Post by Sandwich Lab

A client said it to us plainly: we approved A, users are seeing B. In some auto-creative modes that gap stays invisible until the spend has already landed. This came up across all three of our verticals, so we want to be specific about what we actually do about it, not just name the risk. The first half is the compliance blind spot. In some automated-creative modes the platform breaks your copy apart and reassembles it, even generating new soundtracks and cuts. None of it shows in the daily preview. You find the gap once spend accumulates, or when the client scrolls past the ad themselves — hence that line above. The legal layer raises the stakes. New York's AI disclosure law took effect on June 9, requiring conspicuous disclosure for any ad with AI-generated synthetic performers across Meta, 谷歌, TikTok and others. One missed label is a violation. For teams running AI assets at scale, every creative now needs a pre-launch compliance checkpoint, and most teams we talk to do not have one yet. What we run in our own accounts is two moves: 1️⃣ Technical isolation: turn off flexible-format and high-risk optimization, lock AI to safe operations like aspect-ratio and brightness, no re-editing or rescoring. 2️⃣ A process-first audit SOP: anything with AI-generated elements gets tagged before upload, the AIGC disclosure toggle checked, and for fintech we add a human review layer on top. The second half is brand, and it is where we have lost the most arguments with the algorithm. Platform AI is ruthlessly utilitarian. Put a high-craft brand asset and a low-brow attention-grabber in the same campaign and the latter usually wins the first few days on novelty clicks. The system reads that as better and tilts 80%+ of budget toward it. The metrics look like they are improving while audience quality collapses and back-end ROI follows it down. We have watched that exact curve. There is also the machine voice, grammatically clean AI copy stuffed with lines like "Unleash your inner..." In our resonance-driven verticals, that coldness measurably drops completion and willingness to pay. So we put three guardrails on the AI rather than banning it. Visual: color matrix, layout safe zones and whitespace ratios fixed as workflow constraints. Language: a per-product corpus built from real native reviews, so the AI writes around the usual AI vocabulary. Budget: brand assets and volume assets physically separated at the architecture level, with manual caps. "Dashboards let you observe. Enterprise growth requires governance," The assets you never see should not be making your brand decisions. What does your team check before an AI-assisted asset goes live, and who signs off? #lanbow #enterprise #decision #brand #SandwichLab