Where Marketing Teams Go Wrong With AI—and How to Fix It

AI has become deeply embedded in day-to-day work, but the productivity payoff isn’t automatic. A 2026 Stanford survey of global executives found that about 70% of firms are actively using AI, yet the same survey found more than 80% of firms reported no impact on productivity over the previous three years. For marketing leaders under constant pressure to leverage AI to move faster and accomplish more, that gap is troubling enough. But they also have to navigate an additional challenge: keeping AI from diluting or damaging their brand’s voice.

Efficiency isn’t just about producing something faster. Especially when it comes to marketing, AI should free people to spend more time on work that benefits from human judgment, creativity and connection. That’s an outcome that aligns with what workers themselves say they want from workplace AI: a collaborative relationship where they retain agency and oversight, rather than having AI encroach on the parts of their work that require human judgment.

Members of the Senior Executive CMO Think Tank are experienced marketing leaders with expertise spanning brand storytelling, digital advertising, customer engagement, influencer marketing and the growing role of AI in marketing. Here, they discuss ways marketing teams can inadvertently create more friction with AI and share practical approaches for using the technology to make work simpler, clearer and more valuable instead.

“AI’s default output is the average of everything ever written, and nobody has ever recognized themselves in an average.”

– Cynthia Ferngren, Fractional CMO, Brand Strategist and Founder of Brandsol Agency

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Have AI Surface Insights, Not Do the Talking

For Cynthia Ferngren, Fractional CMO, Brand Strategist and Founder of Brandsol Agency, the problem starts when “efficiency” becomes synonymous with “output.”

“The misuse I see most is teams using AI as a volume machine. More posts, more variants, more outbound—all of it fluent, and none of it specific,” she says. “It feels like efficiency, but it creates friction everywhere downstream: Audiences tune out, response rates fall and the team compensates with even more volume.”

Ferngren points out AI’s most glaring weakness when it comes to content creation.

“AI’s default output is the average of everything ever written, and nobody has ever recognized themselves in an average,” she says.

Ferngren asserts that the better approach is to point AI upstream. 

“Use it to mine customer conversations, synthesize research and find the pattern in a hundred reviews that would take a human weeks to spot,” she says. “Let it do the digging, then have a person say the specific, true thing the digging uncovered. AI should sharpen the shovel, not do the talking.”

Let AI Help You Make Better Decisions

Ramya Chandrasekaran, Chief Communications Officer of The QI Group, says one of the biggest misconceptions is that AI’s primary value is generating content. 

“In my experience, that’s where its value is lowest,” she says.

Instead, Chandrasekaran points to a different set of applications.

“Its real strength is reducing cognitive load: synthesizing information, identifying patterns, stress-testing scenarios and helping teams make better decisions,” she says. “Communications has never suffered from a shortage of content; it suffers from a shortage of clarity.”

For Chandrasekaran, measuring AI’s usefulness by how much more a team can produce misses the more meaningful opportunity.

“The organizations seeing the greatest returns from AI aren’t publishing more,” she says. “They’re making better decisions faster.”

Don’t Let Automation Bury the Point

More information doesn’t necessarily create more understanding. Stefano Marrone, Chief Marketing Officer for Siebert Financial, sees AI-generated excess creating its own form of friction inside organizations.

“One problem many organizations encounter with AI? To borrow a sentiment from philosopher and mathematician Blaise Pascal, ‘I didn’t have time to write a short letter, so I wrote a long one instead,’” he says.

The ability to produce polished, comprehensive documents nearly instantaneously can make it tempting to equate volume with productivity and usefulness.

“AI has increased the amount of content available inside organizations significantly, but it doesn’t mean that content is actually read by people,” Marrone says. “Creating a satisfyingly long document from automated meeting notes often overshadows the benefits of a concise and action-driven synthesis.”

Keep Humans Firmly in Control of the AI Workflow

As organizations experiment with more AI tools and use cases, Charles Stanton, Chief Innovation Officer for Transient Consulting, emphasizes the importance of preparing people to use them responsibly.

“Given the current trajectory, I have continued to encourage clients to invest in training on the ethical use of AI tools,” Stanton says. “As everyone is starting to understand, the quality of input and prompts is a direct determinator of the output quality.”

For Stanton, the big trouble arises when human judgment is replaced.

“The greatest friction increase I have seen is the unchecked use of AI content without sufficient human oversight both before and after,” he says. “AI should not be utilized to replace people. It enhances performance.”

Stanton stresses that, when it comes to creative work, humans have to be heavily involved and view AI as a tool, not an independently capable co-worker.

“There should be a meaningful investment of time and thought when drafting AI prompts and even more time devoted to editing before using any content that is created,” he says.

“Speed from AI is only a win if verification keeps pace with it.”

– Jayashree Rajan, CMO of Nexla

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Review Everything Before Publishing

Fluent AI output can sound authoritative even when its underlying information isn’t. Jayashree Rajan, CMO of Nexla, warns that AI makes it easy to ship content that sounds finished but that many times hasn’t been verified.

“Many marketing teams treat fluent output as technically correct output,” she says. “AI-generated copy often includes stats, claims or comparisons that read confident but aren’t sourced, and if no one checks before publishing, that shows up later as a legal flag, a prospect asking about a particular stat, or a competitor calling out an incorrect comparison. This causes credibility issues and impacts brand reputation.”

The problem can extend beyond factual accuracy when teams alternate between care and complacency.

“It shows up as quality variance: Some AI-assisted pieces get heavy editing, others get shipped raw, so the brand’s voice and rigor become inconsistent piece to piece, which is its own coherence problem,” Rajan says.

She recommends a better approach: Every AI-generated claim gets a source check before it ships, and the quality bar stays constant regardless of whether a draft came from AI or a person. 

“Speed from AI is only a win if verification keeps pace with it,” Rajan concludes.

Don’t Automate the Conversations That Require Human Understanding

Not every friction point is a production problem. Jonas Barck, Director of Customer Marketing at Mentimeter, sees serious trouble when AI becomes a substitute for human presence and understanding.

“The friction shows up when AI is used to avoid a hard conversation instead of having it,” he says. “Someone drafts the layoff message, the pushback and the difficult feedback with AI instead of in person because it feels faster and easier. It isn’t. The person on the other end can tell, and what should have built trust erodes it instead.”

Barck stresses that AI’s role isn’t freeing people from challenging situations; rather, it’s freeing up time so that we can meet them more thoughtfully ourselves.

“AI is excellent at absorbing execution like reporting, first drafts and campaign logistics. It’s terrible at replacing judgment and presence, and hard conversations need both,” he says. “The better approach: Use AI to clear your calendar of the low-stakes work so you have more time for the conversations that don’t scale. That’s the actual trade: not fewer conversations, but more room for the ones that matter.”

Make AI a Team Sport

AI can sometimes increase inefficiency—especially when adoption happens one employee at a time. Emily Popson, Senior Vice President of Marketing at CallRail, explains what happens in a marketing team when each person tries tackling AI on their own.

“Often, teams treat AI as an individual productivity tool instead of a shared resource and end up duplicating work, outputs and maintenance,” she says. “A marketing leader’s job is to deploy resources such as time, people and budget to create value across awareness, acquisition, expansion and loyalty. If everyone runs their own prompts, builds their own workflows and burns credits on the same outputs, you’ve created redundancy that costs more than the original problem.”

Her advice is to treat AI implementation as an organizational capability.

“AI should be a team sport,” Popson says. “Build a shared registry of agents and workflows, create a channel for sharing works in progress, and assign owners to specific use cases so effort doesn’t overlap.

“The bigger unlock is giving the team time and space to build,” she adds. “AI can’t scale if your team only gets to experiment when they find spare time.”

Start With the Friction, Not the Tool

Magda Paslaru, Founder and CEO of THE RAINBOWIDEA, says when AI isn’t used wisely, it can actually complicate processes rather than smoothing them.

“One of the worst uses of AI is adding another layer between a person and the answer they need,” she says. “I see teams automate emails, support or content simply because they can, producing more messages, more approvals and more generic output. That is automation without simplification.”

For Paslaru, deciding where AI belongs must happen before selecting a tool or workflow. By carefully introducing AI where it’s actually needed, teams can realize positive results.

“Start with the friction, not the tool: Identify what customers or employees repeatedly wait for, search for or manually recreate, then use AI to remove those steps,” she says. “Good AI should make the process feel shorter and clearer. If users notice the technology more than the improvement, rethink it.”

“Volume was never the bottleneck. Relevance was.”

– Hastimal Jangid, Co-Founder of Coozmoo Digital Solutions

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Optimize for Meaning Instead of Speed

Hastimal Jangid, Co-Founder of Coozmoo Digital Solutions, sees increased AI-driven content volume becoming a misleading proxy for genuine gains in marketing productivity and effectiveness. That misstep, he says, can rapidly cause serious problems.

“The biggest misuse I see is teams using AI to produce more content faster without asking whether more is actually needed,” he says. “In practice, that looks like AI-generated pages, emails and posts flooding out at a pace no one is reviewing closely, diluting brand voice and burying the content that was actually working. Customers feel it immediately—generic messaging reads as noise, not relevance, and it erodes trust faster than it builds reach.”

Jangid argues that teams need to recognize what AI can and can’t do for them—and stop thinking “more output” is always a desirable goal.

“The better approach is using AI to sharpen judgment, not replace it,” he says. “Let it surface what’s underperforming, identify gaps in what customers are actually asking, and draft first passes that a human then shapes with a real point of view.

“Volume was never the bottleneck. Relevance was,” he concludes. “Teams that optimize for relevance instead of output speed end up with less friction and stronger brand trust.”

Set the Strategy Before You Open the Tool

Lee Salisbury, Founder and CEO of UnitOneNine, brings the issue back to a fundamental marketing responsibility: crafting and refining a brand’s identity and message.

“The misuse is using AI to skip the thinking instead of speeding it up,” he says. “Teams generate 10 versions of an email or a landing page and ship whichever one sounds fine, without anyone having decided what the brand is actually supposed to say. That’s not efficiency; it’s outsourcing judgment, and customers can feel it even when they can’t name what’s off.”

His own use of AI starts only after the underlying decisions have been made.

“I build AI into my own workflow constantly, but only after the strategy and voice are locked, never as a substitute for having one,” Salisbury says. “Use AI to produce more variations of a decision you’ve already made, not to make the decision for you. It should compress production time, not replace the thinking that should have happened before you opened the tool.”

Adding AI Without Adding Complexity

• Use AI to uncover insights rather than simply generate more content. Put the technology to work synthesizing customer feedback, research and other inputs that can help people produce more relevant, specific marketing.
• Judge AI by the quality of decisions it improves, not the amount of content it produces. Look for opportunities to use AI to reduce cognitive load, identify patterns and clarify choices.
• Prioritize concise, useful outputs over comprehensive ones. AI makes it easy to generate lengthy documents, but teams still need to determine what information people actually need to understand or act on.
• Keep human oversight at both ends of the AI workflow. Invest time in thoughtful prompts before generation and rigorous editing afterward rather than treating AI output as finished work.
• Verify AI-generated claims before they reach an audience. Apply the same standards for sourcing, accuracy, brand voice and quality whether content originated with AI or a person.
• Reserve human judgment and presence for conversations that require them. Use AI to absorb lower-stakes execution so employees have more time for sensitive discussions, feedback and other interactions where trust matters.
• Build shared AI practices across the marketing team. Centralize useful agents, prompts and workflows; clarify ownership; and make experimentation visible so employees aren’t unknowingly duplicating one another’s work.
• Start with an actual pain point before introducing AI. Identify where customers or employees are waiting, searching or repeating unnecessary steps, then determine whether AI can meaningfully simplify that experience.
• Optimize AI-assisted marketing for relevance rather than sheer output. More pages, posts and emails aren’t inherently valuable if they dilute the brand voice or make useful content harder to find.
• Set strategy and brand voice before asking AI to execute. Use the technology to accelerate or expand on decisions people have already made rather than outsourcing those decisions to the tool.

Better AI Starts With Better Judgment

The most useful AI applications don’t simply help marketing teams do more. They help teams remove unnecessary work, find useful information faster, make stronger decisions and devote more attention to the tasks that still depend on human judgment. That means measuring success less by output volume and more by whether AI is making experiences clearer, processes shorter and marketing more relevant.

As AI tools become more capable and easier to deploy, the temptation to automate first and evaluate later will only grow. Marketing leaders who resist that impulse—and instead start with strategy, friction and desired outcomes—will be better positioned to turn AI into a genuine source of efficiency rather than another layer of complexity.