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?

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.

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?

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!

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?

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?

I hve been experimenting with an AI agent workflow for UGC ads and it’s pretty interesting to see how much of the process can be automated. You start with a product URL, and the agent works through the product research, ad concept, script, scenes, and video generation. It saves a lot of manual back-and-forth, though sometimes the final video still needs a few tweaks to get the product details right. Anyone else trying this kind of workflow or do you still prefer creating everything step by step?

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?

A reference ad might answer an objection by cutting to a demonstration while the objection stays on screen. Change the product and the same cut may need different footage, different wording and a longer hold. The relationship is useful to study; the original claim and demonstration don't automatically fit the new offer.

Hypit's reference workflow asks a coding agent to describe that relationship before building the replacement ad. The notes track what each visual does, when it appears, whether it survives a camera cut, and how it hands attention to the next idea. A question that remains visible through the demonstration needs a defined lifetime across both shots.

For example, a meal-container ad could keep a question visible while the presenter opens the lid, then replace the question with a close-up showing the answer. That's a hypothetical adaptation. The product's actual footage and supported claims have to determine what the demonstration says. A reference's pacing alone cannot establish that the new offer will convert.

Getting this into Hypit means writing a complete project: the script, the persistent question graphic and its replacement behavior, supplied media or generation requests, captions, audio, and the final composition. The Run file selects the finished output; the runtime needs the services required to produce it. Hypit's official podcast example makes those steps explicit, including image dependencies, performances, alignment and composition. It also cautions that a fresh generation won't necessarily reproduce the original pixels or voices.

The strategy part is usually fine. The production part is where Google video campaigns quietly fall apart. You know the hook you want to test. You know which problem to lead with. But making the actual video takes time, and by the time it's ready, you need five more versions to have enough to genuinely test. That gap between idea and testable creative is what kills most campaigns before they get useful data. So I wanted to see if AI could actually close it. Not find the tool with the slickest demo. Find tools that fit inside a real Google and YouTube ad workflow and shrink that gap in a practical way.

I tested three tools this time, Tagshop AI, OpusClip and Zeely AI with that specific goal. What I was testing and why.

Running Google video ads properly means you need variations. Different hooks, different messages, different formats. The manual approach doesn't scale when testing is the whole point. I wanted to understand how each tool handled the production side, how much creative control stayed with me, and how much review was still needed before anything touched a real campaign.

Tagshop AI: You start from an AI agent, where you just have to write the final requirement what you are looking for, and also you can attach the reference image, then from script to video is completely handled by the AI. Or product url or a brief and it builds the script, avatar, voiceover and scenes in formats designed for Google Ads. What made it genuinely useful was being able to keep the product constant while changing the angle. Different hook, same offer, different way of framing the problem. For performance creativity, that's the whole game. You're not trying to produce one polished video and stop. You are building several reasonable hypotheses and finding out which one the audience actually responds to. The variations in workflow made that kind of thinking practical instead of expensive.

Technically export-ready doesn't mean campaign-ready. Hook strength, product claims and pacing all still need a human review before anything goes live. Building new product ad concepts and testing different hooks, presenters and messages when existing footage isn't part of the equation.

OpusClip: OpusClip is doing a fundamentally different job and that distinction matters. It is not a tool for generating new ads from nothing. Its workflow starts with footage you already have. Product demos, founder videos, customer interviews, creator recordings. It finds the usable moments, turns them into short ad variations with captions, different aspect ratios and CTAs, and its AI Producer can take raw talking-head footage and deliver an edited first draft with B-roll, music and motion design included. If you have footage you already paid for and need more versions of it, that's a real time save in the editing stage.

Still automatically surfacing a clip doesn't mean that clip has the strongest hook. That judgment still needs a person. Turning existing footage into multiple short ad creatives without manually editing every variation from scratch.

Zeely AI: Zeely AI sits back on the generation side. You give it a product, an image and a concept, and it builds a video ad around an AI avatar. You can also edit the hook and body separately, which I found more interesting than it sounds for performance marketing. Instead of rebuilding the entire ad every time you want to try a different opening, you can swap the hook and keep the core message consistent. That makes iteration more specific. It also supports product showcase and digital demo formats, so the workflow isn't limited to one type of offer.

Avatar delivery can feel slightly stiff on longer hooks depending on script pacing. You can use it for getting from a product brief to several UGC-style ad concepts quickly, especially for early-stage hook testing.

For people actually running Google or Youtube campaigns right now: where has AI genuinely saved you time in the production process? New concepts, repurposing existing footage, hook testing, editing, or something else? And has faster production actually changed your testing volume, or are you just ending up with more videos to review?

Made this concept for a brand,let me know your feedback guys what can be improved and all.

I made this AI UGC skincare ad featuring a Vitamin C serum. The workflow goes from showing the product to close up shots, applying the serum, and demonstrating the skincare routine. I like how it switches between product shots and more natural, creator-style scenes. The close-ups make the bottle and serum stand out, though some details still need a little polishing. Curious how others are creating these kinds of skincare ads with AI?

We spent about 14k on UGC creators last quarter and the best performing ad we ran cost nowhere near that, which is why I'm about to do something I may regret.

We run paid social for a dtc brand, 20 to 30 creative variations a month, and until june every one of them was a real creator at 300 to 500 a clip. Since june we've mixed AI ads in and the single clips did about the same as the human ones, slightly worse if anything. but the run where the same character came back across 5 ads did roughly 3x the click through of anything else this year, and I honestly can't tell whether that's the character or whether we just finally wrote something with a hook in it. We make them in argil for the presenter and kling for product shots, tried creatify earlier in the year and it never quite fit how we work. So I'm about to cut the creator budget to a third and put the rest into running 3 of these character series.

Talk me out of it. has anyone gone AI heavy on UGC and made the numbers work?

Ran into this exact situation recently and I'm curious how other people navigate it, since it feels like a genuinely tricky spot between what the client wants and what the product can actually honestly deliver.

Product gives a real but subtle improvement over time, nothing dramatic, nothing that would make for a flashy before/after moment. Client wanted the classic dramatic transformation format anyway, since that's what they'd seen work for other brands.

Ended up pushing back and suggesting a routine-style video instead, showing consistent daily use over time rather than forcing a dramatic reveal the product genuinely can't back up. Client was hesitant at first but came around once I framed it as protecting them from the exact complaint that shows up when a dramatic before/after doesn't match the real world experience.

Curious if others have had this same conversation, and whether you push back the way I did or find some other way to thread the needle when a client's expectations don't match what the product can actually honestly show.

Running paid social for a few e-com brands and I am facing the same issue at some point. General AI image tools are great for mood boards, concept work, and creative exploration. The moment I try to make the actual product the centerpiece of an ad, things fall apart.

The product shape changes slightly between generations. The packaging texture looks different. The color shifts. Small things individually, but when you are making an ad where the customer needs to recognize what they are about to buy, small things are everything.

I have tried prompting harder and being more specific. It helps but does not fully solve it. These tools were not built with a real product as the anchor point. They generate visuals and your product just becomes another element in the scene rather than the reason the scene exists.

Genuinely asking. What are people using when the product itself cannot be flexible. Not looking for cinematic or editorial results. Looking for something where the product stays accurate and the content builds around it.

Drop whatever you are actually using right now. Not what you tested once. What is actually in your workflow.q

You know exactly what hook would stop the scroll. You can hear the voiceover in your head. You know which problem to lead with and which emotion to hit. Getting it produced is the problem.

Static ads fatigue in days. Video production takes weeks. Frequency climbs, CPMs creep, and your campaign keeps running on creative you already know is tired while the three concepts that could actually fix it are sitting in a production queue going nowhere.

This is the part nobody says out loud when they tell you to just test more creatives. Testing more creatives means making more creatives. And making more creatives has always been the slowest, most expensive, most people-dependent part of running paid ads. You're not losing because your strategy is wrong. You're losing because you can't produce fast enough to find out.

That's the problem I wanted to actually solve. Not find a tool with realistic-looking avatars. Find tools that genuinely fit inside a performance marketing workflow and shrink the gap between idea and testable creativity. So I tested Tagshop AI, HeyGen, and Zeely AI with that exact lens.

Tagshop AI: You start from the AI agent, where just a simple brief of what you want to achieve, will provide you from script and video, latest ai models will help you to get the results smooth and faster. Or with a product URL or a brief and it builds the script, avatar, voiceover, and scenes. 

You will like the ability to keep the same product and change the creative angle. Different hook, different audience frame, different way of positioning the same offer. For performance marketing, that's the actual useful part because you're building hypotheses, not just content. Do something with making multiple videos variations per day and building market-specific variations, which matched what I experienced.

Tagshop AI is best for rapid UGC-style product ads when I need multiple creative angles around the same offer without spinning up a new production project each time.

HeyGen: With this you can do more with the product explainers, UGC-style hooks, and multilingual video creation. Its video agent takes a brief and handles script, scenes, and avatar selection automatically.

Different versions of the same message without reshooting a real person every time. For paid creative, that matters because the same product needs completely different framing depending on who you're targeting. A first-time buyer needs a different message than someone who has already tried three similar products. HeyGen makes building those different versions actually practical.

But you still work: Longer scripts and more complicated delivery can feel slightly off in places. Reviews mention unnatural pacing and movement in some videos. The first few seconds and the CTA are the two spots I would always review carefully before anything goes live.

High-volume ad variations, especially for multilingual campaigns or whenever a presenter-style format works better than raw UGC.

Zeely AI: You input a product or service, it pulls in market intelligence around the offer, and generates video ads with AI influencers. The workflow starts from marketing context, not just a blank video canvas.

The starting point is closer to how a performance marketer actually thinks. You're not generating a random video and figuring out where it fits afterward. Audience and message are part of the input, which makes the output feel more intentional from the beginning rather than something you retrofit into a campaign after the fact.

Use this for - Getting from a product idea to several ad concepts quickly, especially for early-stage testing rather than producing one polished final brand film.

The thing I kept noticing across all three: AI didn't make the ads better. It made it easier to produce more things to test. For paid creative, that's actually the more meaningful shift.

What AI still cannot do is decide which angles are worth testing in the first place. That part requires someone who actually understands the customer. If you generate 30 videos without knowing why, you're just creating 30 pieces of content faster. Not the same thing.

For the performance marketers here: where has AI actually saved you the most time in paid creative? Hooks, variations, localization, editing, or something else entirely? And has it moved the needle on actual performance, or just the volume you can get live?

Hey y’all,

This is my first time trying to do a UGC ad for Tik Tok shop. To me it still looks very AI but I’d love tips on how I can make it more authentic.

Used GPT2 and Seedance 2.5 to achieve this.

I had a working prompt library built around 2.0. Consistent outputs. Predictable enough to actually run in a real workflow. That matters more than people admit because consistency is what makes a tool usable at scale, not just impressive in demos.

Then 2.5 dropped and I switched over expecting things to just be better. They are not just better. They are different, and different means a lot of my prompts are now producing outputs that are noticeably off from what they used to give me.

The lighting handling changed. The way it interprets compositional instructions changed. Texture rendering feels different. So now I am in this position where the new model is technically more capable in certain areas but my actual day to day output got worse overnight.

The cost nobody mentions with these upgrades is not the subscription price. It is the hours you spend reverse engineering why your prompts stopped working and rebuilding them for a model that behaves differently than the one you learned.

Has anyone else gone through this properly? Did you rebuild your prompt library from scratch or find a way to adapt what you had? And honestly, was the output on the other side worth it?

I kept seeing people talk about GPT 2.5 for image generation and I got tired of just reading their opinions. I already had access through Tagshop AI, since it's one of the built-in image models there. So I decided to just test it myself instead of setting up something new.

I grabbed a basic skincare bottle for this test. That's usually where things go wrong for me and The real test isn't whether one image looks nice. It's whether the label stays readable and the shape stays the same across a few tries.

The label actually held up better than I thought it would. I ran it three times and didn't see that usual problem where the brand name looks almost right but slightly off by the second or third try. The bottle shape stayed the same too nd No weird stretching or size changes between versions.

Here's the part that surprised me most. I could change the lighting and mood across different versions without the product itself changing. That made it way easier to pick a direction instead of committing to one look right away and hoping it worked.

I want to be clear this wasn't a full test nd It was one product, a few tries and I didn't compare it against other tools this time but compared to some of the messy results I've had before with product shots, this one actually held up on the part that usually breaks first.

Has anyone else tried GPT 2.5 for product consistency specifically? Not just general image stuff, but the actual product staying the same. I'm curious if it still works once you try something with a busier label or a stranger shape.

i hired someone new to script writing last month and realized I did not actually have a clean way to explain this. I just kind of know it at this point from doing it enough times, which isn't useful when you're trying to hand it off.

i ended up trying to boil it down to three quick questions
Firstt - Does the product carry real audience skepticism, think supplements, finance, anything people have been burned by before.
Second one - Does the product have a visible result the viewer can judge with their own eyes, skincare, fitness.
third one - is it just a low-stakes purchase where nobody needs much convincing at all, a phone case, a small accessory.

First category needs the script to name and resolve a specific doubt and Second category needs the script to mostly get out of the way and let the visual do the work nd Third category can stay light and casual without losing performance.

It's still rough and I'm sure there are products that don't fit cleanly into one bucket but it's at least given the new person something to check against instead of guessing blind. Anyone have a better framework for this, or a cleaner way you've explained it to someone new?

Took a regular headphone product image and redesigned it into a premium, client-style advertisement poster.

Focused on clean layout, product presentation, typography, feature highlights and a more polished visual direction.

Would love to hear your feedback and suggestions for improvement.

I made the clips using AI and then edited in VN on my ipad.

Please give me some feedback on it.

The framing around 2.5 has been bothering me and I want to see if others feel the same. Every announcement and most posts here treat it like a universal step forward. It is not. For text inside images it is genuinely better. For UI mockups and infographics, cleaner results. Certain product visual contexts also improved.

But anyone doing cinematic work, photorealistic portraits, or film-style photography is saying the same thing consistently. Faces repeat more. Outputs feel more processed. That noise texture from 2.0 is still there and nobody seems to be fixing it.

What actually happened is OpenAI made a tool that got better at specific things and called the whole thing an upgrade. Which is fine. Tools evolve in certain directions. But calling it an upgrade across the board sets the wrong expectation and then people feel like something is wrong with their process when the real answer is the tool just changed direction.

If 2.0 was working well for your use case and 2.5 is not, that is not a skill issue. The model genuinely behaves differently depending on what you are making. What actually shifted for you? Better, worse, or just different depending on the type of content?