AI can help you stand out — not blend in. Add clear brand guardrails and human judgment, and it becomes a serious creative advantage.
AI isn’t short on hype—or expectations. But meeting those expectations takes more than plugging a tool into your workflow.
According to Superside’s Breakpoint research, 92% of executives expect higher-quality work because of AI. With 79% of teams feeling pressure to implement AI, it’s easy to move fast without setting standards.
Yet most teams still aren’t fully unlocking AI’s potential. Concerns around quality, legal risk, training, and workflow integration are real. AI often gets layered into workflows unevenly—used as a speed boost rather than a strategic tool. When that happens, output gets faster, but not better. And that’s where sameness creeps in.
Used intentionally, AI doesn’t replace creative talent. It amplifies it—giving teams more time to focus on judgment, strategy, and taste. Here’s how creative leaders are using AI to raise the ceiling, not flatten the curve.
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1. Have a clear brand identity and positioning
Might seem obvious, but AI is only as strong as the foundation you give it.
To sharpen AI content’s voice and focus, Kira Klaas, VP of Corporate Marketing at Later, starts with crystal-clear brand identity and positioning. When brand standards are defined upfront, it’s much easier to assess whether AI output actually supports growth goals—or just sounds polished.
That means no generating copy without solid positioning, and no campaign imagery without a brand kit of reference images. Clarity doesn’t slow teams down—it speeds up decision-making and prevents generic output.
2. Avoid the yes-man effect
LLMs are designed to be agreeable. That’s not always helpful.
AI will often validate ideas instead of challenging them—like agreeing that the same copy should run across every social platform, without flagging audience differences. To avoid this “yes-man” effect, Klass recommends pushing back on AI output: ask for alternatives, request critiques, and workshop ideas with real colleagues.
I'd rather see a brand take a creative risk that's aligned with their values than play it safe with generic AI-generated content that could come from anyone.
Sharpen your team’s taste with do’s and don’ts
AI doesn’t create taste—people do.
The more you can engage in tastemaking work together, the more you'll build the muscle to understand why a piece of creative is or isn't working.
One simple habit: generate example content with AI, then review it together as a team. Discuss what works, what doesn’t, and why. Over time, this builds shared judgment—and makes it easier to spot sameness before it ships.
4. Iterate with AI, then curate with your gut
AI makes it tempting to produce more and see what sticks. But volume isn’t the same as learning.
Strong teams use AI to explore options quickly, then step into an editor role—curating, refining, and deciding what’s worth finishing. The goal isn’t more output; it’s better judgment.
For example: A team might use AI to generate 20 headlines or visual directions for a campaign in minutes. Instead of polishing all 20, they quickly eliminate what feels off-brand or repetitive, select two or three strong directions, and invest human time in refining only those. The rest get discarded—no overthinking required.
That shift—from producing everything to choosing intentionally—is where quality actually improves.
5. When it comes to tools, stay agile
AI moves fast—and the tools powering it move even faster.
For Superside’s AI Director, Phillip Maggs one thing is clear: in the AI era, agility isn’t optional. Creative teams that lock themselves into a single platform or model risk falling behind just as quickly as the technology evolves.
When asked how teams can maintain quality while using AI to deliver creative faster and at scale, Maggs’ advice is simple: don’t get too attached to any one tool. As he points out, the sheer volume of companies and startups building creative AI tools means meaningful improvements aren’t an if—they’re a when.
You just have to be very comfortable with switching a lot to be able to maintain quality. Saying okay, here’s my quality benchmark, and I will just stick with this for the next three years with this tooling, training and enablement won’t cut it.
6. Build custom GPTs and keep feeding them context
Generic models produce generic work.
Klaas creates custom GPTs for specific tasks and continuously feeds them company, brand, and audience context—from positioning docs to CRM data. “The best outcomes I’ve seen have been from consecutively adding knowledge as I develop my own thinking,” she says.
Context compounds—and so does quality.
One of Maggs’ core points on quality is knowing where AI excels—and leaning into those strengths. In particular, he points to custom models as a powerful way to maintain consistency at scale.
Custom AI image models trained on a brand’s existing assets allow designers to generate usable, on-brand visuals much faster—without sifting through thousands of off-brand options. The real value isn’t just speed. It’s that these models understand the brand well enough to deliver consistency and quality, even at scale.
7. Lose the blank canvas syndrome
Sometimes, starting is the hardest part.
At Revolut, AI doesn’t replace designers or copywriters—but it does help teams move forward when momentum stalls. When copywriters are overloaded or unavailable, designers use AI to generate on-brand placeholder copy instead of defaulting to lorem ipsum.
“I’ve seen AI really help designers who aren’t as free-flowing with ideas,” Gareth says. “It can give them something to start from.”
8. Lean into the weirdness
Perfect realism is a high bar for AI—and audiences can tell when it misses.
Instead of chasing realism, some teams embrace stylized or playful visuals where AI’s quirks feel intentional. To sidestep the cringe factor that can come with AI content, consider not trying to aim for perfect realism. AI is the perfect tool for wild or fantastical designs because audiences know they’re not looking at real life. That’s not to say realistic AI is impossible, but it’s definitely a higher bar to aim for.
For realism, AI can be tricky. But embrace the weirdness, and you can get fun, humorous visuals.
9. Expand your brand guidelines for AI
AI guidelines aren’t hex codes—but they’re still brand guidelines.
Align teams on which tools to use and how to prompt for on-brand results. Good prompts include guidance on tone, composition, color, and style. When everyone works from the same playbook, AI output gets more consistent—and less generic.
For example: Instead of prompting an image model with “create a lifestyle campaign visual,” a brand might require prompts to specify mood (playful, not polished), color palette (muted neutrals with one bold accent), composition (candid, off-center framing), and what to avoid (stock-photo poses, overly glossy lighting). Those guardrails help AI produce work that feels unmistakably on-brand—without endless revisions.
10. Bring humans in at the right moment
AI is great at generating options—but it still needs human judgment to decide what’s true, relevant, and worth shipping.
As AI speeds up creative workflows, the risk isn’t that teams use it too much—it’s that they trust it too early. Strong teams are intentional about when humans step in: curating early, refining late, and applying expert review before anything goes live.
The takeaway: let AI explore broadly—but bring people in to make the final calls that shape how your brand is perceived.
AI as a catapult to stand out not blend in
AI doesn’t replace creative judgment—it multiplies it.
And the expectations are clear: 93% of leaders believe AI will help teams design faster, and 94% expect it to improve quality. But speed alone doesn’t guarantee distinction. Without strategy, taste, and clear guardrails, it accelerates sameness. With them, it amplifies what makes your team distinctive.
The difference isn’t the tool. It’s how intentionally you use it.
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