Your AI knows what customers do—but only human insight explains why
“For the last ten years, Gong has been doing AI way before it was cool,” said Udi Ledergor, Gong’s Chief Evangelist and former CMO in a recent Insights Unlocked interview. “But up until about 18 months ago, we also understood that sales leaders couldn't give a rat's ass about AI... So if you went to Gong's website... you'd be hard-pressed to find any mention of AI—even though we've been hard at work building these custom models for revenue teams.”
Udi continues:
“Now everyone has an AI strategy... but we focus on the outcomes. How are we going to help you with growth, with predictability, with productivity?,” he said. “Those are the outcomes that they care about... not showing off our technology because nobody cares about that.”
AI experimentation is over. It’s time for AI integration. As the use of generative AI in marketing skyrockets to 116% YoY, CMOs are under pressure to scale faster, automate production, and prove ROI.
But if you rely too much on AI tools and algorithms, you risk creating an insight gap where AI, despite being great at identifying patterns in customer behavior, often fails to understand the emotional reasoning behind it.
And when CMOs make decisions without understanding the “why” behind customer actions, they make high-velocity decisions with low-context insight. These decisions may look efficient on paper, but they quietly erode customer trust and long-term ROI.
That's why CMOs need real-human customer insight to fill that gap and drive AI-powered marketing forward.
The AI acceleration in marketing
The shift toward automation is not just a trend; it is a fundamental restructuring of how marketing teams work. According to The CMO Survey, AI is currently used in 17.2% of marketing activities, a figure projected to reach 44% within the next three years.
From content operations to rapid performance testing, CMOs now expect teams to act as both strategists and machine operators. Here’s why:
- AI allows better scale: allowing faster creation, faster analysis, faster iteration.
- AI can help boost efficiency: reducing time spent on manual processes.
- AI can improve return: unlocking customer value faster and earlier in the journey.
With boardrooms asking every marketing leader the same question, “Where’s the ROI?,” AI becomes the obvious lever. CMOs need to produce more content, personalize more journeys, and analyze more data without doubling their headcount.
But, as investment in these tools grows, so does the need to speed up AI integration and demonstrate that these automated outputs actually resonate with real buyers.
GUIDE
Maximize marketing ROI—before you waste another dollar
The insight gap AI can’t close
Across recent studies on generative AI adoption, marketers highlighted their biggest challenges:
- Ensuring the strategy fits the brand and market
- Capturing audience nuance across segments
- Maintaining emotional resonance in messaging
Although AI can predict, summarize, and generate, it cannot feel. And marketing is still an emotional discipline.
That’s because predictive models and generative pre-trained platforms (GPTs) like Gemini or ChatGPT are trained on historical data. They look backward to guess the future. They may be able to tell you that a specific demographic clicked on a blue button more than a red one, but they cannot tell you if the red button made them feel anxious or if the blue button felt trustworthy.
And that’s the danger: AI doesn't mislead intentionally; its outputs are simply tone-deaf.
When CMOs rely on AI-generated insight without human context, they end up optimizing for behaviors they don’t fully understand.
The rising cost of poor insight
Marketing leaders consistently cite demonstrating the impact of marketing actions on financial outcomes as their top challenge.
If a team uses AI to scale a message that fundamentally misunderstands the customer's motivation, they are not just failing; they are failing at scale.
AI flaws like hallucinations and misinterpreted user data during product launches can lead to wasted ad spend, high churn rates, and brand equity damage. And here’s a simplified breakdown:
Risk Factor | The Consequence |
Wasted ad spend | Targeting audiences based on correlated data rather than causal motivations. |
Product flops | Launching features that AI predicted would be popular, but real users find it confusing. |
Brand erosion | Releasing creative assets that are technically optimized but emotionally tone-deaf. |
To add to that, the cost of fixing a misunderstanding after launch is exponentially higher than validating the concept before a single line of code is written or a single ad is bought.
How high-performing CMOs blend AI and human insight
Platforms that unlock human customer insight with AI-driven tools leverage real user feedback as a "context engine" that validates AI output. It uses AI to generate hypotheses, variations, and segments, and then it uses human insight to validate those outputs.
This hybrid approach reduces bias and increases confidence. Here’s how:
- Testing personalization: AI suggests the segments; human feedback confirms if the personalization feels helpful or invasive.
- Messaging validation: AI writes ten variations of a headline; real users highlight the one that actually compels them to click.
- Creative concepts: AI generates storyboards; humans react to the emotional tone to ensure it aligns with the brand promise.
As an example Joe Chernov, who has led marketing at Pendo and Hubspot, thinks AI could help marketers dial in their buyer persona development, identifying any bias a marketer may bring with them.
“Persona development is way more art than science... and then along comes AI that can provide much more scientific ways of knowing who your ICP is... So you can have a much richer, much more dynamic, evolving, changing ICP through AI,” he said in an interview on Insights Unlocked. “I was really locked on to a definition of our buyer, and I don't even know where that definition came from... If I were CMO there today, I would have the opportunity to challenge this definition... and relieve myself of a bias.”
The new CMO imperative: human-centered AI
The next generation of marketing leaders won’t win by automating more tasks.
They’ll win by combining AI’s computational power with human empathy. They build strategies that move as fast as AI but think as deeply as their customers.
When CMOs fuse AI with real human insight, they unlock:
- Better product and campaign experiences
- More accurate, emotion-aware targeting
- Higher customer satisfaction (already shown to improve by 8.5% with AI-assisted workflows)
The future belongs to the human-validated brand
We are moving past the novelty phase of AI and into the utility phase.
The competitive advantage no longer belongs to the company that can generate the most content the fastest, but to the one that can best align itself with human needs and customer insight.
AI provides the map, but human insight provides the compass. CMOs who learn to integrate these two powerful forces will build brands that are not only efficient but also deeply resonant.
Those who don’t will likely find themselves running very fast in the wrong direction.
Key Takeaways
- AI adoption is non-negotiable: Marketing activities powered by AI are set to more than double in three years.
- Context is king: AI identifies behavioral patterns, but it cannot explain emotional motivations or the "why" behind the actions.
- Risk mitigation: Relying solely on synthetic data can lead to high-velocity mistakes that waste budget and damage brand reputation.
- The hybrid model: The best results come from using AI for generation and scale, and human insight for validation and empathy.
FAQ
Q: Why is human insight necessary if AI models are trained on massive datasets?
A: AI models are trained on historical data, which can contain biases and lack real-time emotional context. Human insight provides current, emotional, and nuanced feedback that historical data simply cannot offer.
Q: Can AI replace traditional market research?
A: No. AI can accelerate data processing and identify trends, but it cannot replace the empathy and deep understanding gained from observing real people interact with your product or brand.
Q: What are the risks of relying only on synthetic users?
A: Synthetic users are simulations based on probability. They can "hallucinate" preferences that do not exist in the real world, leading to product roadmaps and marketing strategies that fail when exposed to actual market conditions.
ON-DEMAND WEBINAR
