AI Search & E-Commerce: How to Make Your Ad Spend Work Smarter, Not Harder

The rise of AI in search, with Google’s AI Overviews and emerging LLM‑driven search, is transforming how fashion and e‑commerce brands are discovered online. For small and medium businesses, the good news is clear: you don’t need to spend more…you need to spend smarter.

1. AI Overviews Are Changing the Organic Landscape

In July 2025, Semrush published a study on how AI Overviews are impacting SEO practices. The study, which analysed over 10 million keywords, shows that AI Overviews now appear in 13.14% of all U.S. desktop queries as of March 2025, up from just 6.49% in January. That’s a 102% jump in two months alone.

The purpose of this article is to unpack what those numbers mean for fashion and e-commerce businesses on a practical level.

While, the share of 13% may sound small, in search terms it’s huge. Even a low double-digit share represents millions of queries daily now answered by AI before a user scrolls down the page. For small brands, this is the slice of organic discovery you could already be losing.

These Overviews tend to target informational queries (about 88% of them), meaning “best of” or “how to” style searches, where users previously clicked into blog content or product guides.

👉 In plain terms: The types of blog posts or style guides that SMEs often use to attract traffic (“5 ways to wear linen this summer”) are exactly what AI Overviews are now summarising. If your site depended on that kind of content for reach, you’ll likely see declining clicks.

Semrush’s visuals illustrate this well:

A graph of the monthly share of queries triggering AI Overviews (January → March spike) shows how rapidly AI is being integrated into results. The steep curve is a warning signal that adoption is accelerating faster than most marketers planned for.

A breakdown by intent type shows informational queries dominate. Commercial/transactional queries are still relatively untouched, which is good news. It means your paid ads and shopping listings for “buy red maxi dress online” still have space to compete.

Meanwhile, zero-click rates, often blamed on AI Overviews, haven’t exploded. In fact, for the same keywords, the rate dipped slightly (from 38.1% to 36.2%) after AI Overviews began showing, suggesting a more nuanced user behaviour.

Rather than killing clicks entirely, AI Overviews may be redistributing them. Users still click through, but now the bar is higher, your content must either be the cited source inside the Overview, or be compelling enough to attract clicks below it.

2. AI Overviews vs. LLM Search: Two Different Paths to Visibility

Google AI Overviews

To understand how Google’s AI Overviews behave in practice, here’s a breakdown of the kinds of queries they target, and where they tend to leave space for paid ads and organic results:

• Appear at the top of search results, synthesising answers from ~3 domains

• Favour low‑CPC, low‑difficulty informational terms (e.g. “what is BMR”, “how to clean shower drain”) (Financial Times)

• Leave transactional queries (e.g., “buy red maxi dress online”) largely untouched, for now

If you are spending ad dollars, don’t compete for clicks where AI is eating the pie. Focus instead on transactional searches where AI Overviews rarely show up, and where your ad spend can still deliver high-intent customers.

Semrush shows this clearly: a scatter of keyword examples where AI Overviews dominate informational but not high-CPC, bottom-funnel terms. This reinforces that CPC campaigns should be weighted towards lower-funnel conversions, not top-funnel awareness.

AI Overview Trigger Examples:

LLM Search (AI Mode, ChatGPT, Perplexity):

Unlike Google’s AI Overviews, which are tightly focused and mostly cite three domains, LLM search casts a wider net. For example, Google’s AI Mode (LLM) pulls from around seven domains in a conversational sidebar, with far less overlap with traditional SERP rankings (Semrush Blog)..

Another important distinction is where LLMs pull their data from. Instead of leaning heavily on official websites, they often cite user-generated content (UGC) such as Reddit threads, YouTube reviews, and influencer blogs. Their responses are also longer and more narrative, weaving together multiple perspectives instead of linking to one authoritative page. 

For fashion brands, this means reviews, influencer mentions, and community discussions can matter as much as ranking on Google.

Monetisation is still evolving, but sponsored or affiliate-style placements are expected. CPC competition will likely shift into this space soon, under new rules.

👉 Practical takeaway: While you can’t yet “buy” placement in ChatGPT results, you can seed your brand into the ecosystems it cites: by encouraging reviews, influencer content, and structured data feeds. When CPC models arrive, those who are already visible organically will have a stronger baseline.

3. Smart CPC Strategy for Fashion & E-Commerce

1. Shift ad spend to high-intent, bottom-funnel queries
Since AI Overviews dominate informational searches, brands should focus bids on transactional queries where ads still shine: e.g., “buy linen maxi dress”, “women’s red evening gown size M”.

2. Lean into visual ad formats
Shopping carousels and video ads sit outside AI Overview blocks and continue to capture attention effectively.

3. Make your product catalogue AI-friendly
This means maintaining up-to-date product feeds (price, availability, SKU, style) and embedding schema markup (product, offer, FAQ, breadcrumb). These increase chances for inclusion in AI Overviews and LLM citations.

4. Boost customer retention
With organic discovery flattening, retargeting and loyalty marketing become even more valuable for maintaining conversion volume. Nurture your existing customers.

5. Prepare for LLM ad formats
Ensure your products and brand are present in sources LLMs draw from (reviews, influencers, UGC), and be ready to compete when CPC-style placements appear in chat-based search environments.

4. The Summary: Work Smarter, Not Harder

Why This Matters

AI Overviews are compressing the upper funnel, but not eliminating it. By understanding where AI currently lands (informational queries) and where it doesn’t (transactional queries), brands can redirect ad spend to where it still delivers return.

Similarly, by preparing for LLM ecosystems, making your content visible, structured, and narrative, you future-proof for the next wave of search: conversational, AI-powered discovery.

For small and medium fashion businesses, this isn’t about bigger budgets; it’s about smarter allocation. At FashionTech Middle East, we believe being data-savvy and structurally ready is your superpower in this evolving AI-first search world.