Poland-based B2B startup Demoboost is betting that product demos can do far more than help close a deal. The Warsaw-based company has raised €2.8 million to turn demos into a source of revenue intelligence, at a moment when B2B sales […]
AI search visibility in beauty is increasingly shaped before a prompt is ever entered.
Brands that appear in generative answers are often those already discussed, validated, and reinforced across social platforms. By the time a user turns to AI search, much of the groundwork has been laid.
Using the beauty category as a lens, this article examines how social discovery influences brand visibility – and why AI search ultimately reflects those signals.
Discovery didn’t move to AI – it fragmented
Brand discovery has fragmented across platforms. AI tools influence mid-funnel consideration, but much discovery happens before a user enters a prompt.
The signals that determine AI visibility are formed upstream. By the time a user reaches generative search, preferences and perceptions may already be set. If brands wait until AI search to influence demand, the window to shape consideration has narrowed.
That upstream influence is increasingly social. Roughly two-thirds of U.S. consumers now use social platforms as search engines, per eMarketer research.
This shift extends beyond Gen Z and reflects how people validate information and discover brands. These same platforms consistently appear among the top citation sources in AI results. The dynamic is especially visible in the beauty category.
In a study our agency conducted with a beauty brand partner, we found that Reddit, YouTube, and Facebook ranked among the top cited domains in both AI Overviews and ChatGPT.
While Reddit is often viewed as an anti-brand environment, YouTube appears nearly as frequently in citation data, making it a logical and underutilized target for citation optimization.
The volume reality: Social behavior still outpaces AI
It’s easy to focus on headline figures around AI usage, including the billions of prompts processed daily. But when measured against business outcomes such as traffic and transactions, the scale looks different.
Social platforms are already embedded in mainstream search behavior. For many users, search-like activity on platforms such as TikTok and YouTube is habitual. Nearly 40% of TikTok users search the platform multiple times per day, and 73% search at least once daily.
Referral data reinforces the contrast. ChatGPT referral traffic accounted for roughly 0.2% of total sessions in a 12-month analysis of 973 ecommerce sites, a University of Hamburg and Frankfurt School working paper found. In the same dataset, Google’s organic search traffic was approximately 200 times larger than organic LLM referrals.
AI search is growing and strategically important. But in terms of repeat behavior, measurable sessions, and downstream transactions, social platforms and traditional search continue to operate at a substantially larger scale.
The validation loop: Why AI needs social
The most critical contrarian point for 2026 is that optimizing for social is also optimizing for AI. Large language models are not primary sources of truth. They function as mirrors, reflecting the consensus formed through human conversations in the data they are trained on.
AI systems also demonstrate skepticism toward brand-owned properties. One study found that only 25% of sources cited in AI-generated answers were brand-managed websites.
At the same time, AI engines prioritize third-party validation. Up to 6.4% of citation links in AI responses originated from Reddit, an analysis by OtterlyAI found. This outpaces many traditional publishers.
There’s also a measurable relationship between sentiment and visibility. Research shows a moderate positive correlation between positive brand sentiment on social media and visibility in AI search results.
Treating video as a “brand channel” or a social-first effort rather than a search surface is a strategic failure.
On platforms such as TikTok and YouTube, ranking signals are shaped by spoken language, on-screen text, and captions – signals AI crawlers increasingly use to “triangulate trust.”
In the beauty category, for example, ChatGPT accounts for about 4.3% of searches, while Google processes roughly 14 billion searches per day. However, for “how-to” and technique-based queries, consumers favor the detailed, personalized guidance of social-first video content.
Science-backed brands such as Paula’s Choice and CeraVe dominate AI-generated results because they publish deep, structured educational content. Meanwhile, more traditional marketing-led brands are significantly less visible.
The phrase “dermatologist recommended” correlates with high visibility in AI results because large language models treat expert social proof as a primary ranking signal, according to the same report.
Breaking the high-production barrier: Creating content at scale
One of the biggest hurdles brands cite is budget. Many believe they need a Hollywood production crew to compete in video environments. That is a legacy mindset.
In today’s environment, high-gloss production can be a deterrent. The current landscape rewards authenticity over polish. Consumers are looking for real people with real skin concerns, not highly filtered commercials.
Optimizing for video discovery doesn’t require filmmaking expertise. Brands can leverage internal talent without adding headcount.
Partner with creator platforms: Platforms such as Billow or Social Native allow brands to work with creators for as little as $500 per video. When mapped to a high-intent query, that investment can drive measurable search visibility outcomes.
Leverage social natives on staff: Often, the strongest asset is internal. Identify team members who are active on platforms such as TikTok and understand platform dynamics. Creating internal incentives or challenges to produce content can generate a steady stream of authentic assets while contributing to culture.
Make strategy the differentiator: A large following is not a prerequisite for visibility. In one case, a TikTok profile built from scratch with one part-time creator at $2,500 per month generated hundreds of thousands of views within 90 days. The focus was not on viral trends, but on meaningful transactional terms that drive revenue.
If a new profile can reach more than 100,000 views per video within three months on a limited budget, the barrier isn’t equipment. It’s clarity on the business case and disciplined execution.
The data is clear. Brands can’t win the generative engine if they’re losing the social conversation.
AI models function as mirrors, reflecting web consensus. If real users on Reddit, YouTube, and TikTok aren’t discussing a brand, AI systems have little to surface.
If marketers wait until a user reaches a ChatGPT prompt to shape perception, the opportunity has already narrowed.
Discovery happens upstream. Validation occurs in the loop between social proof and algorithmic citation.
Translating this into action requires rethinking team structure and priorities:
Stop the silos: Your SEO and social teams shouldn’t speak different languages. Both must focus on search surfaces.
Prioritize the “why” before the “what”: Don’t just fix a technical tag. Build the business case for how social sentiment and expert validation drive market share.
Embrace scrappy execution: Whether through $500 creator partnerships or internal social-native talent, start building authentic assets now.
We’re witnessing a shift from algorithm-driven discovery to community-driven discovery.
It’s agile and multidisciplinary, and when executed well, it can meaningfully impact the bottom line.
As consumers lean into AI search, the industry has focused on the technical “how” – tracking everything from Agentic Commerce Protocols (ACP) to ChatGPT’s latest shopping research tools. In doing so, it often misses the larger shift: conversational search, which is changing how visibility is earned.
There’s a common argument that big brands will always win in AI. I disagree. When you move beyond the “best running shoes” shorthand and look at the deep context users now provide, the playing field levels. AI is trying to match user needs to specific solutions, and it’s up to your brand to provide the details.
This article explains how conversational search changes product discovery and what ecommerce teams need to update on product detail pages (PDPs) to remain visible in AI-driven shopping experiences.
How conversational search builds on semantic search
While semantic search is critical for understanding the meaning and context of words, conversational search is the ability to maintain a back-and-forth dialogue with a user over time.
Semantic search is the foundation for conversational visibility. Think of it like a restaurant: If semantic search is the chef who knows exactly what you mean by “something light,” conversational search is the waiter who remembers that you’re ordering for dinner.
Feature
Semantic search
Conversational search
Goal
To understand intent and context
To handle a flow of questions
How it thinks
It knows “car” and “automobile” are the same thing
It knows that when you say “how much is it?”, “it” refers to the car you just mentioned
The interaction
Searching with a phrase instead of keywords
Having a chat where the computer remembers what you were asking about before
Example
Asking “What is a healthy meal?” and getting results for “nutritious recipes.”
Asking “What is a healthy meal?” followed by “give me a recipe for that.”
AI blends them together. It uses semantic understanding to decode your complex intent and conversational logic to keep the thread of the story moving. For brands, this means your content has to be clear enough for the “chef” to interpret and consistent enough for the “waiter” to follow.
What conversational search and AI discovery mean for ecommerce
I recently shared how my mom was using ChatGPT to remodel her kitchen. She didn’t start by searching for “the best cabinets.” Instead, she leveraged ChatGPT as her pseudo-designer and contractor, using AI to solve specific problems.
Product discovery happened naturally through constraint-based queries:
“Find cabinets that fit these dimensions and match this specific wood type.”
“Are these cabinets easy for a DIY installation?”
Her conversations were piling up, allowing her to reach multiple solutions at once. Her discovery journey was layered. When ChatGPT recommended products to complete her tasks, she simply followed up with, “Where can I buy those?”
Brands and marketers need to stop optimizing for keywords and start optimizing for tasks. Identify the specific conversations where your product becomes the solution. If your data can’t answer the “Will this fit?” or “Is this easy?” questions, you won’t be part of the final recommendation.
“Recommend products” is the top task users trust AI to handle, highlighting a clear opportunity for brands, according to Tinuiti’s 2026 AI Trends Study. (Disclosure: I am the Sr. Director of AI SEO Innovation at Tinuiti.)
For your brand to be the one recommended, your PDPs must provide the “ground truth” details these assistants need to make a confident selection.
Step away from the keyword research tools and stop asking for “prompt volumes.” In an AI-driven world, intent is more important than volume. Before changing a single page, you need to understand the high-intent journeys your personas are actually taking.
To identify your high-intent semantic opportunities:
Audit your personas: Who is your buyer, and what are their non-negotiable questions? If you haven’t mapped these lately, start there.
Bridge the team gap: Talk to your product and sales teams. They know the specific attributes and “deal-breaker” details that actually drive conversions.
Listen to the market: Use sentiment analysis and social listening to find hidden use cases or brand problems. How are people actually using, or struggling with, your product in ways your brand team hasn’t considered?
Map constraints, not keywords: Identify the specific constraints (size, compatibility, budget) that AI agents use to filter recommendations.
How to build PDPs for AI search with decision support
Your PDP should operate like a product knowledge document and be optimized for natural language. This helps an AI system decide whether to recommend the product for a specific situation.
Name your ideal buyer and edge cases
Content should support better decision-making. Audit your PDPs to determine whether they provide enough detail on who the product is best for – and not for. Does the page explicitly name your ideal buyer, their skill level, lifestyle constraints, and deal-breakers?
AI shopping queries often include exclusions, and clearly outlining the important parts of your user search journey will help you understand where your products fit best.
Cover compatibility and product specifications
Compatibility feels synonymous with electronics (e.g., “Will my headphones connect to this computer?”). But think beyond one-to-one compatibility and expand into lifestyle compatibility:
Is this laptop bag waterproof enough for a 20-minute bike ride in the rain, and does it have a clip for a taillight?
Can I fit a Kindle and a book in this purse?
Will this detergent work with my HE washer?
Will this carry-on suitcase fit in the overhead compartment on every airline?
Is this “family-sized” cutting board actually small enough to fit inside a standard dishwasher?
People are searching for how products fit into their lifestyle needs. Highlight and emphasize the features that make your products compatible with their lifestyle.
Breaking down your customer search journey and listening to your customers’ concerns, either through AI sentiment analysis, social listening, or product reviews, will help you understand what you need to be specific about.
Apparel brands should add sizing and fit guidance. Maybe you’re comparing your size 10 jeans to competitors’ sizing, or considering sizing changes based on the cut or style of your other jeans.
Beauty or skincare brands need ingredient combination details. Is this product compatible with other common formulas? Can I layer it over a vitamin C serum?
Toy brands could include important details for parents. Does your product need to be assembled, and how long will it take? Can they assemble it the night before Christmas?
If your biggest customer complaint is understanding when and how to use your products, you’re likely not making it easy enough for them to buy. Better defining your product attributes helps users and LLMs alike better understand your products.
Write for constraint matching instead of browsing
AI shopping discovery is driven by constraints instead of keywords. Shoppers aren’t asking for “the best laptop bag.” They’re asking for a bag that fits under an airplane seat, survives a rainy commute, and still looks professional in a meeting.
PDPs should be written to reflect that reality. Audit your product pages to see whether they answer common “Can I …?” and “Will this work if …?” questions in plain language. These details often live in reviews, FAQs, or support tickets, but rarely surface in core product copy where AI systems are most likely to pull from.
Here’s what transforming your content can look like:
Traditional PDP copy
Laptop backpack
Water-resistant polyester exterior.
Fits laptops up to 15″.
Multiple interior compartments.
Lightweight design.
USB charging port.
PDP copy written for constraints
Laptop backpack
Best for: Daily commuters, frequent flyers, and students who need to carry tech in unpredictable weather.
Not ideal for: Extended outdoor exposure or laptops larger than 15.6″.
Weather readiness: Water-resistant coating protects electronics during short walks or bike commutes in light rain, but is not designed for heavy downpours.
Travel compatibility: Fits comfortably under most airplane seats and in overhead bins on domestic flights.
Capacity and layout: Holds a 15-15.6″ laptop, charger, and tablet, with room for a book or light jacket – but not bulky items.
Lifestyle considerations: Integrated USB port supports charging on the go (power bank not included).
LLMs evaluate how well a product satisfies specific constraints in conversational queries or based on predetermined user preference information.
PDPs that clearly articulate those constraints are more likely to be selected, summarized, and recommended. This type of copy should also help your on-site customers better understand your products.
Just because search platforms change doesn’t mean we should abandon everything we’ve learned in traditional optimization.
Technical SEO fundamentals still heavily apply in AI search:
Can crawlers access and index your site?
Are your product listing pages (PLPs) and PDPs clearly linked and structured?
Do pages load quickly enough for crawlers and users?
Is your most critical content accessible?
In conversational shopping, structured data is playing a different role than it did in traditional SEO strategies. In conversational shopping, it’s about verification.
AI systems use your schema to validate facts before they risk reusing them in an answer. If the AI can’t verify your price, availability, or shipping details through a merchant feed or structured data, it won’t risk recommending you.
Variant clarity is just as important. When differences like size, color, or configuration aren’t clearly defined, AI systems may treat variants as separate products or merge them incorrectly. The result is inaccurate pricing, incompatible recommendations, or missed visibility.
Most importantly, structured data must match what’s visibly true on the page. When schema contradicts on-page content, AI systems avoid recommending uncertain information.
Success on the digital shelf has moved beyond high-volume keywords. In this new era, your visibility depends on how well you satisfy the complex constraints users can provide in a single search. AI models are scanning your pages to see if you meet specific, nuanced requirements, like “gluten-free,” “easy to install,” or “fits a 30-inch window.”
The shift to conversational discovery means your product data must be ready to sustain a dialogue. The goal is simple: provide the density of information necessary for an AI to confidently transact on a user’s behalf. Those who build for these multi-layered journeys will own the future of discovery.
For more than two decades, Amazon has built its dominance by owning every step of the online shopping process. People discovered products on Amazon, researched them on Amazon, and relied on Amazon to deliver them. That control gave sellers little […]
The startup claims that it has developed a "hack-proof" chip that can't be bypassed by devices like Flipper Zero, a widely available hacking tool that can be used to tamper with wireless systems. These chips are linked with digital certificates to verify the authenticity of the products.
Agentic AI is increasingly appearing in leadership conversations, often accompanied by big claims and unclear expectations. For SEO leaders working with ecommerce brands, this creates a familiar challenge.
Executives hear about autonomous agents, automated purchasing, and AI-led decisions, and they want to know what this really means for growth, risk, and competitiveness.
What they don’t need is more hype. They need clear explanations, grounded thinking, and practical guidance.
This is where SEO leaders can add real value, not by predicting the future, but by helping leadership understand what is changing, what isn’t, and how to respond without overreacting. Here’s how.
Start by explaining what ‘agentic’ actually means
A useful first step is to remove the mystery from the term itself. Agentic systems don’t replace customers, they act on behalf of customers. The intent, preferences, and constraints still come from a person.
What changes is who does the work.
Discovery, comparison, filtering, and sometimes execution are handled by software that can move faster and process more information than a human can.
When speaking to executive teams, a simple framing works best:
“We’re not losing customers, we’re adding a new decision-maker into the journey. That decision-maker is software acting as a proxy for the customer.”
Once this is clear, the conversation becomes calmer and more practical, and the focus moves away from fear and toward preparation.
Another important role for SEO leaders is to slow the conversation down. Agentic behavior will not arrive everywhere at the same time. Its impact will be uneven and gradual.
Some categories will see change earlier because their products are standardized and data is already well structured. Others will move more slowly because trust, complexity, or regulation makes automation harder.
This matters because leadership teams often fall into one of two traps:
Panic, where plans are rewritten too quickly, budgets move too fast, and teams chase futures that may still be some distance away.
Dismissal, where nothing changes until performance clearly drops, and by then the response is rushed.
SEO leaders can offer a steadier view. Agentic AI accelerates trends that already exist. Personalized discovery, fewer visible clicks, and more pressure on data quality are not new problems.
Agents simply make them more obvious. Seen this way, agentic AI becomes a reason to improve foundations, not a reason to chase novelty.
Change the conversation from rankings to eligibility
One of the most helpful shifts in executive conversations is moving away from rankings as the main outcome of SEO. In an agent-led journey, the key question isn’t “do we rank well?” but “are we eligible to be chosen at all?”
Eligibility depends on clarity, consistency, and trust. An agent needs to understand what you sell, who it is for, how much it costs, whether it is available, and how risky it is to choose you on behalf of a user. This is a strong way to connect SEO to commercial reality.
Questions worth raising include whether product information is consistent across systems, whether pricing and availability are reliable, and whether policies reduce uncertainty or create it. Framed this way, SEO becomes less about chasing traffic and more about making the business easy to select.
Explain why SEO no longer sits only in marketing
Many executives still see SEO as a marketing channel, but agentic behavior challenges that view.
Selection by an agent depends on factors that sit well beyond marketing. Data quality, technical reliability, stock accuracy, delivery performance, and payment confidence all play a role.
SEO leaders should be clear about this. This isn’t about writing more content. It’s about making sure the business is understandable, reliable, and usable by machines.
Positioned correctly, SEO becomes a connecting function that helps leadership see where gaps in systems or data could prevent the brand from being selected. This often resonates because it links SEO to risk and operational health, not just growth.
For most ecommerce brands, the earliest impact of agentic systems will be at the top of the funnel. Discovery becomes more conversational and more personal.
Users describe situations, needs, and constraints instead of typing short search phrases, and the agent then turns that context into actions.
This reduces the value of simply owning category head terms. If an agent knows a user’s budget, preferences, delivery expectations, and past behavior, it doesn’t behave like a first-time visitor. It behaves like a well-informed repeat customer.
This creates a reporting challenge. Some SEO work will no longer look like direct demand creation, even though it still influences outcomes. Leadership teams need to be prepared for this shift.
Reframe consideration as filtering, not persuasion
The middle of the funnel also changes shape. Today, consideration often involves reading reviews, comparing options, and seeking reassurance.
In an agent-led journey, consideration becomes a filtering process, where the agent removes options it believes the user would reject and keeps those that fit.
This has clear implications. Generic content becomes less effective as a traffic driver because agents can generate summaries and comparisons instantly. Trust signals become structural, meaning claims need to be backed by consistent and verifiable information.
In many cases, a brand may be chosen without the user being consciously aware of it. That can be positive for conversion, but risky for long-term brand strength if recognition isn’t built elsewhere.
Executives care about measurement, and agentic AI makes this harder. As more discovery and consideration happen inside AI systems, fewer interactions leave clean attribution trails. Some impact will show up as direct traffic, and some will not be visible at all.
SEO leaders should address this early. This isn’t a failure of optimization. It reflects the limits of today’s analytics in a more mediated world.
The conversation should move toward directional signals and blended performance views, rather than precise channel attribution that no longer reflects how decisions are made.
Promote a proactive, low-risk response
The most important part of the leadership discussion is what to do next. The good news is that most sensible responses to agentic AI are low risk.
Improving product data quality, reducing inconsistencies across platforms, strengthening reliability signals, and fixing technical weaknesses all help today, regardless of how quickly agents mature.
Investing in brand demand outside search also matters. If agents handle more of the comparison work, brands that users already trust by name are more likely to be selected.
This reassures leaders that action doesn’t require dramatic change, only disciplined improvement.
Agentic AI changes the focus, not the fundamentals
For SEO leaders, agentic AI changes the focus of the role. The work shifts from optimizing pages to protecting eligibility, from chasing visibility to reducing ambiguity, and from reporting clicks to explaining influence.
This requires confidence, clear communication, and a willingness to challenge hype. Agentic AI makes SEO more strategic, not any less important.
Agentic AI should not be treated as an immediate threat or a guaranteed advantage. It’s a shift in how decisions are made.
For ecommerce brands, the winners will be those that stay calm, communicate clearly, and adapt their SEO thinking from driving clicks to earning selection.
That is the conversation SEO leaders should be having now.
A much-expected move by a football powerhouse has arrived with North Dakota State's decision to step up to the college game's highest level, jumping from the FCS Missouri Valley Football Conference to the FBS Mountain West.
On Monday, North Dakota State announced the move on its website, settling a hot topic on social media over the weekend.
In a story published Sunday by the Fargo-Moorhead Forum, NDSU Athletic Director Matt Larsen told the newspaper that the school signed a contract last week with the Mountain West, a Football Bowl Subdivision league. The Missouri Valley is a Football Championship Subdivision conference, a notch below FBS.
Larsen told the newspaper on Sunday that NDSU signed an agreement that same day with the Mountain West. The school will pay the NCAA a $5 million transition fee for FBS membership, and $12.5 million to the Mountain West, the Forum reported, with private donors expected to cover those costs.
NDSU won 10 FCS titles between 2011 and 2024.
The Bison's jump follows their 8-0 record in MVFC play last season and a 12-1 overall record. NDSU's lone loss came in the FCS Playoffs to conference foe Illinois State. The Redbirds ended up advancing to the national championship game in Nashville where they fell in double overtime to Montana State of the Big Sky Conference.
MVFC commissioner Jeff Jackson lauded the departing Bison's performance as a Valley member.
"North Dakota State University has been a distinguished and esteemed member of the [MVFC] for the past 18 seasons, significantly contributing to the conference's unparalleled success. We extend our best wishes to the Bison in their future endeavors and know that the MVFC will continue to maintain its position as the preeminent FCS conference."
As far as the impact on next season's MVFC schedule, Mike Kern, the Valley's senior associate commissioner, confirmed to the Tribune-Star on Monday that the league schedule will be adjusted so all nine teams will play a complete round-robin schedule of eight games. Indiana State was set to play at NDSU on Nov. 7. With the schedule change, that means ISU will now play Northern Iowa. However, dates, times and locations for a new schedule are to be determined, Kern said.
The all-time MVFC series between ISU and NDSU ends with the Bison in control 12-1 with ISU's lone victory in the series coming back in 2012 in Fargo when the Sycamores slipped by NDSU 17-14 in the Fargodome. Last season's matchup saw the Sycamores lose 38-7 in Terre Haute after a tight battle early was interrupted by a thunderstorm delay, and the Sycamores' initial momentum faded.
On Monday, ISU declined comment beyond the Missouri Valley's statement.
The deal will see Uber paying $335 million at the outset to purchase Getir's food delivery business. Uber will also pay $100 million for a 15% stake in Getir's grocery, retail, and water delivery business.
Login
