Was denken Verbraucher*innen
im Jahr 2026 wirklich über KI?

KI-Fans nutzen KI bereits ganz selbstverständlich. Sie haben kein Problem damit, Aufgaben der KI zu überlassen und neue Tools auszuprobieren, sobald diese verfügbar sind.

Dank ihrer Erfahrung mit KI-Tools haben sie allerdings auch höhere Erwartungen an Markenerlebnisse: KI-Fans bemerken zu allgemeine oder sich wiederholende Marketing-Inhalte besonders leicht, insbesondere in E-Mails, Textnachrichten und Produktempfehlungen. So nutzen sie KI:

• Sie nutzen KI mindestens einmal pro Woche, oft für verschiedene Aufgaben.
• Sie betrachten KI als eine Lösung, die ihnen Zeit spart und ihnen den Alltag erleichtert.
• Sie hinterfragen KI-Output nur selten.

KI-Tester*innen sind vom Potenzial der KI überzeugt, überlegen aber noch, wo sie am besten einzusetzen ist. Sie nutzen KI bewusst und nicht gewohnheitsmäßig.

Sie vertrauen der KI in gewissem Maß und verwenden sie monatlich oder gelegentlich für bestimmte Aufgaben.

KI-Skeptiker*innen betrachten KI als ersten Ansatzpunkt und nicht als endgültige Antwort. Sie nutzen KI mindestens monatlich, oft für die Arbeit oder aus Produktivitätsgründen, überprüfen die Ergebnisse aber regelmäßig.

KI-Skeptiker*innen nutzen KI, allerdings unter Vorbehalt. Sie sind vorsichtig, haben aber erkannt, dass KI nützlich und effizient sein kann.

61 % der KI-Skeptiker*innen sind Frauen.

KI-Zögerer*innen nutzen KI selten oder nie. Wenn sie sie nutzen, trauen sie ihr nicht.

KI-Zögerer*innen empfinden KI als unnötig, riskant oder überbewertet. Bisher haben sie weitgehend auf die Nutzung von KI verzichtet.

58 % der KI-Zögerer*innen verlieren das Vertrauen in Marken, wenn diese beim Marketing offensichtlich KI-generierte Inhalte einsetzen.

Back in 2022, the average Google search contained 1-2 words, according to a Semrush report. Now, 30% of AI searches contain 8 words or more, and 52% contain 3–7 words, with multiple descriptors. In other words: rather than searching for a simple word or phrase and doing the rest of the research themselves, today’s consumers are giving AI tools more detail up front and relying on the technology to take the extra steps.

For example, a few years ago, a consumer might’ve searched the term “red coat” on Google. Now, the same shopper might use a more detailed AI prompt, like “I live in Chicago and want a red winter coat that’s waterproof and has a fur lining. I’m looking for something stylish yet practical, with good reviews.”

In addition to adopting this more conversational, detailed approach, 78% of people say they include emotional or personal context at least some of the time when using AI. For example, a shopper might ask for “something to cheer me up,” or “a gift that feels thoughtful.”

Some groups have figured out that it’s best to provide as much detail as possible. Respondents from Asia Pacific are more likely than those in the US and Europe to use 8 words or more. The same goes for Gen Z respondents and daily AI users.

These AI superusers are likely getting better responses and outcomes from using AI, which is encouraging continued usage. On the other hand, baby boomers are more likely than average to use one- or two-word queries, which may not give them the best results—and may be contributing to lower AI trust and overall usage.

While many customers say they don’t fully trust AI, it still influences what they buy: 41% of the consumers we surveyed say they’ve purchased a product AI recommended within the past 6 months. On top of that, an additional 27% say they’ve learned about a new product from AI and then gone on to research it further.

However, AI’s influence varies greatly across different audiences. Here’s what our survey found, based on consumer behavior during a six-month period:

• 43% of AI Enthusiasts bought a new product based on AI suggestions more than once.
• Gen Z and millennials are 21% more likely than the general population to have done so.
• High-income respondents (>$150,000 annual salary) are 64% more likely, possibly because they’re more likely to have used AI at work or home.
• Baby boomers are 58% more likely than the general population to not use AI for product discovery.

In 2026, shoppers aren’t impressed by generic recommendations. “Customers also ordered these products” doesn’t cut it anymore. Instead, consumers are looking for smart, personalized recommendations that complement items they’re buying, elevate the shopping experience, or anticipate their next move.

That’s where customer data makes all the difference. When you collect information about each shopper, you can use AI to make contextual, relevant product recommendations based on details like:

• Past purchases
• Customer service conversations
• Browsing behavior
• Customer preferences
• Demographic information
• Any other data you’ve collected

You can surface these personalized recommendations in your brand’s self-service Customer Hub, cross-channel marketing messages, customer service conversations, or right on your website.

Most often, consumers see low-quality AI on social media, in ads, or in messages from customer service chatbots. They can tell what’s AI when responses are “AI-ish” or images are poorly generated. And practically speaking, people can tell if a response is too quick to be from a human, if it sounds robotic or formal, or if it seems unnatural.

Generally, consumers think that if something could be AI, it probably is. As part of our survey, tested AI-generated images and AI-generated customer service messages to see if people could spot the difference. While 46% of respondents were at least somewhat confident they could distinguish AI, more than 30% incorrectly identified a human customer service response as AI. On the visual side, more than 50% of respondents thought an image was AI-generated when it wasn’t.

Just because a response is obviously AI-generated doesn’t mean it will necessarily annoy consumers. When we asked people what makes them feel understood when interacting with AI, they said they’re looking for clear, relevant answers to their questions, details from current or past interactions, and tailored recommendations based on their preferences, with explanations.

To build trust while using AI, brands have to be careful about where and how they’re using it. The Holdout persona is especially reactive to bad AI: 58% lose trust in brands if they clearly use AI-generated content in their marketing.

In 2026, we predict that brands will lean on their creative sides to craft more human marketing campaigns, even while using AI marketing agents and AI photo editors for help on the production end. You’ll need to curate high-quality content and infuse personality, while staying on brand as you scale your marketing.

Opinions vary, but more than 1 in 5 survey respondents told us that they feel most uncomfortable with inaccurate AI and AI that feels “too personal.”

When we asked what makes people uncomfortable when talking to AI, the top response was “AI that gives inaccurate or misleading answers.” At the same time, 41% of consumers say customer service chats that “don’t feel human” are the top brand experience that feels “too automated,” or not personal enough.

Our takeaway? These findings don’t mean consumers are against AI—they just want a better experience. It’s not enough for your AI agent to reply in seconds if its response is generic or unhelpful. Smarter AI tools can help brands deliver the nuanced, personalized experiences that today’s consumers are looking for.

*See appendix for additional data about experiences that feel “too automated.”