Hey Reader,
We want to start by mentioning that this week marks Himee’s last week working with the WTS team.
Over the past year, she’s brought so much creativity, care & energy to WTS.
We’d love your help amplifying Himee's brilliant portfolio showcasing her work to date. Any company would be incredibly lucky to have her.
If you’re hiring, know someone who is, or can simply engage with & share her post, it would make a massive difference.
We love you, Himee 💙💜
Let's get started =)
Brilliant work by our brilliant members
1) Camille Cunningham - A prompting guide for AI website builders
2) Crystal Carter - How To Get Your Website Ready For AI Agents
3) Helene Jelenc - 5 AI Workflows for Search Marketers
4) Laura I. Abreu - Google’s Smart Bidding Update: Why the Cheapest CPL Isn’t Always the Best Lead
5) Olesia Korobka - Brand Protection In AI Search: How To Audit And Defend Your Brand’s Identity
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If you’d like to recommend resources for future editions (& yes, please recommend your own work!), fill out this form:
12 days left to save £200 on WTSFest London
£399 now → £599 when speakers announced
We’re finalising our speaker lineup & with all the amazing pitches we received - this is not an easy feat!
We know buying a conference ticket before you know who’s speaking can be a bit of a leap.
If you've been before - you know the room is full of kind people, generous conversations, new ideas, & a genuine feeling that everyone wants to see each other succeed.
If you haven't been - take a look at what your network is saying about WTSFest.
Our speakers launch on 5th October & tickets move from £399 to £599 then as well.
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[WTSKnowledge] How AI Search Chooses What to Cite: Specificity, Reviews & Expertise
In research across 10 businesses & ~1,800 locations, a general page earned 61% of citations for a broad query.
When the question became more specific, a dedicated service page jumped to 86% of citations.
The next step after visibility is an important layer - 95% of consumers said they verify AI recommendations before acting on them.
Francesca Belmer breaks down how your website, listings, reviews, & real customer experiences all work together to help AI (& people!) find & choose your brand.
Myth vs Data: SEO in the AI era
The wonderful Danielle Escaro of Oncrawl, asked a group of SEOs (including brilliant WTSers, Crystal Carter & Kelly-Anne Crean) a simple question:
Is there something everyone in the industry believes about AI or SEO that you think is actually wrong?
6 experts. 6 myths busted by data. 6 episodes - 90-second each.
[Webinar] How to Package & Price Your AI Search Services
Antonia & Leon are keen to help agency owners & freelancers expand their services into new, sustainable revenue - all built from your existing AI search expertise.
They'll chat through things like:
- How should you package your AI search work?
- What should you charge?
- Standalone service? Add to existing retainer? Ongoing offering?!
- How to position your new service to get the right clients
Sign up to join live on 15th October or get the recording after
Join us at WTSFest
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October 1st 2026
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March 19th 2027
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From withFrontier:
[WTSNewsletter Series] How to Adapt Your Content for Different Markets
Series Author: Rayne Aguilar
Rayne manages all things content, communications, and SEO at Weglot, an AI website translation tool. She’s from the other side of the world, which is either great branding for a multilingual company or a convenient excuse to shorten the commute for pain au chocolat.
Part 3: Content Adaptation Strategies: How AI Changes What’s Possible (and What It Can’t Replace)
In part 2 of this series on content adaptation strategies, I covered how to approach market-level expression, cultural considerations, and ensuring your organization is fully aligned. Today I’ll be covering how AI changes what’s possible, and what it can’t replace.
Using AI to Adapt Content
The decisions, and documentation I’ve covered so far in this series is a great starting point, but it’s also where gaps can appear if you choose to work with AI translation technology.
For example, tone of voice guidelines written for human readers often rely on abstraction. These can take the form of general descriptors, analogies, or even visual cues to signal intent (such as emojis). Unfortunately, this doesn’t work well for Large Language Models (LLMs).
For example, finance company Qonto, found that feeding their existing guidelines directly into an AI localization assistant model produced poor output. This was because the documentation was written for people, not machines.
In order to improve the model’s output, they needed to rewrite the guidance using specific, concrete instructions with no room for interpretation. In other words, as machine-readable prompts.
Today, Qonto’s documentation now covers a universal tone of voice standard with market-specific sections for each language, complete with examples.
How AI Changes What’s Possible (and What It Can’t Replace)
At one point, Qonto’s local content teams were spending nearly half of their capacity on localization requests from other teams. The work was necessary, but the volume left little room for the editorial and strategic work these teams had to do.
These types of requests were an ideal job for an AI, as the models handle the following things well in a localization workflow:
- Volume. A well-configured assistant produces brand-aligned first-pass translations across multiple languages simultaneously, without bottlenecking on individual team capacity.
- Consistency. Paired with glossary rules or translation guidelines, the model applies the same terminology decisions across every page and language version, regardless of when the content was published.
- Optimization for Search and AI surfaces. Workflows can also incorporate technical elements like hreflang, metadata, URL slugs, internal linking, etc; which are important for both search and AI visibility.
- Speed. Content that previously took hours or days to localize now takes minutes to review and correct.
However, what AI can’t replicate is cultural and editorial judgment, such as whether a tone that works in one market is creating the wrong impression in another.
Kim Reyes from Qonto describes this as a shift toward a more journalistic approach to content: forming genuine opinions and investigating multiple perspectives. While the content team contributes and focuses on this aspect, AI can handle the volume.
Today, Qonto has a two-stage AI workflow to bridge the gap between speed and quality:
- Language-specific assistants handle first-pass translation into each target language
- Then a second AI agent evaluates the output against quality criteria.
The truth is you can’t adapt content effectively for new markets without a considered approach, thorough documentation, governance models, and organizational alignment. Brands that execute content adaptation successfully know their defining values, standards, and voice decisions in every context.
That’s when AI translation models come in – they can help teams scale output without sacrificing quality, and free up time for higher value work.
If you’re interested in seeing how a configured AI translation workflow handles brand voice across every language your business operates in, start a 14-day free Weglot trial.
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- Areej, Erin, & Himee
