Most attractions and destinations still treat AI chat as a support tool. Something that answers hours and ticketing questions so the phone line rings less. That view misses what is actually happening in these conversations. Every question a visitor asks an AI agent is a signal about what they want, when they want it, and what would get them to spend more. Organizations that treat that signal as a customer service log are sitting on data that marketing teams would pay for.

A good example of this comes from Franklin Park Conservatory and Botanical Gardens in Columbus, one of the founding members of the Agentic City program launched by Satisfi Labs this year with Experience Columbus. Over a three-month period, Franklin Park’s AI agent fielded thousands of guest conversations, and the patterns inside those conversations tell a much bigger story than “we answered some questions.”

Start with timing. A large share of the conversations occurred at 10 pm, 11 pm, and later hours, when no staff member is on site to answer the phone. People were asking about hours, ticket availability, membership eligibility, and event registration well after the gates closed. For an attraction, that is a staffing problem solved without adding staff. For a marketer, it is something else entirely. It is a record of intent that would have evaporated by morning.

Also Read: HubSpot’s Aja Frost on Marketing in the Age of AI Search

That intent shows up most clearly around events. When Franklin Park hosted a Gabby’s Dollhouse Meet ‘n’ Greet, the agent absorbed a massive spike in volume, easily a third of all conversations during that window. Guests asked how to register, why a second ticketing step wasn’t appearing, and whether the event was sold out. The agent guided families through a process at scale, without additional hires. But it also surfaced something the events team hadn’t had visibility into before. Teams could see exactly where the ticketing flow was breaking down for real guests, in real time, and in their own words. What began as a customer service interaction ultimately became a source of insight for both the events and marketing teams.

The same pattern revealed something traditional analytics tools rarely catch. Guests asked repeatedly about Museums for All and social services-based discount programs, including questions about specific managed care plans and out-of-state eligibility. These are not casual browsers. They are people actively trying to visit the Conservatory, and the questions show how conversational AI can help visitors navigate access programs and find information that might otherwise be difficult to obtain. That is an audience insight with implications for outreach, programming, and community partnerships that extend well beyond admissions.

Then there is the revenue conversation hiding inside the customer service conversation. Guests asked about weddings, birthday parties, graduation parties, and barn rentals, often with specific dates, venues, and budgets in mind, and frequently at night when the events team was unreachable. Others asked whether the special exhibit changes seasonally, when a specific show is running, and how much the butterfly larvae in the gift shop cost. None of that would show up in a sales report. It shows up only in the conversation itself, and only if someone is positioned to capture it.

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

This is the shift marketers need to make. Unlike traditional FAQ tools, AI-powered conversations do more than reduce support volume. They’re giving marketers direct visibility into audience intent. A guest asking about wedding venues at 10 pm is a lead. A guest’s question about whether an exhibit rotates seasonally is a programming signal. A guest asking about a discount program is an equity and outreach signal. A spike in confused questions about a single event is a UX problem marketing can fix before the next campaign.

Every one of these signals already exists inside the conversations attractions are having with visitors right now. Marketers have spent years trying to infer customer intent from clicks, page views, and conversion data. Increasingly, customers are telling us exactly what they want. The organizations that learn to listen will have an advantage that no dashboard can provide.

The post Your AI Chatbot Knows More Than Your Marketing Team appeared first on MartechView.

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.

German AI translation startup DeepL has acquired a US audio streaming startup whose technology helps attendees at conferences and sports events get the same sound experience regardless of where they are sitting.

DeepL has acquired San Francisco-based Mixhalo for an undisclosed sum to improve its AI translation offering.

The acquisition, which follows DeepL’s layoff of around 250 employees last month, means DeepL will open its first office in San Francisco.

Cologne-based DeepL, last valued at $2 billion, is known for its AI text translation and writing tools. It has also recently moved into voice-to-voice translation, and says more than 200,000 businesses use its translation technology.

Also Read: Payment Experience Is the Foundation of B2B Loyalty

Founded in 2016 by two musicians and a technologist, Mixhalo provides AI-powered, real-time sound in numerous languages at live events such as major sports, entertainment, and conference events. Event attendees install the Mixhalo app, connect it to the concert, and plug in their headphones to get the same sound quality wherever they sit, according to Mixhalo.

Its technology has been used at Metallica and Sting concerts, as well as MLB and NASCAR events, and by brands including Verizon and T-Mobile.

Describing the rationale behind the deal, DeepL, which Mixhalo already uses as its main translation provider, said it was integrating ultra-low-latency audio infrastructure into its offering at large-scale events. “This enables translated speech and captions to reach audiences clearly and instantly, from smaller live settings to tens of thousands of attendees, while preserving the pace and natural fluency of live speech,” the company said.

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

Mixhalo has raised nearly $40 million, including seed investment from Pharrell Williams, and funding from Founders Fund, Fortress Investment, and Cowboy Ventures.

“The team has solved one of the hardest problems in live audio, which is delivering high-fidelity sound to thousands of people at once with basically zero latency,” said Jarek Kutylowski, founder and CEO of DeepL. “Together, we’re building the real-time Language AI layer for communication, so people can understand each other naturally wherever they are interacting, whether that’s in team meetings, customer calls, or even major international events.”

The post DeepL Acquires Live Audio Startup Mixhalo appeared first on MartechView.

The drop in organic traffic that marketers have been quietly watching is not a technical problem. It is a behavioral one — and the data is now clear enough that ignoring it is a strategic choice, not an oversight.

HubSpot‘s analysis of nearly 300,000 businesses on its platform found that organic traffic to customer websites has declined. Not because the sites were poorly optimized. Because the buyers moved. They are asking ChatGPT, Claude, and Gemini the questions they once typed into Google — and, in many cases, forming opinions and making decisions before a single web page ever loads.

The implications for marketers are significant, and a January 2026 survey of more than 3,000 CRM purchase decision-makers worldwide puts a number on them. AI search emerged as the single strongest predictor of purchase intent — ranking ahead of product demos, review sites, and sales calls. Among CRM buyers surveyed, 42% used AI search during their evaluation process. Those buyers were 36% more likely to purchase than those who did not.

The companies that have recognized this shift are already pulling ahead. HubSpot customers actively optimizing for AI search — a discipline the company calls answer engine optimization, or AEO — are generating 20% more traffic from AI visits, 170% more marketing-qualified leads, and 82% more deals than comparable businesses that are not.

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

AEO is a fundamentally different discipline from traditional SEO. Answer engines — the AI systems now mediating the earliest stages of the buying journey — determine whether to recommend a brand based on a combination of social media presence, reviews, third-party coverage, and owned content. The tactics are new. The feedback loops are new. And the core question has changed: it is no longer just about ranking. It is about whether your brand shows up at all when a buyer is asking an AI what to buy.

The shift is already underway. The marketers moving now are the ones their buyers will find. The ones waiting are quietly becoming invisible.

The post HubSpot: AI Search Now Beats Demos in Driving B2B Purchases appeared first on MartechView.

Modernization Is Now a Business Priority

Enterprise modernization has reached a point where delays carry business risk, and careless speed poses equal danger. Many organizations still depend on ERP landscapes shaped by years of local decisions, customizations, acquisitions, compliance needs, and short-term fixes. They run the business, yet often make it harder to see clearly, respond quickly, and scale.

Selective Transformation Offers a More Practical Path

The answer is not a reckless replacement of everything that exists. It is also not a technical conversion that carries yesterday’s complexity into a newer environment. A more responsible path sits between those extremes. Selective transformation gives leaders a way to protect what still has value while removing what has become a burden. Historical data, proven controls, and essential operating knowledge can be retained. Outdated code, fragmented processes, and avoidable variation can be reduced.

A Clean Core Creates Room for Progress

A clean core is not a technology slogan. It is a management discipline. It asks the enterprise to standardize common processes, limit unnecessary customization, govern integrations, and keep the business’s core ready for improvement. When the core is crowded with exceptions, every upgrade becomes harder. Every report becomes debatable. Every innovation effort starts with a cleanup.

For senior leaders, the clean core should be viewed in business terms. Finance needs trusted numbers. Operations need reliable signals. Supply chain teams need visibility across demand, inventory, suppliers, and plants. Compliance teams need evidence that controls are working. Employees need systems that do not force them into manual workarounds. Customers need commitments that can be met. None of this is possible when the foundation is unstable.

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

Public Cloud Readiness Requires Honest Assessment

Public cloud readiness belongs in the same conversation. Public cloud ERP models are attractive because they encourage alignment with standard thinking, shorter deployment cycles, predictable upgrades, and a lower infrastructure burden. They can help growing enterprises move away from fragmented systems and toward a more consistent operating platform. Yet, public cloud should be adopted honestly. It works best when the business is ready to accept standard ways of working.

That distinction matters. Some processes are truly differentiating. Others are merely familiar. A mature modernization program separates the two. The enterprise should not customize a new core simply because the old system carried a certain practice for years. At the same time, critical regulatory, manufacturing, quality, or industry-specific needs should not be dismissed casually. Good leadership wisely uses standardization to create speed, control, and scale.

Industry Needs Should Shape the Modernization Journey

Industry realities make this balance more important. Manufacturers may need better costing across products and plants. Utilities may need modernization without disruption to regulated asset operations. Life sciences companies may need validation discipline, audit readiness, and data integrity. Automotive suppliers may need faster carveout execution, partner integration, and production visibility. Food and dairy businesses may need traceability and recall readiness. Chemical companies may need batch insight, yield control, and margin protection. These challenges are different, but they point to the same principle. The enterprise core must be standardized enough to scale and flexible enough to respect real operating needs.

AI Readiness Begins with the Foundation

Artificial intelligence has made this discussion more urgent. Intelligent tools depend on reliable data and disciplined processes. When data definitions differ across functions, recommendations become questionable. When workflows are unclear, automation may introduce errors more quickly. When integrations are fragile, digital operations become difficult to trust. AI readiness begins long before a model is introduced. It begins with architecture, data, process ownership, and governance.

Also Read: Brands Are Making ‘No AI’ Their Biggest Selling Point

Digital Workers Need Governance and Accountability

The same thinking applies to software agents and digital workers. These capabilities may assist with monitoring, analysis, exception handling, and workflow execution. Their value will depend on control. Each digital worker should have a defined purpose, a clear owner, approved boundaries, escalation paths, and performance measures. Human oversight should remain visible where decisions affect customers, employees, financial results, safety, or compliance. Without that discipline, automation can create risk.

Transformation Must Also Support People

Modernization also has a human side. Employees experience transformation through daily tasks, not through board presentations. They will judge success by whether the new process is clearer, whether training is practical, whether leaders explain the reason for change, and whether the system helps them do better work. When people are left to interpret change on their own, uncertainty grows. When they are supported, adoption becomes more natural.

This requires leadership beyond the technology function. Finance, operations, supply chain, human resources, risk, and business unit leaders must be involved early. Process owners must be willing to make decisions. Governance must be active. Change management must be practical. A modern ERP program should not be handed to IT and reviewed only at milestones. It should be a business transformation enabled by technology.

Business Outcomes Should Define Success

The measure of success should also become more grounded. A successful program should improve cost visibility, working capital control, reporting confidence, inventory accuracy, customer response, compliance quality, productivity, and resilience. These outcomes matter more than the number of features launched. They also matter more than speed if speed comes at the expense of readiness.

The current moment should be treated as a chance to simplify with courage. Aging ERP environments have forced many organizations to make decisions that had been delayed too long. That pressure can be uncomfortable, but it can also be useful. It gives leadership a reason to remove complexity and build a cleaner foundation.

Also Read: Are Brands Losing Credibility in the AI Era?

The Future Belongs to Enterprises That Modernize with Discipline

The organizations that move wisely will not confuse modernization with migration. They will protect continuity while reducing debt. They will adopt public cloud where standardization creates value. They will assess specialized operations carefully. They will prepare data before scaling AI. They will govern digital workers before granting autonomy. They will bring employees through the change with clarity and respect.

Modern ERP modernization is ultimately about confidence. Confidence that the business can change without losing control. Confidence that data can be trusted. Confidence that people can adopt new ways of working. Confidence that intelligent operations can scale responsibly. When clean core discipline, public cloud readiness, industry awareness, AI governance, and business continuity are aligned, the enterprise gains more than a new system. It gains a stronger foundation for the future.

The post Your ERP Is Holding You Back. Here’s How to Fix It. 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.

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SurveyMonkey, the survey and forms platform used by more than 60 million people worldwide, has launched a connector for Claude that lets users create, send, and analyze surveys without leaving the AI interface — turning what would otherwise be a multi-step workflow into a single conversation.

The integration, powered by Anthropic’s Model Context Protocol, is available now through the Claude connector directory. It represents a direct response to how knowledge workers are increasingly operating: inside AI tools, not alongside them.

What the Connector Does

The SurveyMonkey connector for Claude enables four core functions within a single chat interface.

Users can create, edit, and send surveys using natural language — describing what they want to learn and letting the system generate the appropriate questions in seconds. Survey data can be accessed and analyzed in real time, with trends and patterns surfaced directly in the conversation without requiring data exports or platform switching. Feedback from SurveyMonkey can also be combined with other data sources available within Claude, enabling richer, cross-referenced analysis. The entire process — from survey creation through to insight generation — can be managed without breaking the flow of work.

The underlying premise is straightforward. AI tools are most valuable when they operate on real data from real people. SurveyMonkey’s connector is designed to put that data — customer feedback, employee sentiment, market research responses — directly into the environment where decisions are increasingly being made.

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The Broader Context

The launch reflects a structural shift in how enterprise software is being designed and consumed. The model of navigating between specialized tools for each step of a workflow — build in one platform, analyze in another, act in a third — is giving way to AI interfaces that orchestrate multiple capabilities from a single point of interaction.

“Instead of jumping between tools, people expect workflows to happen where they are,” said Eric Johnson, Chief Executive of SurveyMonkey. “With our Claude connector, we’re bringing real survey feedback into the tools where people already work.”

The emphasis on real human feedback is deliberate. As AI-generated content proliferates across business workflows, the signal value of genuine customer and employee responses becomes a more significant differentiator — not less. SurveyMonkey’s integration positions human data as a first-class input to AI-powered decision-making rather than an afterthought.

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Omnisend, the email and SMS marketing platform built for e-commerce, has launched a Model Context Protocol integration that allows merchants to access and operate the platform directly within ChatGPT — without switching tabs, opening a separate dashboard, or learning a new interface.

The integration, now available to all active Omnisend account holders globally, lets marketers analyze campaign performance, identify revenue opportunities, and create and send campaigns using plain-language prompts from within the same environment where they already plan and make decisions.

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The Problem It Solves

The premise behind the integration is straightforward. Marketers are already using ChatGPT as a thinking and planning tool. Requiring them to leave that environment to execute on the insights it generates creates friction — and, in marketing operations, friction tends to mean delayed decisions and missed opportunities.

“MCP is based on a simple idea: people do not want another place to work,” said Bernard Meyer, AI Operations Manager at Omnisend. “They are already using ChatGPT to think through problems, plan campaigns, and make decisions. Instead of asking merchants to come to us, MCP lets Omnisend meet them where the work is already happening.”

What It Can Do

The integration operates across three practical functions.

For performance analysis, merchants can ask direct questions in natural language — such as what drove revenue over the past week, how a recent campaign performed compared to the one before it, or why revenue declined on a given day — without manually navigating reports or comparing dashboards.

For strategic prioritization, the integration surfaces recommended next steps based on store performance and existing marketing activity, helping merchants identify gaps such as missing automation flows or underleveraged audience segments.

For execution, merchants can move directly from insight to action within the same conversation. A prompt asking Omnisend to create a reactivation campaign for customers who have not purchased in 30 days, or to send a weekend sale email to the most engaged subscribers, can be actioned immediately without breaking workflow.

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Access and Data Handling

The integration is available now to all active Omnisend account holders through the Omnisend app in ChatGPT. Access may require a paid ChatGPT plan, depending on the user’s account tier. Users connect their Omnisend account directly within ChatGPT and approve access when prompted. Omnisend says it shares only the data necessary to fulfill each individual request, and users can disconnect the integration at any time.

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Macy’s has launched an AI-powered shopping chatbot called “Ask Macy’s,” built on Google’s Gemini model, and the early numbers are hard to ignore: shoppers who use it spend approximately 4.75 times more than those who do not.

The chatbot rolled out across all of Macy’s digital platforms this week after several weeks of testing with roughly half of the retailer’s website visitors. For a company that has spent a decade navigating declining sales, the results represent one of the more concrete early validations of AI’s commercial potential in retail.

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Why the Numbers Make Sense

Max Magni, Macy’s Chief Customer and Digital Officer, offered a straightforward explanation for the spending gap. Shoppers who engage with the chatbot tend to arrive with a specific purpose — an outfit for an upcoming event, a gift for someone particular — rather than browsing without intent. That specificity translates into higher conversion and larger basket sizes.

Magni also suspects the chatbot is drawing in a younger customer base, a demographic Macy’s has struggled to capture as its core shoppers have aged and its store footprint has contracted.

The two most popular features reinforce that hypothesis. The “complete the look” option suggests accessories to pair with a chosen outfit — a function that mirrors how a knowledgeable sales associate might engage a customer in store. A virtual try-on feature allows shoppers to see how an item looks on them before purchasing, and is available in physical Macy’s locations as well for customers who want to evaluate fit without using a dressing room, according to Chief Stores Officer Barbie Cameron.

Getting the Tone Right

The chatbot did not arrive fully formed. Thousands of Macy’s employees contributed feedback during development, and early versions had notable shortcomings. The original build failed to account for regional climate differences, surfacing the same product selections to shoppers regardless of where they lived. There were also tone problems.

Magni recalled asking the bot for T-shirt suggestions for his son and receiving a response that read: “Here’s a T-shirt for a 10-year-old.” Clinical, transactional, and precisely the kind of interaction that drives customers away rather than toward a purchase.

The revised version handles the same question differently. The bot now responds: “Ten-year-olds can have so much fun with colour — do you want a brighter or more muted colour selection?” The shift is small in technical terms and significant in commercial ones. “The machine continues to learn,” Magni said.

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The Broader Race

Macy’s early results arrive as the competition to define AI-assisted shopping intensifies across the retail industry. Phoebe Gates — Bill Gates’s daughter — founded Phia, a browser extension that compares prices across retailers in real time. Shopping agent Wizard, founded by Marc Lore and Melissa Bridgeford, publicly launched in February after more than four years in development.

The field is crowded, the approaches are varied, and no clear winner has emerged. Macy’s, for its part, is not claiming to have solved the problem.

“Every retailer is trying to figure it out one step at a time,” Magni said. “This is anybody’s game. Nobody has cracked the code.”

What Macy’s does have is evidence — however early — that when an AI shopping assistant is built with enough care, it can change how customers behave in ways that show up directly in revenue. In a retail environment where comparable sales growth of 1.5 percent qualifies as a recovery, that is not a small thing.

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