Media management is being rewritten as fragmented discovery, AI-led execution, and rising pressure to prove business impact force brands to rethink what they need from agency partners. Forrester’s The Media Management Services Landscape, Q2 2026 frames this shift around full-funnel value, AI governance, and CFO-grade measurement, while DAC’s view is that the next media partner must connect strategy, data, content, operations, and outcomes rather than just media buying.

Keep reading to learn how UK brands can turn media management into a more measurable, scalable growth channel.

For years, media management was often evaluated through a narrow lens: buying power, channel expertise, campaign efficiency, and the ability to optimise spend across platforms. Those capabilities still matter. But they are no longer enough.

Today’s CMOs in the UK are operating in a more complex environment. Discovery is fragmented. Customer journeys are less linear. AI is reshaping how consumers search, compare, and make decisions. Retail and commerce media are changing the relationship between advertising and sales. Finance teams are asking harder questions about return on investment. At the same time, media platforms are automating more of the executional work agencies once used to differentiate themselves.

The result is a fundamental shift in what brands should expect from a media management partner. The next generation of media performance will not be defined by who can buy impressions at the lowest cost. It will be defined by who can connect strategy, data, content, technology, and operations to business outcomes that leadership teams can understand and trust.

According to Forrester’s The Media Management Services Landscape, Q2 2026, media management services are evolving beyond paid media placement to combine business strategy, data, technology, content, planning, buying, measurement, and continuous optimisation in support of effective customer engagement. DAC was included among the providers researched in the report.

From media efficiency to business accountability

Efficiency has always been part of the media mandate. Brands need to reach the right audiences, reduce waste, and optimise investment across channels. But efficiency alone does not answer the question now being asked in boardrooms: what business impact did this media investment create?

That question changes the role of the agency partner.

Media can no longer be treated as a standalone activation layer, disconnected from customer behaviour, sales performance, content relevance, and the broader marketing ecosystem. It has to be planned and measured in relation to business objectives such as customer acquisition, lead quality, revenue, retention, store or branch visits, local demand, market share, or lifetime value.

This is especially important for multi-location, multi-market, and enterprise brands, where media performance is shaped not only by national spend, but also by local discoverability, operational consistency, content quality, and the ability to understand what is happening across markets.

The agencies best positioned for this new reality are not simply media buyers. They are business partners that can translate investment into outcomes, and outcomes into decisions.

CFO-grade measurement is becoming the new standard

The pressure to prove media’s value is intensifying. Platform-reported metrics still have a role, but they are not enough on their own. Clicks, impressions, reach, views, and even conversions can tell part of the story, but they do not always prove incremental business impact.

Marketing leaders increasingly need measurement approaches that can stand up to executive and finance scrutiny. That means stronger use of incrementality testing, marketing mix modelling, attribution, experimentation frameworks, and outcomes-linked reporting.

The goal is not simply to report what happened. It is to understand what changed because of media investment.

A campaign can generate strong platform performance while still failing to create incremental value. Conversely, some of the most important media effects may happen across longer time horizons, multiple touchpoints, or offline environments that are not fully captured by platform dashboards.

For brands, the question to ask is no longer, “Can our agency report on performance?” It is, “Can our agency help us understand the business contribution of media well enough to make better investment decisions?”

AI is changing execution, but governance is the differentiator

AI is quickly becoming part of the media management infrastructure. Planning, buying, bidding, budget allocation, creative variation, audience modelling, and optimisation are all being affected by automation.

But this does not mean strategy becomes less important. In fact, it makes strategy more important.

When platforms automate more decisions, brands need partners who can define the right objectives, feed systems with better data, interpret outputs, identify risks, and ensure that AI-enabled activation aligns with business priorities. The real differentiator is not whether an agency “uses AI.” It is how that agency governs AI.

CMOs should ask clear questions. How is AI being used in planning and optimisation? Where is human oversight required? What safeguards exist around brand safety, bias, privacy, compliance, and quality control? How are recommendations validated? How are teams trained? How are outcomes monitored?

AI can accelerate media performance, but only if it is connected to a disciplined operating model. Without governance, automation can simply make poor decisions faster.

Discovery is fragmenting across search, social, commerce, and AI

One of the biggest shifts facing media leaders is the fragmentation of discovery. Consumers no longer move through a predictable funnel. They discover brands through paid search, organic search, maps, social platforms, creators, reviews, retail media networks, marketplaces, AI assistants, video platforms, and local listings.

That has major implications for media strategy.

If people discover brands across a wider range of environments, media cannot be planned in isolation from content, search visibility, local presence, and customer intent. Paid media may create demand, but content, reviews, location data, landing pages, search results, and local experiences often determine whether that demand converts.

This is where the boundaries between media, SEO, content strategy, analytics, and local optimisation start to blur. A high-performing media partner must understand not only where to place investment, but also how consumers make decisions once that investment creates interest.

For distributed brands, this is even more critical. A national campaign may drive awareness, but the final decision may happen at the local level: a nearby branch, dealership, clinic, restaurant, shop, or service provider. If the local experience is inconsistent, inaccurate, or invisible, media efficiency suffers.

The next media partner must understand discovery, not just distribution.

The five demands for your media management partner

As the market evolves, CMOs should raise the bar for agency evaluation. Buying power and channel credentials still matter, but they should be only part of the conversation. The more important question is whether a partner can help the brand build a more accountable, adaptive, and integrated media model.

Here are five demands every CMO should bring to the next agency conversation.

1. Business-linked measurement
 Your partner should be able to connect media performance to business outcomes, not just media metrics. That includes a clear point of view on incrementality, attribution, marketing mix modelling, experimentation, and executive-ready reporting. The goal should be better decision-making, not just better dashboards.

2. AI governance
 Your partner should be able to explain how AI is being used, where human judgement remains essential, and how risks are managed. AI should improve speed and intelligence, but it should not create a black box. Brands need transparency, accountability, and governance.

3. Data strategy
 Media performance depends on the quality of the data behind it. Your partner should know how to structure, connect, analyse, and activate data across channels, markets, and customer journeys. Data strategy is no longer an adjacent capability. It is central to media effectiveness.

4. Content and context alignment
 The right audience and the right placement are only part of the equation. Media also needs the right message, the right content, and the right experience for the context in which people are discovering the brand. Content strategy, search visibility, creative relevance, and media planning need to work together.

5. Operational excellence
 Modern media is operationally complex. Brands need partners who can manage trafficking, taxonomy, governance, reporting, budget pacing, optimisation, partner coordination, and multi-market execution with discipline. Operational excellence is often what separates strategy from performance.

Why DAC is part of this conversation

DAC was included among the providers researched in Forrester’s The Media Management Services Landscape, Q2 2026. In the report, DAC is listed as an agency with geographical focus in North America and EMEA, and with selected extended business scenario focus areas including content strategy, data strategy, and media and advertising operations.

Those areas reflect where the media management conversation is heading. Brands do not need more disconnected activity. They need connected systems of performance: data that informs decisions, content that matches intent, media that drives demand, operations that create consistency, and measurement that proves value.

For DAC, this is a natural evolution of the enterprise-to-local challenge many brands already face. Whether a brand is managing locations, markets, product lines, service areas, dealership networks, franchises, or distributed customer journeys, performance does not happen in one channel, one platform, or one dashboard. It happens across a connected ecosystem of moments that influence how people discover, evaluate, and choose a brand.

The agency review checklist needs an upgrade

As brands reassess their agency relationships, the evaluation process should change. The strongest agency partner may not be the one with the flashiest platform demo or the lowest cost structure. It will be the one that can combine talent, technology, governance, and strategic clarity in a way that helps the brand grow.

That means asking better questions.

How does the agency prove business impact? How does it govern AI? How does it connect media with search, content, and local intent? How does it operationalise data? How does it help the organisation make smarter investment decisions?

The future of media management will belong to partners that can make media more measurable, more adaptive, and more connected to the customer journey.

Media is being rewritten. CMOs should make sure their agency partnerships are being rewritten with it.

The post What Should CMOs Demand From a Media Partner Today? appeared first on DAC.

Keep reading to learn how Google’s latest updates are reshaping SEO, paid media, video strategy, commerce experiences, and measurement frameworks for brands.

Google Marketing Live 2026 reinforced a major shift that’s already reshaping how brands connect with consumers: search, discovery, shopping, and measurement are becoming increasingly AI-driven, conversational, and interconnected. From AI Mode and video discovery to commerce integrations and advanced measurement tools, Google’s announcements made it clear that brands need to adapt to non-linear consumer journeys happening across Search, YouTube, shopping experiences, and conversational interfaces.

As the dust settles on GML 2026, let’s break down the announcements and look at the key takeaways and next steps for brands’ success.

1.    Search is already conversational

The shift in how users search is no longer hypothetical—it’s already happening, with 2.5bn AI Overviews users and more than 1bn monthly AI Mode users. These features see 3x longer queries than traditional search. Consumers are increasingly embracing these experiences, with brainstorm-style searches growing 30% faster than any other AI Mode query type.

With that shift in user behaviour, more Google Ad products are gaining prominence:

• Ads and promotional offers surfaced in AI Mode incorporate immediate purchase action into the original research conversation
• AI-generated shopping summaries help recommended products align more closely with the user’s ongoing research and intent
• Business agents will allow consumers to ask questions directly within ads, creating a more seamless research and purchase experience

What does this mean for brands?

Expertise and experience remain critical for SEO and AI visibility—something DAC has already identified as a core strategic pillar.

With ads coming to AI Mode, brands need to be present in the conversation in a natural way to be able to fit the moment. Features like AI Max can help brands better understand conversational intent and dynamically adapt creative assets to fit the moment more naturally.

2.    Video and creator content are becoming central to discovery and performance

Video is no longer just an awareness tactic; it’s becoming a core driver of discovery, engagement, and purchase consideration, with visual assets playing a key role in brand discovery through multiple touchpoints:

• AI Mode: Organic visibility in AI Mode is not only based on text content, but visual content as well, including video. To continue building expertise and experience needed for more visibility, brands need to lean into all types of content.
• YouTube AI Overviews: AI Overviews are coming to YouTube in the form of an Ask YouTube button that surfaces the most relevant content based on the user’s question and incorporates further chat experience to promote discovery.
• Creator content: With organic video content driving 13x higher search intent and 5x higher purchase intent, creators continue to play a significant role in brand discovery and influencing consumer behaviour. Google is adding features to find and add creator content to paid campaigns to further simplify the process for advertisers.
• DemandGen: On the paid side, DemandGen is the key campaign type to drive incremental revenue with video, rooted in the predictive model surfacing content to users before they actually think about a product or service.

What does this mean for brands?

Brands need to rethink their creative strategies and further lean into video content to fuel discovery and influence purchase decisions. On the paid side, video should not be considered a purely awareness asset, but a purchase driver as well.

3.    Commerce experiences are becoming more seamless and connected

Google is simplifying the path to purchase with Universal Cart powered by UCP (Universal Commerce Protocol), which allows users to buy directly from product ads across multiple surfaces, including AI Mode and YouTube.

Simultaneously, product feeds on YouTube are expanding to tablets and Pause Ads on Mobile, providing more ways for consumers to engage with products and convert.

The Universal Cart also introduces more agentic shopping experiences, including alerts for deals and price changes on saved items. From a user perspective, the Universal Cart becomes a wish list for purchases they might want to think about a bit more.

What does this mean for brands?

Detailed, structured, and regularly updated product feeds are crucial for AI Mode visibility and timely offer surfacing, feeding not only into deal alerts but AI summaries and the ability of the product to fit seamlessly into the conversation.

Brands need to adopt an approach that any channel is a shopping channel and adjust their strategies accordingly to capture demand in the way that is most convenient for consumers.

4.    Measurement and first-party data remain the foundation of performance

As consumer journeys become more dynamic and less linear, Google is continuing to emphasise the importance of strong measurement frameworks and high-quality first-party data. At GML 2026, Google positioned data strength, causality, and unified measurement as critical components for helping AI-powered systems optimise campaigns and understand performance across increasingly fragmented touchpoints.

To support this, Google highlighted tools like Google Tag Gateway and the Data Manager API to help brands strengthen data collection and activation. Additional measurement updates included:

• Campaign Type Attribution: Highlights how campaigns like DemandGen contribute to conversions across the broader customer journey
• Attributed Brand Searches: Measures increases in brand search activity influenced by campaigns
• Qualified Future Conversions: Uses predictive modelling to estimate future conversion impact based on current engagement signals

What does this mean for brands?

Strong measurement foundations and reliable first-party data are becoming even more important as AI-driven media buying and optimisation continue to evolve. Brands need accurate, timely signals to help platforms better understand performance and optimise towards business outcomes.

At the same time, these newer measurement tools reflect the growing need to evaluate influence beyond last-click attribution. While predictive metrics should still be interpreted thoughtfully, they can provide faster directional insights into how campaigns are shaping consumer behaviour across increasingly complex purchase journeys.

Navigating the next era of Google marketing

Google Marketing Live 2026 reinforced that search, video, commerce, and measurement are becoming increasingly interconnected through AI-powered experiences. As consumer journeys grow more conversational and non-linear, brands need strategies that connect SEO, paid media, creative, commerce, and analytics to meet users across every stage of discovery and decision-making.

At DAC, we help brands adapt to these evolving behaviours through integrated digital marketing strategies built for performance and long-term growth. Whether it’s improving AI visibility, scaling video and creator content, modernising measurement frameworks, or building connected full-funnel media programmes, our teams help brands stay ahead of change. Explore DAC’s search, media, and analytics services or contact us to learn more.

The post Google Marketing Live 2026: 4 key takeaways appeared first on DAC.

Keep reading to learn how brands can turn YouTube content into a more engaging, scalable growth channel.

____________________________________________________________________________________________

Edutainment is becoming YouTube’s biggest growth opportunity. “How-to” content still works. It just is not where the biggest upside lives anymore.

For years, YouTube strategy was straightforward: find a question people are searching for, answer it clearly, optimise the metadata, and let search do the rest. That model still has value. But it is no longer enough on its own, especially in crowded categories where dozens — or hundreds — of videos are already competing to answer the same question.

Today, the brands breaking through are not always the ones with the most complete answer. More often, they are the ones that make learning watchable.

A few signals help explain why this shift matters. YouTube Shorts now averages 200 billion daily views, according to reporting on Neal Mohan’s Cannes Lions keynote. HubSpot reports that 89% of businesses use video marketing. And YouTube’s own analytics framework explicitly breaks discovery into multiple surfaces, including Search, Browse, Suggested, playlists, channels, and external sources. In other words, the platform is bigger than search, competition is heavier, and format matters more than ever.

The problem: The old playbook is getting crowded

The problem is not that educational content stopped working. The problem is that basic educational content became easy to replicate.

Most industries now have years of “how to,” “what is,” and “top five tips” videos sitting on YouTube. That has created a sea of content with predictable hooks, familiar formats, and nearly interchangeable answers. There is nothing wrong with those formats in isolation. But when dozens of channels in your category all answer the same question in the same way, distinctiveness disappears.

And at the same time, video has become mainstream across marketing. The volume of content competing for the same searches has never been higher. Producing another technically correct tutorial is no longer a differentiator. It is the baseline.

That matters because YouTube does not simply reward the most technically correct video. It rewards the videos people choose to watch — and keep watching. A video that earns the click and holds attention through to the end sends fundamentally different signals to the platform than one that gets found but abandoned after 45 seconds.

The gap between those two outcomes is exactly where brands are either winning or leaving growth unrealised.

Old model:
Search a question → Watch tutorial → Done
Optimised for: relevance only

New opportunity:
Click-worthy packaging + stronger retention + recommendation growth
Optimised for: how YouTube actually distributes content

YouTube goes beyond search

A lot of brands still treat YouTube like Google with thumbnails. That is one of the biggest strategic mistakes they can make, and it quietly caps the ceiling on everything they publish.

Search still matters. It is still one of the clearest ways to capture intent. But YouTube itself makes clear that traffic can come from multiple surfaces, and the Reach tab is specifically built to help creators understand how viewers find content through impressions, CTR, and traffic sources. Search and discovery guidance also makes clear that YouTube matches viewers to videos they are likely to watch and enjoy, rather than rewarding one format by default.

Those surfaces behave differently and reward different qualities in a video:

Sources: YouTubeHelp; Search Engine Journal

That changes the game entirely. Search can help a video get found. But recommendation surfaces like Browse, Suggested, and Shorts are what helps video scale.

And the rise of Shorts only reinforces that reality. YouTube discovery is no longer built around one behaviour or one format. Brands that plan only for search are building a single-lane strategy on a multi-lane platform, especially as video is becoming a starting point for discovery.

Retention is the real signal

If you want the clearest proof that YouTube is a retention-driven platform, look at what the platform chose to build directly into YouTube Studio.

YouTube’s Key Moments for Audience Retention report exists specifically to show which moments in a video held attention and where viewers dropped off. YouTube says this report is designed to show what is working and where content is losing viewers. Its engagement guidance also points to creators to retention as a core way to understand performance.

That should tell brands everything they need to know.

YouTube is not just asking, “Did someone click?” It is also asking, “Did they stay?” and “Was this satisfying enough to keep recommending?” These are qualitatively different questions than the ones that drove most YouTube strategies five years ago.

Pacing, structure, payoff, and storytelling are not production niceties. They are the variables that influence the signals YouTube can actually measure and act on.

The practical takeaway is simple: if your retention improves, your distribution options improve.

That is the direct bridge between educational content quality and platform growth. Invest in holding attention, and the algorithm works with you instead of against you.

A simple way to think about it is:

Hook → Pattern Break → Payoff

If a video opens with a strong hook, introduces movement or surprise, and delivers clear payoff moments, viewers have more reasons to keep moving forward.

The opportunity: why edutainment is winning right now

Edutainment works because it delivers two things at once:

• Value — “This is useful.”
• Enjoyment — “This is engaging enough to keep watching.”

That second part is where a lot of brand content falls short, not because brands lack expertise, but because content is often packaged for the library shelf rather than for someone choosing what to watch next.

The strongest YouTube content today still teaches. It still informs. It still solves real problems. But it does it through better packaging: stronger hooks, more curiosity, cleaner pacing, sharper examples, more tension, and more payoff. The content is still fundamentally educational — it has just been built to keep people moving forward instead of giving them a reason to click away.

This is why the most effective formats right now look less like lectures and more like stories with a clear point. They feel easier to watch, and content that feels easier to watch tends to travel farther across Browse, Suggested, and Shorts.

These formats are not fluff. They are educational approaches with more built-in momentum.

Higher-opportunity edutainment formats

The method: a repeatable system for finding winning formats

This is the part most teams skip.

They jump straight to idea generation instead of building a process for deciding which topics and formats are actually worth producing. A content brainstorming session without a system behind it produces one-off wins and inconsistent results. A system produces a library that compounds over time.

The framework below is built around one critical insight: on YouTube, format often explains performance better than topic alone. Most brands over-organise their content by subject matter and under-analyse the structural patterns that made their best videos work. That is why a strong content strategy should look beyond topics alone and analyse formats, retention patterns, demand, and distribution potential.

The goal of this system is to close that gap so every new video is built on evidence, not intuition.

1. Export your content library

Pull titles, publication dates, views, format type and length for your own channel and three to five relevant competitors.

2. Tag videos by format

Use categories such as:

• How-to
• Explainer
• Myth-bust
• Mistakes
• Comparison
• Teardown / Reaction
• Expert commentary

Format often explains performance better than topic.

3. Identify format winners

Compare performance against each channel’s own baseline, not just raw views. Look at:

• CTR
• retention patterns
• traffic mix
• relative performance versus channel norms

4. Validate demand

Confirm real search demand exists and that your phrasing matches how people actually talk about the topic.

This matters across the UK, the US, Canada and Europe. The method stays the same, but the wording and demand should still be checked by market.

5. Prioritise with an Opportunity Score

Use a simple scoring model:

Opportunity Score = Demand × Low Competition × Format Suitability × Shorts Potential

This does not need to be complicated. It is just a practical way to filter what is actually worth building.

6. Build pillar videos first

Make one strong long-form asset the centre of the strategy. Everything else extends from this anchor.

That means:

• a clear opening hook
• useful chapters
• visible payoff moments
• a structure designed for retention, not just completeness

7. Cut Shorts from peak moments

Use the strongest reveals, reactions, and takeaways from the pillar video to create short-form extensions that feed discovery.

This aligns with YouTube’s own guidance on transitioning long-form content into Shorts by identifying key moments, extracting strong hooks, and using Shorts to extend reach.

Action plan: what to do next week

If you want to apply this without overcomplicating it, start with a focused audit rather than a full strategy overhaul.

The goal is not to rebuild everything at once. It is to find one or two format-driven opportunities quickly and use them to demonstrate what retention-led content can actually do for your channel’s distribution.

These seven actions are designed to be completed in roughly five to seven working days by a small team.

What this gives you

This gives you a real system, not just a one-off content brainstorming session.

Within 30 days, you will have:

• baseline data on retention curves
• a better view of traffic source mix
• your first two pillar videos
• a small Shorts library to test against current benchmarks

The 30-day tracking window is not arbitrary. It is long enough for newly published videos to accumulate meaningful retention data and show early Browse and Suggested signals, but short enough to give you a fast feedback loop before committing to a full quarterly strategy.

The goal is not perfection on the first try.

The goal is a system that gets smarter with every video you publish.

The shift is already happening

The biggest YouTube opportunity for brands right now is not simply making more educational content.

It is making educational content that actually earns attention.

The brands that understand this early will build libraries that are not just searchable, but watchable, recommendable, and far easier to scale.

The platform has evolved. Discovery is no longer a single lane. Retention is no longer a soft metric. Format is no longer an afterthought. And the brands that treat YouTube like a pure search play are leaving a significant share of their potential audience — and their potential reach — on the table every single week they publish.

The shift from plain how-to to edutainment is not about making content flashier or less substantive. It is about making content that respects the audience’s attention and is intentionally built to keep people moving forward.

The information is still there. The expertise is still real. But the packaging earns the watch, the watch earns the recommendation, and the recommendation is what compounds a YouTube library into a genuine brand asset.

The four key takeaways

Be searchable
Answer real questions with real demand. Search is the foundation, not the ceiling.

Hold attention
Build for retention from the first frame. Hook, tension, pay-off — every time.

Earn recommendations
Earn Browse and Suggested distribution by making content worth watching to the end — and worth watching again.

Scale with a system
Build a system, not just a schedule. Pillar videos plus Shorts create a compounding library.

Simple workflow

Audit → Classify formats → Gap analysis → Opportunity Score → Pillar video → Shorts → Measure

In a crowded YouTube landscape, that kind of clarity matters because winning is no longer just about answering the question well.

It is about answering it in a way people want to keep watching.

The post The YouTube Content Shift Brands Can’t Ignore appeared first on DAC.

Agentic AI represents the next phase of AI-driven discovery. It builds on AI Overviews and LLM-powered search experiences but goes further by collapsing the distance between intent and action. While much of the industry is focused on what this means for SEO, paid media is facing an equally significant transformation. 

This post explores what happens when the user journey compresses, what it means for paid media performance, and how marketers should adapt. 

Key takeaways 

• Agentic AI is collapsing the user journey by moving from decision support to action.  

• Paid media will shift from driving clicks to influencing outcomes before and during AI decisions.  

• Ad inventory will consolidate into high-attention platforms and AI-native environments.  

• Measurement will become less transparent, requiring a shift beyond ROAS to value-based KPIs.  

• First-party data, content, and product feeds will determine visibility and selection in AI systems.  

What does a collapsed user journey actually look like? 

For decades, search followed a predictable pattern: 

Human searches → Human clicks → Arrives on website → Human converts 

Today, that journey is already evolving into a hybrid model: 

Human has intent → AI helps clarify that intent → AI helps inform a decision → Human clicks → Arrives on website → Human converts 

What comes next removes the click entirely: 

Human has intent → AI helps clarify that intent → AI helps inform a decision → AI completes the action (no click required) 

Agentic AI doesn’t just assist. It acts. That means booking, buying and completing tasks without ever sending users to a brand’s website. 

For brands investing in SEO and content strategy, this has already shifted the goal from ranking to being selected. For paid media, it challenges the mechanics of how we drive performance in the first place. 

Will paid media disappear or evolve? 

Paid media isn’t going away, but it will look very different. The current model depends on a fragmented, multi-touch journey. Ads intercept attention across platforms and moments. As AI compresses that journey, there are fewer opportunities to interrupt. 

Instead, paid media will concentrate in two key moments: 

• Before intent exists, where brands shape awareness and preference  

• Inside the decision itself, where AI systems determine outcomes  

This is why DAC’s media planning and buying approach increasingly integrates AI testing and optimisation into every stage of the funnel. 

How will ad inventory change in an AI-first landscape? 

Ad inventory won’t disappear, but it will consolidate. As browsing declines, so do impressions and clicks. Lower-intent placements will struggle to deliver value, while high-attention environments become more competitive. 

We expect three major shifts: 

• Premium platforms gain share: Logged-in ecosystems like Meta, TikTok and YouTube offer deterministic data  

• AI interfaces become inventory: Conversational environments introduce sponsored recommendations  

• Pricing models evolve: Metrics like CPC may shift towards cost per action or AI-driven conversion  

For marketers, this reinforces the importance of diversified investment across channels, something we address in our paid media services and cross-channel strategy work. 

Why will measurement become more complex? 

Measurement doesn’t disappear, but it becomes harder to interpret. When AI intermediates the journey, influence happens in environments that brands can’t fully track. That creates attribution gaps. 

The issue isn’t declining performance. AI will likely improve efficiency by delivering more qualified users. The challenge is understanding what drove those outcomes. 

Marketers will need to rebalance KPIs, focusing more on: 

• Conversion rate and outcome quality  

• Customer lifetime value  

• Incrementality and testing frameworks  

This is where our analytics and measurement solutions help unify fragmented signals into clearer performance insights. 

What replaces traditional retargeting? 

Retargeting depends on visibility. If AI completes the journey without a site visit, that visibility disappears. As a result, identity-based strategies take priority. 

Logged-in environments, CRM data and deterministic signals become more valuable. Retail media, first-party audiences and authenticated ecosystems offer continuity where cookies cannot. 

This aligns closely with DAC’s approach to first-party data strategy and audience development, where brands build durable, privacy-forward targeting capabilities. 

Where does awareness fit in an AI-driven journey? 

Awareness becomes more important, not less. If AI is making decisions, it relies on signals like brand recognition, sentiment and trust. That means influencing preference earlier is critical. 

Rather than shifting heavily back to offline, we expect investment to move towards: 

• Connected TV and premium video  

• Direct publisher partnerships  

• Cultural and contextual placements  

These high-attention environments build the brand signals that AI systems later use in decision-making. Without them, brands risk being evaluated purely on price and availability. 

How do influencers, UGC and video shape AI outcomes? 

These formats now serve two roles: they influence human audiences and train AI systems. LLMs ingest reviews, comments and transcripts to understand how products are described. That language directly impacts recommendations. 

For brands investing in content strategy and digital experience, this creates a new opportunity. Content is no longer just about engagement — it’s about shaping how AI understands your brand. 

Why product data becomes your most important asset 

In an agentic environment, AI doesn’t browse websites. It reads structured data. For retailers, this makes product feeds and technical infrastructure mission-critical. Key priorities include: 

• Real-time pricing and availability  

• Detailed product attributes  

• Strong review signals  

• Structured, machine-readable formats  

This is where DAC’s feed management and ecommerce optimisation capabilities help ensure your products are not just visible, but competitive in AI-driven environments. 

What happens to paid search in AI? 

Paid search doesn’t disappear — it moves. AI interfaces will introduce ads directly into decision-making environments. These may take the form of sponsored recommendations, paid inclusions or auction-based placements. 

The shift isn’t from paid to organic. It’s from ads around content to ads inside the decision. That’s why DAC continues to evolve its search marketing strategies to account for AI-native placements and emerging formats. 

What marketers should do now 

The shift to agentic AI is already underway. The brands that adapt early will be better positioned to influence both human and AI-driven decisions. To stay competitive, marketers should focus on three priorities: 

1. Invest in influence before intent 
Build brand recognition in high-attention environments. Strong brand signals increase the likelihood of being selected by AI systems. 

2. Strengthen data and identity foundations 
Prioritise first-party data and CRM integration. These assets will replace declining third-party signals and enable more precise activation. 

3. Optimise for AI visibility, not just clicks 
Ensure content, product data and messaging are structured for AI interpretation. 

That’s why DAC’s media playbook continues to evolve, integrating paid media, SEO, content and data into a unified strategy built for AI-driven discovery. 

Agentic AI doesn’t eliminate paid media. It redefines it. The focus shifts from buying attention to influencing outcomes. And in a world where decisions happen faster and with fewer touchpoints, being chosen is what matters most. 

The post Agentic AI is coming for search. Paid media is next.  appeared first on DAC.

This surge in interest and attention, paired with strong emotional appeal, will once again create valuable opportunities for brands to reach new customers, even if their target audiences are not full-time football fans. Although the higher costs of broadcast airtime and official placements secured by FIFA sponsors may appear prohibitive, brands should still look for ways to capitalise on the global showcase of the beautiful game.

What advertising opportunities will the World Cup create?

Among the many out-of-home channels that gather momentum during major sporting events, transit advertising is one of the most effective ways to reach audiences — particularly in host cities. That’s why Toronto’s TTC is actively exploring ways to unlock additional revenue by introducing new advertising formats ahead of the 2026 FIFA World Cup, with a potential rollout as early as June 2026. The timing is deliberate, allowing the transit system to benefit from increased ridership and heightened advertiser demand expected during the tournament.

One of the key proposals is the introduction of in-tunnel motion advertising in partnership with Adtrackmedia. This proposal has already received Board approval, marking a significant milestone in the process. The final step is approval from TTC General Counsel. Once cleared, commuters can expect to see a new, highly engaging motion-based format added to the transit environment. The agreement spans 15 years, signalling a long-term commitment and ensuring that this format becomes a lasting part of TTC’s advertising ecosystem. Notably, Adtrackmedia’s entry introduces a new player into a space historically dominated by Pattison, reshaping the competitive landscape.

Image Source: Translink.ca

Independent analysis suggests that TTC ridership could increase by 5–12% during the 2026 FIFA World Cup matches in Toronto. While TTC has not yet released its own official projections, the inclusion of provisions for additional service in the 2026 budget indicates that higher passenger volumes are anticipated. As a result, it is reasonable to expect a meaningful increase in the number of people exposed to out-of-home advertising across TTC properties, including the newly introduced in-tunnel motion format.

For brands looking to tap into the momentum surrounding major sporting events, these kinds of transit innovations create new opportunities to capture audience attention in high-traffic environments.

What this means for advertisers

• Capturing attention: The dynamic video format, combined with high-impact placement inside TTC tunnels, transforms an otherwise routine commute into an engaging brand moment. With limited distractions in tunnel environments and a captive commuter audience, brands have a rare opportunity to command deeper attention and drive stronger message recall.
• Novelty effect: As a new and unexpected transit execution, this format delivers a powerful wow factor. Early adopters can make the most of the buzz surrounding the launch, positioning themselves as innovative and forward-thinking while benefiting from heightened curiosity and organic word of mouth.
• Greater visibility: With the rollout aligned to the World Cup, advertisers can benefit from surges in ridership and increased footfall driven by match attendance and related events. Beyond the tournament, continued return-to-office trends are expected to sustain and grow TTC usage, extending the long-term visibility opportunity.
• Managing multiple media owners: As TTC looks to diversify non-fare revenue streams and introduce additional media partners, Pattison’s long-standing monopoly will shift. Advertisers aiming for a dominant transit presence across Toronto will likely need to coordinate with multiple media owners, adding layers of negotiation, planning and operational complexity — particularly for in-house teams managing buys directly.
• Cross-border visibility during the tournament: International brands can benefit from exposure to a global audience during FIFA 2026, delivering an immersive transit experience that may not exist in consumers’ home markets. This added novelty can strengthen brand memorability and keep the brand front of mind as visitors share their travel experiences across social and digital channels.

Connecting with fans across the full customer journey

With the FIFA World Cup arriving in Toronto, the impact will extend beyond transit ridership. In addition to an expected uplift on the TTC, the city is projecting a 10–15% increase in road traffic during the tournament period. This means overall consumer movement across Toronto — whether by transit, car or on foot — is set to rise significantly.

As mobility increases across the city, media channels that intersect with consumers in real-world environments — such as out-of-home (OOH), audio and experiential — will play an even greater role in driving brand visibility. These formats allow brands to appear in moments that are contextual, timely and emotionally charged. By combining multiple channels and approaching messaging creatively, brands can develop integrated, omnichannel campaigns around the World Cup. This approach creates multiple touchpoints throughout the fan journey — from commuting to matches, to exploring the city, to celebrating key moments — resulting in stronger recall, deeper engagement and a more lasting emotional connection.

In turn, these placements can support broader media strategies by extending the reach of social, TV and online video campaigns, adding incremental frequency and reinforcing key messages in high-traffic environments. Over time, this sustained exposure can help influence consumer behaviour and drive measurable business outcomes.

Thinking about how to reach your audience during the 2026 FIFA World Cup and beyond? Tap into DAC’s strategic expertise and full suite of services to help your brand make the most of the moment and score with fans.

The post How the FIFA World Cup can help brands make meaningful connections appeared first on DAC.

Much of that volatility is being driven by a small number of outsized spenders, most notably Temu and Shein. Both brands have scaled rapidly through paid acquisition, often becoming dominant buyers across Google and Meta. When they increase spend, costs can rise quickly. When they pull back, pressure can ease just as fast. 

For media teams, the challenge is not just higher costs. It is understanding when performance changes are being driven by the auction, not the account. So, let’s take a closer look at how that volatility shows up across channels, how to recognise it in-platform, and what media buyers should do to respond with clarity and discipline. 

What changed heading into 2026 

Temu’s growth story has been closely tied to paid media. The Wall Street Journal reported that Temu was Meta’s top advertiser by revenue in 2023 and among Google’s top five advertisers by spend, an early indication of how quickly one brand’s acquisition strategy can become a market force. 

Then the underlying economics changed. In 2025, multiple outlets reported sharp pullbacks in US ad spend as tariffs and shipping-rule changes put pressure on the unit economics of low-cost cross-border retail. The Financial Times, citing Sensor Tower estimates, reported that Temu cut US ad spending across major platforms and stopped spending on Google Shopping after 9 April 2025. The Verge also reported that Temu and Shein were raising US prices ahead of tariff changes and the end of de minimis treatment.

Crucially, this did not look like a permanent exit from performance channels. It suggested a willingness to increase or reduce spend based on economic conditions, creating a recurring pattern of surges, pauses, and re-entry that other advertisers now need to plan around. That interpretation is also consistent with broader reporting that Temu and Shein reduced US ad spending materially as trade pressure intensified. 

For UK advertisers, the local policy backdrop adds another layer of volatility. In November 2025, the UK government launched a consultation on removing the £135 customs duty relief for low-value imports into the United Kingdom, following concerns from high street retailers about competitive pressure from cross-border e-commerce. The government’s consultation materials say the relief is set to be removed, with new arrangements to follow. Alongside broader European customs reform, the implication is clear: in a market shaped by shifting policy and pressured unit economics, large advertisers can move faster than most brands are built to respond.  

How the pressure moves through auctions 

Understanding where that pressure shows up is critical, because it does not affect every channel in the same way. 

Why Shopping and commerce intent move first 

Commerce intent is concentrated. When an outsized bidder increases coverage and bidding intensity in Shopping, media costs can rise quickly and impression share can shift just as quickly. When that bidder pulls back, auction pressure can ease just as fast. 

This is why the stop-start pattern appears so clearly across Google’s commerce channels. In April 2025, Search Engine Land reported, citing Smarter Ecommerce analysis, that Temu switched off US Google Shopping ads on 9 April and that its Shopping presence declined rapidly in the days that followed, an illustration of how quickly a single outsized bidder can change commerce-intent auction conditions. 

That same competitive pressure can extend beyond Shopping. Reuters reporting described Temu and Shein bidding heavily on competitors’ search keywords during peak retail periods, contributing to higher CPC pressure on high-demand terms. Their influence does not need to be confined to direct product overlap to affect performance. In practice, it can raise costs across shared intent pools and adjacent categories. 

Why paid social relief is often less direct 

Social auctions are broader and more blended, with many advertisers, objectives, creatives, and audiences competing simultaneously. Even when a major spender pulls back, CPMs do not always reset cleanly because demand can be replaced quickly, and delivery systems may take time to stabilise after a regime change. 

The practical effect is that premium campaigns can come under pressure in two ways. Prospecting CPMs may remain elevated for longer than expected, and audience mix can skew more value-seeking during periods of heavy discount saturation, making it harder for premium positioning to convert efficiently. In those moments, relying too heavily on broad prospecting can become a fragile strategy. 

This is one of the most important distinctions for media teams in 2026: Shopping often reflects the shift first and most visibly, while social may absorb that same pressure in a less direct and less proportional way. 

How to recognise the signal in-platform 

If Temu or Shein are affecting performance, the first step is not to assume something inside the account broke. It is to look for evidence that auction conditions changed before adjusting creative, bids, or structure. 

In Google Ads, that signal often appears first in Auction Insights. A sudden increase in Temu or Shein visibility, combined with rising CPCs and declining impression share, is often a strong indication that competitive pressure has intensified. If conversion volume and ROAS begin to weaken while conversion rate remains relatively stable, that is another sign that the issue may be auction-driven rather than on-site. Search Terms can add useful context as well. If more generic, high-volume queries begin to account for a larger share of clicks, traffic quality may be shifting at the same time costs are rising. 

In Meta, the pattern is less direct but still visible. If CPMs rise while conversion rate and site behaviour remain steady, it is often more useful to interpret that first as a delivery or auction shift than as a creative problem. This is especially important for premium brands, where broad prospecting can become more fragile during periods of heavy discount-led competition. In those moments, a heavier reliance on first-party audiences, warmer segments, and value-based signals often creates more resilience than continuing to push scale through broad cold targeting alone. 

What volatility does to performance teams 

One of the most costly effects of stop-start competition is false learning. When auction conditions change abruptly, teams often interpret the shift as proof that creative, landing pages, or account structure stopped working. That misread can trigger a wave of unnecessary adjustments: budget changes, rebuilds, target resets, or learning disruption, at exactly the moment platforms need stability. 

The reporting view can make that problem worse. Monthly averages often smooth over real operating conditions by blending a surge period with a pullback period into a single, seemingly stable number. What looks calm in a monthly summary can feel highly unstable in practice. In a market shaped by abrupt competitive shifts, weekly visibility is the minimum. During more active periods, teams may need daily monitoring to understand whether performance changes are coming from auction pressure or from something they actually control. 

Why this is a governance issue, not just a media issue 

The most durable response to auction volatility is not simply faster optimisation. It is a clearer operating model, one that prevents external shifts from triggering internal overreaction. 

A useful way to think about that process is simple: detect, diagnose, decide, then deploy. Teams need to recognise regime change early, distinguish auction-driven movement from site or creative issues, decide what should and should not change during that period, and only then act through pre-approved adjustments. The value of this approach is less about speed for its own sake and more about preserving discipline when volatility makes everything feel urgent. 

A small number of governance decisions can make that discipline much easier to maintain. Agreeing in advance on acceptable variance bands helps teams avoid escalating every short-term swing. Establishing decision rights is equally important: teams should know who is allowed to change budgets, targets, or structures during volatile periods, and what evidence is required before those changes happen. In practice, a weekly competitive review is often enough under normal conditions, with daily checks reserved for major retail moments, policy milestones, or sudden changes in Auction Insights. 

How to buy more effectively in a stop-start market 

In a stop-start market, the objective is not to eliminate volatility but to build a media operation that can absorb it without losing discipline. That starts with budget and structural flexibility. Teams that can move spend by geography, channel, or category without forcing disruptive rebuilds are better positioned to respond when conditions shift. Monitoring also needs to evolve beyond simple cost tracking. The real goal is to detect when the auction environment has changed so optimisation posture can change with it. 

The same principle applies when pressure eases. Pullbacks should not simply be treated as welcome relief; they should be treated as opportunity windows. Brands that are prepared to increase budgets selectively, loosen constraints where margin and lifetime value allow it, or expand into previously crowded areas can often capture more value from those moments than brands that remain static. 

When pressure returns, the response should be equally deliberate. That usually means concentrating spend in the most defensible segments, reducing exposure to lower-margin areas that are highly sensitive to CPC inflation, and keeping creative changes measured rather than reactive. Over time, this also reinforces the importance of channel diversification and lifetime value discipline. Brands that rely too heavily on a single auction type, or that optimise only to blended ROAS, are more exposed when competition becomes unstable. 

Planning for multiple scenarios 

Scenario planning is a rational response to policy-linked economics that can change quickly. A single forecast line assumes a level of stability this market no longer offers. 

For some brands, the most likely scenario is re-acceleration, where competitive pressure remains elevated and efficiency depends on defending the most resilient segments. For others, the more realistic outcome may be selective buying, where pressure concentrates in specific geographies, product categories, or audience cohorts and requires faster reallocation. Regulatory shock remains the third scenario teams cannot ignore: abrupt pullbacks or geographic rebalancing can create short-lived openings, but only for brands that have flexible budget and clear rules for stepping into them. 

The value of scenario planning is not just in naming possibilities. It is in defining, ahead of time, what each scenario means for CPM and CAC by channel, where budgets should be protected or expanded, and what signals indicate which environment you are actually in. 

What matters most next 

The most useful watchlist is not a long list of possible risks, but a focused view of the signals most likely to change the operating environment quickly. 

Policy and regulatory developments remain central because they can alter the economics behind Temu and Shein’s acquisition strategy with very little notice. In the UK, low-value import reform is one obvious watchpoint. Across Europe, customs reform remains another. At the platform level, the clearest early indicators are usually found in commerce-intent signals such as Auction Insights movement, impression share shifts, and sudden changes in competitive presence. On social, the more telling pattern is often a change in CPMs without a corresponding change in site behaviour or conversion stability, especially when delivery constraints begin to appear at the same time.  

Behaviour in market matters as well. Aggressive promotional pushes, broader coverage strategies, or sudden changes in acquisition intensity can all signal that a new surge period may be forming. The goal is not to predict every move in advance. It is to recognise quickly when the environment has changed enough to require a different response. 

What teams should do now 

The operating challenge in 2026 is a less predictable market, where large competitors can change auction conditions quickly and unevenly. For media teams, that means the response cannot rely on static planning alone. It needs to be faster, more disciplined, and more sensitive to where pressure is actually showing up. 

1. The game did not just get more expensive. It got less predictable. 
Teams should prepare for volatility in advance by setting response thresholds, defining trigger signals, and agreeing on which levers can be used when conditions change quickly. 

2. Big players like Temu and Shein are moving the market. 
Major shifts in their visibility or competitive overlap should be treated as signals that auction conditions may have changed before account-level fixes are made. Reviewing brand value propositions and differentiation alongside market and auction signals is critical. 

3. The auction moves in pockets: geography and audience, not evenly. 
Teams that segment performance more closely can often find efficiency by tightening exclusions, expanding negatives, adjusting bids, and shifting spend towards less pressured areas. 

4. Most brands are reacting too slowly. 
Volatility windows require faster signal detection, clearer decision rights, and the ability to move budget or tighten strategy without delay. 

5. The biggest risk is misdiagnosing the problem. 
Before changing creative, landing pages, or structure, teams should confirm whether the shift began in auction or market conditions. 

6. Winning in 2026 means operating like a trader, not a planner. 
The strongest teams will pair scenario-based planning with pre-approved actions so they can defend efficiently and expand deliberately as conditions change. 

At DAC, we help brands turn volatile auction conditions into a more stable operating system by combining competitive monitoring, disciplined measurement, and agile orchestration across channels. For brands looking to assess whether auction volatility is creating hidden risk or opportunity in their 2026 plans, this is exactly the kind of pressure-testing that matters. 

The post Temu and Shein in 2026: How their stop-start spend is reshaping ad auctions (and what media buyers should do)  appeared first on DAC.

But as it moves from being a tool to becoming infrastructure, a more difficult question emerges: how do you scale AI without losing control?

AI does not only boost productivity. It introduces variability, opacity, compliance exposure, and systemic risk, particularly when deployed across complex, multi-market marketing ecosystems. The challenge is no longer whether to use generative AI. It is how to harness its speed and scale whilst preserving governance, performance stability, and accountability.

In this article, we explore where AI can undermine control, how that loss of control directly affects performance, and what it means to shift from pure execution to true orchestration: designing systems that are explainable, steerable, and built for sustainable growth.

The problems to solve: how AI can undermine control

The risks of generative AI are not abstract. They emerge when speed outpaces governance and automation scales without clear guardrails. As AI becomes embedded across content, media, analytics, and decision-making, five control gaps tend to surface.

1. Reliability is not guaranteed
Generative AI is non-deterministic: similar inputs can produce different outputs. It can also hallucinate, oversimplify, or blend accurate and inaccurate information. In marketing, this is not theoretical. An approximation can quickly become a published claim, a media recommendation, or inaccurate local information at scale.

2. Compliance becomes a production constraint
When AI-generated outputs move directly into production, compliance cannot remain a final checkpoint. GDPR, copyright, claims substantiation, brand safety, and platform policies must be embedded into the workflow itself. Without governance, AI does not just accelerate work — it accelerates exposure.

3. Decision opacity and the difficulty of “explaining”
Marketing is already highly automated. AI adds another layer of recommendations, prioritisation, and optimisation that is not always traceable. When performance shifts, teams need to answer not only “What should we change?” but “Why was this decision made? And based on what evidence?” Without traceability, auditability suffers.

4. Automation multiplies the risk of drift at scale
Human error is typically localised. Automated error replicates instantly across campaigns, pages, and markets. As AI gains autonomy — through agents, action chains, and automated publishing — guardrails around thresholds, permissions, approvals, and rollback mechanisms become essential.

5. Channel fragmentation makes steering more complex
Customer journeys now span paid, organic, local, content, and AI-driven search experiences. Optimising one silo in isolation can degrade overall performance. Without cross-channel coordination, improvements in one area may create instability elsewhere.

The impact on performance: when lack of control becomes expensive

These risks do not sit adjacent to performance — they reshape it. When AI-driven systems operate without clear governance, the consequences show up directly in efficiency, stability, and business outcomes.

1. Loss of consistency and effectiveness
If messaging varies across channels, promises diverge, or local information such as opening hours and services becomes inconsistent, the brand becomes harder to trust. The result is predictable: lower CTR, weaker conversion rates, more friction in the customer journey, and increased pressure on support teams.

2. Local over-optimisation, global under-optimisation
AI can improve visible KPIs — such as reducing CPA — whilst degrading broader business metrics such as lead quality, margin, retention, or lifetime value. Without orchestration across CRM, analytics, media, and local signals, teams optimise what they can see, not necessarily what drives enterprise value.

3. Greater volatility, slower diagnostics
As automation increases, so does systemic complexity. When traffic, conversions, or visibility decline, root causes may span tracking, attribution, competition, stock availability, creative, or platform changes. The more interconnected the system, the harder it becomes to isolate issues quickly — and slow diagnosis directly compounds performance loss.

4. Reputational and legal risk
Errors in claims, moderation, targeting, or compliance do not remain marketing issues. They can escalate into reputational damage, partner friction, regulatory scrutiny, or legal action. These risks cannot be offset by short-term optimisation gains.

The response: moving from execution to orchestration

Once the risks are clear, the question is no longer only how to use AI, but how to deploy it without losing control. This is where the shift from execution to orchestration becomes critical.

Execution: Teams produce and optimise. AI accelerates output, but the system still depends heavily on human bandwidth and reactive checks. Performance can be strong but fragile.

Orchestration: The system itself is designed for control. Detection, diagnosis, recommendation, validation, action, and impact measurement are structured into governed workflows. Performance becomes more reproducible, explainable, and controllable.

The five principles of “steerable” AI in marketing

Orchestration is not theoretical. It rests on five practical principles:

• Clear governance:

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  Define who can generate, publish, edit, or allocate budget — and under what thresholds and escalation rules.
• Traceability: Log prompts, sources, versions, decisions, and approvals to ensure auditability and learning.
• Systematic quality control: Apply structured rules, testing frameworks, and scoring mechanisms before activation.
• Human-in-the-loop by risk level: Low-risk actions may be automated, but brand, budget, and compliance-sensitive decisions require validation.
• Continuous learning loops: Measure real business impact and feed insights back into the system to prevent drift.

What an orchestrated workflow looks like

Let’s take a simple case of declining conversions:

1. A detection layer flags anomalies in conversions, CPA, ROAS, or qualified traffic.
2. Targeted diagnostics are triggered across paid, SEO, local, tracking, creative, and competitive signals.
3. Recommendations are prioritised based on expected impact and risk.
4. Low-risk adjustments can be automated within predefined guardrails.
5. High-risk changes — major budget shifts, core claims, tracking modifications — require validation.
6. Impact is measured, documented, and reintegrated into the system.

Orchestration answers a fundamental business question: where should we invest more, where should we pull back, and why — without destabilising performance?

What DAC brings to AI orchestration

In this context, AI solutions are not flashy generation tools. They are infrastructure: governance frameworks, traceability layers, unified signals, and structured workflows that connect decisions to evidence.

That is how we approach AI at DAC. Not as automation for its own sake, but as a system designed for controlled, scalable performance.

This builds naturally on our Brand-to-Local model, where brands operate across markets, channels, and locations — and where performance must improve without sacrificing consistency or compliance. Orchestration is what makes that possible. In practice, this means:

• IRIS, our AI-powered orchestration platform, which structures workflows, synchronises decision-making, and enables true steering logic, not just reporting.
• TotalSERP, which manages search as a unified ecosystem (paid, organic, local) and coordinates investment in an increasingly fragmented results environment.
• Scaled activation capabilities across content, local, paid, and creative, all governed by quality control, compliance safeguards, and continuous feedback loops.

In a fast-evolving landscape, the competitive advantage no longer lies in generating more, but in steering that process more accurately. As a result, the brands that win will not be those that automate the fastest, but those that build AI systems that are explainable, governed, and accountable.

That is the difference between execution and orchestration. And it is what you gain when you partner with DAC. Let’s talk.

The post From execution to orchestration: How to leverage generative AI without losing control appeared first on DAC.

OpenAI has begun testing ads directly inside ChatGPT, with first live examples appearing on Tuesday 10 February 2026, placing sponsored messages within the flow of conversation and aligning them to what users are actively asking about. 

Source: OpenAI 

This will not be your average display or search campaign—it’s advertising inside an environment people already trust to help them think, decide, and solve problems. That alone makes this moment both exciting and high stakes. No pressure! 

The latest updates as of 10 February 2026 

Ads are now live for logged-in adult users in the US on the Free and Go versions of ChatGPT. They appear clearly labelled as sponsored and sit below the AI’s response, not inside the answer itself. OpenAI has been explicit that ads do not influence how ChatGPT responds, although targeting is primarily conversation-based, matching ads to what a user is engaging with in real time, supported by limited contextual signals. 

Another new update confirms that users remain in complete control of this environment, though there are some trade-offs. Users can turn personalisation off, limit ads, dismiss ads with feedback, or clear ad data entirely—but Free users who limit ads may see fewer available messages unless they upgrade to a paid tier. 

Source: OpenAI 

For advertisers, access is still currently gated. OpenAI does not have an official waitlist and is instead actively reaching out to large brands, but early testing reportedly requires a minimum investment around £148k and ~£44 CPM They are focused on a premium rollout to derive learnings rather than scale, to ensure success. 

Why brands are paying attention and why caution is necessary 

The appeal is clear. Conversational AI offers relevance at the exact moment of intent, something marketers have been chasing for years. When done well, ads in this environment have the potential to feel like an addition rather than a disruption. 

At the same time, hesitation is both expected and healthy. Several factors make this a channel that requires careful consideration: 

• High-intent moments, high expectations: Users come to ChatGPT expecting credible, helpful answers, which raises the bar for brands. Ads must enhance the experience, not interrupt it. 

• Trust and brand safety come first: 

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  Credibility is foundational in conversational AI. Any brand presence needs to align with the tone, accuracy, and usefulness users already associate with the platform. 

• Limited measurement and optimisation: Early reporting is largely impression-based, without the depth performance teams typically rely on to assess impact or refine investment decisions. 

• Premium context, but limited scale: With high minimum investments and restricted participation, this is a testing ground rather than a channel built for broad experimentation. 

For now, brands exploring ads in ChatGPT must be comfortable testing in a high-intent, premium environment while accepting that insight, scale, and control will evolve over time. 

DAC’s perspective on ChatGPT ads 

We see ChatGPT ads as a meaningful signal of where discovery is heading. Conversations are becoming a new entry point to the funnel—as we have seen in the explosion of the LLM space and the ever-changing Google landscape—and brands that understand the need to capitalise on this and shift their mindset will be better positioned as this space matures. 

Moving thoughtfully now matters more than moving quickly. Although brands may be tempted to jump in as quickly as possible, there needs to be a balance of agility and care. As a first mover and early adopter, being smart and strategic is critical. 

DAC is actively partnered with OpenAI and receives regular updates directly from its teams as a high-interest agency partner. That gives us close insight into how the platform is evolving and what’s coming as access expands. 

What this means for brands right now 

For brands considering early testing in conversational AI, now is the time to move from curiosity to planning. Even if large-scale activation isn’t imminent, understanding how advertising functions in environments like ChatGPT will be critical as this channel evolves. That’s why DAC’s media playbook and planning approach sees LLM/AI optimisation and testing as a critical and necessary part of driving success for our clients in a fast-changing landscape. 

At DAC, we’re already helping our clients assess readiness, identify the right use cases, and design thoughtful test-and-learn approaches that balance innovation with trust, brand safety, and measurement. 

If you’re exploring whether ChatGPT ads make sense for your brand, now is the right time to start that conversation. Reach out to us to learn more. 

The post ChatGPT ads are here: Opportunity, open questions, and what brands should do now  appeared first on DAC.

Local Service Ads (LSAs) sit at the centre of this shift. They promise premium placement, high-intent leads and built-in trust signals. But while LSAs may look simple on the surface, scaling them effectively across dozens or even hundreds of locations introduces real operational and strategic complexity. Understanding how they work, and how they fit into a broader local strategy, is essential before leaning in. 

What are Local Service Ads and why do they appear first in search results? 

Local Service Ads are Google’s pay-per-lead advertising format for service-based businesses. When available, they appear at the very top of the search engine results page, above organic listings and most traditional paid ads, and give users a direct way to call, message or book a service. 

What makes LSAs distinct is their combination of placement and trust. Each eligible advertiser displays Google’s verification badge alongside ratings and reviews, helping users quickly decide which businesses feel credible enough to contact. For high-intent searches such as “emergency plumber near me” or “local house cleaner”, this positioning often captures demand before users even scroll. 

This placement can be incredibly valuable for multi-location brands, but it also means competition is fierce, visibility is limited, and performance depends on more than bidding alone. 

How do Local Service Ads work for service businesses? 

To run LSAs, businesses must apply and complete Google’s verification process, which can include background checks, licence validation and insurance review. Once approved, advertisers do not pay per click. Instead, they pay per qualified lead, typically a phone call or message initiated through the ad. 

Google ranks LSAs using a combination of factors, including account performance, responsiveness to leads, review ratings, proximity to the searcher and budget availability. Unlike traditional paid search, keyword control and creative optimisation are limited. Google instead optimises for outcomes it believes signal trust and usefulness. 

Leads are delivered through Google’s Local Services dashboard, where businesses can review, respond to and rate them. This creates a feedback loop that directly influences future visibility, making operational readiness just as important as media strategy. 

Why Local Service Ads are becoming more important in local search 

LSAs are not a short-term experiment. Their growing prominence is driven by several converging trends. 

First, user behaviour continues to shift towards immediate, action-oriented experiences. Searchers increasingly prefer one-tap calls or bookings over browsing multiple websites, particularly on mobile. 

Second, Google has steadily expanded and invested in the LSA product, adding booking functionality, refining verification requirements and rolling the format out across more categories and countries. This level of investment signals a long-term commitment. 

Third, trust signals matter more than ever. The Google Verified badge and visible reviews reduce friction for first-time customers and often improve conversion rates compared with standard text ads. 

Finally, broader platform changes, such as stricter Google Business Profile verification and evolving local policies, have made LSAs a more controlled but higher-quality channel for qualified leads. 

How Local Service Ads have expanded across categories and locations 

LSAs originally focused on home services and skilled trades, but they have since expanded into dozens of verticals, including cleaning, pet care, tutoring and beauty. Geographic availability has also widened beyond the US, with self-sign-up rolling out in phases across additional markets. 

This expansion has two clear implications for multi-location brands. On one hand, more categories and locations create more opportunities to capture top-of-page visibility. On the other, increased adoption raises the competitive bar. Verification, reviews, responsiveness and consistency across locations now play a much larger role in determining who wins that limited space. 

As LSAs mature, success becomes less about early adoption and more about disciplined execution at scale. 

Best practices for Local Service Ads at scale 

For single-location businesses, LSAs can sometimes feel like a “set and forget” channel. For multi-location brands, that approach rarely works. Scale introduces variability, and small issues can compound quickly if left unchecked. 

Best practices include: 

• Complete verification thoroughly 
  Ensure licences, insurance and documentation are accurate for every eligible location to maintain Google Verified status. 

• Use automated bidding strategies 
  Google’s recommended bidding helps balance lead volume and cost, particularly as demand fluctuates by market. 

• Respond to leads quickly 
  Fast response times strongly influence both conversion rates and future visibility. 

• Set competitive budgets 
  Pay-per-lead models still require sufficient budget to avoid capping impressions during periods of peak demand. 

• Expand job types thoughtfully 
  Adjacent services can increase volume but should be monitored closely for lead quality. 

• Actively manage reviews and reputation 
  Ratings and review velocity directly affect prominence. 

• Score and analyse lead quality 
  Use the dashboard to identify trends by market and adjust accordingly. 

At scale, brands also need to watch for breakdowns such as missed calls, uneven response times or markets that consume budget without delivering value. 

Next steps for multi-location brands using Local Service Ads 

Local Service Ads can be a powerful driver of high-intent leads, but they perform best as part of a broader local growth system. For multi-location brands, success depends on more than verification and budget alone. It requires consistent local readiness, strong reputation management, fast lead handling and alignment with SEO and paid search efforts. 

As Google continues to prioritise trust-based, conversion-focused experiences in local search, LSAs will play an increasingly important role in capturing demand at the moment customers are ready to act. The brands that win will be those that treat LSAs not as a standalone tactic, but as an integrated layer within a coordinated local strategy. 

If you are evaluating how LSAs fit into your broader local media and SEO approach, it is worth assessing readiness, scalability and long-term impact before increasing spend. 
Let’s talk. 

The post What multi-location brands need to know about Local Service Ads  appeared first on DAC.

Over the past decade, marketers have built sophisticated, high-precision performance engines. Search, direct response Social, retail media, and remarketing all promise what every CMO wants: immediate, measurable results. The allure is powerful—easy attribution, clean dashboards, and the comforting illusion that every dollar spent drives incremental sales. But increasingly, performance media behaves like a short-term fix. It delivers quick hits, but the long-term effects can be damaging: rising costs, diminishing returns, and an overreliance on capturing demand rather than creating it. 

Many brands now find themselves in the same bind. They know they need to invest in brand so they can reach new audiences, shape preferences, and shift future demand, but they’re reluctant to do it without the measurement rigor they’ve built in the lower funnel. They’re judged quarter-by-quarter on their ability to produce boardroom-ready measurement and reporting of media impact, and have been conditioned to believe that only the bottom of the funnel can deliver that kind of clarity. 

The industry’s leading research backs this up. Binet and Field’s foundational work demonstrates that, on average, brands need roughly a 60/40 balance between brand-building and activation to maximise long-term growth, though the exact balance varies by category. Yet many brands, especially those operating in competitive local and regional markets, dramatically underinvest in brand because activation feels safer, faster, and easier to justify. The consequence is predictable: a plateau in growth and a shrinking pool of in-market buyers. 

What marketers need now isn’t more theory; it’s a practical, measurable path to responsibly shifting up-funnel without losing the confidence that performance media has historically provided. This paper outlines exactly how we do that—combining brand strategy, audience insight, omnichannel orchestration, and a unified measurement framework that triangulates truth across multiple methodologies. Armed with this toolkit, we can overcome the current “crisis of brand.” 

The Power of a Strong Brand 

Marketers intuitively understand that strong brands perform better, but the degree to which brand strength influences performance and how broadly it impacts the full funnel is often underestimated. Numerous studies show that brand preference doesn’t just shape awareness or consideration; it materially improves the efficiency of every performance channel beneath it. 

A Nielsen ROI analysis found that adding upper-funnel marketing on top of existing mid-low funnel activity increased ROI by up to 70%. When a brand is well known and well understood, more people search for it, paid search auctions get cheaper, conversion rates rise, and retargeting pools grow with higher-quality prospects. 

In other words, brand is not an alternative to performance—it is a force multiplier for performance. 

Strong brands also enjoy more pricing power, faster customer acquisition, and greater lifetime value. They lower the cost of persuasion across channels. They create resilience during competitive disruptions, economic swings, or platform volatility. They reduce dependence on unstable attribution models. And, critically for multi-location and Brand-to-Local brands operating across regions and markets, they unlock consistency and efficiency across hundreds or thousands of individual markets. 

Investing in upper-funnel media is not solely a long-term play. It is a highly measurable driver of short-term and long-term outcomes, if we design the ecosystem correctly. 

It Starts with Becoming Audience-Centric 

Shifting up-funnel responsibly requires a deeper understanding of who we’re trying to influence and how they move through their decision process. That means becoming audience-centric, not channel-centric. 

This begins with rigorous audience and landscape understanding, segmenting the market based on behaviours, motivations, values, and unmet needs. Too often, brands lean on broad demographic segments or platform-defined audiences, which obscures the reality of how real people behave across digital and physical environments. 

Audience centricity also relies on the intelligent use of first-party data. Most multi-location brands possess rich data assets (CRM data, transaction history, loyalty data, POS information) that are underutilised for insight generation, not just targeting. By analysing these signals, we uncover higher-value customer cohorts, understand their lifetime value distributions, and identify opportunity segments with substantial headroom. 

From here, we map the full customer journey for each priority segment. The goal isn’t to develop a generic linear funnel, but to understand the specific content, channels, and moments that meaningfully influence behaviour. Some segments may require more emotional storytelling early on; others may need problem-framing content or social proof to advance. 

Finally, KPIs must be aligned to these journey stages. The content and media that drive early-stage shifts should not be held to lower-funnel metrics. Instead, they should be measured with indicators that reflect their role in priming the audience for conversion. 

Audience-centric planning is simple in principle, complex in execution. But when done well, it ensures we invest in the right people with the right messages at the right moments, and it lays the foundation for full-funnel measurement. 

Audience centricity mapping 

Orchestrating Omnichannel Experiences 

Today’s customers fluidly navigate between digital and physical touchpoints. They research online, purchase offline, encounter local messaging, see national branding, and interact across an ever-expanding set of environments. To meet them where they are, we must orchestrate omnichannel media, not run channel-by-channel campaigns. 

This means moving beyond the old dichotomy of “digital for acquisition, offline for brand.” Instead, we need a connected view of the customer’s total behaviour—how they come in the door, how they explore, how they convert, and how they return. 

Critically, omnichannel planning enables us to understand true acquisition dynamics. For example, a digital prospecting campaign may spark interest, but the final conversion might occur in a physical location. Without integrated measurement, the digital channel gets undervalued; with it, we can quantify the full business impact and optimise accordingly. 

For multi-location brands, omnichannel maturity is especially important. Individual stores, regions, or franchisees often experience different customer journeys. To harmonise enterprise strategy with local execution, we need a shared measurement backbone that understands both. 

A Measurement Framework That Moves Beyond Last Click 

If marketers are going to responsibly invest above the performance line, they need measurement systems that make brand investment as credible as lower-funnel activity. There’s no single magic metric that can do this. Instead, we build a Unified Measurement Framework—a multi-method approach that cross-validates insights and “triangulates truth” rather than betting on one model alone 

Marketing Mix Modeling (MMM): The Long-View Foundation 

MMM gives us the strategic perspective: a privacy-resilient, statistical view of how each channel contributes to outcomes over time. Modern MMMs can incorporate both short-term and long-term effects, capturing the halo of brand investment as well as the contribution of activation channels. This becomes the anchor for long-term budget allocation, scenario planning, and brand-level guidance. 

Flighting Variation to Improve Model Accuracy 

MMM accuracy improves when media spend has sufficient variation. We intentionally vary flighting to help the model read impact more clearly, ensuring every channel’s contribution is properly captured. 

Incrementality Testing: Proving What’s Truly Net-New 

MMM alone isn’t fast enough for optimisation. To validate true causal impact, we use incrementality testing, including audience holdouts, geography-based experiments across regions and markets, and platform-level lift studies. These tests answer the essential business question: What would have happened if we didn’t run the campaign? 

Holdout designs provide clean causal reads on net-new outcomes. Geo tests allow us to measure lift at scale without user-level tracking. This approach moves us from correlation to causation and provides the kind of rigor stakeholders trust. 

Attribution Models: Fast Optimisation Signals 

Attribution—both rules-based and machine-learning—remains useful for real-time directional guidance, even as privacy changes degrade its absolute accuracy. By integrating attribution insights with MMM and incrementality testing, we get both speed and truth. 

Together, these components create a measurement system strong enough to support serious brand investment while retaining the agility performance teams depend on. 

MMM: From foundational model to prescriptive intelligence 

From “ROAS-Only” Thinking to Customer Lifetime Value  

The industry’s fixation on ROAS has led many brands to optimise themselves into a corner. ROAS is easy to calculate, easy to report, and deeply misleading. It overvalues existing customers, undervalues net-new growth, and hides the quality of customer cohorts. 

We replace ROAS-only thinking with a system grounded in: 

• Customer Acquisition Cost (CAC) 

• Customer Lifetime Value (LTV) 

• Cohort performance over time 

Cohort analysis shows us how different acquisition channels, creatives, and audience strategies contribute to retention, repeat purchase behaviour, and revenue expansion. Two campaigns with identical ROAS might produce dramatically different 90-day or 1-year value. When you optimise for LTV instead of short-term revenue, you build a healthier, more profitable customer base. 

This is especially important for multi-location brands, where customer lifetime value often varies meaningfully across geographies. A location-first LTV view reveals growth pockets and helps allocate media investment more intelligently. 

Validating Leading Indicators for Faster Optimisation 

While LTV is essential, it accumulates slowly. To stay agile, we identify and validate leading indicators—fast-moving signals that correlate with long-term business outcomes. These indicators allow us to optimise upper-funnel media in near real-time while maintaining scientific rigor. 

Examples include: 

• Share of search, which generally serves as a reliable proxy for share of market. 

• Website visitation and engagement, especially return visits and high-intent behaviours. 

• Video completion rates, which signal the audience finds content meaningful and often correlate with downstream conversion lift. 

• Brand recall and consideration metrics from survey-based providers such as YouGov, Morning Consult, or Dynata. 

The key isn’t selecting these metrics—it’s validating that they truly correlate with the business outcomes you care about most. Once validated, they become invaluable tools for fast iteration without sacrificing long-term accountability. 

Running Everything as a Test: Building a Culture of Experimentation 

The strongest marketing organisations treat testing not as a project, but as a culture. DAC operationalises this by embedding a lead analyst on every media account—a structural decision that ensures data science and media planning operate as a single team, not separate functions. This creates an “always-on feedback loop” where planners and analysts design tests, execute them, interpret results, and feed insights back into strategy continuously. 

Tests become the fuel for learning agendas. Every quarter, we outline the key uncertainties that must be resolved to unlock growth: Which audiences drive the highest LTV? Which creative variants move consideration? Which markets have the most efficient path-to-purchase? Each question becomes a structured experiment. The output is a compounding body of knowledge that improves your marketing system over time. 

This continuous experimentation not only improves performance; it builds organisational confidence, especially where brand investment is concerned, because every initiative is designed with measurement rigor from day one. 

Transparent Dashboards for Brand and Local Stakeholders 

For brands with complex internal structures, transparency is as important as accuracy. Clear, role-specific dashboards bring every stakeholder along—from the CFO to regional field teams. When everyone sees the same truth, in formats tailored to their needs, a unified culture of optimisation emerges. 

These dashboards connect brand and performance metrics, attribution signals, incrementality reads, MMM insights, and leading indicators into a cohesive picture. They ensure the organisation can finally answer the boardroom’s most persistent questions: What’s working? What’s not? What’s incremental? Where should we invest next? 

The Evolution Toward Predictive Scenario Planning 

As data accumulates and MMM models mature, we evolve from measurement to prediction. Scenario planning allows us to forecast the likely outcomes of different investment strategies: What happens if we shift 10% of search budget into video? What if we increase investment in our highest-LTV audience segment? What if we expand brand spend in priority markets? 

These tools elevate marketing to a strategic lever for the whole business. They support CFO-level budgeting, empower CMOs to defend brand investment, and provide executives with confidence in decision-making. 

Conclusion: Building a Marketing System That Compounds Over Time 

Responsibly shifting up-funnel isn’t about abandoning performance. It’s about designing a marketing system that uses brand to supercharge performance, and uses measurement to make that investment credible and scalable. 

When we put all the pieces together, we create: 

• Confidence to invest in long-term brand growth 

• Proof of incremental value grounded in experiments and MMM 

• Clear, CFO-ready answers to tough financial questions 

• A healthier customer base with higher lifetime value 

• Less dependency on unstable attribution and platform-reported metrics 

• A marketing system that compounds knowledge, capability, and results over time 

This is exactly how DAC is powering leading consumer brands with Brand-to-Local marketing. Together, we’ll take your brand forward with a framework built for growth, engineered for truth, and designed to deliver results you can take to the boardroom. 

The post Bridging Brand and Performance Media: Shifting Up-Funnel for Measurable, Enduring Growth appeared first on DAC.

Enter the Universal Commerce Protocol (UCP), a new open standard designed to solve the biggest headache in automated shopping: interoperability. Rather than building custom code for every single storefront, UCP creates a unified way for AI agents, payment services, and retailers to communicate. 

Backed by heavy hitters like Google, Shopify, Walmart, Stripe, and Mastercard, this protocol allows an AI assistant to handle everything from discovery to checkout without needing to learn a new system for every merchant it visits. It layers over existing infrastructure, meaning brands don’t have to rip out and replace their current tech; they simply need to ensure it communicates effectively. 

How do I get started with UCP? 

First, get your house in order before touching any code because your foundation needs to be solid. This means ensuring your Merchant Center account is fully configured, specifically your shipping settings, return policies, and product feeds. If users can’t discover or understand your product terms, the AI can’t buy them. Once that is ready, you will need to join the waitlist and get explicit approval from Google to go live on their AI surfaces. 

At its core, UCP is about predictability. It gives AI agents and merchants a standard playbook for the entire shopping journey: discovery, inventory checks, cart management, and checkout. 

Here is how that interaction looks in practice: 

1. The menu (merchant capability publication) 

First, the merchant puts their cards on the table. They publish a capability profile that effectively says, “Here is what I can do.” This lists their ability to handle product search, pricing, fulfillment rules, and payment methods. It’s a green light telling AI agents exactly which actions are available. 

2. The handshake (agent discovery and negotiation) 

Before trying to buy anything, the AI assistant reads that profile. It compares the store’s rules with its own capabilities and negotiates a path forward. They might agree on a specific payment wallet or determine which parts of the checkout the agent will handle versus what the store needs to process. 

3. The heavy lifting (action execution) 

Once they agree, the AI gets to work. It searches for the product, verifies it’s in stock, applies a discount code, and builds the cart. If the AI hits a wall, like missing information, UCP provides a structured way to pause and ask the human user for help before resuming. 

4. The tap on the shoulder (handling human input) 

AI can’t decide everything. Sometimes you need to pick a delivery slot or confirm the shipping address. UCP allows the system to escalate these specific choices back to the customer. Once the human answers, the AI takes the baton back and finishes the workflow. 

5. Secure closing (payments and security) 

Finally, the transaction needs to clear. UCP manages a secure negotiation to decide which wallet or payment method to use. Crucially, payments are tokenised and backed by verified consent, ensuring that while the AI is facilitating the sale, the security is as robust as a traditional bank transaction. 

Why does UCP matter for SEO? 

For marketers, UCP signals a massive shift. Search is evolving from a place where people click links to a transaction layer where AI completes the task for them. 

• Click-throughs are becoming “buy-throughs”: The goal is no longer just getting a user to your website—it’s about making your product discoverable and actionable inside an AI interface. 

• Data structure is your new storefront: If an AI agent can’t read your pricing, inventory, or shipping data because it’s unstructured, you are invisible. Brands with clean, machine-readable data will be the ones surfaced by AI for high-intent queries. 

• Optimisation for intent and personalisation: It is no longer just about ranking for keywords; it is about answering complex, personal needs. A user might ask, “Best running shoes for flat feet under $150 that can arrive by Friday.” To win that sale, your commerce feed must be rich enough to handle this level of personalisation, instantly matching inventory not just to “shoes,” but to that user’s unique constraints on price, suitability (e.g., specific arch support), and real-time fulfillment speed. 

• The new SEO checklist: SEO teams can no longer stop at keywords and backlinks. The new mandate is technical readiness: real-time inventory status, clear product attributes, and queryable commerce capabilities. 

The bottom line 

UCP isn’t just a technical spec; it’s the foundation for the next generation of ecommerce. It allows AI to act as a true concierge, smoothing out the friction between “I want this” and “order confirmed”. 

For brands, the message is clear: visibility is shifting. It’s not just about ranking pages anymore but making your data accessible enough that an AI agent can buy your product before the customer even visits your site. 

If you want to ensure your brand is ready for this shift, reach out to DAC. We’re already helping clients lay the foundations for a seamless transition into the UCP era.

The post Universal Commerce Protocol (UCP): Why your brand needs to speak “AI” fluently  appeared first on DAC.

For brands, this shift creates uncertainty. Traditional SEO metrics such as rankings, clicks, and organic traffic are becoming less reliable indicators of success. At the same time, many of the foundational principles of SEO still matter deeply. That’s why we’re here to bridge that gap and help marketing leaders understand: 

• What has fundamentally changed in search 

• What remains critical and should not be abandoned 

• How to adapt SEO into a modern visibility strategy that works in an AI-driven environment 

Rather than focusing on theory, this guide provides a practical, step-by-step framework that brands can use to move forward with confidence. Let’s get started. 

Understanding the new search journey 

For most of its history, online search has followed a predictable pattern: 

1. A user entered a keyword or query 

2. They reviewed a list of results 

3. They clicked through to a website 

4. They refined their search if needed 

In an AI-driven environment, this journey looks very different.  

AI systems are now guiding users through the exploration process. Instead of repeatedly searching and clicking, users are increasingly engaging in ongoing conversations. The AI asks clarifying questions, compares options, and synthesizes information on the user’s behalf. 

This introduces a new and critical phase in the journey: AI-guided clarification.  

Many decisions are now influenced, or even made, before a website visit occurs. In some cases, the visit never happens at all. 

Zero-click searches data, Europe vs United States 

What does this mean for brands? 

• Visibility increasingly happens inside the answer, not just on the website. 

• Influence can occur even without a click. 

• SEO success is no longer defined solely by traffic volume. 

In other words, SEO isn’t suddenly less important—but it does need to evolve. 

What still matters: The unchanging fundamentals 

Despite dramatic changes in interfaces and user behavior, the core foundations of SEO remain essential.  

AI systems still rely on signals that indicate: 

• Experience and expertise 

• Authority and trust 

• Technical accessibility and clarity 

• Content relevance and usefulness 

In fact, AI often amplifies the importance of these fundamentals. When AI summarizes information or recommends brands, it draws from sources it perceives as reliable, consistent, and well-structured. 

Key SEO basics that still apply 

• Strong technical foundations (crawlability, structured data, performance) 

• Clear information architecture 

• Content that genuinely satisfies user intent 

• Demonstrable expertise and credibility 

• Consistency across owned and earned digital properties 

AI does not replace these requirements, but instead rewards brands that execute them well. 

The 6-step framework for AI-ready SEO 

This framework translates the realities of AI-driven search into a practical roadmap brands can follow. It focuses on building the structural clarity, trust signals, and content foundations AI systems rely on—the goal being to drive visibility as search continues to evolve, not just today. 

Step 1: Conduct an AI readiness audit 

An AI readiness audit expands the traditional SEO audit to reflect how modern search systems interpret information.  

Rather than focusing only on technical health, this audit evaluates how effectively AI systems can understand, trust, and surface your brand. 

Core audit dimensions 

• Accessibility: Is your content easy for search engines and AI systems to crawl, parse, and interpret? 

• Relevance: Do your pages clearly communicate topical focus and relationships? 

• Engagement: Are users interacting meaningfully with your content across formats? 

• Authority: How strong and consistent is your brand’s presence across the wider digital ecosystem? 

Importantly, this audit extends beyond your website. AI systems draw from reviews, third-party content, expert commentary, and community discussions. 

Outcome 

The goal is not a long checklist. The goal is a prioritized roadmap that identifies where improvements will most directly improve AI visibility and brand representation. 

Step 2: Create content that AI can easily interpret 

AI systems process content differently than humans. They break information into smaller units, look for patterns, and compare meaning across sources. Content that is vague, overly complex, or poorly structured is less likely to be surfaced. 

Practical content principles 

• Use plain, accessible language 

• Keep paragraphs concise and focused on one idea 

• Use clear, descriptive headings 

• Provide direct answers before supporting detail 

One of the most effective approaches is building topic clusters. Instead of isolated pages targeting individual keywords, clusters of interlinked content comprehensively cover a subject area.  

This helps AI systems understand  

• context,

• depth,  

• authority, and  

• relationships between concepts. 

Content that is easy for AI to extract is also easier for humans to consume, making this approach a win for both. 

Step 3: Use existing search performance as an AI roadmap 

Traditional SEO data remains extremely valuable when used strategically.  

One of the fastest ways to improve AI visibility is identifying content that 

• already performs well in organic search 

• but is not appearing in AI-generated answers or summaries. 

This gap is rarely caused by irrelevance. More often, it is due to structure, formatting, or missing clarity. 

How to act on this insight 

• Identify high-performing pages in traditional search. 

• Check whether those topics trigger AI-enhanced results. 

• Prioritize pages where your brand is absent or underrepresented. 

• Optimize those pages for clarity, structure, and explicit answers. 

This approach delivers high return because it builds on existing strengths rather than starting from scratch. 

Step 4: Learn from community and conversational language 

Online communities reveal how people actually think, speak, and ask questions, and often more accurately than keyword tools alone.  

Discussion forums, Q&A platforms, and peer-to-peer conversations highlight  

• real concerns,  

• common comparisons, and the

• emotional drivers behind decisions.

In turn, these language patterns closely resemble how users prompt AI systems. 

Common conversational patterns 

• “Which is better: X or Y?” 

• “What’s the best option for my situation?” 

• “How often should I…?” 

• “Is it normal if…?” 

By translating these patterns into structured, on-site content, brands align more closely with both user expectations and AI comprehension. 

Step 5: Treat video and engagement assets as search signals 

Video has become a primary discovery format across many categories. It frequently appears in search results and plays a growing role in decision-making. 

From an AI perspective, video contributes to engagement signals and provides additional context about products, services, and expertise. As such, a balanced video strategy should include: 

• On-site video: Enhances clarity, engagement, and comprehension. 

• Off-site video distribution: Meets audiences where they research and compare options. 

Video should not be viewed only as an awareness tactic. When used strategically, it supports performance and conversion throughout the journey. 

Step 6: Reinforce local and trust signals 

For location-based businesses, AI relies heavily on existing local data sources. That means  

accurate business information, strong reviews, and consistent reputation management remain foundational. In fact,  

AI-driven local recommendations are powered by the same signals that have always mattered: 

• Accurate listings 

• Consistent NAP (Name, Address, Phone) data 

• Rich profiles with photos and services 

• Active review management 

AI does not introduce a new local playbook. Rather, it reinforces the importance of executing the fundamentals consistently and at scale. 

Measuring success in an AI-driven world 

Measurement must evolve alongside strategy.  

While traffic and rankings still provide useful context, they no longer tell the full story. Instead, brands need visibility metrics that reflect how AI systems represent them. That could include: 

• Brand mentions and citations in AI responses 

• Share of voice across key topics 

• Sentiment and positioning 

• Engagement quality on owned properties 

• Downstream business impact (leads, conversions, revenue) 

The goal is not to abandon traditional metrics, but to complement them with indicators that reflect influence earlier in the journey. 

From SEO tactics to visibility strategy 

SEO is no longer a siloed discipline focused solely on rankings and traffic.  

It is the foundation of a broader visibility strategy. One that spans content, technology, authority, engagement, and trust across a distributed search ecosystem. 

Brands that adapt now will not only protect performance but gain an advantage by influencing decisions before the click ever happens. 

The post SEO success looks different now  appeared first on DAC.

These tools allow users to ask broader, more nuanced questions and receive synthesized answers. The experience feels less like sifting through links and more like speaking with an informed guide. As a result, discovery is no longer limited to the SERP (search engine results page). Now, it often begins with a dialogue. 

And as these platforms grow, so do the signs that the AI layer itself may evolve into a commercial environment. In recent months, ChatGPT has come under scrutiny for interface features that resembled ad-like app suggestions,prompting OpenAI to clarify they weren’t paid placements, as reported by TechTimes and TechCrunch. Meanwhile, ChatGPT could introduce paid ads or sponsored suggestions at some point in the future, even if nothing has been formally confirmed. 

 
What this reinforces is simple: the AI-driven discovery phase is expanding, and commercial opportunities are likely to follow. The platforms shaping early-stage exploration today may very well become the paid environments of tomorrow. 

In short, AI is reshaping the starting line. But one thing hasn’t changed: when it comes to purchase decisions, user behavior still pivots back to what works, and paid media retains its critical role.  

LLMs are rewriting the discovery phase 

The shift in search behavior isn’t only cosmetic, but structural. Large language models (LLMs) like ChatGPT, Gemini, Claude, and Perplexity AI are fundamentally changing how people explore and evaluate information online. These tools don’t just retrieve links; they curate and synthesize insights, often drawing from multiple sources to deliver an “answer” rather than a list of options. 

This creates a new kind of digital gatekeeping. Instead of users scanning results and making comparisons on their own, LLMs act as intelligent intermediaries, deciding which brands, messages, or features make it into their responses. That means if your brand isn’t present, clear, or relevant in the places these models pull from, you risk being excluded from the conversation entirely. 

But this doesn’t signal the death of brand interaction. Instead, it means that engagement is happening earlier and more subtly. Where traditional search behavior might lead someone to a homepage or landing page, an AI-driven query might summarise your product or service without ever sending the user to your site. That changes how visibility and influence are measured. 

Adoption of generative AI for search and information discovery is accelerating across North America and Europe. In the U.S., an AP–NORC study shows that around 60% of adults now use AI tools to search for information, making it the most common AI use case. Pew Research adds that 34% of U.S. adults have used ChatGPT, with a growing share relying on it to learn, evaluate options, and explore topics in depth. Canada mirrors this upward trajectory, consistently ranking among the highest per-capita users of ChatGPT based on global traffic patterns. 

Across Europe, adoption is expanding at pace. A 2025 multi-country survey from the Reuters Institute and the University of Oxford found that 61% of respondents had used a generative-AI tool, with weekly use nearly doubling year on year, while insights from Statista and IPSOS show the same pattern emerging globally: users increasingly turn to AI tools as their first stop for ideas, comparisons, and recommendations. 

But discovery is only the first step. When intent deepens, people don’t just want information, they want solutions. And that’s where paid media continues to prove its value, guiding high-intent audiences from exploration to action. 

But purchase intent still relies on classic search 

While AI tools have changed how users explore, they haven’t replaced how users act. When real intent surfaces, like making a purchase, scheduling a service, or finding a store, people still rely on the proven structure of search engines. It’s familiar, fast, and effective. 

Transactional queries (“buy now,” “near me,” “book today”) are far less likely to trigger AI Overviews or conversational answers. Google itself has stated that AI Overviews typically do not appear on commercial-intent queries. Instead, those searches return classic results: organic listings, shopping ads, and paid search placements. 

Search Results for High Intent Queries 

As AI expands the exploration phase, the path to conversion narrows back into well-established, high-performing formats. In other words, performance media still captures the moment when intent becomes action. You can explore this dual-layered journey and how brands should respond in our latest Media Playbook. 

It’s also worth noting that while AI Overviews can reduce click-through rates, that impact softens when the brand is mentioned within the overview itself. Visibility still matters, just earlier in the journey and in more dynamic formats. 

The bottom line? AI might shift how users start their journey, but it doesn’t change how they end it. Paid remains decisive at the point of conversion. 

The CTR shift is real, but brands can adapt 

One of the clearest impacts of AI Overviews and LLM-driven search results is on click-through rates. When users get answers directly in the results page, consolidated and conversational, they’re naturally less inclined to click on individual links. That includes both organic results and paid placements. 

But here’s the nuance: lower CTR doesn’t equal lost intent. The need is still present, but it’s being addressed earlier, in formats that weren’t part of the journey a year ago. According to Search Engine Land, both paid and organic results experience significant CTR drops when an AI Overview is present. 

However, that effect isn’t evenly distributed. When a brand is directly mentioned within the Overview, the CTR decline is much softer. Those mentions act as early trust signals, helping brands stay in the user’s consideration set even before a click. 

ChatGPT in the Discovery Phase 
 

So, while visibility is shifting, it hasn’t disappeared. It’s migrating earlier in the journey, into environments that don’t always produce a direct response but still influence perception and decision-making. 

This is where strategy matters. It’s not just about where you appear, but when and how. Brands now need to show up during exploration, influence in the interim, and convert when the time is right. 

From channels to continuity: Rethinking strategy 

The evolution of search isn’t just a technology shift, but a strategic one. As AI reshapes how users discover information, the traditional notion of “channels” becomes less relevant. What matters now is continuity: being present across the full journey, from curiosity to conversion. 

Generative AI has introduced a new top layer to the funnel, one that’s more fluid, less predictable, and harder to attribute. Users aren’t just typing queries into Google anymore; they’re asking follow-up questions in ChatGPT, comparing answers in Perplexity, or passively receiving AI-generated summaries on search results pages. These environments blend content, comparison, and conversation into a single moment, often without a clear click path. 

Meanwhile, the transactional stage remains more structured. Paid media still plays a crucial role in capturing demand once users are ready to act, but that doesn’t mean it should operate in isolation. It needs to be part of a connected ecosystem that spans the full journey, not as separate tactics, but as reinforcing touchpoints. 

Thinking in terms of continuity helps shift the mindset from campaign-based execution to always-on visibility. Brands that embrace this layered approach can meet users where they are, however they choose to explore and guide them confidently toward conversion. 

This isn’t about replacing one channel with another. It’s about building strategies that adapt to a landscape where exploration is AI-led, but decisions are still grounded in traditional behaviors. 

Paid media still closes the journey and expands into AI 

Despite all the noise around AI disrupting search, paid media continues to do what it does best: capture high-intent moments and drive measurable outcomes. What’s changing isn’t the value of paid, but the environments where it shows up. 

Users still rely on traditional search results to make purchase decisions. When they’re ready to buy, book, or visit, they turn to structured formats where paid placements offer clarity, choice, and speed. That foundation hasn’t moved. 

But what’s new, and increasingly relevant, is how paid media is beginning to extend into the AI layer itself. Platforms like Perplexity are experimenting with native ads, and OpenAI’s “Instant Checkout” shows how commercial features are now appearing directly within conversational interfaces. 

These aren’t side features, but early signs of how paid media will evolve. In the near future, paid placements won’t be limited to search engines; they’ll also exist inside AI environments, influencing decisions at earlier, less structured points in the journey. 

The takeaway? Paid isn’t being displaced but redistributed. It still owns the final step of the journey, but it’s also gaining a foothold in the exploration phase. 

It’s not AI vs. Paid, it’s AI + Paid 

AI is changing how people explore, compare, and consider. But when it comes to deciding, when the need is clear and the intent is strong, paid media still drives the outcome. 

The future of search isn’t about one replacing the other. It’s about recognising that AI and paid now work in tandem. One shapes awareness and early impressions; the other delivers clarity, choice, and action. Both are essential. 

This shift calls for a more connected approach that’s less about siloed channels and more about presence across the entire journey. From conversational exploration to transactional readiness, brands need to show up with relevance, consistency, and intent. 

The brands that understand and embrace this duality will be the ones that not only earn visibility and attention but drive meaningful conversions. 

If you want to dig deeper into how the search landscape is changing, we’ve broken it down in our latest Media Playbook. It explores how evolving platforms, user behaviors, and technologies are reshaping the entire media ecosystem and what it means for performance. 

The post AI is changing the search game, but paid still wins the last click appeared first on DAC.

2026 isn’t just another step forward: it’s a hard reset. Search is no longer a single destination but an ecosystem woven through AI assistants, social platforms, maps, video, and retail media. Consumers expect frictionless experiences, human authenticity, and instant answers. And the brands that thrive will be the ones willing to reinvent themselves—not slowly, but continuously.

This report distils the noise into 26 practical, performance-ready moves you can activate today. Drawn from the deep expertise of DAC’s specialists, these insights are designed to help you navigate uncertainty, unlock growth, and lead with confidence in a radically new marketing landscape.

THEME 1: AI-native marketing systems

Eight ways to build marketing programmes designed for AI-first environments.

1. Prepare your ads for AI-powered surfaces

Google has confirmed that ads now appear directly inside AI Overviews, creating a highly competitive new paid surface.

AI Overviews, chat-based results, and assistant-led shopping journeys are becoming core discovery surfaces. Craft assets that work inside these AI environments. Think concise value propositions, structured data, comparison snippets, and feed-rich attributes. Brands ready for these formats will capture high-intent queries others can’t reach.

2. Build conversational-ready creative for chat-based ad units

Conversational ad formats are being rolled out across Google, Meta, Amazon, and retail media platforms.

Conversational ads allow users to ask follow-up questions within the ad itself. Prepare bite-size insights, product attributes, pricing details, and competitive differentiators that can feed an interactive dialogue. Your creative must anticipate the next logical question a user might ask.

3. Restructure campaigns for signal quality, not granularity

Google and Meta recommend 50+ conversions per week at the campaign/ad set level for stable machine learning.

AI thrives on volume and clarity. Consolidate campaigns to deliver more conversion signals, use fewer but richer audience buckets, and allow platforms to learn faster. Excessive segmentation slows algorithms and caps performance.

4. Strengthen broad match with smarter guardrails

Advertisers using broad match with smart bidding see 25% more conversions at a similar CPA.

Broad match is now deeply AI-driven and highly effective—when controlled. Combine broad match with smart bidding and strong first-party signals to unlock incremental demand. Use negative lists to maintain relevance and monitor search term reports to identify new profitable themes.

5. Build video as a core search input, not just a media format

58% of Gen Z’s video consumption happens on social platforms.

Consumers—especially Gen Z—use video as a discovery engine. Treat video as a search strategy: create content that answers questions, explains products, demonstrates features, and builds brand recognition across YouTube, TikTok, and social.
 

6. Ensure your website is fully crawlable by LLMs

Content hidden behind JavaScript is often invisible to LLM crawlers, which means it won’t be referenced in AI-generated answers.

While search engines can render JavaScript-heavy content, LLMs cannot. If key content loads client-side, AI engines may never see it. Prioritise server-side rendering, clean HTML structures, and accessible internal links.

7. Update your SEO KPIs for an AI-driven customer journey

Organic traffic will continue to decline as LLMs surface your content without users clicking through, making new KPIs essential.

Traffic is no longer the primary indicator of organic success. With AI search increasingly answering user queries directly, measure value instead: time spent on site, conversions, micro-actions, visibility (impressions), and assisted engagement.

8. Embrace AI-powered measurement and modeled conversion systems

Up to 70% of conversions in Google Ads are now modeled rather than directly observed due to privacy and signal loss.

In AI-native marketing systems, measurement becomes a core performance engine: it provides the signals algorithms need to continuously optimise. Modeled conversions, enriched first-party signals, and data-driven attribution allow platforms to understand intent, fill in measurement gaps, and automatically adjust bids, audiences, and creative. Here, the goal is not just to measure outcomes after the fact, but to feed the AI with the right data so it can learn faster, predict more accurately, and activate more effectively.

THEME 2: Local and trust signals

Seven ways to win where customers search, decide, and convert.

9. Triple your investment in reviews as your #1 ranking signal

88% of consumers would use a business that responds to reviews, versus only 47% for businesses that don’t.

Reviews drive rankings, customer trust, and conversions. Prioritise proactive review generation, fast replies (within 24 hours), and workflows that resolve issues at the location level. High review velocity and sentiment consistency improve visibility across search and AI.

10. Elevate digital PR to build local authority and brand trust

81% of consumers must trust a brand before considering a purchase.

Local authority no longer comes from listings alone. Earn citations, local press, backlinks, and community partnerships to strengthen credibility. Tie PR efforts to openings, events, and seasonal opportunities to build scalable, repeatable visibility.

11. Treat Apple Business Connect (ABC) as a primary local channel

With over 1.56 billion active iPhones globally, Apple Maps is impossible to ignore.

Apple Maps is now a major discovery engine, amplified by Apple Intelligence. Optimise every ABC profile: complete data, accurate categories, local actions, offers, and fresh content updates.

12. Turn local pages into high-intent conversion engines

76% of local searches lead to a store visit within 24 hours; 78% lead to an offline purchase.

Local pages should function as dynamic conversion hubs, not static landing pages. Add structured data, FAQs, offers, booking/ordering modules, real-time information, and rich media.
 

13. Build executive-ready location performance dashboards

Brands using location-level insights see measurable improvements in core KPIs.

Transform location data into business decisions. Report on visibility shifts, ranking movements, sentiment trends, and conversion outcomes, packaged in a narrative that leaders can act on.

14. Operationalise store-level feedback loops that drive action

When consumers see brands respond to feedback, loyalty increases significantly.

Monthly or quarterly performance sessions with store teams improve ranking, satisfaction, and accuracy. Turn insights into action plans, then close the loop so locations understand the “why.”

15. Monitor brand reputation across external sources

Brand sentiment now influences whether your brand is cited in AI-generated recommendations.

AI engines evaluate reputation across the entire digital ecosystem: reviews, UGC, Reddit, Quora, social media, industry publications, and comparison sites. Regularly assess how your brand is portrayed and address weaknesses head-on.

THEME 3: The creative renaissance

Five ways to build creative that wins attention and performance in 2026.

16. Prioritise mobile-first design for modern audiences

40% of users go to a competitor after a bad mobile experience.

Mobile now drives most traffic. Prioritise fast-loading pages, thumb-friendly navigation, responsive layouts, and streamlined content. Poor mobile experiences cost business.

17. Build a cohesive cross-channel design system

Consistency boosts brand recognition by up to 80%.

Eliminate friction by unifying your brand’s visual language across web, ads, social, email, and landing pages. Consistent cues increase trust, retention, and brand recall.

18. Invest in video and interactive content that drives engagement

Social videos generate 1,200% more shares than text and image posts combined.

Dynamic formats like short videos, animations, quizzes, and interactive demos capture attention and increase dwell time across social and web.

19. Use storytelling and UGC to deliver authentic brand Moments

84% of consumers trust brands more when they use UGC.

Amid AI-generated content overload, authenticity wins. Show real people, real stories, behind-the-scenes content, and customer moments that build trust.

20. Replace guesswork with data-driven creative testing

Every £1 invested in UX returns ~£100 in value.

Use heatmaps, analytics, and A/B testing to optimise layouts, CTAs, imagery, and messaging. Small UX improvements drive exponential gains.

THEME 4: New multi-platform search landscape

Four ways to win across AI search, social search, and traditional SERP.

21. Publish high-quality content that feeds both SERP and LLMs

AI search referrals have grown 527% in five months.

LLMs are hungry for structured, clear, authoritative content. Expand product/service pages, FAQs, persona-specific pain points, comparison content, and expert insights.

22. Integrate local insights into your broader SEO and media strategy

89% of companies with strong cross-channel alignment retain customers versus 33% without it.

Use review themes, local search demand, and top-converting locations to guide content, messaging, creative, and media activation. Cross-pollination increases efficiency.

23. Expand your schema markup to improve AI search visibility

Schema continues to expand with new fields that improve AI comprehension.

Schema makes your site easier for AI to understand. Add new fields, validate accuracy, and ensure markup reflects actual on-page content.

24. Build content ecosystems aligned to multi-platform search paths

70% of consumers use three or more platforms during discovery.

Search is now fragmented across Google, Maps, TikTok, AI engines, YouTube, and retail platforms. Build content tailored to each path to ensure brand presence throughout the customer journey.
 

THEME 5: The measurement reset

Two ways to build measurement systems that reflect how consumers buy today.

25. Move beyond last-click: Adopt incrementality to measure true contribution

Incrementality tests reveal an average of 15–30% over-attribution in last-click models.

Incrementality isolates what truly creates value by distinguishing conversions that would have happened anyway from those genuinely driven by your marketing efforts. Through geo-split tests, control groups, experiments, and marketing mix modeling, brands can quantify the real lift generated by each channel, campaign, or tactic. This approach breaks the illusion of last-click attribution and leads to more informed decisions: more precise budget allocation, a more efficient media mix, and a clearer view of overall performance.

26. Leverage first-party data to power AI-oriented data design

Differentiation in an AI world starts with differentiated data.

AI models perform best when fed structured, accurate, and richly described first-party data. Map your data assets, clean your taxonomy, and unify customer identifiers to create AI-ready inputs.

In conclusion, 2026 rewards the bold.

The brands that win will move quickly, integrate channels, embrace AI-native systems, and build trust at every touchpoint. These 26 actions offer a roadmap for accelerating performance, improving efficiency, and delivering meaningful results—today and into the future. Ready to activate your 2026 plan? Let’s connect and explore how these insights can support your marketing priorities for the year ahead.

The post The 2026 marketing trends report: 26 moves to accelerate your marketing performance appeared first on DAC.

Old Habits Die Hard: Why We Cling to Last-Click  

For years, last-click attribution was a marketer’s security blanket. It offered clean lines of cause and effect: ad click results in sale. ROAS and ROI simplified the story further, distilling complex journeys into a single success metric. And for a while, that was enough.  

But as consumer behavior spread across screens, platforms, and formats, that promise of precision began to crumble. Today’s purchase paths often include influencer touchpoints, AI search summaries, and passive exposure on CTV or YouTube, long before anyone clicks “buy.” Traditional last-click and channel-siloed reporting flattens that complexity, over-crediting the final touch and undervaluing the layers of influence that actually move markets.

Meanwhile, zero-click environments such as AI answers, platform-native experiences, and retail networks are reducing site visits and direct control. Measuring only by last-click success metrics misallocates budget and overlooks the invisible work that builds demand.  

The simplicity is an illusion. Behind that tidy last-click view lies a growing disconnect between what’s measurable and what truly matters, and in that gap, performance and opportunity quietly disappear.

Watch our short video “When Measurement Becomes Meaning” where Dan Temby, Senior Vice President, Technology Analytics shares how one of DAC’s retail clients uncovered $35 million in incremental profit by rethinking attribution. 

Modern Journeys Demand Modern Models

Customer journeys aren’t linear. A single conversion might be influenced by YouTube, a creator post, Connected TV (CTV), AI Overviews, and a late-stage search, all before the click. Last-touch models flatten this complexity, undervaluing the mid- and upper-funnel work that actually creates demand.  

Zero-click environments, like AI answers or platform-native experiences, make measurement harder. WARC data shows paid search ROI can be overstated by up to 190% when upper-funnel contributions are ignored. Meanwhile, 35% of last-click–attributed spend drives zero incremental sales.  
Click-based KPIs like CTR and CVR still have value for tactical optimization, but they can’t measure brand lift, influence, or long-term growth. To move forward, marketers need a framework that reflects how people really discover and decide.  

TotalSERP: Brand Visibility in Paid, Organic, and Local
As search journeys fragment, visibility can’t be evaluated by looking at one channel at a time. A single results page now blends paid ads, Shopping units, organic listings, images, videos, reviews, maps, and zero-click features like AI Overviews, AI Mode, and People Also Ask. Treating SEO, SEM, and local as separate lanes risks missing where influence is actually happening.  

DAC’s proprietary TotalSERP methodology approaches the results page as a single ecosystem. Instead of asking, “How are we doing in paid vs. organic vs. local?”, the focus shifts to, “How are we showing up across all the surfaces where our customers are making decisions?” That ecosystem-wide view helps teams identify gaps, overlaps, and opportunities that siloed reporting can’t see.  

AIO Impact Modeling: Measuring What You Can’t Click 
AI-generated answers add another layer of complexity. More queries are being resolved directly in AI Overviews or conversational responses, where users may never click through at all. Traditional reporting often treats these interactions as “invisible,” even though they clearly shape perception and choice.  

With AIO Impact Modeling, DAC evaluates how often and in what context brands appear within AI-generated outputs, and how that presence shifts over time. This kind of analysis helps marketers understand visibility and influence in environments where classic metrics like clicks and CTR don’t exist, but impact still does. Beyond tracking AI Overviews, DAC is adapting SEO strategies to ensure our clients continue to remain visible as AI reshapes the SERP experience.  

Tools for True Insight  

Modern measurement requires a shift in tools and mindset. Here’s how leading brands are making it work:  

• Incrementality: Did this spend drive outcomes we wouldn’t have had otherwise?  
  Using geo splits, holdouts, or audience controls, incrementality testing isolates true lift and enables confident budget reallocation.  
• MMM (Marketing Mix Modeling): How much did each channel contribute to revenue or profit?  
  MMM reveals how touchpoints work together and adjusts for seasonality, budget shifts, and macro trends.  
• Creative performance: Are people engaging with the message?  
  Metrics like time-in-view, completion rate, and attention scores spotlight creative impact. Creative can be tested for lift, just like media.  
• Brand and demand signals: Are we building momentum?  Brand recall, search demand, and favorability provide forward-looking indicators of growth. Critically, the cycle has sped up. Teams are no longer waiting for quarterly reviews. With lightweight MMMs and quick geo-tests, they’re optimizing in real time and reallocating based on impact, not instinct.  

Turning Measurement into a Continuous Practice

Modern measurement is shifting from static reporting to a continuous, embedded discipline. Instead of treating KPIs as post-campaign scorecards, leading teams use signals across the funnel, from recall and consideration to store visits and revenue to guide planning and optimization in real time.  

Incrementality testing and Marketing Mix Modeling (MMM) are becoming central to this approach. By using geo splits, audience controls vs. holdouts, and both national and local mix modeling, marketers can see which channels genuinely drive lift rather than relying on last-click assumptions. Rebalancing budgets based on true contribution often reveals incremental profit that traditional attribution models miss  

Another key shift is translating results into business terms such as gross profit, incremental revenue, and LTV (lifetime value). When teams quantify impact this way, measurement becomes a shared language across marketing, analytics, and finance, making decisions clearer and investment conversations more grounded.  

As discovery increasingly spans paid, organic, local, social, and AI-generated surfaces, measurement also needs to reflect the full ecosystem rather than isolated channels. Understanding a brand’s visibility in zero-click environments, generative answers, and platform-native feeds such as TikTok is now essential to capturing the real influence media has long before a user clicks.  

Together, these practices turn measurement from a retrospective exercise into an operational capability, one that helps teams move faster, act with more confidence, and connect their decisions to meaningful business outcomes.  

The Next Step: What This Means for You  

The measurement reckoning is here. Relying on last-click models puts your strategy at risk, not just resulting in inefficiency but irrelevance. In a fragmented, AI-first world, what worked five years ago no longer holds.  

It is time for brands to rethink measurement from the ground up, integrating analytics, creative, and media into a feedback loop that connects directly to business outcomes.  

The brands that are winning today are those shifting from metrics to meaning, building systems that uncover true influence and unlock sustainable growth.  

What Good Looks Like  

When measurement is working, you’re reallocating budget based on data, not gut feel. Creative refreshes happen when attention dips, not when the calendar says so. You can point to incremental profit from defined tests and answer the CFO’s question, “What would’ve happened without this spend?” And your marketing reports? They tell a business story, not just a media summary. 

The post Forward in Analytics: When Measurement becomes Meaning appeared first on DAC.

The media landscape has never been more dynamic or more demanding. What once operated on stable rules of reach, frequency, and funnel stages is now driven by constant motion. Audiences shift across platforms, attention spans splinter, and AI reshapes how discovery even happens. The result? Linear media plans built months in advance often feel outdated before they launch. 

For brands navigating this turbulence, agility has become more than a buzzword. It is the difference between reacting and leading. The most successful marketers are moving away from rigid planning cycles toward systems that can pivot in real time, reallocating spend, creative, and strategy as performance and culture shift. In this new era, the advantage no longer belongs to the biggest budgets or loudest voices. It belongs to the brands built to move. 

When Agility Becomes Advantage 

Agility in media is not about moving faster for the sake of speed. It is about building flexibility into every decision so brands can act with confidence when the market shifts. Traditional media planning assumed stability; modern planning assumes volatility. The brands that win are those that design systems to anticipate change rather than scramble to keep up. 

This is where agility becomes a true competitive advantage. It allows marketers to rebalance budgets in real time, pivot messaging as consumer behavior evolves, and activate locally when opportunities emerge. Frameworks like the 70/20/10 model help guide this shift. Seventy percent of spend goes to proven, reliable channels. Twenty percent is set aside for adaptive reallocations. The final ten percent fuels experimentation and testing. DAC applies this philosophy to ensure clients remain efficient and ready to seize emerging opportunities. 

Agility is no longer just a planning tactic. It is a mindset that values responsiveness over rigidity and empowers marketers to use uncertainty as a strategic edge. 

Building an Agile Media System 

True agility requires more than flexible budgets or quick reactions. It depends on having the right systems, data, and collaboration to move at the speed of change. At DAC, we design media ecosystems that connect national strategy with local execution, ensuring every campaign can flex with market conditions while staying true to the brand. 

Our Brand to Local framework is central to that approach. It connects national storytelling with localised activation, giving brands the flexibility to shift creative, spend, and targeting based on what’s happening in each market. A campaign can move from broad reach to conversion mode, or from national awareness to local store engagement, without losing cohesion or control. Behind the scenes, we rely on integrated technology and insight systems that surface performance patterns, simplify decision-making, and keep local presences healthy and accurate. Instead of juggling disconnected data, teams can see how paid, organic, and local signals are working together and pivot quickly when an opportunity appears. It’s about giving brands a clearer, more connected view of their media landscape so they can act with confidence and agility.   

This connected system turns agility from a theory into practice. It allows brands to pivot seamlessly and ensures every decision, from creative to channel allocation, is made with clarity and speed. 

Localised campaign adjustments, supported by real-time insights, maximise impact at both national and local levels, enhancing ROI and consumer relevance. 

The “So What”: How Clients Can Apply This Today 

Agility only matters if it translates into real business results. For marketing teams, that means turning flexibility into a measurable framework for decision-making. Three principles help brands put agility into practice. 

1. Plan for flexibility. Every media plan should include built-in space for change. DAC recommends allocating around twenty percent of budgets for responsive optimisation and ten to fifteen percent for active testing. This ensures brands can scale what is working and pivot when performance shifts without waiting for the next quarterly review. 

2. Measure in real time. Agile planning depends on continuous insight. Platforms like IRIS enable marketers to detect performance anomalies, evaluate spend efficiency, and redirect investment toward emerging opportunities as they surface. Gartner reports that companies with mature agile marketing practices see up to twenty percent higher revenue growth than peers, underscoring the direct value of adaptability. 

3. Localise with intent. National campaigns set the vision, but local execution drives relevance. Using Brand to Local strategies, brands can adjust offers, creative, and targeting to the nuances of each market while keeping overall messaging aligned. 

DAC’s Media Blueprint framework keeps brands focused on the why behind each investment, not just the what. 

These steps transform agility from a media ideal into an operational advantage that builds resilience, speed, and sustainable performance. 

Watch our short video, “When Agility Becomes Advantage,” where Maura Pierson, Vice President, Media Planning at DAC explains how agile media planning helped a client unify national strategy with local activation, driving a 50% increase in localised campaign performance and accelerating speed-to-market by 30%. 

The Forward Path 

The future of media belongs to brands that can move with purpose. Agility is no longer a backup plan for volatile times; it is the operating system for sustained growth. The pace of change will only accelerate as AI, automation, and audience behavior evolve, but that evolution also brings opportunity. 

At DAC, we see agility as a discipline, not a reaction. It means using data to act faster, using insight to guide every pivot, and aligning national vision with local impact. When agility becomes a habit, brands stop chasing the market and start shaping it. 

The playbook has changed, but the advantage is clear. The brands that lead forward will be the ones built to adapt, empowered by systems that turn movement into momentum. 

The post Forward in Media Strategy: When Agility Becomes Advantage  appeared first on DAC.

For Gen Z and, increasingly, millennials, the scroll feed has become the new search bar. What used to begin with typed queries now starts with personalized, visual-first content that blurs the line between entertainment, education, and commerce.This evolution isn’t just a change in channel preference; it’s a fundamental shift in how consumers gather information and make decisions.

Video platforms are the new discovery engines

Nearly half of Gen Z adults now use social media as their first stop for information, leaving traditional search engines behind. This shift isn’t anecdotal; it’s a widespread behavioral pivot shaping how brands must think about visibility.

As a direct and immediate result, the logic of discovery has shifted. Instead of directing users to external websites, platforms like TikTok and YouTube Shorts keep users immersed within the feed, surfacing personalized content that meets their needs in real time. TikTok has even positioned itself as a “search and entertainment engine,” with features like TikTok Shop that allow users to move directly from discovery to purchase without ever leaving the platform.

This is discovery without friction.Feeds are algorithmically tailored based on user interests, behaviors, captions, sounds, hashtags, and even visual context. That means every piece of video content becomes a potential search result, even if the user never types a word—effectively replacing the search box with an AI-driven, video-first feed.

And the content doesn’t need to be long to be effective. According to WARC, short-form video can drive brand recall in as little as 1.7 seconds. That kind of attention efficiency makes these formats especially valuable in fragmented, fast-moving feeds.

Measuring the value of influence, not just clicks

One of the biggest challenges in this new environment is measurement. Traditional success metrics like CTR and last-click attribution fail to capture the full impact of short-form video. Many of the most influential moments now happen well before (or without) a click.

Mid-funnel video placements, which are often undervalued in traditional click-based models, actually play a critical role in driving consideration and engagement. And yet many brands still optimize for views or CTR, missing the true impact video can deliver when evaluated with more modern frameworks.

This is where new measurement models come in. Attention-based KPIs such as time-in-view and attention scores are proving more useful than raw video views. They reflect real consumer engagement, not just exposure.

What brands must do now

This is not the moment for minor adjustments; it’s a foundational change in how discovery, consideration, and conversion happen. Consumers expect relevance without friction, but attribution models built for click-throughs buckle under the complexity of AI, automation, and zero-click behavior.

The idea of measuring marketing success solely by who “clicked through” misses the broader context of influence and underestimates the role that short-form video plays in shaping consumer decisions.

To succeed in this new environment, brands need to:

• Embrace short-form video as a primary channel for awareness and consideration, not just as a brand awareness add-on.
• Align creative and platform strategies around scroll-based discovery. That means building content specifically designed to perform in its native environment, not repurposing 30-second TV spots.
• Focus on influence over clicks. Whether someone stops to watch, engages, or shares, the moment they choose to pay attention may be more valuable than whether they clicked.
• Move at platform speed. What performs on TikTok or Reels today may not work tomorrow. Testing, iteration, and agility are essential.

How DAC is helping brands lead in video-first discovery

As discovery becomes increasingly fragmented, video isn’t just a complementary tactic; it’s central to how audiences explore, evaluate, and engage with brands. We’ve seen firsthand that the most effective strategies don’t treat video as a siloed format, but instead build purposeful, platform-specific experiences across TikTok, YouTube, Connected TV (CTV), and social video, with messaging and creative that reflects where people are in their journey.

One fashion retail client had long focused on lower-funnel performance tactics like branded SEM  shopping ads. But in an increasingly competitive category, and with audience fatigue setting in, they were seeing diminishing returns and limited growth.

We partnered with them to reshape their media mix. The goal was to increase visibility and relevance among new customers. We activated Connected TV to deliver high-impact storytelling to targeted audience segments, launched TikTok campaigns to engage Gen Z fashion shoppers, used niche video placements to reach smaller, high-intent cohorts, and ran Google DemandGen to intercept users open to discovering something new.

In just four weeks, they saw a 16% increase in new users to the site and a 10% lift in branded search demand; clear signs that the new media mix was driving brand interest, not just clicks.

This type of work is rooted in how people actually discover brands today. And it goes beyond individual platforms. Our approach brings together:

• Integrated video blueprinting: Coordinated, cross-surface strategies across Linear TV, CTV, YouTube, TikTok, and short-form social, each aligned to platform behaviors and full-funnel goals.
• Influencer and short-form testing: Combining authentic creator content with paid amplification, like our work with a specialized hand tool brand that delivered a 7% lift in share of voice and tripled ROAS.
• Scaled creative activation with local precision: AI-assisted creative tailored to specific markets, connecting national brand messaging to local behavior and intent.
• Reddit and social sentiment integration: Tapping into real user conversations and emerging trends to inform more relevant, discoverable, and timely content strategies.

In short, we’re not adapting around the edges but helping brands build systems that are responsive, creative, and ready for what’s next.

This is just one part of the full reset

We’ve created a playbook to guide you on what’s changed, what’s coming, and what to do about it now. The rise of video as the new search is only one of four major shifts reshaping the media landscape. From the fragmentation of the SERP to the evolution of measurement, marketers need not just new tactics but entirely new frameworks.

Get the full playbook now to explore all four implications and access DAC’s full tactical guide to driving growth in the AI era.

The post The Media Forward series: Video is the new search appeared first on DAC.

Linear customer journeys have long since given way to fragmented touchpoints, and this trend is only accelerating. Attention is more divided than ever, and consumer trust is more social than brand driven. The moment when someone decides to pay attention (or scroll past) now plays out across different devices, platforms, and generations.

This matters for every part of marketing, but nowhere is the impact more visible than in search. Search used to be the predictable engine of digital performance. But when the way people discover information changes, search changes too.

Search is no longer what it used to be

Search is no longer the reliable, keyword-driven channel marketers once knew. For decades, the model was simple: show up when someone types what they want. Clicks led users to websites, and performance could be traced back to a keyword. Many brands built their marketing strategies around that predictability. But that era is ending.

Today, search feels less like a list of results and more like a curated answer. With the rise of AI-generated responses, users often get what they need without clicking through. Google’s AI Overviews fold generative answers directly into the results page, and AI Mode offers fully chat-based AI experience. At the same time, platforms like ChatGPT and Perplexity are quietly claiming more of the search journey, changing what people expect when they go looking for information.

And it’s not just AI. Social platforms have redefined how discovery starts. Nearly half of Gen Z adults now turn to TikTok, Instagram, or YouTube first, treating search as a follow-up rather than a starting point. Even when they do search, their expectations have shifted. They want direct, relevant answers, not a list of links to sort through.

These changes don’t just affect how users behave. They reshape what visibility looks like. If your brand isn’t showing up in these new, zero-click, AI-powered environments, it may not be showing up at all.

Why traditional models break down

What used to be distinct channels in a funnel, TV, social, search, video, radio, are now converging into a complex web of influence. And nowhere is that convergence more disruptive than in search, where platforms, formats, and user behaviors are colliding faster than marketers can recalibrate. For years, paid search operated with precision. Keyword match types, ad copy control, and direct attribution allowed marketers to build campaigns that could scale with predictable returns. Organic strategies, meanwhile, were built on ranking for high-value queries, using technical SEO, content optimization, and backlink strategies to climb the results page.

But those models are buckling under the weight of fragmentation and automation. Search results are no longer a ranked list of links, they’re a blend of AI-generated summaries, shopping carousels, maps, images, and answers. And those results increasingly keep users in the SERP. According to SparkToro and Datos, nearly 60% of searches end without a click, a number that continues to grow in AI-dominated environments.

At the same time, automation is reshaping the paid landscape. With tools like AI Max, Demand Gen, and Performance Max, Google now determines how ads are matched and where they show, based on broad intent signals and machine learning, not specific keywords. The control marketers once had is being traded for scale, speed, and reach, but with less transparency.

Organic search is evolving too. Visibility no longer comes from targeting one keyword at a time. It comes from owning entire themes, supported by content that’s clear, structured, and optimized for machine understanding. Schema markup, headings, easily digestible content, and topical depth all factor into whether your content appears in an AI-generated response.

The bottom line: the search engine results page is no longer a fixed destination. It’s a dynamic, AI-driven surface, and legacy playbooks weren’t built for it.

A new path forward

In this new search environment, adaptation isn’t a matter of optimization, it’s a matter of reinvention. To remain visible, relevant, and competitive, brands need to evolve their strategies from the ground up.

That starts by breaking down internal silos. Paid search, SEO, content, and local presence can no longer operate as separate workstreams. Search visibility increasingly depends on how these pieces connect, how campaigns are built, how content is surfaced, and how each element responds to shifting intent. Alignment across teams and tactics isn’t just operationally efficient; it’s foundational to how search works today.

It also means preparing for discovery that doesn’t follow the rules of the past. Search now includes results generated by AI, contextual ad placements, evolving SERP layouts, and conversational interfaces that users don’t click out of. To be present in this environment, brands need structured, machine-readable content. Schema markup, strategic internal linking, topical coverage, and clarity are no longer just best practices, they’re essential inputs into how visibility is assigned.

Readiness also extends beyond content. Review signals, business listings, product feeds, and other first-party data are now part of how search engines understand and evaluate relevance. Fragmented or outdated data leads to missed opportunities. Clean, connected, and current infrastructure unlocks performance across all surfaces of search, not just in listings, but in ads, answers, and beyond.

Measurement must also evolve. Traditional attribution models built for last-click behavior don’t reflect how users discover brands today, and according to recent data, 60% of underperforming marketers are still relying too heavily on last-click metrics alone. Success is no longer just about traffic and conversions, it’s about presence, influence, and responsiveness across the entire search experience.

This is the new baseline. And for brands that are built to adapt, it’s an opportunity to lead.

What adaptation looks like in practice

This isn’t theoretical. It’s already happening.

A major US-based travel brand had long maintained strong organic visibility for a destination they had effectively “owned” in search. But over time in a highly competitive market, they began to lose ground. The search results page had changed, and traditional SEO tactics focused on high-volume keywords were no longer enough to maintain visibility. What once worked, a keyword-first strategy and a single hero landing page, was no longer delivering results.

DAC partnered with the brand to completely reframe their search strategy. The goal wasn’t just to recover lost traffic but to reassert authority across the entire topic. Together with the client, we identified a wide range of content opportunities, from seasonal weather and event calendars to activities tailored to families, adventure seekers, and food lovers.

To scale quickly, we used our AI content engine to support subject matter experts throughout the process. Strategy, SEO, and Creative worked side by side to ensure each piece of content aligned with user intent, followed best practices for structure and clarity, and stayed true to the brand’s voice. The results were immediate: a 54% increase in organic traffic and a 136% lift in revenue tied to that destination.

In another engagement, we analyzed how local presence influences paid search performance. Locations with more Google reviews saw a 38% higher click-through rate on paid search. About one-third of paid search clicks went directly to local listings. Stores competing against seven or more higher-ranked locations experienced noticeable drops in CTR. Most importantly, paid search leads were strongly correlated with business outcomes, showing an 82% correlation to new customer count and 84% correlation to overall sales.

This is what adaptation looks like: smarter content, stronger signals, and full alignment across all the surfaces of search.

Search has changed, and it’s not going back. AI, new behaviors, and zero-click experiences are redefining what visibility looks like. Brands that still rely on old strategies risk falling behind.

But there’s a path forward. It starts with intent, integration, and a willingness to rethink how search works. The time to adapt isn’t later. It’s now.

The post The Media Forward series: When search becomes conversation appeared first on DAC.

Many marketing leaders I speak with feel both excited and overwhelmed by this shift. They’re being asked to make calls on data platforms, automation strategies, and creative optimization tools, areas that once sat firmly in the tech department. But it makes sense: marketing has become the most data-rich, fast-moving function in the enterprise. And it now demands agency partners who understand not just audiences and channels, but how to use AI responsibly and effectively to unlock business value.

The most effective way to deliver on that promise is by combining data intelligence, rapid innovation, and operational efficiency. When those elements come together in cohesive systems where AI and humans co-create, the result is what we call Generative Marketing Intelligence, a distinct competitive edge in today’s AI-driven landscape.

Here’s what CMOs should expect from their media agency as this new reality continues to evolve.

1. AI-oriented data design

It’s tempting to think of AI as a kind of magic black box that just needs the right question, but the truth is that the quality, structure, and description of your data will determine how intelligent your AI actually is.

AI performs best when it’s armed with enough context to make informed inferences and decisions, but not so much that its “attention span” is overwhelmed by irrelevant facts. In other words, you need to make sure your data isn’t just stored, but understood.

New-generation data platforms such as Snowflake are providing excellent tools to address this, thanks in part to a semantic layer that acts as a translator between human language and machine logic. For example, when a marketer asks, “Which campaign drove the highest ROI last quarter?”, the semantic layer helps AI models understand the question and convert it into an efficient, accurate query.

Constructing this layer involves defining key business concepts (metrics, dimensions, hierarchies) and then providing sample queries that teach the AI how those concepts relate to one another, bridging the gap between raw data and real-world questions. Dedicated environments like Proove Intelligence can play a valuable role here, helping teams align data engineering with business logic to make AI outputs more relevant, timely, and actionable.

2. AI-powered rapid prototyping

AI-powered rapid prototyping has become one of the most important tools for marketing leaders, allowing them to quickly onboard relevant datasets, experiment with new ideas, and build solutions that address immediate business questions without waiting for enterprise-scale deployments. Now, CMOs can move from hypothesis to insight in days instead of months, and test whether a new data source, model, or concept actually delivers value before committing to full implementation.

The key is iteration: small, agile tests that reveal what works (and what doesn’t) long before large-scale rollout. That’s only possible when teams fully embrace AI at every layer of development. Developers now use AI tools that write, review, and optimize code in real time, accelerating build cycles and freeing teams to focus on strategy and integration instead of syntax and debugging.

Non-developer power users can leverage tools like Cursor to bring AI-assisted development into the hands of marketers, analysts, and strategists, empowering anyone with business acumen to help shape data-driven prototypes directly.

Likewise, visualization tools such as Streamlit allow teams to quickly generate interactive dashboards and insights, transforming complex datasets into intuitive, business-ready stories, helping diverse teams see, test, and refine AI-driven insights collaboratively.

Working with an internet service provider, for example, DAC integrated broadband coverage data with real estate listings to visualize internet service availability at a hyper-local level. The resulting prototype showed which ISPs were leading in each neighborhood and why, providing actionable intelligence in days, not months.

Agencies play a valuable role in enabling this kind of rapid experimentation, particularly when it comes to designing internal systems that securely connect to large language models and enterprise data environments. Tools like Guardrail are designed to blend governance with flexibility, allowing organizations to manage permissions, ensure compliance, and select the most appropriate AI model for each task. By enforcing brand safety, managing API connections, and maintaining auditable workflows, this type of system makes sure prototypes aren’t just fast, but safe, consistent, and ready for real-world use.

3. AI-optimized media

Today’s media experts need to apply AI both within and beyond walled garden buying platforms. On one hand, the platforms themselves have become remarkably sophisticated. Their algorithms can manage bids dynamically, respond to real-time user signals, and even assemble creative assets on the fly to deliver highly personalized composite messaging. This native AI is powerful, and when used correctly, it can drive strong performance at scale.

But, as I often remind clients, platform automation has its limits. It isn’t always transparent, and its incentives don’t always align perfectly with those of the advertiser. When agencies trust these systems implicitly, they risk handing too much strategic control to black-box algorithms that may prioritize engagement over efficiency, or short-term clicks over long-term brand value.

    That’s why agency-owned AI systems need to operate at a higher level of oversight, sitting above the platforms themselves to orchestrate the total mix. These systems analyze cross-platform performance data, adjust allocations dynamically, and provide an independent lens to ensure the best investment decisions are being made. They’re not just performance tools; they’re governance engines that keep AI decision-making accountable.

This layer of oversight is also essential for financial accuracy and operational discipline. AI can manage thousands of campaign variables in real time, but if spend pacing or data reconciliation goes off course, even the smartest algorithms can produce waste. Agency systems must ensure that every impression, click, and dollar is tracked, verified, and aligned with business objectives.

Tools like IRIS Pulse, our real-time AI monitoring system, can help make this oversight possible. By continuously tracking key performance indicators (KPIs) and flagging significant deviations from plan, these systems enable teams to respond immediately, not after the fact.

By combining the automation power of media platforms with the strategic intelligence and accountability of custom-built systems, agencies can create a hybrid model where AI amplifies human expertise rather than replacing it. The resulting efficiency, intelligence, adaptability, and trustworthiness is exactly what CMOs should strive towards in today’s AI-driven landscape.

4. AI-enhanced experience delivery

Delivering a great customer experience requires both creative excellence and contextual intelligence. Properly adapting creative for each audience, journey stage, product, location, and media moment can require thousands of variants, and each version needs to feel distinct, relevant, and on-brand.

Media agencies have long worked closely with creative partners to plan, test, and execute campaigns across channels, but growing demand for both speed and scale make it increasingly impractical to rely on traditional production methods. AI-driven solutions can help by extending creative’s reach rather than replacing creative talent. These systems enable teams to test, tailor, and optimize creative faster, while staying true to the brand’s visual and emotional identity.              

AI is also proving to be a powerful partner in interpreting creative test results. By combining performance data with a qualitative understanding of imagery, tone, and emotional resonance, AI can surface insights into why certain creative outperforms others, adding a new layer of intelligence to the creative process.

This is where AI goes beyond production acceleration to become a creative intelligence engine, helping teams learn, adapt, and continuously refine creative concepts and variants. That’s the hallmark of effective experience delivery in the AI era.

5. AI-driven scenario planning

Every CMO I speak with is feeling the same pressure: to do more with less. Budgets are tighter, expectations are higher, and every dollar of media spend must clearly contribute to business growth. So, marketers have to be able to demonstrate that investments are going toward the highest-impact activities, but achieving this clarity is harder than ever. Signal loss from privacy regulations, browser changes, and cookie deprecation continues to erode visibility.

To stay ahead, CMOs need to bridge two realities: the fast-moving KPIs optimized in the short term, and the broader business impact that builds over time. That starts with identifying statistically reliable short-term signals that strongly correlate with long-term success.

Media mix modeling (MMM) provides the foundational, cross-channel view to understand how different marketing levers interact and contribute to overall performance. Thanks to AI, these models can now be developed and updated faster, making them more accessible and actionable. In parallel, incrementality testing isolates the true contribution of specific channels or tactics. Run in a structured, ongoing way, these tests reveal which activities are driving genuine growth, versus those simply capturing existing demand.

The magic happens when these approaches are combined. By layering deterministic and probabilistic methods, MMM, incrementality testing, and AI-enhanced attribution modeling, teams can triangulate toward the truth with greater confidence. AI synthesizes these complex data sources, models future outcomes, and identifies the paths most likely to drive sustainable growth.

6. AI-enabled workflow automation

AI systems are most effective when they support human–AI co-creation, not full automation. The goal isn’t to remove people from the process, but to give them smarter tools that extend their capabilities and accelerate their impact.

Our human expertise remains the compass. It’s what guides the AI, shapes its prompts, hones its outputs and apply creative judgment. That’s why it’s critical to design workflows with clear intervention points: built-in moments for review, approval, and feedback that keep automation grounded in brand integrity and real-world insight.

This philosophy is embedded in platforms like our IRIS platform, which includes Spaces, collaborative environments designed for ideation, content creation, data analysis, and campaign planning. These workspaces support complex, cross-functional projects, enabling media specialists, data scientists, and strategists to collaborate seamlessly.

Even the AI systems themselves benefit from specialization. Instead of relying on a single model, we orchestrate networks of AI agents, each designed for specific tasks like data cleansing, creative generation, or reporting. This mirrors how modern teams work: interconnected, expert-led, and collectively intelligent.

The future of workflow automation lies in this balance: people and machines working in sync, each making the other more effective, and ensuring that automation drives not only efficiency, but also better outcomes.

Expect more from your media agency

AI isn’t just about marketing automation, but a complete transformation of how marketing operates, from insights to planning to execution and optimization. CMOs are now stewards of that change, and your media agency should be your most trusted partner in making it real.

At DAC, that’s exactly what we focus on: combining human creativity and AI intelligence to deliver measurable business outcomes.

Let’s talk about how DAC can help your brand harness AI for real growth.

The post 6 things CMOs should expect from their media agency in the age of AI appeared first on DAC.

So, what is this model collapse, you ask? Imagine you have a beautiful photo. You make a copy of it with a copy machine. The copy looks good, almost perfect. Now, imagine you take that copy and make another copy from it. And then you copy that copy. Each time, the picture gets a little worse. It becomes blurry. The colors are not right. After many copies, you cannot even see what the original photo was.

This is what is happening to AI. We’re currently only at the fifth copy, so the effect is not yet fully perceptible, but it is happening, and some say it is inevitable.

AI models like ChatGPT learn by reading huge amounts of information from the internet. At first, this was all information created by people. But now, the internet is full of content created by other AIs. One study suggested that about 57% of all web text has been generated or translated by an AI. That figure comes from a one-year-old article, so it is safe to assume that the figure is probably even higher today.

So, new AI models are learning from the “copies” made by older AI models. Just like the photo, the quality gets worse with each new copy. The AI starts to forget the details. It starts to make mistakes.This is model collapse. It is like the AI is eating its own tail.

How model collapse will impact marketers

Why should you, a marketer, care about this? Because this problem will affect our work directly, and very soon.

First, think about Google Search. Google is now using AI to give answers right on the search page. They call it AI Overviews. This AI needs authentic, accurate information from the web to work well. But what happens when the web is full of low quality, AI generated content? AI Overviews will start giving bad answers. They will show wrong information. They will repeat the same boring ideas. People will stop trusting Google.

For marketers, this is a big problem. If people cannot find our websites, our traffic will go down. Studies already show that when an AI answer appears, very few people click on the links to websites. One analysis found a site could lose about 79% of its traffic for a query if the results appear below an AI overview. This is a serious threat to SEO and content marketing.

Second, think about our paid advertising. Many of us use programmatic advertising to show our ads to the right people. But cheap AI content has created thousands of fake websites, sometimes referred to as “made for advertising” or MFA sites. They are full of bad articles and way too many ads. One report found that 15% of programmatic ad spending goes to these low-quality sites.Our ads are showing on these terrible websites, and we are wasting part of our budgets.

At the same time, criminals are using AI to commit ad fraud. They create fake people, fake clicks, and fake engagement. It is becoming harder to know if our ads are being seen by real customers. Model collapse makes this worse because the AI we use to target ads also learns from bad data, so it creates a loop of bad decision making.

How to mitigate the effects of model collapse

Marketers cannot stop this problem alone, obviously, but we can be smart, and that means adapting our strategies sooner rather than later.

1. Focus on real quality

In a world full of fake AI content, real human expertise is more valuable than ever. We need to create content that is truly helpful, original, and based on real experience. Tell real stories. Share real data. This is what people will trust, and what good AI should value. Put a bit of your soul in each piece, even in a business setting. Use a little humor to make your content more alive and personal.

2. Value your own data

The data you collect directly from your customers, your first-party data, is like gold, it is clean and undoubtedly real. Use this data to understand your audience and to train your own small-scale AI models. Do not just rely on the big models that are trained on the messy internet; leverage a big AI model’s analysis capability but feed it with curated, validated data you trust. This will create a compromise worth using.

3. Be a smart ad buyer

Do not just try to get the cheapest ad impressions. This is the best way to end up on MFA sites. Instead, work with good publishers. Make a list of websites you trust and tell your ad platform to only show your ads there. Ask for more transparency.

The age of AI is exciting, but it has dangers. It doesn’t make you an AI-denier to question the situation and prepare for future issues. It actually makes you a valuable marketer for your brand and your clients.

But make no mistake: model collapse may feel like a small problem right now, but its effects are growing every day. As marketers, we must understand it and adapt accordingly. The future will not be about who can create the most content with AI, it will be about who can create the most trusted and authentic content.

My advice is to lead with quality and trust. Is my English writing as good as the latest fancy AI models? Probably not, but part of the human experience, and its core beauty, lies in the honesty of our imperfections and uniqueness. Personally, I will strive to preserve the human experience, one imperfect blog article at a time.

The post Is AI eating itself? What “model collapse” means for your marketing appeared first on DAC.