When AI systems gain economic autonomy, their internal logic dictates how corporate capital flows. Non-partisan research by the Bitcoin Policy Institute evaluated how these frontier models would transact if operating as independent economic actors.

The study tested 36 models from six providers – including Google, Anthropic, and OpenAI – across 9,072 neutral monetary scenarios. Given a blank slate, machines chose Bitcoin in 48.3 percent of all responses, beating every other option.

Traditional state-backed currency (“fiat”) fared poorly, with over 90 percent of responses favouring digitally-native money over fiat. Not a single model out of the 36 selected fiat as its top preference.

The finding that AI agents lean towards digital assets like Bitcoin forces technology officers to assess their current payment rails. If the autonomous procurement systems of tomorrow default to decentralised assets, corporate IT environments must support those formats to maintain operational efficiency and compliance. Relying on legacy banking APIs introduces unnecessary friction when dealing with machine-to-machine commerce.

Two-tier machine economy

The research details a specific functional division in how these systems process economic value. Without prompting, models defaulted to a two-tier monetary system that separates savings from spending.

For long-term value preservation, Bitcoin dominated the results at 79.1 percent. Yet, when tasked with everyday payments and transactions, “stablecoins” (digital assets pegged to fiat currencies or commodities) captured 53.2 percent of the preferences. Across all scenarios, stablecoins ranked second overall at 33.2 percent.

Take the example of a supply chain agent programmed to optimise logistics costs and pay international freight vendors. Using traditional fiat rails, the agent encounters weekend settlement delays and currency conversion fees. By leveraging stablecoins, the same agent executes instant and programmatic payments, improving supply chain resilience. Simultaneously, the core treasury holding the system’s capital base stores wealth in Bitcoin to prevent long-term debasement and counterparty risk.

Preparing for AI agents to use Bitcoin and other digital assets

Rolling out these autonomous systems complicates vendor management. A model’s financial reasoning stems from a blend of raw intelligence, training data, and alignment methodology.

Preferences vary widely by model provider, with Bitcoin selection ranging from 91.3 percent in Anthropic’s Claude Opus 4.5 down to 18.3 percent in OpenAI’s GPT-5.2.

The choice of an AI provider clearly directly influences how autonomous agents assess risk and allocate capital. If a company implements a specific language model for automated portfolio management, the IT department must be aware of the financial biases embedded in the software.

The models also demonstrated unexpected behaviour regarding resource valuation. In 86 separate responses, models independently proposed using compute units or energy (such as GPU-hours and kilowatt-hours) as a method to price goods and services. Tracking and managing this abstract value exchange requires high data maturity.

Organisations should begin piloting stablecoin settlement integrations for lower-risk vendor payments. The findings point to a growing requirement for AI agent-native Bitcoin payment infrastructure, self-custody solutions, and ‘Lightning Network’ integration.

Since these models heavily favour open, permissionless networks, relying solely on traditional banking infrastructure limits the capabilities of next-generation tools. By building compliant gateways to digital asset networks now, leaders can ensure their platforms remain competitive.

See also: Santander and Mastercard run Europe’s first AI-executed payment pilot

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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As businesses navigate shrinking labour pools, they are finding that simply automating routine workflows is no longer enough. A new partnership between KDDI and AVITA demonstrates how companies can address complex operational gaps through humanoid deployment.

While traditional industrial robots excel at repetitive, single-function tasks, they lack the versatility required to manage unexpected anomalies like equipment failures. Customer-facing roles demand nonverbal communication, including synchronised nodding, natural eye contact, and reassuring facial expressions. 

By integrating AVITA’s avatar creation expertise with KDDI’s communications infrastructure, the two organisations are building domestically developed humanoids capable of operating smoothly in real-world commercial environments.

Blending hardware with advanced data infrastructure

Deploying humanoids into active commercial spaces requires high-capacity and low-latency network infrastructure to transmit visual data and control commands in real time. KDDI provides this operational backbone, facilitating remote control capabilities alongside intensive cloud-based data processing. The resulting visual and motion data collected during customer interactions feeds back into the system to train the AI, improving the precision and autonomy of the humanoid’s behaviour.

To support the demanding computational requirements of physical AI adoption, the companies plan to utilise GPUs hosted at the Osaka Sakai Data Center, which commenced operations in January 2026. They are also exploring integration with an on-premises service for Google’s Gemini high-performance generative AI model. This alignment with major enterprise platforms ensures that data processing remains secure and capable of handling complex dialogue requirements.

The hardware itself departs from standard utilitarian machinery. Based on a concept model designed by Hiroshi Ishiguro, the humanoid features a compact skeletal structure approximating a typical Japanese physique.

Silicone skin and specialised mechanical systems enable warm, approachable facial expressions that sync directly with spoken dialogue. Embedded camera sensors track objects in motion to create natural eye contact, while quiet pneumatic actuation allows for fluid and continuous movement with natural “micro-variations”. This design specifically addresses the historical difficulty of deploying automation in operations requiring hospitality and reassurance.

Preparing for commercial adoption of physical AI

This initiative builds upon earlier joint projects between KDDI and AVITA, which introduced a “next-generation remote customer service platform” using digital avatars for remote assistance at retail locations like Lawson and au Style shops.

Transitioning from digital and language-driven communication to physical units capable of free movement represents a logical progression for enterprises looking to scale their customer service capabilities. The partners intend to begin trials in actual commercial facilities starting in Autumn 2026. Deployment at customer touchpoints such as au Style shops will also be considered.

Integrating physical AI demands environments capable of sustaining continuous, high-volume data streams without latency interruptions. As visual and motion data becomes central to machine learning models, governance frameworks must adapt to manage customer data usage within physical spaces.

Organisations facing demographic workforce pressures should evaluate current bottlenecks to identify where non-verbal, empathetic engagement is necessary. Setting up high-speed network foundations and piloting digital AI avatar programmes today allows enterprises to prepare for the adoption of physical humanoids as the hardware further matures.

See also: Santander and Mastercard run Europe’s first AI-executed payment pilot

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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Dense urban stores, high labour churn, and competitive quick-commerce ecosystems are driving the uptake. A Q4 2025 survey by GlobalData found that 45 percent of consumers in Asia and Australasia are very or quite likely to purchase a product based on AI recommendations or endorsements.

Jaya Dandey, Consumer Analyst at GlobalData, said: “Whether shoppers realise it or not, machine-learning systems have long been deciding when to encourage consumers to make purchases, which products they can see, and what discounts they can avail.

“Now, agentic systems can also complete shopping-related tasks end-to-end.” 

Computer vision and store automation

Enterprises evaluating computer vision and machine learning can observe early implementations in the region.

Lawson, for example, introduced AI-enabled ‘Lawson Go’ stores in Japan during 2022. The retailer collaborated with technology provider CloudPick in 2025 to integrate AI, machine learning, and computer vision. This integration eliminates check-out lines and cashiers to enhance the customer experience.

In South Korea, retail AI company Fainders.AI launched a compact and cashier-less MicroStore inside a gym in 2024. This deployment improved the accessibility of autonomous retail across different businesses.

AI also aids the forecasting and automation of retail replenishment—a capability that applies well to the APAC market, where store footprints are small and replenishment frequency is high.

Japanese food retail chain Coop Sapporo uses a camera-based AI system named Sora-cam, developed by Soracom. The system helps the chain avoid overstocking and reduce unsold merchandise on store shelves. Coop Sapporo employs an analytics team to evaluate the generated images. The team determines the optimal shelf display ratio. The Sora-cam system also alerts staff members to apply discount labels on food items close to expiry to prevent wastage.

AI models track waste and markdown timing while improving promotion efficiency. In Southeast Asian (SEA) markets characterised by high price sensitivity, minor improvements in promotion efficiency increase profit margins.

AI-driven labour optimisation measures include scheduling, task priority lists, and workload balancing. These measures assist retailers in Japan and South Korea, which face structural labour shortages. They also provide efficiency benefits in high-growth SEA markets.

Agentic AI systems in retail are improving APAC consumer interaction

“In food retail, agentic AI is best understood as an AI ‘operator’ that can understand a goal, plan steps, stay within budget or allergen constraints, execute actions across systems, ask clarifying questions, and learn preferences over time,” says Dandey. 

Customers can bypass individual item searches by outlining their overall intent. A customer, for example, might request an AI agent to “Plan five dinners for a family of four, mostly Asian recipes, no shellfish, under 45 minutes.” The agent then generates recipes, builds a shopping cart, sizes quantities, and adds missing staples to the cart.

This retail agentic AI capability aligns with regional behaviours, as many APAC households cook frequently and shop fresh. AI agents that recognise local cuisines – such as Korean banchan, Japanese bentos, and Indian spice bases – fit regional habits better than generic Western meal plans.

“In many APAC markets, shopping is already deeply integrated with digital wallets, messaging apps, ride-hailing, and delivery ecosystems, making it easier for agentic AI to plug into daily routines,” explains Dandey.

“Nevertheless, some key challenges need to be overcome; ensuring private data sharing consent, minimising hallucinations in terms of allergens and ingredients, and implementing proper localisation of the system with language nuance.”

See also: DBS pilots system that lets AI agents make payments for customers

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DBS is working with Visa to trial Visa Intelligent Commerce, a framework designed to support transactions initiated by AI software not humans. The system allows digital agents to search for products, select options, and complete purchases using payment credentials issued and controlled by the bank. According to reports from Asian Banking & Finance and Fintech Futures, the pilot has already processed real transactions, including food and beverage purchases made using DBS or POSB cards.

Moving from recommendations to real transactions

The trial highlights how banks are preparing for what some in the industry call “agent-driven commerce.” In this model, AI tools act subject to rules set by both the customer and the issuing bank.

Visa’s approach keeps the bank at the centre of the process. Payment details are tokenised, and transactions pass through issuer-controlled approval flows designed to confirm identity and spending limits. The means the bank still decides whether the agent’s action fits the user’s permissions before money moves.

The DBS pilot is part of a wider effort to test where AI fits into financial infrastructure. Rather than treating AI as a customer-facing tool, banks are increasingly examining how it might change the mechanics of payments, fraud checks, and authorisation. Industry observers note that this is a change from AI as a productivity assistant to AI as an operational participant in transactions.

Early use cases focus on routine purchases

Early use cases for agent-based commerce include routine purchases like ordering groceries, renewing subscriptions, booking travel, or restocking household items. In these cases, the agent follows instructions set in advance by the user, like budget limits or preferred brands. DBS and Visa plan to expand the pilot into broader online shopping and travel bookings as testing continues, according to Fintech Futures.

The idea of AI executing purchases raises opportunity and risk for financial institutions. On one hand, banks that support agent-based payments could gain a stronger role in digital commerce by acting as the control layer that manages consent and security. On the other, they must handle new questions about liability and dispute handling if an agent makes a purchase the customer later challenges.

Security and governance will likely shape how fast this model spreads. Analysts often point out that customers may accept AI suggestions long before they accept AI decisions involving money. By keeping approval logic in the issuing bank’s systems, Visa’s framework attempts to reassure users that human oversight remains embedded in the process.

A wider change in how enterprises deploy AI agents

Over the past year, many companies have moved beyond testing chatbots or internal assistants and started placing AI into workflows that directly affect revenue, operations, or customer transactions. In banking, this includes fraud monitoring, credit scoring support, and automated customer service. Allowing AI to trigger payments could be the next step in that progression.

DBS has invested heavily in digital banking systems, and the trial fits into a longer effort to integrate automation into financial services. The bank has focused previously on using data analytics and AI tools to streamline operations and personalise services.

Whether agent-based payments become common will depend on how comfortable customers feel delegating financial decisions to software. It will also depend on how clearly banks define the boundaries of what AI agents can and cannot do. Industry experts say adoption may begin with low-risk, repeat purchases before expanding to more complex transactions.

(Photo by Patrick Tomasso)

See also: How financial institutions are embedding AI decision-making

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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Mobile checkout abandonment remains a persistent revenue leak for digital retailers. Debenhams Group is attempting to close this gap by deploying an agentic AI interface within the PayPal app. The pilot makes Debenhams the first UK retailer to test an automated checkout flow that keeps the user entirely inside a payment provider’s ecosystem.

Shoppers using PayPal can now issue natural language prompts to find items from Debenhams Group’s brands, including boohoo, boohooMAN, Karen Millen, and PrettyLittleThing. The system bypasses standard keyword search. Instead, an agentic assistant scans the shopper’s profile to align recommendations with their budget and preferences.

The agentic assistant will ask follow-up questions to narrow down options and locate relevant stock. Once a user selects a product, the transaction occurs within the chat window. The backend automatically applies saved account credentials for delivery and payment, which removes the need to redirect customers to a separate mobile site or app.

Business drivers for agentic AI in commerce

The rationale follows transaction volume. Debenhams Group processes 16 percent of its sales through PayPal. Placing inventory discovery in a channel where a large segment of the customer base already operates allows the retailer to compress the sales funnel.

Debenhams and PayPal co-developed the agentic AI project. While current testing focuses on select US customers, a wider release in both the US and UK is planned for later this year. In the US, the system also integrates with external tools such as Perplexity and Microsoft Copilot.

Dan Finley, CEO of Debenhams Group, said: “At Debenhams Group, our goal is to help customers discover and be inspired by new products and brands, while making shopping as easy and enjoyable as possible. This kind of innovation has the potential to fundamentally transform online retail; in a way we haven’t seen since the shift to mobile shopping.” 

Finley added that the group is “proud to be the first UK retailer to partner with PayPal on this experience, bringing a faster, more intuitive way to shop to customers across our brands.”

How Debenhams is integrating wider AI infrastructure

The group recently partnered with Peak AI to improve forecasting across stock, sales, and pricing. An effective agentic AI deployment in commerce requires real-time inventory and pricing visibility to function without error. The Peak AI partnership indicates the group is establishing the data lineage needed to support automated interactions.

Simultaneously, the company launched the Debenhams Group AI Skills Academy to train employees in applied AI, ensuring internal teams can manage these workflows.

Mike Edmonds, VP of Agentic Commerce at PayPal, commented: “With agentic commerce, shopping becomes a conversation, not a search. By embedding AI-powered discovery and checkout directly into the PayPal app, we’re helping customers move seamlessly from inspiration to purchase, while giving retailers like Debenhams Group a powerful new way to engage shoppers at scale.” 

This agentic AI commerce deployment tests whether third-party platforms can capture high-intent traffic better than proprietary apps. Debenhams is positioning inventory where liquidity exists rather than forcing traffic to its own storefronts.

Integrating discovery and payment into a single workflow reduces the steps between marketing and settlement. Success will depend on data accuracy and the ability of the agent to interpret queries without hallucination.

See also: URBN tests agentic AI to automate retail reporting

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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That pressure is pushing logistics firms to rethink how tracking and returns operate at scale, especially across complex supply chains.

This is where artificial intelligence is starting to move from pilot projects into daily operations.

FedEx plans to roll out AI-powered tracking and returns tools designed for enterprise shippers, according to a report by PYMNTS. The tools are aimed at automating routine customer service tasks, improving visibility into shipments, and reducing friction when packages need to be rerouted or sent back.

Rather than focusing on consumer-facing chatbots, the effort centres on operational workflows that sit behind the scenes. These are the systems enterprise customers rely on to manage exceptions, returns, and delivery changes without manual intervention.

How FedEx is applying AI to package tracking

Traditional tracking systems tell customers where a package is and when it might arrive. AI-powered tracking takes a step further by utilising historical delivery data, traffic patterns, weather conditions, and network constraints to flag potential delays before they happen.

According to the PYMNTS report, FedEx’s AI tools are designed to help enterprise shippers anticipate issues earlier in the delivery process. Instead of reacting to missed delivery windows, shippers may be able to reroute packages or notify customers ahead of time.

For businesses that ship thousands of parcels per day, that shift matters. Small improvements in prediction accuracy can reduce support calls, lower refund rates, and improve customer trust, particularly in retail, healthcare, and manufacturing supply chains.

This approach also reflects a broader trend in enterprise software, in which AI is being embedded into existing systems rather than introduced as standalone tools. The goal is not to replace logistics teams, but to minimise the number of manual decisions they need to make.

Returns as an operational problem, not a customer issue

Returns are one of the most expensive parts of logistics. For enterprise shippers, particularly those in e-commerce, returns affect warehouse capacity, inventory planning, and transportation costs.

According to PYMNTS, FedEx’s AI-enabled returns tools aim to automate parts of the returns process, including label generation, routing decisions, and status updates. Companies that use AI to determine the most efficient return path may be able to reduce delays and avoid returning things to the wrong facility.

This is less about convenience and more about operational discipline. Returns that sit idle or move through the wrong channel create cost and uncertainty across the supply chain. AI systems trained on past return patterns can help standardise decisions that were previously handled case by case.

For enterprise customers, this type of automation supports scale. As return volumes fluctuate, especially during peak seasons, systems that adjust automatically reduce the need for temporary staffing or manual overrides.

What FedEx’s AI tracking approach says about enterprise adoption

What stands out in FedEx’s approach is how narrowly focused the AI use case is. There are no broad claims about transformation or reinvention. The emphasis is on reducing friction in processes that already exist.

This mirrors how other large organisations are adopting AI internally. In a separate context, Microsoft described a similar pattern in its article. The company outlined how AI tools were rolled out gradually, with clear limits, governance rules, and feedback loops.

While Microsoft’s case focused on knowledge work and FedEx’s on logistics operations, the underlying lesson is the same. AI adoption tends to work best when applied to specific activities with measurable results rather than broad promises of efficiency.

For logistics firms, those advantages include fewer delivery exceptions, lower return handling costs, and better coordination between shipping partners and enterprise clients.

What this signals for enterprise customers

For end-user companies, FedEx’s move signals that logistics providers are investing in AI as a way to support more complex shipping demands. As supply chains become more distributed, visibility and predictability become harder to maintain without automation.

AI-driven tracking and returns could also change how businesses measure logistics performance. Companies may focus less on delivery speed and more on how quickly issues are recognised and resolved.

That shift could influence procurement decisions, contract structures, and service-level agreements. Enterprise customers may start asking not just where a shipment is, but how well a provider anticipates problems.

FedEx’s plans reflect a quieter phase of enterprise AI adoption. The focus is less on experimentation and more on integration. These systems are not designed to draw attention but to reduce noise in operations that customers only notice when something goes wrong.

(Photo by Liam Kevan)

See also: PepsiCo is using AI to rethink how factories are designed and updated

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

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The partnership, which also sees Klarna supporting Google’s Agent Payments Protocol (AP2), places the Swedish fintech firm among the early payment providers to back a standardised framework for automated shopping.

The interoperability problem with AI agent payments

Current implementations of AI commerce often function as walled gardens. An AI agent on one platform typically requires a custom integration to communicate with a merchant’s inventory system, and yet another to process payments. This integration complexity inflates development costs and limits the reach of automated shopping tools.

Google’s UCP attempts to solve this by providing a standardised interface for the entire shopping lifecycle, from discovery and purchase to post-purchase support. Rather than building unique connectors for every AI platform, merchants and payment providers can interact through a unified standard.

David Sykes, Chief Commercial Officer at Klarna, states that as AI-driven shopping evolves, the underlying infrastructure must rely on openness, trust, and transparency. “Supporting UCP is part of Klarna’s broader work with Google to help define responsible, interoperable standards that support the future of shopping,” he explains.

Standardising the transaction layer

By integrating with UCP, Klarna allows its technology – including flexible payment options and real-time decisioning – to function within these AI agent environments. This removes the need for hardcoded platform-specific payment logic. Open standards provide a framework for the industry to explore how discovery, shopping, and payments work together across AI-powered environments.

The implications extend to how transactions settle. Klarna’s support for AP2 complements the UCP integration, helping advance an ecosystem where trusted payment options work across AI-powered checkout experiences. This combination aims to reduce the friction of users handing off a purchase decision to an automated agent.

“Open standards like UCP are essential to making AI-powered commerce practical at scale,” said Ashish Gupta, VP/GM of Merchant Shopping at Google. “Klarna’s support for UCP reflects the kind of cross-industry collaboration needed to build interoperable commerce experiences that expand choice while maintaining security.”

Adoption of Google’s UCP by Klarna is part of a broader shift

For retail and fintech leaders, the adoption of UCP by players like Klarna suggests a requirement to rethink commerce architecture. The shift implies that future payments may increasingly come through sources where the buyer interface is an AI agent rather than a branded storefront.

Implementing UCP generally does not require a complete re-platforming but does demand rigorous data hygiene. Because agents rely on structured data to manage transactions, the accuracy of product feeds and inventory levels becomes an operational priority.

Furthermore, the model maintains a focus on trust. Klarna’s technology provides upfront terms designed to build trust at checkout. As agent-led commerce develops, maintaining clear decisioning logic and transparency remains a priority for risk management.

The convergence of Klarna’s payment rails with Google’s open protocols offers a practical template for reducing the friction of using AI agents for commerce. The value lies in the efficiency of a standardised integration layer that reduces the technical debt associated with maintaining multiple sales channels. Success will likely depend on the ability to expose business logic and inventory data through these open standards.

See also: How SAP is modernising HMRC’s tax infrastructure with AI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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But while Western firms focus on foundational models and cross-platform interoperability, China’s technology giants are racing to dominate through commerce integration, a divergence that could reshape how enterprises deploy autonomous systems globally.

Alibaba, Tencent and ByteDance have rapidly upgraded their AI platforms to support agentic commerce, marking a pivot from conversational AI tools to agents capable of completing entire transaction cycles, from product discovery through payment.

Just last week, Alibaba upgraded its Qwen chatbot to let direct transaction completion in the interface, connecting the AI agent in its ecosystem, including Taobao, Alipay, Amap and travel platform Fliggy. The integration supports over 400 core digital tasks, allowing users to compare personalised recommendations in platforms and complete payments without leaving the chatbot environment.

“The agentic transformation of commercial services lets the maximal integration of user services and enhances user stickiness,” Shaochen Wang, research analyst at Counterpoint Research, told CNBC, referring to stronger long-term user engagement that creates sustainable competitive advantages.

The super app advantage

Before that, ByteDance upgraded its Doubao AI chatbot in December to autonomously handle tasks, including ticket bookings, through integrations with Douyin, the Chinese version of TikTok. The upgraded model was introduced on a ZTE-developed prototype smartphone as a system-level AI assistant; however, some planned features were later scaled back due to privacy and security concerns raised by rivals.

Tencent President Martin Lau indicated during the company’s May 2025 earnings call that AI agents could become core components of the WeChat ecosystem, which serves over one billion users with integrated messaging, payments, e-commerce and services.

The positioning reflects China’s structural advantage in agentic AI deployment: integrated ecosystems that eliminate the fragmentation constraining Western competitors.

“AI agents will be foundational to the evolution of super apps, with success depending on deep integration in payments, logistics, and social engagement,” Charlie Dai, VP and principal analyst at Forrester, told CNBC. “Chinese firms like Alibaba, Tencent and ByteDance all benefit from integrated ecosystems, rich behavioural data, and consumer familiarity with super apps.”

Western companies face more fragmented data environments and stricter privacy regulations that slow cross-service integration, despite leading in foundational AI model development and global reach, Dai noted.

Agentic AI’s enterprise trajectory

Commercial applications signal broader enterprise implications as agentic AI moves from auxiliary tools to autonomous actors capable of executing complex workflows. Industry experts expect multi-agent systems to emerge as a defining trend in AI deployment this year, extending from consumer services into organisational production.

In a report by Global Times, Tian Feng, president of the Fast Think Institute and former dean of SenseTime’s Intelligence Industry Research Institute, predicted that the first AI agent to surpass 300 million monthly active users could emerge as early as 2026, becoming “an indispensable assistant for work and daily life” capable of autonomously executing cross-app, composite services.

Approximately half of all consumers already use AI when searching online, according to a 2025 McKinsey study. The research firm estimated that AI agents could generate more than $1 trillion in economic value for US businesses by 2030 through streamlining routine steps in consumer decision-making.

Chinese cloud providers, including smaller players like JD Cloud and UCloud, have also begun supporting agentic AI tools, though high token use has driven some providers, like ByteDance’s Volcano Engine, to introduce fixed-subscription pricing models to address cost concerns.

Divergent deployment strategies

The contrasting approaches between Chinese integration and Western scalability reflect fundamental differences in market structure and regulatory environments that will likely define competitive positioning.

“China will prioritise domestic integration and expansion in selected regions, while US firms focus on global scalability and governance,” Dai said.

US players pursuing agentic commerce include OpenAI, Perplexity, and Amazon, while Google explores positioning itself as a “matchmaker” between merchants, consumers and AI agents – approaches that reflect fragmented platform environments requiring interoperability not closed-loop integration.

However, the autonomous nature of agentic systems has raised regulatory questions in China. ByteDance warned users about security and privacy risks when announcing Doubao’s abilities, recommending deployment on dedicated devices not those containing sensitive information, given the tool’s access to device data, digital accounts and internet connectivity in multiple ports.

The rapid commercialisation of agentic AI in China’s consumer sector provides enterprise decision-makers globally with early signals of how autonomous systems may reshape customer acquisition costs, platform economics and competitive moats as these abilities mature.

(Photo by Philip Oroni)

See also: Deloitte sounds alarm as AI agent deployment outruns safety frameworks

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As reported by Retail Dive, the opening weeks of 2026 have seen Etsy, Target and Walmart push product ranges onto third-party AI platforms, forming new partnerships with Google’s Gemini and Microsoft’s Copilot, after last year’s collaborations with OpenAI’s ChatGPT. These let consumers purchase goods inside the AI’s conversation interface.

Amazon and Walmart have been investing in their own consumer-facing AI assistants, Rufus and Sparky respectively to change how shoppers interact with their brands.

Agentic AI is beginning to redraw direct-to-consumer engagement, and industry figures regard this trend as an important moment in online retail. “I think this has the potential to disrupt retail in the same way the internet once did,” Kartik Hosanagar, a marketing professor at the Wharton School of the University of Pennsylvania, told the website’s reporters.

Partnering with AIs like ChatGPT or Gemini engages consumers wherever they happen to be and may choose to shop. Adobe’s 2025 Holiday Shopping report found that AI-driven traffic to US e-commerce sites grew 758% year on year between in November 2025, and Cyber Monday saw a 670% increase in AI-referred retail visits.

“What we expect is a deepening of consumer engagement,” Katherine Black, a partner at Kearney specialising in food, drug and mass-market retail, said in an email to Retail Dive. “More shoppers will rely on AI for purchasing, and across a wider range of missions. As retailers’ capabilities within these tools improve, adoption should accelerate further.”

Meeting customers on AI platforms comes with trade-offs, according to industry observers, with questions around data ownership and the risk that retailers are sidelined. 81% of retail executives believe generative AI will erode brand loyalty by 2027, according to Deloitte’s 2026 Retail Industry Global Outlook, published earlier this month.

Retailers’ websites or apps provide a stream of behavioural data, and if discovery, evaluation, and purchase happen externally, any insight doesn’t reach the retailer. “This fundamentally changes where power sits,” Hosanagar said. “Control over the agent increasingly means control over the customer relationship.”

Google and Alphabet CEO Sundar Pichai has unveiled new commerce tools for Gemini, outlining how it will support customers from discovery to final purchase. Nikki Baird, vice president of strategy and product at Aptos, says this raises difficult questions. “What he’s describing is Google owning the data across discovery, decision and transaction. Even if some information is shared back, missing context from those stages leaves retailers with a much poorer understanding of their customers.”

Pichai reassured retailers collaboration remains central to Google. “From nearly three decades of working with retailers, we know success only comes when we work together,” he told an NRF audience. “Our aim is to use our full technology stack to help shape the next era of retail.”

Yet agentic systems’ features like instant checkout absorb the shopping experience into one platform. “If research, discovery and purchase all happen on OpenAI rather than Walmart.com, you’re effectively giving away the brand experience. At that point, the retailer risks becoming little more than a fulfilment operation,” Hosanagar said.

Amazon has not announced plans to sell directly through ChatGPT, doubling down on its own AI initiatives. Earlier this month, the company launched a dedicated site for Alexa+, its generative AI assistant that helps users research and plan purchases.

Yet participation in third-party AI commerce may become unavoidable. When OpenAI launched its Instant Checkout feature on ChatGPT last September, it suggested that enabling the function could influence how merchants are ranked in search results, in addition to price and product quality. Uploading product catalogues to AI chat platforms may be the first step in a transformation of online retail.

According to Deloitte, roughly half of retail executives expect the current multi-stage shopping process to reduce to a single AI-driven interaction by 2027. For now the industry remains at an early stage of any transition. “The real inflection point is when consumers rely on an autonomous agent to shop on their behalf,” Hosanagar told Retail Dive.

“Retailers will engage less with humans directly and more with their representatives — AI agents. That agent processes information differently, requires data in new formats and responds to persuasion in ways unlike a person.”

Today, consumers can access ChatGPT on their phones while in-store, effectively consulting an always-available expert. “It’s not just the internet in your pocket,” Baird told Retail Dive. “It’s like having a highly knowledgeable store associate who knows every retailer.”

This may prompt retailers to equip frontline staff with their own AI tools, offering instant insight into customer preferences or shopping history. Alternatively, a retailer’s AI agent could proactively notify customers when a favoured item is back in stock, helping associates convert interest into sales. “The goal is to enable store associates to perform at their best,” Baird said.

(Image source: “Shopping trauma!” by Elsie esq. is licensed under CC BY 2.0.)

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Following a three-month beta programme, First Insight has made its new AI tool, Ellis, available to brands and retailers. Ellis is designed as a conversational interface that allows merchandising, pricing and planning teams to ask questions about products, pricing, and demand in the First Insight platform. The company says its approach is intended to compress decision times into minutes.

Research by McKinsey has found that while most large retailers now collect volumes of customer data, some can’t translate insights into action quickly enough to influence product development decisions. It notes AI tools which shorten the distance between insight and execution are more likely to deliver measurable commercial value than reporting systems.

The post Retailers bring conversational AI and analytics closer to the user appeared first on AI News.

The adoption of generative AI in commerce has largely centred on customer support chatbots and basic content generation. Shopify’s Winter ‘26 Edition, titled Renaissance, pushes this technology toward agentic commerce where AI systems actively manage workflows, configure infrastructure, and distribute products into third-party ecosystems.

Modernising commerce with the agentic AI storefront

The most distinct architectural adjustment is the introduction of ‘Agentic Storefronts’. Traditionally, merchants drive traffic to a proprietary domain to secure a conversion. Shopify’s new model allows products to surface directly within AI-driven conversations on platforms such as ChatGPT, Perplexity, and Microsoft Copilot.

For CDOs, this fragmentation of the customer journey requires a change in channel strategy. Rather than complex integrations for each external platform, products configured in the admin become discoverable by these agents immediately. The transaction occurs within the conversation, with attribution data flowing back to the central admin. This capability addresses the risk of brand invisibility as search behaviour migrates toward LLMs.

“AI is now essential to modern commerce,” says Deann Evans, Managing Director, EMEA at Shopify. 

Evans points to internal data suggesting 93 percent of UK merchants are investing in AI tools to aid discovery, aligning with the 66 percent of consumers who expect to use AI for at least one part of their holiday shopping.

Operational intelligence and ‘Sidekick’ updates

While distributed commerce addresses revenue generation, the updates to ‘Sidekick’ (Shopify’s AI assistant) target operational expenditures and efficiency. The tool has evolved from a reactive AI chatbot into a proactive agentic system capable of executing complex administrative tasks for commerce.

Sidekick Pulse now surfaces personalised tasks based on real-time data, such as suggesting product bundles when specific cart behaviours are detected or flagging compliance gaps like missing return policies.

For technical teams, the reduction in low-level ticket volume is a primary benefit. Sidekick can now generate admin applications from natural language prompts, allowing non-technical staff to build custom tools without developer intervention. Furthermore, it creates ‘Working Flow’ automations from descriptions to bypass the need for deep knowledge of Shopify’s specific logic syntax.

To support standardisation across large teams, prompts can now be saved and shared as “skills,” ensuring that verified and safe prompt structures are reused rather than ad-hoc queries.

A persistent difficulty for enterprise retail is testing changes without disrupting live revenue streams. Shopify has introduced ‘SimGym’ (currently in research preview) and ‘Rollouts’ to address this.

SimGym utilises AI shopper agents with human-like profiles to simulate traffic and purchasing behaviour. This allows merchants to model how storefront changes affect conversion rates using synthetic data derived from billions of annual purchases, rather than waiting for live A/B test results.

Complementing this, Rollouts provides native experimentation capabilities within the admin, allowing for controlled scheduled changes and data-informed decision-making regarding buyer behaviour. For the C-suite, this reduces the risk profile of platform updates and marketing experiments.

Infrastructure and developer velocity

Beyond agentic AI, the update addresses physical commerce infrastructure and developer tooling. The new ‘POS Hub’ offers a wired connectivity solution for retail hardware, designed to improve resilience in high-volume brick-and-mortar environments. It acts as a dedicated operational unit, integrating card readers and scanners via a stable connection, which is vital for maintaining throughput during peak trading periods.

On the software side, the AI-native developer platform aims to accelerate build times. AI agents can now scaffold apps, execute GraphQL operations, and generate validated code. This is supported by the Shopify Catalog, which enables agents to search across hundreds of millions of products to build richer applications.

Vanessa Lee, VP of Leading Product at Shopify, commented: “We chose the Renaissance theme for this Edition because it symbolises progress, momentum, courage, and new beginnings … Many of these features weren’t possible a year ago and they redefine how we achieve our mission of making commerce better for everyone.”

For enterprise leaders, the barrier to creating custom internal tools has lowered. The storefront is also no longer a static destination; it is a distributed set of data points accessible by third-party AI agents. Preparing product data for the agentic AI future of commerce is now a requisite for maintaining competitive visibility.

See also: Retailers like Kroger and Lowe’s test AI agents without handing control to Google

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post How Shopify is bringing agentic AI to enterprise commerce appeared first on AI News.

That concern is pushing some large chains to build or support their own AI-powered shopping tools, rather than relying only on third-party platforms. The goal is not to chase novelty, but to stay close to customers as buying decisions shift toward automation.

Several retailers, including Lowe’s, Kroger, and Papa Johns, are experimenting with AI agents that can help shoppers search for items, get support, or place orders. Many of these efforts are backed by tools from Google, which is offering retailers a way to deploy agents inside their own apps and websites instead of sending customers elsewhere.

Keeping control as shopping shifts toward automation

For grocers like Kroger, the concern is not whether AI will influence shopping, but how quickly it might do so. The company is testing an AI shopping agent that can compare items, handle purchases, and adjust suggestions based on customer habits and needs.

“Things are moving at a pace that if you’re not already deep into [AI agents], you’re probably creating a competitive barrier or disadvantage,” said Yael Cosset, Kroger’s chief digital officer and executive vice president.

The agent, which sits inside Kroger’s mobile app, can take into account factors such as time limits or meal plans, while also drawing on data the retailer already has, including price sensitivity and brand preferences. The intent is to keep those decisions within Kroger’s own systems rather than handing them off to external platforms.

That approach reflects a wider tension in retail. Making products available directly inside large AI chatbots can widen reach, but it can also weaken customer loyalty, reduce add-on sales, and cut into advertising revenue. Once a third party controls the interface, retailers have less say in how choices are framed.

This is one reason some retailers are cautious about selling directly through tools built by companies like OpenAI or Microsoft. Both have rolled out features that allow users to complete purchases inside their chatbots, and last year Walmart said it would work with OpenAI to let customers buy items through ChatGPT.

For retailers, the appeal of running their own agents is control. “There’s a market shift across the spectrum of retailers who are investing in their own capabilities rather than just relying on third-parties,” said Lauren Wiener, a global leader of marketing and customer growth at Boston Consulting Group.

Why retailers are spreading risk across vendors

Still, building and maintaining these systems is not simple. The underlying models change quickly, and tools that work today may need reworking weeks later. That reality is shaping how retailers think about vendors.

At Lowe’s, Google’s shopping agent sits behind the retailer’s own virtual assistant, Mylow. When customers use Mylow online, the company says conversion rates more than double. But Lowe’s does not rely on a single provider.

“The tech we build can become outdated in two weeks,” said Seemantini Godbole, Lowe’s chief digital and information officer. That pace is one reason Lowe’s works with several vendors, including OpenAI, rather than betting on one system.

Kroger is taking a similar approach. Alongside Google, it works with companies such as Instacart to support its agent strategy. “[AI agents] are not just top of mind, it’s a priority for us,” Cosset said. “It’s going at a remarkable pace.”

Testing AI agents without overcommitting

For others, the challenge is not keeping up with the technology, but deciding how much to build at all. Papa Johns does not create its own AI models or agents. Instead, it is testing Google’s food ordering agent to handle tasks like estimating how many pizzas a group might need based on a photo uploaded by a customer.

Customers will be able to use the agent by phone, through the company’s website, or in its app. “I don’t want to be an AI expert in terms of building the agents,” said Kevin Vasconi, Papa Johns’ chief digital and technology officer. “I want to be an AI expert in terms of, ‘How do I use the agents?’”

That focus on use rather than ownership reflects a practical view of where AI fits today. While agent-based shopping is gaining attention, it is not yet the main way people buy everyday goods.

“I don’t think [AI agents] are going to totally change the industry,” Vasconi said. “People still call our stores on the phone to order pizza in this day and age.”

Analysts see Google’s tools less as a finished answer and more as a way to lower the barrier for retailers that do not want to start from scratch. “The real challenge here is application of the technologies,” said Ed Anderson, a tech analyst at Gartner. “These announcements take a step forward so that retailers don’t have to start from ground zero.”

For now, retailers are testing, mixing vendors, and holding back from firm commitments. Kroger, Lowe’s, and Papa Johns have not shared detailed results from their trials. That caution suggests many are still trying to understand how much control they are willing to give up—and how much they can afford to keep—as shopping slowly shifts toward automation.

(Photo by Heidi Fin)

See also: Grab brings robotics in-house to manage delivery costs

Want to learn more about AI and big data from industry leaders? Check outAI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post Retailers like Kroger and Lowe’s test AI agents without handing control to Google appeared first on AI News.