Category: AI Market Trends

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Top spy agencies say AI cyber threats will impact you within months. Here’s why

The global surge in AI cyber threats is no longer a distant problem for corporate data centres, according to an urgent public warning from the world’s most powerful intelligence alliance. On June 22, 2026, the cybersecurity chiefs of the Five Eyes nations—comprising the US, UK, Canada, Australia, and New Zealand—issued a rare joint intelligence briefing stating that upcoming artificial intelligence models will supercharge offensive hacking capabilities on a timeline measured in months, not years.

While the advisory specifically tells corporate executives to overhaul their network defences, the rapid evolution of these tools means everyday internet users are about to face a much shiftier digital landscape.

The massive shift in AI cyber threats

The intelligence brief highlights an immediate danger: advanced, upcoming models like OpenAI’s “GPT-5.5-Cyber” and Anthropic’s “Mythos” are actively lowering the technical barriers for digital crime. Rogue actors no longer need elite coding skills to build complex, devastating software exploits.

Instead, automated digital agents can scan internet-connected infrastructure around the clock to find software vulnerabilities before human engineers can patch them. This drastically shrinks the safety window that technology companies rely on to keep user applications secure.

How does this hit home for regular users?

When criminal networks use automated tools to breach large databases, the immediate consequence is the theft of regular consumer data. Your personal information, saved passwords, and cloud backups are the ultimate targets in these accelerated corporate intrusions.

Furthermore, bad actors are leveraging conversational models to generate hyper-personalised phishing scams at an industrial scale. This trend is hitting the Asia-Pacific (APAC) region particularly hard, with countries like India recording a staggering 165% spike in ransomware incidents in early 2026 due to AI-assisted targeting.

Rather than relying on easily spotted, poorly written spam emails, automated systems can scan your public social media profiles to write flawless, highly convincing messages designed to steal your credentials.

Fighting back with the same tech

The primary challenge facing cyber defenders is that machine-paced offence naturally moves faster than human-led detection. According to the World Economic Forum’s Global Cybersecurity Outlook, a massive 94% of corporate executives identify AI as their top threat vector, yet two out of three organisations report moderate to critical cybersecurity talent shortages.

Network administrators are finding it impossible to review and deploy traditional security patches manually when rogue AI agents can discover and exploit a software vulnerability within minutes.

The Five Eyes alliance emphasises that the most effective way to withstand these accelerating AI cyber threats is to deploy automated defences. Security teams are actively integrating defensive artificial intelligence models to monitor unusual behaviour and isolate network breaches.

For individual users, the basic rules of internet safety are becoming mandatory. Turning on multi-factor authentication and deleting old, unused online accounts remain the most effective ways to break the automated chain of an AI-driven attack.

See also: AI web search risks: Mitigating business data accuracy threats

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 23, 2026
Computer vision deployments drive retail productivity gains

Computer vision deployments are driving retail productivity gains as operators automate physical shelf tracking to protect eroding margins.

This hardware deployment directly addresses the persistent in-store execution failures currently costing the industry billions. A study authored by Coresight Research – in partnership with technology providers Simbe and RELEX Solutions – calculates the exact cost of these operational shortfalls.

Inefficiencies consume 6.4 percent of gross sales across the sector. Hardware, mass merchandise, and grocery categories will surrender $196.4 billion to these operational failures in 2026. The monetary value of these losses is jumping 21 percent over the previous year. This deficit vastly outpaces the three percent projected sales growth for the entire sector.

Nine in ten retailers report active difficulties managing their shop floors. Empty shelves and inaccurate pricing structures directly suppress operating margins. Margin erosion exceeds five percent for 89 percent of operating businesses.

Full-scale deployments of store intelligence platforms operate across 60 percent of enterprise footprints. This adoption rate represents an 18-percentage-point jump year-over-year.

Experimental pilot programmes account for a mere 18 percent of current market activity. The adoption curve skews heavily toward top-tier enterprises. 73 percent of retail companies generating over $5 billion in annual revenue maintain fully scaled deployments.

Mid-market operators lag behind, with only 42 percent of sub-$1 billion companies achieving similar deployment maturity. Treating physical stores as separate entities from digital channels degrades customer lifetime value. Capital expenditure directly targets out-of-stock tracking, automated pricing, planogram verification, and assortment planning.

Production deployments in hardware and grocery

BJ’s Wholesale Club provides a documented case study of applied shelf digitisation. The operator deployed Simbe robotics platforms to monitor inventory and price accuracy across its locations.

Management used this hardware foundation to generate digital twins of individual warehouse clubs. This application established real-time visibility systems previously absent from their physical operations.

BJ’s applied these digital models to route planning for online orders and curbside fulfillment. The engineering team recorded a 40 percent year-over-year improvement in picking efficiency through this data application. CEO Bob Eddy reported the technology enabled the company to elevate quality standards within fresh merchandise categories.

Grocery operator Albertsons applies AI to automate complex retail operations. The grocer targets $1.5 billion in productivity gains spanning three fiscal years. CEO Susan Morris explained: “We will be equipping our merchants with AI-driven insights and automated execution to optimise pricing, promotions, and assortment decisions, transforming category management and driving margin improvement.

“Our vision is the future where intelligent automation guides these decisions, freeing our people to focus on strategy and innovation.”

Flaws in deployment sequencing

Many organisations prioritise the installation of pricing software while ignoring foundational sensor infrastructure. 43 percent of surveyed technology leaders direct their capital toward pricing optimisation software.

Supplier collaboration platforms rank second in priority, attracting investment from 36 percent of operators. Only 33 percent of these organisations invest in the shelf digitisation hardware required to feed accurate data into those pricing models.

This hardware includes the sensors and cameras needed to verify physical stock availability. Store intelligence deployments require strict sequencing to function properly. Retailers must first digitise the shelf, deploy data analytics, install inventory tracking software, and finally execute pricing automation.

This inversion of the technology stack creates downstream data failures. Markdown algorithms process outdated inventory counts when physical tracking sensors are absent. Mispricing rates hit 13 percent in 2026, marking a four-point increase since 2024.

Pricing and promotional execution dominates the priority list, presenting an active difficulty for 92 percent of operators. Kim Anderson, VP of Store Operations at Schnucks Markets, states that shelf data must precede all other implementations. Without accurate physical inventory monitoring, downstream applications fail to meet their performance targets.

Out-of-stock events remain severely disruptive, with 52 percent of operators ranking inventory availability as highly demanding. Operators attempt to fix multiple problems simultaneously, with 40 percent directing capital toward three or more operational inefficiencies at once.

Labour reallocation and efficiency metrics

Lowe’s demonstrates the financial impact of automating the associate workflow through its ‘Perpetual Productivity Improvement’ initiative. Executive VP of Stores Joseph McFarland directed the deployment of workforce management tools and inventory solutions to eliminate redundant associate tasks.

The engineering rollout saved 80 non-productive labour hours per store on a weekly basis. Lowe’s advanced the initiative by deploying full shelf replenishment technologies powered by AI to track stock depletion in real-time.

Management distributed financial bonuses to the workforce based on documented productivity enhancements. The company issued $5,000 to associate store managers and varied payouts to hourly staff.

Broad industry data validates the performance metrics recorded by Lowe’s. The deployment of intelligence applications drives a 14 percent average reduction in time spent on manual store tasks. 86 percent of organisations record defined decreases in manual assignment hours.

Retailers report distinct performance disparities based on total revenue. 56 percent of operators generating over $5 billion report advanced reductions in task completion times, compared to only 36 percent of mid-market companies.

Organisations cite operational efficiency as their primary investment objective, followed closely by the unification of store data. Retailers expect these tools to generate new capital, with 40 percent of leaders seeking to establish alternative revenue streams like retail media networks.

Securing market competitiveness

Store intelligence technologies function as an interconnected ecosystem rather than standalone fixes for isolated problems. Deploying these systems without a coherent sequencing plan forces operators to build upon an unstable foundation.

Establishing real-time, shelf-level visibility proves strictly necessary before attempting to scale downstream software. Pricing automation, supplier collaboration platforms, and inventory forecasting applications require verified physical data to generate accurate outputs.

Customer behaviour responds directly to correct operational upgrades. Proper deployments increase customer lifetime value by 11 percent across the sector, while conversion rates improve for 50 percent of the operators executing physical automation frameworks.

48 percent of companies record increased enrollment in their loyalty programmes following system integration. Accurate pricing and consistent stock availability elevate online review metrics for 47 percent of surveyed operators.

Retailers compounding value through integrated, properly sequenced hardware and software capabilities possess a distinct market advantage over competitors accumulating disconnected applications.

See also: HSBC expands AI banking partnership with Google Cloud

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 18, 2026
Microsoft sells OpenAI models in China. OpenAI and Anthropic won’t.

Microsoft has quietly become the main supplier of OpenAI models in China, selling the technology to the country’s largest internet companies even as OpenAI and Anthropic keep their own models out of the market on intellectual-property and misuse grounds. The arrangement, detailed this week by Bloomberg, hands Microsoft a position no other American AI vendor holds: it sells the GPT series to Chinese firms that the model’s own creator will not deal with directly.

The scale is not trivial. ByteDance has been Microsoft’s largest AI customer in recent years, running largely on OpenAI models, and is on track to spend more than US$1 billion a year on Microsoft’s AI and cloud services, people familiar with the matter told Bloomberg. Ant Group, Meituan and Tencent also buy AI models through Azure, though Ant says it develops its own models and that its core products do not rely on outside systems.

Inside Microsoft, the growth has been celebrated rather than played down. Azure’s AI revenue in China expanded faster than in any other sales territory, roughly tripling in the financial year to June 2025 after climbing about 400% the year before, then-chief commercial officer Judson Althoff told staff at a July 2025 sales meeting, according to a transcript reviewed by Bloomberg.

Althoff described Microsoft as the one company “bringing those two places together,” meaning the AI hubs of the US West Coast and China’s east. President Brad Smith has separately told US lawmakers that the China business accounted for roughly 1.5% of the company’s revenue in 2024.

Why OpenAI models in China run through Microsoft alone

The reason comes down to Microsoft’s singular contract with OpenAI, which lets it set its own terms for selling GPT models abroad. Both OpenAI and Anthropic have declined to sell into China directly, and Anthropic’s models are absent from Microsoft’s China line-up altogether. That leaves Microsoft acting as the intermediary for models whose makers have decided the Chinese market is too risky to serve.

Risk is the recurring tension. OpenAI has privately pressed Microsoft to do more to stop Chinese customers from “distilling” its models, Bloomberg reported, a technique that uses one model’s outputs to train another. Microsoft points to automated monitoring and a rule that it sells only to established companies rather than individual developers.

Yet sources told Bloomberg that Chinese buyers face no heightened scrutiny, and synthetic data generated from the models is difficult to police. To limit its exposure, Microsoft does not host the OpenAI models on Chinese soil; customers reach them over the internet from data centres elsewhere, Singapore among them.

The contradiction sharpens when you look at what Microsoft hosts alongside GPT. It added DeepSeek’s R1 to Azure AI Foundry in January 2025, and this month confirmed to Axios that it is testing a fine-tuned, Azure-hosted version of DeepSeek-V4 as a cheaper option for Copilot Cowork, the enterprise agent currently powered by OpenAI and Anthropic models. So Microsoft is selling a Chinese model into Western businesses while selling American models into Chinese ones, taking the margin on both legs of the trade.

Whether the balancing act survives the politics is another matter. The China business is contentious in Washington, where lawmakers have cast the country’s AI push as a threat to American industry, and OpenAI’s private objections could grow louder. For now, Microsoft owns the market for OpenAI models in China, and it is the only player being paid by both sides.

See also: China’s DeepSeek V3.2 AI model achieves frontier performance on a fraction of the computing budget

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.

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June 18, 2026
Insurers pivot AI strategy toward core risk underwriting

AI investments by insurers are now expected to generate tangible business value beyond mere efficiency.

According to findings in the 2026 Evident AI Index, insurers are now embedding AI technologies into workflows that directly influence underwriting discipline and capital allocation.

Christian Preece, Insurance Director at Evident, says: “For years, insurers have competed on AI ambition, but now the focus is shifting from what insurers are building to the value they’re creating. In itself, it’s a sign of AI maturity to have the internal capability to measure these figures and be confident enough to disclose them.

“As the first industry leaders disclose hard return on investment data, they’re providing the kind of evidence that shareholders and boards have been looking for in light of increasing concerns around the costs of AI, and we can expect to see more insurers going public in the coming year.”

While the broader insurance workforce experienced a contraction of 2.2 percent over the past year, the AI-specialist headcount expanded by 32 percent across the 30 insurers tracked in the report. This personnel shift highlights a transition from building data foundations to the integration and optimisation of business-specific AI use cases.

Data engineering remains a component of this investment, yet its relative share of the talent stack is declining as roles focused on AI development and software implementation gain priority. AI specialists now represent one in every 50 employees at insurers included in the Index.

Executive structures are also adapting to these requirements. Nearly 40 percent of the insurers indexed now designate a senior leader with explicit responsibility for AI. Most of these appointments occurred within the last 12 months, creating a new level of executive oversight for AI-driven growth.

This governance is vital as firms shift from isolated point solutions toward agentic AI systems that coordinate actions across multiple stages of the policy administration and claims lifecycle. Notably, the adoption of agentic AI has surged, with one in four newly disclosed use cases now showing evidence of agentic orchestration, compared to one in twenty only six months prior.

Zurich sets an example

Zurich serves as an example of this transition, rising from 12th position to 4th in the global rankings by emphasising a shared platform model over decentralised experimentation.

The insurance giant deployed ZurichIQ, a modular generative AI platform integrated into underwriting, claims, legal, and service operations. This architecture provides a unified environment for various functional tools, such as PolicyIQ for contract comparisons and GuidelinelQ for enforcing underwriting standards.

Hurdles in such deployments typically involve maintaining oversight across diverse business lines. Zurich manages these risks through a dedicated committee that governs AI investment and model risk management. The platform approach allows the insurer to push AI capabilities into daily production while maintaining a consistent governance framework, which is reinforced by internal training programs like the £1.3m AI apprenticeship initiative.

Ericson Chan, Group Chief Information & Digital Officer at Zurich, said: “Being recognised as the biggest AI growth insurer in the Evident AI Index is not simply a reflection of technology adoption; it signals a broader transformation from use cases to enterprise-wide execution and change.

“This recognition reinforces our conviction in our AI360 strategy, embedding intelligence into workflows, decisions, and customer outcomes across the value chain. AI is no longer a technology initiative. It is becoming Zurich’s operating system.”

Focus on risk selection and ROI

With claims typically accounting for 60 to 80 percent of premium income, even minor improvements in fraud detection and risk selection produce a disproportionate financial impact compared to general administrative cost reduction.

Insurers are now directing venture capital and internal innovation efforts toward data sources that enable more dynamic analysis of climate volatility and cyber threats. A critical marker of this maturity is the ability to quantify and disclose financial returns.

Manulife, Generali, and Intact Financial have led this effort, publicly reporting AI-driven value. Projections indicate these three firms will generate over $1 billion in AI-driven value by the end of their respective reporting periods. This transparency provides the hard data shareholders demand regarding the costs of AI deployment, effectively mandating more rigorous performance measurement across the sector.

Success in the next phase of industry adoption depends on the ability to translate these technical investments into better underwriting results. Market leaders Allianz (which now holds the largest AI talent pool in the industry and has registered 900 AI use cases worldwide) and AXA maintain top positions by demonstrating sustained investment across innovation, talent, and transparency pillars.

Barbara Karuth-Zelle, Member of the Board of Management and Group COO at Allianz, commented: “AI didn’t change our ambition. It accelerates how we deliver on it at scale.

“Behind this ranking are thousands of moments: a claim processed faster, a customer experience reimagined, a partner better connected, a colleague freed up for what truly matters. And we are determined to keep going—an inspiring, transformative journey.”

See also: Accenture: Consumers show growing trust in AI shopping agents

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 Insurers pivot AI strategy toward core risk underwriting appeared first on AI News.

June 16, 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
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
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.

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The post Aviva deploys AI to stop £230M in sophisticated insurance fraud appeared first on AI News.

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

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

June 9, 2026