How Gerber Childrenswear Uses Agentic AI To Scale Content Authentically While Protecting Brand Safety

Retail is entering the era of agentic commerce, where AI does more than respond and starts executing real work across research, content, and customer engagement. The opportunity is clear, especially for lean teams looking to scale output and insight without scaling headcount. The constraint is just as real. For legacy brands, especially in high-stakes categories, every gain in speed has to hold up against strict brand safety standards. The advantage now goes to the teams that can expand what AI does without compromising what the brand stands for.

To gain an expert's perspective, we sat down with Alicia Littleton, Director of E-commerce at Gerber Childrenswear. With over 15 years of experience in e-commerce and a former role as an Adjunct Professor of Digital Strategy at Clemson University, she holds unique perspective on how to quietly pressure-test AI behind the scenes. Her focus is on using internal AI capacity to expand what a relatively lean team can accomplish, all while protecting a legacy baby brand’s strict safety rules.

Gerber spent 18 months rolling AI out internally before exploring anything customer-facing. Backed by executive buy-in, the initiative began with an intensive two-day series led by an AI industry expert, followed by real-time working sessions and ongoing monthly conversations with the full leadership team. "We're a really big brand, but we are also a small company at the same time. So how can we use AI agents to build a stronger workforce? It gives us the leverage to explore opportunities that in the past we would have used a consultant for," Littleton says.

Once an internal foundation was in place, the team turned its attention to a massive scaling challenge to begin applying it to real contextual problems. Some industry benchmarks suggest that to be best-in-class, brands must update product pages as often as eight times a year. For a legacy company managing close to 7,000 SKUs, doing that solely through traditional, heavily vetted internal copy processes is mathematically impossible, so Gerber began exploring how AI agents could handle content enrichment at scale. But the challenge wasn't just about volume.

• Blanket statements: In the baby category, brand safety standards are equally non-negotiable, as the stakes of getting content wrong are especially high. "We're in the baby space," Littleton says, highlighting the stakes of automated content. "You don't want to accidentally put out there that something is safe for a baby when it's not. Or little things like an AI suggesting a parent put a blanket in a crib, which is dangerous. So I think that there's a lot more rules and restrictions on what kind of content we can push out."

• Automating the red tape: Some more risk-averse CMOs may see implementing AI here as a risk to brand safety, but Littleton's team sees it as an enforcement mechanism. "The capabilities this technology is bringing into the space are allowing us to still maintain those guardrails and even checks and balances without having a human element in the mix," she says. "Let's just be honest. We're humans. We make mistakes too. So I think that it's actually more beneficial in some ways to have all of those guardrails checked over by AI."

• Bad data out: The key to getting this balance between safety and scale right, she says, means getting the underlying data right first. "Bad data in, bad data out," Littleton warns, emphasizing that foundational elements are non-negotiable. "If you don't have the content, regardless of how great the technology is, you're still not going to win. The need for content is never changing." To scale AI-assisted guard railing effectively, she argues a solid content strategy always comes first.

The urgency isn't just driven by operational needs. Agentic adoption is also a direct response to cultural evolution. A growing segment of shoppers consumes information differently now, expecting more digestible messaging across seamless omnichannel experiences. They also expect it fast. Littleton points out that this behavioral reality is a primary driver behind practical AI adoption, helping teams deliver more relevant content at the speed customers expect.

• Microwave mentalities: "We live in a microwave society," Littleton says. "They want things fast; they want it quick. We absorb information at such a fast pace that we want it faster. What AI introduces is the ability to process faster even for us, because it's summarizing everything in a way we can digest much quicker."

• Innovate or expire: Keeping pace with that evolution changes how executives think about vendors and MarTech decisions. Over time, a brand's needs and a vendor's roadmap naturally move in different directions. To stay aligned, leaders now build regular reviews of their tech stack into how they operate as the landscape rapidly evolves. "If you're not moving forward, you're moving backward in technology," she says. "There's no opportunity for us to stand still with where we're at right now. If we're not actively looking forward to how we're going to be in that AI space, then a year from now, you probably wouldn't even want to talk to me. It'll be old news."

For Littleton, the ultimate test of the role is resisting the urge to fix only the most immediate problem. The whole strategy of the internal rollout, agentic workflows, and vendor audits only holds together if the person leading it can keep one eye on the horizon. "If you're trying so hard to solve today's problem, by the time you solve it tomorrow, you're not going to have tomorrow's problem solved," Littleton says. "That, I think, is one of the most challenging parts of this role, but also the most rewarding. It's taking yourself out of the dumpster fire and looking at the whole picture."