2026 AI Experience Benchmark
AI builds consumer trust, bad experiences break it.
AI is accelerating consumer trust in brands, but that trust is very fragile. The difference comes down to how effectively teams can understand and act on these moments with the right data.
Our survey of 1,500 consumers and 750 digital leaders across the US and UK, combined with anonymized, aggregated behavioral data from Quantum Metric, reveals how trust in AI is formed, where it breaks down, and how teams can better support it.
Key takeaways
AI is driving higher-intent customer loyalty.
10%
higher average monthly CVR YoY between 2025-2026.
98%
of consumers make repeat purchases from AI-recommended brands.
But, AI customers will also abandon at the first sight of friction.
2X
more likely for AI-referred to abandon when facing friction.
81%
won't return to an AI-referred brand after an issue.
Digital teams are investing in the wrong AI priorities.
46%
of consumers want brands to prioritize AI for search support.
52%
of digital leaders prioritize AI to automate customer support.
Likely due to their own barriers to data trust and AI adoption.
62%
are concerned about misinterpreted data and incorrect outcomes.
54%
higher confidence in data quality is needed to boost AI trust.
Section 1
How is AI changing customer discovery and conversion rates?
For consumers today, digital journeys increasingly begin with AI, with 1 in 5 consumers starting their search on an AI platform.
And they aren’t using it to validate decisions they’ve already made:
- Only 13% go to AI to confirm an existing choice.
- Half of consumers use AI to find the best option, often uncovering new brands.
No surprise then that AI-referred traffic is seeing ~111% annualized growth, more than doubling year over year.
But, that growth isn’t steady. Platform findings show adoption accelerates during seasonal periods, even outside of the holidays.
Typically, when a channel scales, conversion rates decline. AI traffic is the exception. The graph, AI-referred traffic and conversion rate, shows average monthly CVR is already 10% higher in 2026 than 2025 and is increasing 5% month-over-month.
The chart below, YoY trends in AI-referred traffic growth: By industry, shows that growth is not happening evenly across industries.
Retail leads in scale, while travel stands out for both sustained conversion performance and higher-value transactions, with AI-driven cart values nearly 2x higher than traditional traffic.
What does it all mean?
AI is driving traffic, and more importantly reshaping how trust is built.
- Consumers are discovering brands earlier, arriving with stronger intent, and placing confidence in AI recommendations before ever engaging directly.
- And even if AI traffic includes more browsers than buyers today, what those users experience will shape whether that trust holds.
EXPERT TAKES
What trends in AI traffic are digital brands seeing today?
“We’re seeing AI-driven traffic concentrate around high-intent moments like flight offers and booking management, as well as key informational pages. It suggests customers are arriving with a clearer sense of what they need, using the experience to validate recommendations and find specific answers.”
José Manuel García Muñoz / Digital Business Analyst & CRO, Air Europa
Section 2
How patient is AI traffic with digital experience errors?
Spoiler alert: not very.
Consumers trust AI, but that trust varies by different products or services. As seen in the survey response chart below:
- 18% would fully trust AI for lower-cost purchases or travel bookings
- 67% trust AI for style-based recommendations like clothing, books, and music
- 64% trust AI for cable, streaming, or mobile phone recommendations
- 45% would trust AI recommendations for financial products
Added to this, 46% of consumers believe AI favors certain brands, but they still expect those recommendations to hold up once they take action.
That expectation makes the digital experience critical. When a consumer follows an AI recommendation to a brand site, tolerance for friction is low.
- 2 in 5 consumers say they will leave a site recommended by AI after a single issue
- 81% say they are unlikely to return.
Platform data confirms this. The chart, Abandonment vs. errors, shows, AI-referred users are over 2x more likely to abandon when an error is introduced.
This reflects a fundamental shift in behavior. Traditional users explore, retry, or navigate around issues. AI-referred users arrive with a clearer expectation and when that expectation isn’t met, they leave.
What does it all mean?
AI has annihilated any margin for error.
- Consumers are arriving from AI with higher intent, stronger expectations, and less patience for friction.
- As AI continues to shape how consumers discover and evaluate brands, the challenge for digital teams is no longer just optimizing for conversion, it’s ensuring that every experience delivers on the promise AI has already made.
EXPERT TAKES
Where should brands optimize their experience for AI-referred traffic?
“As more customers turn to AI tools to solve problems, expectations for speed and accuracy are increasing. When information is incomplete or inconsistent, it becomes harder for those tools to clearly represent your products, introducing friction at a point where customers are less willing to tolerate it.”
Tyler Moore / Ecommerce Product Manager, LLBean
Section 3
Where do consumers actually want AI in the digital brand experience?
So, if we are trusting and using AI more than ever, wouldn’t that mean we want more AI once we arrive at a website?
Not exactly: Half say AI has not improved their experience
There’s a disconnect between what brands are building and what consumers need. The survey response chart on AI investments shows:
- Half of digital teams prioritize automating customer communications via chat or contact center, the least desired AI application for consumers (20%).
- 46% of consumers want AI to optimize search support, yet fewer than 20% of digital teams prioritize today.
Another disconnect: 2 in 3 digital leaders primarily focus on conversion rate as a mark of AI success, while just 37% look at voice of customers surveys or other customer feedback.
KPIs like conversion rates matter, but are better understood in the context of other unstructured data. Without it, digital teams could be misreading AI performance.
For example, our platform data shows: Time on site for AI-referred users is ~25% lower than traditional traffic. On the surface, this could suggest lower engagement.
However, the chart, Time on site by traffic type, shows that in early 2025, AI-referred users spent 2X more time on sites versus traditional traffic, likely to validate AI recommendations.
As adoption grew, time on site decreased while conversion rates increased, suggesting consumers are now trusting AI recommendations more.
What does it all mean?
AI is redefining how to make digital experience investments.
- Not every AI investment should be designed to drive immediate conversion. AI can play a critical role in building long-term loyalty and retention.
- When investing in AI-powered experiences like customer support or automation, brands need to be more intentional about when and how those tools are used.
EXPERT TAKES
What's the best way to consider AI investments?
“The next phase of AI in retail isn’t just about driving transactions—it’s about deepening relationships. This makes it critical for brands to be thoughtful about how and when AI-powered experiences, like customer support and automation, are introduced into the customer journey.”
Chris Colyer / Worldwide Head of Retail Industry Partnerships, Google Cloud
“As AI-driven traffic grows, having visibility into where it comes from and how it behaves is critical. We’re investing in dashboards and internal AI tools to better understand performance and identify friction points faster, while continuing to validate those insights against real customer behavior.”
José Manuel García Muñoz / Digital Business Analyst & CRO, AirEuropa
Section 4
What does adoption of AI look like for internal digital teams?
The biggest hurdle in AI adoption, digital teams still don’t trust the technology.
Skepticism within digital teams comes down to concerns about analysis and how and where data is sourced. The survey response graph on AI skepticism shows:
- 62% of digital leaders are concerned about misinterpreted data and incorrect outcomes
- 36% say AI insights most often break down at the point of analysis and interpretation
When asked why, digital leaders point to a fundamental gap: the data foundation behind AI applications feel incomplete and/or lack context. The survey response chart on AI trust in digital decisions making shows:
- Today, only 34% of digital leaders feel confident their data foundation can support AI-driven decision-making.
- The majority believe higher confidence in data quality and greater transparency are needed to boost AI trust.
As teams adopt multiple AI tools across functions, fragmentation also becomes a barrier: 1 in 4 digital leaders say disparate AI platforms hinder adoption, citing integration challenges and the inability to connect insights across systems.
Despite these challenges, 65% of digital leaders say they would trust AI for high-impact decisions if the right data foundation and transparency were in place.
What does it all mean?
Trust in AI doesn’t start with the model, it starts with the data.
- While “garbage in, garbage out” is a common refrain, the bigger issue is often more subtle: incomplete data, disconnected systems, and a lack of business context can be just as damaging.
- This is why so many AI pilots fail. Not because the technology isn’t capable, but because the inputs aren’t sufficient to produce actionable, trustworthy outcomes.
- The success of any AI investment depends on the ability to build a connected, reliable data foundation that reflects the full customer journey.
EXPERT TAKES
How are brands navigating AI adoption for their teams?
“We’re making sure all customer experience AI pilots are rooted in “utility” with a clear problem we’re trying to solve and how we’ll measure success. This informs strong persona-based prompts to surface meaningful insights and guides our team as we vet the AI analysis. It helps to think of AI like a dialogue, you might have to re-phrase for clarity, use both open-ended and leading questions; like a conversation, it’s one perspective to consider in the context of others.”
Jeff Phillips / Director of CX Operations, American Medical Association (AMA)
“AI adoption is accelerating, but without a unified data foundation, it risks becoming fragmented and underutilized. When insights are trapped across disconnected systems, organizations can’t fully realize the value of AI. Encouragingly, a majority of digital leaders are ready to trust AI for high-stakes decisions—what they need is confidence in the data behind it. Building integrated, transparent data ecosystems is what will ultimately turn AI from a collection of tools into a true strategic advantage.”
Brian Kane / Head of Global Strategic Partnership Management, Google Cloud
What does this mean for your AI experience?
- Don’t just think about how you show up in AI search, but how you build connections with customers early and often to build trust.
- Getting every experience right is more important than ever. Just because more discovery happens on AI, doesn’t mean you don’t need visibility into what happens on your site.
- Consider looking outside of typical KPIs and incorporating VoC feedback into the context of your digital data to determine when and how consumers use AI.
- If your team can trust their AI tools, they can build the right AI investments to boost your experience and capitalize on the loyalty opportunities AI traffic is bringing to your door.
Learn how Quantum Metric is taking on the next phase of AI with Felix Agentic.
Capturing more data points than traditional analytics, Felix Agentic is built on a foundation that takes you from question to customer understanding and action, faster.