Winning the 2026 Holiday Shopping Season

Retailers preparing for the 2026 holiday shopping season take note: It won’t be enough this year to pick the right products to promote or to accurately assess what consumers are willing to spend. Instead, you must also focus more deliberately on how people decide what to buy. That’s because there’s an increasingly powerful advisor separating businesses from their customers.

Three in four consumers now trust AI more than a close friend to advise on a purchase, according to Accenture’s 2026 Consumer Pulse study involving more than 25,590 people from 16 countries. When shoppers are willing to give AI that much influence, you need to make sure your products, people, and channels are working with AI to show up as the best option. You also need to follow through in ways that exceed expectations. Reduced to essentials, that means:

• Making choices easier for shoppers
• Reinventing frontline roles to build shopper confidence
• Overhauling operating models for AI-mediated shopping

To better appreciate why these actions are necessary, take a look at what our research highlighted about last year’s peak shopping season in the United States. It looked steady on the surface. Even as growth in the past few years has been largely attributed to inflation-led price increases, retail sales did rise about 4 per cent year over year between November 1 and December 21 (excluding autos). Holiday sales landed in the 3.7–4.2 per cent growth range, crossing US$1 trillion. And although the average selling price of goods grew 6–7 per cent year over year, shopping volume rose by 2 per cent, indicating that shoppers did not pull back despite concerns about inflation and economic stability.

Beneath the surface, though, consumers were evolving rapidly. They were looking more closely at what they were getting for their money. They compared harder and refused to commit until they felt certain they had found real value. E-commerce traffic rose around 10–12 per cent, but according to our research, time spent on retail sites jumped 28–35 per cent.

That extra time translated into people doing more work per purchase. In fact, 77 per cent of consumers felt overwhelmed by too many options, and 75 per cent felt stressed about making the right decision. Many people shopping online (83 per cent) abandoned carts due to frustration or indecision.

“We believe the 2026 peak shopping season will reward retailers who successfully build customer experiences as a single system across physical and online domains.”

They also began turning to generative AI for help. About 47 per cent of consumers said gen AI inspired confidence when they felt unsure and helped them narrow gifting options from a daunting array to a manageable number.

Physical stores, for their part, held their own. A third of younger shoppers (Gen Z and Millennials) said they preferred in-store shopping outright, as did, surprisingly, a third of those who identified themselves as heavy gen AI users in our survey. And ultimately, in 2025, they accounted for 73 per cent of holiday payment volume. But more shoppers arrived in-store after researching online, using the physical experience to compare and commit, gaining a confidence that no screen could provide.

We believe the 2026 peak shopping season will reward retailers who successfully build the customer experience as a single system across physical and online domains. Shoppers expect clarity on demand, across channels, with a human touch. And they won’t mind shifting to another retailer or retail format (online or physical) if their first choice does not provide the answers they seek.

That’s why we believe a successful 2026 holiday shopping season requires retailers to take the following three steps while there’s still time.

STEP 1: Making choices easier for shoppers

 Most retailers treat shopper confusion as a marketing problem. And yes, it can look like one. Our survey participants told us they felt bombarded by advertising, overwhelmed by too many options, and stressed about making the right decision.

That’s marketing, isn’t it? Not solely, no. The issue, at its core, is operations. Abandoned carts and a spike in returns mean lost revenue, higher expenditure, and a shopper who may not return—all of which are operational line items. That is why the fix starts with the architecture of the shopping experience. The idea is to engineer clarity for consumers just as the organization does for supply chain reliability.

Begin by reducing the cognitive load: making the path to purchase shorter and the right products easier to find. That means fewer near-duplicate items, clearer comparisons, and simpler “good, better, best” framing in high-choice categories where decision stress spikes. Stores, too, will need to evolve and give shoppers a reason to show up. More stores won’t give retailers the next retail advantage. Having moreright ones will. Ones that are designed for value and meaning through community and engagement.

This discipline also extends to merchandising decisions. Reducing choice overload means auditing high-decision categories for near-duplicate products, rationalizing range depth where variety adds friction rather than value, and curating shelf and floor presentation around the decisions shoppers actually face.

The key is the ease with which shoppers can move ahead from “what should I buy?” to “yes, that works,” irrespective of the channel with which they start or end their journey.  

STEP 2: Reinventing frontline roles to build shopper confidence

The store visit had a change in character in 2025. Shoppers were aware of their choices. What they needed was a fast, certain, and compelling answer. That put pressure on the already overwhelmed frontline staff. Ensuring that these individuals meet that pressure this season means rethinking what the frontline staff is for.

Consider someone who is coached, connected, and well equipped to function as a concierge, inspiring the shopper, guiding the decision, and executing the purchase without friction. In everyday categories like food and grocery, they’ll help a shopper achieve an outcome and add an unexpected suggestion that turns a routine visit into something memorable. In high-value categories like electronics and luxury, they’ll assume the role of an associate who understands what a shopper is trying to achieve and surfaces what hasn’t been considered. That’s what’s needed now, and not just in premium, exclusive contexts.

To make it happen, organizations need to support frontline staff with the right tools and with continuous coaching so they can use the tools fluently in front of shoppers. Alleviate their stress to the extent possible, and they will improve the atmosphere for customers, by extension. At the end of the day, the idea, as a recent World Economic Forum briefing note points out, is to build an always-on change muscle, given the continuous evolution of AI-led technologies and, consequently, workflows.

Real-time visibility into inventory, promotions, and policies enables instant query resolution. Simple decision scripts for the moments that trigger hesitation give associates the fluency to convert uncertainty into commitment: “Which one is best for a first-time user?” “Will this work with what I already own?” That kind of guidance closes the trust gap that digital guidance still struggles to fill.

Some examples that show progress? Starbucks’ gen AI-powered Green Dot Assist lets baristas ask questions like, “What are the ingredients of this seasonal beverage?” and get an instant response that helps them provide superior customer service.  Similarly, Ace Hardware’s Hey ARMA offers store staff quick access to product knowledge, project advice, and recommendations. It also supports in-store scenarios, for example by helping staffers compare products and even discuss items a shopper might have purchased elsewhere.

The physical environment itself needs to support this. Intelligent stores—where inventory data is live, layout is designed around decision moments, and staff have instant access to the same information a shopper might have found online—remove the conditions that erode confidence. When the infrastructure works, the associate does not have to compensate for it. And the store becomes a system for building certainty.

Beyond these two steps, retailers can undertake a broader operating model overhaul to enable AI-mediated commerce over the next few years. But they must start now.

STEP 3: Overhauling operating models for AI-mediated shopping

 Gen AI now acts like the front door for many shoppers. People use it to brainstorm gifts, compare options, narrow choices, and build confidence. That creates a strategic leak: If discovery and comparison happen inside someone else’s AI ecosystem, the relationship and data drift away from your brand. As AI earns more of that trust, the leak widens.

The stakes rise further as shopping becomes more agentic. Consumers are open to delegating purchase decisions to AI agents that research, compare, and transact on their behalf. When that happens, the brand relationship risks being bypassed entirely. Brands that are not visible, verifiable, and easy to act on inside these systems will not be in contention.

Competing in this environment requires rebuilding the operating model around AI. That means redesigning demand forecasting, inventory allocation, and fulfilment, so the business can absorb peak-season complexity without proportional cost increases. It also means getting product data right at the source: specific, consistent, and structured so that AI systems can surface, evaluate, and rank your brand accurately.

But that can’t happen overnight. In fact, most retail executives told us that they’re in the early to middle stages of readying their brand for agent shopping. They are activating product data, reviews, and discovery but not yet connecting the full journey from chat to checkout. Our survey reveals that just 5 per cent of U.S. retailers expect their brand’s shopping journey to be fully gen AI-optimized by the end of 2026.

In that context, the road to AI optimization raises a host of difficult questions. If every retailer deploys gen AI and cleans up product data, where does their differentiated advantage come from? How will brand loyalty evolve as AI eases consumers’ ability to compare and switch? How should gen AI fit into the company’s identity as a retailer? As Accenture research on agentic commerce shows, most organizations will concentrate on making their brands unmissable and attractive to agents. A select few with proprietary data and authority in a particular category may also decide to offer their own agent—one that others (people and agents) turn to for help.

However, the key to winning the 2026 holiday season will be a combination of operational rigour and prioritization clarity. Resist the urge to solve everything at once, at least until the 2026 shopping season. Go instead to where shoppers need guidance most. Start where decision stress runs highest: categories such as gifting, electronics, and beauty, where shoppers seek reassurance and where gen AI use rises with complexity. Then pilot AI-powered gift assistants that help shoppers shortlist, bundle, and compare. Ensure that your value is legible to agents: sharp differentiation, provable claims, and product data structured so that an AI can surface, evaluate, and act on it without friction.

Beyond this season, look to leading retailers whose activities are starting to show what’s possible. Target, for example, paired its in-store experience with two app capabilities: the “Store Mode” map and the AI-driven “Gift Finder.” Together, the two tools help shoppers find the right present faster through navigational help and tailored gifting suggestions. Consider, too, the fast-expanding supporting ecosystem. Gemini’s AI Mode for shopping, for example, draws on Shopping Graph’s 50 billion products to help users generate ideas and find inspiration through conversational search.

So act now because what happens in December depends on it. The clarity, the AI infrastructure, and the frontline capability that convert a stressed shopper into a confident buyer take months to build and test. And this work cannot be compressed into the run-up to the season. Those who wait will find themselves improvising at a time when excellence matters most. But retailers who start now will arrive at the peak with a system they’ve already tested and adjusted. The shopper, sharper and less forgiving than last year, will see the difference.

The authors would like to thank Priya Verma and Regina F. Maruca for their contributions to the article.