Creative teams have never had more tools at their disposal—and those tools can do more than ever before. Across marketing and design, new developments in AI have made it possible to create passable first-draft copy, imagery, and video at the push of a button.
But it hasn’t necessarily made creative work simpler. AI can produce outputs that might look superficially acceptable, but require a great deal of editing. And it has increased the volume of work that teams have to wrangle. "Content proliferation is immense," says Shannon Butler, Dropbox's VP of Design and Research. "There's a lot of innovation, but it's created new complexity in how we manage ideas and outputs."
As adoption becomes widespread—99 percent of marketers say they’re already using AI in some form—a new shift is now happening. AI is starting to remove friction from the creative process itself by tackling bottlenecks: helping teams easily find context, shorten iteration cycles, and improve decision-making.
These three use cases show how AI is reshaping creative work before, during, and after ideas are made…
Reinventing Workflows: Finding Context In the Chaos
For Joyce Hwang, the workday used to mean juggling decks, email chains, and Slack threads just to piece together context for a campaign. Now, Dropbox’s VP of Marketing asks the company’s own AI tool, called Dropbox Dash, which pulls work from different apps into one place. It connects to workplace software such as Slack, Asana, and Canva to search across files, conversations, and project materials in order to surface assets or answer questions.
“Previously, getting up to speed meant toggling between tools and pings—this lets me search the way I think,” she says. “I can just ask questions in plain language—'What changed in the launch deck?', 'Who's leading the design on this project?'—and it brings everything together."
Dash means she no longer worries whether she’s in the right version of a file. “We all know how many times we’ve named a file v_final and then v_final_2,” Hwang says with a laugh. “I don’t even look at the file, I just have a conversation: ‘What’s the latest version of this?’ It’s really freed me up from the mundane tasks.” The result is it allows her to put more effort into what she calls “the first and last mile” of creativity. This is the part of the process that can’t be outsourced to AI—refining ideas, making decisions—and where human input has outsized impact.
Dash also keys into a wider trend for AI: universal search and knowledge management tools—something to which the new wave of AI is well suited, thanks to its ability to understand natural language and wrangle unstructured data across multiple modalities. Hwang, for example, can describe an image in plain language, and Dash will surface the relevant file. “It’s like having an assistant that understands what you’re looking for, not just what something is called,” she says.
The ambition is for tools like this to move from being reactive to proactive, not merely providing an answer to a search, but anticipating what you’ll ask for next. Hwang expects Dropbox Dash’s predictive capabilities to grow as she and her team continue using the system. "It learns how your team works, so it can anticipate what you'll need,” she says. “That’s how it’s able to be more personal and coordinated.”
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Reinventing Production: Shortening Iteration Cycles
Once teams can find and align around their work, the next challenge is execution. And as content creation becomes easier, differentiation becomes harder. “The battleground for organizations is not creating generic content,” says Daniel Hulme, Chief AI Officer at ad agency group WPP. That’s why marketers are going beyond simply using a chatbot straight out of the box. Instead, they upload ‘brand voice assets’ to it—guidelines, examples of previous work, product descriptions—to train the AI to understand what kind of content is right for a specific brand.
Some agencies are taking this idea further. WPP has developed “Brand Brains”—AI systems customized even more deeply to individual clients using advanced techniques such as fine tuning. These are used to help execute creative work at scale and form part of the company's AI platform for marketing, WPP Open. “The goal of a Brand Brain is producing production-grade, brand-accurate, differentiated content,” Hulme continues, “and that last mile is actually extremely hard.”
Humans come up with the creative ideas in the first instance, but the Brand Brains execute them. These models generate imagery, video, and more, that matches up with brand guidelines and any compliance needs.
When cloud-computing company CoreWeave scheduled its Nasdaq IPO for March 2025, for example, WPP agency AKQA was tasked with announcing it. In a matter of weeks they did what would usually take months, creating a campaign that ran across multiple channels—social, online, TV, Times Square billboards—with the help of the Brand Brains within WPP Open.
The sheer speed of this campaign is a testament to the tech, but the real impact for Hulme is that the brand was able to work fast enough to capitalize on the announcement. “There are millions of moments today that are being missed,” he says, “where brands are failing to put their products in front of the right people at the right time.” Hulme imagines a future where a Super Bowl-quality ad could be produced, with the help of AI, in response to any micro or macro moment. “It allows us to be more efficient, but more importantly, more effective.”
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Reinventing Testing: Focus Groups on Tap
Executing a creative idea is one thing, but how do you know if that idea is actually any good in the first place? The traditional way is focus groups—but these are expensive, slow, and usually happen late in the process.
Interest has been growing around how AI chatbots can serve as digital focus groups, since they can be told to adopt different marketing personas in order to do some basic idea testing. Marketers taking this approach often provide the chatbot with extensive prompts covering a persona’s goals, challenges, personality, and more.
But this has its limitations. The personas aren’t reflective of real people—they are merely assumptions—and this is what a number of startups are trying to solve.
Hamish Brocklebank, founder of Brox, has created ‘digital twins’ of tens of thousands of real people. Each one answers a slew of surveys, and is paid for their input, “so we can then test new ads or concepts against realistic, high-fidelity models of real consumers”, Brocklebank explains. “You can literally upload an ad, choose your audience—say, people who subscribe to Netflix and drink coffee three times a day—and two hours later, you get your results.”
The IRL versions of Brox’s synthetics continue to answer surveys too, to keep up with economic, social, and political changes.
As cost is not an issue, Brox’s clients often test 50 or even 100 versions of an idea, and will start much earlier in the creative process. A phone company, for example, used Brox’s synthetic focus groups to test what new features to promote in a new marketing campaign—they wanted to know which of over 50 options would appeal to different audiences.
Being able to road test ideas at an early stage is a huge time saver, but it can also reveal unexpected insights: outliers that spur new creative concepts. “The dirty little secret in market research is that people know the answers to most of the questions,” says Brocklebank. “The real value comes in finding that alpha at the edge, an answer you didn’t expect.” Those early, more accessible tests could also potentially give creatives much needed back up when pitching unusual ideas. A great response to ‘this has never been done before, how do you know it will work?’ is ‘here’s a whole load of data’.
That’s the thread across AI’s integration into operations, content-creation and testing—it’s not just giving creatives time back, it’s also providing them with more power. As Dropbox’s Hwang says: “It’s not about replacing creativity, it’s about removing friction so we can actually do more of it.”
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