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AI-generated ad creative has produced measurable gains in Admiral Media’s published client work: +45% ROAS for Star Chef 2, a 50%+ cut in creative production time for StoryBeat, and a 32% lower CPA for dynamic creatives than for static ads in a retargeting test. Results still vary by account, so the fair way to judge AI creative is on campaign results, not on claims.
This article presents three case studies from Admiral Media’s client work, with the published numbers for each. If you’re evaluating whether AI generated ad creative is ready for your business, these are the results to weigh.
What Case Studies and Data This Article Contains
- Star Chef 2 (99games): AI creative production with a structured testing matrix of 60+ variations. +45% ROAS, +55% CTR, -18% CAC.
- StoryBeat: AI across the creative process, from translation to testing. 50%+ less time spent on creative production.
- Food delivery business in South Africa: dynamic creatives for retargeting. +77% spend and a 32% lower CPA than static ads.
- The workflow behind Admiral Media’s AI Creative Factory, from brief to launch and back into the next brief.
- How to judge a creative partner’s experience with subscription apps before you hire one.
Every figure above comes from the published case study linked next to it.
Why AI Creative Performance Is Now Measurable at Scale
Two things have changed that make the AI creative case studies below possible. First, AI creative production has matured to the point where output quality is platform-ready across video, static, and AI UGC formats. Second, performance marketing platforms have evolved to reward creative variety, with algorithmic systems that identify winning creatives faster and more accurately when given larger creative pools to work with.
These two factors compound. Better AI production quality means creatives can actually run at scale. More creatives running means more data. More data means faster identification of winners. Faster identification of winners means better campaign performance, which validates more investment in AI creative production. The feedback loop is self-reinforcing.
The third case study below shows this effect inside a single account: once dynamic creatives multiplied the number of variations in rotation, spend grew while CPA fell.
Case Study 1: Star Chef 2, +45% ROAS, +55% CTR, -18% CAC
Star Chef 2 is a free-to-play cooking and restaurant management game from 99games with a broad casual audience. As Star Chef 2 scaled, creative demand outpaced what the in-house team could deliver, and manual workflows created a hard ceiling on both testing velocity and spend. 99games needed to scale creative production without losing Star Chef 2’s visual style and brand.
Admiral Media implemented an automated AI production workflow with fast-paced iteration, generating large volumes of ad variants while preserving Star Chef 2’s visual style and brand guidelines. A structured A/B testing matrix of 60+ variations isolated the winning elements by hook, visual and format, and real-time ROAS and engagement data fed the next round of prompts.
Results across the engagement:
- +45% ROAS: return on ad spend improved
- +55% CTR: click-through rate increased
- -18% CAC: customer acquisition cost decreased
Shilpa Bhat, VP Games at 99games, noted that Admiral Media helped them scale creative production while staying consistent with Star Chef 2’s visual style and brand. The structured testing approach helped identify several new creative concepts that performed strongly and expanded 99games’ testing capabilities.
The Star Chef 2 case illustrates one way AI creative agency results come about: through the testing surface area that AI production makes possible. The case study names the bottleneck directly: manual workflows had put a hard ceiling on testing velocity and spend. With 60+ variations in the testing matrix, the team found several new concepts that performed strongly, which a handful of manually produced ads would have been less likely to surface.
Case Study 2: StoryBeat, 50%+ Less Time Spent on Creative Production
StoryBeat‘s ads needed rapid production without sacrificing impact, and traditional content creation methods were a slow and costly bottleneck.
Admiral Media’s focus was on refining and iterating StoryBeat’s best-performing ads rather than creating new concepts from scratch.
Admiral Media integrated AI into every step of StoryBeat’s creative process, from translation to testing. AI automated translation and sped up image sourcing, which allowed more ad variations to be created faster and tested in real time, within the same budget and timeframe.
The results centered on time saved in creative production:
- 50%+ reduction in creative production time: the time spent on creative production was cut by 50% or more
The StoryBeat case shows why production speed matters in performance creative: faster production means more ad variations can be tested within the same budget and timeframe.
Case Study 3: Dynamic Creative Optimization, +77% Ad Spend, -32% CPA
This case used dynamic creative optimization (DCO). The client in this dynamic creatives case study is a food delivery business in South Africa, and its retargeting relied on static creatives, which led to ad fatigue. Manually creating and managing large numbers of ads for different locations and promotions was also resource-intensive.
Admiral Media used dynamic creative elements to personalize ad content by product category, running through a programmatic platform built for dynamic banners. A product feed and automated tools generated the ad variations, reducing the need for manual work.
The dynamic approach produced more than 2,140 unique variations and sizes from a feed of 160 elements, and tripled the volume of creative assets served compared with all the static creatives the account had run before.
- +77% increase in ad spend: spend was scaled up while the dynamic creatives kept a lower CPA than the static ads
- -32% decrease in CPA: cost per acquisition was 32% lower for the dynamic creatives than for static ads during the test period
This result matters because spend and efficiency improved together, which is normally hard to achieve as spend scales.
What These Results Have in Common
Looking across these case studies, several factors recur.
Volume Creates the Testing Surface That Finds Winners
In each of these cases, production was automated to deliver more creative variants than manual work had allowed. The brands that test the most creatives tend to find the most winners. AI production doesn’t just make creative cheaper; it makes the testing surface large enough to find high-performing combinations that would otherwise go undiscovered.
Performance Data Drives Creative Decisions
In these case studies, creative decisions were guided by testing and performance data rather than intuition. What hooks drive the highest watch-through rates? Which value propositions produce the lowest CPA? What visual styles correlate with subscription conversion rather than just install volume? These questions can only be answered systematically at scale, which is why AI creative agencies for performance marketing brief creative from performance signals rather than brand preference.
Speed Compounds Over Time
The StoryBeat and dynamic creative cases both show how faster creative production lets more ads be made and tested. Each week faster is another week of performance data informing the next creative iteration.
The Human Layer Makes AI Creative Scalable
Admiral Media’s AI creative work pairs AI production with human strategy and quality review. AI produces the volume, and people decide what is on brand and what ships.
How the AI Creative Factory Workflow Runs
The results above come from a repeatable process rather than one-off experiments. Admiral Media’s AI Creative Factory runs in five steps:
- Ideation and brief: the team concepts the idea and sets the creative direction. Inputs can be a brief, competitor ads or performance data.
- Creative brief: hooks, scripts and formats are mapped to channel and audience.
- AI production: variants are generated with AI, then edited and quality-checked by people. Generation candidates are kept separate from finished ads.
- Post-production: music, subtitles, color and the other finishing touches.
- Ready to launch: ads are delivered to spec for Meta, YouTube, TikTok and other channels.
After launch, an integrated feedback loop closes the cycle: test results shape the next brief. In the Star Chef 2 engagement, that meant feeding real-time ROAS and engagement data into the next round of prompts.
Learn more about how AI creative agencies produce 100+ ad variants monthly, or explore the complete guide to AI creative agencies to understand the full scope of what this model delivers.
How to Evaluate AI Generated Ad Creative Results for Your Business
When assessing whether AI creative results translate to your context, the most important question is how to structure an engagement to achieve strong results rather than mediocre outcomes.
Strong AI creative results usually depend on a few things: giving the testing program enough time to accumulate data before drawing conclusions, providing a large enough creative pool to generate meaningful signals, and maintaining a feedback loop between creative performance data and the next production cycle.
The brands that see weak results typically cut the program too early, over-constrain the creative brief to the point where meaningful testing is impossible, or treat AI production as a cost-cutting measure rather than a performance strategy, optimizing for cheaper creative rather than better creative.
The case studies above came out of structured testing, and results vary by vertical and competitive context.
How to Judge a Creative Partner’s Subscription App Experience
A common buyer question is which agencies have proven experience with subscription apps and can actually help reduce CAC, rather than general performance shops that also make ads. Use the same standard this article uses for its own evidence:
- Ask for published case studies with numbers. Look for cost per subscription, CAC or CPA figures tied to a named client, not only install volume or creative output.
- Ask what the creative is optimized toward. For a subscription app, winners should be picked on subscription or trial events, not on clicks or cheap installs.
- Ask how testing works. How many variants go into each cycle, how winners are chosen, and how results feed the next brief.
- Ask who reviews AI output. A person should check every ad for brand fit and platform policy before it ships.
- Ask what a switch would look like. A good partner should start from your existing ad accounts, creative history and current winners rather than from zero.
As an example of the evidence to ask for: in Admiral Media’s FET case study, a subscription dating app, cost per subscription fell 66% and subscriptions grew 162%, with 60+ ad iterations across 9 unique concepts in the creative testing framework. Admiral Media was founded in 2019 by Andre Kempe and is rated 4.9 on Clutch from 48 reviews. To see whether this approach fits your app, talk to our team.
Frequently Asked Questions
Do AI generated ad creatives really perform as well as human-made ads?
In performance marketing contexts, AI-generated creatives can match or outperform traditionally produced ads, depending on the account and the testing setup. The case studies above report improvements in ROAS, CTR, CAC and CPA, and time saved in creative production. The advantage is not the AI itself but the volume of testing it enables: more variants tested means more winners found. Traditional production quality is high, but the volume needed for effective performance testing is often not achievable at traditional costs.
How long does it take to see results from an AI creative program?
The timeline depends on the account, the spend level and how many creatives are in testing. AI creative programs usually build results over time, because each batch of creative builds on what the previous batch showed, so judging a program too early can understate what it delivers.
What types of ad formats work best with AI creative production?
AI creative production covers the full range of performance ad formats: video (including AI UGC), static image, animated variants, and copy/headline testing. Video tends to show the most dramatic performance differences because the creative variable space is larger and the production cost savings are greatest versus traditional video production.
How many ad variants should I plan to test?
A brand that wants a reliable set of winners should plan to test a large pool of variants rather than a handful. Higher-spend brands managing multiple campaigns and markets should plan for more. Volume like this is difficult to reach through traditional production, which is where AI-powered creative production helps.
What makes Admiral Media’s AI creative results different from generic AI tools?
The difference between deploying self-service AI tools and working with a managed AI creative agency is the strategic layer. Generic AI tools handle production. A managed program adds creative strategy, human quality review, and a feedback loop between campaign performance and creative decisions. The results above come from this combination, not from AI production alone. The tool is the engine; the strategy is the driver.
