Consumers accept AI as a shopping advisor, but not a buyer
Seventy-five percent of U.S. consumers say they are comfortable with AI helping them decide what to buy. That figure reflects how quickly the behavior has spread in only a few years. The same research found that the more people use AI for shopping, the more comfortable they become with it.
There is a clear boundary, though. Consumers separate agentic shopping from agentic purchasing. They accept AI that researches products, compares options, finds better deals and narrows the choice. They resist AI that completes the transaction without a sign-off. Sixty-four percent favor AI suggesting brands they would not normally consider. Only 20% are comfortable with AI acting independently. Making a purchase without approval is the single biggest trust-breaker, cited by 55% of consumers.
The same pattern shows up on the buy side. Most advertisers and commerce media network operators want agentic AI to do the majority of the work, with human approvals built in.
What is agentic AI advertising?
Agentic AI advertising is the use of AI systems that can carry out multi-step advertising and commerce tasks on their own, such as researching products, comparing options, optimizing campaigns, or proposing media-buying actions, within limits set by a human operator.
It differs from older automation in one way. Traditional automation follows fixed rules. An agentic system takes a goal, decides the steps, and adjusts as new information arrives, a distinction IBM draws between agentic and generative AI. In most current deployments, it stops short of the final decision and hands that to a person.
Two related terms are worth defining here:
- AI media buying agent: a system that selects inventory, sets bids, and reallocates budget toward a stated goal, then surfaces the results for human review.
- Generative engine optimization (GEO): the practice of structuring brand content and data so AI assistants cite or recommend it when a user asks for a product or category.
Why this is a problem worth acting on
For a decade, the rules of commerce media were stable. Success came from winning visible ad placements, buying the right audience segments, and measuring clicks. Agentic AI changes where the advantage sits.
When a shopper asks an AI assistant to find the best running shoe under a set price, the assistant returns a shortlist. Products that are not on that shortlist are effectively invisible to that shopper. The ad position on the page stops being the deciding factor. Inclusion in the AI's recommendation becomes the deciding factor.
The industry has noticed. In the Koddi research, 84% of commerce media leaders said they would invest in opportunities designed to increase their visibility inside AI-generated answers and recommendations. Sixty-one percent are already moving that money out of performance and paid-search budgets. The funding is shifting before the standards have settled.
That creates three concrete operational problems:
- A new gatekeeper. The AI shortlist sits between brand and consumer. If your products and data feeds are not structured for AI retrieval, you lose presence at the exact moment a purchase decision forms.
- Fragmented measurement. Competing AI ecosystems use different feed structures, checkout flows, and attribution models. A brand operating across several of them is solving multiple measurement problems at once. In the study, 33% of respondents said current gaps in measurement and attribution were holding back investment.
- Budget committed before proof. A third of advertisers plan to allocate between USD 250,000 and USD 1 million to agentic AI products over the next 12 months. At that level, finance teams expect demonstrated return, and the attribution to prove it often does not yet exist.
What brands can do: options for the agentic shift
There is no single fix. The response is a set of moves chosen against where a brand currently has gaps. Here are the main options, with the operational trade-offs.
1. Structure data and content for AI inclusion (GEO)
The first move is making sure AI systems can find, read, and trust your product information. That means clean product feeds, structured data, explicit specifications, and content written so individual passages make sense on their own. The foundational GEO study by Aggarwal and colleagues found that content edits such as adding citations, quotations, and statistics measurably raised how often generative engines selected a source.
Trade-off: this is low-cost and within a brand's control, but it competes for attention with paid placements and takes weeks to show effect.
2. Buy paid inclusion and sponsored recommendation slots
Commerce media networks are building paid formats for the agentic layer: paid inclusion in shortlists and sponsored recommendation positions. These are the direct equivalent of the old sponsored placement, moved into the AI answer.
Trade-off: this buys presence quickly, but standards and pricing are immature, and over-reliance on paid inclusion does nothing to build the organic trust signals AI systems weight.
3. Build agent-specific measurement and attribution
Because attribution is the stated blocker, measurement is where serious budget is going. In the research, 92% of respondents plan to invest in agent-specific measurement and diagnostics, and 80% would invest in measurement tools tied to AI-mediated journeys. The brands that solve attribution across fragmented ecosystems first will have the evidence to keep scaling while competitors stall.
Trade-off: high effort and cross-functional, but it is the capability that unlocks every other investment, because nothing scales without proof of return.
4. Keep humans in the decision loop by design
The market is not heading toward full autonomy. In the Koddi study, only 3% of commerce media leaders favored environments where AI runs with an almost free hand, and staff is limited to oversight. The brands pulling ahead are not the ones automating the most. They are the ones building infrastructure where human and AI collaboration works reliably at scale, across different networks and data environments.
Trade-off: designing approval gates adds process, but it matches both consumer trust limits and internal risk tolerance, which reduces the chance of a costly autonomous error.
5. Run campaign optimization on a self-learning engine
This is where the engine behind the campaign matters. Adello's mobile DSP optimizes at the level of the individual ad impression rather than broad audience segments. Its deep-learning prediction system predicts the conversion probability of every single impression, follows users through the full funnel (interactions, landings, sessions, conversions) rather than clicks alone, and carries learnings from one campaign into the next instead of starting from zero each time. The system runs automatically, while an operations team monitors for outliers.
This is the practical shape of "agentic, with a human in the loop" that the research describes. The optimization runs on its own at impression scale; people set the goals and watch the edges. For fraud and traffic quality, AdCTRL layers on top of buying to detect suspicious patterns in real time, delivering 99%+ fraud-free traffic verified by third parties. That addresses the measurement-trust gap directly, because budget committed to agentic products needs clean traffic to produce attribution anyone will believe.
Trade-off: a managed, self-learning approach reduces manual optimization load and the cold-start penalty on new campaigns, but it requires sharing goals and data with a platform partner rather than running everything in-house.
6. Concentrate effort at research and recommendation, not checkout
The data points to where consumer acceptance is highest. People are open to AI-mediated discovery and comparison. They are not open to AI spending their money unprompted. The opportunity in agentic commerce is concentrated at the research and recommendation level, where acceptance is high, and brands can genuinely be discovered.
Trade-off: this narrows focus and may feel like leaving the transaction on the table, but it aligns spend with the behavior consumers actually permit, which protects against trust damage.
A short comparison
| Option | Speed to effect | Cost / effort | What it fixes |
| GEO / data structuring | Weeks | Low | AI invisibility |
| Paid inclusion slots | Immediate | Medium | Short-term presence |
| Agent-specific measurement | Months | High | Attribution gap |
| Human-in-loop design | Immediate | Medium | Trust + risk |
| Self-learning optimization engine | Ongoing | Medium | Performance + fraud |
| Focus on research/recommendation | Immediate | Low | Acceptance mismatch |
Why now
The behavior is already mainstream, and the budget is already moving. Three-quarters of U.S. consumers accept AI as a shopping advisor, 84% of commerce media leaders are investing in AI visibility, and a third of advertisers are committing up to USD 1 million each to agentic products within the year. The next 18 months will bring new monetization models and advertiser formats built specifically for agentic environments.
The work that creates an advantage, clean data feeds, structured content, working attribution, and a self-learning optimization layer takes time to build. Brands that start now will have evidence and presence when the standards settle. Brands that wait will be buying inclusion into shortlists they had no hand in shaping.
Commerce media is moving from surfaces to systems, and from impressions to outcomes. The brands that do well in this shift are not the ones that hand everything to the machine. They are the ones that let AI do the research, optimization, and heavy lifting, keep people on the decisions that matter, and build the infrastructure that makes the two work together. The distance between the companies that understand this and those that do not is widening month by month.
Created with the help of AI.
AI agents already run billions in ad spend, and most agentic projects still fail
Two of the most widely deployed agentic marketing systems in the world are not startups. Meta Advantage+ and Google Performance Max manage billions of dollars in ad spend through autonomous AI decision-making. Meta's Advantage+ line alone is generating roughly $60 billion in annualized revenue. AI adoption across advertising has climbed in step, with social and video channels leading at 85–86%.
As Viant's CEO put it, autonomous media buying is no longer theoretical.
The deployment record is harder. Gartner projects that more than 40% of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value, and weak risk controls. A 2025 RAND meta-analysis found that around 80% of enterprise AI projects fail to deliver their promised business value — roughly twice the failure rate of conventional software.
For a marketing team, the decision that matters is which approach survives contact with real data, a compliance review, and a real budget. A clean demo says little about that.
What a marketing AI agent is
A marketing AI agent is a software system that takes a goal, breaks it into steps, and carries those steps out across connected tools with limited human supervision. It reads the current state of a campaign or audience, decides the next action, executes that action through an API or platform, observes the result, and adjusts. The defining traits are autonomy, a goal it optimizes toward, and the ability to act rather than only suggest.
This is the practical meaning of agentic AI in advertising: software that runs a loop of perceive, decide, act, and learn, instead of waiting for a person at each step.
How an agent differs from automation and a chatbot
Rule-based automation, assistants, and agents are often grouped together. They behave differently in production.
| Capability | Rule-based automation | Chatbot / assistant | AI agent |
| Triggers | Fixed if-then rules | Responds to a prompt | Sets its own sub-steps toward a goal |
| Decision-making | None | Suggests; you decide | Decides the next action itself |
| Action | Executes a preset task | You act on the output | Acts across tools via APIs |
| Adapts to new data | No | Only within a reply | Yes, adjusts as results change |
What marketing AI agents do across the funnel
The functions below are operational today across major ad platforms, marketing clouds, and specialist tools.
- Media buying and bidding. Agents adjust bids, expand query matching, and reallocate budget continuously. Google AI Max, an AI layer applied to Search campaigns and announced at Google Marketing Live in May 2025, typically lifts conversions by about 14% at a similar CPA or ROAS, according to Google's own data. Meta Advantage+ and Performance Max operate on the same principle.
- Creative production and dynamic creative optimization. Agents generate and test ad copy, images, and video, then shift spend toward the variant that performs.
- Audience building and targeting. Agents define and refine segments from first-party and behavioral signals, increasingly without manual demographic inputs.
- Autonomous ad optimization. Agents monitor accounts continuously, pause weak placements, and scale winners. This is the layer behind AI-powered campaign management and self-improving platforms such as Adello's own AdCTRL optimization engine.
- Predictive targeting and pacing. Agents forecast conversion probability to guide bids and budget, the core idea behind predictive ads.
- Content and generative engine optimization. Agents produce structured content so a brand is cited inside AI answers from systems like ChatGPT, Gemini, and Perplexity — the practice known as generative engine optimization, or GEO.
- Cross-platform orchestration and reporting. Agents pull results across Meta, Google, and TikTok, normalize attribution, and recommend or execute budget shifts.
Why do marketing AI agents break between the demo and production
Most marketing AI agents look excellent in a demo. The demo runs on a clean dataset, one channel, and no compliance review. Production is a different environment. The agent meets fragmented ad accounts, a CRM that was never built to be queried in real time, brand rules that live in someone's head, and a data protection officer with questions. The space between those two settings is where projects stall.
Four causes are concrete and measurable.
- Integration friction. Gartner research reports that about 70% of developers face integration problems connecting agents to existing systems, and roughly 42% of enterprises need eight or more data sources to make an agent useful. Marketing data is among the most fragmented: ad platforms, a CDP, a CRM, analytics, and creative libraries rarely share a schema.
- The black-box and lock-in tradeoff. Platform-native agents optimize toward the platform's goals, which are not always the advertiser's. An independent Wicked Reports analysis of more than 55,000 Meta campaigns found new-customer acquisition cost through Advantage+ more than doubled between May 2024 and May 2025, from $257 to $528. Autonomy without transparency can raise CAC quietly.
- Data ownership and compliance. Switzerland's revised data protection act (revDSG), in force since September 2023, requires controllers to show where personal data is processed and stored, who can access it, and on what legal basis. Agents that route data through US-controlled inference add contractual and assessment overhead.
- Total cost of ownership. Per-action or per-conversation pricing is the visible portion. Implementation, knowledge-base maintenance, model updates, and human supervision often exceed the licence line in year one.
Four ways to put a marketing AI agent into production
When a team decides to deploy an agent, the market offers four categories of vendors. The clearest examples below come from customer service, where agentic AI is most mature, but the build-versus-buy choice and the compliance, integration, and cost dynamics transfer directly to marketing agents. A fuller side-by-side sits in this 2026 buyer's comparison.
1. Multi-market generalist platforms
A fast-scaling vendor that covers many markets and channels under one contract. The clearest example is Wonderful AI, a Tel Aviv vendor founded in early 2025 that had raised about $284 million by March 2026 at a $2 billion valuation and expanded across several European and EMEA markets. It covers voice, chat, and email with a stated 80% resolve rate, and positions on per-market linguistic fluency.
Where it fits: organizations operating across many markets that want a single vendor and are comfortable with a hyper-growth partner still building its governance footprint. Where it does not: compliance teams that need data residency and processor mapping before signing. Pricing is enterprise-negotiated, and the track record in regulated verticals is still short.
2. Regional specialist platforms
A scoped platform with local legal standing and fast deployment. Typewise, a Zurich scale-up (Y Combinator S22) co-developed with the ETH Zurich AI Center, is used by around 60 enterprises including Unilever, DPD, and Brack.ch for written customer service. In February 2026 it launched an AI Supervisor Engine for multi-agent orchestration. Stated benchmarks include a 50% or higher reduction in agent effort, deployment in one to two days, ISO-certified and GDPR-compliant infrastructure, and outcome-based pricing.
Where it fits: written-channel use cases that want a Swiss legal counterpart and quick deployment. Where it does not: voice-first work, or scope that extends well beyond customer service.
3. Platform-native and CRM-embedded agents
An agent layer inside a stack the organization already runs. Salesforce Agentforce is available to Salesforce Enterprise Edition customers and above. As of April 2026, its pricing page lists a free Foundations tier, consumption at $500 per 100,000 Flex Credits (about $0.10 per action), $2 per customer-facing conversation under a fixed model, or per-user add-ons at $125 per user per month; Agentforce 1 editions start at $550 per user per month, on top of Enterprise ($165 per user per month) or Unlimited ($330). Third-party estimates put the first-year total for a ten-person team near $140,000 once licences, implementation, and training are included.
The same pattern exists in pure marketing. Google AI Max and Meta Advantage+ are platform-native agents that live inside one ad ecosystem. The benefit is the speed of activation. The tradeoff is optimization toward the platform's goals and the difficulty of forecasting consumption costs.
Where it fits: organizations already standardized on the platform with clean data inside it. Where it does not: fragmented data outside the platform, or buyers who want control over where optimization decisions point.
4. Custom-built agents (Lab51 by Adello)
A custom agent is built around a defined scope, deployed on infrastructure the buyer controls, and integrated directly into the data sources that matter. The 2026 architecture is well understood: a curated knowledge base, a retrieval layer (a vector database with hybrid keyword and semantic search), a defined response matrix, channel integration through Model Context Protocol or direct APIs, and a benchmark dataset signed off before launch.
A representative mid-complexity scope is indicative rather than fixed: about 8 weeks to build the knowledge and retrieval engine, 8 to 12 weeks to integrate channels, implementation in the range of USD 70,000–90,000, plus model monitoring in the low hundreds per month. Voice and multimedia agents sit at the higher end. These figures scale with data complexity and channel count.
For a marketing team, the custom model addresses the production problems directly. Data stays in a defined environment, which matters under revDSG. The agent connects to the ad, CDP, and analytics stack the team actually runs on rather than a generic layer placed on top. And the cost is a one-time build plus low ongoing fees, instead of per-seat or per-conversation licensing that grows with volume.
Lab51, by Adello, builds in this category for Swiss and DACH enterprises, with revDSG-compliant deployment patterns, a defined 8-week knowledge-base build, and phased channel integration. For marketing, that means an agent wired into owned campaign and audience data, integrated with the systems already in use.
Where it fits: regulated organizations, teams whose differentiation depends on specific workflows, and buyers who prefer a build-and-own cost model. Where it does not: very generic needs with no compliance constraints, where a fast SaaS deployment wins on time-to-value.
Want to discover how AI agents can drive efficiency and growth for your business? Fill out the form today, and one of our sales specialists will contact you shortly.
The four models side by side
| Approach | Best for | Data residency control | Integration depth | Pricing model | Time to value |
| Multi-market generalist | Many markets, one vendor | Vendor-managed; assessment needed | Broad, generalist | Enterprise-negotiated | Fast |
| Regional specialist | Written-channel work, local counterpart | Swiss / ISO / GDPR | Scoped to its domain | Outcome-based | Very fast (days) |
| Platform-native / CRM | Teams already on the platform | Platform-controlled | Deep in-platform, limited outside | Consumption or per-seat | Fast if data is in-platform |
| Custom build (Lab51) | Regulated, workflow-led, build-and-own | Buyer-controlled | Deep, to chosen sources | One-time build + low monthly | Weeks (≈8–20) |
Why this is a 2026 decision, not a 2027 one
Three forces are moving at once.
The failure rate is documented and high. Decisions made on hype rather than fit surface as cancelled projects 12 to 24 months later, after the implementation cost is already sunk.
Procurement takes time. In Swiss-regulated industries, compliance review, a data protection impact assessment, and an IT security review for a new external processor typically run two to four months. Starting in Q2 2026 means production by late 2026 at the earliest.
The operational gap compounds. Teams that deploy effective agents in 2026 build data assets, prompt libraries, and process knowledge that grow more valuable each quarter. Teams that wait face a baseline that keeps moving.
The practical move for buyers is to sequence the decision. Define the scope, the channels, the data sources, the compliance constraints, and the success metric before the first vendor demo. Then use that scope as the testing ground for every category.
Marketing AI agents have moved from pilot to production faster than most teams have built the governance to run them. The capability is real and already running paid media at scale. The differentiator in 2026 is fit: matching the deployment model to your data, your compliance posture, and your budget. Define what success looks like first, then choose the approach that can reach it.
Created with the help of AI.
January 2026 Opened With a 3.9% Drop
According to the European Automobile Manufacturers' Association, new vehicle registrations in the EU fell 3.9% year-over-year in January 2026, to 799,625 units. Gasoline-powered cars fell 28.2%. France was down 48.9%, Germany down 29.9%, Italy down 25.5%, and Spain down 22.5%.
Volkswagen is reported to be roughly 500,000 vehicles short of its annual targets — equivalent to two full plants' output. Audi is shedding 7,500 jobs by 2029.
Porsche is about 3,900. Suppliers Bosch, ZF, Continental, and Schaeffler announced sweeping cuts in 2024 and are still cutting.
Meanwhile, Chinese entrants are taking a share. BYD's battery electric vehicle (BEV) registrations rose 86% in January 2026. Leap Motor rose 357%.
This is the operating environment for automotive marketing in 2026.
Why the Squeeze Is Harder This Time
Past downturns were cyclical. Production paused, showrooms eventually refilled, and demand returned. 2026 is structural.
The supply side is cutting capacity, not pausing it. When suppliers disappear, model lineups narrow. Marketing teams end up with fewer products to sell and longer gaps between launches.
The buyer has already moved online. The Cox Automotive Car Buyer Journey Study 2025 reports that car buyers now spend around 14 hours and 19 minutes researching online, roughly 7 hours of it on specific vehicle research. They visit an average of 4.6 websites before contacting a dealer. Over 70% use a smartphone as their primary research device.
The electrification message has to be rebuilt. Dutch BEV registrations jumped over 30,000 units in December 2025 on tax scheme changes, then fell 35.4% in January 2026. Campaigns written six months ago are already outdated in several markets.
The old media mix — TV brand campaign plus dealer print ads plus paid search plus some Facebook — no longer matches the buyer or the product cycle.
Six Moves That Work Now
Each move below addresses a specific gap that 2026 has exposed. They are not sequential. Most automotive brands will run several in parallel.
1. Activate First-Party Data Before Third-Party Signals Fully Disappear
First-party data is data you own: test-drive requests, service records, configurator sessions, newsletter signups, CRM records.
Audience segments built on 2022 third-party cookie data are decaying. First-party data is the asset that survives the change. It is what direct-to-consumer competitors are already building on.
Practical steps:
- Unify web, dealership, and aftersales data into a single addressable record per household.
- Suppress existing customers from acquisition campaigns to avoid wasted spend.
- Retarget unfinished configurator sessions with offers tied to the exact trim chosen.
- Build lookalike audiences in cookieless environments using your own customer base as the seed.
2. Move Budget and Measurement to Mobile
Over 70% of automotive internet shoppers use a smartphone during the car-buying journey. Desktop-first analytics still dominate many OEM dashboards, which is a mismatch.
A mobile-first stack for automotive marketing in 2026 includes:
- Vertical video creative (9:16) built for the phone, not the TV.
- Mobile DSP platforms that can bid on in-app inventory where buyers research.
- Click-to-call and click-to-dealer-locator actions are native to the ad unit.
- Geolocation targeting around dealerships, service centres, and competitor locations.
Programmatic mobile ads now account for the majority of premium mobile inventory. Skipping this layer means advertising against the wrong screen.
3. Use Contextual Video Targeting Across YouTube, TikTok, and Pinterest
With signal loss growing, contextual advertising is rebuilding as the primary way to match message to audience on video platforms. The logic is direct: show a car ad next to content the buyer is already consuming — motorsport, road trip videos, EV reviews, luxury lifestyle, family vlogs — rather than inferring a demographic profile from cookies.
This is where Adello's PXLSTRM applies directly. PXLSTRM is a patented AI-powered contextual video targeting solution for YouTube, TikTok, and Pinterest. It analyses video content, dialogues, and on-screen objects, and clusters millions of videos into behaviour-aligned categories, including Automotive, Sport, Luxury, Travel, and Shopping.
An Adello benchmark comparison showed PXLSTRM delivering +53.9% more relevant impressions and +118% more relevant channels against TrueView, while lowering eCPM by 29.38%. For automotive campaigns that need brand-safe placements at scale — particularly in markets with restricted content environments — this is a measurable advantage.
4. Rebuild Audience Targeting on Deep Behavioural Signals
Segment-average optimisation — the classic programmatic approach — is no longer accurate enough when lead volumes are shrinking. Optimisation at the individual impression level is what Adello's deep-learning DSP does. Users exposed to an ad are followed through the full conversion funnel, not just to the click. Algorithms predict the conversion probability of every single impression, taking prior data and new signals into account.
Adello's Audience Class includes Automotive as a dedicated segment, built on in-app and contextual signals. For OEMs and importers, this enables:
- Lookalike modelling from existing service customers.
- Suppression of non-converting traffic types before the bid.
- Full-funnel measurement from impression to dealership visit.
- Brand safety and fraud detection are applied pre-bid, so invalid traffic is eliminated before spend.
More than 500 advertising partners, including BMW, use Adello's platform across Europe, North America, and Asia.
5. Extend Campaigns into Programmatic DOOH and CTV
Two channels are growing while linear TV contracts:
- Programmatic Digital Out-of-Home (pDOOH): highway screens, petrol stations, shopping centres, transit hubs. Bought in real time, with creative variations by daypart, weather, or inventory availability.
- Connected TV (CTV) ads: streaming inventory on smart TVs, measurable at the household level, and addressable with first-party data.
The real shift is combining them. Mobile programmatic, pDOOH, and CTV now run through the same DSP and measurement layer, which means one audience and one attribution framework across screens. Industry analysts at eMarketer flag CTV and pDOOH among the fastest-growing programmatic categories through 2026. Running them as a coordinated omnichannel strategy — rather than three separate buys — is what turns them into a reach and frequency system instead of parallel line items.
6. Treat Generative Engine Optimisation (GEO) as a Core Channel
The Cox Automotive study found that approximately 19% of all vehicle buyers and 25% of new-vehicle buyers in the US used AI chatbots (ChatGPT, Copilot) or AI-generated search overviews (Google, Gemini) during research in late 2025.
If the brand page is not structured for AI retrieval, it is invisible at that moment. GEO practices that matter for automotive pages:
- Definition-first paragraphs for every model, trim, and financing term.
- FAQ blocks with standalone question-and-answer pairs, marked up with the FAQ schema.
- Specific, cited statistics rather than generic marketing claims.
- Content freshness — AI engines prefer content that is on average 25.7% fresher than traditional search citations.
Brands cited in AI overviews see +35% organic clicks and +91% paid clicks compared with those not cited. The cost of being absent is measurable.
Summary Table: What Each Move Solves
| Move | The 2026 gap it closes | Primary channel |
| First-party data activation | Third-party cookie decay | CRM, DSP |
| Mobile-first stack | 70%+ buyers on smartphone | In-app, mobile web |
| Contextual video targeting | Signal loss on social video | YouTube, TikTok, Pinterest |
| Deep audience targeting | Segment-average inaccuracy | Programmatic display, video |
| pDOOH and CTV | Linear TV decline | Out-of-home screens, streaming |
| GEO | AI search displacing Google | Owned web content |
Why Now, Not Next Quarter
Three conditions make waiting expensive.
Share is being redistributed in real time. When an EU market drops 48.9% on ICE, and Chinese BEV entrants grow triple-digit, the 2027 order book is being shaped now by the brands still advertising.
Measurement changes compound. Cookieless adoption, AI-driven search, and first-party data consolidation each add a layer of complexity every quarter. A team that starts rebuilding in Q4 2026 is two years behind a team that started in Q4 2024.
Media inflation is returning. As automotive marketers who paused in 2024–2025 come back, CPMs on video and mobile are rising. Early commitments lock in better rates.
What customers must do: audit the current mix against these six moves, identify which one closes the biggest gap, and deploy a first test within the next 60 days. A test on a single model line is enough to produce the internal evidence needed for a full-year plan. Request a demo with Adello to scope the first test.
The automotive marketing playbook of 2019–2022 no longer fits the 2026 market. Sales are down, competition has shifted east, and the buyer has moved to mobile and AI-assisted search. Brands that align their media stack — first-party data, mobile, contextual video, deep audience targeting, pDOOH and CTV, GEO — will protect share while the market corrects. The ones that wait will be paying higher CPMs to reach a smaller audience.
🚙 Marketing budget cut for 2026, but the showroom targets didn't move?
Book a 20-minute scoping call with Adello. We'll map your current media mix against the six moves above and show where the biggest gap is.
FAQ
Why is contextual advertising replacing cookie-based targeting for automotive? Third-party cookies are being deprecated, and audience segments built on them lose accuracy quarter by quarter. Contextual advertising matches ads to the content being consumed instead. AI-powered solutions such as PXLSTRM analyse video content, dialogue, and on-screen objects to assemble precise, brand-safe audience clusters at scale.
What role does programmatic advertising play in automotive in 2026? Programmatic advertising is the default buying method for display, mobile, video, CTV, and DOOH. It enables real-time bidding at the individual impression level, brand safety and fraud detection pre-bid, and unified measurement across channels.
How is AI changing automotive search and research? Around 19% of all vehicle buyers and 25% of new-vehicle buyers used AI chatbots or AI-generated search overviews during research in late 2025. Brands cited in AI overviews see a +35% organic and +91% paid click uplift. Pages need GEO optimisation — structured data, definition-first paragraphs, and fresh statistics — to be retrieved by these engines.
Is mobile advertising still the priority for car brands in 2026? Yes. Over 70% of automotive internet shoppers use a smartphone as their main research device. A mobile DSP platform, vertical creative, and geolocation targeting are required, not optional.
AI Shopping Agents Are Already Processing Billions in Transactions
During Cyber Week 2025, 20% of global e-commerce orders were influenced by AI agents, according to Salesforce. AI chatbot traffic to U.S. retail sites grew 670% year-over-year during that same holiday season, as Adobe reported. And in January 2026, Google CEO Sundar Pichai announced the Universal Commerce Protocol (UCP) at the National Retail Federation conference — a clear signal that the infrastructure for AI-completed purchases is no longer theoretical.
McKinsey projects the global agentic commerce opportunity at $3 trillion to $5 trillion by 2030, with up to $1 trillion in U.S. B2C retail alone. AI platforms are expected to account for 1.5% of total U.S. retail e-commerce sales in 2026 — roughly $20.57 billion — nearly quadruple the 2025 figures, per EMARKETER.
These are not projections from AI enthusiasts. These are numbers from McKinsey, Morgan Stanley, and Forrester. The shift is measurable, and it has direct consequences for how brands advertise.
What Is Agentic Commerce?
Agentic commerce is an online shopping model where AI agents make purchasing decisions across the entire buying journey — from product discovery through checkout and post-purchase support — on behalf of the consumer.
The consumer sets intent and constraints. The AI agent handles research, comparison, selection, and in some cases, the transaction itself.
This is different from conversational commerce, where a chatbot recommends a product and the consumer still completes the purchase manually. In agentic commerce, the agent queries product catalogs, evaluates pricing and availability in real time, checks shipping and return policies, and can execute the checkout.
Think of it this way: a chatbot that suggests a moisturizer based on your skin type is conversational commerce. An AI that queries multiple skincare brands, compares ingredients and prices, and selects the best option within your budget — that is agentic commerce.
Why Agentic Commerce Changes Everything for Advertising
For over two decades, digital advertising operated on a predictable model. Brands competed for attention on search results pages, product listing pages, and social feeds. The consumer clicked, browsed, compared, and purchased. Advertisers measured clicks, impressions, and conversions.
Agentic commerce removes the session layer. The consumer no longer opens ten tabs. They no longer scroll through product listing ads. They describe what they need, and the agent handles the rest.
This creates a structural problem for advertisers. If the AI agent does not surface your product, your brand is not part of that transaction. The consumer never sees your ad, your listing, or your landing page. The entire traditional advertising funnel — awareness, consideration, conversion — gets compressed into a single AI-mediated decision.
U.S. advertisers will spend $71.98 billion on retail media in 2026, up 18.7% from 2025, according to an EMARKETER March 2026 forecast. AI shopping agents that bypass traditional search and browse behavior directly reduce the value of sponsored product placements, display ads, and keyword advertising.
The metric that matters is shifting. Click-through rate measures human interaction with a page. In agentic commerce, the more relevant metric is the AI citation rate — whether a shopping agent retrieves, references, or recommends your product during fulfillment.
How Agentic Commerce Works: The Three-Layer Architecture
When a consumer types a request like "Find me running shoes under $120, size 10, that ship before Thursday," the AI agent activates three distinct layers:
Intent Layer. The AI model parses the request, extracting constraints: budget, size, delivery window, brand preference, and return policy requirements. This is where natural language understanding meets structured product queries.
Commerce Layer. The agent queries product catalogs, pricing services, inventory feeds, and shipping estimators through structured APIs. It evaluates options against the consumer's constraints and ranks results by relevance, trust signals, and fulfillment reliability.
Transaction Layer. For approved purchases, the agent communicates with the merchant's checkout systems using protocols like the Universal Commerce Protocol (UCP) or the Agentic Commerce Protocol (ACP). Payment is processed through pre-authorized methods, and the agent handles order confirmation, tracking, and returns.
The critical insight: this architecture does not rely on websites, product pages, or traditional ad placements. It relies on structured data, API accessibility, and machine-readable product attributes.
The Key Players Building the Agentic Commerce Ecosystem
| Platform | Protocol / Product | Status (Q1 2026) |
| Universal Commerce Protocol (UCP), AI Mode checkout, Business Agent | Active. Brands like Keen Footwear and Pura Vida are already selling through it | |
| OpenAI / Stripe | Agentic Commerce Protocol (ACP), ChatGPT Instant Checkout | Live since September 2025 for 900M+ weekly ChatGPT users |
| Microsoft | Copilot Checkout | Active. Brands like Keen Footwear and Pura Vida already selling through it |
| Shopify | Agentic Storefronts | Available to millions of Shopify merchants as of March 2026 |
| Perplexity | Instant Buy (PayPal-powered) | Live with conversational product discovery and checkout |
| Amazon | Rufus (proprietary agent) | Restricted ecosystem. Sponsored prompts available |
Google, OpenAI, and Microsoft are building open or semi-open ecosystems. Amazon is taking a more controlled, proprietary approach. For advertisers, this fragmentation means maintaining product data for multiple agent ecosystems simultaneously.
What Brands and Advertisers Must Do: 5 Actionable Strategies
1. Treat Product Data as Advertising Infrastructure
In an agentic commerce environment, your product catalog becomes your most important advertising asset. AI agents do not read display ads. They read structured data feeds.
If your product data is incomplete, inconsistent, or lacks machine-readable attributes, the AI agent skips your product without a human ever seeing it. The difference between a "correct" catalog and an "optimized" catalog is no longer a marginal performance lift. It determines whether you are in or out of the conversation.
Priority actions: enrich product descriptions with real use-case language, add structured attributes for compatibility, accessories, substitutes, FAQs, and keep pricing and inventory synchronized in real time.
2. Invest in AI Engine Optimization (AEO) Alongside SEO
SEO was built for keyword-based search. AI agents operate differently. They interpret intent, evaluate tradeoffs, and surface products based on structured relevance, not keyword density.
Answer engine optimization and generative engine optimization are becoming operational requirements. This means structuring content so that AI systems can accurately represent your products when consumers ask questions across ChatGPT, Gemini, Perplexity, and Copilot.
Bain & Company estimates that 30% to 45% of U.S. consumers already use generative AI to research and compare products. If your brand does not appear in these AI-driven discovery moments, you lose consideration before the consumer even reaches a traditional advertising surface.
3. Build Structured Fulfillment Signals for Agent Legibility
AI shopping agents struggle with ambiguity. They need delivery windows, shipping costs, and return terms that are structured, comparable, and consistent. If this information is unclear across channels, the agent defaults to a competitor whose offer is easier to execute.
This is a form of competitive advantage that has nothing to do with creative or messaging. It is purely operational. Make your delivery promises, return policies, and availability data machine-readable and consistent across every channel.
4. Run Contextual and Cookieless Advertising to Reach Consumers Before Agent Delegation
Here is the part most advertisers are not discussing yet: agentic commerce compresses the funnel, but it does not eliminate the need for brand awareness and preference. Consumers still need to form opinions before they instruct their AI agents.
A consumer who tells their agent, "find me waterproof running shoes under $150 with next-day delivery," has already been influenced by something — a contextual video ad they saw during a trail running review, a brand mention in a comparison article, or a recommendation from a creator they follow.
This is where contextual targeting and cookieless advertising become critical. Brands that reach consumers in the right context — while they are still forming preferences — can shape the instructions consumers give to their AI agents.
Adello's cookieless advertising stack and its mobile DSP further support this approach, delivering programmatic advertising across mobile, DOOH, and video channels without reliance on deprecating identifiers.
The outcome: brands that invest in contextual, privacy-compliant advertising build the upstream awareness that shapes downstream agentic purchases.
5. Prepare for Agent-to-Agent Negotiation in B2B
Forrester predicts that by the end of 2026, 1 in 5 B2B sellers will face AI-powered buyer agents delivering dynamically generated counteroffers. This means advertising and pricing strategies need to account for machine-to-machine interactions, not just human decision-makers.
B2B advertisers should begin structuring their pricing, terms, and product data for agent consumption now — before competitors establish presence in these emerging channels.
Why Acting Now Matters: The Window Is Narrow
Retailers that deployed AI capabilities between 2023 and 2024 saw 14.2% sales growth, compared to 6.9% for those without AI capabilities, according to Capital One Shopping research. The performance gap is accelerating, and it applies to advertising strategy as well.
Shoppers directed to retail sites from AI platforms are 30 times more likely to make a purchase, per Adobe data cited by EMARKETER. That is an extraordinary conversion signal — but only for brands whose products the AI agent can find, evaluate, and recommend.
The practical window for establishing presence in agentic commerce ecosystems is 2026. The protocols are being standardized now. The consumer behaviors are forming now. The brands that structure their data, advertising, and fulfillment for AI agent accessibility in this window will have a measurable advantage over those that wait.
The Advertising Playbook Is Being Rewritten
Agentic commerce does not replace traditional advertising overnight. Consumer trust is still developing — only 46% of shoppers fully trust AI recommendations, and 89% still verify information before purchasing, according to the IAB. High-value and identity-sensitive purchases will remain human-directed for now.
But the direction is clear. The consumer journey is moving from pages to conversations. From clicks to delegated decisions. From keyword-based discovery to structured-data-driven recommendations.
Advertisers who prepare for this shift now — by investing in AI agents for advertising readiness, contextual targeting, cookieless advertising solutions, and structured product data — will be positioned to capture demand in a channel that is growing at multiples of traditional e-commerce.
The brands that will succeed in agentic commerce are the ones that are already visible, already trusted, and already structured for the machines that will increasingly do the shopping.
Zurich, 23.03.3026 - PXLSTRM, Adello's AI-based contextual advertising spinoff, now supports Pinterest campaigns in Germany, Austria, and Switzerland. The expansion adds Pinterest to PXLSTRM's existing contextual solution for YouTube and TikTok.
PXLSTRM applies AI-driven contextual targeting to video and image platforms without relying on third-party cookies.
For advertisers already using PXLSTRM on YouTube and TikTok, Pinterest adds an additional audience that is largely unreachable on other channels.
The incremental reach versus TikTok alone is approximately +50%. Adding Pinterest to an existing PXLSTRM campaign extends coverage to a segment that current platform combinations miss.
Initial Pinterest campaigns through PXLSTRM show a 10% CPA improvement and a 4:1 ROAS in case studies. Further campaigns will shed light on the scale-out of these initial numbers.
PXLSTRM processes behavioral signals across multiple dimensions and correlates these with proprietary contextual data. The result is audiences instead of demographics alone. Leveraging proprietary contextual data and domain-specific know-how is what makes the solution unique.
Pinterest has more ~24.5 million users in DACH and reaches 29–36% of the online population. 55% of users report purchase intent — compared to 12% on Instagram and Facebook. 98% say they have discovered new products on the platform. 44% of German Pinterest users fall into high-income households. This proves that while Pinterest is smaller in absolute numbers, it can add a highly engaged audience for certain target groups.
About PXLSTRM
PXLSTRM is a pioneering AI-driven video advertising technology company, a spin-off of Adello. By analyzing video content—including dialogues, objects, and visuals—PXLSTRM ensures that ads reach the right audience with unmatched precision. Advertisers leveraging PXLSTRM’s technology experience engagement and conversion rate improvements of over 100%. Brands looking to optimize their Return on Ad Spend (ROAS) while ensuring contextual relevance and brand safety are encouraged to connect with the PXLSTRM team.
Contact: info@adello.com
The AI Visibility Paradox
Today, 75% of a marketing agency's inbound leads come from visibility in ChatGPT and similar AI platforms, not traditional search. Meanwhile, 95% of B2B marketers have adopted AI content tools. Yet only 39% report improved performance.
This gap exposes a structural problem: Brands across technology, healthcare, real estate, and tourism are producing exponentially more content while simultaneously losing visibility where it matters.
This is why it is happening: AI-powered answer engines now extract up to 48% of their response content from open sources without directing users to brand websites. Your content becomes a source, not a destination. The question isn't whether your audience can find you through traditional search anymore. It's whether AI systems recognize your brand as authoritative enough to cite when making recommendations.
What Changed: From Clickable to Citable
For two decades, digital marketing operated on a simple premise: Rank high in search results, capture clicks, convert visitors. Traditional SEO rewarded keyword optimization, backlink profiles, and page speed. The conversion funnel was linear.
Today, that model is breaking.
AI assistants now sit between your brand and your audience. Most queries never generate a website visit. Zero-click searches are the new normal.
This creates three compounding problems. First, traditional traffic metrics no longer correlate with influence. A brand can lose 40% of its website visitors while simultaneously increasing its impact on purchase decisions if AI platforms frequently cite its expertise.
Second, content volume has become a liability. The average B2B brand publishes 5x more content than in 2020, creating internal competition for attention.
Third, AI-generated content floods every channel with grammatically correct but strategically hollow material that commodifies entire categories.
Solutions for AI-Era Brand Relevancy
Build Content Designed for Extraction, Not Just Reading
AI systems need clear signals to understand when and how to cite your brand. This requires structural changes to how content is created.
Implement extractable frameworks. Each piece should include one-sentence takeaways, clear definitions, specific methodologies, and quantified outcomes.
AI rewards precision. Instead of "our platform improves efficiency," write "reduces processing time from 14 days to 3 days through automated workflow routing." The second version gives AI systems concrete information they can extract and attribute.
Create topic clusters with authoritative depth. Rather than publishing 50 surface-level blog posts, develop comprehensive resources on 10 core topics. Each cluster should include primary research, case studies with specific metrics, and documentation of methodology. AI platforms favor sources that demonstrate subject matter depth over breadth.
Establish First-Party Data Architecture for Privacy-Compliant Targeting
Third-party cookies are gone. Contextual signals and first-party data now determine targeting precision.
Build zero-party data collection mechanisms. Interactive tools, preference centers, and value exchanges generate data that users actively provide.
Implement progressive profiling. Rather than demanding 12 form fields upfront, collect 2-3 pieces of information per interaction across multiple touchpoints. After five interactions, you have comprehensive account intelligence without friction.
Connect data sources into unified customer views. Most organizations collect first-party data in six separate systems that never communicate. CRM, marketing automation, website analytics, event registration, support tickets, and product usage each hold partial pictures. Integration creates targeting accuracy that third-party cookies never could.
Limitation: First-party strategies only work when traffic exists. Brands with limited awareness need complementary approaches to generate initial engagement.
Implement Thought Leadership Programs With Verifiable Expertise
AI platforms favor sources that demonstrate original thinking backed by empirical evidence.
Only 4% of B2B organizations rate their thought leadership programs as "leading", creating an opportunity for brands willing to invest in substantive content.
Document proprietary methodologies transparently. Share the frameworks your organization uses to solve problems, including decision criteria, evaluation models, and implementation sequences. When an AI platform needs to explain how businesses approach a specific challenge, your methodology becomes the reference point.
Partner with academic or industry research institutions. Third-party validation carries more weight than self-published claims.
Time investment is substantial. Meaningful thought leadership requires 200-400 hours annually per topic area, combining research design, data collection, analysis, and content development. Most brands underestimate this.
Optimize for Multi-Platform Search Ecosystems
Traditional SEO focused on Google. AI-era visibility requires presence across ChatGPT, Perplexity, Gemini, Bing AI, Meta AI, and vertical-specific platforms.
Each platform pulls from different source hierarchies. ChatGPT heavily weights Wikipedia, Reddit, and established news sources. Perplexity favors recent content with clear attribution. Gemini integrates more real-time data. Optimization requires platform-specific strategies.
Create "source-ready" content formats: Structured data, clear attribution, FAQ sections, and comparative tables make extraction easier for AI systems.
Monitor AI platform citations directly. Traditional analytics track website traffic but miss AI visibility. New tools are emerging to measure how often brands appear in AI-generated responses. This becomes the primary visibility metric, replacing search ranking position.
Build Community-Led Growth Engines
As AI commodifies information access, community participation becomes a differentiation strategy. B2B buyers trust peer recommendations in private Slack groups and LinkedIn communities more than vendor content.
Create owned community spaces. Rather than relying on third-party platforms, establish brand-hosted forums where practitioners share challenges and solutions. This generates first-party data about actual problems while building authority through facilitated expertise sharing.
Activate employee advocacy systematically. Individual professionals have greater reach and trust than corporate accounts on most platforms. Provide frameworks and resources, but allow authentic voices rather than scripted messaging.
Invest in experiential marketing. Physical events, workshops, and invite-only demonstrations create relationships that algorithms cannot replicate. Post-pandemic research shows 78% of B2B marketers are increasing experiential budgets.
Leverage AI for Operational Efficiency, Not Content Generation
The brands succeeding with AI use it to amplify human expertise rather than replace it. AI handles research synthesis, data analysis, and draft structure while humans provide perspective, judgment, and expertise that only comes from direct experience.
Implement AI-assisted workflows. Use AI to generate content outlines based on keyword research and competitive analysis. Have subject matter experts add proprietary insights, specific examples, and nuanced judgment that AI cannot replicate. Edit for voice and precision. This creates content that scales production while maintaining differentiation.
Apply AI to personalization at scale. Generate variant messaging for different industries, company sizes, and roles based on a core narrative.
Use AI for performance prediction. Train models on historical campaign data to forecast which messages, formats, and targeting strategies will perform best for upcoming launches. This reduces testing cycles from months to weeks.
Maintain editorial standards regardless of AI involvement. The output must reflect your organization's expertise and perspective. AI is a production tool, not a strategy replacement.
Why Relevancy Requires Immediate Action
AI search adoption is accelerating faster than mobile search did. ChatGPT reached 100 million users in 60 days. Traditional SEO took 18-24 months to show results because ranking improvements were gradual. AI citation either happens, or it doesn't. There is no page two.
The current window represents a first-mover advantage. AI platforms are still establishing their source hierarchies and citation patterns. Brands that become authoritative sources now get embedded in these systems as they scale. Waiting 18 months means competing against established citation patterns rather than creating them.
Budget reallocation matters more than budget increases. Most organizations can fund AI-era strategies by reducing spending on declining channels. Traditional display advertising, generic content production, and third-party data purchases should decrease. First-party infrastructure, research programs, and contextual precision should increase.
The alternative is passive citation at best, invisibility at worst. Your competitors' content will be cited when AI platforms answer questions in your category. Your expertise becomes context for their recommendations. This compounds over time because AI systems reinforce existing citation patterns through algorithmic learning.
The New Measure of Brand Relevancy
Digital marketing is transitioning from an attention economy to an authority economy. Volume no longer correlates with impact. Visibility through AI citation, thought leadership recognition, and community influence determines which brands shape buying decisions.
This requires different metrics, different skills, and different organizational structures than traditional demand generation. But it also creates an opportunity for brands willing to invest in substance over scale. The technology companies, healthcare providers, real estate firms, and tourism operators that build extractable expertise, privacy-compliant targeting capabilities, and authentic community engagement will own category authority in the AI era.
The ones that continue optimizing for yesterday's search algorithms will become invisible where decisions are actually made: in conversations with AI assistants that never mention their brand at all.
2025 is coming to an end - what a year for marketing and ads!
Things are changing faster than ever, forcing marketers to stay on their toes and constantly keep up.
So, what is 2026 preparing for us? Let’s take a look into the future - no magic, no esoterics, just a clear observation of the trends. 😉
AI + strategic creativity beats AI + automation
What? Marketers around the world rushed to adopt AI automation, and suddenly, a new trend has arrived: AI + strategic creativity.
If you feel like you missed the thread, AI + strategic creativity means keeping a human in the loop to preserve brand voice, precision, and avoid generic output.
How? For example, AI localizes ads for five markets, and a human validates cultural fit and emotional accuracy. Or a strategist defines the core idea, audience tension, and brand angle, while AI generates variations.
Briefly: AI accelerates execution. Humans own meaning, taste, and risk.
Back to authentic content
This trend derives from the previous one. Suddenly, companies are back to hiring copywriters and designers to use a naïve style - imitating imperfect, human-like drawing.
In fact, users are tired of seeing generative AI everywhere. We miss something truly authentic. Brands, recognising this, are stepping closer to their audience.
Next year, the winners will be brands that deliver authentic, human-made content and celebrate it.
Important: AI is here to stay, but as a companion, not the main character.
Image credit: kittl.com
Mobile first
Mobile ads and mobile adoption have remained a dominant trend over the past several years. Today, the absolute majority - over 70 % of internet users - access the web via mobile, and more than 60% of global traffic already comes from smartphones.
People don’t “go online” anymore; they are online by default, through their phones: scrolling, searching, shopping, watching, and interacting in short, high-frequency moments throughout the day. Mobile marketing naturally follows attention, and attention lives in the pocket.
Social commerce
Speaking of mobile, we can’t avoid social commerce. Recently, TikTok fully stepped into commerce, following the same direction Instagram and Meta took earlier, turning social platforms into discovery-to-purchase ecosystems.
This shift reinforces the need for platform-specific content strategies. And by the way, our recent PXLSTRM update for TikTok, reaching the most relevant audiences through smarter ad placement, we’re already seeing 2-3x higher landing-page engagement.
Thus, in 2026, marketing must be both content-aware and commerce-aware because attention, intent, and conversion now live in the same feed.
GEO optimisation
In addition to Search-Everywhere Optimization (SEvO), Generative Engine Optimization (GEO) is coming into the spotlight. GEO enhances your content so it’s easily understood, trusted, and cited by AI systems such as ChatGPT, Google AI Overviews, Perplexity, and Copilot.
When users ask AI tools for recommendations or explanations, your content gets selected, summarized, and cited as the source. In GEO, instead of chasing keywords, you focus on clarity, context, and authority.
The good news is that GEO is still in its early adoption phase, which means the brands that adapt first are the ones that will win in 2026 and beyond.
Video
Video will remain the leading format in 2026. It aligns perfectly with how people consume content today: fast, visual, mobile-first, and low-effort.
Short-form video remains the most engaging format across platforms, while long-form video is evolving into an education and trust-building tool.
At the same time, video is becoming more contextual, shoppable, and performance-driven. In 2026, brands that win with video won’t be the loudest, but the clearest, most relevant, and most intentional. See how to do that.
DOOH and geodata
DOOH powered by geodata will be a defining trend in 2026. When combined with mobile audience intelligence, it allows brands to detect audiences passing by DOOH screens, re-engage them later on mobile with native, interactive ad formats, and measure how exposure translates into real-world visits and footfall. This creates a full loop: from physical presence to mobile interaction to performance insights, including location sensitivity, timing, and message effectiveness.
FOOH - Out of Home goes viral
Technically, Fake Out of Home (FOOH) is very different from classic DOOH. Nevertheless, this format is gaining traction, especially on social media, and strongly resonates with younger audiences.
FOOH isn’t limited by screens, locations, or physical inventory. It’s limited only by imagination: brands can place themselves anywhere, bend reality, and create visually striking moments designed purely for sharing, conversation, and cultural relevance.
Unique chance of increasing Share of Voice
The last trend is the bottom line of everything we’ve discussed in this article.
There’s no secret that 2026, alongside all the exciting innovations, may also bring economic recession, uncertainty, and reduced consumer spending.
In response, many brands will shut down or significantly cut their marketing activity - and that’s a big mistake. In times like these, the smartest move is to make lemonade out of lemons: occupy the space others leave behind, increase your share of voice, and be the loudest when everyone else goes silent.
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On Friday, June 13, 2025, PXLSTRM hosted the event “The Future of Video Advertising in Switzerland”. This event brought together top industry leaders to discuss the next era of video advertising defined by AI, shifting media consumption habits, and increasing personalization.
With participation from major Swiss media players like Swiss Post, Zattoo, and TWmedia, as well as thought leadership from LAB51’s Mark Forster, the event explored both the challenges and the opportunities shaping the industry today.
Video Advertising at a Crossroads
PXLSTRM CEO Wei Phung welcomed the guest. This was followed by a keynote speech of Mark Forster, where he highlighted the importance of video for today’s advertising.
Legacy TV is transforming, and video consumption is becoming more fragmented and digital. While traditional TV providers continue to serve as curators of quality content, distribution models and viewer behavior have evolved.
Now, there is a demand for new metrics for measurement reach and effectiveness across both linear and digital platforms.
The AI Revolution in Video Ads
Wei Phung, in his presentation, introduced PXLSTRM, an AI-powered contextual video targeting technology. He outlined the four key evolutionary stages of this technological solution:
- From keyword-based targeting to semantic and sentiment analysis, PXLSTRM is moving beyond surface-level metadata and grasping the true meaning and tone of the video content.
- PXLSTRM’s algorithms are capable of identifying logos, objects, dialogues, and even emotional nuance. This helps not just to understand what’s shown, but how it feels.
- Real-time emotional targeting enables advertisers to serve the right message at the precise moment a viewer is most receptive, based on live content and sentiment cues within the video stream.
- PXLSTRM's system is capable of generating up to 500 contextual keywords per minute of video, turning passive media into rich, data-driven ad opportunities.
- All of this with zero reliance on cookies, ensuring privacy compliance while achieving maximum relevance.
According to internal analysis of over 40 campaigns in 2024, PXLSTRM led to an average +110% improvement in contextual relevance, while delivering significantly higher engagement, brand safety, and viewer retention across the board.
Also, Wei Phung has presented a PXLSTRM's Toolset for the Future:
- PXLSTRM Analytics: Deep campaign audits, optimization insights, and competitor benchmarking
- PXLSTRM Video: Semantic and context targeting on platforms like YouTube, soon expanding further
- PXLSTRM Live!: Real-time, mood-based ad delivery for CTV platforms
These tools combine to deliver emotionally intelligent advertising — ads that feel right in the moment and stick in memory. With this level of granularity and automation, PXLSTRM is positioning itself at the forefront of a new era in video advertising, one that is smarter, safer, and more attuned to human experience.
Panel Discussion: Where Are We Heading?
Later, the industry leaders from influential media companies sat down in a panel discussion moderated by Mark Forster. This panel, “Where is Video Advertising Going?” featured:
- Goce Nicoloski, Zattoo
- Alexander Schmitt, Swiss Post
- Oliver Schönfeld, TWMedia
- Wei Phung, PXLSTRM
Together, they addressed the need for improved cross-platform measurement and attribution to accurately capture audience reach across both linear and digital channels. They also highlighted the importance of context-aware advertising that can adapt in real time to the viewer’s mood and the specific moment of content consumption. Underpinning these discussions was a shared commitment to leveraging AI not only for efficiency and precision but also in ways that are ethical, transparent, and aligned with user expectations.
The event finished with an apéro and networking session, offering guests the opportunity to engage in more personal conversations and connect directly with participants and speakers.
This event wasn’t just a showcase; it was a dialogue. The participants from some of the most important organizations in the Swiss video advertising industry (including Mediapulse, WEMF, Admeira, Goldbach, etc.) shared their perspectives and contributed significantly to the depth and relevancy of the discussion. Stay tuned, the transformation of video advertising is just getting started!
PXLSTRM today announced its strategic expansion into the dynamic Asia-Pacific market, with an initial focus on Southeast Asia. Following its successful entry into Germany, the innovative video analysis & targeting solutions company headquartered in Zurich, Switzerland, is rapidly gaining traction in the region. PXLSTRM is already partnering with several of the “Big Six” media agencies and delivering its cutting-edge solutions to global brands in the Food & Beverage and Entertainment industries.
This expansion highlights PXLSTRM’s mission to become a global leader in AI-powered contextual video advertising.
“Our expansion into Asia underscores our commitment to becoming a truly global pioneer in video analysis and targeting,” said Wei Phung, CEO and Co-Founder of PXLSTRM. “This dynamic market presents invaluable opportunities for innovation and market leadership. Asian markets are evolving at an accelerated pace, and users here show a strong appetite for adopting new technologies. This creates an exceptional environment for us to learn, innovate, and refine solutions that benefit all our markets.”
PXLSTRM is establishing its presence across the Asia-Pacific (APAC) region, beginning with Southeast Asia (SEA). To drive this growth, the company has strategically hired local talent in Malaysia and Singapore—experienced adtech professionals with deep industry ties. Their expertise positions PXLSTRM for rapid advancement and impactful local execution in SEA.
The company’s unique contextual video targeting solution uses advanced AI to analyze visuals, dialogue, context, and sentiment in video content. This generates a significantly higher volume of actionable data, empowering the AI to ensure ads appear in the most relevant and brand-safe environments. This has resulted in an average relevance uplift of over +110% for video advertising campaigns.
About PXLSTRM
PXLSTRM is a pioneering AI-driven video advertising technology company, originally spun off from Adello. By analyzing video content—including brands, logos, objects, activities, and dialogues—PXLSTRM ensures ads reach the right audiences with unmatched precision. Advertisers leveraging PXLSTRM’s technology have seen engagement and conversion rates improve by over 100%.
Brands looking to maximize their Return on Ad Spend (ROAS) while ensuring contextual relevance and brand safety, especially within the fast-growing APAC region, are encouraged to connect with the PXLSTRM team.
PXLSTRM has officially expanded into the German market following its participation in the AdVantage Microsoft Advertising event in Munich.
Organized by Sowespoke AG, a Microsoft Advertising Channel Partner, the event provided a key platform for emerging agencies to explore Microsoft Advertising’s ecosystem and cutting-edge solutions.
At the event, Wei Phung, CEO and Co-Founder of PXLSTRM, introduced the company’s AI-powered video ad solution, PXLSTRM, to an audience of rising agencies within the SWS Alliance, an initiative led by Sowespoke AG. The alliance, consisting of nearly 100 digital marketing agencies across Germany now has access to PXLSTRM’s advanced contextual video advertising technology.
“Germany represents a crucial market for PXLSTRM’s growth, and our partnership with the
SWS-Alliance ensures that emerging agencies can leverage AI-driven contextual targeting
to maximize ad relevance and effectiveness,” said Wei Phung.
Transforming Video Advertising with AI Precision
PXLSTRM enhances contextual targeting by analyzing video streams with AI and understanding visuals, dialogues, context, and sentiment. This ensures that ads are delivered within the most relevant content, optimizing engagement while maintaining brand safety. With an average relevance improvement of +110%, the solution empowers advertisers to deliver highly targeted messaging in the right context.
About PXLSTRM
PXLSTRM is a pioneering AI-driven video advertising technology company, originally a spin-off of Adello. By analyzing video content—including dialogues, objects, and visuals—PXLSTRM ensures that ads reach the right audience with unmatched precision. Advertisers leveraging PXLSTRM’s technology experience engagement and conversion rate improvements of over 100%. Brands looking to optimize their Return on Ad Spend (ROAS) while ensuring contextual relevance and brand safety are encouraged to connect with the PXLSTRM team.