AI adoption in holiday shopping is rising
AI is making serious headway in holiday shopping, but we’re not at full throttle yet. The numbers show momentum, but they also show friction. Adobe’s 2025 consumer survey reports that 38% of shoppers have already used AI for online purchases, and 52% intend to use it this holiday season. Even more striking, Capgemini paints a clear upward curve in consumer preference, 58% now favor AI over traditional search engines for recommendations, up from 25% the year before.
That’s a sharp, fast climb. These aren’t marginal gains. They signal that AI isn’t a trend, it’s a transition. But here’s the caveat: not everyone is on board. A separate set of data from PissedConsumer.com found that nearly 70% of respondents don’t plan to use AI at all for holiday shopping. Read that again. That’s a direct contradiction.
What does this tell you as a business leader? We have a disconnect, not in the technology, but in perception. AI is proving itself to early adopters, but across broader markets, it’s still contending with cultural inertia and trust issues. The tech has arrived, but mass consumer belief in it hasn’t.
That’s where your opportunities, and challenges, live. As AI adoption grows, the C-suite needs to stay focused on real-world application, not theoretical capability. Don’t wait for consumers to warm up; instead, lead with AI strategies that are meaningful, simple to engage with, and respectful of user skepticism. Show up with clarity, not complexity.
At this point, we’re not asking whether AI has value. That question’s been answered. The real task is scaling that value in a way that builds confidence, not confusion.
Low consumer trust and satisfaction with AI are impeding widespread adoption
Despite growing interest in AI-assisted shopping, a large segment of consumers simply doesn’t trust the tech. That’s not a software problem, it’s a perception and delivery problem. According to Akeneo, only 45% of consumers trust AI assistance in shopping contexts. Just 38% are satisfied with AI-led customer service. These aren’t power users rejecting the idea, they’re the everyday buyers you’re relying on for scalable growth.
The hesitation stems from recurring issues. People doubt the accuracy of AI-generated product suggestions. They’re seeing inconsistencies, irrelevant results, and in some cases, outright hallucinations, responses from AI that are simply wrong. That undermines confidence fast. Add to that growing concern about transparency, how their data is being used, whether prices and conditions are current, and you get clear resistance. Not from all users, but from enough to matter.
If you’re leading a retail, eCommerce, or tech vertical, this should be a priority. Trust drives adoption cycles. Your AI tools need to do more than produce outputs, they need to prove reliability, accuracy, and consistency over time. If users feel like they’re not in control, or don’t understand what’s happening with their data, they’ll switch off. And most won’t come back quickly.
Customer confidence isn’t just about feature sets. It’s about experience. If the AI delivers one flawed recommendation, that impact is magnified in the mind of the buyer, especially during high-stress periods like the holidays. Your team should treat AI quality control the same way you’d treat product quality control: with discipline, accountability, and constant iteration.
This moment is high leverage. If you build trust now, through clear data disclosures, consistent experiences, and support layers that make the technology feel accountable, you’ll earn long-term adoption. Miss this, and you’ll need to win them back later with twice the effort.
Transparency and clear practical applications are key
Now we’re at the point where adoption meets resistance head-on. Consumers don’t automatically reject AI, they reject unclear value and opaque behavior. Whether your company is deploying AI for product recommendations, customer service, or backend personalization, the same rule applies: Explain what the tech is doing, and why. Make it obvious how it improves the customer’s experience.
A lot of shoppers are open to AI in concept but unsure when they’re interacting with it, and even more unsure about what’s happening in the background. They want to know how their data is being used, what decisions are automated, and where their input fits into the equation. When businesses avoid answering those questions directly, they create friction. And that friction slows adoption, even if the technology works.
Your customers won’t sit through long disclosures. You don’t need to flood them with details, but you do need to be clear. Plain, visible messaging that explains AI’s role, its benefits, and how preferences or data usage can be modified or opted out of, that’s the baseline expectation now. It’s not a burden. It’s an advantage you can lead with.
Practical use cases must be the centerpiece. Holiday shopping is high-pressure for consumers. If you’re using AI to save them time, show it clearly. If recommendations help them move faster through gift selection, say so, then ensure the outcomes are accurate. This is where you rise above the noise. Poor implementation creates skepticism. Thoughtful use focused on solving real problems builds momentum.
Adobe reports that over half of online shoppers intend to use AI this season. Capgemini shows a strong jump in AI preference for shopping recommendations, up from 25% to 58% in the past year alone. That growth happens only when consumer experience lines up with the promised benefit.
Give people visibility. Let them opt out when they want to. And ensure the outcomes you promise hold up under regular use. That’s how you build AI into something people don’t just use, but trust.
AI should be used to reinforce and enhance customer service
AI in customer service is useful, but only when it stays focused on solving real problems. When buyers struggle to get clear answers or feel like they’re trapped in endless automated loops, it damages your credibility. Current satisfaction rates tell the story: just 38% of consumers report being satisfied with AI-powered customer support, according to Akeneo. That’s low, and it reflects a mismatch between expectations and delivery.
If you’re leading a retail or service-driven business, here’s the straightforward move: use AI to support your workforce, not replace it. AI can handle repetitive requests efficiently. It can manage triage, filter inquiry types, pull up policy details, or track orders. But when it comes to nuanced issues, product complaints, order disputes, emotional interactions, those don’t belong in the hands of a script-driven system. They need people who can listen and respond in context.
The human layer is essential. But with AI streamlining the operational load, your teams get more time to handle complex requests properly. That shift improves morale, speeds up resolution, and leads to more consistent customer satisfaction. It’s not about pushing people out. It’s about clearing space for them to focus where they actually have leverage.
You also can’t ignore transparency here. Customers should always know when they’re speaking with AI. Don’t mislead. Be upfront and offer a human pathway when needed. That mechanism, human fallback, drives trust and prevents frustration.
This is where long-term value is built. During the busy holiday season, speed and clarity matter more than ever. Use AI to raise the baseline. Let your people raise the standard. That balance works across industries and use cases. It’s not about scaling for tomorrow, it’s about delivering today while earning loyalty that lasts past peak season.
Key takeaways for leaders
- AI adoption is rising but fragmented: AI is gaining traction in holiday shopping with consumer usage rising to 52%, yet 70% still report no intention to use it. Leaders should align AI rollouts with clear value to narrow the adoption gap and capture early majority users.
- Trust gaps are slowing AI momentum: Only 45% of consumers trust AI assistance, and just 38% are satisfied with chatbot experiences. Executives should reinforce reliability and transparency to reduce skepticism and improve user confidence.
- Clear utility and transparency drive adoption: Consumers are more willing to use AI when its purpose, data usage, and benefits are made explicit. Organizations should prioritize plain-language messaging, data disclosures, and opt-out features to earn trust and scale adoption.
- Augment, don’t replace, customer service: AI should support human service teams by handling simple inquiries, freeing staff for high-impact tasks. Leaders should invest in AI triage systems that improve efficiency while maintaining human backup to preserve service quality.
