Molly Ploe

The 2026 Content Marketing World event was the first time it was combined with The Market Research Event (TMRE), put on by Informa. Since I was there among both groups, I got to hear from people who have very different perspectives that I wouldn’t have been exposed to otherwise. Even so, I was surprised to hear such similar concerns in the way our industries are changing right now.

Content marketers and market researchers don’t necessarily sit in the same corner of the professional world. One group is often focused on messaging, brand voice, distribution and content performance. The other is more likely to be focused on audience understanding, survey design, interviews and data quality.

But with AI now affecting all of our work, it’s only natural that we’re wrestling with many of the same questions. We just encountered them in different forms.

AI Can Speed Up Production — but Not Judgment

One of the clearest shared concerns was about the speed of AI versus the appropriateness of what it produces.

At CMWorld, this came through in conversations about content creation. Marketers talked about using AI to draft blog posts, campaign assets and other branded materials much faster than they could before. But speed on its own wasn’t what excited them most. In many cases, it was also what made them uneasy.

We’ve noticed this theme in our own research, too. In Brafton’s 2026 State of AI Adoption in Marketing Teams report, 55.3% of marketers said AI helps them complete tasks more quickly. But in that same report, 70% said thin or generic-sounding content is their biggest concern about AI output. Marketers want efficiency, but not at the expense of originality, relevance or brand fit.

At TMRE, I heard a very similar concern from the research side. AI can generate survey questions and research materials quickly, but that doesn’t mean those materials are thoughtful, unbiased or well suited to the context. The output may look complete, but that doesn’t make it trustworthy.

That was one of the strongest common threads across both groups: AI can accelerate production, but it can’t replace the judgment required to decide whether the result is any good.

Quality Means Different Things, but It Still Matters to Everyone

The specific quality concerns weren’t identical, but they definitely shared a resemblance.

For CMWorld attendees, quality often meant questions like these: Does this piece say anything original? Does it sound like us? Is it useful enough to publish? Are we creating something valuable, or are we just flooding our content library with more material to review?

For TMRE attendees, quality showed up in a different format: Are these questions clear? Are they leading? Will this prompt produce meaningful responses? Does this output actually make sense for the research objective?

Those are different disciplines with different deliverables, but the underlying issue is the same. In both cases, AI makes it easier to generate something that looks finished before it’s actually ready.

Human Review Is Still the Real Safeguard

Another major point of overlap was the continuing need for human review.

Across both events, I kept hearing versions of the same idea: the content or research material still needs to be reviewed by a person who can think critically about it.

That matters on the CMWorld side because branded content has to do more than fill space. It has to reflect the company’s message, align with the audience’s expectations and fit the channel where it appears. A human reviewer has to be able to spot when the output feels generic, off-brand or strategically misaligned.

It matters just as much on the TMRE side. Research materials need to be logically sound, contextually appropriate and sensitive to the situation they’re being used in. That kind of review requires actual expertise, not just a quick skim.

Our AI in Marketing research found that human oversight remains standard, with most AI output still being edited, fact-checked or rewritten before it’s ready to use. AI may help people get to a first version faster, but it doesn’t eliminate the need for someone who knows what good looks like.

CMWorld Conversations Also Pointed to Consistency and Strategy

Another thing I heard from CMWorld attendees was concern not just about individual outputs, but about what happens when AI becomes embedded across a whole content operation.

It’s one thing to generate a single article or campaign asset. It’s another to ensure that many people across a team are using AI in a way that still produces a consistent body of work. If everyone is using different tools, different prompts and different standards, it becomes much harder to maintain a recognizable voice or a coherent strategy.

That concern felt especially important because marketers aren’t just trying to create content faster. They’re trying to create content that supports business goals. If AI helps teams produce more, but not with any strategic consistency, the perceived efficiency gains represent a false economy.

That part of the conversation made the overlap with TMRE even more interesting to me. Researchers were often talking about accuracy and trustworthy datasets, while marketers were often talking about consistency and strategic direction. But both concerns ultimately come back to the same point: process matters.

What About the Next Generation of Professionals?

One of the more interesting concerns I heard had less to do with the quality of today’s output and more to do with the development of tomorrow’s professionals.

Experienced marketers and market researchers came up in a pre-AI world. We learned by doing. We made mistakes, revised our work, figured things out the long way and gradually built judgment through repetition and experience.

That process wasn’t always efficient, but it was formative.

So what happens if newer professionals enter the workforce in an environment where AI smooths over too much of that learning curve? If the tool handles the messy first draft, the rough survey design or the early-stage problem solving, how will people develop the instincts that come from wrestling with the work themselves?

We’ve found that many professionals haven’t had many opportunities for AI-focused training. Our 2026 AI in Marketing report found that 61% of marketers said they aren’t currently, or have never, received formal AI training and are just learning as they go. Only 21% said they were undergoing training or expected it soon, and another 18% said only some people at their companies were set to receive it.

We also saw this pattern when we surveyed marketers on their generative engine optimization plans. When respondents were asked whether they had attended training or education sessions about GEO, 40.85% said they had attended an event or webinar on their own, while only 7.75% said they had attended in-house training.

In other words, many professionals understand they need AI-era skills, but are still being left to build them largely on their own.

I don’t think this problem has a simple solution, but professionals across disciplines — marketing, market research and so many others — are beginning to wonder how to upskill for AI-assisted work. Whether thinking about content creation or research practice, there was real concern about what gets lost if newer professionals are never required to build foundational skills for themselves.

The Answer Is Structure, Not Blind Adoption

What I keep coming back to is that the solution isn’t to reject AI outright, but to build a structure around how it gets used.

There still need to be standards. There still need to be quality checks. There still need to be training sessions, review processes and clear expectations for what good work looks like.

For marketers, that may mean governance around brand voice, review workflows and strategic use cases for AI-generated content. For researchers, it may mean stronger controls around methodology, wording, privacy and the appropriateness of the tools being used.

The details vary, but the broader requirement is the same: people need enough expertise to identify what’s wrong with imperfect output, understand why it’s wrong and know how to improve it.

That may mean refining the prompt. It may mean changing the process. Or it may simply mean doing the work the old-fashioned way and writing it better yourself.

Ethics and Governance Were Another Shared Thread

Quality wasn’t the only area where marketers and researchers seemed aligned. Ethics and governance came up repeatedly as well.

At CMWorld, these concerns often surfaced through questions of security, brand safety and responsible use. People want to know what information is safe to put into a system, whether the platform is secure enough for their organization and how to use AI without creating reputational or legal problems.

At TMRE, the ethical concerns sometimes became even more explicit. One person pointed out the ethics of dynamic pricing models. Another raised the enormous concern around entering health care or personal information into AI systems.

Having concrete answers to these questions helps professionals adopt new technology more confidently. But in our AI in Marketing research, we found that 58% of marketers still lack formal governance structures around AI. Adoption is moving quickly, but policy is still catching up.

That’s where critical thinking comes back into the picture again. Ethical AI use doesn’t just depend on the platform. It also depends on the decisions people make while using it.

Transparency is part of that, too.

One market researcher who works with elderly populations said she was surprised to find that people even in their 80s didn’t mind being interviewed by an AI, as long as they understood what was happening. That observation echoes something I’ve noticed in marketing practices, too: people are often more open to the technology than we assume, provided we’re honest and clear about how it’s being used.

What the Combined Event Revealed

What made the combined CMWorld and TMRE event so interesting was not just that it brought two professional groups into the same space. It was that, once the conversations started, the overlap in their concerns became so obvious.

We may come from different sides of the marketing world, but AI has infiltrated it all.

And when that happens, the same core questions start surfacing everywhere: How do we maintain quality? How do we train people well? How do we use these tools ethically? How do we stay transparent? How do we make sure speed doesn’t come at the expense of thoughtfulness?

Those are not content-marketing questions alone, and they’re not market-research questions alone, either. They’re broader professional questions now, and we’re all going to have to answer them.

Closing Thought

If there was one thing this event made clear to me, it’s that AI is creating a shared reckoning across disciplines that used to feel more separate.

Content marketers and market researchers may not do the same work, but we are now facing many of the same decisions. In both cases, the answer is less about whether to use AI at all, and more about whether we’re willing to pair it with the structure, scrutiny and responsibility it requires.