Optimizely’s latest report, AI & The Click-less Customer, surfaces the scale of the shift.

According to its research, 52% of consumers frequently use AI to research products, and 14% now begin their shopping journey on platforms like ChatGPT and Gemini.

For younger shoppers in particular, this isn’t niche behavior; it’s now the default. The study notes that shoppers aged 18-44 are “around three to four times more likely to use AI daily to research products and services than those aged 55 and over.”

Another striking, and arguably worrying, statistic from the report, given the inconsistencies of AI summaries, is that 42% of consumers are willing to trust AI-generated product summaries without clicking through to a website.

If search engines once served as the high street for digital commerce, AI is fast becoming the front door. Brands, however, are largely standing inside, hoping customers still choose to knock.

A Discovery Channel Businesses Don’t Control

As AI tools work their way into shopping behavior, organizations must realize that the starting conditions of the customer journey are very different. Customers now get a summary, not a set of links. They see a shortlist, not a funnel.

In discussing the report, Optimizely’s SVP of Marketing, Tara Corey, captured the stakes clearly:

“AI isn’t just changing how people shop, it’s rewriting the rules of how brands are found.”

“The moment a shopper decides to learn more, they expect instant clarity, trust, and speed. If you’re not visible or ready in that moment, someone else is.”

That immediacy is crucial. AI-generated answers effectively compress discovery, comparison, brand familiarity, and early consideration into a single exchange.

If customers don’t feel compelled to click through, the traditional levers that retailers rely on (UX design, A/B-tested product pages, meticulously crafted landing pages) don’t matter until much later, if at all.

This is why Optimizely highlights the rise of GEO (Generative Engine Optimization) as a new battleground.

Rather than concentrating on ranking, brands need to prioritize representation by featuring accurately in AI summaries, with consistent product data and clear value propositions that AI can interpret and surface.

For Corey, Black Friday reinforces this point, claiming that the shopping holiday “has always been a test of performance. Now it’s a test of discoverability.

“The brands that understand how to show up in AI platforms, not just search engines, will be the ones consumers engage with first.”

ChatGPT Pushes Further into Product Research

The timing for this shift is amplified by OpenAI’s rollout of shopping research in ChatGPT, a feature designed specifically to guide buying decisions.

According to the company, the feature allows users to simply describe what they want with a prompt, such as “Find the quietest cordless stick vacuum for a small apartment”, and ChatGPT will “ask smart clarifying questions, research deeply across the internet, review quality sources, and build… a personalized buyer’s guide in minutes.”

OpenAI claims that the tool “turns product discovery into a conversation,” pulling up-to-date information on price, availability, reviews, specs, and images.

Indeed, the company didn’t mince words about how widespread this behavior already is, stating that “hundreds of millions of people use ChatGPT to find, understand, and compare products.”

The tool is built on a specialized version of GPT-5 mini trained for shopping tasks. It “reads trusted sites, cites reliable sources, and synthesizes information across many sources to produce high-quality product research,” while adjusting recommendations based on user feedback.

It’s also transparent:

“Your chats are never shared with retailers. Results are organic and based on publicly available retail sites.”

With nearly unlimited usage made available across ChatGPT plans for the holiday season, OpenAI has effectively turned its product research feature into a mass-market shopping assistant at the exact moment Black Friday demand peaks.

OpenAI is also narrowing the gap between product discovery and conversion with the introduction of Instant Checkout, a new feature that allows U.S. ChatGPT users to buy items directly within the chatbot.

The rollout begins with Etsy sellers, with Shopify merchants such as Glossier, SKIMS, and Spanx set to follow.

The system currently supports single-item purchases and will expand to multi-item carts and additional regions over time. It’s powered by the open-sourced Agentic Commerce Protocol, developed with Stripe, which enables secure transactions between AI agents, shoppers, and merchants.

Stripe users can activate it with minimal code adjustments, while others can connect through the Shared Payment Token API or the Delegated Payments specification.

For shoppers, the experience is seamless: product recommendations appear organically, payment and shipping details are confirmed inside the chat, and purchases are completed without switching platforms.

Merchants retain full control as the merchant of record, managing payments, fulfillment, returns, and support through their existing systems. OpenAI notes that while sellers pay a small fee on completed transactions, customer pricing remains unchanged.

The Journey No Longer Starts with the Brand

Optimizely’s data shows that 66% of consumers still start on search engines, but AI is eating into that lead quickly.

When AI tools handle product discovery before a brand’s website ever loads, the customer journey begins in a space the brand doesn’t own and can’t directly shape.

And while 45% of marketers say they have a GEO strategy, only 27% feel fully prepared for customers who first encounter them via AI.

That gap is becoming critical. Optimizely’s data from Black Friday 2024 underlines the intensity of peak-season traffic, which sees a 65% increase in website visits, 99.98% uptime, and over 7,400 A/B tests run across the weekend.

These numbers reflect how much brands still rely on their own channels to convert interest into purchase. But if AI increasingly determines whether customers ever reach those channels, discovery could become redundant.

Consumers are already signaling what they trust, with 31% saying they’re more likely to trust an AI-generated summary if it comes from a known brand, and another 31% preferring a mix of brand and product information.

It is clear that right now, the brand still matters – AI just mediates the introduction.

What Retailers Need to Do Next

Across Optimizely’s report, the following three priorities emerged:

1. Structure Product Data for AI, Not Just Search Engines

Optimizely stresses that AI platforms are becoming “the new front door to digital experiences” and that GEO ensures brands “show up accurately in AI-generated answers.”

Accurate and accessible product data is foundational for that.

2. Treat AI Summaries as an Extension of Your Brand

Optimizely notes consumers will often trust AI summaries without clicking, and trust increases when information comes from a brand they know.

This implies brands must manage how they appear inside AI answers – which is the core of GEO.

3. Prepare Your Site for Late-Stage Arrivals

The concept fits the report’s narrative: customers come to a brand’s site later, after AI does the early filtering.

Optimizely also highlights the need for site performance during peak loads (traffic surges, uptime, speed).

A New Era of Shopping Behavior

ChatGPT’s new feature doesn’t replace retail websites, but it reshapes their role. They’re no longer hubs for early research; they’re destinations customers reach after an AI-guided shortcut.

AI is removing friction from the top of the funnel, but it’s also removing brand influence. The brands that adapt quickly will treat conversational AI as the new storefront, not a secondary channel.

And the next generation of shoppers is already there.

The timing matters. Shoppers expect speed and personalization in the same breath. Around 71% say they actually want AI built into the shopping journey. They’re not asking for gimmicks. They want better stock visibility, quicker service, and recommendations that actually fit. Miss those marks and loyalty drops fast.

Amazon has already shown where this is heading: robotics in its fulfilment centres have cut costs by roughly 25%, a sign that retail automation solutions can shift margins as well as customer experience.

Tech giants are moving quickly, too. Salesforce, Google, and Microsoft are building AI agents to automate frontline support and back-end operations alike. It’s the “agentification” of the enterprise – automation that doesn’t just support the business but runs through it.

Challenges Retailers Must Overcome

One of the reasons retail automation is gaining so much attention right now is that the right tools can genuinely solve real-world problems – the kind that hold brands back. Right now, retailers have a lot of issues to overcome. The systems they already have don’t connect. Processes run in silos. Customers fall through the gaps. The result is frustration on both sides of the checkout.

Automation has the potential to tackle issues like:

• Disconnected inventories: A shopper checks a website, sees an item listed as available, makes the trip, and finds nothing on the shelf. The reverse happens too: stock piling up in storerooms with no visibility online. Without automation tying together store systems, warehouses, and ecommerce data, managers are left to guess.
• Cart abandonment: More than seven out of ten online baskets are abandoned before payment, a persistent drain on digital sales. Some of that is down to clunky checkout flows. But much of it comes from poor timing: slow shipping updates, lack of payment options, or no personalized nudge to finish the order.
• Poor customer experience: Customer experience is another sore spot. Fragmented journeys cost U.S. businesses an estimated $136.8 billion a year in lost loyalty. It’s the same pattern every time: a customer starts with live chat, follows up by phone, then gets a completely different answer by email. Each handoff repeats the pain. Without retail automation solutions that unify data, every channel feels like a different company.

As Gartner warns, “limitless automation” is a myth. But the goal isn’t automating everything. It’s automating the right things, with the right guardrails, to fix broken journeys.

Retail Automation Use Cases and Benefits

The impact of retail automation shows up in the basics: how goods flow, how shelves stay full, how support teams respond. When it works, it links the back office to the customer in one thread. When it doesn’t, it becomes just another layer of friction.

The following use cases show where the biggest opportunities lie.

Supply Chain & Logistics

Retail supply chains face constant pressure. Surges in demand, shipping delays, and rising costs. The systems built years ago weren’t built for the pace of modern ecommerce. Automation is starting to bridge that gap. AI now forecasts demand spikes, reroutes deliveries, and even triggers restocks without human input. The payoff: fewer empty aisles, lower transport costs, less waste.

Analysts at NetSuite note that automation in logistics can trim lead times significantly while also cutting excess inventory. Amazon’s own network shows the effect at scale, using AI-driven workflows to manage thousands of sites, speed up decisions, and reduce overheads.

Inventory Management & Forecasting

Inventory has always been retail’s balancing act. Too much stock ties up cash and fills warehouses. Too little drives customers to competitors. The gap between online and in-store data only makes it harder.

Retail automation can close that gap. Machine learning models forecast demand more accurately, pulling signals from sales patterns, seasonality, and even local events. IoT sensors and ERP integration push updates in real time, so a store manager isn’t left guessing what’s on hand. One company, FLO, reduced lost sales by 12% just with AI-powered demand forecasting, allocation, and replenishment tools.

Elsewhere, by connecting systems and automating core workflows, ThredUp reduced manual bottlenecks and kept inventory moving efficiently through its marketplace. That meant quicker processing times, fewer errors, and a smoother experience for both sellers and buyers.

Smarter Customer Service

Customer service is often the first test of a retailer’s brand. It’s also one of the hardest to scale. Long queues, repeated questions, and inconsistent answers push customers away.

This is where retail automation has some of the clearest wins. Many firms now use AI agents to cover FAQs, returns, warranty requests, and basic order updates. That shortens queues and frees staff to focus on tougher cases.

Proactive outreach also helps cut down on cart abandonment and cancellations. At a deeper level, automation is reshaping the shopping experience itself. L’Oréal, for example, used Salesforce’s Agentforce to unify data and automate service interactions. Customers received consistent, personalised responses across every channel, turning routine contacts into relationship-building

Revenue Growth & Marketing

Automation goes beyond efficiency; it drives sales. Ecommerce automation tools are now used for predictive pricing, upselling, cross-selling, and tailored offers at scale. Customer Data Platforms bring scattered records into a single profile, enabling true personalisation. That data fuels real-time campaigns designed to anticipate customer needs and lift conversion rates.

By automating parts of its customer experience, marketing, and sales strategies, Simba Sleep generated more than £600,000 in additional monthly revenue. The company’s AI agent now does the work of 8 full-time employees, freeing human staff up for other work. The automation didn’t just cut costs. It created a direct and measurable growth impact.

Enhancing Employee Experience

Retail isn’t just about customers. Employee experience matters too. High turnover and burnout are expensive. Automating repetitive work helps keep staff engaged, while workforce scheduling tools ease pressure during peak demand.

For example, by automating key workforce processes, Lowe’s saved over $1 million in just eight months. The benefits went beyond the bottom line – supervisors reported higher satisfaction, and frontline staff were able to focus on more meaningful work.

Great Southern Bank also achieved similar results, watching attrition rates fall by 44% after building intelligent automation into workflows. This is clear evidence that automated retail tools don’t replace staff. They make jobs more rewarding by removing the least engaging parts of the day. That has a direct impact on retention.

Unlocking Business Insights

Retail runs on data. But in most organizations, that data is split. Marketing has one view. Ecommerce has another. Service teams work with something different again. By the time reports land on a desk, the moment to act has already passed.

Retail automation changes that. Automated systems connect the dots between platforms and feed AI models that can see patterns in real time. Which product lines are about to sell out? Which promotions will flop? Who looks ready to walk?

A single view of the customer makes the difference. That’s why retail automation solutions now often include Customer Data Platforms. When Vodafone brought its records together in one place, engagement rates jumped by nearly 30%, and teams were able to build more effective journeys without risking burnout.

The gains aren’t limited to revenue. Automation can also catch compliance issues, broken workflows, or supply chain weak spots before they turn into costly problems.

Best Practices for Retail Automation

The potential of retail automation is huge. But so are the risks. Without a clear plan, projects can misfire – frustrating customers, raising compliance concerns, and wasting money. The retailers that succeed tend to follow a few clear rules.

• Get the data foundation right: Automation is only as good as the information it runs on. If customer records are scattered, bots will give inconsistent answers and supply chains will make the wrong calls. That’s why many retailers are investing in Customer Data Platforms. A CDP pulls together records from marketing, sales, service, and ecommerce. One view. One source of truth. Without that, everything else is shaky.
• Set guardrails: Gartner has already warned about the danger of chasing “limitless automation”. Not every process should be automated. Not every customer interaction should be handed off to AI. The best deployments use escalation rules, monitoring, and clear ownership so nothing gets lost.
• Avoid generic automation: Customers spot it instantly. A one-size-fits-all chatbot that can’t see their order history does more harm than good. Graia has called out this problem in CX, showing that automation has to be tuned to the business and the customer journey, not just bolted on.
• Train the workforce: Automation changes jobs. It takes away repetitive tasks, but it also requires staff to know how to work with AI systems. The best companies invest in training and create “automation champions” on the front line. That reduces fear and speeds up adoption.
• Measure what matters: Metrics like call volume or handle time don’t show the true impact of automation. Smarter measures include containment quality, safe deflection, and revenue lift. Tools like Scorebuddy now track the performance of AI agents directly, adding oversight where it’s needed most.

Don’t jump in trying to automate everything. Automate carefully, with the right data, the right checks, and the right training.

The Future of Retail Automation: Growth, Loyalty, and Smarter Operations

The role of retail automation has shifted. It’s now about reshaping the sector end-to-end – supply chains, inventory, customer service, and marketing. When used well, automation and AI cut costs, trim waste, and improve both staff and customer experiences.

But there are risks too. Fragmented data, overuse of bots, and weak oversight can undermine trust faster than they deliver returns. Success depends on planning: build solid data foundations, set limits, train teams, and track outcomes that go beyond call times or ticket counts.

Automated retail is already here. The retailers that move carefully but with intent will be the ones winning the next decade, with leaner operations, more loyal customers, and stronger margins.

US retailer Target recently stirred up lively debate on Reddit by introducing a “10-4” policy, which requires employees within 10 feet of customers to smile, make eye contact and use welcoming body language, and those within 4 feet of to actively greet shoppers and offer assistance.

Motivating employees to go the extra mile for customers isn’t always straightforward, and what works in theory may not always work in practice. For some employees, traditional benefits may hold little appeal.

Researchers at the University of Bath have found that retail customer service staff, such as cashiers or shop assistants, prefer free meals or team outings to health benefits or gym memberships. These kinds of benefits will encourage greater company loyalty, better customer service and higher sales.

The study, co-authored by Professor Jens Nordfält of the University of Bath School of Management and published in the Journal of Marketing Research, looked at five categories of company-sponsored wellness benefits: food, social, mindfulness, physical and health. Free meals and events such as happy hours and company picnics were the most effective at inspiring workers to deliver better service, as employees felt more valued and developed a stronger sense of loyalty to their employers.

These loyal employees were more likely to care about their company’s well-being and deliver stronger performance, service quality and customer assistance. That better service, in turn, translated into higher sales, the study found. Professor Nordfält, who is also co-director of the Bath Retail Lab, said:

“Our study showed food had the biggest impact on employee motivation, followed by social gatherings. Mindfulness activities also helped. But physical and health perks like gym memberships or flu-shot drives had the least effect.”

The rising popularity of wellness programs prompted the research, said the study’s co-author, Professor Dipayan Biswas of the University of South Florida Muma College of Business.

Investing in the Right Benefits Can Pay Off in Customer Satisfaction

More than 90 percent of companies around the world offer some form of employee benefits, and global spending is projected to exceed $90 billion annually, the report notes.

With enterprises feeling the pressure on budgets, they might be inclined to cut their employee benefits to manage costs. But as the research shows the right types of benefits can have a tangible impact on sales, the key is to find the balance so that the perks pay for themselves.

“The onus is on benefits professionals to understand what people really want,” said Sarah Jefferys, Head of Reward Consulting, Gallagher’s Benefits & HR Consulting Division in the UK. Gallagher’s Workforce Trends Report, released earlier this year, emphasized the importance of clear communication to gain insights and shape benefit programs that work. Jefferys added:

“The key to getting good engagement and positive ROI on benefits hinges on employers communicating the options available and being open to adjusting strategies to meet the shifting needs of employees.”

The advice for employers is to find ways to introduce cost-effective perks that will have the biggest impact on employee motivation, creating tangible benefits for the staff and the company.

“The recommendations for any business, small or large, is when you’re having these wellness programs, the ones that foster nourishment and connection have stronger downstream effects,” said Biswas.

Professor Nordfält added that the research pointed to a way to reduce food waste, as one of the main sources is discarded grocery items. “[I]nstead of discarding aesthetically unattractive foods that many consumers do not buy, stores can provide them as benefits to their employees to make them feel more valued, which in turn might lead to more responsive customer service.”

Retailers are moving quickly to integrate these intelligent agents into their e-commerce platforms, aiming to deliver faster recommendations, more personalized deals, and seamless checkout experiences just in time for the busiest shopping season of the year.

Shoppers are also warming up to the idea of AI-powered assistance. Early adopters plan to consult AI to search for gift ideas, specifically citing ChatGPT as a tool, according to survey data from Bread Financial.

Those users are likely to continue their holiday shopping journeys online, through marketplaces like Amazon, social media platforms, or retailer apps.

“For the 2025 holiday season, AI-assisted search is positioned to be the first step in a shopping journey that begins and ends online,” Bread Financial notes, adding that data-savvy retailers optimizing for tools like ChatGPT could gain a competitive edge.

AI agents can shift the shopping journey from reactive to proactive, anticipating customers’ needs by learning from their behavior and predicting intent.

Unlike traditional chatbots, which answer questions in a transactional way, AI agents can reason, make decisions, and act on a shopper’s behalf. They bring together data from large language models (LLMs), real-time data, user preferences, and contextual insights, learning and improving with each customer interaction.

“We’re at the very start of the Agentic Web — where AI assistants don’t just find information on the web for us, they navigate it with us — and it will dramatically alter how we do things every day,” including shopping, said Dave Anderson, VP of Product Marketing at Contentsquare, adding that AI is already transforming online shopping behaviors:

“Intelligent AI agents are starting to compare prices, automatically apply coupons, and even complete purchases on their own. Customers are learning how to ‘set it and forget it,’ and letting agents do all the work for them.”

Retailers Turn to AI Agents to Power Smarter Shopping

Retailers are taking note, with Walmart emerging as a high-profile example.

The U.S. retail giant is leaning fully into “agentic commerce” this holiday season, rolling out five new AI-powered tools to help in-store and online shoppers find gifts, organize celebrations, or spruce up their homes to welcome guests.

The In-Store Savings feature in Walmart’s app lets customers find the best Black Friday, Rollback, and clearance deals as they navigate the aisles, with options to filter items, compare prices, and locate products in-store. Saved wish lists can be sorted by aisle for faster shopping.

AI-generated audio summaries for more than 1,000 premium beauty products make gift-buying easier on the go, while Sparky, Walmart’s GenAI-powered assistant, can now curate party supplies and recipe ingredients in real time.

For a more immersive experience, the retailer is also inviting customers into its augmented reality 3D showrooms using its Retina platform, where users can explore holiday-themed spaces and add products to their carts directly from interactive scenes.

Walmart hopes these features will encourage shoppers to engage with its app for their holiday purchases, as “when they use the app while they shop in stores, they spend 25 percent more on average than on trips when they don’t use the app,” Tracy Poulliot, SVP of Shopping Experiences at Walmart U.S., said in the announcement.

Behind the scenes, Walmart is deploying AI to enhance efficiency and freshness. Its Agentic AI for Freshness helps teams predict demand, reduce waste, and keep shelves stocked, while a multi-agent delivery system optimizes driver routes and delivery timing.

Other upgrades include a GenAI customer support assistant, dynamic real-time delivery windows, and digital twins that pre-empt equipment failures.

Walmart has embraced various forms of AI-first shopping, recently partnering with OpenAI to let customers complete purchases directly within ChatGPT using Instant Checkout.

Doug McMillon, Walmart’s President and CEO, framed the shift:

“For many years now, e-commerce shopping experiences have consisted of a search bar and a long list of item responses. That is about to change. There is a native AI experience coming that is multimedia, personalized, and contextual.”

Other retailers are also gearing up to deliver agentic e-commerce for the holiday season.

At the recent Salesforce Dreamforce event, homewares retailer Williams Sonoma announced its use of Agentforce 360 and Data 360 to bring AI agents into its online shopping experiences.

Alongside a customer service agent that delivers personalized responses, the retailer is developing a “sous chef” agent on Agentforce called Olive to help customers plan menus and discover products on its website ahead of the holiday season.

Olive will draw on data such as past purchases to infer intent and create customized Thanksgiving plans, complete with menus, shopping lists, and recommendations for Williams Sonoma cookware, ingredients, and table décor.

Similarly, Denmark-based jewelry retailer Pandora is gearing up for the holiday season with the debut of its shopper-facing AI agent Gemma, built on Salesforce’s Agentforce Commerce platform.

Unveiled during Dreamforce, Gemma was created to bring personalized service to the company’s digital channels. The jeweler typically increases its staff during peak holiday demand, but capacity can be stretched even with the additional support.

Traditional chatbots handled basic “Where’s my order?” questions, but Gemma aims to replicate the brand’s in-store service online, drawing on order history, product details, and research insights to guide gift selection.

A companion agent, Clara, handles post-purchase requests and FAQs, freeing up human staff to focus on higher-value customer interactions.

AI Traffic to Retail Sites Surges

Data underscores the growing impact of AI on retail. In its online shopping forecast for the holiday season covering the November 1 – December 31 period, Adobe predicts a 520 percent year-on-year rise in AI traffic to U.S. retail sites, following the first material surge of 1,300 percent seen last year.

Its survey of 5,000 consumers found that one-third have used AI for online shopping, and of those around 30 percent have used it for gift inspiration, while 36 percent were looking for deals, and 40 percent sought product recommendations.

Retailers of all kinds are increasing their use of AI beyond basic chatbots, data from Talkdesk’s third annual AI Holiday Shopping Report shows.

Among retailers that have already adopted AI, 85 percent are now using it for predictive analytics, up 24 percentage points from last year, and 83 percent have deployed virtual agents into their shopping experience, also up 24 points.

They are slightly more cautious about AI agents in customer service, reflected by an increase of 20 points to 79 percent.

Nearly two-thirds are leveraging AI to deliver personalized pricing and promotions to customers. Confidence in the technology is rising just as quickly, as 80 percent of retailers believe AI will help boost holiday sales and sharpen their competitive edge, and 79 percent expect it to strengthen customer loyalty, according to the survey.

Talkdesk’s data shows that retailers already using AI are widening their competitive gap with laggards.

“While retailers can’t stop that shift, they can study and leverage it,” ContentSquare’s Anderson said.

“It’s crucial to step into this next era of digital commerce by getting back to important basics: building analytics that capture how customers are using AI, like agent-powered traffic, search patterns, and conversion metrics.”

Still, transparency remains a sticking point, as Talkdesk’s survey shows 84 percent of consumers want to know when AI is involved, and most expect stronger oversight. While many are open to sharing preferences or basic personal details in exchange for more tailored experiences, few are comfortable disclosing sensitive data.

To earn that trust, retailers will need to balance personalization with privacy, offering meaningful value in exchange for data, such as discounts or loyalty points, and clearer communication about when AI is at work.

“Revenue and customer loyalty are going to be driven by how well brands prepare for a future where human and AI shoppers share the same carts,” Anderson said. “It’s not just about optimizing for Google anymore — it’s about optimizing every system that defines the future of digital experiences.”

As AI agents become part of the holiday shopping journey, retailers that deliver seamless, personalized features will set the bar for how consumers spend in 2026 and beyond.

“AI-assisted search is almost certain to grow in holidays to come,” Bread Financial noted, a trend that suggests agentic commerce may soon become a standard expectation rather than a novelty.

The report revealed that both enterprises and consumers were more willing to adopt AI in customer service. 

The tech company leveraged its Agentic Enterprise Index to uncover trends in the first 6 months of the year, revealing that the quantity of AI Agents across businesses who adopted them increased on average by 119%. 

Salesforce saw an increase in consumer willingness to adopt AI, with 94% of consumers choosing to interact with the AI agent when asked, as well as nearly 60% choosing to frequently use AI agents after claiming they had become more helpful during the past year. 

Furthermore, employee willingness has increased, with agent interactions having grown 65%, along with 35% longer conversations between AI and employee. 

This increase in AI adoption highlights the speed at which AI integration will not only be implemented but accepted in the workforce. 

Earlier in the year, Salesforce revealed that 80% of global HR chiefs agreed that by 2030, most workforces will become hybrid, with a likely future of humans and AI agents working together. 

From a customer service perspective, this allows human agents to focus on complex issues to drive customer relationships, whilst the AI agent completes the human’s repetitive tasks, without worrying about how it will affect customer loyalty. 

AI Agent Adoption Not Yet Universal

Within its report, Salesforce detailed how AI agent adoption rates varied depending on the sector. 

In financial services, for example, employee interactions with AI agents had grown 105% each month during the first half of 2025, whilst 38% of their consumers who chose to interact with AI agents reportedly favored performing money management tasks with the AI. 

Ryan Teeples, Chief Technology Officer at 1-800-Accountant, claimed that “with Agentforce now resolving up to 60% of incoming requests, we can confidently address routine inquiries like tax return statuses, which frees our team to focus on more intricate tasks while still providing swift, secure and personalized support.” 

In travel and hospitality, the industry saw AI agents and employee interactions growing at 133% each month during the first half of 2025, whilst also seeing a higher activity amongst business users compared to other industries surveyed. 

Demetri Salvaggio, Senior Director, Business Development & Client Operations at Engine, highlights how tailoring their AI agents has increased its capabilities for improved routine task handling:

“This has been critical in achieving our reduction in average handle time by 15%.” 

In retail, AI agents and employees were increasing their interactions at a monthly rate of 128%, with 200% of consumers who regularly interacted with AI in retail agreeing that their overall experience with the retail industry has been improved. 

Calvin Anderson, Vice President of Digital Commerce Operations & User Experience at SharkNinja, explains how adding digital and AI enhancements to improve and support a consumer’s shopping experience would strengthen their impression of the brand, stating:

“This not only enhances brand loyalty through tailored recommendations but also allows our team members to focus on more complex, meaningful interactions.” 

The report suggests that AI agent adoption is likely to rise, with 60% of consumers who use these agents agreeing that they have become increasingly more helpful during the past year. 

Interestingly, although the vendor predicts the growing influence of AI agents, it also implies a greater reliance upon humans, with human agent escalation activity increasing by 10% between Q1 and Q2 in 2025. 

Arguably, this increase in human escalation is a natural byproduct of more customers having to interact with AI agents in the first place. As the technology becomes more advanced and customers become more adept at interacting with it, it is likely that these escalations will decrease, potentially making human agents surplus to requirements.

From the Taylor Swift Eras Tour meltdown to the very recent Oasis ticketing controversy in the UK, it has become shorthand for poor CX. But beyond the headlines lies a deeper lesson — one about legacy systems, scaling, and trust.

This article provides five simple lessons that a company running a high-demand business can learn from Ticketmaster’s blushes.

1. Scale for the Peaks, Not the Averages

Ticketmaster’s core challenge is one many CX leaders dread: millions competing for limited stock in tight windows.

Its proprietary “Smart Queue” exists to throttle load and shield downstream systems. Yet in peak moments, customers still report getting frozen, misclassified as bots, or arriving at checkout only to find their preferred tickets gone.

The lesson? Don’t just test for normal load — simulate real-world stress, use cloud elasticity, and build adaptive load balancing so your journey holds up when it matters most.

2. Transparency Before Surprise

Take the Oasis 2025 tour row: cheaper ticket blocks vanished rapidly, leaving only higher-priced options — and fans had no advance warning.

While the UK Competition and Markets Authority has pressured Ticketmaster to give clearer advance notice before using variable pricing, that doesn’t go far enough.

In CX, surprises are failures. Whether fee structures or tiered inventory, customers deserve clarity upfront, not just at checkout.

3. Anti-Bot Defense Without Alienating Fans

Scalpers must be stopped, but Ticketmaster’s anti-bot systems often misfire, resulting in genuine buyers getting booted from queues after long waits.

The balance is delicate — too loose, and scalpers win; too strict, and you alienate your real users. For CX teams, false positives erode trust just as surely as undetected fraud does.

4. Edge Modernization Isn’t Enough

Ticketmaster’s “Host” system — its decades-old seat management backbone — is generally regarded as rock solid. The friction appears to come in the newer layers: APIs, bot checking, and queuing logic built to interface with web and mobile fronts.

To its credit, Ticketmaster employs Fastly’s edge network and containerized microservices to scale workloads. It has also invested in cloud automation and APIs for real-time partner integration.

But winding modern services around old systems brings its own risks. Wrapping new functionality over legacy systems may work temporarily, but it also introduces latency, bottlenecks, and fragile integration points.

5. Trust Is the Ultimate Currency

Late-revealed fees, sudden price jumps, hidden lockouts — each instance reinforces the perception that the system is rigged against everyday users.

Trust can’t be cached or containerized. It must be earned via systems that deliver, communication that’s honest, and respect for customers’ time and money.

Ticketmaster remains dominant thanks to Live Nation’s power, but its CX cracks are widening. Regulators, competitors, and consumers are taking notice. Artists are wondering if alienating their fans is worth the revenue trade-off.

For any business in a high-demand digital space, the warning is clear: don’t just modernize the edges — redesign the experience from ground up where and when you can. Reliability, transparency, and fairness are table stakes for winning trust.

Deaf and hard-of-hearing customers who use LSF can now connect with Amazon’s customer service team through a video call.

They can start a call via the “accessibility help page” or “Contact Us” section on the Amazon website using a computer, smartphone, or tablet equipped with a camera. No special software is required.

The service connects users with professional LSF interpreters who facilitate real-time communication with Amazon support agents.

Amazon plans to extend the service to fully cover its customer service opening hours in France from 06:00 to midnight daily and to integrate French Cued Speech (Langage Parlé Complété, or LPC) by October 2025.

Cued speech helps deaf or hard of hearing people to distinguish sounds to better understand a speaker when lipreading.

The company already uses its Mobile Amazon Video Interpreting Stations (MAVIS) internally to provide virtual sign language interpreting for its deaf and hard-of-hearing employees in Europe, so that they can access services and communicate with colleagues in their primary language.

Yet, extending sign language support to its customers is a massive step in making customer support more accessible.

The French National Federation of the Deaf (FNSF) estimates that 100,000-200,000 people use LSF in France as their primary way of communicating. While many deaf people in France can read and write French, written French is often their second language. By offering access to its customer support services in LSF, Amazon ensures that these customers can explain their needs clearly and receive help without a language barrier.

“This initiative reflects our commitment to creating inclusive experiences for all our customers,” said Samira Bejnouni Terlier, Head of Customer Experience in France at Amazon.

We want everyone to be able to benefit from the same level of support, regardless of how they communicate.

Given this objective, Amazon may grow the program across Europe, as it did with its virtual sign language interpreting services for employees, and perhaps further afield over time.

Empowering Customers Through Inclusive Service

As customer expectations around the availability and efficiency of customer support increase, providing enhanced accessibility is key to customer satisfaction.

Offering the option to use sign language in customer service interactions will help companies like Amazon identify and better serve vulnerable customers.

Relying on written communication with deaf and hard-of-hearing customers can present challenges, particularly when dealing with complex issues like account troubleshooting, returns, or billing. Moreover, decision trees that default to calling the customer over the phone to solve problems cause frustration.

Services that support sign language via video enable customers to interact independently without having to rely on third parties like family members, which is becoming increasingly tricky with new customer authentication methods. Instead, helping vulnerable customers resolve problems on their own terms promotes independence and self-confidence.

It can also facilitate faster problem resolution, as live interpretation via video allows for immediate, back-and-forth dialogue, so that support agents can resolve issues faster than through written communication.

Ultimately, this improves the customer experience and builds trust and loyalty, as customers who feel seen and supported are more likely to become repeat customers and recommend the company to others.

Additionally, empowering customers by removing barriers to communication, such as by providing support in sign language, demonstrates a commitment to equality beyond mere compliance.

By providing sign language support, companies like Amazon set an example for other businesses to follow, helping to normalize accessibility in mainstream services.

Now, as part of the Kao Corporation, it continues to scale its global footprint.

Naresh Krishnamurthy plays a big role in that effort, leading digital customer experience transformation at Molton Brown.

In doing so, he aims to connect the company’s legacy with the future of digital CX. Here are five ways in which his team is doing so.

1. Follow a More Experiential Loyalty Model

Traditionally, brands in the luxury space looked at customer loyalty through transactions, i.e., points and purchases. Molton Brown has evolved this into an experiential model using SAP CX.

Sharing more, Krishnamurthy said:

We now offer exclusive previews, personalized fragrance layering advice, tailored content, and even gamification features like our Fragrance Finder on the website.

Behind the scenes, Molton Brown also uses SAP Commerce Cloud and Master Data to create a single customer view that crosses customer-facing departments.

2. Embrace “Phygital”

“Phygital” is the practice of blending physical and digital experiences. Molton Brown is embracing a phygital strategy.

For instance, it’s ensuring the product assortment a customer sees on its website mirrors what’s available in their closest store, eliminating confusion and strengthening consistency.

Now, it’s looking to go further by uplevelling its personalization strategy, so in-store, online, and AI assistants can anticipate customer needs and provide tailored recommendations.

That strategy hinges on real-time data orchestration. So, for example, if a customer browses woody fragrances online, that preference is captured in SAP. When they later book a boutique consultation and scan their loyalty profile on arrival, the associate sees a single customer view. That includes fragrance preferences, online interactions, and campaign responses, enabling the associate to make more relevant suggestions.

3. Leverage “Non-Gimmicky” Gamification

As noted, Molton Brown launched a Fragrance Finder on its e-commerce website. This offers a gamified experience where customers answer personality- and preference-based questions. It then recommends products tailored to their responses.

“It’s not just a gimmick; it’s a way to immerse customers in our brand story,” explained Krishnamurthy. “The focus is on engagement, not just conversion.” He continued:

All the data flows back into our single customer view. This means when a customer later visits a boutique, associates already understand which fragrances have resonated with them.

Additionally, Molton Brown has layered in exclusivity. For example, loyalty members can unlock rewards, previews, or special access through these gamified interactions.

Meanwhile, seasonal experiences, such as Molton Brown’s annual Christmas games, add to this engagement while staying aligned with the brand’s luxury positioning.

“It’s a modern, digital-first way to make luxury interactive,” concluded Krishnamurthy.

4. Get Strategic on Specific Social Channels

At first, many brands jump on a new social channel to open a new door to the business. Yet, Molton Brown is always cautious to create a unique experience through its channels.

For instance, it sees Instagram as key for unveiling campaigns, sharing artistry, fragrance creation, craft, and heritage through rich imagery.

Meanwhile, TikTok is more playful and focuses on short-form engagement, such as fragrance layering tips or seasonal rituals.

“Different generations gravitate to different channels, so we tailor content accordingly,” said Krishnamurthy. “What’s critical is that all of this isn’t siloed.”

“Whether a customer discovers us via Instagram, plays with the Fragrance Finder online, or later visits Covent Garden, the journey is continuous.”

While doing so, Molton Brown recognizes preferences across channels through its SAP CX stack, customer data platform (CDP), and loyalty systems.

Ultimately, its goal isn’t to chase every platform, but ensure one consistency: wherever customers interact, they experience the brand promise.

5. Prepare for a Future of Conversational Commerce

In April 2025, OpenAI rolled out shopping in ChatGPT following rumors of a big Spotify partnership.

The announcement beckoned a future of conversational commerce, and Molton Brown is closely monitoring these developments.

Krishnamurthy explained: “The opportunity for us isn’t just about visibility, it’s about how we show up as a luxury house.

Our focus is on using AI to guide discovery in a way that feels authentic, personal, and true to our heritage.

For example, if a customer asks for the best fragrance on a summer evening, Molton Brown hopes AI will guide them with the expertise and storytelling they expect from an associate.

This focus is particularly important in fragrance and body care, a category that is naturally harder to sell online. By using AI and personalization, Molton Brown bridges the gap between digital and in-person discovery.

Yet, Krishnamurthy also urged his team’s caution. “Luxury is about balancing innovation with exclusivity,” he summarized. “Our priority is a connected experience across all channels.”

What Else Is Molton Brown’s CX Team Focusing On?

Alongside all the above, Molton Brown continues to innovate around the post-purchase experience.

Recently, it has introduced tailored replenishment reminders, fragrance layering rituals, and seasonal discovery boxes. These aim to extend the luxury experience beyond the point of sale.

Krishnamurthy also named sustainability a top priority after noting a trend in the number of customers asking how sustainable its products are.

As such, the company has pledged to go further with its sourcing and refillable packaging. “It’s integral to our customer experience and brand promise,” Krishnamurthy confirmed.

Yet, Krishnamurthy and his team are constantly considering new ways to innovate around digital customer experiences. To stay up to date, follow him on LinkedIn.

However, a new trend and demand are emerging – a movement from generic platforms to tailored vertical solutions.

It is also safe to say that retail is spearheading this charge, with unique pain points, evolving customer expectations, and the need for specialist CX tools heightened.

While it is a growing trend, this shift towards verticalized solution approaches has murmured away across the CX realm for some time.

SequenceShift Takes Vertical Solutions Approach

Take AWS, for example. It adopts a vertical solutions approach by leveraging deep, purpose-built services to address unique challenges and opportunities within healthcare, automotive, financial services, retail, and more sectors.

Part of this change is centred around SequenceShift, which has recognized this change and has been an early adopter.

It is positioning itself as a future-facing partner for the retail sector through agent-assisted and self-service phone payments, which have achieved AWS Retail Competency certification.

On this, Dimitri Muntean, Managing Director at SequenceShift, explained, “Everything is becoming more specialized. And it makes sense – different customer groups, whether by industry or size, like startups, SMBs, or enterprises, all have unique needs.”

Trying to build a one-size-fits-all solution often results in something that doesn’t fully meet anyone’s needs. When you design for specific requirements, the solution fits better.”

This approach helps meet this unique requirement and can solve problems the customer has yet to encounter.

The SequenceShift Strategic Move: The Retail Competency

SequenceShift has been widely recognized for these efforts, most recently when awarded as an AWS partner for retail software competency – which is no mean feat.

Muntean added, “Achieving this competency allows us to show that AWS trusts us and our solution to deliver for retail clients. It is a way of proving that we work well in that space. That was the main driver [to obtain the award].”

This award, which recognizes competency across verticals, enables SequenceShift to be a differentiator in this field.

It is not just a shiny badge for SequenceShift, it has real world tangible benefits for customers.

Trust, a key chain in the armour of good CX, is at the heart of SequenceShift’s philosophy.

For Muntean, it comes down to credibility. He told CX Today, “AWS has effectively given us a stamp of approval that says, ‘These guys know what they’re doing in retail.’

“So, for companies in that space, it’s a signal that we’ve been vetted and our solution meets key requirements.”

Outside of trust, customers can take advantage of enhanced integrations, sector-specific configurations such as retail, and domain expertise.

These factors all umbrella into a faster time to value for retail clients.

Naturally, focus is geared toward improving the CX for retailer’s clients, but retailers themselves can benefit from more relevant data-driven insights and address real world challenges.

In this sense, one of the core challenges is PCI-DSS compliance.

SequenceShift’s Payline Addresses Complex Issues

This is where Payline comes in. It removes its clients’ environments from the PCI-DSS scope when their customers make self-service payments over the phone.

Captured payment information gets encrypted and sent via SequenceShift to the preferred payment provider.

Muntean explained that users get “Access to the best of both worlds; the ultimate flexibility and power from Amazon Connect and industry grade PCI-DSS security from Payline by Se.

Take Sonos, for example. Due to rigorous PCI standards, they faced significant challenges in processing payments over the phone.

This limitation impacted their ability to generate sales over the phone and deliver a high-quality customer experience in their global contact centers.

Some of these challenges included the need for seamless integration with Service Cloud Voice (SCV) and Amazon Connect and subsequent missed opportunities to upsell during calls due to restrictions on phone purchases.

SequenceShift’s Payline integrates with Service Cloud Voice and Amazon Connect for secure, PCI-compliant phone payment processing.

The ability to quickly build a cost-free PoC simplified the selection process and instilled confidence in the solution’s capabilities.

PoC Within Days and Implementation Within a Month: Sonos Experienced Reduced Time-to-Values

This action by SequenceShift resulted in the PoC being built within days, implemented within a month, and provided Sonos with compliant phone payment handling.

While compliance is a compelling uses case for why vertical CX solutions are no longer just options but a competitive advantage, it is also worth noting that reduced time-to-value and implementation costs.

As vertical CX platforms come pre-configured with relevant tools, businesses can reduce time spent on customization and integration, leading to faster ROI.

Through solutions like Payline, SequenceShift remains committed to supporting the evolving needs of the retail sector.

For full details on SequenceShift’s solutions, click here.

In doing so, it introduced virtual try-ons, price tracking, and an agentic checkout, with the latter threatening to kill the checkout page.

As an important prelude, Google first announced a new search experience that is now available in the US.

“AI Mode in Google Search” is a more conversational approach to search, which provides AI-generated answers to long, complex queries.

According to the company, it can also offer “helpful, personalized” suggestions based on a user’s past searches.

Yet, the new Google Search doesn’t only answer questions; it completes tasks, including those related to commerce.

The New Google Search Shopping Experience

AI Mode pulls in information from the web and Google’s real-time data.

As such, Google can bring together Images and its Shopping Graph, which has over 50 billion product listings that it constantly updates.

That combination allows users to type the details of a product they’re looking for into AI Mode. The search function then presents a browsable mosaic of images and shoppable products.

Users can then refine their search, browse recommendations, and ask AI Mode questions about a product.

That product may be an item of clothing. In this scenario, Google offers a “try-on” feature that will help users virtually try on clothes to get a feel for how styles might look on them.

In doing so, they can lay the product over a picture of themselves, thanks to a custom image generation model, as pictured below.

Introducing the feature, Vidhya Srinivasan, VP & GM of Ads and Commerce at Google, explained: “The try-on feature uses body mapping from the image itself to aid in the accuracy of the item of clothing and the person. This technology is the most state of the art at scale feature of its kind.”

Now that the customer has found something they like, AI Mode helps the consumer find the best price and buy it with a new, agentic checkout feature.

All this, directly in Google search results.

Yet, perhaps the customer wants it cheaper. In this case, they can set an alert, so Google continuously checks websites where the product is available and lets the user know if the price drops.

When that happens, they get a notification – via Google Shopping – on their smartphone. If they wish to buy, a checkout agent will add the right size and color to their cart.

“I can choose to review all my payment and shipping information, or just let the agent buy it for me,” concluded Srinivasan.

Interestingly, Amazon added a similar checkout feature to its app earlier this year. Yet, Google’s announcement could have much bigger consequences for the future of payments, commerce, and retail.

Google Has Nuked the Checkout Page – Fintech CEO’s to go to DEFCON One?

Announcements such as this point towards the seismic shifts in the commerce and payment industry.

Indeed, Google is not alone in reinventing the shopping experience, with major fintech players moving rapidly to embed AI-powered transactions into daily commerce.

After announcing shopping on ChatGPT, OpenAI has brought in Instacart CEO Fidji Simo to lead consumer operations, while Perplexity has integrated Stripe into its chatbot.

Meanwhile, Visa, Mastercard, and PayPal announced their own agentic commerce services in April.

The battle for who controls the critical moments before, during, and after payment has begun.

Making this point, Simon Taylor, Head of Strategy & Content at Sardine, posted on LinkedIn:

For decades, merchants optimized checkout pages for conversion: A/B tests on button colors, reducing form fields, one-click purchasing, c art abandonment emails Now Google’s agentic checkout could make all that irrelevant.

The implications are clear: whichever platform perfects agentic commerce first will likely shape the next decade of commerce.

With AI poised to redefine how transactions happen, fintech companies must adapt – or risk becoming obsolete in a world where AI doesn’t just assist in payments, but actively executes them.