Issue 2: How Adore Me Uses AI to Accelerate Content Creation

TL;DR

Adore Me, the direct‑to‑consumer lingerie brand and certified B‑Corp under the Victoria’s Secret umbrella, needed to scale content production across thousands of products and multiple languages without ballooning its team or sacrificing brand voice. The marketing and product teams partnered with Writer’s AI Studio to build role‑specific AI agents that generate SEO‑optimized product descriptions, translate copy for international launches and draft stylist notes for subscription shipments. By putting generative AI at the centre of its content supply chain, the brand went from long, manual workflows to an automated system that delivers high‑quality content in minutes.

The Source: Writer's Room

The Use Case

Adore Me sells dozens of new styles every month and operates in multiple countries. Before AI, every product description, translation and stylist note was crafted by hand, often outsourced to freelancers or agencies. This bottleneck slowed time‑to‑market for new collections, made localization expensive and threatened consistency as the brand scaled.

The team identified three repeatable content workflows that could be automated:

1. Product descriptions: Writers spent about 20 hours per batch drafting descriptions that matched Adore Me’s tone and included the right keywords.

2. Localized copy: Launching a new country often took months because each description and web page had to be translated manually.

3. Stylist notes: Subscription boxes include personalised notes from stylists. Drafting them at scale was repetitive and time‑consuming.

Using Writer’s AI Studio, Adore Me trained agents on its existing style guides and product language. Each agent was assigned a narrowly defined role – product description generator, Spanish translator for Mexico launches and stylist note drafter – and allowed to produce drafts that humans then reviewed and approved. This “AI in the loop” approach preserved quality while dramatically speeding up production.

The Results

• 40% increase in non‑branded search traffic as a result of SEO‑optimised product descriptions.

• 2,900 product descriptions created in minutes using AI agents instead of the 20 hours it previously took per batch.

• Launch a new market in 10 days instead of months thanks to automated translations.

• Adore Me also reports that stylist notes can be drafted in a fraction of the time and that the team now spends its hours refining and approving content rather than generating first drafts.

“We see limitless potential with generative AI.” – Ranjan Roy, SVP of Strategy at Adore Me

What Changed

Before adopting AI:

• Content teams spent days writing descriptions, translations and notes.

• Launching into a new geographic market could take months because every page needed translation and localization.

• The brand’s SEO strategy relied on manual research and copywriting.

• Stylist notes were repetitive and time‑consuming to produce.

After adopting AI:

• Product descriptions and translations are generated by specialized AI agents in minutes, then refined by humans.

• New‑market launches can happen in about 10 days because localized copy is produced quickly.

• SEO content is more consistent and targeted, leading to a 40 % increase in non‑branded search traffic.

• Stylists spend more time personalizing their notes and less time writing them from scratch.

Our POV: Why Marketers Should Care

Adore Me’s case demonstrates that AI doesn’t replace creativity – it removes the drudgery. By training agents on a style guide and clearly defined tasks, the brand avoided the “one size fits all” outputs that plague generic AI content generators. The human team still sets strategy, checks quality and refines messaging. AI simply handles the high‑volume, low‑risk work.

For lean marketing teams under pressure to produce more content across more channels, this offers a template:

• Define discrete, repeatable tasks where AI can help (e.g., product descriptions, translations, briefs).

• Train AI on your brand voice and style, not on generic prompts.

• Keep humans in the loop for final approval and nuanced language.

• Measure and iterate; AI is only as good as the data and guidance you provide.

The big strategic shift is moving from a reactive, project‑by‑project approach to a proactive content supply chain that is always on and always learning. The faster you can generate, test and refine content, the faster you can learn what works and adjust your marketing.

How to Measure It

To replicate or benchmark Adore Me’s success, focus on metrics that reflect both efficiency and performance:

• Time to market: Track how long it takes to launch a new product or geographic market before and after AI adoption.

• Hours saved per role: Measure the reduction in human writing time for product descriptions, translations and stylist notes.

• SEO performance: Monitor non‑branded search traffic, organic rankings and impressions. A sustained lift like Adore Me’s 40 % increase suggests the AI content is doing its job.

• Content velocity: Count how many unique pieces of content you can produce per week or per campaign.

• Quality and engagement: Use A/B testing to ensure AI‑assisted content performs at least as well as manually created content.

What This Signals for the Future

The Adore Me case shows that generative AI is moving beyond experiments into core marketing infrastructure. Content supply chains that once relied on manual labour and third‑party agencies can now be built in‑house with a combination of AI agents and human editors. This doesn’t just lower costs – it shortens feedback loops, allowing brands to learn faster and personalize messaging at scale.

As more companies adopt AI for marketing operations, the bar for content quality and speed will rise. Brands that invest in training AI on their voice and workflows will build defensible advantages. The bigger opportunity isn’t just efficiency, it’s freed‑up human creativity. When machines handle routine drafts, humans can focus on strategy, emotion and brand storytelling – the things machines still can’t do.