AI in Design: Creative Shortcut or Brand Liability?

When we first started our AI & Purpose series, we asked creative, strategy, and research partners across our network how AI was showing up in their work. What we heard reflected a moment in time. Early experimentation, cautious curiosity, and a lot of “it depends.”

Since then, the pace of AI adoption has accelerated. The tools are more visible. Expectations around speed and efficiency have shifted. And our own thinking has evolved too.

This post reflects where that conversation has landed, particularly from a design leadership perspective. Not as a verdict on AI, but as a clearer articulation of where it helps, where it creates risk, and why discernment matters more than enthusiasm.

When AI Helps

When we first fielded our survey, many respondents described using AI in limited, practical ways. Early brainstorming. Rough concept exploration. Repetitive or mechanical tasks that slow teams down.

When we ran our survey, 63.6% of respondents said they primarily used AI for early brainstorming or ideation, often to save time on repetitive tasks.

Used carefully, AI can:

• Help teams get unstuck at the very beginning of a process
• Reduce friction in repetitive production work
• Support accessibility through captioning, resizing, or formatting
• Function as a starting point for internal drafts or mockups

Even then, few people described AI as improving originality or strengthening creative craft. Most framed it as a head start, not a solution.

We’ve seen this done thoughtfully. In the EdTech space, Brightspace has used AI-powered creative systems to scale campaigns while maintaining strict brand guardrails and visual consistency. The technology isn’t replacing strategy; it’s operating within it.

We think that distinction matters. AI can support creative work. It does not author it. Ideas, judgment, and responsibility still sit with people.

Four Design Signals We’re Paying Attention To

In the survey we ran, concerns about quality and fit surfaced clearly.

59.1% of respondents said AI-generated outputs felt off-brand or generic. While we didn’t ask directly about time spent fixing AI assets, 31.8% noted that AI diluted originality and 18.2% said it made their work feel generic, pointing to a deeper friction around craft and control.

We’ve come to see that “generic” is often just the most visible symptom of deeper shifts happening in design. As AI becomes more embedded in creative workflows, these aren’t just risks to mitigate, but signals worth paying attention to. From a design leadership perspective, four patterns continue to surface, particularly for values-driven organizations.

1. Value Misalignment (Not Just “Generic” Output)

The most obvious critique of AI-generated design is that it looks bland or generic. But for purpose-driven brands, the deeper risk is misalignment.

Design choices signal values. When visuals are generated through pattern recognition rather than intention, they can quietly conflict with what an organization stands for, particularly in environmental, cultural, and community-based work. What looks efficient on the surface can feel careless or disconnected underneath.

We’ve already seen how this plays out with several high-profile AI-driven campaigns facing backlash for feeling disconnected or underdeveloped (Coca-Cola and McDonald’s Christmas campaigns) 

For organizations built on trust, that gap matters.

2. Cultural Flattening and Loss of Authorship

AI understands patterns, not culture. When design work involves representing people, communities, or lived experience, relying on generative tools introduces real risk. Responsibility for accuracy, respect, and nuance cannot be outsourced to systems trained on incomplete or flattened data.

Authorship still matters. Who made something, how it was made, and whose perspective shaped it are not technical details. They are ethical considerations.

3. Human Labour Becoming a Brand Signal

We’re seeing more brands intentionally move away from digital perfection. In-camera effects, handcrafted visuals, and visible imperfections are being used to signal care, intention, and investment in people.

This isn’t necessarily about nostalgia, we see it as a response to saturation.

As automated aesthetics become more common, human labour itself is becoming more visible as a differentiator. Choosing to invest in creative craft, rather than replace it, increasingly communicates values in a way no brand statement can.

Merriam-Webster’s “Actual Intelligence” explicitly positions human thought as the differentiator in a synthetic landscape. The campaign does not reject AI outright. Instead, it reframes intelligence as something grounded in judgment and meaning.

In a landscape where frictionless generative output is increasingly accessible, the visible presence of human effort becomes part of the message. How something is made now carries meaning alongside what is made.

4. Performative Efficiency and Virtue Signaling

AI is often framed as a way to do more with less. And for small teams under pressure, that promise is appealing.

But when AI is used to signal responsibility or progress without accountability, it risks becoming performative. Efficiency can quickly turn into a shortcut that undermines credibility, especially in sectors where trust and relationships are central.

We’re also seeing brands like Blue Diamond’s Almond Breeze Jonas Brother’s campaign mocking “AI slop,” but the exaggerated concepts were created using AI tools. It signals superiority over the technology while still participating in it.

For social profit and community-focused organizations, the cost of getting this wrong is reputational, not just aesthetic.

The Grey Area

The grey area is no longer about whether AI is ethical in theory. It is about speed, pressure, and defaults.

AI is increasingly embedded in the tools teams use every day, whether they actively seek it out or not. Expectations around turnaround time and cost are shifting, and the pressure to adopt is real.

In our survey, nearly half of the respondents noted that AI was already embedded in the platforms they use every day, whether or not they actively sought it out

Within Ramp, these conversations are ongoing. We are actively navigating what responsible use looks like, not just in the outputs we produce, but in the outcomes those choices create. That includes acknowledging why AI holds appeal, while also questioning what gets lost when efficiency becomes the primary driver.

Skepticism does not mean resistance. It means taking responsibility for the consequences of what we make.

Questions We’re Asking Ourselves

Rather than offering prescriptions, these are the questions guiding our thinking right now:

• Where does AI genuinely support our work, and where could it erode trust?
• What does authorship mean in our design and brand work today?
• What values are embedded not just in what we make, but in how we make it?
• What are we unwilling to automate, even if it would be faster or cheaper?
• How do we ensure efficiency does not come at the cost of connection?

These are not settled questions. They should not be.

Up next

In the final post in this series, we step back from design to look at the bigger picture. Not how AI changes workflows, but how easily tools can be mistaken for strategy.

AI may change how work gets done. It won’t replace strategic clarity, strong positioning, or the work of building trust. That’s where we’ll close out this series.

Follow the Series: AI & Purpose
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