7 AI Advertising Case Studies with Real Numbers

Most AI advertising "case studies" are vendor decks with a hero metric and no context. The seven below cleared a simple bar: a named brand, a described intervention, and a number that was reported publicly rather than whispered in a sales call. They also split cleanly into two tooling classes worth keeping straight, generation (making the creative) and optimization (targeting and assembling it), because the numbers mean different things depending on which one produced them. Where a case is impressive but the metric is soft, we say so. The goal is not a highlight reel. It is a set of interventions you could actually copy, with the honest caveat attached.

TL;DR

• Generation wins show up as cost and speed: Kalshi's $2,000 primetime spot, Klarna's six-weeks-to-seven-days image cycle, Monks' 97% production cost cut.
• Idea-led generation wins show up as reach: Heinz turned a model's bias into a billion-plus impressions.
• Optimization wins show up as ROAS and CTR: Omneky's 6x ROAS, Novo Nordisk's 24% open-rate lift.
• Read the tooling class before you read the number. A ROAS lift from targeting is not evidence that generated creative works, and vice versa.
• What to copy is in each entry. The pattern: use generation for volume and cost, use optimization to find the winner inside that volume.

1. Klarna: the operational case (generation + workflow)

What they did: Built an internal AI image pipeline (using DALL-E and Midjourney-class tools) to produce marketing visuals at scale. Numbers: Over 1,000 AI-generated images in Q1 2024, image development cut from about six weeks to seven days, roughly $10 million in annual marketing cost savings including a $6 million reduction in image production, and a 25% cut in external supplier costs, all while running more campaigns. What to copy: Treat generation as a throughput system, not a hero-spot generator. The compounding win is the cycle time. When making the fiftieth variant is nearly free, you test more and find better. This is the creative volume thesis in numbers.

2. Heinz "AI Ketchup": the idea-as-AI case (generation)

What they did: Noticed that image models reliably render "ketchup" as a Heinz-shaped bottle, and built the campaign around it: "This is what ketchup looks like to AI." Numbers: Over a billion impressions, reported at many multiples of the media investment, with social engagement around 38% above benchmark. What to copy: The strongest generation ideas are the ones that could not exist without the model. Heinz did not use AI to make a normal ad cheaper. It made an ad whose entire concept depends on a property of generative models. That is the highest-value use, and the hardest to reverse-engineer, because it starts from an insight about the tool.

3. Kalshi: the cost-floor case (generation)

What they did: Aired a 30-second NBA Finals spot in June 2025 made almost entirely with Google's Veo 3, via filmmaker PJ Accetturo. Numbers: About $2,000 in production cost, two days of work, 300 to 400 Veo generations trimmed to 15 clips, over three million views on X, roughly 95% below conventional production. What to copy: Match the register to the tooling. The ad is deliberately chaotic and absurd, a style where Veo's occasional weirdness reads as intentional. The cost number is only reproducible if you pick a concept that flatters generated footage instead of fighting it.

4. Monks + Hatch: the performance-creative case (generation)

What they did: Media.Monks used its Monks.Flow AI workflow with Google Gemini to produce and iterate ad creative for Hatch's Restore sleep device. Numbers: 31% improvement in cost per purchase, 80% jump in click-through rate, 46% lift in on-site engagement, 50% reduction in production hours, and a reported 97% cost reduction. What to copy: This is the cleanest evidence that generated creative can move performance metrics, not just save money. The move is to feed a high-volume variant pipeline into a proper test, then let the CTR and CPP data pick the survivor. It is the variant testing workflow run at production scale.

5. Novo Nordisk: the copy-generation case (generation)

What they did: Used Phrasee's language-generation platform to optimize email subject lines and copy. Numbers: Click-through rates up 14%, open rates up 24%. What to copy: Generation is not only visual. Language models tuned on brand voice and tested against real opens can beat a copywriter's best guess on the specific, unglamorous surface of a subject line. The caveat: this is optimization dressed as generation, the win comes from testing many machine-written options, not from one brilliant line. Budget for the volume, not the single output.

6. Omneky + New Sapience: the full-funnel case (generation + optimization)

What they did: Used Omneky's AI image generation plus predictive scoring to run a crowdfunding campaign for New Sapience. Numbers: $460,000 in direct contributions toward a $1.18 million total raise, 6x ROAS, about $160 cost per investment, and over 52,000 link clicks. What to copy: This is the combined play, generate the creative variants and let a scoring model route spend to the winners. The 6x ROAS is a full-stack result, so credit is shared between the creative and the targeting. That shared credit is exactly why you should keep the two tooling classes separate in your own reporting.

7. Lidl France + Marcel/Bria: the scale case (generation)

What they did: Publicis's Marcel platform, using Bria's text-to-image models, produced localized retail visuals for Lidl France. Numbers: 1.7 million AI-generated visuals in three weeks. What to copy: The number here is throughput, not performance, and that is the honest framing, there is no ROAS attached. But for a retailer running thousands of SKUs across regions, the ability to generate 1.7 million on-brand visuals in three weeks is itself the result. When your problem is volume and localization, throughput is the metric that matters. Just do not confuse it with proof that the creative converts.

The pattern across all seven

Two tooling classes, two kinds of number.

Generation cases report cost, speed, and reach. Klarna's cycle time, Kalshi's $2,000, Monks' 97% cut, Lidl's 1.7 million, Heinz's billion impressions. These prove the same thing from different angles: making the creative got radically cheaper and faster, which lets you make and test far more of it.

Optimization cases report ROAS, CTR, and conversion. Omneky's 6x, Novo Nordisk's open rates. These prove that assembling and targeting the creative got smarter.

The trap is attributing an optimization number to your generated creative, or a generation cost saving to your targeting stack. Keep them in separate columns. The full win, the one Omneky and Monks each caught a piece of, is stacking them: generate the volume, then optimize inside it.

What this means for brand teams

The reproducible move in every generation case is the same. Lower the cost of a variant toward zero so you can afford to make dozens, pick a register that flatters the tooling, and run a real test to find the winner. That is not a vendor pitch, it is what Klarna, Kalshi, Monks, and Heinz each did in their own register. The cost collapse behind all of it is the same one reshaping agency pricing.

If you want to run the generation half of that stack across every major image and video model on one canvas, then hand the survivors to your optimization tools, start on 8frame and clone a variant workflow. For the campaigns that won on craft rather than numbers, see the best AI-made ads of 2026.