AI Has Changed Content Production
AI has made content production faster, cheaper, and more scalable than ever. That is not a warning. It is a fact. Brands that ignore this shift are already falling behind competitors that have built AI into their workflows.
However, a gap is opening inside that efficiency story. Data now confirms it, and senior marketing leaders are starting to name it publicly. At Cannes Lions 2026, Samsung’s Europe CMO Benjamin Braun said it plainly: artificial intelligence will become a universal capability. Emotional intelligence will become the competitive advantage.
His follow-up was equally direct. Over the prior year, he had seen too much cost-efficient but poor-quality production. That work would ultimately harm the brands it should have helped.
Why Emotional Intelligence Still Matters
This is not an argument against AI. It is an argument for knowing what AI cannot do. Brands must make sure people still handle the work that requires empathy, judgment, and emotional intelligence.
This post makes that case with evidence, not opinion. It also explains what the right model looks like when AI and human creative leadership work together. The answer is not less AI. It is smarter AI. Experienced people must hold it to a higher standard because they understand what brand-building requires.
What Is Emotional Intelligence in Brand Marketing?
Emotional Intelligence Reads Human Context
Emotional intelligence in brand marketing is the ability to understand what consumers feel and what they need to feel before they act. It helps a brand create messages that change behavior instead of simply sharing information.
This skill also helps marketers recognize cultural timing and hidden consumer tension. A strong creative decision comes from empathy, judgment, and human understanding, not only from pattern matching.
Why Emotional Intelligence Differs From Data
Data intelligence tells you what happened. AI-assisted creative production shows what has worked before. Emotional intelligence helps teams decide what will work next for this consumer, in this category, at this cultural moment.
That judgment comes from experience, observation, and real engagement with human behavior. A training dataset cannot fully reproduce it.
Why Authenticity Matters Commercially
This distinction matters because brand storytelling and campaign creative are designed to create feeling, desire, and connection. Research published in Frontiers in Psychology in early 2026 found that AI can support creative inspiration, but human creators still need to add authentic emotional expression.
For functional content, such as product specifications, tutorials, and data-driven copy, AI can perform well. Brands can also disclose its involvement with less risk. However, emotional brand-building content works differently.
Perceived authenticity is one of the main ways brand content builds trust. AI-first production often struggles to deliver that authenticity because it can imitate patterns, but it cannot fully understand human meaning.
The Evidence: What Happens When AI Runs the Creative Process
The research on consumer responses to AI-generated brand content has been consistent enough across multiple studies and data sources that it constitutes a finding, not a trend.
Consumer trust is eroding in direct proportion to AI adoption
A 2026 study by Fractl and Search Engine Land surveying over 1,000 US consumers found that in 2025, 20% of consumers said heavy AI use would reduce their trust in a brand. By 2026, that number had risen to 39%, nearly double in twelve months. The study notes directly: scaling content output with AI is no longer a neutral operational decision.
Separately, Klaviyo and Datalily data from December 2025 found that only 7% of consumers say visible AI-generated marketing content makes them trust a brand more, while 31% say it makes them trust the brand less. Emplifi research found that 52% of consumers say they would stop buying from a brand after an inauthentic experience, and 91% expect brands to disclose when they are using AI in marketing.
These figures describe a structural tension that most marketing teams have not yet fully absorbed. AI adoption inside marketing organizations is near-universal and accelerating. Consumer trust in AI-generated content is declining at a comparable rate. The gap between those two curves is where brand equity is being lost.
Consumer preference for human-made content is not nostalgia. It is signal.
Canva’s 2026 Marketing AI Report found that 78% of consumers say they would rather see ads made by people, even if AI could produce better ones, and 87% believe the best advertising still requires a human touch. A Billion Dollar Boy study found that consumer preference for AI-generated content has dropped from 60% in 2023 to 26% in 2026, a collapse in preference across just three years of mass AI adoption.
This is not irrational consumer behavior. It reflects something genuine about how brand trust is built. Consumers can feel when content is assembled from patterns rather than built from understanding. They cannot always articulate what is missing, but the response is consistent: the brand feels less real, less connected, less worth paying attention to. For brands operating in categories where emotional resonance drives purchase decisions, that perception has direct commercial consequences.
The brands that learned this lesson the hard way include major consumer names. In 2025, Polaroid, Heineken, Aerie, Cadbury, and DC Comics publicly walked back AI-generated ads after consumers responded that something felt off. Coca-Cola’s earlier AI-generated holiday campaign was widely described as soulless despite the company’s enthusiasm for the technology. These are not small brands with niche audiences. They are marketing-sophisticated organizations with significant resources, and they still misjudged where the line sits between AI efficiency and brand authenticity.
The quality problem is accelerating, not stabilizing
Canva’s research found that mentions of AI slop in media monitoring data increased ninefold in the period leading up to 2026. Among marketing leaders, 41% say it is becoming a real challenge for their organizations. This is not a fringe concern raised by creative traditionalists. It is a quality management problem that is now mainstream enough to have its own terminology and to be named from the main stage at the world’s largest creative marketing festival.
The underlying mechanism is straightforward. AI tools are trained on existing content. They produce outputs that are statistically similar to what has worked before. When every brand is using the same tools trained on the same data, the result is content that is technically competent, tonally familiar, and distinctively nothing. The output lacks a genuine point of view about the consumer. It avoids the cultural tension the brand should own. Instead, it fills the brief without doing the harder work that makes the brief matter.
Volume without distinctiveness is not a content strategy. It is noise production at scale.
What AI Does Well, and Where It Belongs in the Production Process
Making the case for emotional intelligence as a competitive advantage is not the same as making the case against AI. The distinction matters because brands that overcorrect, abandoning AI tools in favor of pure human production, will sacrifice real efficiency gains for a marginal quality improvement that consumers cannot detect in functional content categories.
The evidence is clear that AI performs well in specific roles within a content and brand production workflow.
Structured, functional content at scale
For product descriptions, SEO content, email personalization, data-driven copy variants, and technical documentation, AI performs at a level that matches or exceeds human output on most quality measures, and does so at a fraction of the cost and time. Adobe’s 2026 State of Marketing report found that 53% of senior executives using generative AI report significant improvements in team efficiency. Marketing teams using AI tools save an average of 11 hours per week on task-level production, representing a genuine productivity gain that creates capacity for higher-order creative work.
Frontiers in Psychology research confirms this distinction directly. For functional content, AI’s strengths in data integration and processing are a real advantage, and its involvement does not undermine consumer trust in the way it does for brand storytelling and emotional campaigns.
Research, synthesis, and creative briefing
AI is a powerful tool for processing large bodies of consumer research, competitive analysis, and cultural data into structured insights that inform creative strategy. It can synthesize patterns across market data faster than any human team, identify gaps in brand positioning, and surface the consumer tensions that brief writers need to start from. In this role, AI makes the human creative work better, not cheaper.
IDC’s 2025 Marketing Playbook describes this as the shift from AI-assisted production to co-creation with AI. The distinction is meaningful. AI-assisted production uses AI to execute tasks that were previously done by humans. Co-creation uses AI to expand what human creatives can see and know before they make the decisions that require emotional judgment.
Creative testing and iteration
AI tools can run concept and copy testing at a scale and speed that transforms the briefing process. Kind Snacks used generative AI in 2025 to accelerate creative testing, build synthetic audiences, and personalize ads in days rather than months. That is a legitimate efficiency gain with no brand quality cost, because the human creative judgment was applied upstream in determining what was worth testing, not replaced by the testing process itself.
The pattern that emerges from the evidence is consistent. AI performs best when it is accelerating or expanding human judgment, not replacing it. The production workflows where AI generates the highest return are the ones where experienced human strategists and creatives set the direction, and AI handles the execution, variation, and optimization at scale.
Where Emotional Intelligence Cannot Be Automated
There are specific creative decisions in brand strategy and content production where emotional intelligence is the input, and where removing it from the process produces detectably worse output regardless of how well the AI is prompted.
Reading cultural timing
Knowing when a brand can speak to a cultural moment, and when doing so would feel opportunistic or tone-deaf, requires genuine understanding of the consumer’s emotional context. AI can identify that a cultural conversation is happening. It cannot tell you whether your brand has earned the right to enter it, or whether the moment is one of tension that the audience will resent being marketed at through. That judgment is built from human experience with how culture actually moves, not from pattern recognition on historical data.
This is why campaigns that feel culturally relevant are increasingly rare even as the volume of culturally-adjacent content production increases. AI makes it easier to respond to culture quickly. It does not make it easier to respond correctly.
The unspoken consumer motivation
The most powerful brand strategies are built on consumer insights that consumers cannot articulate themselves. They are derived from observation, ethnographic research, and the kind of empathic inference that comes from genuinely trying to understand why someone makes a decision, not just whether they made it. AI can surface patterns in behavioral data. It cannot infer the emotional logic behind a behavior that does not leave a clean data trail.
As McKinsey research confirms, AI brings speed, pattern recognition, and precision. Humans bring context, strategy, and creativity. AI can suggest copy variants, predict customer behavior, or summarize user feedback, but it does not know why your brand exists or what story you want to tell. Those are not gaps that better prompting closes. They are gaps that require a different kind of intelligence.
The creative decision under pressure
The moments in a campaign development process where the work is either made or lost are rarely the ones that involve execution. They are the moments where someone has to decide whether a creative idea is genuinely right, or just safe. Whether the consumer insight is real, or a comfortable assumption. Whether the campaign platform holds across channels, or falls apart the moment it hits executions the room has not considered yet.
Those decisions require experience, judgment, and the willingness to challenge the brief. Automation cannot make those calls. Senior creative and strategic talent must make them, because those decisions determine whether production builds brand equity or merely fills space.
The Model That Works: Human Leadership, AI Leverage
The brands that are producing the most effective content in 2026 are not the ones using the most AI or the least AI. They are the ones that have been clearest about where each belongs.
AB InBev’s Creative Marketer of the Year keynote at Cannes Lions 2026 described their approach as a sandwich: technology in the middle, with human judgment touching the start and the end of every process. That framing captures the principle. The creative vision is set by people who understand the brand, the consumer, and the cultural context. AI handles speed, scale, and variation in the execution layer. Human review closes the loop, catching the quality failures that AI cannot detect because it has no standard for what brand authenticity feels like.
Samsung’s Benjamin Braun:
Samsung’s Benjamin Braun described the same principle from a different direction. AI is a super tool. Using it does not lower the bar for creative quality. It raises the responsibility of marketers to protect it. The implication is direct: the brands that benefit most from AI efficiency are the ones with the strongest creative leadership, because they have the judgment to use AI in the roles where it creates value and to prevent it from operating in the roles where it destroys it.
For content production specifically, this means building workflows that look different from the ones most organizations inherited from the pre-AI era. Creative briefing becomes more rigorous, not less, because it is doing more of the work that used to happen in production. Consumer insight and strategy work happens upstream of any AI-assisted content generation. Human review is embedded in the workflow as a quality gate, not bolted on at the end as an afterthought. And the measure of success shifts from content volume, which AI handles easily, to content effectiveness, which requires both.
Adobe’s 2026 research found that only 7% of marketing organizations have embedded AI in ways that deliver measurable business results. The gap between AI adoption and AI-driven business impact is not a technology problem. It is a strategy and creative leadership problem. The organizations closing that gap are the ones that have been clearest about what AI is for, and equally clear about what it is not for.
What This Means for the Agency Partner Conversation
The AI production efficiency question is now a standard part of how CMOs evaluate agency partners. Most agencies can demonstrate AI tooling. Fewer can demonstrate that they have built a model where AI efficiency and human creative judgment are genuinely integrated rather than layered on top of each other.
The questions worth asking are not about which AI tools an agency uses. They are about where human judgment sits in the creative and production process, how the agency protects brand quality standards when production volume increases, and what evidence they can provide that their AI-assisted work performs differently from content that audiences correctly identify as machine-made.
Those questions separate agencies that have integrated AI thoughtfully from those that have adopted it primarily to reduce cost. The former produces content that competes. The latter produces the AI slop that Samsung’s CMO warned the industry about from the Cannes stage, and that the consumer trust data confirms is already eroding brand equity for organizations that have prioritized volume over quality.
Agency Squid approaches content production as a strategic capability, not a cost center. Our work connects brand strategy directly to production, ensuring that the emotional intelligence and consumer insight built at the strategy level are present in every execution decision, and that AI tools are used where they accelerate that work rather than where they would compromise it. If you are evaluating your content production model or your brand strategy approach, both service pages outline how we think about this in practice.
The Standard Is Getting Harder to Meet, Not Easier
The irony of universal AI adoption in content marketing is that it has made differentiation through emotional resonance more valuable, not less. When every brand can produce more content faster and at lower cost, the brands that stand out are not the ones producing the most. They are the ones producing work that feels genuinely human in the moments when that matters commercially.
The consumer data is unambiguous on this point. Trust in AI-generated brand content is declining. Preference for human-made advertising is strong and consistent across demographics. The brands that manage to hold both, AI efficiency in the right roles and human creative leadership in the ones that require it, are building a competitive advantage that their competitors cannot close simply by adopting better tools.
Emotional intelligence is not a soft skill in this context. It is a commercial capability. The brands and the agency partners that treat it that way are the ones that will be worth paying attention to when the next wave of AI tools levels the production floor again.
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