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Making execution as intelligent as everything upstream of it.

Most retailers enter 2027 with a real AI strategy–demand sensing, personalization, contact center automation. What they underestimate is how fast that strategy stalls when the fulfillment layer can't keep pace.

Shoppers don't experience your demand forecast. They experience whether the order shipped from the right location, arrived when promised, and got resolved without friction when something went wrong. That all happens in execution.

The retailers who win in 2027 will deploy AI where the operational cost lives: autonomous exception resolution, real-time inventory rebalancing, fulfillment logic that adapts without an IT ticket. In a margin-compressed environment, the execution layer is where the advantage gets built–or lost.

Retailers spent the last decade optimizing acquisition and checkout. The next competitive battleground is what happens after the purchase and using the refund experience as the $0 CAC for the next repurchase.

In 2027, the brands that win will treat refunds, returns, and post-purchase resolution as core infrastructure–not back-office workflows.

Consumers increasingly expect immediate resolution when something goes wrong. Waiting 5–10 business days for a refund now feels broken in the same way waiting days for a payment would feel broken. At the same time, retailers are under pressure from rising customer acquisition costs, higher dispute volumes, tighter margins, and increasing operational labor costs across customer support and finance.

The retailers pulling ahead are reframing refunds from a cost center into a strategic operating lever impacting:

• Customer lifetime value.
• Chargebacks and disputes.
• Working capital.
• Support efficiency.
• Open-to-buy flexibility.
• Customer retention.

“Refund to original tender” will increasingly become viewed as a legacy constraint rather than a customer strategy.

Over the next few years, we’ll see leading retailers build more dynamic post-purchase experiences: instant refunds, flexible disbursement options, closed-loop recovery mechanisms, and automated resolution workflows integrated directly into the customer experience.

The future of retail won’t just be defined by how merchants take payments.

It will be defined by how intelligently merchants refund them.

How to cultivate and, more importantly, sustain brand affinity.

The two core trends that will impact brand engagement and building are 'The agentic commerce' trend, and "Fracturization of customer base.

The agentic commerce trend has the potential to shift the products to the lowest common denominator–price. If an agent is shopping on behalf of the customer, then how does the retailer evoke emotion in that transaction? Essentially, retailers will have to work with a marketplace, albeit an agentic marketplace.

Fracturization of customers raises the challenge for retailers to be authentic without diluting or eroding trust or minimizing or losing large swaths of the audience. This is an outcome of the society becoming openly opinionated and highly vocal about their affinities, which are now a multitude of polar extremes.

This is the environment retailers have to navigate. They either pick a set of like-minded audience segments and become hyper-focused on them, or try to avoid extremes. The most successful retailers would truly navigate their own path and identity, rather than build their brand by pandering or succumbing. These retailers will not have to fear the agentic commerce, nor will they have to worry about losing segments. Audiences will seek them out.

Meeting shoppers where their expectations are already being set.

Shoppers are increasingly starting their journeys on ChatGPT, Perplexity, and Google’s AI overviews, getting answers, comparisons, and recommendations before they ever land on a retailer’s site. By the time they arrive, they've already experienced a standard of guidance that most onsite search experiences weren't built to match. That’s the gap retailers need to close by 2027.

In a survey of 4,000 shoppers across the U.S. and U.K.*, nearly 60% said they're more likely to buy when guided to exactly what they need. 67% expect conversational assistance to become core to the experience, a figure that rises to over 80% among Millennials and Gen Z.

The answer isn’t a chatbot bolted alongside search but a unified experience that understands intent and responds accordingly. Giving shoppers products when they’re ready to buy, and answers when they’re still deciding. The search bar is already where most shoppers go first. The opportunity is to make it do more: handle a specific query with precision, and handle an open-ended question with genuine guidance, without forcing the shopper to switch modes or tools.

The retailers that pull ahead will be the ones who make intent-aware guidance feel native to the experience, not a separate assistant, not a different interface, but search that understands what a shopper is trying to accomplish and helps them get there quickly.

Relevance by 2027 means delivering clarity as much as results.

Source: https://explore.coveo.com/report/2026-commerce-relevance-report

The data layer powering their ESP and CRM. Brands have spent the last few years bolting AI onto tools that only see what they already own: purchase history, email opens, on-site events. That's a fraction of how shoppers actually behave. As AI agents move from novelty to default–drafting campaigns, segmenting audiences, deciding who gets which message–they'll only be as smart as the shopper data feeding them. The 30/60/90-day engagement segment that's anchored retention marketing for a decade doesn't have enough signal to fuel that future. In 2027, the brands that pull ahead won't be the ones with the biggest lists or loudest sends. They'll be the ones who can answer harder questions: which anonymous visitor is about to convert for the first time? Which "engaged" subscriber is actually drifting? Which shoppers got excluded too soon–and why? Volume is no longer a strategy. Intelligence is. Every retention leader heading into 2027 should be asking the same question: does our marketing stack know more about our shoppers today than it did a year ago? If the answer is no, that's the gap AI alone won't close.

Agents will become first-class citizens in Commerce. The 'storefront' will exist wherever a shopper's AI agent happens to be. Commerce infrastructure will serve agents as well as human customers. Merchants who have evolved their approach from 'digital commerce is a destination', to 'digital commerce is an agentic operating system' will win. Those who have prepared their product data and can seamlessly orchestrate as merchant of record across agentic networks will be converting customers in moments they never could have reached before. Those who haven't invested in the relevant data, protocols and partners will simply be absent from the journey.

The unglamorous groundwork that will determine who actually wins in agentic commerce.

While headlines focus on agentic checkout experiences and rapid advances like Google’s UCP updates, the real differentiator will be the quality of the underlying commerce infrastructure that agents rely on to operate. Most retail systems were designed for human interaction–fragmented APIs, inconsistent product data, and workflows built around manual decision-making. That architecture simply does not translate into a world where AI agents discover products, assemble baskets, and execute transactions autonomously.

Winning in 2027 will depend on whether retailers have already invested in structured, machine-readable, real-time commerce data. This includes catalog enrichment for LLM discovery, accurate inventory and pricing signals, and operational systems that expose workflows in ways agents can execute reliably.

In practice, this means shifting investment away from isolated AI pilots toward foundational “agent readiness”: generative engine optimization of product data, unified commerce APIs, and operational redesign that enables automation across the full commerce lifecycle.

The companies that treat this as infrastructure work–not experimentation–will be the ones able to plug into rapidly evolving agent ecosystems as they scale. Everyone else will find themselves technically “integrated” but practically invisible.

That retail, QSR, and grocery brands will stop thinking about their apps and websites as purely transactional tools and start viewing them as engagement ecosystems. This next shift in loyalty will be less about who has the smoothest checkout flow and more about who gives customers a reason to come back even when they are not actively shopping. We already see brands exploring new ways to drive repeat engagement through non-gambling games, surveys, and reward-based experiences that allow users to earn store credit, discounts, perks, or loyalty currency directly within the existing app experience. By 2027, I believe the conversation will shift from “Should we offer reward-based engagement?” to “How do we create the most engaging experience for our users?”

By 2027, stores will operate as intelligent service hubs, not just checkout points. Self-service will accelerate as shoppers scan items on phones, use self-checkout more often, and handle payments, pickup, and returns with less friction.

Associates will become more effective because they’ll have mobile tools, real-time inventory, AI-assisted recommendations, and customer context that help them sell, fulfill, and solve issues anywhere in the store.

Personalization will also become more practical. AI-driven recommendations, loyalty offers, and contextual insights will shape interactions across channels. AI will reshape commerce, but winners will be retailers that pair intelligence with execution.

Stores will also act more like mini-fulfillment nodes for pickup, ship-from-store, exchanges, and returns. That will make inventory accuracy, fulfillment speed, and coordination between digital and physical operations true competitive differentiators.

The retailers that win in 2027 will combine frictionless technology, operational agility, and stronger human engagement to deliver a unified commerce experience that feels connected, responsive, and trustworthy.

Product discovery is no longer limited to on-site search. Shoppers are finding products across marketplaces, social platforms, and increasingly through AI-driven experiences. If product data is not structured, enriched, and optimized for these environments, products simply won’t be found.

By 2027, the retailers that win will be those who connect shopper intent to product visibility across every channel. This requires a shift from fragmented tools to a unified approach that combines search, personalization, merchandising, and product feed management into one intelligent system.

It is no longer enough to drive traffic. Brands must ensure their products are visible, relevant, and ready to convert wherever discovery happens.

Retailers who invest in intelligent discovery will turn every shopper interaction into growth, while those who do not risk losing visibility, conversion, and ultimately revenue.

AI shopping agents become the new search bar. Amazon’s Rufus, ChatGPT Shopping, and Perplexity are reshaping product discovery. By 2027, brands that haven’t optimized for LLM-driven recommendations. Through structured product data, conversational content, and answer-engine SEO will lose visibility before a shopper ever reaches a product detail page. “Ranking” on Amazon will mean ranking inside an AI’s answer in addition to a results page.

TikTok’s Halo Effect goes full-funnel. Brands are already seeing measurable lift on Amazon and DTC sales from TikTok Shop activity–the TikTok Halo. In 2027, winners will treat TikTok as a commerce engine and a marketing channel, and tie creator-led content directly to retail media performance across every marketplace they sell on.

Multichannel orchestration replaces single-channel mastery. Shoppers research on TikTok, ask Rufus, compare on Walmart, and buy on Amazon–often in the same session. Brands managing each channel in isolation will leak margin to those running unified, AI-orchestrated strategies where a signal on one channel informs bidding, creative, and inventory decisions on every other.

Meeting customer expectations. Despite rising costs, global turmoil, and an ever-volatile market, customer expectations are higher than ever before. That’s because retail shopping options have only continued to grow, while the collective buying power of retail customers has shrunk in turn.

This massive divide has resulted in shoppers, rightly, demanding more from their retailers. Things like higher convenience, more personalization, easy and seamless returns, and consistent, omnichannel buying experiences. In other words, customers expect tailor-made shopping experiences designed with them in mind. Experiences built off of customer data, powered by an intelligent and versatile supply chain that’s intended to keep pace with their rapidly changing demand requirements.

Customer loyalty is no longer guaranteed. Retailers must meet customer expectations in order to survive in this new status quo. Any who are either unwilling or unable to meet expectations are doomed to falter against more versatile competition.

Making sure the AI they already have can actually do the work.

Every ecommerce team is being asked the same question right now: how are we integrating more AI? Most answer by adding more tools. Every analytics platform shipped a chat feature this year. Every dashboard has a sparkle icon on it. None of it has changed the operator's actual day.

The conversation I keep having with VPs of Ecommerce goes like this: their team noticed add-to-cart dropped 9% week over week. They want to know why. The data lives across their analytics tools, ecommerce platform, email marketing, CMS, session tool, error monitoring, replays, etc. Three weeks later, they have a hypothesis. Not an answer. The bottleneck was never the model. It's attention. Small teams don't have 17 hours to spend in a heatmap. So the AI tools sit unused. Data-rich. Insight-poor.

What 2027 will reward is a different starting point. Stop bolting AI onto existing tools. Start with the data layer your AI gets to read–purpose-built for ecommerce, not a generic event log. Then put pre-packaged workflows on top of it. Workflows that don't just answer questions, they complete the work end-to-end, with a human in the loop on the final approval.

The question retailers should be asking heading into 2027 isn't "do you have AI?" It's "can your AI actually do anything?"

Move beyond the false tradeoff between customer experience and abuse prevention in the contact center. Today, most service agents operate with limited visibility into the true identity and risk profile of the customer engaging them. As AI accelerates service automation, it is also accelerating exploitation through synthetic identities, account takeovers, and GenAI-powered refund and return abuse.

The next wave of retail innovation will be defined by Risk-Aware Identity Intelligence: the ability to resolve and assess customer trust in real time across a broad commerce network, directly within the customer service workflow. Rather than treating every customer interaction the same, retailers will dynamically calibrate trust based on identity confidence, behavioral intelligence, and historical risk signals.

This will fundamentally change how contact centers operate. Service agents and AI-powered CX systems will use real-time identity intelligence to intelligently adjust the “trust dial” for each interaction. High-confidence, high-value customers will move through frictionless “green lanes” with instant resolutions, refunds, and loyalty-enhancing experiences. Higher-risk identities will encounter targeted verification and precision friction designed to stop abuse without degrading the broader customer experience.

This is not just a fraud prevention strategy. It is a revenue and loyalty strategy. Retailers that successfully operationalize risk-aware identity intelligence will reduce abusive refund claims, policy abuse, and non-fraud chargebacks while simultaneously improving customer satisfaction and retention.

Increasingly embrace selling stock before it arrives at the warehouse, as a more flexible inventory strategy to maximize sales.

For many retailers today, “available to sell” still means physically in stock. That assumption is limiting and out-dated. Consumer demand moves faster than production cycles, customer acquisition costs continue rising, and AI-driven discovery is creating unpredictable purchase intent. Brands are increasingly being pushed to convert demand the moment it appears, not days, weeks or months later when inventory arrives.

Instead of relying on out-of-stock notifications, brands increasingly want an "always-on” sales channel to bridge the gap between demand and on-hand inventory timing. With a pre-order strategy, brands can continue converting customers when that stock is on a ship or still at the factory, while maintaining clear delivery expectations and operational control. This avoids delayed revenue at best, or completely missed revenue at worst.

The broader trend is that retail is becoming more demand-led and less inventory-led. In a volatile market, the winners won’t necessarily be the brands with the most inventory. They’ll be the brands that can sell more flexibly and intelligently across their supply chain.

Structuring their product content for agentic commerce.

Who's selling their products when no one's browsing their website.

AI agents are already discovering, comparing, and recommending products on behalf of consumers. By 2027, a meaningful share of online purchases will be influenced, or outright executed, by an AI that never visited your homepage, never saw your banner ad, and doesn't care about your loyalty program.

Most retailers are still optimizing for eyeballs: SEO, paid media, conversion rate on their dot-com. That playbook assumed a human was on the other end. Increasingly, it's not.

The retailers who win in 2027 will be the ones who make their products machine-readable, machine-trustworthy, and machine-transactable. That means structured product data that AI can reason about. It means fulfillment infrastructure that agents can query in real-time–what's in stock, how fast can it ship, what's the return policy. It means showing up in the channels where agents are shopping, not just the channels where humans are browsing.

SEO got you indexed. The next era, call it GEO, call it agentic readiness, gets you trusted. And only trust gets you recommended.

The biggest mistake will be treating this as a 3-5 year horizon. Mobile commerce moved faster than anyone predicted. This will move faster still.

Making sure their products are categorized correctly in their sales tax platform.

Sales tax rules are more counterintuitive than most retailers realize. A Snickers bar and a Twix bar are taxed differently, because one contains flour and the other doesn't. In Minnesota, a sports uniform is tax-exempt but the cleats you purchase with it aren't. A pair of shorts sold as casualwear and the same shorts listed as board shorts or swim trunks can fall into entirely different categories. Get any of these wrong and you're either over-collecting from customers or sitting on a liability you don't know about.

Most retailers don't know they have these errors. And until recently, they could get away with it.

As AI becomes standard in state audit workflows, the era of sampling-based audits is ending. Historically, auditors would pull a handful of product categories, check for errors, and extrapolate an error rate across the full catalog. If your miscategorized SKUs didn't land in the sample, you were fine. That's no longer a safe assumption.

Past audits may have gone smoothly because they didn't catch everything, but the next ones will. Retailers who do this work now won't have to worry about it later.

Employees will become AI-native by default. Everybody will be able to produce 7/10 work–including your competitors. Retailers need to figure out how and why they plan to differentiate when creative, copy, etc. becomes a commodity.

Operational clarity. Because AI is about to make content a commodity.

For years, product listings, descriptions, and channel feeds were the bottleneck. That bottleneck is disappearing. AI now handles creating, attributing, enriching, and distributing product content at scale across Amazon, Walmart, TikTok Shop, and wherever else your customers are. What used to take teams of people is becoming table stakes.

That shift changes what winning looks like. When everyone has great content, the differentiators become inventory accuracy, channel coverage, fulfillment speed, and pricing discipline. Retailers who treat their product catalog as a single source of truth and build the infrastructure to push that data cleanly across every channel will have a durable advantage.

The brands struggling today are not losing because of bad content. They're losing because they built their business in silos. AI won't fix that. Retailers who invest in operational clarity now will be positioned to leverage every new capability as it emerges, rather than chasing each one from behind.

Turning their post-purchase experience into a retention engine–not an afterthought.

Most brands still treat the moment after checkout as the end of the customer relationship. It's not. It's the beginning of the next one. The brands that will win in 2027 are the ones that use every post-purchase touchpoint–tracking, returns, reviews, loyalty–as a deliberate moment to build trust and drive the next purchase.

The data is clear: acquiring a new customer costs 5–7x more than retaining an existing one, and repeat customers spend on average 67% more than first-time buyers. Yet most retail tech stacks are still built around acquisition. That imbalance is the single biggest missed opportunity in ecommerce right now.

What makes this actionable in 2027 is the convergence of AI and unified data. Brands no longer need five disconnected tools to manage post-purchase. The retailers who pull their tracking, review, loyalty, and support data into one system will be able to personalize at scale, predict churn before it happens, and turn every delivery into a loyalty moment.

The retailers that crack this won't just have better retention metrics–they'll have a structural cost advantage that compounds over time. That's the real competitive moat for 2027.

Not just using AI to move faster, but using it without losing touch with the customer. Over the next year, every retailer will have access to better tools for summarizing feedback, spotting patterns, and flagging anomalies. That part will not be differentiating for long.

What will separate winners is what happens after the signal. Too many teams still confuse a metric with an insight. A rise in returns gets labeled a product problem. Lower engagement gets read as weak loyalty. In reality, the customer may be confused, uncertain, or running into friction that no dashboard can interpret on its own.

Retailers planning for 2027 need to build the human layer around AI: the judgment to understand what customers are actually experiencing, the discipline to connect data to context, and the ability to turn that understanding into action quickly. AI will absolutely make teams more efficient. But trust, loyalty, and long-term value will still come from how well a brand understands people.