Category: enterprise

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  • 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

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  • 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

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  • DeepSeek Value Rises To $45bn In First Funding Round

    China’s biggest state-backed chip investment fund reportedly in talks to lead AI start-up’s funding round, as valuation more than doubles
  • France’s Genesis AI Debuts First Model, Shows Robotic Hand

    Start-up Genesis AI backed by former Google chief Eric Schmidt builds model to power robots for delicate or complex tasks
  • 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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