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AI Trusted Less Than Social Media and Airlines, With Grok Placing Last, Survey Says

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By Ty Pendlebury

More Americans are concerned about the loss of personal interaction from AI than they are about potential job loss.

Google Gemini is the most trusted AI platform among its competition, but many people still have concerns about the technology, according to an American Customer Satisfaction Index poll released Thursday.

In ACSI’s results, AI scored an overall customer satisfaction score of 73 on a scale of 0 to 100, which the authors noted was slightly below social media (74), airlines and mortgage lenders, but in line with energy utilities.

Of the five platforms mentioned in the survey, Google Gemini led with 76, followed by Microsoft Copilot (74), Claude and ChatGPT (both 73), and Grok and Perplexity (both 71). Meanwhile, TikTok (77) and YouTube (78) both scored better than the AI platforms.

Gemini is one of the most prolific AI services, with access via smart speakers, TVs, phones and computers, while most ChatGPT users access the AI tool via the ChatGPT website or mobile app, and Grok via social media platform X.

The ACSI poll found that 43% of respondents said reduced human-to-human interaction is their main concern, followed by job loss for future generations (37%) and their own job risk (31%), based on interviews with 2,711 US adults.

Baby Boomers were the most sceptical generation in the poll, with 35% saying they are very concerned about AI’s effects, compared to just 6% who view it extremely favourably.

Disconnect between AI adoption and perception

While platforms such as ChatGPT have up to 1 billion weekly users, there is still a disconnect between AI’s adoption and public perception of it, which is driven by concerns over privacy, the spread of misinformation and the loss of jobs.

“Consumers spent the last decade learning to distrust how social media platforms handle their data, and AI’s privacy scores suggest they’re carrying that scepticism forward,” said Forrest Morgeson, associate professor of marketing at Michigan State University and director of research emeritus at the ACSI.

21% reported an “extremely favourable” outlook toward AI, while an equal 21% said they are “very concerned about the consequences.”

These results were in line with another poll published by YouGov this week, which found that only 29% think the positive effects of AI outweigh the negative ones, while 36% think its net effects are negative.

It’s worth noting that more than half of the people interviewed (56%) had no recent experience with AI, but of the 44% who did, half of them use AI at least once a day, and the usage went up with people who earned over $100,000 a year.

Last month, an NBC poll suggested that AI was one of the least-liked things in America, but it was still more popular than the Democratic Party.

TV and home video editor Ty Pendlebury joined CNET Australia in 2006, and moved to New York City to be a part of CNET in 2011. He tests, reviews and writes about the latest TVs and audio equipment. When he’s not playing Call of Duty he’s eating whatever cuisine he can get his hands on. He has a cat named after one of the best TVs ever made. 

Feature image credit: Getty/SOPA Images

By Ty Pendlebury

Sourced from C NET

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How Agencies Survive AI, According To The Man Who Built Advertising’s Biggest Empire

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By Jodie Cook,

Summary

Sir Martin Sorrell advises agencies to adapt to AI by implementing five key strategies: compress creative production with output-based pricing, personalize content at scale, become validators of AI-generated work, drive radical efficiency by automating internal processes, and democratize knowledge within the organization.

The old way of running an agency is dead. If you own or operate a services business, whether that’s an agency, a consultancy, or any company where clients pay for your expertise, the ground is shifting under your feet. AI can create content faster and cheaper than your team. Clients expect more for less. Production lines that took weeks now take hours.

Agencies in the next ten years will look nothing like agencies in the last ten. The same is true for anyone in the knowledge economy who serves clients for a living.

I sat down with Sir Martin Sorrell at FII Priority Miami 2026 to ask him how agencies survive what’s coming. Sorrell is the founder and executive chairman of S4 Capital, the digital-first marketing services company operating under the brand Monks. Before that, he built WPP from a £1 million shell company into the world’s largest advertising group, with over £15 billion in revenue and 200,000 people across 113 countries. He ran it as CEO for 33 years, making him the longest-serving chief executive in the FTSE 100. If anyone knows what happens when an industry gets disrupted, it’s him.

I founded and sold a social media agency. Looking back at my team of 20 people, I can see which roles AI would have replaced and which ones would have become more valuable. Sorrell sees the same pattern playing out across the entire industry. When I asked him what agencies should do now, he gave me a 5-step process for staying relevant. This applies to any business where you trade expertise for money, and it starts with client work.

5 ways to keep your agency alive in the age of AI

Compress your creative production

AI is already cutting the cost and time of visualisation, copywriting, and content production. Sorrell was direct about the business model problem this creates. “We’re paid on time taken,” he said. “So you have to shift the model to output-based pricing, either on a unit asset basis or subscription.” The agency that charges by the hour while AI does the work in minutes will lose every time.

Audit how you charge. If your revenue depends on how long tasks take your team, you’re exposed. 

Maybe you’re the founder who bills 40 hours for a content package that AI helps you produce in 10. That gap is your vulnerability and your opportunity. Close it before your clients do the maths.

Personalise at scale

The second step is using AI to produce huge volumes of personalised assets. Where you once created one campaign and hoped it landed, now you produce dozens of variations tested against specific audiences. Sorrell sees this as an expansion of opportunity. More content, more formats, more touchpoints. The business model shifts again toward output pricing because the volume of work explodes.

Think about your own content output. If you’re still producing a single version of each deliverable, you’re leaving performance on the table. Use AI to create variations. Test them. Let the data tell you what resonates with each segment of your audience. The agencies and consultancies that think bigger about what they can offer, producing ten times the output at a fraction of the old cost, will win the clients who want results measured in numbers.

Become the validator

Media planning and buying will become totally algorithmic. Humans stay at two points in the process. The ideation at the start and the checking at the end. The middle, where junior staff once spent their days planning and placing, gets automated. The agency’s role becomes validation. Nobody will take a platform’s recommendation at face value. “You’re not going to say, I agree with the Google plan. You’ll want to check,” Sorrell said.

Position yourself as the person who scrutinises the machine’s work. If you run a consultancy or an agency, your value is in judgment, not in execution. The media buyer that is age 25? That role disappears. The experienced strategist who can look at an AI-generated plan and say “this is right” or “this is wrong” becomes irreplaceable. Build that skill in yourself and your team.

Drive radical efficiency

Sorrell described a joint venture with Nvidia, AWS, and Adobe on outside broadcasting using AI. The result was an 80% reduction in cost. That number is already a reality. Every service business has processes that cost more than they should because humans have always done them. AI changes the equation.

Go through your operations and find where the money leaks. Identify the tasks your team does that a machine could handle faster. Maybe it’s reporting, maybe it’s scheduling, maybe it’s the first draft of every deliverable. The savings are huge. An agency that operates at 80% lower cost on its production can either increase margins or pass savings to clients and win more work. Both options beat standing still.

Democratise knowledge

Sorrell’s fifth step was the one that most people overlook. He talked about using AI to spread knowledge across an organisation so that silos break down. He pointed to Jensen Huang running Nvidia with 50 direct reports and no one-to-one meetings. “AI spreads knowledge as long as you enfranchise people and give them access,” Sorrell said. “You get rid of the silos.”

Most agencies and service businesses hoard information in the heads of senior people. Junior team members wait for briefings that come too late. AI changes this. Give your team access to shared knowledge systems. Let AI summarise client histories, surface past work, and distribute learning across the company. The business that shares what it knows internally will move faster than the one that keeps everything locked in the founder’s head. Stop controlling information and start building systems that make everyone smarter.

How the man behind advertising’s biggest empire says you stay relevant in the age of AI

Sorrell told me that reduced employment is coming, but the number won’t be the 95% that some predict. The agencies and businesses that survive will be leaner, faster, and built around these five steps.

Compress creative production. Personalise at scale. Become the validator. Drive radical efficiency. Democratise knowledge. Whether you run an agency, a coaching practice, or a consultancy, the same process applies. Adapt now or spend the next few years watching someone else take your clients.

Feature image credit: SIR MARTIN SORRELL

By Jodie Cook,

Find Jodie Cook on LinkedIn. Visit Jodie’s website.

Sourced from Forbes

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How AI is making ad creative faster — and more fair

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By Nikhil Nanivadekar

The shift toward AI is not just about producing ads faster, it’s about giving creative capability to everyone, writes Amazon’s Nikhil Nanivadekar.

The following is a guest piece written by Nikhil Nanivadekar, principal engineer, consumer ad experiences at Amazon. Opinions are the author’s own. 

Advertising has always celebrated creativity, but for many brands, it came with real constraints. Big ideas required big budgets, specialized teams and long production cycles. Speed was a luxury, and experimentation carried risk. For too many businesses, the gap between a great idea and a great ad felt impossibly wide.

Artificial intelligence is breaking down these barriers. When the barriers to experimentation fall, creativity rises and more innovative storytelling becomes possible for everyone. This is not a distant promise. It is happening right now, across industries and businesses of every size.

The rules of advertising are being rewritten in real time. According to the Marketing AI Institute’s “2025 State of Marketing AI Report,” 74% of marketers now say AI is very important to their success over the next 12 months, up eight percentage points from 2024. That momentum is only accelerating.

This shift is not just about producing ads faster. It is about giving creative capability to everyone. Mom-and-pop shops can now be seen and heard in ways once reserved for the biggest brands. The challenge has never been a lack of ideas. Small businesses have always had compelling stories to tell. The barrier has always been bringing those ideas to life at a level that competes for attention. AI is changing that equation, putting sophisticated creative capabilities in the hands of businesses of all sizes and letting their stories finally shine.

Amplifying creativity and agility

One of the biggest shifts is how creative work gets produced. Small and mid-sized brands that once relied solely on simple product-shot ads now use AI to transform product images into lifestyle scenes, convert detail pages into audio ads and develop simple ideas into full TV commercials, all with a single prompt. What once required weeks of production planning can now happen in minutes.

But agentic AI tools are changing the game, letting teams test wildly different approaches in minutes instead of weeks. Customers report spending less time on administrative work and more time on big ideas.

For example, when Molly’s Suds set out to create a streaming TV ad, they didn’t start with a storyboard, an agency brief or a production crew. Instead, they experimented by using Creative Agent — Amazon Ads’ new conversational, agentic AI tool.

Creative Agent analysed the images, product copy, reviews and brand details from the product detail page to understand Molly’s Suds’ tone, customer value proposition and visual style. From there, the tool guided the advertiser through brainstorming, script development, scene planning, voice over selection and final video production.

This is one example of AI tools turning a difficult and expensive process into a streamlined, exciting new creative possibility.

Democratizing the advertising process

While increased speed and efficiency delivered by AI is important, it’s the access and breaking down barriers that is perhaps the most important change AI is driving.

Brands once side-lined by constraints are now stepping into creative spaces as active players, bringing fresh perspectives and diverse voices that make advertising richer for everyone. This momentum is visible among Amazon sellers themselves. By the end of 2024, nearly one in five Amazon sellers were using AI-powered creative tools, with the majority being small businesses discovering for the first time what it feels like to compete at the highest level.

The impact is not just philosophical, it is measurable. McKinsey’s “State of AI in 2025” report shows that revenue gains from AI appear most commonly in marketing and sales. We believe broader access to creative capabilities translates quickly into real business outcomes. When more businesses can tell their stories effectively, everyone wins.

AI-powered creative tools are now foundational for brands of all sizes. They accelerate production, enhance storytelling and deliver a level of sophistication that once required massive budgets and large teams. But beyond the efficiency gains and the impressive statistics, what excites me most is what this means for the future of advertising itself. The result is a more level playing field, one where imagination becomes the most valuable currency, and where any brand with a great idea and a great story has a real chance to be heard.

Feature image credit: peshkov via Getty Images

By Nikhil Nanivadekar

Sourced from MarketingDive

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Why Social Capital And Human Networks Are Important In The Age Of AI

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By Cathy Hackl

In an era where artificial intelligence is reshaping industries at an unprecedented pace, the value of human connections and social capital has never been more critical. Take the story of retired Army Sergeant Major, Michael Quinn, a former senior military leader who transitioned into a successful entrepreneurial and executive career. Leveraging LinkedIn, Quinn built a robust network that not only facilitated his remarkable transition from the military to the private sector but also skyrocketed his success and positioned him as one of the world’s leading experts on leadership and social capital. His story is a testament to the transformative power of social capital and human networks in today’s fast changing digital landscape.

The Essence Of Social Capital And Why it Matters

Social capital refers to the networks of relationships among people who live and work in a particular society, enabling that society to function effectively. In the professional realm, social capital is the currency of influence, built through trust, mutual respect, and shared experiences. It’s what turns a simple introduction into a long-term professional relationship and a casual conversation into a lucrative business opportunity.

The Relevance Of LinkedIn

In this digital age, platforms like LinkedIn have become indispensable tools for building and maintaining social capital. LinkedIn offers a dynamic space where professionals can showcase their expertise, connect with peers, and discover new opportunities. It’s a platform that has proven its worth time and again, not just for job seekers but also for thought leaders and executives looking to expand their influence and impact.

“LinkedIn is no longer just a networking tool. It is the most powerful personal branding platform in the professional world,” highlighted Maha Abouelenein, Founder & CEO of Digital and Savvy and personal branding expert . “ We’re entering a world where job titles matter less, AI can mimic expertise, and a single moment can define or destroy a reputation.”

For Abouelenein, the real currency isn’t visibility. It’s credibility, and the leaders winning on it aren’t necessarily the most experienced in the room. They are the clearest, the most consistent and the most intentional.

With more than 1.2 billion members across over 200 countries and territories, LinkedIn remains the world’s largest professional network and one of the most dynamic platforms for building modern social capital at scale. It has evolved far beyond a digital résumé repository into a global arena for ideas, leadership, and opportunity. Within that ecosystem, LinkedIn Top Voices, an exclusive group of professionals recognized for consistent thought leadership and meaningful contributions, represent a small but powerful cohort shaping conversations across industries. Their presence highlights something important: in a network of this magnitude, credibility, insight, and authentic engagement rise to the top. In the age of AI, platforms like LinkedIn don’t just connect people, they amplify trusted voices and accelerate influence.

According to Quinn, Chief Growth Officer of Tenova LLC, HireMilitary and a 3x Linked Top Voice, there are three things that make LinkedIn incredibly valuable.

“First, LinkedIn is the social media platform where industry decision makers spend their time,” added Quinn. “Second, LinkedIn focuses on trust & safety, removing hostile comments from your post before you see them and third you can choose your desired audience by connecting strategically with the people you want to reach and then sharing information that would interest them.”

Building Success Through Networking

Sandy Carter is another shining example of the power of social capital in action. Recently recognized as a LinkedIn Top Voice in AI Tech, Carter, who happens to be a Forbes Digital Assets contributor, leveraged her network to amplify her influence further and drive her career forward even more. Despite already having global recognition as a tech leader, with leadership roles at IBM, AWS and now at Unstoppable Domains, Sandy has used LinkedIn strategically.

Her approach to LinkedIn goes beyond simply posting content. Sandy treats the platform as a two-way conversation, consistently engaging with her community, elevating other voices, and sharing lessons from her decades of building multi-billion dollar businesses. It is this intentional, relationship-first mindset that has set her apart.

“Your network is your net worth, but only if you invest in it authentically. I have always believed that social capital is built by giving first: sharing knowledge, opening doors for others, and showing up consistently. LinkedIn gave me a platform to do that at scale, and the returns have been extraordinary, from partnerships and speaking invitations to a global community of women I am proud to champion,” said Sandy Carter, Chief Business Officer and Founder.

Her journey underscores the importance of actively engaging with and contributing to professional communities. By doing so, she not only expanded her reach but also created a platform to share her insights, thereby strengthening her social capital.

Today, Sandy’s influence extends well beyond corporate boardrooms. As the founder of Unstoppable Women of AI and Blockchain, she has trained over 55,000 women across 92 countries in emerging technologies. She also hosts Marketing Companion by Sandy Carter, a top 1% podcast and winner of two marketing awards, where she shares actionable insights on AI and marketing leadership. Her LinkedIn presence has become a launchpad for all of these efforts, proving that when social capital is invested with purpose, it can create impact on a global scale.

Social Influence: A Top Skill for the Future

According to the World Economic Forum’s Future of Jobs report, leadership and social influence are among the fastest-rising skills in the global economy, signalling a structural shift in what organizations now value. As AI systems take on analytical, operational, and even creative tasks, competitive advantage is moving away from pure technical execution and toward distinctly human capabilities. The leaders who stand out are not simply those who understand technology, but those who can guide people through transformation, build alignment in moments of uncertainty, and translate complexity into clarity.

Social influence, in this context, is not about popularity or personal branding. It is about trust at scale. It is the ability to convene the right people, shape strategic conversations, foster collaboration across industries, and mobilize networks toward action. Social capital provides the network foundation, while social influence is the ability to activate and direct that network with credibility and purpose. In an AI-accelerated world where change is constant, influence becomes infrastructure. Those who can cultivate meaningful relationships and activate their networks thoughtfully will not just adapt to disruption, they will help define what comes next.

Leadership and social influence are some of the most crucial skills for the future, emphasizing its importance in navigating the complexities of the modern professional landscape. As automation takes over routine tasks, the ability to influence, lead, and connect on a human level becomes a defining factor for success.

The Human Moat In An AI World

As we navigate the complexities of AI acceleration, the role of social capital cannot be overstated. It’s an essential component of thriving in the modern professional landscape. The stories of Michael Quinn, Maha Abouelenein and Sandy Carter are powerful reminders that, even in a world increasingly dominated by technology, the human element of connection remains irreplaceable. Investing in social capital is not just a strategy, it’s an essential component of thriving in the modern professional landscape.

As the age of AI continues to unfold, those who master the art of building and nurturing social capital will find themselves at the forefront of innovation and leadership. Embracing the power of human connection is not just about staying relevant—it’s about leading the charge in a future where technology and humanity converge.

As the age of AI continues to unfold, those who master the art of building and nurturing social capital will find themselves at the forefront of innovation and leadership. Embracing the power of human connection is not just about staying relevant—it’s about leading the charge in a future where technology and humanity converge.

Feature image credit: Michael Quinn

By Cathy Hackl

Find Cathy Hackl on LinkedIn. Visit Cathy’s website.

Sourced from Forbes

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In The Age Of AI, Your LinkedIn Profile Is Only Half Your Brand

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By William Arruda

In the early years of personal branding, before LinkedIn became the default professional destination, I encouraged clients to create their own personal websites. It was a powerful way to introduce yourself to the people who are checking you out. Because you own your website, you control the narrative, structure, and context.

LinkedIn Emerges As Your Professional Home Base

When LinkedIn officially launched in 2003, it gradually evolved into a powerful platform for communicating your experience, credibility, and point of view. It came with some big advantages over having your own site:

  1. An instant network. LinkedIn is the de facto professional social media platform, providing a community of people eager to engage with you.
  2. Ease of creation and updating. Building and maintaining a website takes more effort than updating a profile on an established platform.
  3. Budget. There’s no need to pay for your own design, hosting, maintenance, and updates.

LinkedIn also helped normalize an important idea: if you are serious about your career, you are responsible for managing it. LinkedIn became the online home for your résumé, your network, and your professional reputation. It was the sole professionally focused social media platform. Over time, it became the place to tell the world who you are and to learn about other professionals. That’s still true today. Often, when people want to learn about you, they open a browser, go directly to LinkedIn, and type your name in. And even if they start their research with Google, your profile shows up near the top, so it’s usually what gets clicked. That has been the case for over two decades. But now, there’s a new game in town. You’ve probably heard of it. It’s called AI.

AI Can Play A Big Role Than LinkedIn In How You Are Perceived

Increasingly, your first impression may be delivered by an AI-generated summary instead of a direct visit to your profile or website. For years, when people wanted to learn about you professionally, LinkedIn was often the first stop. And if they googled you, your LinkedIn profile was among the top links. Today, though, if someone searches your name on Google, the first thing they may see is an AI-generated overview before any traditional links. That matters because a large share of Google searches now end without a click. 58.5% of U.S. searches and 59.7% of EU searches resulted in zero clicks. In many cases, the searcher decides the summary gave them enough to move on.

Here’s the challenge: AI systems tend to draw more confidently from content that is openly accessible on the web. Because much of LinkedIn lives inside a walled garden, it may be less visible and less useful to AI systems than content published on your own website. Google still operates at a much larger search scale than ChatGPT, even as AI search behaviour grows quickly. LinkedIn still has more than a billion members and remains a powerful place to build visibility, share ideas, and strengthen professional relationships. But it has a limitation in the AI era. Much of its value lives inside a platform that AI systems cannot access as easily or as fully as the open web.

The New System Requires A Focus Both On Web Search And AI Search

The answer is not LinkedIn or AI. It is LinkedIn and the open web. That’s pretty much how most technological advances happen. When radio arrived, newspapers did not disappear. When television arrived, radio did not vanish. New channels rarely erase old ones. They change how attention gets distributed. As AI strategist Matt Strain puts it, “You need to make sure your content is visible to both Google and AI. Strain added, “If your best work lives inside walled gardens (LinkedIn, newsletters, private communities, paywalls), it can vanish from the AI research cycle. In addition to focusing on LinkedIn, publish a searchable home base on your own website, then earn third-party mentions (interviews, podcasts) that validate your credibility.” That’s the strategic shift many professionals have not yet made. They’re polishing the version of themselves that lives inside LinkedIn while neglecting the version of themselves AI can actually read, summarize, and cite. As AI becomes even more prevalent, it’s essential that you post valuable, relevant content to get it referenced in AI summaries.

The Real Advantage: LinkedIn Plus An AI-Readable Home Base

When you manage your digital identity as an ecosystem, you increase the odds that no matter how someone searches for you, they find a clear, credible, and compelling picture of who you are and how you deliver value. Your LinkedIn profile may still rank highly for your name, but if an AI-generated summary appears first and satisfies the searcher, they may never click through to it. That is why zero-click behaviour matters so much now.

Having your own website may seem like overkill or a bit self-centered, but it’s actually key to being visible, known, and found in the age of AI. Strain explained, “Traditional SEO trained us to think in keywords. AI answer engines behave more like a researcher. They look for clear explanations and narrative context that they can summarize with confidence. One of the simplest formats is structured Q&A with a short story behind the answer. Focus on making your expertise easy to extract.” Storytelling is key, and your website allows you to position yourself with this type of content. The good news is that building a strong personal website is simpler than most people think. Follow these steps:

  1. Buy your domain name.
  2. Define your brand identity system – the colours, fonts, and imagery that convey your brand differentiation.
  3. Decide if you want to do it yourself or hire someone.
  4. Create a homepage that clearly states who you help, how you help, and what makes you different.
  5. Add a strong About page written in natural language, not résumé language.
  6. Include proof: media mentions, testimonials, speaking topics, articles, books, podcasts, and case studies.
  7. Publish a few pages or articles that answer the questions people actually ask about your expertise.
  8. Make your content easy for both humans and AI to understand with clear headings and an organized structure. Avoid business jargon.
  9. Link your site to your LinkedIn profile and link your LinkedIn profile back to your site.
  10. Keep it current so both search engines and AI systems find fresh signals of credibility.

Use LinkedIn And Your Personal Website To Increase Your Visibility

Having your own website gives you something LinkedIn cannot fully give you: control over structure. You decide the pages, the questions you answer, the proof points you feature, and the language that explains your value. That makes your expertise easier for both search engines and AI systems to interpret. LinkedIn remains the best platform for building relationships, showing activity, and signalling professional relevance in real time. Your website is not a replacement for that. It is the foundation beneath it. For years, LinkedIn was your most important professional first impression. In the age of AI, it is still important, but it is no longer enough. To be accurately understood and easily found, you need both a strong LinkedIn presence and an AI-readable home base on the open web.

Feature image credit: Getty

By William Arruda

Find William Arruda on LinkedIn. Visit William’s website.

William Arruda is a keynote speaker, bestselling author, and personal branding pioneer. He works with leaders to help them deliver magnetic, mesmerizing, and memorable presentations in-person and online.

Sourced from Forbes

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AI Is Collapsing the Shopping Funnel

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BY DHRUV PATEL

For most small and mid-sized (SMBs) e-commerce businesses, the hardest part of growth today isn’t building a better checkout. It’s adapting to how radically shopping behaviour has changed.

A few years ago, researching a major purchase might have taken 30 minutes across multiple tabs—comparing prices, reading reviews, checking availability. Today, that same research happens in a single ChatGPT prompt: “Find 10 stores selling a PlayStation 5, compare bundles, and tell me the best deal based on my preferences.”

AI-driven search has compressed what used to be a predictable funnel into seconds. And when the funnel collapses, checkout stops being just a conversion point. It becomes the only moment where you still have control.

The funnel still exists, but it’s collapsing fast

On a basic level, commerce hasn’t changed. Customers still learn, decide, and buy. What has changed is speed.

AI-driven discovery has compressed research cycles that once required multiple searches and comparisons. Payments have compressed too. Wallets, tokenization, and one-tap checkout have removed nearly all friction from buying.

Customers now arrive at e-commerce sites from everywhere at once—AI search, social feeds, creator links, comparison tools—often making decisions in seconds. More channels mean less control over how they get to you.

But that fragmentation also creates a new advantage.

Checkout is no longer the finish line. It’s the one moment where every signal finally converges and where growth can be won or lost.

This shift is driving what many operators describe as distributed commerce: a model in which buying decisions, monetization, and growth are shaped across channels, brands, and platforms, then executed in a single moment at checkout.

Why context now drives revenue

Historically, most commerce systems treated checkout as context-free. Once a shopper reached the cart, intent was assumed to be fixed.

That assumption is becoming expensive.

How a customer arrives matters. A shopper who compared prices across multiple sites is likely price-sensitive. Someone coming from social may be inspiration-driven. A customer landing from AI search may already be optimizing for speed or value.

In distributed commerce, upper-funnel signals must shape what happens at the transaction moment—what products appear, which offers surface, and how monetization works.

Delivering the same experience to fundamentally different buyers doesn’t just leave revenue on the table. It weakens trust.

For SMBs, this means a shift in focus

For small and mid-sized businesses, distributed commerce isn’t about doing more across every channel. It’s about concentrating leverage where control still exists.

Instead of trying to master every acquisition surface, the priority becomes making smarter decisions at the transaction itself. Instead of treating checkout as the end of the journey, it becomes the place where signals are interpreted and acted on in real time.

The shift isn’t about complexity. It’s about focus.

Infrastructure still matters more than headlines

AI dominates headlines, but infrastructure determines whether distributed commerce actually works.

Three layers are becoming essential:

1. Product and catalogue infrastructure: It enables brands to offer relevant complementary products without owning inventory, fulfilment, or returns. Shared catalogue models allow adjacent products to appear naturally at checkout while fulfilment remains distributed.

2. Payments infrastructure: This has become table stakes. Embedded wallets and tokenized cards make transactions fast and invisible, regardless of who fulfils the order.

3. Data infrastructure: This allows businesses to collaborate without exchanging raw customer data or exposing competitive intelligence. Signals move, ownership doesn’t.

Without these layers working together, relevance breaks down at the exact moment it matters most.

Measurement in a post-impression world

As commerce and media converge, impressions matter less than outcomes.

Growth leaders are increasingly focused on a simpler question: Would the purchase have happened anyway? Customer acquisition cost, unit economics, and incrementality are replacing attribution theater.

Channels embedded inside the transaction are uniquely positioned to answer whether they truly influenced behavior—especially when you can see how long customers spent on your site and whether they were returning customers.

The real competitive advantage

The biggest obstacle to adopting distributed commerce isn’t technology—it’s adaptability.

Rigid organizations struggle to test new formats, rethink data foundations, or change how monetization works. More resilient companies experiment continuously, refining their systems before competitors force the issue.

The long-term opportunity is clear: Blur the line between advertising and commerce while preserving trust and economics. In distributed commerce, ads function as utility and relevance becomes native.

For founders and operators, the takeaway is straightforward. The next generation of commerce platforms won’t be built around pages or funnels. They’ll be built around context, connectivity, and collaboration. AI has already changed how customers arrive. Now it’s time to change what happens when they do.

Feature image credit: Getty Images

BY DHRUV PATEL

Sourced from Inc.

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Customer reviews become a key battleground as AI revolutionizes product discovery

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By Julia Waldow

Brands are chasing after a traditional, but powerful tool in the age of AI-powered search engines: customer reviews.

Platforms like ChatGPT and Perplexity are reshaping how customers discover products online — and data shows they’re making recommendations based on everything from key search terms to user feedback.

When asked if reviews seem to impact whether his products are recommended by AI agents, Eric Edelson, CEO of direct-to-consumer tile company Fireclay Tile, said, “One million percent.”

“Our reviews are really in-depth, which I think plays super well to [large language models],” he added. A quick test by Modern Retail found that ChatGPT lists Fireclay Tile as the “best DTC tile company in California, based on reviews.”

Now, to better pop up in AI search results, brands are placing a bigger emphasis on getting more customers to leave reviews. In recent months, for instance, dog food brand Pawco started offering customers $20 off if they left a review after their third order. Meanwhile, Fireclay Tile has become “more deliberate” about soliciting reviews, Edelson said. The company sends customers personalized notes asking for reviews, and it holds contests among employees to see who can drum up the most reviews.

This is important, as more people are turning to AI engines as a trusted source for product research. In the U.S., ChatGPT users are making more than 84 million shopping-related queries weekly, per Stackline. “These AI engines are very, very good at doing web search and discovery for you,” Adam Brotman, a former Starbucks and J.Crew executive and the co-founder of the applied AI company Forum3, told Modern Retail. “They present you with an answer to your shopping questions — price and features and reviews — and save you time.”

Reviews provide a trove of crucial information to LLMs. But some platforms are more open than others about giving AI engines access to reviews. In 2024, OpenAI signed a deal with Reddit to “bring Reddit data to ChatGPT,” which would include users’ posts about products and services. But, as Modern Retail has reported, Amazon has quietly blocked OpenAI-related bots from crawling Amazon.com content, including reviews.

Reviews as trust drivers

Pawco, which was founded in 2021, tends to earn new customers from paid advertising, as well as Google and customer referrals, said Ryan Bouton, vp of growth for Pawco. Pawco sells fresh-food subscription meals and treats and is known for its salads and protein bars for dogs. It’s launching a new sub-brand called Genius Dog, which curates products around monthly themes, such as “movie night” or “tea party.”

As Pawco looks to capture more market share, sourcing reviews has “definitely grown in importance,” Bouton said. “People don’t just switch their dog’s food unless they really trust [the product],” he explained. “So, reviews in that realm have become important to us. … And, in the last six months, AI search optimization has become a big focus of what we’re trying to unlock, especially as a smaller brand who’s fighting against bigger brands.”

Pawco, though, is also finding that the timing of reviews is important. The company doesn’t ask for reviews right away, knowing that pets and their owners need weeks to adjust to its products and see the benefits. Pawco usually solicits reviews after two weeks for a standalone order, or, for a subscription product, after three reorder cycles.

Pawco’s $20-off-for-a-review deal only applies after a customer receives their third order. “We figure if someone has stuck with us for three orders, it shows our investment and their loyalty,” Bouton said. Two or three years ago, Pawco “definitely wouldn’t have been offering that $20 off,” he said. But now, he explained, “We’re a lot more focused on reviews as being a core part of our strategy.”

Nik Kacy, who owns their own gender-free footwear and accessories brand, also finds that reviews are helpful for brand reach. “We send out automated requests for reviews,” they told Modern Retail. “I definitely try to tell folks [to write a review], because I don’t really have an ad budget. Everything’s by word of mouth.”

At Pawco, customers are sharing that they’ve learned about the brand by searching terms like “best food for dogs with allergies” on AI search engines. “We’ve seen our first orders coming in and growing month over month, from ChatGPT and other AI search platforms,” Bouton said. “That’s where consumers are discovering brands now, the same way they used to be discovering brands on Google.”

Incentives and encouragement

Customer reviews are crucial, but not common, in the design world, said Fireclay Tile’s Edelson. The company serves clients from Starbucks to Salesforce to home owners.

“Reviews are insanely powerful, and they add credibility and assurance,” Edelson said. But compared to, say, the restaurant industry, reviews “don’t happen as much” with interior design and construction, he explained. “Professionals [like contractors or interior designers] are less likely to leave reviews,” he said. “And for homeowners, [a remodel] is such a drawn-out experience that, by the time they finish, … it’s the last thing on their mind. We have to encourage people to post reviews.”

Knowing that AI engines are pulling from reviews, Fireclay Tile is now stepping up how it sources reviews. “I’m kind of obsessive about asking for the review,” Edelson joked. The company has a Slack channel called #ClientSuccess, in which salespeople share positive customer anecdotes. “My response is, ‘Awesome! Please ask for a review,’” Edelson said. Edelson writes notes to every customer, soliciting reviews, and Fireclay Tile periodically makes donations on a customer’s behalf, in exchange for a review. In the past, it has given money to support national parks.

Internally, Fireclay Tile is providing incentives for employees to solicit reviews, too. It runs different contests around who can get the most and best reviews. Recently, Fireclay Tile acquired Fox Marble, a countertop installation company, and offered team members $25 if they got a five-star review from a customer. Edelson also maintains a spreadsheet of ratings and reviews from competitors, to see how Fireclay Tile stacks up.

However, brands are finding that it’s not just reviews that are important — it’s also where customers are posting them. While companies are featuring reviews on their websites and social media channels, AI search engines are increasingly pulling from reviews on public forums like Yelp or Tripadvisor. It’s a bit of a return to where things were 10-15 years ago, Edelson said. His company asks people to write reviews on all types of forums, to up their odds of getting surfaced.

“We have this incredible rich content on our site that LLMs are seeking, but also, the Reddits and Googles are very powerful again,” Edelson explained. “So, we’re always bouncing back and forth trying to get people to leave multiple reviews in different places. We’re just trying to find the wins, where we can.”

Feature image credit: Ivy Liu

By Julia Waldow

Sourced from ModernRetail

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Failure: The Secret Sauce In Successful Gen AI Strategy

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By Dr. Gleb Tsipursky

Leaders who replace blame with post-mortems and psychological safety are seeing stronger AI adoption, as teams experiment more freely and turn failed pilots into long-term business gains.

Generative AI rewards those who embrace constant iteration. Instead of fearing errors, treat them as essential data. Every strange output reveals how the system actually thinks, providing the edge you need to master the tool.

AI offers the rocket fuel that propels innovation forward and enables organizations and teams to overcome challenges and manage risks. This is especially true in a field as unpredictable and transformative as Gen AI. When we talk about innovation, we must acknowledge that failure is not the opposite of success, but a crucial part of it.

Gen AI solutions, by their nature, demand iteration, testing, and refinement. Not every experiment will hit the mark immediately, if at all.

De-Stigmatizing Failure in Gen AI Strategy

The traditional corporate landscape often views failure through a punitive lens. This leads to fear and risk-averse behaviour. Employees who experience setbacks might worry about career repercussions, public embarrassment, or losing credibility.

This mindset is a death knell for innovation, suffocating the exploratory nature of Gen AI work, where trial and error are not just common, but essential.

Research by McKinsey shows that companies cultivating a culture of innovation and embracing failure greatly outperform their peers in implementing technology, with 21% of weak innovators succeeding in digital transformations compared to 45% of strong innovators. This underscores the undeniable link between embracing failure and achieving tangible business success.

So, how do we dismantle this culture of fear? We need a seismic shift in how we perceive failure, starting at the top.

Leaders must actively cultivate an environment where calculated risk-taking is not just tolerated, but celebrated. Employees need to know that their careers won’t be derailed by experiments that don’t pan out. Instead, the focus should be on the insights gained from every experiment, regardless of the outcome. Each “failed” project is a treasure trove of data.

Consider a recent engagement where I consulted for a mid-sized regional retail chain struggling to personalize its marketing efforts. This company, with around 500 employees and $200 million in annual revenue, was eager to leverage Gen AI to improve customer engagement.

Initially, they were hesitant. The leadership team was concerned about the potential for wasted resources and the stigma of failed projects.

We began by implementing a small-scale pilot project using Gen AI to tailor email marketing campaigns. The first few attempts fell short of expectations. The personalized content didn’t resonate as anticipated, and click-through rates remained stagnant at a measly 2.5%.

However, instead of viewing this as a failure, we treated it as a learning opportunity. We conducted a thorough analysis and discovered that the initial customer segmentation model was too broad, resulting in generic messaging that didn’t appeal to specific customer interests.

We also found that the tone of the AI-generated content didn’t align with the brand’s voice, with a formality score 15 points higher than their usual communications.

The Power of Post-Mortem Analysis for Gen AI Strategy

When an experiment doesn’t go as planned, the knee-jerk reaction might be to find someone to blame. This is counterproductive and stifles learning. A constructive approach involves a detailed post-mortem analysis.

What went wrong? Why did certain methods fail? How can we adjust our approach in the future? These questions are not about assigning blame, but about extracting knowledge.

We’re not looking for scapegoats; we’re searching for understanding. Were there gaps in the data or model training? Did we misalign the Gen AI tool with the business problem we were trying to solve?

Systematically answering these questions creates a roadmap for future success. This analysis also helps build institutional knowledge, ensuring that the entire organization benefits from individual teams’ learnings.

In the case of the retail chain, the post-mortem analysis of the initial Gen AI marketing campaign revealed critical insights. We refined the customer segmentation model, focusing on more granular data points like purchase history, browsing behaviour, and demographic information, increasing the number of segments from 10 to 25.

We also fine-tuned the Gen AI model to generate content that better reflected the brand’s personality, adjusting the formality score down by 15 points to match their existing brand voice.

The subsequent campaigns, informed by these learnings, showed significant improvement. Within three months, the retailer saw a 25% increase in click-through rates, rising from 2.5% to 3.125%, and a 15% rise in conversion rates, jumping from 1% to 1.15% from their email marketing efforts. They also received a 10% increase in positive customer feedback regarding email content relevance.

This translated to a noticeable uptick in sales directly attributed to the Gen AI-driven campaigns, with an eventual 8% increase in sales from email marketing.

This experience underscored the importance of embracing failure as a learning opportunity. By openly analysing what went wrong and adjusting our approach, we were able to unlock the true potential of Gen AI for this organization.

It’s worth noting that the organization saved an estimated $50,000 in marketing costs within six months by switching from broad marketing campaigns to more targeted Gen AI driven campaigns. And that was the first project of many, which overall improved their bottom line by over $300,000 in a year. Such a case study clearly illustrates how real businesses gain real, financially-relevant benefits from applying the approach of viewing failure as a learning opportunity when implementing Gen AI.

Building a Gen AI Strategy of Shared Learning and Resilience

An open and transparent approach to failure helps facilitate shared learning. When failures are openly discussed and analysed, it allows teams to learn from one another’s mistakes, accelerating the organization’s overall learning curve.

Instead of burying failed experiments, organizations should create forums where teams can present their findings, both successful and unsuccessful, to the broader group. This practice democratizes the learning process and reduces the likelihood of repeated mistakes, while simultaneously creating trust and openness.

Leaders can also encourage peer support networks, where employees involved in different Gen AI initiatives can offer advice and share lessons learned from their own successes and failures. This creates a communal learning environment, where the responsibility for Gen AI success is shared, rather than resting solely on individual teams.

These forums also allow for cross-functional collaboration, where failures in one department can provide insights that benefit another. This cross-pollination of ideas can lead to new approaches and methods for leveraging Gen AI that would not have emerged if failures were hidden or minimized. Moreover, organizations can take a proactive approach by building controlled environments where risk-taking is encouraged and the consequences of failure are minimized.

Innovation sandboxes — safe, controlled spaces for testing new technologies and processes — allow teams to experiment with Gen AI without the fear of disrupting core business operations. Such environments encourage risk-taking because the potential downsides are contained, allowing teams to focus on learning and improving rather than avoiding mistakes.

Creating a psychologically safe environment is paramount. This means a workplace where employees feel free to take risks, voice their ideas, and engage in creative problem-solving without fear of retribution if things don’t go as planned. This sense of safety is essential for encouraging experimentation, particularly in the context of Gen AI, where uncertainty is high.

A lack of psychological safety leads to a “play-it-safe” mentality, where employees only propose ideas they are confident will succeed. This limits the organization’s capacity to push boundaries and innovate. In contrast, when employees know that failure will be met with support rather than blame, they are more likely to take bold steps.

Leaders can foster this environment by publicly acknowledging the efforts of teams who take risks, regardless of the outcome, and by consistently framing failures as opportunities for growth.

An article by Forbes highlights the importance of psychological safety in driving innovation. It emphasizes how leaders can create a culture where employees feel empowered to take risks. Additionally, a study by Google, discussed on their re:Work platform, found that psychological safety was the most important factor in team effectiveness.

Failing to Gen AI Success

Ultimately, creating a culture where failure is viewed as a natural part of innovation enables the organization to remain agile and responsive. In a field as dynamic and quickly progressing as Gen AI, staying ahead requires continuous learning, which can only happen when employees feel empowered to experiment, fail, and try again.

Organizations that embrace failure as part of the process will not only see greater innovation but will also build a more resilient and adaptive workforce, capable of navigating the complexities of AI adoption with confidence and creativity.

Failure, when approached with the right mindset, is not an ending but a beginning. It’s the secret sauce that fuels the engine of innovation, driving us toward a future where Gen AI transforms our businesses and our world.

By Dr. Gleb Tsipursky

Dr. Gleb Tsipursky, called the “Office Whisperer” by The New York Times, helps tech-forward leaders replace overpriced vendors with staff-built AI solutions. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his forthcoming book with Georgetown University Press is The Psychology of Generative AI Adoption (2026). Prior to that, he wrote ChatGPT for Leaders and Content Creators (2023). His cutting-edge thought leadership was featured in over 650 articles in prominent venues such as Harvard Business Review, Fortune, and Fast Company. His expertise comes from over 20 years of consulting for Fortune 500 companies from Aflac to Xerox and over 15 years in academia as a behavioural scientist at UNC-Chapel Hill and Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio

Sourced from Future of Work

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AI bots crawl retail sites 198x more than Google, new report warns

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Sourced from PPC.Land

AI bots crawl retail sites 198x more per visit than Google, bot traffic rose 5.4x in 2025, and 80% of websites are exposed to agent spoofing, per new research.

A new industry report published this week by Retail Economics, Amazon Web Services, Botify, and DataDome has put a precise number on how dramatically artificial intelligence has disrupted the underlying mechanics of retail discovery – and it is a number that should concentrate minds across search, e-commerce, and advertising alike. For every single visit OpenAI’s systems deliver to a retail website, those same systems perform 198 crawls. Google, by comparison, generates one visit for every six crawls. The disparity, drawn from analysis of approximately 200 retail and e-commerce websites, illustrates how AI platforms interact with the web in a fundamentally different way from the search engines that have shaped digital marketing for the past two decades.

The report, titled “The Future of Search and Discovery: A strategic playbook to understand agentic commerce,” is based on a consumer survey of 6,000 nationally representative respondents across the UK, US, and France, conducted in November 2025. Its conclusions range from quantitative measurements of bot traffic growth to qualitative assessments of consumer trust, and it arrives at a moment when agentic commerce infrastructure is being built at pace across every major platform.

Bot traffic has multiplied – and skewed analytics

The headline infrastructure finding is stark. According to Botify’s analysis, AI-driven bot traffic across the approximately 200 retail and e-commerce websites examined increased 5.4 times during 2025, with the index moving from a baseline of 100 in the first quarter to roughly 640 by the fourth quarter. The growth was not linear. A particularly sharp acceleration occurred in the weeks preceding September 2025, when crawl intensity rose sharply as AI systems refreshed and ingested product data. Shortly after, OpenAI expanded its commerce-related capabilities, including agent-led shopping and in-chat purchasing features. Visits from OpenAI to retail websites then increased 200% month on month in September 2025 following that rollout – a direct illustration of the relationship between platform-level capability updates and referral traffic patterns. PPC Land reported on OpenAI’s instant checkout launch on September 29, 2025, covering how the Stripe-backed Agentic Commerce Protocol enabled direct purchases within ChatGPT conversations.

The category-level picture is even more granular. According to the report, food and grocery experienced a 29-times increase in AI-driven bot traffic over the course of 2025, driven by the high volatility of prices and stock levels that make the category valuable for AI systems to monitor continuously. Home and DIY saw an 11-times increase. Electronics and appliances also crawled significantly. The divergence reflects a structural insight: AI systems treat retail categories differently based on how frequently data changes, not purely on commercial significance or retailer performance.

The scale and velocity of this automated traffic introduces a measurement problem that retailers have only begun to grapple with. According to the report, AI bot systems generate high-volume, concurrent requests that are not always distinguishable from human browsing in traditional analytics. The consequences are concrete. When Google removed a technical shortcut – the &num=100 parameter – that many tracking tools relied on in early September 2025, Botify’s enterprise retail clients reported search impressions fell by approximately 67%, while clicks stayed largely flat and average position appeared to improve. Click-through rate growth then increased by approximately 150%, not because performance had genuinely changed, but because the data was no longer contaminated by synthetic bot impressions. Much of the apparent growth in impressions had been driven by AI bots capable of making 100 or more simultaneous requests, not by real consumers.

Nearly 80% of websites exposed to agent spoofing

Perhaps the most operationally urgent finding in the report concerns security. DataDome, the bot and agent trust management company that co-produced the research, analysed 698,214 live websites using a spoofed “ChatGPT AI assistant” user-agent. The result: 79.7% did not block or challenge the impersonation attempt. Of those, 79.2% returned a “200 OK” response code, meaning the spoofed agent was admitted without challenge. Only 17.2% returned a “403 Forbidden” response.

This is not an abstract vulnerability. According to the report, spoofable user agents and incomplete IP lists make it difficult for retailers to distinguish legitimate AI agents from stealth or human-driven automation using shared infrastructure. The practical effect is that malicious actors can clone weakly declared AI agents to exploit pricing, inventory, or checkout flows. The report notes that DataDome’s threat research team, Galileo, recently identified that 80% of AI agents do not declare themselves properly when visiting websites. That figure underpins a broader argument that retailers face “skewed performance metrics that undermine commercial decisions and expose them to fraud.”

The risk is asymmetric. Blocking all AI traffic to protect against spoofing carries a different cost: if brands do not allow AI bots to find and use content on their websites, according to the report, those systems may find data elsewhere – from third-party review sites, forums, or competitors. PPC Land has tracked how Amazon chose the restrictive path, blocking AI bots from OpenAI, Anthropic, Meta, Google, and Huawei in August 2025, a strategy that runs in parallel with Amazon’s development of its own proprietary AI shopping tools.

The 1-in-198 ratio and what it means for discovery

The visit-to-crawl ratio is worth dwelling on. It signals that for OpenAI’s systems, the primary purpose of engaging with retail websites is not delivering visitors but rather ingesting, validating, and comparing information within their own interfaces. Discovery and evaluation increasingly happen inside AI interfaces before a consumer ever reaches a retailer’s site. This challenges the foundational assumption of SEO: that being crawled translates, over time, into being visited.

The report frames this as a shift in where influence operates. According to Botify’s data, Google drives one visit per six crawls, compared with one per 198 for OpenAI. In practical terms, a product that ranks highly in Google search still generates traffic directly. A product evaluated by an OpenAI agent may shape a recommendation without ever producing a referral visit. Conversion attribution, session metrics, and bounce rate become less meaningful as a result. Brainlabs reported earlier in 2025 that AI search visitors can be worth 4.4 times more than traditional organic traffic, but that premium depends entirely on the visitor arriving at a website in the first place – an outcome the 1-in-198 ratio suggests is far from guaranteed.

The report introduces a taxonomy of AI-led traffic that distinguishes between training crawlers (such as GPTBot from OpenAI and ClaudeBot from Anthropic), live retrieval crawlers (such as ChatGPT-user and Perplexity-user, which fetch fresh content in real time), index-building crawlers (such as OAI-SearchBot and PerplexityBot), AI assistants and shopping agents (such as ChatGPT, Microsoft Copilot, Gemini, and Amazon Rufus), agentic browsers (such as Perplexity Comet, ChatGPT Atlas, and Gemini integrated into Chrome), and malicious or exploitative bots(unauthorised scrapers, competitive intelligence bots, and automated fraud traffic). Each category carries different implications for governance and access policy. PPC Land reported on OpenAI’s revised ChatGPT crawler documentation in December 2025, which created different compliance standards for different crawler types.

JavaScript invisibility and the structured data imperative

A separate technical finding deserves attention among search and e-commerce professionals. According to the report, most AI bots cannot read content rendered in JavaScript. If a brand’s product data – pricing, availability, specifications, reviews – sits behind JavaScript, AI systems will see only a stripped-down version of the page. The report illustrates this with a comparison: a shoe product page viewed by a consumer shows size, colour options, materials, price, and promotional details; the same page seen by most AI bots shows only a handful of visible text labels and a stripped visual shell.

The consequence is direct. If AI systems cannot access or interpret a retailer’s data, that retailer may never appear in AI-mediated discovery. The report places structured, authenticated, and accessible data at the centre of its five identified forces of disruption, alongside discovery shifts, infrastructure requirements, LLM evolution, and measurement change. Poor metadata or inconsistent taxonomies can make products invisible to AI crawlers entirely. PPC Land reported in December 2025 on Google’s documentation clarifications around JavaScript rendering for error pages, reinforcing the same underlying technical vulnerability.

The report identifies Answer Engine Optimisation (AEO) as the growth layer built on top of traditional SEO. Traditional keyword rankings, organic impressions, click-through rate, domain authority, and bounce rate – the standard dashboard of digital marketing performance – were built for a world of links and human clicks. They do not show how AI agents see, interpret, and act on content. The report proposes a new generation of performance metrics: agent inclusion rate (what proportion of products or pages are recognised and surfaced by AI agents), discovery visibility (presence rate across multimodal environments), engagement confidence index (how often consumers act on AI-surfaced results), structured-data coverage, trust signal strength, visibility-to-sale ratio, and discovery ROI index. These are emerging standards, not yet widely deployed, but the report argues they are necessary to understand commercial impact in an AI-mediated environment. An SEO expert released a related AI search content optimisation checklist in June 2025 that addressed similar requirements around server-side rendering and structured data coverage.

Consumer adoption: 73%, but trust lags

The consumer survey component of the report draws from 6,000 respondents across the UK, US, and France surveyed in November 2025, with 2,000 per country. According to Retail Economics, 73% of consumers across the three markets have consciously used AI in some form over the past twelve months. Of those, 38% have used AI assistants specifically for shopping tasks including product ideas, suggestions, or comparisons. A further 34% have used AI features on retailer websites or apps. Twenty-one percent have used AI tools to make decisions or support purchases.

The US records the highest adoption rate at 73%, with France at 69% and the UK at 68% – closer to each other than might be expected given differences in digital culture. Among 18-to-24-year-olds, approximately one in four use AI assistants regularly and one in five use them day-to-day. Among those aged 55 and older, fewer than one in ten report day-to-day use. The gap widens further when examining AI use relative to other discovery channels: among 18-to-24-year-olds, AI assistants and social discovery channels exert influence that matches or exceeds traditional search engines in the discovery phase.

Trust, however, tells a different story. Thirty-two percent of consumers across the surveyed regions say they do not trust AI-enabled search and discovery. Whereas 38% feel comfortable with recommendations from tools like ChatGPT, Microsoft Copilot, and Gemini, far fewer are willing to let those systems act on their behalf. Nearly half – 49% – say discovery is something they want to do themselves, not something to outsource. The report describes this as a “key tension in the shift towards agentic commerce: people value the benefits afforded by AI, but don’t yet feel fully confident to delegate decisions.”

The trust gap is structured by age and income. Higher-income consumers exhibit greater confidence in AI systems, likely reflecting greater familiarity from work settings. Middle-aged, high-affluence consumers emerge as the most AI-trusting segment. Least affluent consumers show the lowest trust, where concerns about risk, accuracy, and control are most acute.

Which categories and missions face the earliest exposure

The report maps retail categories by consumer trust in AI-led discovery and willingness to use AI, weighted by typical spend. Electronics and appliances consistently lead across all three markets. Purchases in this category involve technical specifications, rapid product cycles, and meaningful price differences – exactly the conditions where AI assistance in comparison and shortlisting is most valued. Travel and leisure sits close behind. Clothing and footwear shows rising exposure, with large online ranges and frequent browsing creating fertile ground for AI-led personalisation.

Categories sitting lower on both axes include jewellery, beauty, and homewares – purchases that involve emotional, tactile, and personal judgements where consumers still seek human reassurance. Food and grocery shows strong regional variation: the US shows higher openness to AI assistance in grocery discovery, while France reflects a stronger food culture centred on freshness and physical inspection.

Shopping missions follow a parallel gradient. According to the survey, consumers show the highest willingness to delegate to AI for “considered or technical purchases” and for “buying gifts for others” – both missions involving uncertainty, high information load, and benefit from structured comparison. Routine replenishment sits at the bottom of the willingness scale across all three markets. The pattern is consistent: AI assistance is welcomed where decisions feel cognitively demanding, and resisted where habitual or emotional judgement dominates.

Amazon Rufus provides a commercial datapoint that anchors these projections. According to the report, more than 250 million customers used Rufus during 2025, with interactions up 210% year on year. Customers who use Rufus while shopping are over 60% more likely to make a purchase during that session. Amazon’s full-year financial results subsequently confirmed that Rufus generated nearly $12 billion in incremental annualized sales during 2025, with more than 300 million customers using the tool throughout the year.

Four consumer personas and three readiness workstreams

The report identifies four distinct shopper personas in relation to AI-assisted discovery. AI-first optimisers (10% of the total, skewing younger at an average age of 38) use AI assistants as their primary discovery tool and show 47% complete trust in AI for research and comparison. Assisted explorers (55%, average age 42) welcome AI as a practical co-pilot for shortlisting and comparison but want to remain in the approval loop. Guarded adopters (16%, average age 53) use AI in controlled, low-risk ways but scrutinise results and hesitate before delegating meaningful decisions. Human loyalists(19%, average age 62) rarely use AI for shopping and require concrete evidence of benefit before adopting more meaningfully.

The strategic section of the report organises its recommendations into three readiness workstreams. The first concerns traffic policy for AI bots and agents – establishing which systems should be allowed, blocked, limited, or monetised, with continuous trust assessment and dynamic behaviour-based security. The second concerns data readiness and product information management – standardising product attributes, metadata, and taxonomy to create a single machine-readable source of product truth, and testing how AI crawlers actually extract and interpret that data. The third concerns on-site AI experiences – building conversational, voice, and embedded-agent user experiences that complete the discovery-to-purchase loop without losing the customer to a competitor’s AI interface.

Cloudflare’s launch of pay-per-crawl in July 2025 and its subsequent Markdown for Agents service in early 2026represent infrastructure-level responses to exactly these workstreams, creating mechanisms for retailers to control and monetise AI access to their content while reducing the token cost of that access by approximately 80%.

The report concludes that early-mover advantages are emerging, but brands that delay action risk becoming harder to find, harder to trust, and easier to replace. The age of agentic search and discovery, it argues, will arrive gradually – but the transition is already underway, and hastening.

Timeline

  • 2010-2014 – Keyword search dominates retail discovery; consumers type exact phrases into search engines with results ranked on keywords and page relevance.
  • 2014-2017 – Behavioural and personalised search takes hold; retailers use cookies and browsing history to introduce recommendation engines.
  • 2017-2019 – Mobile, social, and voice discovery expands search beyond text through smartphones, Alexa, Siri, Facebook, YouTube, Instagram, and TikTok.
  • 2019-2021 – Visual and contextual discovery arrives with Amazon Lens, Pinterest Lens, and Google Lens enabling image-based shopping.
  • 2022-2024 – Generative discovery begins; ChatGPT and Google AI Overviews transform search into dialogue, summarising and comparing products.
  • August 7, 2023 – OpenAI announces GPTBot; major websites begin implementing blocks within two weeks. Coverage on PPC Land
  • 2024 – Bot traffic exceeds human website visitors for the first time, according to Imperva data cited in Brainlabs research. Coverage on PPC Land
  • May 2024 – Google launches GoogleOther-Image and GoogleOther-Video crawlers for research and development data gathering. Coverage on PPC Land
  • Q1 2025 – AI-driven bot traffic baseline established at index 100 across approximately 200 retail and e-commerce websites analysed by Botify.
  • April 18, 2025 – Microsoft launches Copilot Merchant Program for retail integration. Coverage on PPC Land
  • April 28, 2025 – OpenAI introduces shopping features to ChatGPT, reporting over 1 billion weekly searches. Coverage on PPC Land
  • July 1, 2025 – Cloudflare launches pay-per-crawl service in private beta. Coverage on PPC Land
  • July 16, 2025 – SEO expert warns Google’s AI could eliminate website clicks amid deteriorating crawl-to-visit ratios. Coverage on PPC Land
  • August 21, 2025 – Amazon blocks AI crawlers from OpenAI, Anthropic, Meta, Google, and Huawei. Coverage on PPC Land
  • Early September 2025 – Surge in AI crawl intensity at retail websites precedes OpenAI’s commerce capability expansion; Google removes &num=100 tracking parameter, causing apparent 67% drop in search impressions.
  • September 29, 2025 – OpenAI launches Instant Checkout for ChatGPT with Stripe partnership and Agentic Commerce Protocol. Coverage on PPC Land
  • September 2025 – OpenAI commerce capabilities expand; visits from OpenAI to retail websites increase 200% month on month, per Botify analysis.
  • October 6, 2025 – Independent analyst questions commercial viability of agentic commerce despite ChatGPT checkout launch. Coverage on PPC Land
  • November 2025 – Retail Economics consumer survey of 6,000 respondents conducted across UK, US, and France.
  • November 13, 2025 – Google launches agentic checkout and AI shopping tools for the holiday season. Coverage on PPC Land
  • November 17, 2025 – Google Search Console adds annotations; Google’s AI Mode gains agentic features including table reservations. Coverage on PPC Land
  • November 25, 2025 – UK research shows 85% of consumers planning AI-assisted holiday shopping would trust agents to place orders and pay. Coverage on PPC Land
  • Q4 2025 – AI-driven bot traffic reaches 5.4x the Q1 2025 baseline across retail websites analysed by Botify.
  • December 9, 2025 – OpenAI revises ChatGPT crawler documentation with significant policy changes. Coverage on PPC Land
  • December 18, 2025 – Google clarifies JavaScript rendering behaviour for error pages. Coverage on PPC Land
  • January 8, 2026 – Microsoft launches Copilot Checkout with PayPal, Shopify, and Stripe integration. Coverage on PPC Land
  • Early 2026 – Cloudflare launches Markdown for Agents, reducing AI token costs by 80%. Coverage on PPC Land
  • February 2026 – Amazon confirms Rufus generated nearly $12 billion in incremental annualised sales during 2025, with over 300 million users. Coverage on PPC Land
  • March 5, 2026 – Greenough Agency pitches the Retail Economics/AWS/Botify/DataDome report to PPC Land.
  • March 7, 2026 – “The Future of Search and Discovery: A strategic playbook to understand agentic commerce” published by Retail Economics, AWS, Botify, and DataDome.

Summary

Who: Retail Economics, Amazon Web Services, Botify, and DataDome published the report. The consumer research covers 6,000 nationally representative consumers in the UK, US, and France. Key data contributors include Botify’s analysis of approximately 200 retail and e-commerce websites, and DataDome’s security test of 698,214 live websites. AJ Ghergich, Global VP of AI at Botify, is available for comment on the findings.

What: A 35-page strategic report measuring the scale and commercial implications of AI-driven crawling and agentic discovery in retail. Core quantitative findings include: AI bot traffic grew 5.4 times during 2025; OpenAI generates 1 visit per 198 crawls compared to Google’s 1 visit per 6 crawls; 79.7% of websites are unprotected against AI agent spoofing; 73% of consumers have used AI in some form; and 38% have used AI specifically for shopping tasks. The report introduces a new taxonomy of AI traffic types and a set of next-generation performance metrics for the agentic era.

When: The consumer survey was conducted in November 2025. The bot traffic analysis covers the full calendar year 2025. The report was published today, March 7, 2026.

Where: The report covers retail and e-commerce markets across the UK, US, and France for consumer data. The bot traffic analysis draws from approximately 200 retail and e-commerce websites globally. The security analysis of agent spoofing covers 698,214 live websites internationally.

Why: AI systems now function as gatekeepers between brands and consumers, shaping consideration sets before shoppers ever visit a retailer’s site. The combination of rapidly escalating bot traffic, widespread vulnerability to agent spoofing, and the invisibility of JavaScript-rendered content to most AI crawlers creates material commercial risk for retailers who have not yet adapted their data infrastructure, traffic governance, and measurement frameworks to the agentic era. The report argues that early-mover advantages are already emerging and that delay increases the risk of being excluded from AI-mediated discovery entirely.

Sourced from PPC.Land

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How Your Business Can Survive — And Thrive —When AI Takes Over Shopping

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By Tomas Gorny

A recent report shows what might be the biggest shift in the history of consumer activity: AI is starting to take over shopping. Everything your business has done to build brand loyalty is at stake.

AI agents “can behave differently from human shoppers: they prioritize price, user ratings, delivery speed, and real-time inventory over brand familiarity or loyalty,” Boston Consulting Group reports. “This has the potential to reshape how retailers compete and how purchase decisions are made.”

There’s no time to waste in meeting this new reality. This year, 52% of consumers plan to use generative AI for online shopping, according to Adobe. Some will use it to simply get a list of options, and may pick their favourite brand from among them. But when they see those options listed together without brand identities or logos, price is even more likely to be the differentiator.

And soon, agentic AI will take on even more of the buying process. It’s like having “a personal shopper who deeply understands your preferences, lifestyle, and budget, effortlessly curating tailored product recommendations from thousands of options,” BCG says. “Your shopper seamlessly anticipates your needs, secures the best prices, and completes transactions autonomously.”

The way business works today, organizations will lose a lot through “disintermediation,” in which consumers bypass a brand’s e-commerce platform altogether, the report adds. “The growth of zero-click search and agent-driven interactions is eroding direct traffic—along with the retailer’s ability to observe, influence, and understand consumer behaviour at scale.”

There are steps businesses can and must take now to prepare.

Your Own Agentic Experiences

The most important step is to create your own platforms that make consumers want to come directly to you. These platforms must offer all the same end-to-end shopping as third party AI applications like ChatGPT, Gemini, and Claude.

The good news is that your business has a big advantage in this battle: proprietary information about customers. By combining that information with the power of AI, you can provide experiences that make people want to shop there.

Building an agentic experience requires taking all the data you have about each individual customer and using it to personalize their journey, making them feel recognized and valued. That means leaving nothing on the table. Be sure to gather every piece of information from every interaction your brand has ever had with each customer, across any and all channels.

A well designed Unified Customer Experience Management (UCXM) platform can achieve this. It serves not only as a way to consolidate information, but also as a system for everyone across a company to collaborate. This way, people across different functions access the same records; update them in real time; and contribute to finding solutions to customer challenges.

Since these tools keep learning over time, they become more precise and successful, guiding each customer to their best possible experience. Everything about an agentic experience can be hyper-personalized, from an agent’s voice to its manner of speaking to the products and services it recommends.

Building a ‘moat’

“To drive revenue growth and improve ROI, business leaders may need to commit to transformative AI possibilities,” McKinsey says. “As the hype around AI subsides and the focus shifts to value, there is a heightened attention on practical applications that can create competitive moats.”

Your brand is like its own castle. The experience of going to it can be so enjoyable that customers keep visiting. And by ensuring that competitors can’t get in — that they’re unable to access your precious customer data — you keep the experience distinctive.

Of course, this is not the only part of the solution to the seismic shift ahead in how people shop. BCG notes that brands must also work to ensure discoverability on popular AI tools, through both “earned visibility” and paid opportunities.

On this, brands have an opportunity as well. Adobe found that when people arrive at a retailer’s site from a generative AI source, they are 10% more engaged, with 32% longer visits and a 27% lower bounce rate. “This indicates that with AI tools, shoppers are becoming more informed and focusing on the most relevant retailers during the research/consideration phase,” Adobe’s report said.

With a UCXM in place, your brand can know exactly how a customer arrived at your site each time, and use that to tailor their experience.

As with so many other revolutions, the AI revolution presents an opportunity. By developing new strategies, you can compete and win in new ways. The key is to see the possibility — not just the problem.

Feature image credit: Getty

By Tomas Gorny

Sourced from Forbes

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