I recently came across a company called Mercor, and for once my reaction to an AI story wasn’t excitement, panic, or the mild existential dread I’ve started taking with my morning coffee. It was anger.

If you haven’t heard of it, Mercor is a marketplace that pays “experts” to train AI to replicate professional work — law, finance, consulting and, yes, marketing. Humans review outputs, define what’s good and bad, create problem sets and feed judgment back into the machine so it improves. It’s a company built by founders in their twenties who haven’t actually done any of the jobs they’re now trying to replicate. Or any job, frankly.

Silicon Valley has been confidently building things it doesn’t understand since before most of its founders were born. That’s practically the business model.

What stopped me wasn’t the audacity. It was the question hiding inside it.

The Question Nobody’s Actually Asking

The job descriptions sound credible enough. Experts analyze branding, consumer behavior, marketing performance. They evaluate AI outputs, provide structured feedback. The listed qualifications look solid — MBA, PhD, five-plus years in digital or growth marketing.

And then it hit me. Who the f#%k are these experts?

Not as an insult. As a genuinely serious question. Because if AI is being trained to think like marketers, someone is deciding what “thinking like a marketer” actually means. And that decision will shape more work than any creative brief ever written.

To be fair, this isn’t just Mercor. LinkedIn is already testing similar AI training marketplaces. An entire category is forming around “human-in-the-loop” systems — pay people to refine outputs, inject judgment, teach the machine what good looks like. Mercor claims millions of vetted experts and millions paid out daily. LinkedIn is reportedly offering up to $150 an hour for the privilege.

We’ve stopped experimenting with how AI learns. We’re industrializing it.

Competence Is Not Brilliance. And We’re Confusing the Two.

Here’s the problem. Marketing doesn’t work the way these systems assume.

The qualifications Mercor uses as entry criteria — MBAs, PhDs, five years of experience — aren’t signals of greatness in this business. They’re signals of competence. And competence and brilliance are not the same thing, however loudly we pretend otherwise on LinkedIn.

Marketing is one of the few professions where the real value isn’t credentialled. There’s no licensing body for taste. No exam for cultural instinct. No certification for knowing when something is technically correct and completely wrong. (If there were, half the ads running right now would fail it.)

So, we default to what we can measure. Degrees. Job titles. Years in the industry. All fine proxies for showing up. Terrible proxies for being brilliant. And now we’re using those proxies to train the machine.

The Numbers Tell a Story. It’s Not a Flattering One.

McKinsey’s latest data puts AI adoption at 88% of organizations using it in at least one business function — up from 78% just a year earlier. McKinsey & Company is consistently among the most active areas. Generative AI use has surged even faster, now deployed regularly by 79% of organizations. This is not a niche experiment on the fringes. It’s already shaping output at scale.

The IAB found that 83% of ad executives say their company has now deployed AI in the creative process, up from 60% just two years ago. And — here’s the bit that should make you put down your coffee — 82% of those executives believe Gen Z and Millennial consumers feel positively about AI-generated ads. Only 45% of consumers actually do.  

It gets worse. That gap between advertiser perception and consumer sentiment has actually widened — from 32 points in 2024 to 37 points now. 

The people building and training these systems are already misreading the audience they’re trying to influence. Now they’re encoding that misunderstanding into the models themselves. We are, with great efficiency and considerable funding, teaching AI to be confidently wrong.

Among Gen Z, the backlash runs even deeper: 30% describe brands that use AI for ads as “inauthentic,” 26% say “disconnected,” and 24% say “unethical.”  These aren’t fringe opinions. They’re your next generation of customers telling you exactly how they feel — and the industry is moving in the opposite direction.

Smartly’s research confirmed that only 13% of consumers trust ads created entirely by AI, while 48% trust ads co-created by a person with AI support. People can feel the absence of judgment, even when they can’t articulate it. That instinct has a name. We used to call it taste.

So, if we can’t agree on what good looks like now, what exactly are we teaching the machine? The answer — and I say this with affection for the industry I’ve spent my career in — is the average. AI doesn’t invent mediocrity. It scales it.

What “Good” Actually Looks Like

You can see this clearly in the work. When AI is used well, it’s almost never the source of the idea. It’s the amplifier.

Heinz used AI to generate ketchup imagery, and the outputs consistently looked like Heinz. The technology proved a brand truth that already existed. Cadbury used AI to let small businesses create ads featuring Shah Rukh Khan (India’s biggest Bollywood star and the face of Cadbury for decades), scaling a strong human idea across thousands of executions. Virgin Voyages built a personalized AI invitation system around Jennifer Lopez — concept first, execution second.

When AI replaces judgment instead of supporting it, the cracks appear fast. Coca-Cola’s AI-driven holiday creative was technically polished and emotionally hollow. Mango, the Spanish fast-fashion retailer, rolled out AI campaigns across dozens of markets that were efficient, scalable, and utterly indistinguishable from everything else in the category.

AI doesn’t kill creativity. It exposes whether there was any to begin with. Which, for a significant portion of the industry, is uncomfortable news.

The Availability Problem

Now go back to Mercor. Tens of thousands of contractors. Millions paid out daily. A global network of experts feeding judgment into machines.

Here’s the uncomfortable reality. The best marketers aren’t doing this work. They’re not sitting at home grading AI responses for $100 an hour. They’re running brands, making decisions, killing bad ideas, and taking the kinds of risks that machines — trained on consensus — would never recommend.

So, who is doing it? People who are smart, capable and — crucially — available.

Availability is not authority. But in a system like this, it becomes the selection criteria. The machine isn’t being trained on excellence. It’s being trained on whoever showed up. That’s how you get mediocrity as a dataset.

The Holding Groups Figured This Out

The large holding groups already understand this risk — and they’re moving accordingly.

WPP’s Agent Hub, built into its WPP Open platform, codifies roughly 30 years of proprietary Brand Asset Valuator data — the world’s largest and longest-running study of brand equity — alongside behavioral science frameworks and what WPP calls a “Creative Brain” drawing on 150 years of accumulated creative intelligence. They’re not crowdsourcing judgment. They’re working to protect it.

Omnicom, WPP and Havas each unveiled AI operating systems at CES 2026, all converging on the same idea: agencies as managed ecosystems of AI agents, built on proprietary data, wrapped in compliance, plugged into end-to-end marketing execution.  

The common thread isn’t technology. It’s whose knowledge is doing the training. Because once you dilute standards at this scale, you don’t quietly get them back.

The Wrong Question Is the One Everyone Keeps Asking

Everyone wants to know whether AI will replace marketers.

Wrong question.

The right question is: who is teaching AI what marketing is?

If the answer is “a large pool of reasonably qualified, currently available people,” we’re not building better marketing. We’re building faster average. And average, at scale, is a very expensive way to disappear.

Where does the judgment in your organization live — and are you protecting it?  I’d genuinely like to hear what you think.

Sources: McKinsey & Company, Interactive Advertising Bureau (IAB), IAB / Sonata Insights, Smartly, WPP, Storyboard18 

Now that AI has permeated every aspect of business and the pace of its development suggests it’s coming for all our jobs, it’s easy to imagine a future where marketing research is entirely automated. With its vast computing power, AI could soon dominate this field, processing data faster and more accurately than human teams ever could.

I should probably preface this article by admitting that I’ve always harbored a distinct mistrust for much of the research brands conduct around the marketing space, particularly regarding its impact on creative output. In my experience, research is often a sanity check—a way to justify or rule out ideas. It may suggest what not to do but rarely provides actionable insights on what to pursue. If research were that effective, we’d live in a world of only hit songs, blockbuster movies, best-selling novels, and advertising campaigns that never miss.

I’m also drawn to the Observer Effect, which suggests that the simple act of observation may influence the subject being observed. Simply put, “The act of asking the question changes the answer.” This inherent limitation underscores the challenges of relying solely on research, even as AI promises to eliminate human biases (though maybe insert a few of its own).

AI has already disrupted industries by automating tasks previously thought to require human intuition and creativity. In marketing research, it promises to transform how we collect, analyze, and apply data. From consumer sentiment analysis to campaign optimization, the technology seems unstoppable. But can it truly replace human oversight?

The Current Necessity of Human Expertise in Marketing Research

While AI excels at data processing, humans remain crucial for interpreting nuanced insights, especially in qualitative research. For instance, Procter & Gamble relies on human analysts to decode cultural nuances in global campaigns, ensuring messages resonate across diverse markets.

Even in numbers-driven industries, human involvement is critical. According to a 2023 Forrester report, 68% of companies that use AI for market research still rely on human teams to validate insights and translate them into actionable strategies.

Moreover, AI algorithms often inherit biases from their training data. Human oversight is essential to identify and correct these biases. For example, Facebook’s ad-targeting AI was found to skew results based on racial and gender biases in a 2023 audit, prompting the platform to double its hum an oversight team.

Creativity thrives on intuition and risk, areas where research alone falls short. Nike’s campaigns balance AI-driven insights with human-led storytelling, crafting narratives that connect emotionally with audiences worldwide. Their 2023 “You Can’t Stop Us” campaign integrated AI data to identify trending themes but relied heavily on human creativity to deliver its powerful, unifying message.

How AI is Revolutionizing Marketing Research
Platforms like Oomiji integrate open-ended questions and provide real-time analysis, challenging traditional focus groups by offering deeper, more immediate insights. In a recent case study, Oomiji helped a mid-sized retailer boost customer satisfaction by 35% by identifying previously overlooked concerns.

Similarly, Qualtrics reported that companies using their AI-driven tools cut research timelines by 40% while increasing actionable insights by 25% in 2023. SurveyMonkey has rolled out advanced AI analytics, allowing businesses to generate reports in under an hour, a process that once took weeks.

The future of focus groups may lie in virtual environments led by AI, offering cost savings and scalability. In 2023, Coca-Cola piloted VR-based focus groups, which provided deeper behavioral insights, reducing campaign misalignment by 30%.

AI tools offer instant tabulation of survey data, delivering polished reports in minutes. However, despite their efficiency, these reports often miss contextual nuances. A 2023 study by McKinsey revealed that 48% of companies using automated tools faced misinterpretations in at least one major campaign, underscoring the need for human oversight.

The Potential for Full Automation in the Future
Advanced AI can predict consumer trends with remarkable accuracy, potentially eliminating the need for human hypothesis testing. Case in point: Amazon’s predictive analytics not only improved product recommendations but also increased conversion rates by 22% in 2023.

AI platforms are also starting to develop creative content based on data-driven insights, such as ad copy and design variations. Levi Strauss experimented with AI-generated designs last year, saving 15% on production costs while maintaining high consumer approval rates.

However, the leap to full automation isn’t without hurdles. Gartner projects that only 12% of organizations will achieve fully automated marketing research workflows by 2025, primarily due to trust and ethical concerns.

The Limits of AI Today: Trust, Accuracy, and Ethics
Despite its capabilities, AI struggles with transparency. How it arrives at conclusions is often a black box, raising trust issues. According to a recent PwC survey, 72% of business leaders expressed concern over the opacity of AI-driven insights.

Errors in automated sentiment analysis can mislead campaign strategies. For example, Wendy’s faced significant backlash in 2023 after AI misinterpreted customer sentiment, leading to a poorly received promotion that cost the company $10 million. The misstep highlighted the dangers of relying too heavily on AI without human checks.

Embracing a Hybrid Future
While AI will continue to enhance marketing research, human expertise will remain critical for ensuring accuracy, creativity, and ethical compliance. Businesses must embrace AI while maintaining a strong human element, ensuring the best of both worlds in their marketing research endeavors.

Sources:
• Statista, “AI Use in Marketing,” 2024.
• Forrester, “AI in Market Research,” 2023.
• PwC, “Trust in AI Systems,” 2024.
• McKinsey, “State of AI,” 2023.

Who Should Read This Article:
Business and marketing leaders, brand managers, and corporate strategists looking to explore the transformative power of co-branding and partnerships will find this article invaluable.

Top Insights Readers Will Gain:
• Key strategies for identifying and securing the ideal co-branding partner to complement and enhance your brand’s value.
• Real-world examples of successful partnerships that showcase the potential for expanded customer reach and innovative product offerings.
• Best practices and cautionary tales to navigate the complexities of co-branding, ensuring mutual benefits and sustained growth.

Companies worldwide are harnessing Artificial Intelligence (“AI”) to drive cost savings, enhance customer experiences, and achieve business goals. With over 80% of executives in the retail and consumer space anticipating their businesses will use AI automation by 2025, and the integration of AI across marketing functions already becoming pervasive, our industry is obviously experiencing a major transformative shift – hopefully, for the betterment of our profession.

However, as AI’s role in marketing deepens—from ad targeting and content personalization to interacting with customers via chatbots—the question of transparency arises. Should marketers disclose when content is AI-generated?

Considering that about half of marketers now claim to use AI for content creation (certainly more, who use AI without acknowledgment), and a significant 90% have employed AI tools to automate customer interactions, this issue is now front and center in the debate about the ethical use of AI.

The stakes are high: while AI promises efficiency and personalization, a lack of transparency can lead to mistrust and skepticism among consumers.

Current Legal and Ethical Landscape
As AI technologies rapidly integrate into various sectors, particularly marketing, the regulatory framework remains notably undeveloped. There are currently no specific laws that mandate the disclosure of AI-generated content in marketing practices globally. This regulatory vacuum leaves marketers at liberty to decide whether or not to inform their audiences about the use of AI in content creation. However, this lack of guidance does not come without its challenges. As AI’s capabilities and applications continue to grow, so does public and regulatory scrutiny.

Recent discussions among lawmakers and industry leaders indicate a growing concern about the transparency of AI applications. These conversations often revolve around the potential for future regulations that could mandate certain disclosures to maintain fair competition and consumer trust. For marketers, staying informed about these potential changes is crucial, as the direction of these regulations will significantly impact how AI can be used in future marketing strategies.

I argue that that we should be ahead of this impending regulation rather than wait for it to drive the process.

The ethical implications of non-disclosure in AI-generated content are significant and multifaceted. One poignant example of the risks associated with undisclosed AI usage is the experience of CNET. The tech news giant faced a backlash when it was revealed that many of its finance-related articles, purportedly written by “CNET Money Staff,” were actually generated by AI. This revelation came after numerous errors and instances of plagiarism were found within the content, leading to corrections in 41 out of 77 AI-written stories. Such incidents not only raise ethical questions but also highlight potential reputation risks that can lead to a loss of credibility and trust among consumers.

The concerns extend beyond individual companies to the broader implications for the marketing industry. Transparency in AI usage helps maintain a level playing field and fosters an environment of trust and reliability. When companies fail to disclose AI involvement, especially when errors or misleading information come to light, it can damage public perception not just of the company involved but of AI’s reliability and integrity in marketing. This can lead to increased skepticism and resistance from consumers who may feel deceived or manipulated by undisclosed AI-driven content.

For marketers, these ethical considerations are not just about adhering to non-existent regulations but about proactively establishing trust with their audience. By addressing these ethical challenges head-on and choosing transparency, marketers can enhance their brand’s reputation and build stronger relationships with their consumers. This proactive approach not only mitigates potential ethical pitfalls but also positions companies as leaders in responsible AI usage, an increasingly important trait as consumers become more aware of and sensitive to the role of AI in the content they consume and interact with daily.

Impact on Brand and Consumer Trust
Sports Illustrated experienced significant backlash when it was revealed that certain articles assumed to be written by human journalists were actually created using AI. This disclosure from a revered media giant not only surprised readers but also sparked a broader debate over the authenticity and reliability of journalistic content. The incident highlights the potential risks associated with using AI in content creation without proper transparency, especially in industries where credibility is paramount. The controversy underscored the importance of maintaining trust with the audience, which is crucial for publications that rely heavily on their reputations for accuracy and integrity.

In contrast to the Sports Illustrated incident, the Associated Press (AP) uses AI to generate some of its content, such as minor league baseball reports and corporate earnings stories. AP has been transparent about its use of AI, which has helped mitigate potential backlash. This proactive disclosure has not only maintained its credibility but also demonstrated how AI can augment journalistic capabilities without compromising ethical standards.

Consumer demand for transparency is strong and increasing. Nearly 50% of U.S. consumers oppose the use of technologies like Photoshop or generative AI in social media posts for commercial purposes without appropriate disclosure. This sentiment is mirrored in how consumers respond to content creation across different platforms.

Transparency not only meets consumer expectations but also fosters trust and loyalty—88% of marketers report that transparent AI use has enabled them to personalize customer journeys more effectively. Additionally, in the context of AI-driven chatbots, transparency significantly enhances consumer receptiveness and trust. For instance, 57% of B2B marketers in the U.S. use chatbots to improve audience engagement, but acceptance is much higher when consumers are aware they are interacting with AI. Brands should take note!

Practical Guidelines for Marketers
If you agree that transparency in AI usage is essential for maintaining consumer trust and adhering to ethical marketing practices, it is time to adopt a few basic guidelines.

1. When to Label AI Content:
   Disclosure is crucial whenever AI substantially contributes to or fully generates content. This includes scenarios where AI has:
   • Fully generated an article, report, or any other form of content.
   • Significantly aided in drafting, data analysis, or any process that shapes the core content.
   Of course, disclosure may not be necessary for minor AI contributions that assist with non-essential tasks such as data collection or grammar checks, as these do not significantly influence the content’s integrity.
2. How to Label AI Content:
   Effective disclosure involves clarity, visibility, and accuracy in conveying the extent of AI’s involvement:
   • Placement of Disclosures: Position disclosures prominently, typically at the beginning or end of the content, to set the right expectations as the user engages with the material.
   • Language to Use: Use straightforward language that can be easily understood. Avoid technical jargon unless it is appropriate for your target audience.
   • Example for Minor AI Use: “This content was enhanced by AI to ensure accuracy.”
   • Example for Full AI Generation: “This article was generated entirely by artificial intelligence based on current data and trends.”
   • Example for Significant AI Assistance: “AI was used to gather data and draft initial insights for this content, which were then thoroughly reviewed and enhanced by our editorial team.”
   • Consistency: Maintain a uniform approach to how AI disclosures are formatted and presented across all platforms to prevent confusion about the nature of AI involvement.
3. Developing an AI Policy:
   Brands should formulate a comprehensive AI policy that:
   • Defines AI-generated vs. AI-assisted content.
   • Specifies scenarios requiring disclosures.
   • Guides training and compliance to ensure all team members understand and adhere to these standards.
4. Monitoring and Evaluation:
   Most important, we must continuously assess the impact of AI-generated content through:
   • Analytics: Track engagement and compare the performance of AI-generated content versus human-generated or assisted AI-generated content, perhaps via A-B testing.
   • Consumer Feedback: Gather insights on how the audience perceives AI-generated content to adjust strategies as needed.

By adhering to these guidelines, marketers can navigate the complexities of AI integration in content creation transparently and ethically. This approach not only upholds brand integrity but also aligns with consumer expectations for authenticity and transparency in digital content.

Preparing for Future Changes
As AI technology evolves and becomes more ingrained in content creation, marketers globally must stay proactive in anticipating potential regulatory changes. It’s crucial to monitor international developments in AI regulation, as changes in major markets like the EU, USA, or Asia often influence global standards and can set precedents that impact worldwide practices.

Marketers should regularly review updates from international tech and legal news to stay informed about new regulations. Developing flexible marketing strategies and AI policies will allow quick adjustments to meet new regulatory requirements.

By staying informed and adaptable, marketers can effectively manage the impact of regulatory changes on their AI-driven content strategies, ensuring compliance and maintaining trust with their global audience.

Embracing transparency and preparedness in the evolving landscape of AI-driven content is essential for marketers looking to sustain trust and stay ahead of regulatory curves. By implementing robust disclosure practices and staying attuned to global regulatory trends, marketers can not only comply with current standards but also shape future conversations about the ethical use of AI in content creation. This proactive approach will enhance consumer confidence and secure a competitive edge in a digitally driven marketplace.

Full Disclosure: AI was used as an assistant to gather stats, summarize research and proofread this article. Skewed analysis, heavy-handed opinions and typos…well, that was all me.