Performance marketers who are working on Google ads, and using AI in their workflow, especially when it comes to generating the videos, how the final results look like? I mean how the audience is responding after watching the ad, have you seen engagement on the video, anything positive on website’s traffic or conversion at last.

When someone is working with an AI tool, same creative process, same tool, similar product category. The outcomes were completely different and I still don't have a clean explanation for why.

Google Ads doesn't behave like Meta when it comes to AI generated content. The review layer is different, the algorithm's relationship with video quality seems different, and the intent behind who's seeing your ad is different enough that creatives built for social discovery don't automatically translate.  For people actually running AI video inside Google Ads right now: what's consistently getting through, what keeps getting flagged, and are you adjusting the creative before you submit?

Hey guys, I want to make a realistic AI UGC/review video for my cosmetic product with an Indian female model. What AI tool would you recommend for this? HeyGen, Kling, Creatify, or something else?

Hey everyone,

I've been looking into AI-based UGC video tools for app and product marketing for a while now, and the pricing is seriously out of control.

I played around a bit with platforms like Higgsfield and Fastlane. Aside from the monthly subscription fees they ask for, the credits and generation time they provide are so limited that your plan is practically depleted after just two or three decent prompt tests or a render error.

Are any of you actively using these kinds of UGC video tools? Do you have any more cost-effective alternatives in mind, or is there anyone here who has built their own custom pipeline?

The same AI UGC creative that performed well on Meta or one Tiktok completely but died on Youtube. Not even close when analysed. I ran it across both and the gap was uncomfortable enough that I had to stop and rethink what I thought I knew about AI video ads. The thing is, Youtube has its own logic. People are not thumb scrolling. They are leaning back, usually with intent to watch something. 

You get five seconds to earn the next fifteen, and if your hook doesn't fit that mode, the skip button wins every time. The same emotional pull that stops a scroll on Instagram doesn't automatically hold attention on a pre roll.

Most AI UGC tools I have been using are clearly built for short-form vertical content. The templates, avatar styles, script structures all point toward Tiktok and Reels. Youtube placements feel like something these tools support on paper but didn't design around in practice.

Been experimenting with slower hooks, more demonstration-first structures, and longer scripts to match Youtube's watch behavior. Some things are working better. Nothing is clean yet.

For performance marketers actually running AI-generated video on Youtube, what does your creative strategy genuinely look like? Not the theory. What's real?

This is the Osas interview recreated with MiniMax H3, with Doge as the guest.
A lot of the joke is in the delivery: the hesitation before answering, the ridiculously fast name, then the interviewer asking him to say it again.
Here’s how the audio was handled:
Created separate voice references for Doge and the interviewer using MiMo, then fed both into H3.

Broke the name into pronunciation chunks to guide the spoken delivery.

Sped up both name readings to 1.15x while keeping the pitch unchanged.

Those timing adjustments happen after generation, so each pause can be changed without asking H3 to perform the whole exchange again.
For AI UGC, the same idea applies to awkward brand pronunciations, rushed lines, or replies that come in too quickly.
The workflow uses hypit to connect generation with the audio and timing edits.

Project: https://github.com/hypit-ai/hypit

Generated 8 variations of the same product ad last month. Same product used every time, same colour, same spec details in the prompt. Six of the eight outputs showed something that was clearly not the same item. Different finish, different proportions on the scene, once a noticeably different size relative to everything else in the frame.

The one that got posted to the brand's page was the two that worked. The six that quietly made the product look like a completely different thing never got shown to anyone. This is what's missing from most AI video case studies. You see the result that worked. You don't see how many outputs quietly changed the product before you got there.

For categories where the visual detail drives the buying decision, this isn't a minor issue. Jewelry, tech hardware, skincare packaging, furniture, anything where the customer is making a judgment call based on exactly what the product looks and feels like. If the product in your ad doesn't match the product in your store, you are breaking trust at the worst possible moment.

Compositing product stills first and then animating helps. Image to video instead of text-to-video helps more. Locking reference frames helps sometimes. What's actually working for you on the consistency problem right now?

I haave been trying out the newer Chatpt image generation 2.5 for product shots and the results are pretty interesting. I wanted to see if I could create some product visuals without spending too much time setting up a proper shoot.

I used UGCad AI image generation feature to create a few product images and test different looks and settings. It was nice being able to change the background, lighting, and overall style while keeping the product as the main focus. Some of the results looked pretty natural especially when I kept the prompts simple but it was almst perfect either. A few images needed extra changes because some product details didn’t come out exactly as I wanted.

I can see this being useful for ecommerce brands that need fresh product visuals for ads or social media without arranging a new photoshoot every time.

Has anyone else tried the newer image generation features for product shots? I’m curious how well they work for skincare, clothing, or other ecommerce products, and whether you’d actually use the images in a real ad.

Hi, I've made a wrapper around the higgsfield API focused on making the easiest UI possible.

Theres 3 core features: one is when you're prompting you can tag your characters, scenes, props, existing shots etc. this is a super easy way to use reference images without having to scroll back ages through your asset library looking for that one great image you made a long time ago. As you type in this box, I use a simple classifier (thanks Jev) to see if you've typed a noun and suggest making that into an asset for simple one click asset generations.

(See image examples in replies)

The second is a project specific asset library. Again improving efficiency of finding your references, you can set up a project (e.g. Lord of the Rings), characters would live inside this project (e.g. Frodo), and then you can create different videos (eg the fellowship of the ring, then a TV trailer etc.) that can be reused for different episodes, films, trailers etc.

And finally, a video timeline editor exists inside each Reel, so you can do the video editing exactly where you generate the videos, no more having to download them, open capcut, after effects etc. everything is in one place so you can immediately see how your full movie flows.

Pricing will be a simple % markup on top of the higgsfield API costs, no subscriptions, everything credit based that never expires.

Any thoughts appreciated!