THE AI ENGAGEMENT PARADOX: Why AI Is Making Marketers Smarter, and Some Customers Feel More Overlooked Than Ever

Here is a statistic that should give every C-suite leader pause: 93% of marketers say artificial intelligence helps them understand their customers’ wants and needs more accurately. Yet only 53% of consumers agree that brands are accurately predicting those wants and needs. That is a 40-point gap, and it represents one of the most critical blind spots in modern customer engagement.

This is the central finding of Braze’s 2026 Global Customer Engagement Review, drawing on surveys of more than 2,200 VP-level marketing decision-makers, 4,000 adult consumers, and behavioral data from over 6 billion user profiles. The findings deliver a strategic reckoning that every business owner and marketing leader needs to internalize: AI is not the whole answer. In fact, deployed without discipline, it can quietly erode the very customer relationships it promises to deepen.

Let’s break down where the gaps are, where the opportunities lie, and what you can do about it.

Why Consumers Still Feel Overlooked

The paradox at the heart of today’s AI-powered marketing landscape is this: the tools have never been more powerful, yet brand communications have felt more and more generic. According to the Braze report, 52% of consumers say that most brands they encounter online look and feel identical, with no memorable distinction. In an era when AI promises hyper-personalization at scale, that is a damning indictment.

52%  of consumers say most brands online look identical with no memorable distinction

53%  of consumers say brands are accurately predicting their wants and needs

The root cause is a fundamental misalignment between what AI can theoretically do and what brands are operationalizing. Most organizations are deploying AI to optimize for marketing efficiency rather than customer value. The result is faster, cheaper, more voluminous communications that consumers still tune out.

The data-sharing problem compounds this. Only 26% of consumers are willing to share demographic or purchase data with AI agents, and a full 27% refuse to share any data at all. This is not consumer irrationality, it is a rational response to a broken value exchange. Brands are collecting data but failing to demonstrate that sharing it results in meaningfully better experiences.

A telling real-world example: a major apparel retailer invested heavily in an AI-driven recommendation engine, only to surface products customers already owned, categories they had no interest in, and price points misaligned with their spending patterns. The AI was technically personalizing, but on incomplete, siloed data. The result was irrelevance at scale.

The structural fix is real-time data infrastructure. Only 55% of marketers are updating and leveraging customer information in real time through data streaming architecture. That means nearly half of all marketing organizations are making AI-driven decisions on stale data. In customer engagement, yesterday’s signal is tomorrow’s misfire. The Braze data confirms the payoff of getting this right: 70% of top-performing “Ace” brands exceeded their revenue goals in 2025, and they are 30% more likely to use AI to predict customer churn, purchase intent, and key lifecycle actions.

"Knowing more about your customers does not automatically mean serving them better."

How AI Is Starting to “Shop” for Your Customers

One of the most consequential shifts in the customer engagement landscape is the rise of AI intermediaries. Consumers are increasingly using tools like ChatGPT, Google Gemini, and Anthropic’s Claude not to interact with brands directly, but to do their shopping, research, and decision-making on their behalf. Rather than visiting your website, a growing cohort of consumers is asking their AI assistant to find the best deal, discover new products, or manage brand relationships entirely.

19%  of consumers currently use AI intermediaries to interact with brands

46%  will use AI intermediaries by year’s end – a 1.4X year-over-year increase

The implications are profound. When a consumer’s AI agent is evaluating your offer, your loyalty program, and your product catalog, the rules of engagement change entirely. The AI agent does not respond to emotional appeals or brand storytelling. It optimizes for the criteria its user has instructed it to prioritize: typically price, convenience, and relevance. If your brand cannot compete on those dimensions within the AI-mediated layer, you lose the customer before they ever interact with you directly.

Already, 63% of marketing leaders say AI intermediation has weakened their ability to connect with customers and maintain direct relationships. Consider what this means for a quick-service restaurant brand: a consumer might simply instruct their AI agent to “order dinner” and the agent determines which restaurant, which items, and which loyalty offer to apply. The brand with AI-readable product data, accessible loyalty APIs, and real-time pricing wins the transaction. The brand without that infrastructure is invisible.

Consumers turn to AI agents primarily for better deals (35%) and easier product discovery (32%). Both are addressable. Brands that build for AI discoverability, through structured data, API integrations, and presence in AI-native environments, can turn this shift into a strategic advantage. The strategic move is not to resist AI intermediaries but to meet your customers where they are going.

"When AI shops for your customers, your marketing must speak to algorithms before it speaks to people. Most brands are not ready for that."

What It Really Takes to Balance Automation with a Human Touch

AI, by itself, is not a customer engagement strategy. It is an infrastructure layer, and without the right strategy and human oversight behind it, even the best models will consistently underdeliver. The third major finding from the Braze research makes this unmistakably clear: the human element in customer engagement is not a nice-to-have. It is a competitive differentiator.

When brands use AI without maintaining a human touch, 35% of consumers feel disappointed, and 27% feel frustrated. Right now, only 46% of consumers believe brands are successfully maintaining that human quality in their AI-powered communications. That means the majority of consumers find AI-driven brand interactions lacking in warmth, empathy, or basic contextual judgment.

The brands getting this right are doing several things consistently. According to the research, 53% maintain a human touch through advanced personalization, ensuring every AI-generated communication reflects individual customer context rather than broad segment assumptions. Another 53% are transparent about their use of AI, which builds consumer trust rather than eroding it. Fifty percent have humans reviewing AI-led engagement before it reaches customers. And 44% train their AI systems on real customer feedback, creating a continuous improvement loop grounded in actual human responses.

The Infrastructure Imperative

AI is an amplifier. Without real-time data streaming, cross-channel orchestration, and unified customer profiles, even sophisticated AI models will underperform. Starbucks’s Deep Brew initiative illustrates what the gold standard looks like: millions of loyalty members receive a genuinely personalized app experience because it is built on a real-time, unified data foundation. Customers do not notice the AI, they just notice that the brand seems to understand them. That is the bar.

Practical Actions for Business Leaders

The research translates into six concrete moves executives should prioritize:

• Audit your data infrastructure before scaling AI. Siloed, delayed data is the primary reason AI personalization fails. Invest in unification and real-time streaming as foundational prerequisites.

• Define your value exchange explicitly. Communicate clearly what customers gain by sharing their data, and then deliver on that promise consistently. When brands accurately predict customer needs, 23% of consumers are more likely to purchase again, and 30% are more likely to stay loyal.

• Prepare for agentic commerce now. Structured product data, loyalty program APIs, and presence in LLM-native environments are no longer future-state considerations—they are current competitive requirements for high-value customer segments.

• Institutionalize human review. Build human oversight into AI-driven campaign workflows. This is not a lack of confidence in AI, it is mature understanding of where AI still fails, particularly in contextual and empathetic judgment.

• Be transparent about AI. Proactively communicate when and how you use AI in customer engagement. In an environment saturated with algorithmic skepticism, transparency is a brand differentiator.

• Build feedback loops. Fifty-five percent of top-performing brands use ongoing feedback mechanisms, surveys, NPS, behavioral analytics, to keep their AI systems grounded in real customer voices. Your customers’ input is your best model-training data.

The Bottom Line

The 2026 Global Customer Engagement Review delivers a clear verdict: the 40-point gap between marketer confidence and consumer satisfaction is not a technology failure, it is a strategy failure. It is what happens when organizations deploy AI in service of internal efficiency goals rather than customer value creation.

The brands that will win the next chapter of customer engagement are those that treat AI not as a replacement for human-centered strategy, but as its highest-fidelity expression. They will invest in the real-time data infrastructure that gives AI the context it needs to make genuinely intelligent decisions. They will build transparent, value-driven customer relationships. They will prepare for an agentic commerce environment where algorithms evaluate their offers before humans ever do. And they will maintain the human oversight and empathy that ensures AI remains a tool in service of people, not the other way around.

The AI engagement paradox is real. But it is solvable. The roadmap is in the data. The brands willing to do the hard strategic work. not just the technology deployment, will emerge with deeper relationships, stronger brand equity, and a competitive position that no amount of AI spending alone can replicate.

Ready to Close the Gap?

If your business is ready to move beyond the noise and build a customer engagement strategy that actually works, let’s talk. Angelo Ponzi and the team at Craft help business owners and executive leadership teams translate data-driven insights into customer-centric growth strategies, from auditing your martech stack to preparing for the agentic commerce era.

Start the conversation today at craftmarketingandbranding.com, because in a world saturated with AI-generated noise, authentic human-led strategy is your greatest competitive differentiator.