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.

In its third-quarter earnings call, the technology platform saw a strong earnings result following significant investments in AI.

When questioned on the future of agentic e-commerce, Amazon claimed that most of these third-party agents currently lack the capabilities required to deliver ‘acceptable’ customer experiences. 

Amazon CEO, Andrew Jassy, expressed difficulty in finding a third-party to partner with for AI shopping agents. 

He said, “Right now, I would say the customer experience is not – there’s no personalization.  

“There’s no shopping history. The delivery estimates are frequently wrong. The prices are often wrong.”

He also notes that despite its sudden upsurge, creating AI agents is still “harder than it should be.” 

Currently, these agents are unable to deliver the required seamlessness and reliability requirements that consumers expect. 

However, Jassy remains determined that the future of agentic commerce will greatly improve the online shopping experience, narrowing down personalized choices using accurate user context. 

Amazon has also announced its plans to continue investing to keep up with AI trends, after adding more than 3.8 gigawatts of AWS power capacity during the past 12 months, doubling the capacity levels of 2022, as well as expecting to add another gigawatt by the end of the quarter. 

Despite AI adoption still being at the start of its journey, Amazon believes this can benefit its customers by introducing the features early on to avoid the competition. 

This investment has included the recent launch of Project Rainier, a multi-data center housing almost half a million Trainium2 chips to advance its agentic AI systems and support AI-based services, expecting to double by the end of the year. 

Success in AI Features

 AWS has already seen successful innovations with AI in retail this quarter. 

Currently, the company is already working with several internal AI features to improve online shopping experiences. 

Rufus

An AI assistant designed for consumers to ask shopping-related questions, recommendations, and product comparisons to offer source-generated responses.

This feature saw 250MN active customers, with a monthly interaction increase of 140% and a year-on-year growth of 210%. It also found that customers were 60% more likely to buy an item by using Rufus. 

AI-Powered Audio

The GenAI-powered audio feature simplifies shopping experiences, allowing users to listen to a summarized audio description of the product, as well as analyzing customer reviews and product details to give you access to the needed information at the top of the page.

This feature has now been enabled for millions of products, with AWS reporting a total of almost 3 million minutes listened.  

Amazon Lens

Lens has been enhanced with an AI visual search tool that reduces product discovery time by allowing you to scan products using product images and bar codes.

The tool also now offers Lens Live, which can be used to find products as well as showing similar recommendations via a swipeable carousel in real-time, seeing a monthly customer usage of 10 million. 

Key Earnings Results

The big news was Amazon Connect hitting $1BN in annual revenue after improving its efficiency and personalization capabilities for both agent and customer.

This was a result of 12 billion minutes in customer interactions with AI handling each year for AWS customers such as Toyota, American Airlines, and Capital One.

Other notable figures include:

• In annual revenue, Amazon saw a 12% increase of $180.2BN  
• Across year-on-year increase, the company saw its improved growth rate of 20.2%, its highest in 11 quarters 
• Its operating cashflow still saw upwards of 16% increase at $130.7BN despite major investments in AI 
• However, its free cash flow decreased from $47.7BN the previous year to $14.8BN 

Between 15:45 UTC on October 29 and 00:05 UTC on October 30, configuration change caused node failures in Azure Front Door (AFD), Microsoft’s global, cloud-based service that handles content delivery and routing, according to Azure’s status page.

A whole host of Azure services experienced latencies, timeouts and errors, including Azure Communication Services (ACS). The voice and SMS messaging channels in Dynamics 365 Contact Center are built on top of ACS, which enables organizations to provision phone numbers, enable voice calls and manage SMS capabilities through ACS directly.

The outage disrupted customer services across multiple industries, including airlines, retail and gaming, all of which use Azure for their backend operations including point-of-sale systems, customer portals, and mobile apps.

Alaska and Hawaiian Airlines, which have recently merged, reported that the Azure hosts several of their key services, including their websites, which meant that customers were unable to book or check in for flights online.

Travelers who were unable to check in were advised to see an airport agent for their boarding pass and plan for additional time in the terminal, as there were long lines.

“Our teams worked quickly to stand up our backup infrastructure to allow our guests to book and check-in for their flights online while minimizing operational disruptions,” Alaska Airlines stated. The airline added that it would bring the affected systems back online “once Microsoft resolves the issue from its end.”

Heathrow Airport also reported problems with its website, while retailers like Starbucks, Costco, and Kroger experienced interruptions to their websites and mobile apps, indicated by spikes in reports on outage monitoring site DownDetector.

Microsoft’s own services were not immune, as its customers were unable to log in to Xbox Live, Minecraft, and other Microsoft gaming platforms, and access to 365 productivity tools like Outlook, Teams and SharePoint went offline for enterprise users as well as consumers.

The timing of the outage was ironic, given that Microsoft reported its quarterly earnings with its investor relations website down ahead of the analyst call.

The company reported 40 percent year-on-year growth in its Azure revenues, indicating how deeply entrenched the cloud infrastructure is across industries.

The outage served as a reminder of the critical role cloud infrastructure plays in modern customer service delivery and the risks of centralized dependencies.

Successive Outages Expose Systemic Dependence on Cloud Giants

While the AWS outage originated from Amazon’s servers in Northern Virginia, affecting the US East Coast and parts of Western Europe, the Azure outage spread further, with Downdetector indicating disruptions from parts of Asia as well as the U.S. and Europe.

Initial reports had suggested the outage was global. Vivek Dodd, CEO at Skillcast, told CX Today:

“Another outage following so closely after the recent disruption at AWS highlights just how dependent the economy has become on major technology providers.”

“When these big platforms experience downtime, the ripple effects are immediate and widespread across industries—disrupting financial systems, logistics networks, and retail operations.”

As digital transformation brings more enterprise systems into the cloud, many companies are struggling to establish resilience plans to keep up with the growing risks.

“The assumption that leading cloud platforms will always be available has left some enterprises exposed when outages occur,” Dodd said.

Enterprises need to proactively embed operational resilience and third-party risk management as core priorities to mitigate the risk, rather than being reactive and treating them as afterthoughts.

“Building true resilience requires more than technological redundancy; it demands foresight, planning and training,” Dodd said.

One way to do this is by avoiding full reliance on cloud infrastructure, by using hybrid approaches so that their systems have redundancies.

“Organizations should implement hybrid or multi-cloud models, such as diversifying across providers to avoid overreliance on a single vendor, establish clear crisis-response protocols and invest in company-wide compliance and resilience to handle these situations efficiently,” Dodd explained.

Because when third-party infrastructure goes down, it is the enterprise that suffers the impact on its customer experience.

“Incidents like this serve as a powerful reminder that resilience isn’t just about technology, it’s about protecting trust, productivity, and customer confidence in a digital-first economy.”

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.

During the conversation, host Andrew Mayne quizzed Altman over the potential for OpenAI to insert advertising into the much-loved AI model.

Many have touted the move, with the company projected to burn through $26BN and accumulate $14.4BN in losses this year, per the New York Times.

Addressing the speculation, Altman claimed that he’s “not totally against” the idea. Yet, they aren’t sure what an advertising product would look like.

“I can point to areas where I like ads,” said the CEO. “I think ads on Instagram are kind of cool [and] I’ve bought a bunch of stuff from them. But I think it would be very hard to get it right; it would take a lot of care.”

Altman then underscored the high degree of trust in the independence of ChatGPT’s outputs, and appeared unwilling to break that.

Indeed, the OpenAI head honcho believes faith in web search has fallen, as with social media, because “you can tell you’re being monetized.”

He continued: “How much do you believe you’re getting what the company truly thinks is the best content for you, versus something designed to interact with ads? There’s a psychological effect there.

For example, I think if we started modifying the output stream from the LLM (large language model) in exchange for who paid us more, that would feel really bad, and I would hate that as a user. That would be a trust-destroying moment.

Of course, there are alternative methods, such as inserting adverts outside the ChatGPT stream. Yet, Altman stressed: “It would have to feel really useful to users and really clear that it was not messing with the LLM’s output.”

The idea of adverts becomes even more complex, given OpenAI’s recent move to release a new shopping experience, where users can find products/services, compare them, and follow a link to make the purchase.

For those unfamiliar with the experience, check out the video below.

Reports of OpenAI teaming up with Shopify to deliver an end-to-end commerce experience within ChatGPT underscore its intention to build upon this.

In doing so, ChatGPT may soon become the go-to place for many shoppers, with it already getting 5.24BN hits each month, per SEMrush.

That prospect is exciting, but could cause concerns if OpenAI introduces an advertising product. After all, with ads, consumers may question whether their search results and comparisons are independent.

Thankfully, OpenAI appears to be against such actions, for now.

More Moves By Open AI to Transform Customer Experiences

OpenAI’s innovation team hasn’t taken the summer off, releasing a series of data connectors last month that link ChatGPT with popular business applications.

Amongst those apps is HubSpot, the widely utilized CRM offering.

With its connector, brands can pull customer data into ChatGPT, run deep research queries against it, generate new data, and pull that back into the CRM.

In doing so, CX leaders using HubSpot may run sophisticated analytics projects via the familiar ChatGPT interface to refine customer targeting strategies, spot new sales opportunities, forecast staffing needs, and more.

On the launch, HubSpot’s CTO declared it a “big day in HubSpot history.”

However, the move asked some uncomfortable questions for conversational intelligence providers, with some even suggesting it signaled “the death of Gong.”

That’s a question up for debate. Yet, it underscores how ChatGPT is changing the landscape, not just in terms of how customers interact with brands but in how brands evolve their tech stacks.

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.

The retail giant is preparing for a future where AI-powered shopping agents, not human customers, drive purchasing decisions, price comparisons, and brand loyalty.

Walmart recognizes AI-driven shopping assistants as an entirely new type of customer, distinct from traditional consumers. 

On this, Walmart is developing its own AI-powered shopping agents, accessible through its app and website. These agents will handle routine tasks such as reordering weekly groceries and assembling shopping baskets based on user prompts.

However, the company is also preparing for a future where consumers may opt for third-party shopping agents built by tech firms, ensuring its systems are adaptable to external AI-driven purchasing solutions.

In effect, AI agents won’t just facilitate transactions; they’ll redefine how retailers deliver customer experience.

Rethinking CX for Machine-Driven Commerce

This evolution demands a fundamental reassessment of customer experience strategies. 

Retailers must ensure their platforms and services align with AI-driven preferences, enabling automated purchasing with confidence.

Speaking on this announcement through LinkedIn, Sirte Pihlaja, CEO of Shirute, explained: “Walmart’s proactive investment positions it to shape how the retail landscape adapts to artificial intelligence, potentially giving it a competitive edge.

The shopping experience of the future won’t just be personalized, it will be fully automated, with machine customers driving loyalty, efficiency, and expectations.

The Future of Retail: Personalization Meets Automation

As retailers adapt, AI shopping assistants will become key drivers of consumer engagement, setting new benchmarks for speed, accuracy, and seamless purchasing experiences.

Walmart anticipates the development of an industry-wide protocol that will enable third-party shopping agents to interact with retailers’ proprietary AI systems seamlessly. 

This would allow retailers to transfer personalized product recommendations from their platforms to external shopping assistants, ensuring a more integrated and user-driven purchasing experience.

Walmart is the latest in a growing number of retailers and enterprises looking to get ahead and not fall behind in the new rise of AI-led commerce experience. 

Take Amazon, for example, its new ‘Buy for Me’ feature allows users to purchase products from third-party websites directly through the Amazon app, with AI handling the transaction on their behalf.

But this isn’t just an incremental upgrade; it marks a fundamental shift in e-commerce. 

Then, consider how OpenAI recently announced that ChatGPT will let users shop directly from their search results.

The news comes as search is becoming increasingly popular through ChatGPT, with over one billion searches in one week in early May alone.

This announcement then fanned the flames of rumors surrounding an integration between OpenAI and Shopify.

The epicentre of the rumors was a code leak suggesting that OpenAI tested native shopping capabilities with Shopify. 

Retailers Currently Spinning Plates as Consumers Evolve to Become More Tech Savvy 

While retailers grapple with the changing landscape of the shopping experience, they will also have their eyes firmly set on how AI agents or “machine customers” will transform customer support. 

Machine customers are AI-driven digital assistants designed to handle service tasks for human users, such as interacting across customer service channels.

Indeed, Google has recently made several moves to usher in the future of “machine-to-machine” customer service, with the implication that contact centers will leverage AI agents to converse with customers, too.

Most notably, Google’s newly introduced Ask for Me feature enables some Google search users to leverage an AI agent that calls businesses on their behalf.

The newly launched Buy for Me feature empowers users to buy products from third-party websites directly within the Amazon app, with AI completing the purchase on their behalf.

But this isn’t just a logical next step for the e-commerce giant — it signals a much bigger shift: Amazon is not just expanding its retail reach, it’s redefining how we shop altogether. AI is no longer just recommending; it’s transacting.

However, Amazon isn’t the first one on this track.

In November 2024, the AI-powered answer engine Perplexity introduced its Pro Shopping Assistant, allowing users to browse, compare, and shop with direct links to third-party retailers curated by AI. Their approach hinted at a vision where AI connects users to products across the web, eliminating friction at every step.

Perplexity positioned it as a way to “shop smarter, not harder,” with a focus on transparency, price comparison, and convenience — a familiar theme now echoed by Amazon.

Now, Amazon builds on that foundation and pushes it even further by turning product discovery into action by introducing embedded, AI-powered purchasing into their already dominant shopping platform.

Amazon’s version fully integrates the checkout experience and uses agentic AI to take action throughout the customer journey on your behalf, filling out forms, submitting orders, and syncing delivery tracking, all while staying within the Amazon ecosystem.

And here’s the twist: Jeff Bezos is at the helm of Amazon and a key investor in Perplexity. It’s no coincidence that we’re now watching two AI-first powerhouses align their visions to define the future of digital commerce, each from a different angle.

What we’re seeing is a strategic stacking of capabilities, with both companies exploring how AI can revolutionize not just how we shop but who (or what) is actually doing the shopping.

While Amazon didn’t invent agentic AI, it is mainstreaming it, thus making this advanced capability part of everyday customer experiences for millions.

What Is “Buy for Me”, and Why Does It Matter?

Amazon’s Buy for Me allows users to shop from third-party brands without ever leaving the Amazon app. Using a combination of Amazon’s proprietary Nova AI system and Anthropic’s Claude AI, the feature performs tasks on the user’s behalf like opening external websites, auto-filling information, and completing purchases on third-party sites.

This is one of the first major commercial deployments of agentic AI, intelligent systems that don’t just suggest actions but actually take them. It redefines convenience for customers, shifting from tapping and swiping to simply delegating.

Order tracking remains within the Amazon app, but customer service and returns are still handled by the original retailer, maintaining visibility and accountability for these brands.

Currently, the feature is in limited testing in the U.S. on iOS and Android, with a few partner brands. However, Amazon’s pilot programs tend to scale fast.

Enter Amazon Alexa Plus: The Voice of Future Commerce

In February 2025, Amazon unveiled Alexa Plus, a generative AI-powered upgrade to its iconic voice assistant. Alexa has transformed from a scripted helper into a more conversational, context-aware, and capable assistant.

Unlike earlier versions, Alexa Plus can engage in natural conversation, understand context, and take more complex actions across your devices and accounts.

It means your voice assistant won’t just research product features and remind you to reorder products. It will suggest better deals, compare brands, place the order for you, and maybe even initiate the whole process before you say a word based on patterns in your behavior.

With Alexa Plus, Amazon is bridging the gap between voice-driven interfaces and agentic AI. Users can now engage in natural, back-and-forth conversations. It’s a future where shopping becomes an invisible, intuitive, and almost ambient experience.

For customers and the future of shopping, this changes everything.

Imagine telling your digital assistant that you need new equipment for your hobby. You say: “Alexa, I need new running shoes for muddy trails,” and instead of serving a list, Alexa asks follow-up questions about brand preference, budget, and color and then places an order through Buy for Me to complete the purchase autonomously, even if the shoes are sold on a different retailer’s site.

That’s how conversational commerce gets real.

The experience doesn’t stop at reactive commands. Alexa Plus is also capable of proactive assistance.

It can predict your purchasing behavior, keep track of when you’re running low on essentials, and prompt you with suggestions like “You’re almost out of coffee. Would you like to reorder the same brand or try something new?”

This type of interaction transforms Alexa into a personal shopping assistant that anticipates needs rather than waits for instructions. It might suggest bundled savings based on your habits and preferences before you even think

This evolution pushes the assistant, not the seller, to the forefront of the customer experience.

Combined with Buy for Me, Alexa Plus positions Amazon to own the interface for commerce, whether you’re browsing, talking, or letting AI shop autonomously.

Loyalty begins to shift from retailers to the AI interface that knows your needs, remembers your history, and does the work for you. When this assistant is embedded into your home, your devices, and your routines, Amazon essentially becomes the operating system for everyday commerce.

The Bigger Picture: Shopping Without Shopping

A clear theme emerges when we look at Perplexity Pro, Buy for Me, and Alexa Plus side by side: whether users are browsing visually, engaging by voice, or allowing AI to act entirely on their behalf.

It’s not just about shopping faster; it’s about making shopping invisible, intuitive, and effortless.

This leap from chat-based AI to action-based AI represents a paradigm shift that paves the way for the age of agentic AI, i.e., tools that are not only intelligent but autonomous enough to perform tasks with minimal input.

Customers are moving from active browsing to delegated decision-making. The future is not just about faster checkouts but eliminating the checkout altogether.

While the principles of Agentic AI have existed for years in research and innovation labs, Amazon’s and Perplexity’s implementations bring it squarely into the commercial spotlight.

These systems understand your intent, navigate digital environments, curate options, make decisions, and execute purchases fully autonomously.

This raises key questions for customer experience leaders, marketers, and retailers on how brands will preserve identity and engagement when interfaces like Alexa or Perplexity own the customer relationship.

Furthermore, data privacy has become even more critical. We need to start thinking about how to build trust in AI that shops for us, anticipates our needs, and handles sensitive information like credit cards and shipping data.

This new model signals a future where consumers rely on machine customers to complete tasks. And it is a future that demands reimagining CX.

Designing for Human and Machine Customers

We are entering a future where AI is the new front desk, the new sales rep, and increasingly, the one holding the credit card.

Amazon’s Buy for Me and Alexa Plus are not just features. They are signals of a shift toward automated commerce ecosystems, where the customer journey is less about browsing and more about belonging to a platform that knows you.

To succeed in this new reality, businesses must design experiences not only for people but for the intelligent agents acting on their behalf. If AI is doing the shopping, your digital experience must be readable, actionable, and optimized for machine customers.

The age of B2A (Business-to-Agents) in commerce has arrived. The question is: are we ready to serve AI agents as our primary customers? And what does loyalty look like when a machine is your most loyal shopper?

Would you trust an AI like Alexa Plus or Buy for Me to shop on your behalf? Is your customer experience strategy ready for a world where your next buyer might not be human? Have your say on CX Today’s Reddit Community.

Thanks to Sirte Pihlaja for sharing this article with CX Today.  

Sirte Pihlaja, a globally recognized “AI Whisperer” and CX thought leader, pioneers Agentic AI in customer and employee experiences.  

As CEO of Shirute and Head of Team at CXPA Finland, she helps organizations navigate AI-driven transformation with human-centric business models and CX and AI governance frameworks. 

For more insights from Sirte Pihlaja, follow her on LinkedIn or subscribe to her podcast series AI Experience Podcast