Beyond Text: How Marketing Teams Are Actually Using AI-Generated Visuals in 2026

For the first two years of the generative AI boom, most of the marketing conversation was about writing — blog drafts, ad copy, email subject lines. That conversation has moved on. The bigger shift happening inside marketing teams right now is visual: images, short-form video, and illustration work that used to require a designer, a stock license, or a production budget.

That shift shows up clearly in adoption data. <cite index=”42-1″>Recent industry surveys find that 56% of marketers now use AI to create short-form videos, and 53% use it to generate images</cite>. This isn’t a niche experiment anymore — it’s becoming a standard part of how campaigns get built, alongside the AI writing tools most teams already adopted.

Why Visual Content Became the Next AI Frontier

Text generation was the easier problem to solve first: language models had more training data, and the output was easier to edit by hand. Visual generation lagged for a while, then caught up fast. <cite index=”45-1″>Generative AI in marketing and advertising was valued at roughly $5.8 billion in 2024 and is projected to grow to over $22 billion by 2028</cite>, and a meaningful share of that growth is concentrated in image and video tools rather than text.

The practical driver is cost and speed, not novelty. A single product photoshoot or short ad video can take days and thousands of dollars to produce through traditional channels. AI generation compresses that into hours, which matters most for teams running high-volume campaigns — seasonal promotions, A/B tested ad creative, or localized versions of the same asset for different markets.

What Marketing Teams Are Actually Generating

It helps to break this down by use case rather than treat “AI visuals” as one category, because the tools and workflows differ quite a bit depending on the output:

Product and lifestyle imagery. Teams generate variations of product shots — different backgrounds, angles, or model presentations — without rebooking a photoshoot for every SKU or seasonal update. This is one of the fastest-growing use cases in e-commerce marketing specifically.

Short-form ad and social video. Rather than editing a single long-form video down into ten platform-specific cuts, teams are increasingly generating short clips directly from a brief or from existing footage. Some newer video models, like Seedance 2.5, have been built specifically around producing this kind of quick, coherent short-form clip rather than long-form cinematic output — reflecting how much of current ad spend is going toward vertical, sub-30-second formats.

Stylized and illustrated content. Not every marketing asset needs to look photorealistic. Illustration-style generation — used for social graphics, character-driven brand mascots, or stylized promotional art — has its own dedicated tools, such as PixAI, which focuses specifically on anime and illustration-style output rather than trying to be a general-purpose photo generator.

The common thread across all three is specialization. The early wave of AI image tools tried to do everything reasonably well; the current generation of models tends to do one thing — short video, photorealism, illustration — noticeably better.

Where Teams Get This Wrong

The failure mode worth watching for isn’t quality — most current-generation tools produce genuinely usable output. It’s workflow fragmentation. A marketing team ends up with one subscription for product photos, another for short video, a third for illustration work, and no shared library connecting any of them. Every project starts from a blank upload, and nobody remembers which tool produced which asset six months later.

Platforms that consolidate several generation types under one account — pairing something like Pollo AI’s Seedance 2.5 for video with an illustration-focused model like PixAI in the same workspace — solve a real operational problem here, less because any single model is best-in-class and more because campaigns rarely need only one type of visual output. A single product launch might need photorealistic hero images, a short vertical ad, and a stylized social graphic, and stitching those together across three separate tools and three separate asset libraries is where a lot of the theoretical time savings from AI actually get lost.

A Practical Starting Point

If your team hasn’t formalized an AI visual workflow yet, start narrower than you think you need to. Pick one recurring asset type — weekly social graphics, or short-form ad variations for one campaign — and run it through an AI-assisted process end to end for a month. Track two things: actual time saved versus your previous production process, and how much manual cleanup each output needed before it was publish-ready.

That data point matters more than any general industry statistic, because “AI saves marketing teams time” is true on average but varies enormously by use case and by how well the workflow is set up. A team that generates visuals in a fragmented, tool-hopping process will see far less benefit than one that treats asset reuse and workflow consolidation as part of the plan from the start.

The broader trend is clear enough that it doesn’t need much forecasting: visual content generation is following the same adoption curve text generation did two years ago, just compressed into a shorter timeline. The teams getting ahead of it aren’t necessarily using the flashiest individual model — they’re the ones who’ve figured out how to make AI-generated visuals a repeatable part of production, not a one-off experiment.