For a long time, contextual advertising worked like a matching game. A recipe page showed a cookware ad. A marathon training guide showed running shoes. It was relevant, privacy-safe, and it worked at scale.

But people don’t experience a web page as a list of keywords.

They read a recipe differently on a rushed Tuesday night than on a slow Saturday morning. They feel something when they watch a tense final quarter or a quiet documentary. And what they feel shapes what they notice, remember, and decide.

That’s where relevance reaches its ceiling. An ad can sit next to the right topic and still miss the moment. And as the signals the industry once used to track users fade, that gap matters more than ever.

Most contextual advertising services still stop at classifying content. The next step is understanding the moment: what people are interested in, how they feel, and what they’re ready to do next. At Seedtag, we call it Neuro-Contextual Advertising. Here’s how contextual got here, and what comes next for brands and agencies.

Key Takeaways

• Contextual advertising services place ads based on the content people engage with, not on who they are.
• Unlike behavioral advertising, contextual targeting doesn’t need to track users or rely on third-party data.
• Relevance has a ceiling: an ad can match the topic and still miss the moment.
• Contextual advertising has evolved from keyword matching to natural language processing, and Neuro-Contextual Advertising is the next step: understanding the moment.
• Liz, our Neuro-Contextual AI, reads interest, emotion, and intent to align ads with how a moment feels, without personal data.

What Are Contextual Advertising Services?

Contextual advertising services help brands serve ads based on the content of a web page, video, or stream, rather than on a person's browsing history. The ad follows the context. If someone is reading about a weekend camping trip, an outdoor gear brand can appear in that same environment.

It's a simple principle, and one of the oldest in advertising.

Long before digital, brands chose magazines, TV shows, and radio programs because the content matched their products or services. Contextual ads bring that same logic to the open web, video, and CTV, at scale and in real time.

So how does contextual advertising work in practice? Each time content loads, the technology analyzes it and decides whether it's a good fit for a campaign. If it is, the ad is served. If it isn't, the impression is skipped. The quality of that decision depends on how deeply the technology understands what it's reading.

That's where contextual advertising services start to differ.

Contextual vs. Behavioral Advertising: Context Over Identity

Behavioral advertising was built on a different question. Instead of asking what someone is looking at, it asked who they are. It tracked users across sites, built profiles from past clicks, visits, and purchases, and used those profiles to target ads, often relying heavily on third-party data.

For years, audience targeting meant exactly that. But profiles have limits.

A profile describes someone’s past. It says little about what they need right now. The same person who researched a new car last month may be planning a family dinner today. Nothing about them has changed. Only their context has.

Contextual targeting starts from the present instead. It reads the environment someone is in and the content they’ve chosen to engage with, which is often the clearest signal of current interest available. And because it doesn’t need to know who someone is, it respects privacy by design.

This shift isn’t about giving up on audiences. It’s about reaching them through what they’re experiencing, rather than through who a dataset says they were.

Context tells you where someone is. It doesn’t yet tell you how they feel there. That’s the gap the next stage of contextual is built to close.

Traditional Contextual Advertising Services: Keywords and Categories

Traditional contextual advertising typically works in one of two ways: matching keywords, or classifying a page by topic and category. Both have moved the industry forward. Both still stop at the page.

Here’s a closer look at how each one works

Keyword-Based Contextual Advertising

The first generation scanned a page for specific words and matched them to campaigns. Early tools like Google AdSense helped popularize this approach, displaying ads that matched the terms on a page.

It was a breakthrough at the time. But keywords miss nuance. “Apple” could be a fruit or a brand. A word like “shot” means something very different in a basketball recap than in a news report.

Keywords also miss tone, which makes brand safety harder. Picture an airline ad next to a news story about a plane crash. A keyword match might see “flight” and approve the placement, even though the context calls for the opposite.

Topic and Category Classification

A more advanced approach classifies content by topic and category, often using natural language processing. Instead of matching single words, it analyzes full sentences to understand what a page is actually about. Some systems can also assess sentiment across text, images, and video.

The result is more accurate targeting and stronger brand safety controls. Still, it understands the page, not the person reading it. 

It can tell that an article is about family cars. It can’t tell whether the reader is daydreaming, comparing prices, or ready to book a test drive.

What Is Neuro-Contextual Advertising?

Here at Seedtag, we see Neuro-Contextual Advertising as the next evolution of contextual. Combining neuroscience and AI, it starts from a simple premise: the content people engage with reveals not just what they’re reading or watching, but how they feel.

Who someone is rarely changes from one hour to the next. How they feel, and what they’re focused on, can change in an instant. That’s why Neuro-Contextual Advertising looks at three signals that shape how people respond to advertising. Interest determines whether someone is engaged at all. Emotion affects how easily a new message connects with what they already know. Intent determines whether they’re in the right situation to act.

It’s also why viewability alone isn’t enough. An ad can be seen without ever being processed.

Liz, our Neuro-Contextual AI, reads these signals across the content people consume on the open web, video, and CTV. Where traditional approaches stop at what a page is about, it interprets the moment around it, all without personal data or identifiers. Brands and agencies can then align with that moment across the full funnel, from building awareness to driving action.

The difference is backed by science. In our neuroscience research, developed with Prof. Moran Cerf of Columbia University, Neuro-Contextual ads drove 3.5x higher neural engagement than non-contextual ads, and a 26% stronger emotional response than standard contextual ads.

What This Shift Means for Brands, Agencies, and Publishers

• For brands, the question changes. It’s no longer just where an ad should appear, but what someone is experiencing when they see it.
• For agencies, measurement changes too. Clicks and viewability show that an ad could be seen. They say little about whether it was noticed, felt, or remembered. Planning needs signals that reflect how people actually respond, and audiences they can look inside, rather than segments they have to take on trust.
• For publishers, quality content gains new value. When advertising responds to how content makes people feel, the stories that hold attention and spark emotion become the most valuable environments, not just the ones with the most traffic.

And across the open web, video, and CTV, that understanding can move from screen to screen, without following the people themselves.

From Context to the Moment

Contextual advertising began as a way to decide where an ad should appear. Neuro-Contextual Advertising takes it further, helping brands understand the people on the other side of the screen without needing to know who they are.

Keywords told us what a page said. Classification told us what it meant. Neuro-Contextual Advertising helps us understand how it feels.

That’s the next shift for every marketing strategy. Brands that win won’t just reach audiences. They’ll resonate with people, one moment at a time.

For most of digital advertising’s history, discovery and purchase lived in different places. People found a product in one channel, researched it in another, and bought it somewhere else, often after a long chain of clicks, forms, and checkout pages. Every step in between was a chance to lose them.

Social platforms are closing that gap. On TikTok, a single moment can hold the whole journey: people discover a product, watch a creator explain it, and buy it without leaving the app. A platform once seen mainly as a place to build awareness has become a place where brands drive real business results.

Two forces are making that possible. Creators give products a voice audiences already trust. And AI in digital advertising is removing friction from nearly every step in between, from producing creative to finding the right audience to measuring what worked.

In this episode of AdTech Heroes, our host Jon Wallett sat down with David Kaufman, VP of Product Management and Head of Monetization Product Solutions & Operations at TikTok, to explore how those forces come together. 

They talked about integrated commerce, how brands of every size are using TikTok Shop, and how automation and agentic AI are reshaping creative production, media buying, and measurement. At the center of it all is the goal that shapes Kaufman’s work: removing every bit of friction between a client’s investment and the business result it’s meant to drive.

Key Takeaways

• When discovery and purchase happen in the same place, the hard part of selling online shifts from building a storefront to finding the right audience.
• Brands use TikTok Shop in different ways: as a primary channel for small businesses, a category accelerator for retailers, and a product testing ground for large CPG companies.
• On creator-led platforms, creative increasingly does the work of targeting by letting the message find its audience.
• Agentic AI for media buying gives marketers a choice between full automation and precise, hands-on control.
• Measurement credibility depends on third-party validation and on letting the client define what value means.

How TikTok Shop Turns Discovery Into Commerce

TikTok’s roots are in short-form video, and early on, brands saw it mainly as a way to build brand equity. Today, the platform supports a much wider range of business objectives, and more of them are about performance.

TikTok Shop has made social commerce part of everyday shopping.

Kaufman walked through the traditional online journey: see an ad, click it, visit a website, create an account, enter payment details, add to cart, check out. Every one of those steps is friction. Every step also loses people along the way, which weighs on engagement and conversion rates. On TikTok Shop, most of those steps disappear. People see a product, like it, tap it, and buy it.

The power of commerce at the point of discovery, he said, can’t be overstated.

It also changes what’s hard about selling online. Earlier in his career, as an entrepreneur, Kaufman worked in distributed e-commerce, sharing product data across the social web. Many of his clients believed building the website was the hardest part. It wasn’t. On TikTok Shop, the platform, its users, and its creator ecosystem do much of that work, helping brands find their audience faster and see results sooner.

“Finding your audience is the hard part.”

How CPG Brands and Retailers Use TikTok Shop to Test and Grow

Not every brand comes to TikTok Shop for the same reason. Kaufman described three distinct patterns, each shaped by the size and mechanics of the business.

Small and medium-sized businesses often treat it as another sales channel, and sometimes the main one. Around seven and a half million SMBs in the US use TikTok solutions. Some run their own websites, and some don’t. Either way, integrated commerce is easy to set up, and sales happen naturally as people discover products in their feed.

Established retailers tend to enter one category at a time. Health and beauty has been especially strong, which fits how much of the platform’s content already lives there. “Get ready with me” videos and hair and makeup tutorials create a natural home for products people want to see in action.

The third pattern may be the most surprising.

Large CPG (consumer packaged goods) companies without a direct-to-consumer presence are using TikTok Shop as a product testing ground. How do people respond to a new snack flavor? What kind of engagement does a new beverage concept earn? Instead of waiting on traditional research, these brands get real-time insights from real buyers. Fulfillment is the main challenge on the back end, and brands can either manage it themselves or work with TikTok.

The result is a hybrid of sales and market research, and it isn’t limited to large companies. For emerging brands, a low barrier to entry makes it easier to test an idea before committing to a bigger launch.

Why Creators Power Integrated Commerce on TikTok

If TikTok Shop is where the sale happens, creators are often what make it happen.

Kaufman pointed to TikTok’s creator ecosystem as one of its biggest strengths. Through the TikTok One platform, brands can work directly with creators who have already built trusted voices and the audiences brands want to reach.

That authenticity is changing performance advertising too. With Smart+, TikTok’s automated performance solution, a brand can bring a large product catalog and let the system match the right product to the right person in the right format. 

The creative is evolving as well. Five or ten years ago, a product ad might have been a static image against a white background. Now, brands can bring in creators to explain and enrich the product, and TikTok Shop sellers can work with affiliate creators who make content specifically around what they sell.

‍On a platform like TikTok, creative has become the targeting. Rather than defining every audience in advance, brands can let their message find the people who respond to it. Personalizing content becomes less about who someone is and more about what they choose to engage with.

What Is AI in Digital Advertising and How Has It Evolved?

Behind the creators and the commerce sits a lot of AI. Kaufman’s view of it is a calm one: AI in advertising is not scary at all.

The industry has relied on ranking for years. What we used to call machine learning, we now call AI, and AI models have long decided which ad appears in front of which person. Kaufman called it maybe the most established and valuable use case for AI anywhere.

He remembers when that work was manual. As an entrepreneur, he found those kinds of insights through hands-on data analysis. Today, they live inside AI algorithms, built into how ads are delivered.

What’s new is where AI shows up next. Some use cases are small but practical: fixing aspect ratios, correcting color, or adding promotional overlays across a catalog of hundreds of thousands of assets. These are repetitive tasks that would be nearly impossible to do by hand at scale. Others are more ambitious. Through TikTok’s Symphony platform, AI enables brands to generate scripts and video from a prompt, or to create a digital avatar that explains a product.

What Is the Difference Between Traditional AI and Agentic AI in Digital Advertising?

Traditional AI works in the background. It ranks, predicts, and optimizes within rules a team has already set. Agentic AI goes a step further. It can understand a goal, plan the work, and act on it.

Kaufman sees TikTok’s role as offering both paths.

A marketer can upload a CRM file of their highest-value customers for a retargeting campaign, or turn on automation and let the system find those customers on its own. Teams that want a deterministic setup can program their business logic against the campaign management API (application programming interface). Others are testing the MCP (Model Context Protocol) offering TikTok announced at TikTok World in May. It lets an agent trained on a brand’s business logic connect directly to the ads platform and manage campaigns.

According to Kaufman, the most sophisticated advertisers are doing a little of everything to learn what works. That’s the real promise of agentic AI for media buying: giving teams the freedom to decide where automation helps and where precision matters more.

How Can Marketers Use Agentic AI in Digital Advertising to Streamline Creator Briefs and Targeting?

Creator marketing has long been one of the most manual parts of social media advertising. Finding the right partner meant researching profiles, scrolling through social media posts, and reaching out directly or tracking down a business manager.

This is where agentic AI in marketing starts to change the work. With natural language processing, a marketer can describe a goal in plain words and get back a shortlist of relevant creators, suggested outreach, and draft briefs. On TikTok, that happens through the Symphony agent, part of what Kaufman calls an agentic buying flow.

What doesn’t change is the need for judgment. An agent can surface options, but teams still decide which creators genuinely fit the brand and which messages will feel authentic to their audience.

AI avatars have a role too, but a different one. If a brand has already defined its audience and wants to test many versions of a message quickly, generative AI makes that possible without a camera crew or a long production timeline. When it comes to authenticity, though, most of the excitement Kaufman hears from advertisers is still about creators.

Why AI-Powered Digital Advertising Campaigns Need Independent Measurement

As commerce and automation grow, so does the need for proof. Sophisticated brands build marketing strategies across every channel, so measurement can’t stop at one platform’s walls.

Measurement was one of the defining topics at Cannes Lions this year, and the message from brands was consistent: they want partners that aren’t checking their own homework. That’s why third-party validation and multi-touch attribution matter as much as the numbers a platform reports on its own.

‍Kaufman added a principle worth remembering: the client owns the definition of value. For some brands, that means brand lift surveys. For others, it’s media mix models or in-house business intelligence teams. Either way, the platform’s job is to understand how each advertiser defines success and deliver against it.

Premium Formats and the Power of the Moment

Not every outcome is a sale. Some brands need to own a moment.

TopView, the ad people see when they first open the app, is built for advertisers who want a high share of voice around a moment that matters to their business. Pulse Premier lets brands appear alongside content tied to major events such as the World Cup, often to extend sponsorships they run on other channels.

Timing matters too. During the Super Bowl, TikTok found clear spikes in engagement during game breaks, a strong signal for sponsors to run complementary content on the platform. The exception was Bad Bunny’s halftime show, when engagement dipped. Some moments simply command full attention.

It’s an idea we come back to often. Attention clusters around moments, and the brands that understand those moments as they happen are the ones that earn real engagement.

Future Trends: Where Commerce, Creators, and AI Meet

The future trends Kaufman described point in one direction: less friction between discovery and purchase. Creators give products a trusted voice. Integrated commerce turns interest into action in a few taps. And AI adoption is speeding up everything around it, from creative production to buying and measurement.

The brands that grow fastest will treat discovery, creative, and commerce as one connected experience, built around the people they want to reach.

Automated media buying has changed almost every part of how campaigns get planned, priced, and delivered. Programmatic advertising and real-time bidding now move faster than any human could, and machine learning models decide which ad space and ad inventory fit a brand in milliseconds.

Will AI replace media buyers and turn paid advertising automation into the whole job? Not at all, because the parts of this job that actually move a client's business rarely show up in a dashboard.

In this episode of AdTech Heroes, I sat down with Sascha Lock, Executive Director, SVP of Integrated Investment at Hearts & Science, to talk about what automation has genuinely changed in media buying, what it hasn't touched, and why the agencies that win are the ones who treat data-driven tools as a starting point rather than the whole answer.

Key Takeaways

• Automated media buying has made the buying process faster, but it hasn't replaced the relationship-driven work that determines whether a campaign succeeds.
• AI in advertising is following the same path mobile did: it will keep growing until it stops being a separate conversation and simply becomes how digital advertising works.
• Programmatic advertising and first-party data give agencies more signal, but clients still need a human point of view to translate that signal into a decision.
• Real-time bidding has automated pricing, shifting negotiation from "what's the best price" to "what's the best strategic partnership."
• The most valuable habit in media buying right now is filtering. There's more data and more platforms than any one person can track.

Why Automated Media Buying Still Needs a Human at the Center

Ask most people what an "investment" role in media buying actually involves, and they'll picture spreadsheets and numbers moving between columns. Lock pushed back on that idea early in our conversation. His role, he explained, is much more about relationship capital than transaction management.

That distinction matters more as automation takes over the mechanical parts of the buying process. When machine learning and buying platforms handle pricing and placement, the value an agency brings shifts toward judgment: knowing which of the thousands of available options actually fits a client's goals, and being willing to say so.

Lock compared it to any real relationship, built on direct but kind communication and consistency. Automated media buying can optimize an auction. It can't build that trust on its own.

‍ "The actual product isn't everything. You're buying service, and you're buying commitment, and you're buying this mutual strive to do better and to grow together."

Will AI Replace Media Buyers? Not Anytime Soon, Because Context Still Matters

Lock drew a comparison that's hard to argue with. Mobile advertising, he pointed out, used to get its own line item on every media plan. Eventually it stopped being a special category and simply became how advertising works.

He believes artificial intelligence is on the same track. Right now, AI in advertising gets a dedicated conversation on nearly every panel and podcast. Eventually, it will stop being a separate topic and just become the infrastructure underneath everything, the same way connectivity and speed are for the internet today.

That's a useful way to think about whether AI replaces media buyers and paid advertising automation makes the role obsolete. History in this industry tends to be cyclical. Each wave feels revolutionary in the moment, but what people actually need from a partner changes far more slowly than the tools do.

How Programmatic Advertising and First-Party Data Are Reshaping Client Conversations

With hundreds of CTV platforms and thousands of potential partners now available in the US alone, no single person can be an expert on everything. Lock described his role as a connector: someone who listens to every new product release, filters out what's genuinely relevant, and brings a clear point of view to the client.

That filtering depends on data, but the decision itself still depends on people. Clients see the same headlines about programmatic advertising and first-party data that agencies do. What they need isn't more information. It's a trusted read on what that information means for their business, and someone willing to make a recommendation rather than just present options.

From Transactional to Strategic: How Real-Time Bidding Changed Negotiation

Real-time bidding automated a huge part of what negotiation used to mean. Ask a newcomer to define negotiation, Lock said, and they'll usually describe getting the best price. That's still true, and it still matters.

But once pricing and inventory decisions run through an algorithm, the conversation left for humans is a different one. It's less about the number on a line item and more about building something together: better flexibility, stronger strategic terms, and closer collaboration between everyone at the table. 

Some of the most productive moments, Lock noted, still come from putting the right minds from a media partner, a client, and an agency in the same room to work through a problem together.

What a Strong, Data-Driven Buying Process Actually Looks Like

When I asked Lock what a strong modern media partnership requires, he didn't point to a platform. He pointed to three habits: transparency, accountability, and speed.

Transparency means being upfront when something goes wrong, not just when it goes right. Accountability means showing up when you say you will, and explaining clearly when you can't. Speed means pivoting quickly once the data shows something isn't working, rather than over-analyzing a decision that's already clear.

The Two Questions Every Agency Should Be Asking Clients Right Now

Lock narrowed the most important client conversations down to two questions. The first is simple to ask and hard to answer well: what does success actually look like, and what sources of truth will we agree to measure it by?

The second is about risk tolerance. Our guest described a rough 70/20/10 framework many teams use: most investment into what's already proven, a portion into promising opportunities, and a smaller share for bolder tests. That last bucket should expand or shrink with how much risk a client can absorb, and it's worth revisiting often.

Getting clear on goals and risk tolerance matters more than any new platform. It's the foundation everything else in the buying process gets built on.

Toward the end of our conversation, I asked Lock what superpower he'd want in ad tech. His answer was "Mr. Transparency": the ability to see exactly what an algorithm is doing and why, not to fight the technology, but to bring that insight back into planning. It's a fitting note to end on. The tools keep getting faster. What agencies bring to the table is still deciding what that speed should be used for.

You can watch the full conversation with Sascha Lock above.

Publishers, agencies, and brands don't always sit on the same side of the table. But at the end of the day, they're facing the same challenge.

For publishers, the question is how to create content relevant enough that brands and agencies want to advertise within it. For agencies, it's where to place their clients and which environments are truly brand safe. For brands, it's where to invest to build a closer connection with their customers. The perspectives are different, and so are the priorities. But the challenge is shared: as the way people discover information keeps evolving, quality content needs to keep being found, valued, and sustained.

That challenge was one of many conversations we brought to Barcelona. From September 15 to 17, the Seedtag Innovation Summit gathered clients and partners from the United  States, Latin America, and Europe, opening at the Joan Miró Foundation.

During the Summit, Brian Gleason, our CEO, added another layer: "We're more connected than we've ever been, but we're more distracted than we've ever been." For Brian, finding out how to connect in that moment is the most important thing, whether you're a brand, an agency, or a publisher.

The agenda combined dedicated sessions for the supply side and the demand side with joint sessions that put both in the same room. Topics ranged from the agentic shift and AI search to attention measurement, brand safety, CTV, creative, and life after the traffic. At lunches and dinners, the conversations kept going.

Across all of them, one thing became clear. The challenges facing the adtech ecosystem aren't local. They're global. And they become easier to solve when both sides look at them together.

Key Takeaways

• Consumers are more connected and more distracted than ever, and connecting with them in the moment matters for brands, agencies, and publishers alike.
• The same person makes different decisions in different situations, shaped by signals like time of day, screen, weather, and place.
• Fewer impressions carry identifiers, but audiences haven't gone anywhere, and reaching them requires a new structure.
• Interest, emotion, and intent shape how people process advertising, while viewability alone says nothing about that processing.
• Publishers, agencies, and brands face the same challenges from different perspectives, and the conversation is global, spanning the United States, Latin America, and Europe.

What Are the Biggest Challenges Facing the Digital Advertising Landscape?

Throughout the Summit, Brian Gleason put the consumer at the center of the conversation. For him, it's the one thing the industry doesn't focus on enough. We talk about acronyms, LLMs, and chats, but rarely about what consumers are doing every day or what their world is like.

And that world is full of noise. Attention is fleeting, and capturing it is hard. At the same time, clients are under pressure to grow and differentiate, and Brian raised an open question about what happens to them as products become commoditized in AI chat environments.

Grego Martínez, our Chief Product Officer, pointed to another challenge: media buying itself.

In CTV, most buying happens at the platform level, yet streaming services rarely have exclusive content. To illustrate the point, Grego shared a simple example: in Spain, Harry Potter is available on eight different platforms. So when a brand buys one of them, it's buying the service, not the moment. It learns nothing about the show being watched, the emotion it creates, or the hour and day it's being seen.

Buying through demand side platforms (DSPs) has a similar limitation. Brands and agencies can target IAB categories, keywords, domains, geography, or day part, but combining all of those signals to build moments manually is very complicated. The signals exist, but programmatic fragmentation keeps them apart.

Why the Adtech Ecosystem Is Moving From Understanding the Who to the Moment

For Grego, solving that fragmentation starts with a simple idea: the same person can make very different decisions depending on the situation they're in.

Picture someone commuting to work on a rainy Monday morning, phone in one hand, about to lose connection in five or six minutes. Any message has to be fast. Now picture the same person on a sunny Sunday morning, planning the week ahead with Monday off. They might spend 90 seconds on a powerful long video, one that takes them to the next step.

Nothing about them has changed. Only the moment has.

The same happens with a recipe. On a Tuesday evening after work, someone looking one up wants something fast. On a Saturday morning, with family and friends coming over, they're far more open to a message tied to that interest. The article is exactly the same. The moment is not.

That's why content is only one piece of context. The hour, the day of the week, and the season all matter. So do the type of screen someone is using, the weather, and the place they're in. On their own, these signals won't massively improve advertising. Combined, they create a scenario where advertising works much better.

Moments also change across environments. On mobile, things happen faster. On desktop, people are usually sitting down and paying more attention. CTV is often watched in a group rather than alone.

"We've spent more than 20 years understanding the who. Now, I think the future will be won by those who understand the moment much better," Grego said as he closed his session.

Audiences Were Never Identifiers

Hannes Sieling, our VP of Data & AI, started from programmatic advertising's long reliance on identifiers. They gave data teams structure, plus the reach to find people again. Today, fewer impressions carry them, but the reality hasn't changed. The same people are consuming the same content. And audiences were never identifiers in the first place.

What the industry needs is a new structure, built around the content people consume. That's the idea behind vector-based targeting: grouping similar content together so brands and agencies can find the environments closest to their goals, while balancing reach and precision.

Hannes also pointed to the technology that makes this possible: "Embedders are a little bit the hidden champions, the unsung heroes of the whole rise of large-language models."

The real question is what counts as similar. For him, the answer needs to go beyond clicks or categories and help find the right environment at the right moment. Understanding how people actually process what they see is a key part of that answer.

The Science Behind Attention, Emotion, and Intent

Professor Tino Meitz from the University of Münster explained how people process advertising. Who we are stays fairly stable, but how we feel and what we're focused on can change in milliseconds. When people consume media or advertising, that state of mind matters most.

That's also why viewability isn't enough. It shows that an ad could be seen, but says nothing about whether it was actually processed.

Three contextual signals shape that processing:

• Interest: If people aren't motivated to watch a show, they're unlikely to engage with the advertising within it.
• Emotion and affect: The brain relates new situations to past experiences, so a context that connects to familiar feelings makes new information easier to process.
• Intent: People need to be in the right situation for a message. If you want to buy a mid-sized SUV, you probably won't walk into a Ferrari dealership.

Tino was careful to separate affect from emotion: "Affect is something you can't govern. It's something that's happening, it's also, to a certain extent unconscious. Emotion is an evaluation of your affect afterwards."

How Can Supply and Demand Collaborate Across the Adtech Ecosystem?

Rob Beeler, who led the Two Sides, One Signal working session, explained why collaboration and supply and demand alignment matter. “Both sides are trying to solve the same things. This is a relationship-driven industry, and relationships are built by talking to each other in person. When both sides start working together, he said, they can be more effective for advertisers”, he said.

Kevin Donker, Head of Programmatic & Media Automation at Omnicom Media, brought that same spirit to the agentic conversation. For Kevin, agentic buying is still in its infancy, and the industry will have to come together to adopt protocols that make it reliable. When that happens, the productivity gains from AI should be reinvested in people.

In his view, the winners will be organizations that use AI to boost creativity, leadership, and judgment. The ones left behind will simply put an AI layer on top of an already fragmented workflow.

Brian Gleason echoed that idea in his closing remarks. He pointed to the feedback shared over the three days on different ways of working, ad inventory sources, and ways to measure. He also stressed the importance of trust: when you know you can trust someone, you know they'll do what they say they're going to do.

"There are a lot of pressures and challenges we think about in the world, and the things we shared over the last few days were brilliant. That's what we love to do: share information. It's an ecosystem that works together, and that's what we're trying to create", Brian Gleason. 

How Does Neuro-Contextual Advertising Bridge the Gap Between Supply and Demand?

If both sides face the same challenge, they also need a shared way to understand the moment. That's what Neuro-Contextual Advertising offers.

Liz, our Neuro-Contextual AI, reads signals of interest, emotion, and intent to understand the moment behind each impression, without personal data or identifiers. For brands and agencies, that means reaching people when it matters most. For publishers, it means their content is valued for the moments it creates.

And it goes beyond advertising. Bringing value back to the open web helps keep quality content alive, and that's a responsibility the whole industry shares.

From Barcelona to the Next Conversation

Over three days, publishers, agencies, and brands from the United States, Latin America, and Europe approached the same questions from different perspectives. How do we capture attention? How do we reach audiences without identifiers? How do we make sure quality content keeps being found and valued as the way people discover information changes?

For us, at Seedtag, those conversations reinforce the vision behind Neuro-Contextual Advertising: understanding people's interest, emotion, and intent in the moment they're in.

It's not about knowing who. It's about understanding the moment.

Thank you to everyone who joined us in Barcelona. If you'd like to keep the conversation going, our team is here.