Category: AI Business Strategy

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HarmonyOS 7 steps into the AI gap Apple left open in China

Four days after Apple confirmed that Siri AI would not launch in China, Huawei took the stage in Dongguan and declared HarmonyOS 7 the beginning of the agent era. The gap Apple could not fill, Huawei has moved into with an architecture built specifically for it.

What HarmonyOS 7 actually changes

The headline change is the HarmonyOS Intelligent Agent Framework 2.0, which restructures the OS around what Huawei calls an “intent-as-service” model, compressing what previously required multiple app navigation into a single natural-language command.

At the centre of this is Xiaoyi, Huawei’s AI assistant, rebuilt from a conventional voice tool into what the company describes as a system-level intelligence agent. Xiaoyi now controls over 2,100 system-level capabilities and coordinates with more than 2,000 third-party AI agents developed across Huawei’s developer ecosystem.

Richard Yu, chairman of Huawei’s Consumer Business Group, framed the release as a generational inflexion point: “In 2019, HarmonyOS was born. In 2023, native HarmonyOS apps began. In 2026, HarmonyOS enters the Agent era.”

Underneath sits openPangu 2.0, Huawei’s updated foundation model, with 505 billion parameters in its Pro version and 92 billion in the Flash variant, both supporting 512K context windows. On-device models at 30 billion parameters are due on Kirin chips by autumn 2026. HarmonyOS 7 also delivers a 15%-plus performance improvement over HarmonyOS 6.1, according to Huawei’s own benchmarks.

The task execution rate claimed is above 90%, though that figure is Huawei’s own and has not been independently verified.

The market position is consolidating

The numbers shared at HDC 2026 reflect a shift that has already happened. In Q1 2026, HarmonyOS held 19% of China’s smartphone OS market against Apple iOS at 16%, with Android at 65%. HarmonyOS first overtook iOS in China in Q2 2025, according to Counterpoint Research.

That trajectory matters more than any single feature because China is simultaneously the market Apple cannot currently operate in at the AI level and the one Huawei has fully optimised for. The agent network Xiaoyi coordinates includes partnerships with Ctrip for travel planning and Ant Medical for health data analysis, services woven into the Chinese consumer stack that Apple’s architecture does not reach.

Where the limits are

The scope of the challenge to Apple needs calibrating. HarmonyOS 7 is currently in developer beta, with the stable consumer release expected this autumn. The 2,000-plus AI agents are anchored in the Chinese app ecosystem.

The platform counts more than 400,000 applications and services, which is significant but still a fraction of what Apple’s App Store carries. Huawei’s ambitions to take HarmonyOS international remain aspirational for now.

There is also a design note that softens any clean divergence narrative: HarmonyOS 7 adopts the same Liquid Glass aesthetic Apple introduced with iOS 26, and Samsung brought to One UI 9. Visual language converges even as underlying architectures and regulatory environments pull in opposite directions.

The longer arc

HarmonyOS exists because of US sanctions. When Huawei lost access to Google’s Android in 2019, it built its own OS from necessity. By January 2026, over 90% of Huawei devices were running the fully homegrown version. That forced independence is now a structural advantage in the one market where Apple cannot currently deploy its headline AI feature.

Sanctions built the platform. Regulatory friction cleared its path.

See also: Siri AI arrives with Google inside, and much of the world is locked out

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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June 15, 2026
Accenture: Consumers show growing trust in AI shopping agents

Consumers are showing a willingness to let AI agents take on more shopping-related tasks, according to new research from Accenture.

The company’s 2026 Consumer Pulse Research, based on a survey of 25,590 consumers across 16 countries, found that 74% of respondents would trust a personal AI agent more than their best friend to make a purchase on their behalf.

The report described this as a move beyond the use of chatbots or search tools. In this context, an AI agent refers to software that can act on a consumer’s behalf within set permissions. It can shop, negotiate, resolve complaints, manage subscriptions, and, in some cases, complete purchases.

Consumers are ready to delegate

The survey found that 74% of consumers would allow an AI agent to handle routine tasks. These include deal negotiation, complaint resolution, subscription renewals, and product reorders.

Accenture said this level of delegation does not mean consumers are ready to hand over every decision. Instead, the findings suggest that consumers are more open to delegating parts of shopping that feel repetitive, time-consuming, or low-risk.

The report also found that 32% of consumers would ask an AI agent to make a purchase decision on their behalf within defined limits. These limits could include budget and brand preferences, with other conditions set by the user.

In that scenario, the AI agent would choose the best available option, but the consumer would still review and approve the purchase before payment. The report categorised this as delegated decision-making, separate from task execution and autonomous purchasing.

Autonomy still has limits

A smaller group of consumers is open to AI agents completing purchases without final approval. The report found that 9% of respondents would allow an agent to initiate and complete purchases within defined boundaries.

The payment stage recorded lower openness to autonomous agent decisions. Accenture said only 12% of consumers are open to agents making purchase decisions autonomously at the payment stage.

The report identified several conditions that affect consumer willingness to delegate more control. These include data safeguards, configurable permissions, and instant override options. Clear recourse, platform reputation, and perceived neutrality also affect trust.

Consumers are more comfortable with AI agent autonomy in parts of the journey where effort is high and emotional stakes are lower. The report pointed to negotiation and post-purchase support as areas where consumers showed greater openness.

The report said recurring services ranked highest across stages of delegation, while lifestyle and travel purchases showed a sharper drop as autonomy increased.

It also said consumers are more likely to keep control over choices linked to identity or personal enjoyment. A consumer may delegate routine grocery restocking but still want to choose a hotel room, clothing item, or experience directly.

What it means for brands

The report said AI-assisted shopping requires brands and retailers to make product information clear and machine-readable. If consumers use agents to compare options, pricing, availability, policies, and claims will also need to be easy for agents to assess.

AI agents can compare brands using structured attributes and verified claims. They can also weigh price-to-value ratios and fulfilment records. The report said this affects how brands appear across digital channels, including search engines, marketplaces, and social platforms.

The report found that 56% of all consumers would tell their AI agent which brands to consider. Among behaviorally loyal consumers, 37% said they would allow an agent to switch brands if it found a better fit.

The report linked brand switching to factors such as fit, price, availability, and service performance.

Accenture also found that consumers are interested in agents that can work across providers. The report said 61% want an agent that can shop across multiple grocery retailers on their behalf, while 71% want an agent that can plan and book a complete trip across airlines, hotels, and activities.

Brands and retailers need product data, pricing, availability, policies, and claims to be readable by the systems agents use to evaluate options, according to the report.

The main reasons cited were existing knowledge of shopping preferences, trust built through service and support, and access to a broad selection of products and services.

The report listed several possible roles for brands and retailers in AI-assisted commerce. Some may build their own agents, while others may integrate data, inventory, and services into platforms that consumers already use.

The report cited verified information, clear inventory, transparent pricing, and reliable fulfilment data as factors that can help agents evaluate brands more easily.

It also found that 71% of consumers expect generative AI to influence at least half of their spending decisions over the next 12 months.

The report also found that 63% of consumers want agents to shop for their “idealised self.” Examples include helping them make healthier choices or stay within budget. Some respondents also want agents to support more intentional upgrades.

Among active generative AI users, 26% said they had already bought a more expensive item because AI increased their confidence in the decision. The same proportion said AI had led them to increase their basket size.

Stores still matter

The survey also asked consumers how AI could affect stores. It found that 87% believe AI will affect the role of stores. Another 31% said stores will become more important for creating moments of enjoyment.

The findings show lower openness to full automation than to routine task delegation. It shows a more selective pattern, with consumers delegating routine or lower-risk tasks while retaining control over purchases that involve personal preference, risk, or emotional value.

The report said some brand evaluation could take place inside agent-led comparison systems before consumers visit a website, app, or store.

(Photo by Growtika)

See also: Visa ChatGPT integration enables AI agent retail purchasing

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 Accenture: Consumers show growing trust in AI shopping agents appeared first on AI News.

June 15, 2026
Coinbase for Agents: Automating portfolio trading with AI

Coinbase for Agents connects AI to financial execution channels to automate trading and payments directly from user portfolios.

Large language models process vast quantities of data but lack direct integration with active financial portfolios. Individuals frequently employ these models to evaluate market developments or research investment opportunities. These software tools possess the capacity for complex reasoning but cannot execute financial transactions on behalf of the user.

Coinbase for Agents enables autonomous digital entities to execute trades, process payments, and manage balances within user-defined parameters.

Terminal-based systems use command-line interfaces to manage the connection. This route fits development environments such as Claude Code, Codex, or OpenClaw. The command-line architecture integrates directly into established local development toolchains. This implementation path lowers token expenditure during high-frequency tasks and accommodates extensive local customisation. Setting up this configuration involves installing specific skill packages via the Coinbase Developer Platform documentation and generating dedicated API keys.

Web-centric software arrangements, meanwhile, rely on the Model Context Protocol. MCP establishes a direct integration path for web-based agent environments like ChatGPT or Claude Web. It permits a rapid connection via a single account login procedure. This method functions without requiring manual API key creation or complex local configuration files. A remote MCP option will become available in the near future that will allow individuals to link their financial profiles using standard single sign-on features without writing code.

Portfolio allocation and execution

Account holders can program specific distribution rules, instructing an automated agent to establish or maintain targeted asset ratios.

As an example, a portfolio manager might select a target distribution consisting of 60 percent Bitcoin, 20 percent Ethereum, and 20 percent Solana. The agent executes this directive over extended timeframes spanning multiple months. It assesses real-time pricing data and positions limit orders to purchase assets when market valuations decline by five, ten, or fifteen percent. The software captures these brief market pullbacks to accumulate assets automatically.

Coinbase’s current system supports spot and derivatives trading but is working on expanding the protocol to include index funds, standard corporate equities, commodities, and prediction markets.

The autonomous assistant monitors available cash balances around the clock to keep funds productive. It distributes idle capital to generate rewards or highlights specific asset positions that require direct human attention.

Integrating the x402 protocol allows these agents to interact with external commercial systems. Coinbase introduced this agentic payment protocol last year to provide software agents with a standard method for economic interaction. Agents deploy capital via this protocol to purchase computing resources, analytical models, and proprietary market data to inform their decisions. Upcoming x402 integrations will standardise these automated purchases across web services.

Data collection determines the efficacy of automated trading logic. An agent assigned to execute a dollar-cost averaging plan into Ethereum uses historical metrics to optimise market entry. The system retrieves thirty days of hourly pricing statistics to pinpoint historical low points during the day and can then establish a recurring daily market purchase of $20 timed precisely to those optimal windows. The automated routine executes daily for two weeks based on a single initial command.

Security controls and compliance

Agents operate exclusively inside isolated portfolios to safeguard broader financial holdings. This design prevents the autonomous entity from viewing or accessing unauthorised balances.

Users already retain total control over the operational boundaries. However, upcoming platform updates will introduce explicit rulesets for fine-tuned governance. Users will dictate maximum transaction volumes, specific permitted assets, and absolute spending limits.

The platform subjects all agent-initiated payments to standard transaction monitoring and “Know Your Transaction” validation. Users receive automated compliance verification without building internal monitoring systems.

Coinbase’s latest product launch marks the expansion of a broader consumer product suite that began with the 2024 launch of AgentKit, which provided tools for embedding crypto wallets into software systems. The subsequent introduction of the x402 protocol and the release of Coinbase for Agents finalises the financial execution layer.

Alternative connection options exist for everyday investors who prefer simple interfaces. Coinbase Advisor operates natively inside the primary consumer application. This integrated agent provides automated recommendations and financial guidance directly to users. The assistant holds formal registrations with both the SEC and the CFTC as a financial advisor. For retailers, commercial merchants can deploy Coinbase Payments to accept automated transfers from these autonomous systems.

See also: Visa ChatGPT integration enables AI agent retail purchasing

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.

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

The post Coinbase for Agents: Automating portfolio trading with AI appeared first on AI News.

June 12, 2026
Xebia: Why AI agents fail without the right data foundation

If your remit is to help your organisation add AI agents to accelerate its processes, you have to start at the foundation – and that means making your data available for AI consumption. Agentic AI scales on data strength, as Niels Zeilemaker, global CTO at Xebia, explains.

“If you don’t think about that, you can build the best agent, but it will never be able to find the correct data; maybe it will misinterpret the data, maybe it will join different fields together in your data which should never be connected,” explains Zeilemaker. “And these mistakes are not necessarily the fault of the agent. It’s the fault of your foundation, which is not ready for AI agents.”

One area to particularly consider, Zeilemaker notes, is data cataloguing. It’s not a new concept, but the game changes for agents. “If you’re setting up a data catalogue for an organisation only consisting of humans, there’s always a fallback,” he says. “If there’s something not really well documented, you can pick up the phone, walk to a colleague, and have a sort of back door, in ‘how should I work with this particular set of data?’

“Agents don’t have such a back door. They have to rely on the data catalogue, what’s written there, and if the description is wrong, the agents will not perform.”

Xebia’s focus is to help organisations turn AI strategy into production-ready solutions which drive real transformation faster. The company’s core values include being people first and quality without compromise, but perhaps the most important, as Zeilemaker sees it, is sharing knowledge – such as at events like TechEx Global North America, at which Xebia participated.

“I think sharing knowledge is very important for us, and it also allows us to be a bit ahead of the curve, adopt quickly to new changes in the market, because everybody has this eagerness to find out new things, and to share what works, what doesn’t work,” says Zeilemaker. “By pushing a lot into this sharing knowledge and innovation, we try to also pick a couple of domains where we want to be the authority.”

Data and AI is evidently one of those areas. At AI & Big Data Expo, Zeilemaker told attendees how to build this AI foundation and unify their fragmented data landscapes. It was an honest account of how combining purpose-built AI agents with expert engineering compresses a 12- to 24-month timeline into a fixed-price, milestone-bound engagement.

The overarching thread for this is what Xebia calls Agentic Data Foundation (ADF), which extends the data platform to host agents, and then make use of them both in customer-facing use cases and internal processes. While there has always been a big appetite in migrating from legacy to modern platforms, Xebia is seeing more customers asking for an approach to more quickly – and reliably – migrate into data platforms. Zeilemaker says this is where consultant and customer are co-developing the solution.

“Agents have to rely on the data catalogue and what’s written there – and if the description is wrong, the agents will not perform”

“After doing migrations the old-fashioned way, and accelerating some with LLM coding, we are now integrating this into the data platform, making use of the additional context it can provide to accelerate migrations even further,” he says.

That accumulated experience is what shaped Xebia Axis: Agentic Data Foundation, Xebia’s answer to helping enterprises make their data AI-ready faster than any alternative.

Another weapon Xebia has in its arsenal is Xebia ACE: AI-Native Software Engineering, a framework which embeds AI across an organisation’s entire software development lifecycle (SDLC). Done right, delivery can be accelerated by up to 40%, while legacy transformation costs are cut by up to 70%.

Zeilemaker notes that Xebia ACE is particularly useful for larger enterprises who ‘maybe still want to stick to a particular governance or way of working while doing SDLC’. Yet there is a bigger picture here. Zeilemaker uses vibe coding as an example. “If you think about vibe coding, everybody can create an app, but nobody is daring to actually push these apps into production,” he says. “If you adopt ACE, you still get a lot of the benefits of the acceleration of LLMs, but you’re still having the same quality end results as you’re used to in the past.

“If you’re looking to make the switch to using LLMs in coding, Xebia ACE will give you a very nice framework to use, without the risk, or any drawbacks of doing dark factory LLM and hoping for the best – and losing a bit of control or governance in the process,” adds Zeilemaker.

For enterprises, that control is key. With so much code being generated, the AI-driven SDLC could become a security weakness through vulnerabilities. Zeilemaker argues it’s something the industry still needs to figure out to a degree, but notes with interest the recent move by Anthropic to release a pull request reviewer.

“It’s an interesting one, which we’ll probably see more of,” he says. “There will be very lengthy pull request reviews, which you apply whenever you go and try to do a new production release. And then you add a very senior team member in the form of an LLM to your process, which does a sort of third-party review.

“I think that’s an interesting angle with what we’re going to see more of in the future.”

Ultimately, wherever organisations are in their journey, from assessing their data readiness to being ready to build, Xebia is able to help get the foundations right – and create the transformations on top of it.

Photo by fabio on Unsplash

Want to learn more about AI and big data from industry leaders? Check out 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.

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June 11, 2026
Visa ChatGPT integration enables AI agent retail purchasing

Visa has linked its payment infrastructure to ChatGPT, enabling AI agents to recommend retail products and execute financial transactions.

The deployment removes human intervention from the final stages of the retail funnel. Autonomous agents will now process user prompts, evaluate merchant catalogues, and complete the checkout process using Visa’s payment rails at any supporting merchant.

Previous retail AI integrations restricted automated purchasing to single-vendor environments. Retailers built proprietary chatbots confined entirely to their own inventory. Visa’s integration bypasses closed-loop architecture.

The payment giant connects the open-web reasoning capabilities of a large language model directly to a universal transaction network. Users simply command the agent to procure an item, and the model handles the vendor selection, product comparison, and financial settlement.

Enterprises should be aware that commercial transactions will increasingly execute without a human buyer ever seeing a retailer’s website, digital advertisement, or promotional email.

Restructuring retail data for AI agent buyers

Marketing departments design campaigns around human psychology, emotional triggers, and visual merchandising. AI agents operate on pure data evaluation.

When ChatGPT receives a mandate to purchase a specific product type, it parses technical specifications, aggregated sentiment scores, and pricing structures. Display ads and user interface optimisations hold zero weight in the model’s selection criteria.

Retailers will need to expose machine-readable inventory data. Search engine optimisation transitions into language model optimisation. The algorithms driving ChatGPT rely on structured data feeds, clear API documentation, and explicitly-formatted product attributes to evaluate whether an item meets the user’s parameters. Merchants failing to maintain high-quality, structured metadata will find their products invisible to the autonomous agent.

Personalisation occurs entirely on the user’s device or within the user’s secure LLM profile. The AI retains the consumer’s past preferences, sizing requirements, budget constraints, and brand affinities. Instead of the retailer attempting to guess the consumer’s needs through tracking cookies and site behaviour, the agent arrives at the digital storefront with a highly-specific procurement mandate.

Completing a transaction without human intervention requires a secure, automated handshake between the reasoning engine and the payment gateway. Visa provides the financial layer necessary to establish trust in an inherently untrusted agentic environment. Traditional checkout flows require manual data entry, CAPTCHA verification, and two-factor authentication loops. These mechanisms block autonomous agents.

Visa implements programmatic tokenisation to solve the authentication problem. The user pre-authorises the ChatGPT environment with specific spending parameters. When the LLM decides on a purchase, it generates a single-use payment token through the Visa network. The agent transmits this token via API to the merchant’s backend systems. The transaction settles exactly like a standard digital wallet payment, bypassing the visual user interface completely.

A digital storefront requiring multi-page navigation or mandatory account creation introduces failure points for the agent. Enterprises actively deploying headless commerce architectures possess an advantage. They can process the agent’s payload, confirm stock levels, and execute the payment token in milliseconds.

Enterprises track bounce rates, session durations, and cart abandonment to understand consumer behaviour. An AI agent does not browse—it queries an endpoint, extracts the necessary data, and either executes the payment or terminates the connection.

Retailers must develop new telemetry to measure agent interactions. Tracking the frequency of API queries from known LLM IP addresses replaces tracking unique human visitors. Understanding why an agent selected a competitor’s product will require analysing the structural differences in product data feeds rather than running A/B tests on website layouts.

Customer retention strategies also need adjustment. An autonomous agent evaluates the market fresh with every prompt unless explicitly instructed by the user to reorder a specific brand. Loyalty programmes must be engineered into the payment token or the user’s LLM profile. If the AI cannot automatically apply a loyalty discount during its background calculation, the merchant loses the pricing advantage intended to secure the repeat purchase.

Managing and securing the agentic AI supply chain

Prompt injection attacks could theoretically manipulate an agent into purchasing from malicious vendors or authorising inflated transactions. Visa’s network acts as the final validation layer, applying fraud detection models to the incoming token requests.

Businesses face the secondary challenge of managing automated returns and customer service queries initiated by the AI. If the delivered product fails to meet the parameters defined in the original prompt, the user can instruct the agent to reverse the transaction.

In this scenario, the AI will autonomously navigate the merchant’s return policy, initiate the refund request, and generate the necessary shipping labels. Retail customer service operations must deploy their own automated systems capable of negotiating directly with the consumer’s agent.

Visa’s ChatGPT integration confirms the enterprise transition from human-operated software interfaces to autonomous digital proxies. The customer is no longer necessarily a human navigating a web browser, but an algorithm executing a script.

See also: Aviva deploys AI to stop £230M in sophisticated insurance fraud

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.

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

The post Visa ChatGPT integration enables AI agent retail purchasing appeared first on AI News.

June 11, 2026
McDonald’s tests Google-backed AI drive-thru ordering system

McDonald’s is testing a new AI system that can take drive-thru orders and support restaurant operations.

The system, called ArchIQ and nicknamed “Archy,” was introduced during the company’s Worldwide convention, according to Restaurant Business. It is being tested at five McDonald’s locations in the United States, though the company has not named the restaurants involved.

A video shared on X by a McDonald’s franchise owner showed the system greeting customers, processing order changes, displaying the final total, and asking customers to pull ahead for pickup.

A demonstration shared on X by the franchisee account McFranchisee showed the system taking orders in English and Spanish. The account said the system has processed more than one million transactions, with about 90% of orders completed without being escalated to staff.

The same account said ArchIQ can respond when repeat customers ask for their usual order. McDonald’s has not provided technical details on how that feature works.

ArchIQ is being developed with Google. According to McFranchisee, McDonald’s restaurants in the US are receiving Google Edge Cloud blades ahead of the rollout.

McDonald’s previous AI ordering test

ArchIQ is McDonald’s latest AI test for drive-thru ordering. The company previously worked with IBM on an automated ordering system across more than 100 restaurants.

McDonald’s ended that pilot in 2024 after customer complaints over order errors. The earlier IBM test was followed by customer videos showing incorrect orders, including one case in which the system reportedly added more than $250 worth of chicken nuggets.

After ending the IBM partnership, McDonald’s said it would continue exploring voice ordering technology.

Restaurant operations support

ArchIQ is not limited to customer ordering. McFranchisee said it can monitor restaurants and alert managers to possible issues.

According to McFranchisee, the system can alert managers if a freezer is down. It can also flag kitchen bottlenecks or other problems that need attention.

McFranchisee described ArchIQ as both an ordering tool and a management-support tool.

The test forms part of McDonald’s new growth plan, called “McDonald’s > NEXT.” The company said the plan is intended to improve restaurant operations and unit economics.

McDonald’s reported a large digital customer base in its 2025 results. The company said systemwide sales to loyalty members across 70 markets rose 20% to nearly US$37 billion in 2025, while 90-day active loyalty users rose 19% to nearly 210 million at year-end.

McDonald’s CEO Chris Kempczinski said in a press release that the strategy is aimed at the company’s next phase of growth and productivity.

The company has also referenced restaurant upgrades and possible menu changes under the same plan, but has not provided detailed information.

Automation and service

In a company memo, Kempczinski said more of the customer journey is becoming automated, leaving fewer chances for guests to interact with crew members. He said that it raises the standard for hospitality when customers interact with staff.

QSR Magazine’s 2025 Drive-Thru Report, citing Revenue Management Solutions, said drive-thru traffic remained negative month after month and hovered between minus 5% and minus 8% in 2025.

Other fast-food chains have also announced AI-powered drive-thru ordering systems, including Taco Bell and Wendy’s.

Jonathan Maze, editor-in-chief of Restaurant Business, told ABC News that companies often present drive-thru automation as a way to free employees for other tasks. The McFranchisee account said the system could reduce the need for workers to take orders in noisy drive-thru lanes.

Some X users responding to the ArchIQ demonstration said they preferred interacting with human workers. Others supported a more automated ordering process.

McDonald’s has not said when ArchIQ could be expanded beyond the five test locations. The company has said the system is intended to improve speed and accuracy while supporting customers and crew.

The company’s AI drive-thru system remains in limited testing.

(Photo by Boshoku)

See also: Walmart’s AI workflows meet the realities of the balance sheet

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 McDonald’s tests Google-backed AI drive-thru ordering system appeared first on AI News.

June 10, 2026
Aviva deploys AI to stop £230M in sophisticated insurance fraud

Aviva has uncovered a record £230 million in insurance fraud claims and is using AI tools to counter the growing problem.

The battleground has changed, and the culprits are also coming armed with a new generation of tools. We’re now in an environment where AI is being used not just to defend against fraud, but to perpetrate it.

The insurance industry has long dealt with opportunistic dishonesty. A bumped car suddenly needs four new doors, or a minor slip becomes a life-altering injury. However, according to Aviva’s data, the nature of the deception is getting deeper, more sophisticated, and harder for the human eye to catch.

Aviva is fighting fire with fire, deploying its own AI to uncover these elaborate schemes.

Countering the AI-powered insurance fraud factories

Aviva reports that scammers are now using AI to generate convincing fakes of car accident scenes. These aren’t clumsy photoshop jobs; they’re detailed, plausible images that can easily fool a claims handler working through a heavy caseload.

The same generative AI tools are being used to create fake documents, from invoices for repairs that were never done, to medical reports that have no basis in fact. Fraudsters don’t need access to a network of corrupt garages or medical professionals to back up their story. They just need a subscription to an AI service and a bit of imagination. The AI handles the rest, producing official-looking documents that can pass a cursory inspection.

An individual or small group can now generate the supporting evidence for dozens of high-value claims without ever leaving their desk. How do you validate reality when reality itself can be so easily and cheaply faked?

Aviva’s response has been to build an AI-powered defence system that can operate at the same scale and speed as the threat. While the company is understandably tight-lipped about the exact architecture, you can piece together what a system like this needs to do.

At its core, the AI detective carries out pattern recognition at scale. The AI sifts through millions of data points from current and past claims, learning what a legitimate claim looks like—and, more importantly, what it doesn’t.

When a new claim comes in, the system is cross-referencing everything. Does the damage in the photo match the physics of the described accident? Do the timestamps on the documents make sense? Has this vehicle registration number appeared in other suspicious claims? Are the repair costs quoted on the invoice out of line with the thousands of other similar repairs in the database? It’s a level of forensic analysis that would be impossible to perform manually on every one of the thousands of claims filed each day.

From organised crime to exaggerated claims

It’s important to note that this isn’t all about organised criminal gangs. A portion of that £230 million figure comes from what the industry calls “claims inflation.”

Claims inflation is the more common fraud where policyholders or service providers pad the bill. For instance, a garage might add unnecessary repairs to a quote, or an individual might exaggerate the value of items stolen in a burglary.

Here, too, AI is proving to be a heavy-duty tool. By analysing vast datasets of repair costs and market values, the system can instantly flag when a quoted price is an outlier. It can compare the cost of a replacement part from one garage against the average from hundreds of others in the same region for the same make and model.

The goal of Aviva’s AI isn’t to outright deny claims, it’s an augmentation tool for their human investigators. The AI acts as a filter, sifting through the noise to surface the most likely instances of fraud. This human-in-the-loop approach is essential for ensuring fairness and preventing the system from becoming a black box that makes decisions without oversight.

What Aviva is doing provides a potential route for any customer-facing enterprise in the age of generative AI. The same technology that creates these threats is also the most effective way to combat them.

As it becomes easier to fake everything from identities to invoices, the only viable defence is an intelligent system that can learn, adapt, and spot deception at a scale that humans alone can’t match.

See also: Weis Markets adds Instacart AI-powered shopping carts to stores

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.

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

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June 9, 2026
How C3 AI agents will automate predictive maintenance for Shell

Shell will use agents from C3 AI to shift from basic anomaly detection towards fully-automated predictive maintenance.

The global energy giant is building on their current use of the C3 AI Reliability Suite, which already keeps tabs on more than 30,000 crucial pieces of equipment across upstream and downstream operations. Shell now intends to lean heavily into autonomous AI agents, putting them in charge of the entire maintenance lifecycle.

Going from that first warning sign all the way to a completed repair, this level of automation strips away the need for constant human oversight and makes sure the company’s resources are pointed exactly where they are needed most.

“This expanded partnership with Shell proves what’s possible when enterprise AI is fully operationalised at global scale for predictive maintenance—reducing unplanned downtime and delivering hundreds of millions of dollars in economic value,” said Stephen Ehikian, President of C3 AI.

“Shell has built mature AI predictive maintenance programs on our platform, and together we’re now pushing into agentic AI, advancing how this technology can further transform reliability, safety, efficiency, and operational performance.”

C3’s AI agents help Shell move past basic anomaly detection

In the beginning, Shell used machine learning simply to spot odd patterns in sensor data, giving engineers an early heads-up before things broke. To pull this off, the system ingests a massive amount of real-time operational technology (OT) data and mixes it with business context from ERP platforms such as SAP.

The next step introduces AI agents built for actual reasoning and independent action. While older systems stopped at pinging an engineer when things looked unusual, this next-generation framework independently investigates why an alert fired in the first place.

Once it pinpoints the root cause, the agent steps up to draft precise work orders, confirm part availability in the inventory, and generate procurement requests.

C3 AI’s platform handles the heavy lifting, providing a model-driven space to easily integrate high-frequency sensor feeds with structured financial and maintenance logs. These AI capabilities are trained to learn the normal operating baselines for specific gear, like pumps, turbines, and compressors.

The agentic layer sits on top of this foundation. Operators configure an individual agent for a given piece of equipment by defining its objectives and permitted responses. If the core machine learning models detect a deviation from normal operations, this agent activates, gathering extensive contextual data to build a complete picture of the situation. This context usually includes recent maintenance history, environmental conditions, and upstream process variables.

Using all that information, it suggests a fix backed by solid evidence. Human operators can then easily approve or override the plan. As the system proves itself over time, Shell can fully automate its responses to certain types of alerts. Connecting straight into systems like SAP is critical here, allowing the agent to work inside the exact same workflows that human planners already use.

The real impact of agentic AI for predictive maintenance

Putting agentic AI to work at this scale tackles the classic “last mile” headache in predictive maintenance. Many industrial companies can predict failures just fine, but turning those insights into fast, efficient action remains a challenge. Usually, engineers still have to manually dig through alerts, investigate the causes, and write up the work orders themselves.

Shell wants to shrink that timeline. By letting AI handle root cause analysis and work orders, the delay between a predicted failure and the actual fix drops. That directly improves equipment uptime and protects production.

Moving to a model where repairs only happen when the equipment condition actually demands it naturally saves money, simply because nobody is wasting time tinkering with perfectly fine machinery. Leaving healthy hardware alone also means it lasts much longer.

On top of the cost savings, stepping in before a catastrophe hits makes the whole operation much safer and cuts down on environmental risks, which is always top of mind in the energy sector.

“What Shell and C3 AI have built on Azure over the past several years is exactly what enterprise AI should look like—real applications, running in production, delivering measurable value at global scale,” commented Sandy Gupta, VP GISV, Software Development Companies at Microsoft.

This expanded rollout shows that we are finally talking about practical industrial AI production workflows instead of just algorithms. Rather than just the prediction itself, the real value comes from the system’s ability to act on it with barely any human oversight.

See also: Meta Business Agent drives AI-powered conversational commerce

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The post How C3 AI agents will automate predictive maintenance for Shell appeared first on AI News.

June 9, 2026