marketing
AI Made PR and Marketing Work Faster. But It Didn’t Fix Your Biggest Inefficiency.
marketing 23 Apr 2026
By Carey Madsen, VP and CMO, The Fletcher Group
94% of B2B buyers now use AI during the buying process, and most marketers are working hard to insert their brands into those buyer recommendations. But you’re probably making it harder than you need to.
Here’s a scenario that plays out every day in B2B: a company earns a strong media placement in a respected trade publication. The story is sharp, well-positioned, and reaches the right audience. Then it disappears. Posted once on LinkedIn, shared internally, and forgotten. Sales never sees it. The website never references it. No one writes a follow-up post that builds on the insight. The executives who could have amplified it don’t.
This is what happens when PR and marketing operate in silos. Coverage and content don’t travel far and in 2026, that has consequences that go beyond missed amplification. It affects how often your brand appears in AI-generated answers.
The way B2B buyers research and evaluate vendors has changed fundamentally over the past two years. Buyers no longer follow a neat funnel. They may read a trade article, which prompts a question, so they ask ChatGPT or Claude. The answer frames their next steps, which might include a visit to your website to read an FAQ or case study, an industry report, or to a competitor’s site instead.
If your messaging isn’t aligned and repeated across these channels, you haven’t made your brand known; and it’s difficult for buyers to find you, because they don’t know what you solve for. In a nutshell, vague messaging gets skipped, while consistent messages gets cited.
How Do B2B Buyers Research Vendors in 2026?
Forrester’s 2026 State of Business Buying report shows that purchasing is more collaborative, and dependent on validation from trusted sources than in previous search eras. Buyers rely on what Forrester calls a “buying network” internal stakeholders plus analysts, peers, and earned media — to validate what they learn from any single channel, including AI tools.
The Forrester data paints a clear picture of just how early these decisions are forming:
· 92% of B2B buyers enter the process with at least one vendor in mind, and 70% of the journey happens before sales engagement
· 9 out of 10 C-suite decision makers say they are more receptive to thought leadership than traditional marketing materials
· 94% of buyers use generative AI during the buying process, but 20% report inaccuracies—leading them to validate AI outputs against third-party sources
Buyers use AI as a data point, then confirm what they find through media, analysts, LinkedIn, and your owned content. If your brand shows up in only one of those places, you’re missing other essential validation opportunities.
Why Do LLMs Favor Brands with Multi-Channel Presence?
This is where buyer behavior and AI visibility intersect. LLMs pull from media coverage, brand content, social conversations, and third-party validation to shape the answers buyers see. Brands that appear across more source types tend to be cited more often and with more context.
The rules of AI-fueled search are evolving in real time, but several patterns are already clear enough to act on:
· Earned media drives the majority of AI citations. Muck Rack found that 82% of citations come from earned sources
· Brand search volume is a stronger predictor of AI citation than traditional SEO authority like backlinks
· LLMs do not share the same resource pools, so, appearing on a wide range of relevant channels—owned, paid and earned—is necessary to be cited by all the most popular LLMs
In practice, this means disconnected or incomplete efforts across PR and marketing teams create visibility gaps that competitors can fill. When PR, content, and executive visibility aren’t aligned, you reduce the number of trusted signals AI systems rely on.
How Does One Asset Become Five?
The real value of integration is making one success work four times harder. This helps large companies absolutely dominate their space and lets smaller firms punch above their weight through efficient use of resources.
Here’s what that looks like in practice. Take a single starting point: your company releases original data or research on a trend that matters to your buyers.
• Earned: The research is pitched to key trade publications and tier 1 business outlets. Stories are published, your CEO is quoted with a distinctive point of view.
• Owned: The research becomes an un-gated blog post and report on your website, structured with clear headers, FAQ sections, and schema markup so both Google and LLMs can parse it effectively. Key data points are formatted as standalone, citable claims that start showing up in other earned media.
• Shared: Your CEO and other executives post their own take on LinkedIn — not identical reshares, but distinct perspectives that create multiple entry points for key audiences. The company page amplifies with a summary post linking to the blog.
• Third-Party/Paid: A LinkedIn sponsored content campaign targets decision-makers in your key verticals. An analyst briefing results in an informed industry expert that validates the narrative for media and prospect inquiries. The research serves as the foundation of a presentation or webinar at an industry event.
Does Integrated PR and Marketing Require a Large Budget?
No. In fact, smaller teams are often better positioned to do this well from day one, because they can’t afford to be spread too thin. Even some larger brands can’t activate all channels at scale, and trying to do everything at a surface level is worse than doing two things well. But whatever you do invest in, do it well, and set your campaigns up to compound across channels rather than exist in isolation.
A single earned media placement that nobody amplifies, repurposes, or references on your website is a missed opportunity — and that’s true whether your budget is $50,000 or $500,000. A blog post that answers a question your buyers are asking but never gets shared by an executive or promoted to a targeted audience is content that only works in one way, instead of four or five.
Integration is a mindset about how assets get used, not a mandate to spend more. Start with what you have. Make each piece of content and each media win work across every channel you can reach.
The Outcome: Consistent Presence Where Buyers Look
The B2B buyer’s journey is no longer a path you control. It is now made up of a network of sources — and increasingly, a network that AI tools reference on their behalf.
When PR, content, social, and paid efforts work together, your brand appears more consistently across those sources. That consistency builds consensus and ultimately, trust.
Creative Over Signals: Rethinking Attention as Performance Across Omnichannel Advertising
marketing 20 Apr 2026
How is fragmentation across DOOH, mobile, and retail media impacting marketers today?
What are the biggest challenges brands face when trying to unify these channels?
How can advertisers create a more seamless omnichannel experience across these touchpoints?
What role does data play in bridging the gap between DOOH, mobile, and retail media?
Are there specific technologies or platforms helping reduce fragmentation effectively?
How can brands better measure ROI across multiple channels without siloed data?
What trends are you seeing in programmatic advertising across DOOH and mobile?
How important is personalization when connecting retail media with DOOH and mobile campaigns?
What advice would you give to marketers just starting to integrate these channels?
Start by understanding where your audience is in the real world and how your brand can show up meaningfully in those everyday moments. Then focus on creative that is contextually relevant, not just reaching people but actually capturing attention and leaving an impression. Most importantly, treat these touchpoints as opportunities to create engaging, memorable experiences that bring a bit of energy and delight to their day.
Looking ahead, how do you see the future of unified advertising across DOOH, mobile, and retail media evolving?
Where Brands Become Experiences: The Rise of Experiential Retail Spaces
marketing 20 Apr 2026
Gen Z and Gen Alpha don’t see malls as places to shop. They see them as places to experience, linger, socialize and find joy. For them, physical spaces are social platforms. They’re coming for discovery, content creation, and shared moments, not just transactions.
Gen Z expects environments that are dynamic, immersive, and constantly evolving. They want spaces that give them something to do, content to capture, and experiences to share. That fundamentally shifts the role of the mall from retail destination to cultural stage.
2. What inspired Westfield to transform malls into broadcast and experiential hubs?
We recognized a simple truth: Attention has fragmented, but physical environments and tangible experiences still command attention at scale if you design them correctly.
Our properties sit at the intersection of commerce, culture and community. By evolving them to operate at their full potential, they evolve from places people visit to platforms brands can activate and amplify. It’s about turning passive foot traffic into active audience engagement.
3. Can you explain the idea of “the physical environment as media”?
Traditionally, media has been something you place into an environment. We’re flipping that.
The environment itself becomes the media channel. Every surface, every screen, every spatial moment is an opportunity for storytelling. Instead of interrupting people, brand moments have become part of the experience. You’re not just seeing a campaign - you’re actually inside it. That creates deeper emotional resonance and dramatically increases memorability.
4. How do creator-led launches and live cultural events fit into this new strategy?
Creators are today’s cultural distributors. They don’t just amplify moments—they define them.
By operating spaces that are production-ready and broadcast-capable, we allow brands to launch products, host premieres, and stage cultural moments that live simultaneously in the physical and digital worlds.
A creator-led launch at Westfield doesn’t just reach the audience in the room, it cascades across social platforms in real time, turning a single event into a global moment. A great example of this is the BTS x Arih retail ramen launch that took place the weekend of April 10th – which was posted about across TikTok and Instagram feeds.
5. What makes Westfield Century City’s new space unique compared to traditional malls?
Westfield Century City is purpose-built for this new era. It’s not retrofitted—it’s designed from the ground up as a hybrid of venue, media platform, and cultural hub. Our hero event space in LA, The Atrium, acts as the “town square of LA,” with integrated infrastructure that supports large-scale productions, premieres, and brand activations seamlessly.
What makes it unique is the convergence: event space, high-impact media, and audience density all working together in one cohesive ecosystem.
6. Could you tell us more about The Centurion and its role in this transformation?
The Centurion is a defining example of where retail and media are headed. It’s not just a screen—it’s a broadcast surface engineered for live moments, real-time content, and cinematic storytelling. With high-resolution LED, optimal sightlines, and integrated production capabilities, it enables brands to create experiences that feel more like live entertainment than advertising.
Its role is to anchor the entire media ecosystem at Century City, turning the space into a stage where brands can premiere and participate in culture.
7. How does real-time impression measurement benefit brands and advertisers?
Measurement is what turns experiential from art into science. With real-time analytics like dwell and attention time, we can quantify impact in ways that weren’t possible before. Brands can understand not just how many people saw something, but how they engaged with it. This capability closes the loop between physical experience and performance marketing, making experiential a measurable and scalable channel.
8. Why are social-first and viral activations becoming so important in retail spaces?
Because the true audience is no longer limited to who’s physically present. The most successful activations today are designed to travel—visually, emotionally and culturally. If it doesn’t translate beyond the four walls, it isn’t scalable.
We think about every experience through a dual lens: how it feels in person and how it performs afterwards. When you get both right, you create exponential reach.
9. What impact will these changes have on brand storytelling and customer engagement?
Brand storytelling is becoming more immersive, more participatory, and definitely more immediate.
Instead of telling consumers what a brand stands for, we’re creating environments where they can experience it firsthand. That shifts engagement from passive consumption to active involvement.
The result is stronger emotional connection, higher recall, and ultimately, greater brand affinity. We’re seeing this across categories, but especially in entertainment. As just announced – Apple TV+ is hosting a truly immersive two weekend-long activation “Think Apple TV" (April 23-April 26 and April 30-May 3) featuring interactive experiences from a lineup of series including: Pluribus, Margo’s Got Money Troubles, The Morning Show, Shrinking, Your Friends & Neighbors, Imperfect Women, Slow Horses, and Stickfan. The installation will offer fans the opportunity to experience their favorite shows up close like never before.
10. What future trends do you see shaping the next generation of retail experiences?
We’re moving toward a world where retail, media, and entertainment fully converge. You’ll see more real-time, adaptive content—experiences that change based on audience behavior. More integration with creators and communities. More seamless connections between physical spaces and digital ecosystems, from AR to live streaming to commerce.
And most importantly, you’ll see a continued shift toward purpose-built environments—spaces designed not just to sell products, but to host culture. The future of retail isn’t about transactions. It’s about moments and the brands that create them will win.
AI may shape the search, but retail media still wins the sale
marketing 17 Apr 2026
AI is rapidly changing how Australians shop. It is making product discovery faster, easier and more personalised, and giving consumers new ways to compare prices, assess options and narrow their choices.
But discovery is not the same as conversion. Shopfully’s 2026 State of Shopping research shows that while AI is becoming a powerful tool for comparison and recommendation, the final purchase decision still depends on something more immediate: whether the product is available nearby, competitively priced and relevant in that moment. That is where retail media matters most.
AI is changing how shoppers discover
Australian shoppers are more deliberate than ever. They are price-conscious, research-driven and increasingly comfortable using digital tools to stay in control of what they spend.
Digital has long played a role in this behaviour, with around 81% of Australians researching products online before purchasing in-store. AI is now accelerating this shift. It removes friction from the research phase, giving shoppers instant access to comparisons, tailored recommendations and real-time alternatives.
Our research found that 71% of shoppers are already using AI to compare prices across retailers, 43% use it to track price drops, and 38% rely on AI for personalised product recommendations. For retailers, this wider top of funnel creates a more competitive and complex path to purchase.
Retail media is where decisions are won
As AI expands the discovery phase, it also makes it easier for shoppers to delay commitment. Shopfully’s research shows 67% of shoppers are spreading their spend across multiple retailers to secure better value. Loyalty is weaker, comparison is easier, and the route to purchase is no longer linear.
That creates a new challenge for retailers. It is no longer enough to be discovered. Retailers also need to win the decision at the point where a shopper is ready to act.
Retail media plays that role by turning intent into action. It gives retailers a way to reach high-intent shoppers with information that is locally relevant and immediately useful: whether a product is in stock nearby, available at the right price or backed by a timely promotion. In a shopping journey shaped by AI, those details are often what close the sale.
Turning AI‑led discovery into real‑world sales
To convert AI-led discovery into sales, retailers need to focus on three things.
First, they need to be visible at the moment of decision, not just the moment of discovery. As shoppers compare options across channels, brands must show up when consumers are actively weighing where to buy.
Second, real-time retail data needs to be connected to that experience. Inventory, local store availability and current pricing should not sit in separate systems if retailers want shoppers to move from interest to action quickly.
Third, promotions need to be more dynamic and more relevant. In an environment where shoppers can compare alternatives instantly, generic messaging is easier to ignore. Retailers need offers that reflect intent, timing and location if they want to convert consideration into purchase.
Just as importantly, the path from digital discovery to store visit must feel seamless. The less friction there is between finding a product and buying it, the more likely a shopper is to convert.
The future belongs to retailers who can bridge the gap
AI is reshaping the top of the funnel, but it is also making competition harder. The easier it becomes for shoppers to compare products, prices and retailers, the harder it becomes to win a sale on visibility alone.
The retailers that succeed will be those that can bridge discovery and decision making. AI may influence what shoppers consider, but retail media is what helps turn that consideration into action with the right offer, in the right place, at the right time.
Building for the Agentic Era: How AI and Identity Are Transforming Audience Data Activation
marketing 16 Apr 2026
Three forces of change are converging, creating a real sense of urgency.
The industry’s identity foundation is collapsing. Third-party cookies and shared device IDs aren’t viable anymore. Publishers without a robust first-party identity strategy are already feeling the revenue impact.
Privacy regulation isn’t slowing down and continues to shape how data can be collected, shared, and activated. It’s forcing decisions about consent management and data governance in the infrastructure, not as an afterthought.
And then AI is changing what's possible fast enough that organizations are racing to understand what it means for their workflows before the window for competitive advantage closes.
Brands and publishers are recognizing that these three aren't separate problems. The ones that understood early on that solving identity and privacy is the prerequisite for unlocking AI are in the best position. You can't build intelligent, automated workflows on top of fragmented or non-compliant data. They’re moving away from trying to survive cookie deprecation towards building the foundation that lets them win in an agentic ecosystem.
2. Optable describes its platform as enabling “agentic collaboration.” For readers who may be new to that concept, how does agent-based technology change the way organizations discover, activate, and collaborate around audience data?
Right now, most of the work in audience discovery, planning, and activation is manual. A publisher receives an RFP, a team member spends hours or days querying data, building a proposal, and setting up a deal. Each step requires human intervention.
Agentic collaboration lifts the burden, using AI agents to take over querying data, building audiences, negotiating parameters, and activating campaigns. This shift keeps a human in the loop for quality control rather than execution.
This means publishers can respond to more RFPs, faster, with richer and more tailored audience proposals, and buyers can discover and activate against premium inventory without waiting for a sales rep to call them back.
What makes this work is that the intelligence moves to where the data lives, rather than the data moving to where the intelligence is. That distinction matters enormously for privacy and for data ownership.
3. Many brands and publishers have invested heavily in first-party data, but unlocking its full value can still be challenging. What approaches are you seeing organizations take to turn these data assets into scalable revenue opportunities?
The gap between having first-party data and utilizing it is still wide for a lot of organizations. A recent survey we did with Digiday found that only 4% of publishers have more than half their audience data identifiable via first-party signals. So even the publishers who have invested are still in the early innings.
The organizations that are closing that gap fastest are doing three things:
They're building a proper identity foundation. They’re not just collecting emails. They’re actually resolving those signals across web, mobile, CTV, and audio into a unified identity graph that makes the data actionable at scale.- They're investing in the infrastructure to enrich and activate that data in real time. They’re moving away from manual processes because the programmatic market rewards speed and precision.
- They're putting AI agents to work. They're replacing manual processes by deploying agents to query their first-party assets, build custom audiences, and push them to activation platforms in minutes. They’re ready for the buyer agents that are already searching and evaluating their inventory.
When those pieces come together, they see first-party data working the way it’s supposed to.
4. Collaboration between brands, publishers, and partners has historically required complex integrations and data sharing. How are privacy-enhancing technologies and clean-room environments enabling more secure and efficient data collaboration across the ecosystem?
The old model of manual data sharing is slow, risky, and technically demanding on both sides.
Clean rooms addressed the privacy concern. Advertisers can bring their data into an isolated environment to match against publisher audiences. What comes out is insights and activatable audiences, not raw data.
What's new is that AI and agentic workflows can remove the remaining friction. Data collaboration can feed directly into agentic pipelines that can build audiences, model lookalikes, and automatically push to activation platforms. We call this process agentic collaboration. An advertiser can onboard into an Optable clean room in minutes, and we've had clients go from match to live campaign in hours.
When collaboration is that fast and frictionless, it can become a regular part of how deals get done.
5. Identity continues to be a central component of effective audience activation, particularly as marketers work across multiple channels like web, mobile, and connected TV. How should organizations think about building an identity strategy that supports both reach and accuracy?
The biggest mistake I see is organizations acting like they can resolve identity with a single solution. Different channels operate with different identity signals. Cookies and hashed emails for web, device IDs with consent limitations for mobile. CTV has IP and device IDs but might require server-side integration without the use of tags.
An identity strategy that only works for web leaves revenue on the table. A flexible, multi-channel approach requires a unified identity graph that can span all of those environments from a single platform, with a policy-driven resolution layer that selects the strongest available identifier for each channel and activation path.
Our ID Switchboard manages resolution across UID2, LiveRamp, ID5, Yahoo ConnectID, Epsilon, and others in real time, selecting the best option per environment without requiring publishers to retag or rewrite integrations when they add a new partner or channel.
Identity solutions that optimize purely for match rates often trade precision for scale in ways that hurt performance and confidence. The organizations building durable identity strategies are the ones treating accuracy and consent as non-negotiable. And they’re the ones that will expand their addressable reach over time.
6. Interoperability across platforms, identity frameworks, and marketplaces has become increasingly important in today’s advertising ecosystem. How does enabling collaboration across multiple partners and environments create new opportunities for marketers and publishers?
Historically, interoperability has been waylaid by too many solutions creating dependency rather than connectivity. Proprietary stacks made it hard to work with other partners or adapt as the ecosystem evolved. But interoperability is the key to a successful agentic marketplace.
Optable is a founding member of the Ad Context Protocol (AdCP), which is about enabling AI agents across the advertising ecosystem to communicate, transact, and optimize across organizational boundaries. No single platform should own the entire workflow; the connections between systems working together is where the value is.
A standard like AdCP facilitates agentic collaboration. It means publishers’ inventory and audience data become discoverable and transactable through AI-powered buyer workflows they couldn't access before. On the buy side, marketers can discover premium inventory and act on valuable first-party data from publishers in their own environments.
Agentic buyers are already looking and deciding who gets premium spend, and a publisher who isn't agent-ready is invisible to those workflows.
7. Looking ahead, how do you see AI-driven collaboration and agent-based workflows shaping the future of audience intelligence, monetization, and partnership models across the marketing ecosystem?
The workflows that define how advertising gets planned, transacted, and optimized today were designed for a world where humans painstakingly managed every step. That world is changing fast.
What I'm most excited about, and what I'm seeing early evidence of with our clients, is that agentic workflows go well beyond speeding up existing processes. Agents operating across publisher first-party data can surface audience insights faster than a human analyst, optimize in-flight against real signals rather than relying on post-campaign reports, and close the loop between planning and outcomes in ways that were impossible before.
Publishers with the right infrastructure can offer demand-side access to their audiences and inventory in ways that didn't exist a year ago. Advertisers can run real-time scenario modeling against publisher first-party data without a week of back-and-forth.
The best-performing relationships we're seeing aren't just vendor-client arrangements. We're working with partners like Scope3, Newton Research, and Chalice AI to build agents together that serve use cases none of us could address alone.
The organizations that will lead through this transition are starting with solid data infrastructure and identity foundations, because AI is only as good as the data it operates on.
AI Agents, Protocol Wars, and the Future of Shopping: Inside Agentic Commerce with Botify
marketing 16 Apr 2026
For people hearing the term for the first time: what is agentic commerce and how is it different from AI-powered shopping or a chatbot on a retail site?
Agentic commerce represents a significant shift in the future of retail.
Chatbots are reactive, working in a turn-based way: you say something, and the chatbot responds, reacting to user input in a fixed workflow. Meanwhile, AI-powered shopping might personalize recommendations or curate options, but you, the human shopper, make the actual browsing and buying decisions.
Agentic commerce is fundamentally different. The entire shopping experience rests with AI agents. It’s goal-based, powered by fully autonomous agents that own every step from discovery to purchase to returns and even subscription management. These agents don’t just respond like chatbots; they proactively plan, sequence steps, and crucially, use tools (like APIs) to execute. We’re not technologically there yet, nor are consumers, but this is where the retail industry is heading.
After OpenAI announced Instant Checkout and then appeared to pull back, what did that reveal about what’s actually feasible (technically, commercially) right now?
The industry is not quite there when it comes to fully autonomous, end-to-end transactions. The research and discovery phases are moving quickly—shoppers already use conversational AI for product discovery but the transactional side remains a major challenge.
With OpenAI’s Instant Checkout, it was clear that the discovery phase worked, but actual transactions didn’t scale. There are many real-world complexities, like real-time inventory, sales tax integration, and fraud detection—all the unglamorous but absolutely critical logistics for agentic commerce to work.
Plus, there isn’t enough adoption among retailers and merchants yet. Think about the launch of electric vehicles. The vision, direction, and interest were there, but the charging-station infrastructure wasn’t. It doesn’t mean the EV vision failed; it means we put the cart before the horse. For OpenAI, they proved people want and will use AI for discovery and research, but the underlying transactional and infrastructure stack needs to catch up before agentic commerce can really deliver on the full promise.
What else are you seeing emerge in the agentic commerce space?
Right now, we’re seeing what I’d call a “protocol war”—Open AI’s Agentic Commerce Protocol (ACP) vs. Google’s Universal Commerce Protocol (UCP). Both ACP and UCP are designed to be a universal language for agents to communicate with existing tech stacks, similar to how we have one standard for apps to communicate via APIs. The big players are jockeying for the technical framework that will win and be used by the masses.
Amazon, interestingly, is holding its cards close and hasn’t come out to support ACP or UCP or introduced its own protocol. They’re showing signs of leaning into agentic commerce, but crucially, they’ve also walled off a lot of their content, blocking outside AI platforms from finding and crawling it. If you don’t have access to Amazon, Target, Walmart, and a few others, you’re missing the heart of retail. Ultimately, the winning protocols will need buy-in (or at least access) from these giants to come out on top.
What parts of agentic commerce are most likely to stick over the next 12–24 months? How should retail marketing and e-commerce teams adapt their strategies to remain competitive?
AI-powered research and discovery isn’t going away—full stop. Consumers are embracing LLMs, and there’s no turning back.
But for brands, being crawlable to search and AI platforms is no longer enough. Behind the scenes, structured data and product feeds become even more critical. No matter which technologies win the protocol war, agents will need clean, structured, up-to-date data from retailers’ product feeds to perform discovery and (eventually) transactions.
Product feeds need to be highly structured, AI-optimized, and adaptable, or brands risk being left out of AI-driven recommendations. Think of feeds and structured data as the sitemaps of agentic commerce: they’re foundational and agnostic to who wins the standards battle. And this is why we just launched Botify Agentic Feeds, to automate the creation and delivery of AI-ready, protocol-compliant product feeds. With Agentic Feeds, retailers can ensure their products are always accessible and compelling to AI agents, whether by enriching feeds with reviews and Q&A content or by adapting immediately as protocols evolve. The brands that win will be those that become the trusted data source the agents turn to.
More broadly speaking, I also think AI visibility as a KPI will stick. Retailers want to know: “Is my brand or product appearing in AI-powered shopping experiences?” That’s different from traditional rank tracking, and it’s rapidly becoming top-of-mind for CMOs.
To remain competitive, brands must focus on infrastructure and feeds that power AI responses. What OpenAI tried to do with Instant Checkouts will likely not become a reality this year—the technology must mature, and consumer trust must grow. But so much can be done today to ensure your brand has the right foundation and competitive edge to succeed, no matter where the industry goes next.
If AI agents are crawling retail sites more aggressively, what new opportunities and risks does that create for marketing and e-commerce teams, and how should they adapt their crawling/traffic policies?
The explosion in bot traffic is massive. Retailers experienced +5.4x increases year-over-year in AI bot visits, and that’s on top of the massive growth we saw the year prior. Even more notably, for every one OpenAI user session, brands see an average of 198 bot crawls—for Google, it’s one session per six crawls. What that shows us is that discovery isn't necessarily happening on brand sites anymore. For marketing teams, the risk comes from failing to adapt measurement. Traditional metrics, like website traffic, no longer capture the full picture. As consumers discover products directly through AI platforms like ChatGPT, they may never visit your site until they’re ready to purchase. Without understanding where and when you appear in AI search, and without optimizing for it, you miss opportunities to strengthen visibility and generate new revenue.
More broadly, the upside is that AI platforms can be seen as a new discovery channel with massive potential. Every time a new discovery platform has emerged, like social in the 2010s, early adopters have won market share. The brands investing in high-quality, structured product data and robust site infrastructure today will be the ones that win as these agentic channels mature. Retailers should monitor bot policies, ensure their site is crawlable and data-rich, and use the opportunity to outmaneuver slower competitors.
Botify works with some of the world’s leading retailers and e-commerce brands. What are you hearing from your customers about the future of AI in retail? What are they most excited about, and what’s keeping CMOs and e-commerce leaders up at night?
Brand leaders are excited first by the promise of a new, measurable revenue channel and the chance to outpace competitors by adopting new, AI-driven models early. And the savvy CMOs realize that you can’t build visibility in AI search (AEO/GEO) unless you already have a strong SEO foundation. The promise is that investments in search visibility now compound across both human and AI/agentic channels. It’s exciting to know that so much of what we’re already doing by traditional means, like SEO, will still have gains tomorrow.
But there’s also a lot that keeps leaders up at night. Loss of control over the customer journey is one of the biggest concerns. For years, CMOs have painstakingly mapped personas and multi-step journeys. Now, the 12+ step journey is collapsing to one or two, and it’s not even a human making the purchase. Suddenly, the customer is an algorithm, using its own tools and reasoning to make decisions.
92% of AI-Assisted Shoppers Say AI Shapes What They Buy. Is Your Content Keeping Up?
marketing 16 Apr 2026
During the 2025 holiday season, generative AI and AI agents drove an estimated $262 billion in global retail revenue, accounting for roughly 20% of total sales. Traffic from AI search channels like ChatGPT and Perplexity doubled year over year. Shoppers referred from AI-powered search converted at nine times the rate of social media referrals.
What does this mean for how brands reach consumers? I see it playing out on three fronts:
● An evolution of existing channels
● The emergence of new agentic channels
● And the need for new marketing operational models
To gauge how far these shifts have actually reached consumers, Skai surveyed 1,000 U.S. shoppers about how they’re using GenAI throughout their shopping journey.
The implication for marketers is clear: the consumer journey is being rewritten in real time.
The habit gap is your window of opportunity
Consumers know AI can help them shop. 86% are aware they can use ChatGPT for shopping. 55% have knowingly used a retailer AI assistant like Amazon Rufus or Walmart Sparky. Nearly half (48%) used AI for product research in the last 30 days.
But 30% say they simply haven’t considered using AI for shopping. The barrier isn’t skepticism or distrust. It’s just not part of their routine yet.
The advertiser side tells a similar story. Skai and Stratably’s 2026 State of Retail Media survey found that 63% of advertisers are already using GenAI, but only 3% are seeing meaningful impact. Consumer behavior is moving, but advertiser readiness isn’t keeping pace.
That gap between awareness and habit represents an early-adoption window. As AI gets more embedded in shopping platforms and the experience gets smoother, that 30% will decline. Brands need to start building presence, test what increases visibility, and figure out who owns AI discoverability.
92% say AI research influenced their purchase.
When consumers use AI for shopping, they’re using it to get smarter before they buy. The top tasks cluster around information gathering: comparing products or brands (37%), finding deals and discounts (32%), checking reviews and pros/cons (30%), and finding product recommendations (28%).
And it’s working: 92% of those who used AI for product research say it influenced their purchase decision. Nearly three-quarters (73%) take further action after an AI recommendation, whether that’s asking follow-up questions, clicking links, or visiting retailer sites. AI is actively shaping what consumers consider and what they ultimately buy.
With this level of AI influence on purchasing decisions, optimizing for AI-readable content can’t stay a side project. Your product feeds, structured data, and brand information need to be built for machines as well as humans. That’s a workflow change, and potentially a new role. Someone needs to own the intersection of content, data, and AI discoverability.
Two-thirds of consumers click through. 29% of Gen Z buy directly.
The influence goes beyond research. Two-thirds of consumers (65%) have clicked from an AI tool directly to a retailer site. This isn’t passive browsing. Consumers are following AI recommendations to the point of purchase.
Gen Z leads here. They use AI for comparison shopping at 1.5x the rate of Boomers (44% vs. 30%). And 29% of Gen Z have made a purchase directly through ChatGPT’s shopping feature, compared to just 5% of Boomers. Shopping queries on AI platforms are growing faster than any other category, and referral traffic is converting at rates retailers cannot ignore.
In performance terms, AI is behaving like a high-intent referral channel layered above existing retail infrastructure.
That has implications for measurement. Most brands can track paid search, paid social and retail media performance with precision. Far fewer can measure how they appear within AI-generated results, or which product attributes and data signals influence recommendations.
That’s not a media gap. It’s a capability gap.
Replenishment-Heavy Categories Lead, Especially Among Gen Z.
When consumers show openness to AI-driven purchasing, it’s concentrated in predictable categories. Groceries and household essentials lead at 25% comfort with AI auto-purchase, followed by entertainment and media (23%), beauty (20%), and electronics (20%). Replenishment beats consideration. Categories with predictable repeat purchases see higher AI acceptance than those requiring personal judgment.
Gen Z accelerates the timeline. 67% are comfortable with AI auto-buying within set rules, compared to just 19% of Boomers. A majority of Gen Z say they would buy through AI instead of going to a retailer site directly.
Replenishment-heavy categories like grocery and household essentials will see AI-driven purchasing integrated into existing retail platforms first. If you’re in those categories, treat AI optimization with the same urgency you bring to search and retail media today. Start building your agentic playbook in these categories: test formats, learn what influences recommendations, and establish benchmarks.
Conclusion: As the consumer journey is being rewritten in real time, what can advertisers do?
The latest holiday season proved that AI is a present reality contributing hundreds of billions in revenue. Our survey reveals the nuance beneath the headlines: consumers are embracing AI as a research tool while remaining cautious about handing over purchase decisions. But that caution is evaporating fastest among Gen Z, which is a preview of where mainstream behavior is heading over the next three to five years.
The consumer data confirms what the broader market signals have been pointing to. Existing advertising channels are evolving as AI reshapes discovery and research. A new agentic channel is emerging with real, measurable activity. The marketing organizations that will win aren’t the ones bolting AI tools onto existing workflows. They’re the ones rethinking how their teams, data, and media strategies work together across all three fronts.
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