In late 2022, before most marketers had worked out what to do with ChatGPT, Aja Frost was already making the case internally at HubSpot that large language models were about to change how people find software — and that HubSpot needed a strategy for it before the shift became obvious.

That pitch turned into a cross-functional initiative spanning growth, product, engineering, brand, and communications. The result: HubSpot became the most visible CRM in LLM responses, with citations up more than 4,000% and AI-referred demand up over 1,850%. It also led to HubSpot’s acquisition of Xfunnel and the launch of the HubSpot AEO Grader, the first free tool designed to help companies understand their visibility in AI answer engines.

Frost is now HubSpot’s Senior Director of Global Growth and Paid, leading the teams responsible for top-of-funnel demand through SEO, LLM optimization, and paid media. In this conversation, she talks about why the website has become the last stop — not the first — on the modern buyer journey, what it actually means to give algorithms more control without handing over strategy, and how a company that sells marketing software to marketers is thinking about AI automating a significant chunk of what those marketers do.

Excerpts from the interview; 

Your role sits at the intersection of growth, paid, and AI. What does your focus look like today?

My team owns top-of-funnel demand for HubSpot, with a focus on paid, SEO, and AEO. We sit at the intersection of Marketing, Sales, Analytics/Ops, and Product. Right now, a significant portion of my team’s time is on answer engine optimization (AEO) — making sure HubSpot shows up in the answers buyers are getting from ChatGPT, Gemini, and Perplexity — and turning what we’ve learned into a playbook our customers can use. Part of that work was recognizing there was no good way for marketers to see how the AI search landscape was shifting, so we built HubSpot’s AEO Sensor, a free tool that tracks AI visibility, citation, and traffic trends by industry. This helps people understand whether their strategy is working or the underlying models are changing.

Also Read: AI Ads Will Win Only If They Earn Consumer Trust

How has HubSpot’s growth playbook evolved from the inbound era to the age of AI search?

For years, inbound marketing was the playbook. It worked because buyers were searching on Google, and we met them there. Now, people are going to ChatGPT or Gemini, having an in-depth conversation where they identify a problem, evaluate solutions, make a shortlist, and then, and only then, go to your website. The website has gone from an early stop on the buyer’s journey to the final one. In response, HubSpot has stopped targeting high-volume educational keywords and started building visibility in answer engines. We’re getting less traffic, but it’s much more valuable: Customers who arrive after doing their research in an LLM convert at about 3x the rate of traditional search visitors. They’re pre-qualified and ready to purchase. 

How do you scale paid growth globally without losing local relevance?

When going global, marketers typically run a single playbook everywhere or fully decentralize, letting each region do its own thing. Neither works. We use a global operating system that includes shared segmentation, KPIs, and a shared narrative. Execution is local: we adapt channels, messaging, and offers to each market’s buying behavior and digital maturity. Lastly, we test before we expand. A campaign earns its way into new markets based on data, not assumptions about what should translate. 

Paid media has changed dramatically. How has HubSpot adapted, and what’s working today that wasn’t two years ago?

Paid got harder when cheap targeting stopped being reliable. iOS changes, cookie disruption, and rising CPMs have pushed the industry away from easy scale. We’ve had to get much more disciplined about where paid adds value — and much less dependent on third-party signals. First-party data and well-built intent signals are far more impactful now than broad reach. That has also changed what we optimize for. Traffic volume is no longer the North Star. We care more about visibility, branded demand, conversion rate, pipeline quality, and revenue because they are better indicators of whether we’re actually influencing buyers throughout a fragmented journey. 

In practice, that means paid is less about buying broad top-of-funnel traffic and more about amplifying strong signals and strong creative around higher-intent destinations. What’s working now that probably wouldn’t have worked two years ago is this combination of first-party precision, off-site amplification, and integrated paid support. The channels and content types that drive AI citations, such as YouTube, newsletters, podcasts, and Reddit forums, also happen to be where buyers spend time. Today, 90% of HubSpot’s leads come from non-blog sources, with YouTube leads up 100% and newsletter leads up 90%. The paid strategy follows the same logic. 

Also Read: The Agency-Led E-commerce Model Is Changing

How much control should marketers give AI-driven ad platforms—and where do you draw the line?

We’re giving algorithms more room than we used to, but we aren’t handing over the strategy. The most important parts are still in human hands: who we want to reach, what counts as real intent, what message we want in the market, and how we judge success. The machine can optimize delivery, but it shouldn’t define the objective.

Automation is worth it when it operates within strong guardrails and leverages first-party data, explicit intent signals, and strong creative. You’re giving up tactical control, not strategic control, and that’s only worth doing when the algorithm is paired with strong inputs and rigorous measurement. If you’re using automation with weak data or vague goals, it’s just spending efficiently against the wrong objective. 

Where has AI delivered the biggest measurable impact across HubSpot’s growth operations?

AI isn’t something my team does off to the side; it’s part of our day-to-day operating model. On the creative side, we use AI to generate creative and scale testing, produce search ad variations, and make sure assets are on-brand and speak to our persona before they go live.  Our internal heuristic is: use AI to go faster, but keep a human in the loop. We’re also using AI in optimization and execution, including value-based bidding, dynamic personalization, and A/B testing at scale. The results are pretty incredible: AI-referred demand is up 1,850%, email personalization has driven an 82% improvement in conversion rates, and 94% of HubSpotters use AI weekly.

Also Read: Payment Experience Is the Foundation of B2B Loyalty

As AI automates more marketing work, how is HubSpot redefining the marketer’s role?

AI is automating many traditional marketing activities, including content production, campaign setup, reporting, and personalization. But internally and across our customer base, we’re seeing demand for marketing “judgment” or taste rise.  AI raises the ceiling on what a marketing team can do — it doesn’t lower the floor on strategic thinking. At HubSpot, 94% of our team uses AI weekly. We’re not asking whether to use it; we’re asking whether the output is driving outcomes. Building on that, we don’t tell customers that AI replaces the marketer. We’re showing them how to use it to do more of the work that matters.

The post HubSpot’s Aja Frost on Marketing in the Age of AI Search appeared first on MartechView.

Comcast Corporation announced its intention to separate into two independent publicly traded companies through a tax-free spin-off of NBCUniversal and Sky. Upon completion of the transaction, Comcast shareholders will own shares in both companies, creating two focused industry leaders, each with significant scale, strong financial profiles, and distinct strategic opportunities.

The proposed separation reflects Comcast’s track record of positioning its businesses to compete in rapidly changing markets. As technological innovation, consumer behavior, and competitive dynamics continue to reshape both media and communications, Comcast’s board and management team believe each company will be better positioned to pursue its own strategic priorities, invest for growth, and create long-term shareholder value as independent entities.

Brian L. Roberts will continue to be actively involved in the leadership of both Comcast and NBCUniversal, working in partnership with the CEOs of each company. Mike Cavanagh will serve as Chief Executive Officer of NBCUniversal, while Michael Angelakis, Comcast’s former Chief Financial Officer, will become Chief Executive Officer of Comcast following completion of the separation. In the interim, Angelakis will join as a Strategic Advisor.

Comcast, a leading technology company serving residential and business customers through its broadband, wireless, and entertainment platforms, will focus on delivering exceptional customer experiences backed by the nation’s largest converged network, which reaches more than 65 million homes and businesses. With one of the nation’s fastest-growing wireless businesses and the industry’s leading business services platform, Comcast is positioned for long-term growth as a standalone connectivity company.

Also Read: Why the CMO Now Owns the Privacy Problem

NBCUniversal will become a premier global media and entertainment company, anchored by its growing theme parks division, Universal film and television studios, NBC and Telemundo networks, Peacock, Bravo, and Sky, its European media business. The company will be powered by a portfolio of world-class intellectual property, a deep content library, and exceptional strength across sports, news, and entertainment.

“This is a very exciting day for our company,” said Brian L. Roberts, Chairman and Co-Chief Executive Officer of Comcast Corporation. “The transaction we are announcing will unlock a more entrepreneurial management approach and open up a multitude of new opportunities for each business. Mike Cavanagh will lead the new NBCUniversal media and entertainment company as CEO. His vision is for a unique, independent, focused company that will be home to some of the industry’s most valuable brands and assets across theme parks, film, television, streaming, sports and news. I am also incredibly pleased to welcome Michael Angelakis back as Comcast’s CEO. Michael’s deep knowledge of the business and passion for technology will serve us well as we continue to take bold actions in today’s competitive environment.”

“Both companies begin this next chapter from positions of strength,” said Mike Cavanagh, Co-Chief Executive Officer of Comcast Corporation. “Comcast will continue to build on its leadership in connectivity, while NBCUniversal, together with Sky, will have the scale, brands, content and financial resources to compete as a premier global media and entertainment company.”

Also Read: Your Rebrand Is Failing. Have You Tried Listening?

“Comcast’s exceptional assets, entrepreneurial roots, deep customer relationships, and strong track record of innovation and technological leadership provide a powerful foundation for the future,” said Michael Angelakis. “Together, we will build on those strengths, execute aggressively, invest for growth, and pursue new opportunities to create value for our customers, colleagues and shareholders.”

The separation is expected to be completed through a tax-free spin-off to Comcast shareholders in approximately one year, subject to customary conditions including final approval by Comcast’s board of directors, receipt of tax opinions, regulatory approvals, and completion of financing arrangements. NBCUniversal will have the same dual-class share structure as Comcast. Comcast expects to retain a stake of up to 19.9% in NBCUniversal for up to one year after the spin, which it intends to monetize in a tax-efficient manner over time. Comcast intends to establish a strong investment-grade balance sheet for each business, providing both companies with significant financial flexibility to pursue their respective growth strategies.

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TELUS Digital, a global technology service provider specializing in AI-powered digital customer experiences and future-focused digital transformations, today announced a partnership with ElevenLabs, an AI company that transforms how people and businesses communicate with the world. The partnership makes TELUS Digital a preferred implementation partner for ElevenAgents, ElevenLabs’ AI voice agent platform. ElevenAgents handles high-volume, routine interactions and routes complex or sensitive ones to human frontline teams, who receive better-qualified work and more capacity for the customers who need them most.

Under the partnership, enterprises can contract with ElevenLabs directly and work with TELUS Digital to deploy and run the voice AI technology. TELUS Digital will lead implementation, integration, and governance for clients adopting ElevenAgents, drawing on expertise across the major customer relationship management, CX, and telephony platforms ElevenLabs connects to, including Genesys, Twilio, Amazon Connect, Zendesk, and Salesforce, along with the orchestration, monitoring, and human capabilities clients need to run voice AI at scale. TELUS Digital’s role will continue beyond go-live, with ongoing managed services and operational support to keep voice AI running well in production, and to grow it as client needs evolve.

“Deploying AI agents at enterprise scale is harder than it looks. The technology has to hold up in a live operation with real customers, real complexity, and no margin for a bad experience,” said Ashish Uchil, Head of Business Development and Partnerships at ElevenLabs. “TELUS Digital understands that. They bring operational depth and engineering expertise to take ElevenAgents from deployment to something customers actually experience. That’s exactly the kind of partner we want to build with us.”

Also Read: Payment Experience Is the Foundation of B2B Loyalty

TELUS Digital’s expertise in conversational AI for customer service spans voice, generative AI, and agentic automation, backed by something few implementation partners can match: it runs customer operations at scale itself, and employs more than 900 AI engineers with a deep understanding of enterprise-grade AI delivery. It applies a forward-deployed engineering model, embedding its engineers within client operations to build and refine solutions close to the real work.

Voice AI enables frontline teams to handle far more customer conversations than they can on their own, in each customer’s own language. AI agents like ElevenAgents resolve common requests and connect to back-end systems to take action mid-conversation, such as updating an account or booking a follow-up. They can also reach customers proactively at the moments that matter, such as onboarding a new account. This extends what a customer care operation can cover, while freeing teams to spend their time where people make the difference.

ElevenAgents produces natural, human-sounding speech across more than 70 languages with low latency. The agents talk, listen, and take action in real time, resolving issues, triggering workflows, and delivering accurate answers grounded in a company’s own data. When a customer switches languages mid-call, the agent follows; when they interrupt or hesitate, the conversation keeps moving. The result is a customer experience that’s consistent, on-brand, and available at scale, without having to rebuild from scratch for every market or channel.

Also Read: Your ERP Is Holding You Back. Here’s How to Fix It.

“Voice is how people most naturally communicate, and it is fast becoming a primary way they connect with brands and customer care teams,” said Will Mayo, Senior Vice President, Commercial, TELUS Digital. “Voice AI can get customers the answers they need in the moment, without wait time or being passed between agents. That frees up our frontline teams to spend their time on the conversations that need them most. ElevenAgents is built for this, and it can deliver that experience to every customer, at scale.”

TELUS Digital already uses ElevenLabs to train its own contact center agents through its proprietary Fuel iX Agent Trainer. The company onboards thousands of new agents each year, and the platform generates lifelike voice and chat simulations so they can rehearse a full range of customer situations, from routine questions to difficult complaints, before taking live calls. After more than 90,000 simulations as of June 2026, the approach has reduced onboarding time by 20%, with early signs of lower agent turnover as better-prepared agents start the role with more confidence.

At TELUS Communications, an ElevenLabs voice agent proactively reached newly activated home internet customers in their first 90 days, a proof-of-concept designed and built by TELUS Digital. The voice agent confirmed service setup, walked customers through their first bill, and answered early onboarding questions before customers needed to reach out, while account changes, promotions, service troubleshooting, and any request for human support stayed with TELUS’ human agents. Transparency was built in: the agent identified itself as an AI at the start of every call and before any account information was discussed, and the call ended if a customer declined to continue.

Also Read: Is AI About to Make Media Buying an Endless Experiment?

“Our team members are the heart of customer care at TELUS, and they’re who customers turn to for the complex and sensitive issues where human judgment and empathy matter most,” said Jelena Bajic, Global Vice President, Global Operations Excellence, TELUS Communications. “When customers sign up for home internet, those first moments matter. We wanted to welcome every new customer proactively, before they hit a problem, with personalized onboarding that gets them set up for success. With ElevenLabs, we can deliver that personalized first experience at scale. It frees our team members to do what they do best: handle complex issues and build real relationships.”

TELUS customers who received a welcome call were less than half as likely to cancel within their first 30 days as the average new internet customer, and gave the welcome calls an average score of 8.5 out of 10. Building on these results, TELUS Digital and ElevenLabs will go to market together to bring ElevenAgents into live customer service for enterprise clients across telecommunications, financial services, utilities, retail, and other sectors with high-volume customer engagement needs.

TELUS Digital and ElevenLabs are co-hosting a VIP reception for a select group of technology and AI executives during Customer Contact Week in Las Vegas, on Tuesday, June 23, 2026, from 6:00 to 9:00 pm PDT.

The post TELUS Digital, ElevenLabs Partner on Voice AI for CX appeared first on MartechView.

Football has spent years obsessing over what happens on the pitch. Sinch wants to know what happens at the microphone.

The communications platform, which powers more than 900 billion customer interactions annually, has launched the xC Tracker — an AI-powered tool that analyses every pre- and post-match press conference delivered by all 48 national team managers at this summer’s tournament, and ranks them by how often they reach for football’s most familiar phrases rather than saying something worth hearing.

The name is deliberate. Where xG measures the probability of a shot becoming a goal, xC — expected clichés — measures the probability of a manager telling you the team “gave 110 percent” or that there are “no easy games at this level.” A high xC score is not a compliment.

The tracker monitors communication patterns across six languages — English, Spanish, Portuguese, French, German, and Arabic — and follows each manager from the opening match through to the final. It tracks how scores shift after wins and losses, whether pressure makes managers more or less predictable, and which coach will ultimately be crowned the winner of what Sinch is calling soccer’s Press Conference Ballon d’Or.

“Soccer gave us xG. We thought it was time someone measured xC,” said Robert Gerstmann, Chief Evangelist and Co-Founder of Sinch. “Anyone who has watched a post-match interview knows that certain phrases come up again and again. At Sinch, we help businesses communicate effectively under pressure every day. We wanted to find out whether soccer managers do too.”

The premise is more pointed than it sounds. Sinch’s core business is helping companies deliver the right message to the right person at the right moment — at scale, under pressure, across languages. The xC Tracker is an application of that same expertise to one of the most-watched, most-analyzed, and most linguistically predictable stages in global sport.

Also Read: Are We Using AI to Help Customers or Avoid Them?

Whether a manager is facing questions after a last-minute winner or a group-stage exit, the challenge is the same as every brand faces in a crisis: say something real, or say something safe. The tracker will tell you, in real time, which way each of the 48 went.

Live rankings and real-time data are available throughout the tournament at expectedclichetracker.com.

The post Sinch Launches AI Tool to Track World Cup Manager Clichés appeared first on MartechView.

Something remarkable has been happening in boardrooms and brand strategy sessions across North America and Europe. Companies that spent nearly three years racing to announce AI integrations, AI-powered experiences, and AI-driven personalization are now doing what would have seemed professionally suicidal in 2023: publicly walking some of it back.

The hot new trend in marketing, it turns out, is hating on AI — or at least being seen to.

This is not a fringe movement. It is a strategic recalibration happening at some of the most commercially sophisticated consumer brands in the world, driven by a simple and increasingly hard-to-ignore insight: in a market saturated with artificial intelligence, the most powerful differentiator available to a brand may be genuine humanity.

The Starbucks Signal

The most striking recent example is Starbucks. In May 2026, the company retired its AI-powered inventory-counting system built by NomadGo across all its North American stores, just nine months after deploying it as a centerpiece of CEO Brian Niccol’s “Back to Starbucks” turnaround strategy.

The problems were operational and embarrassing. Employees and managers across multiple locations described the system frequently miscounting and mislabeling items — confusing similar milk types, missing items entirely during scan sessions, and, in at least one case, failing to recognize a peppermint syrup bottle in a promotional video Starbucks itself had uploaded to showcase the tool. That promotional video, along with the original blog post announcing the rollout, was quietly deleted from the company’s website before the retirement was announced.

At launch, Starbucks had promoted the technology as a way to free workers to focus on what matters: crafting beverages and connecting with customers. The floor reality inverted that promise entirely — the AI system created more work, not less, and the friction showed up precisely at the human moments the brand could least afford to compromise.

For a chain that leans heavily on drink customization and frequent limited-time items, any friction in inventory accuracy can quickly affect sales, waste, and customer satisfaction. Starbucks is not retreating from technology entirely — Niccol is rolling out a generative AI chatbot for staff built on Microsoft’s Azure platform. But the NomadGo failure is a clear signal that AI deployed without operational rigor in a brand built on human warmth and reliability can do more harm than it solves.

Dove’s Decade-Long Head Start

Starbucks may be the most recent and most dramatic example, but Dove understood this dynamic earlier than almost anyone. The brand’s “Real Beauty” campaign, launched in 2004, was built on a single contrarian insight: in a category flooded with aspirational, heavily retouched imagery, showing real women — unaltered, diverse, ordinary in the best sense — would be more commercially effective than following category convention.

In 2024, marking the campaign’s 20th anniversary, Dove formalized what had been an implicit creative principle into an explicit public commitment. “At Dove, we seek a future where women decide and declare what real beauty looks like — not algorithms. Pledging to never use AI in our communications is just one step. We will not stop until beauty is a source of happiness, not anxiety, for every woman and girl,” said Alessandro Manfredi, Chief Marketing Officer at Dove.

The timing was not incidental. As generative AI began flooding advertising with synthetic models, algorithmically optimized faces, and artificial perfection, Dove’s commitment to real images became more valuable, not less. The contrast did the work — and audiences responded.

Also Read: Why the CMO Now Owns the Privacy Problem

The Broader Backlash

Dove and Starbucks are not isolated cases. Porsche released a hand-drawn holiday advertisement with an explicit statement that no AI was used, and it received significant praise in the comments from audiences who recognized and valued the human craft behind it. Polaroid launched a billboard campaign with the line “AI Can’t Generate Sand Between Your Toes,” connecting with consumers on a personal level amid screen fatigue and phone exhaustion.

Aerie made “No AI” a trust promise, extending its long-running no-retouching stance into a clear, modern pledge. Heineken’s “real friends” wearable campaign flipped the AI companionship conversation into an offline invitation.

The year 2025 marked a clear shift, as brands began highlighting human effort and labeling their products “100% human” and “no AI”,  labels that are becoming the digital equivalents of “organic” or “non-GMO” in food marketing. Bob Hutchins, CEO of Human Voice Media, described it plainly: “We are at a tipping point where the superabundance of algorithmically-generated content, ‘AI slop, ‘ is making human-generated work a luxury good.”

Merriam-Webster agreed with the diagnosis, designating “slop” its word of the year for 2025.

The Backlash Economy

What these brands are responding to is consumer skepticism that is not a niche concern — it is mainstream, measurable, and commercially consequential.

A Nielsen study found that 55 percent of consumers feel uncomfortable with websites that primarily use AI-generated content, and 4 out of 5 said brands and media organizations should be transparent about their AI use in content creation. A 2025 study from the Nuremberg Institute for Market Decisions found that simply labeling an ad as AI-generated makes people see it as less natural and less useful, lowering ad attitudes and willingness to research or purchase.

The market, in other words, is creating a premium for authenticity precisely because authenticity has become scarce. When something abundant becomes rare, its value rises. Human-made, genuinely considered communication was once the default. AI has made it exceptional almost overnight — and the brands that recognize this before their competitors do will capture a meaningful and durable advantage.

The Risk of Overcorrection

It would be a mistake, however, to read this as a simple rejection of AI. The brands navigating this moment most effectively are not the ones abandoning technology entirely. They are the ones being selective and transparent about where AI adds genuine value and where it subtracts human value.

Starbucks, notably, is not walking away from AI — it is walking away from AI that failed operationally and degraded the human experience on which its brand depends. Dove is not anti-technology; it has created Real Beauty Prompt Guidelines to help people use generative AI more responsibly and inclusively. The distinction is not AI versus no AI. It is a judgment about where each belongs.

Also Read: Hyper-Automation Is Over. Agentic AI Is What Comes Next.

What Comes Next

The irony at the center of this moment is rich. The technology that promised to make marketing more efficient, more personalized, and more effective has, at scale, made it less trusted, less differentiated, and less human. And the brands that spent the most aggressively to automate their way to relevance are discovering that the most relevant thing they can do right now is slow down, show up as people, and say something worth saying.

Starbucks is retooling its in-store operations to put human connection back at the center. Dove is running photographs of real faces. Porsche is hiring animators. Polaroid is putting up billboards about sand between your toes.

The slop, as Merriam-Webster put it, “oozes into everything.” And the brands pulling back from it are finding that the space they reclaim is worth considerably more than what they gave up.

The AI arms race is not over. But the counter-movement has started — and, in marketing, as in most things, it’s where the most interesting money gets made.

The post Brands Are Making ‘No AI’ Their Biggest Selling Point appeared first on MartechView.

For years, digital marketing was all about scale. Brands that published frequently, optimized aggressively, and maintained a constant presence across platforms often gained the greatest visibility. 

But the rise of generative AI has fundamentally changed that reality. Today, nearly anyone can use ChatGPT, Claude, or any other AI tool to produce endless streams of blogs, social media posts, videos, and thought leadership content at minimal cost and extraordinary speed.

The content ecosystem today is saturated with AI slop, and readers aren’t blind to it. You’ve likely scrolled past bland, repetitive ads, seen those generic LinkedIn posts, and probably rolled your eyes at the blatantly AI-generated articles. It’s no wonder that as AI slop increasingly masquerades as real content, readers are becoming more selective about who they trust.

This is creating a new challenge for PR and marketing: How do you create credibility in an environment where authenticity itself is being questioned?

Also Read: Hyper-Automation Is Over. Agentic AI Is What Comes Next.

More Isn’t Always Better

Many organizations still approach PR as a numbers game, measuring success through impressions, reach, or volume. But AI has changed the metrics of success.

Let’s take marketing as an example. On Instagram, users are inundated with AI-generated ads that they tend to scroll past rather than engage with. An AI-generated ad that flashes across a social feed may technically register as a view, but that does not mean it resulted in trust or engagement. In many cases, audiences are actively tuning out.

For PR, a very similar dynamic is emerging across earned media, brand storytelling, and thought leadership. Since AI tools make it easy to produce “good enough” content at scale, readers are subconsciously placing greater value on content that feels intentional, specific, and, most importantly, human. What stands out is not perfectly polished messaging, but clarity of voice and authenticity of perspective.

In the age of AI, trust is increasingly tied to transparency. Audiences want to understand not only what brands are saying, but how they are communicating and why. If AI is being used to support content creation, brands should be open about it. Attempts to obscure or over-automate communication risks worsening this skepticism, especially among younger audiences already wary of algorithm-driven media environments.

That skepticism is growing. A recent Nielsen study found that 55% of 6,000 respondents felt “uncomfortable” on websites that primarily use AI-generated content, and 4 out of 5 respondents said media organizations must be transparent about their AI use, particularly in content generation.

Use AI to Make PR More Human, Not Less

That’s not to say AI has no place in modern PR work. The reality is that AI tools can be extremely valuable when used strategically – not just to produce content faster, but to understand audiences more deeply and communicate with greater precision.

AI is exceptionally good at identifying patterns, analyzing audience behavior, and surfacing trends. PR teams can use AI-powered social listening tools to track how conversations evolve across platforms in real time by monitoring keywords, brand mentions, and emerging narratives as they take shape. They can analyze comment sections and forum discussions to see how people react to messaging, not just how many people saw it. 

Crucially, this shifts PR back to one of its fundamentals: listening. In practice, that may involve using AI to analyze thousands of comments to identify recurring questions or frustrations, or to compare how different audience segments respond to the same message. You could also track the exact words and phrases audiences use, and reflect that language back in communications so your messaging feels natural rather than imposed.

Used well, this allows PR professionals to move beyond guesswork. Instead of broadcasting generic messages, they can tailor communications that reflect real concerns, cultural nuances, and audience priorities. AI can streamline research, assist with drafting, and help PR teams respond faster in the ever-changing media landscape. But more importantly, it can help them listen better by grounding decisions in real audience insight, not assumptions.

Still, no AI tool can generate authenticity. That remains a human trait. AI can tell you what resonates, but it’s up to you to decide why it matters and respond with judgment, context, and honesty.

Also Read: AI Is Supercharging Returns Fraud. Retailers Are Behind.

Authenticity Is Now Table Stakes

What does this mean for brands and PR professionals? With trust now harder to earn, brands must reconsider some of their long-standing assumptions about engagement. 

First, consistency matters more than volume in this new age. For years, many companies prioritized constant output to stay visible across every platform and news cycle. Search engine algorithms of the past favored high linkback rates, so writing for the SEO machine made sense as a way to drive organic search traffic.

That won’t work today, with audiences deluged with content and search itself deprioritizing content farms. 

In this new climate, brands must publish for humans. Companies that communicate clearly, thoughtfully, and consistently over time are more likely to build credibility than those that chase every trending topic or publish for visibility alone.

Second, specificity is becoming increasingly valuable. Generic messaging designed to appeal to everyone often resonates with no one. People are drawn to content that feels informed, focused, and grounded in real expertise or lived experience. That could mean sharing concrete insights, addressing niche concerns, or offering a distinct point of view instead of repeating industry clichés. Given how much content is out there, specificity will help brands sound more human and less interchangeable.

Third, prioritize engagement over impressions by measuring the quality of audience relationships. High view counts and viral reach may look impressive in reports, but they will not necessarily translate into trust or loyalty. Target a smaller audience that has a high likelihood of actively engaging with your brand, sharing your content, and building a lasting relationship with you. An approach that prioritizes honesty, clarity, and a personal touch will help you establish a real relationship that no amount of AI slop can draw away.

Finally, recognize that authenticity cannot be automated. AI may assist with execution, but credibility is still a byproduct of human insight, lived experience, and hard-won expertise. 

Also Read: The Death of Batch-and-Blast Email Marketing

The Future of PR Will Be More Human

Ironically, the explosion of AI-generated content may ultimately increase the value of human communication.

As audiences become more skeptical of messaging of all stripes, qualities like transparency, depth, and authenticity will help content stand out. The future of PR will be defined by content that can help build the strongest relationships and the deepest trust.

The post Are Brands Losing Credibility in the AI Era? appeared first on MartechView.

Artificial intelligence-powered search and answer engines have overtaken traditional search engine optimization as the leading content distribution channel for B2B technology marketers — and most organizations are not yet producing content designed to perform in that environment. That is the central finding of a new report from 10Fold, a B2B technology communications agency, based on a survey of 400 marketing decision-makers across the United States and Europe.

The report, titled The Visibility Reset: How AI Search Is Changing B2B Content Strategy, was conducted in partnership with research firm Sapio Research and marks a significant shift in how B2B buyers discover, evaluate, and validate solutions. Fifty-two percent of respondents ranked AI-generated search and answer engines as their most effective content distribution channel — displacing SEO from the top position it has held for years.

A Readiness Gap

The urgency of that shift is broadly understood. More than half of respondents said visibility in AI-generated search is very important, and 15 percent called it a top strategic priority. Yet the majority — 41 percent — said only between a quarter and half of their content had been created or updated for AI-driven search in the past year.

The gap between recognition and readiness is the report’s most consequential finding. B2B organizations understand that the rules of content visibility have changed. Most have not yet adapted their content portfolios to reflect that understanding.

The nature of the challenge is also shifting. In a traditional search environment, visibility depended on ranking. In an AI search environment, it depends on credibility, specificity, and authority — on producing content that AI systems judge worth surfacing, citing, and presenting to buyers as a trustworthy source.

“The companies that win will not be the ones that publish the most AI-generated content,” said Susan Thomas, chief executive of 10Fold. “They will be the ones that create content worth finding, citing, and believing.”

Also Read: Peak Martech? The Landscape Has Plateaued, but the Real Story Lies Beneath

Credibility as the New Currency

The top content challenge cited by respondents — named by 31 percent — was earning visibility from credible sources to support stronger discovery. Differentiating in an AI-saturated market ranked as the second biggest barrier at 29 percent, followed by producing sufficient high-quality content at 23 percent.

The findings point toward a growing premium on original research, expert perspectives, and content supported by credible third-party validation — through trusted publications, analyst firms, peer reviews, and industry influencers. In an environment where AI systems are synthesizing answers from authoritative sources, being cited matters more than being clicked.

Traffic Metrics Are Being Redefined

One of the most persistent concerns about AI-generated search is that it reduces website traffic by delivering answers before buyers click through to a company’s site. The 10Fold data offers a more nuanced picture. Forty-two percent of respondents said both visibility and traffic increased as a result of AI-generated search.

More significantly, the metrics by which marketers define content success are shifting. AI and search visibility were the most frequently cited success metrics at 40 percent, ahead of marketing-qualified leads at 33 percent and brand awareness at 31 percent. Meanwhile, 85 percent of respondents said lead quality improved over the past 12 months — including 32 percent who said it improved significantly — suggesting that while AI search may be changing the volume and nature of inbound traffic, it is not necessarily degrading the commercial value of leads.

Human Oversight Remains Uneven

The report also examines how B2B marketing teams are balancing AI-generated content with human editorial judgment. Thirty-nine percent of respondents said they use a balanced collaboration between AI and humans to develop content. Another 21 percent use AI-generated drafts with human review, and 8 percent produce content that is mostly AI-generated.

Governance practices, however, remain inconsistent. Roughly a third of respondents said that every piece of AI-developed content is reviewed by both a subject-matter expert and an editor. But 9 percent said they do not review AI-generated content at all, or only spot-check it — a meaningful exposure, given that accuracy and data privacy were cited as the top barriers to AI adoption by 30 percent and 29 percent of respondents, respectively. Only 38 percent of companies reported having a formal, enterprise-wide AI usage policy in place.

Also Read: Three Myths That Are Keeping Brands Away From AI

What Marketers Are Doing About It

B2B marketers are already experimenting with a range of tactics to improve visibility in AI-powered discovery environments. The most common approach — cited by 44 percent — is improving product and solution explainer content. Creating content that answers role-specific buyer questions was cited by 39 percent, and producing quote-ready summaries or key takeaways by 35 percent.

The tactical picture suggests an industry in active transition: aware of the new rules of visibility, experimenting with responses, but not yet operating with the systematic content infrastructure that the AI search era will ultimately require.

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Nearly all workers describe themselves as curious. Less than a third say their workplace rewards them. That gap, and what is driving it wider, is the subject of SurveyMonkey’s 2026 State of Curiosity report — and its findings arrive at a moment when the question of what humans contribute alongside artificial intelligence has never been more consequential.

The report, based on a survey of 1,925 US workers conducted in April, introduces the concept of “curiosity capacity” — defined as the ability to stay open, ask sharper questions, and keep learning in an environment where AI produces polished answers faster and easier than ever before. The central argument is pointed: as AI commoditizes outputs, the differentiator is no longer what workers produce. It is the questions they ask, the assumptions they challenge, and what they notice that AI missed.

Also Read: CES 2026: The Year “Physical AI” Claimed the Real World

Three Forces Draining Curiosity at Work

The report identifies three workplace dynamics that are systematically suppressing curiosity on the job.

The first is what the report calls the AI middleman. Directors and vice presidents are nearly three times as likely as individual contributors to use AI instead of asking a colleague a question — 33 percent versus 12 percent. Conversations that once built shared judgment and organizational understanding are being replaced by prompts, quietly eroding the connective tissue of institutional knowledge.

The second is the scroll reflex. More than a third of workers who use AI say they accept AI-generated responses as-is or after only a cursory check — even though 58 percent say they trust colleagues more than AI. The path of least resistance is winning over the habit of deeper inquiry.

The third is the efficiency squeeze. Pressure for speed has compressed the space available for exploration and discussion. Only 38 percent of workers describe most meetings as genuine forums for open discussion and idea exploration. More than half say that additional unstructured time would help them be more curious at work.

The Cost of Not Asking

The organizational consequences are already visible. Half of workers say they have had to redo work because the right questions were not asked at the outset. Forty-six percent say they have witnessed time or money wasted because assumptions went unchallenged.

Yet the conditions that would correct this are precisely those that current workplace norms discourage. Nearly half of workers — 44 percent — say that asking too many questions makes them look incompetent. Among Gen Z workers, the numbers are particularly striking: 41 percent report pretending to understand something they do not, 45 percent feel pressure to already know the answer, and 42 percent have stayed silent because they felt they had already asked too much.

“AI allows us to impersonate leadership without doing the hard work of actually leading,” said Anne Morriss, founder of The Leadership Consortium, whose commentary features in the report.

Jack Soll, a distinguished professor of management and organizations at Duke University’s Fuqua School of Business, offered an equally pointed observation: “AI might make us individually smarter, but the opposing force is going to make us all the same — which might make it harder to be creative and innovative.”

Also Read: Visa Bets Big on AI Commerce, Unveils New Partnerships and Innovations

What Workers Say They Need

The report also surfaces a clear picture of what workers believe would help. Seventy-seven percent want more opportunities to brainstorm with colleagues. Seventy percent want greater psychological safety to ask questions without consequence. Sixty-one percent want stronger connections across teams, and more than half want both reduced workload and more unstructured time in the working day.

“Curiosity isn’t the problem,” said Katie Miserany, Chief Communications Officer and Head of Global Marketing at SurveyMonkey. “The way we work is. We hope this inspires everyone to start designing workplaces that strengthen curiosity capacity instead of draining it.”

The full report is available at surveymonkey.com/curiosity/state-of-curiosity-report.

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Jellyfish has appointed Stephanie Parry as Managing Director for its UK and Ireland business, succeeding Nick Fettiplace, who is leaving the agency after fourteen years.

Parry joined Jellyfish in 2023 as Executive Vice President of Client Management, leading accounts for Boots, Deckers, Apple, and Workday. Before joining the company, she served as Global Managing Director and Managing Partner for BlackRock at Mindshare New York, and held the role of Global Media Director at WPP’s Red Fuse Communications, where she led the dedicated Colgate-Palmolive team.

In her new role, Parry will be responsible for the performance and growth of the UK and Ireland operation, with a focus on client impact, AI capability development, and collaboration across the wider Brandtech Group.

“Jellyfish has built a powerful model at the intersection of talent, technology, and creativity,” Parry said. “My focus will be on translating our integrated capabilities — from AI-driven media and creative to emerging areas like generative engine optimization — into tangible business outcomes for our clients, while continuing to invest in the people and culture that make us unique.”

Also Read: Why the Future of Advertising Is Built on Probability

Nick Emery, Global Chief Executive of Jellyfish, said Parry brings “a rare combination of sharp commercial instinct and deep client understanding” and has been instrumental in strengthening both client relationships and the company’s business presence since her arrival. “She knows what it takes to drive growth in a complex, global environment,” he added, “and consistently builds teams that people want to be part of.”

Fettiplace’s departure after fourteen years marks the end of a significant chapter for the UK operation. No details of his next move were disclosed.

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Adobe has unveiled a new agentic AI platform aimed at marketing and creative teams, as the company moves to extend its existing AI infrastructure into a more comprehensive operating layer for agencies and enterprise brands.

The platform, called Adobe CX Enterprise, builds on the company’s Adobe Experience Platform Agent Orchestrator, which Adobe says now powers more than one trillion digital experiences annually across its enterprise customer base. CX Enterprise adds what Adobe describes as “agent skills” — reusable instructions that can be deployed across different AI agents — and is designed as an open system capable of integrating into any technology stack, not just Adobe’s own.

The platform includes two core intelligence products. Adobe Brand Intelligence is described as a continuous engine for enforcing brand consistency, learning from inputs such as review feedback, rejected creative assets and editorial annotations, then making those insights available to agents working on content production. The second, Adobe Engagement Intelligence System, functions as a decision engine for customer communications — determining the next best offer, message or action for a given consumer based on lifetime value rather than clicks and conversions alone, and optimising toward defined business goals.

Also Read: AI Isn’t Killing PR. Bad Measurement Is.

Adobe also introduced a product called CX Enterprise Coworker, which assembles and coordinates specialised AI agents around specific business objectives. In the company’s own example, a marketing team targeting a 3% improvement in cross-sell performance could instruct Coworker to identify the relevant agents and tools, assemble audience segments, creative assets and performance data, draft a campaign plan for human approval, and then execute and monitor the campaign once approved.

Beyond the platform itself, Adobe is formalising partnerships with six of the world’s largest agency holding companies — Dentsu, Havas, Omnicom, Publicis, Stagwell and WPP — to deliver joint solutions in areas including content supply chain management and brand visibility in AI-powered search. The company says 20,000 companies have built operations on its technology.

Also Read: Part 3: Authority, Not Attention, Wins in 2026

A separate set of system integrators — including Accenture, Capgemini, Cognizant, Deloitte Digital, EY, IBM, Infosys, PwC and TCS — are using Adobe’s agentic capabilities to build vertical-specific solutions for their own clients. Expanded integrations with AI platforms from Amazon, Anthropic, Google, IBM, Microsoft and OpenAI are intended to make it easier for brands to connect Adobe workflows with tools from other providers.

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