When Assembly announced the rollout of Stagwell Search+ across APAC last month, it described the move as a fundamental shift away from search rankings and toward “share of prompt.” The idea is that as AI-driven search experiences increasingly shape brand discovery before users ever reach a website, traditional search metrics may no longer provide a complete picture of visibility.
The timing is significant. APAC has emerged as a global leader in AI search adoption, with 78% of users reporting weekly usage, according to Assembly. That scale makes the region both an opportunity and a stress test — multiple languages, cultural contexts, and varying model adoption across markets means a brand can appear authoritative in one market while remaining invisible or misrepresented in another.
Stagwell Search+, built in partnership with emberos, was developed to help brands monitor how they appear across AI models and languages. The platform then helps teams take action across paid, owned, earned, and shared media to improve visibility.
We spoke with Stephanie Wong, SEO Director, APAC at Assembly, to understand what the shift to AI-driven discovery means for brand marketers across the region, how “share of prompt” works in practice, and what separates the brands getting it right from those still relying on traditional measures of search performance.
Google recently announced its biggest overhaul to Search in 25 years — AI Mode has crossed a billion monthly users, and queries are doubling every quarter. How significant is that moment for brand marketers?
It’s hugely significant – it is a structural shift, not just a product update.
This is the point at which AI search stops being a feature and becomes the default behavior. When AI-driven interactions are used at scale, they quickly reset expectations. Search is no longer just a retrieval mechanism; it is becoming one of the primary environments where brand meaning, credibility, and preference are formed.
As AI search proves its ability to resolve real needs, usage compounds. This creates a powerful feedback loop that reinforces trust, driving repeat behavior, and accelerating user growth.
Search is no longer just a retrieval mechanism; it is becoming one of the primary environments where brand meaning, credibility, and preference are formed.
For brand marketers, this means discovery is shifting from “ranking for keywords” to “being the brand that AI recommends.” Users also are moving from keyword-based queries to more complex, conversational prompts. They are asking for recommendations, comparisons, and validation. That moves search much closer to the point of decision.
Brand meaning is increasingly being shaped by machine-generated systems before consumers interact directly with a brand. How much does that change how marketers need to think and operate?
Historically, brands had far more control over their narrative through owned channels and paid media. Now, a large part of that narrative is being crafted independently by AI systems, using signals from across the open web. Your consumers are making decisions about your brand before they even land on your website.
This shifts the focus away from messaging in isolation toward how machines interpret a brand. Authority, consistency, and clarity across paid, owned, earned and shared channels become critical because those are the signals AI models rely on when generating answers.
Your consumers are making decisions about your brand before they even land on your website.
It also requires moving from campaign-based thinking to system-based thinking. You are not just launching an activity anymore. You are continuously shaping a layer of information that AI systems draw from.
The brands adapting fastest recognize that AI visibility cannot be treated as a siloed channel. It reflects the combined impact of all online media working together to shape how a brand is understood, and recommended, on LLMs.
APAC presents a uniquely fragmented environment, with multiple LLMs, languages, and cultural contexts. What does that look like on the ground for brands operating across the region?
Fragmentation is already a defining characteristic of APAC, and AI is amplifying that rather than simplifying it.
Different markets are adopting different models. Language is not just a translation challenge; it also affects how queries are structured and how responses are generated. Cultural context also shapes what is considered relevant, credible, or persuasive.
Fragmentation is already a defining characteristic of APAC, and AI is amplifying that rather than simplifying it.
On the ground, this means a brand can appear strong and authoritative in one market, while being inconsistent or absent in another, depending on how those models are trained and what data they prioritize.
For regional brands, this makes a standardized approach ineffective. There needs to be central governance of brand signals, but also enough flexibility to adapt locally within each market and model. Visibility has to be measured market by market and model by model. Without that level of detail, it is very easy to operate with significant blind spots.
How do you define “share of prompt” in practical terms — and how can brands tell whether they’re gaining or losing visibility in AI-driven discovery?
Share of prompt reflects how a brand shows up within AI-generated answers, which is increasingly where discovery and consideration are happening before a user ever visits a website.
In practical terms, it measures how often your brand is included when AI models generate responses to relevant prompts, how prominently it appears, and how it is positioned in relation to competitors. It is not just visibility, but influence within the answer itself.
To understand whether you are gaining or losing ground, brands need consistent tracking across prompts, models, and markets. That is particularly important in APAC, where visibility is not uniform.
The signal that matters is whether your presence is increasing across the prompts that shape decisions, and whether your brand is being surfaced in a way that strengthens consideration rather than diluting it.
Assembly recently rolled out Stagwell Search+ across APAC to help brands monitor how they appear across AI-driven search environments. When the system identifies that a brand is invisible or misrepresented in a particular model, what happens next? How do teams respond to those signals in practice?
When Search+ Operating System identifies a gap through Sonar, the agent immediately quantifies the severity of the issue.
From here, it gives teams clarity on where and how that gap is happening across specific prompts, specific models, or markets. It then generates Fix Packs through Pilot which are assignable work units designed to change the underlying data that AI models rely on. The Fix Packs are not isolated to SEO or website changes alone. It looks at the full ecosystem across paid, owned, earned, and shared media – something that typically requires multiple specialists on one campaign to achieve.
Teams will then take targeted actions to strengthen these signals, but it’s important to note they do not just respond to every signal. They use a Predicted ROI Efficiency score within Pilot to prioritize which Fix Packs will provide the greatest lift in Share-of-prompt. Once a Fix Pack is implemented in Flow, it becomes a starting point for the platform to measure the lift, creating a feedback loop that enhances the accuracy of the model confidence index as more Fix Packs are implemented.
What makes Search+ a different approach to this problem — and what were the limitations of how brands were trying to manage AI visibility before?
Search+ represents a shift from passive observation to active control.
Previously, brands had no real way to manage AI visibility. It was either treated as a black box or approached through traditional SEO tactics that weren’t built for how LLMs generate answers. Teams could spot issues, but they couldn’t diagnose root causes, prioritize actions, or prove impact.
Search+ changes that by turning AI visibility into a structured, governable system.
Instead of guessing, brands can now quantify how they appear, understand why gaps exist, and identify the signals driving performance — powered by the Brand Knowledge Graph, which continuously maps how models understand the brand across the ecosystem.
Most importantly, it closes the loop.
Where other solutions stop at insight, Search+ translates diagnosis into targeted Fix Packs — clear, assignable actions across owned, earned and paid media — and measures the impact through a closed-loop system tied to real outcomes.
In short, Search+ moves brands from reacting to AI visibility to actively managing it as a measurable, controllable driver of business performance.
The platform is designed to guide human decision-making rather than automate changes directly into platforms. Why was it important to maintain that human layer?
The decision to maintain a human layer is deliberate — and central to how Search+ differentiates.
Most solutions are optimizing for speed and scale, often driving automated actions or mass content generation. This leads to “AI slop” — high-volume, low-differentiation activity that erodes trust and weakens how models understand a brand over time.
Search+ takes a different approach.
The OS is agentic — it continuously monitors visibility, identifies gaps, and predicts impact — but it is intentionally not built to auto-execute. Instead, it surfaces what should change and why, leaving decisions to practitioners who understand brand, context, and market nuance.
This matters because AI search is probabilistic, not deterministic. Automating responses without oversight risks inconsistency, misinformation, and misaligned positioning — especially in complex markets like APAC.
Search+ is therefore a decisioning system, not a production engine:
- Detects and prioritizes opportunities across POES
- Translates insight into actionable Fix Packs
- Measures impact in a closed-loop system
Execution remains human-led.
That balance enables precision over volume — protecting brand integrity, avoiding short-term optimization, and ensuring actions align to broader strategy.
In a market moving towards automation, Search+ is deliberately built to do the opposite: augment human decision-making, not replace it with scale.
In general, for brands navigating the shift in consumer search trends, what separates the ones getting it right from the ones that aren’t?
The difference comes down to whether brands have recognized that the moment of influence has already moved upstream — into AI-generated answers.
The brands getting it right understand that discovery and consideration now happen before the click, inside experiences where options are framed and recommended by models. They are actively managing how they are represented and compared within those answers, not just how they rank outside of them.
They are also not treating this as an SEO problem. Instead, they take a system-level approach, aligning content, PR, social, paid, and broader signals to shape how AI models interpret their brand — rather than optimizing channels in isolation.
The brands getting it right understand that discovery and consideration now happen before the click, inside experiences where options are framed and recommended by models.
Critically, they prioritize quality over volume, avoiding the trap of mass AI-generated content and focusing on building credible, differentiated signals that models can trust.
Finally, they measure success differently — focusing on visibility, representation, and share of prompt, not just rankings or traffic.
The brands falling behind are still optimizing for a link-based version of search, reacting too late in the journey — after decisions have already been shaped.
