and that scrutiny now lands early in the funnel, on free users, before any contract exists. At the same time, agentic usage is quietly breaking the models teams rely on to spot buying intent: when a meaningful share of product activity isn't human, traditional PQL scoring starts misfiring. This is a session on treating compliance and privacy posture as a growth lever instead of a blocker and on getting your funnel AI-ready before it costs you conversions. Jagbir Kaur will share a framework for instrumenting trust friction as a first class signal, tiering the compliance surface so most questions resolve without a human and rebuilding PQLs so they hold up when both AI features and AI users are in the mix. The through line is a metric enterprise PLG teams don't track and should: 'time to trust'.