AI Applications

In June, Nvidia announced agreements with Microsoft and other PC makers to develop AI- and agent-ready PCs for Windows based on its RTX Spark chip. However, details about these machines were scarce at the time. Now, Microsoft has revealed more information about its new Surface Laptop Ultra, which is powered by the RTX Spark chip.

The Surface Laptop Ultra comes in two base models: one starting at $2,600 and another with a more powerful chip that starts at $3,700. Prices increase from there, with options for additional memory and storage up to $5,900. Microsoft notes that its highest-end device is already out of stock.

Microsoft also introduced the Surface RTX Spark Dev Box, a workstation powered by the RTX Spark chip, priced at $6,000. This PC comes equipped with Microsoft development tools like VS Code, GitHub Copilot CLI, WSL, and PowerShell 7. The company emphasizes that these devices are designed to run AI models on-device locally, without requiring cloud computing or additional costs.

The revamped Windows 11 operating system includes a feature called ‘Execution Containers,’ which makes it easier for developers to sandbox AI agents. This feature will be available for all Windows 11 users, according to Microsoft CEO Satya Nadella. At the event in San Francisco during Tech Week, Nadella explained that having just an AI model is not enough; rather, one needs to orchestrate and integrate multiple models with context and memory.

Nadella highlighted the importance of a ‘harnessed layer’ that can handle multiple models, context, and action space. He emphasized that Microsoft’s goal is to enable this capability for all developers, not just those using Microsoft apps. This aligns with the company’s vision for the Windows platform going forward.

The new Surface PCs are clearly targeted at developers, which is why Microsoft is offering up to $1,000 off for customers who trade in a MacBook Pro. Macs have become popular among developers and AI enthusiasts, particularly in Silicon Valley. However, these powerful graphics processing units also enable content creation, video processing, and gaming.

Microsoft was not the only company announcing Nvidia-RTX Spark AI Windows PCs on Wednesday. Dell revealed details about its previously announced Dell XPS 16 Creator Edition PC, which is available for preorder now at Best Buy with a later October delivery date. The price of this device is $3,800.

NVIDIA has opened preorders for its new line of laptops, the RTX Spark series, which are designed to run on Windows PCs. The company made this announcement at a recent event in San Francisco, where it also showcased its partnership with Microsoft. According to NVIDIA’s recap of the event, the two companies have been working together for several decades and have now reached a significant milestone in their collaboration.

The event featured a fireside chat between Jensen Huang, founder and CEO of NVIDIA, and Satya Nadella, CEO of Microsoft. The conversation was hosted by Sriram Krishnan, a former senior White House policy advisor on AI. During the discussion, Huang highlighted NVIDIA’s long-standing support for Windows and Microsoft, stating that without Windows, there would be no GeForce.

Nadella credited Huang’s consistency in vision as a key factor in bringing the two companies to this moment of collaboration. He emphasized the importance of making the desktop the most secure place for agents to execute, which is why Microsoft has been working on its Execution Containers (MXC) technology. MXC allows agents to run safely and persistently in the background under operating system control.

Microsoft’s executive vice president of Windows and Devices, Pavan Davuluri, also spoke at the event about the company’s efforts to build primitives directly into Windows for agent security and governance. He pointed out that Microsoft Security and Agent 365 are key components of this effort. Nadella returned to the theme in his conversation with Huang, reiterating the need for a secure environment for agents.

Huang stated that MXC will revolutionize how agents are built and deployed, much like Windows and DirectX did for applications. He emphasized that NVIDIA’s approach would form the foundation of the next generation of IT. The partnership between NVIDIA and Microsoft is expected to have significant implications for the development and deployment of AI models in various industries.

The RTX Spark laptops will run on a full NVIDIA AI stack, which includes CUDA platform support natively. This means developers can move their models, tools, and workflows without rewriting them. The systems are designed to handle large-scale AI workloads, with some configurations capable of running up to 120 billion parameters locally. One notable example is the Qwen 3.8 Flash Next model, which has a 125B parameter count and a 51B n-gram.

NVIDIA also highlighted its fifth-generation Tensor Cores with NVFP4 support in the RTX Spark laptops. This will enable hardware-accelerated AV1 video encode and decode for creators. For gamers, the company promises to run AAA titles at 1440p above 100 frames per second using DLSS 5, Reflex, and G-SYNC.

The event also previewed NVIDIA’s DGX Station for Windows, which is described as a deskside AI supercomputer that brings GB300 Grace Blackwell-class AI infrastructure directly into the Windows ecosystem. This system will allow users to run frontier-class models locally without needing to rent a cluster. The DGX Station for Windows runs on the GB300 Grace Blackwell Ultra Desktop Superchip and offers up to 748 GB of coherent memory.

NVIDIA’s product page lists several key features, including access to Linux AI toolchains through the Windows Subsystem for Linux. The company also notes that the vast majority of Fortune 500 companies are standardized on Windows, making this move significant for enterprise developers. NVIDIA is taking notification sign-ups for availability and expects DGX Station for Windows in the fourth quarter of 2026 from several major manufacturers.

The RTX Spark laptops will be available starting October 16, 2026, with preorders opening on October 7. The systems are designed to handle a range of tasks, including running large-scale AI models locally without needing cloud infrastructure. NVIDIA has partnered with several major manufacturers to bring the RTX Spark series and DGX Station for Windows to market.

The Surface Laptop Ultra is one example of an RTX Spark laptop that will be available from Microsoft. This device features up to 128 GB of unified memory and can run AI models locally without needing cloud infrastructure. NVIDIA’s Blackwell RTX GPU combines with the Grace CPU in a single chip, delivering up to one petaflop of FP4 AI performance.

NVIDIA has also announced that its DGX Station for Windows will be available from several major manufacturers, including ASUS, Dell Technologies, GIGABYTE, HP, MSI, and Supermicro. The system is expected to have significant implications for the development and deployment of AI models in various industries.

Microsoft has introduced its latest laptop, the Surface Laptop Ultra, which is designed to handle demanding artificial intelligence (AI) tasks. The device was unveiled at an event in San Francisco and features a powerful Arm-based Nvidia RTX Spark processor.

The Surface Laptop Ultra is positioned as a competitor to Apple’s MacBook Pro laptops for AI workloads, video editing, and gaming. With a price tag of $2,599, it is Microsoft’s most expensive laptop yet, but the company claims its performance justifies the cost.

Pavan Davuluri, executive vice president of Windows and devices at Microsoft, highlighted the device’s capabilities in an interview with Yahoo Finance. He noted that the Nvidia RTX Spark processor features up to 128GB of unified memory, allowing it to run complex AI models more efficiently than other laptops on the market.

The Surface Laptop Ultra is likely to appeal to early adopters across both consumer and enterprise spaces, according to Tom Mainelli, group vice president of device and consumer research at IDC. He emphasized that cost remains a significant factor in purchasing decisions for many users.

However, as AI workloads become more prevalent, the need for local processing capabilities will grow. This is because certain tasks can be handled by free, locally installed models on your PC rather than relying on expensive cloud-based services like OpenAI’s GPT-6 Astra.

Micorosft demonstrated its Surface Laptop Ultra’s performance in a video clip showcasing it running multiple demanding applications simultaneously, including the video game ‘Gears of War: E Day’ and the video editing platform DaVinci Resolve. This level of multitasking would be challenging for standard laptops to handle.

The device offers significant improvements over Apple’s MacBook Pro with its M5 Pro chip in terms of AI performance. Microsoft claims that the Surface Laptop Ultra generates AI images 4.3 times faster than a MacBook Pro and video content 6.2 times faster.

Despite the ongoing shortage of memory and storage chips, which has led to increased prices for devices across the industry, device makers have managed to insulate themselves from the worst of sales declines so far.

Just a month after launching on iPhone, Meta is bringing its AI-powered assistant Muse to the iPad. This expansion marks another significant step for the company in making these tools accessible across various platforms.

The new dedicated app offers users a more detailed look at their accounts and tasks. With this addition, Muse has reached 6.6 million installs since its launch on iOS and Android back on September 8, according to Sensor Tower’s estimates.

Muse lets users connect their accounts for staying organized and handling tasks like booking reservations or ordering groceries. To make this work smoothly, Meta is continuously adding partners that appear in Muse as ‘connectors.’ Users can add these connections selectively based on what parts of their online life they’re comfortable letting an AI agent manage.

Meta’s efforts have led to the inclusion of several new connectors for small businesses like Asana, Canva, and Dropbox. The company has also updated its list with newer additions such as Notion and Granola. Moreover, Meta recently announced more retail partners – Best Buy, Gap, Sephora, Walmart, and Wayfair are now part of this effort.

These new connectors allow the agent to shop those retailers’ sites and complete transactions on users’ behalf. But this technology is still in its early stages, and issues can arise when websites mistakenly block AI agents thinking they’re malicious bots. To address this problem, Meta has announced it’s working with industry partners on an open standard that would enable personal AI agents to identify themselves to businesses.

This technological solution aims to help businesses distinguish between genuine users and malicious bots, which should improve the user experience and reduce errors. In its effort to make Muse more useful for both individuals and small businesses, Meta is taking steps towards developing this crucial technology with industry partners.

SAP has announced the launch of its new suite of autonomous customer experience (CX) artificial intelligence assistants, designed to streamline and automate various aspects of customer-facing workflows. The announcement was made during SAP’s recent Connect conference in Las Vegas. These AI-powered tools aim to move beyond standalone chatbots and content-generation platforms by integrating with underlying operational systems across marketing, commerce, sales, service, and operations.

The development follows the release of SAP’s Engagement Cloud Customer Loyalty Index, which highlighted the growing influence of AI on customer decision-making processes. According to Jessica Keehn, Chief Marketing Officer at SAP, Autonomous CX is designed to address a critical disconnect between how businesses organize CX and how customers experience it in reality. ‘Customer experience has historically been siloed across separate functions and systems,’ she explained.

Keehn emphasized that Autonomous CX enables AI to coordinate across these boundaries, allowing marketing, commerce, sales, and service teams to work together seamlessly throughout the customer journey. This integration is crucial for businesses seeking to provide a cohesive and efficient experience for their customers. The new suite of assistants includes campaign, content, shopping, quoting, incentive compensation, and service planning and operations tools.

The Campaign Assistant, for instance, can activate campaigns using real-time data from both marketing systems and the enterprise resource planning (ERP) system. This enables businesses to create more timely and relevant marketing efforts that are aligned with their overall business objectives. The Content Assistant supports marketers in generating personalized content quickly by reducing manual work associated with adapting content across different channels.

Other assistants, such as the Shopping Assistant, validate customer and product information to speed up order preparation while minimizing errors. Meanwhile, the Quoting Assistant helps sales teams create complex quotes more efficiently, ensuring accuracy and compliance. The Incentive Compensation Assistant streamlines processes for managing incentive compensation data, providing clearer visibility into compensation details.

The Service Planning and Operations Assistant connects customer service activities with planning, logistics, and operational workflows to help teams coordinate the entire journey. Keehn stressed that these assistants are designed to span multiple systems, enabling sales professionals to quickly create complex quotes tailored to their customers’ needs while ensuring accurate quoting.

SAP is also extending its Order Management Services across both SAP and non-SAP ERP environments to enable organizations to manage demand, inventory, and fulfillment for a wide range of customers. Furthermore, the company’s Reltio platform, now generally available as part of SAP Business Data Cloud, serves as the data foundation for this approach by bringing together customer, supplier, and product records to provide shared context for AI agents.

The recent Customer Loyalty Index report from SAP reveals that 35% of consumers prefer AI-driven recommendations over advice from friends or family. Additionally, 32% regularly use AI to help decide what products to buy, while a further 33% have purchased items recommended by AI systems. However, the study also highlights that sole reliance on AI does not eliminate the need for brands to get their underlying experience right.

Keehn acknowledged that people are increasingly seeking to simplify their buying experiences and leverage AI capabilities to reduce noise and streamline selection processes. Nevertheless, poor implementation of AI can negatively impact how customers perceive a brand’s overall value proposition. As such, CX teams must ensure AI recommendations are based on accurate and current information before adding more autonomous capabilities.

Customer control is becoming an increasingly significant aspect of the picture, with 60% of consumers wanting a shared-control model between humans and AI. Furthermore, 56% expect human support to remain available when interacting with automated systems. This underscores the need for clear boundaries around what AI can do independently and where customers can intervene or request human assistance.

The introduction of personalization must also be handled carefully, as 60% of consumers feel more loyal to brands that make them feel personally valued. However, 30% agree that irresponsible data use could cause them to leave a brand. Keehn warned against the risk of over-personalization, stating that ‘good personalization isn’t about showing customers how much data you have about them; it can feel intrusive.’

For CX leaders, these findings highlight rising expectations for AI models built around accurate context, customer control, and relevant personalization with human support available when automated experiences reach their limits. As businesses continue to explore the potential of autonomous CX tools, they must prioritize a comprehensive approach that balances technology-driven innovation with human-centric values.

The restaurant industry has been facing tough times, with top chain restaurants experiencing slowed sales in 2025. Consumers have been cutting back on dining out, but certain sectors like coffee, beverages, and snacks continue to thrive.

In a recent panel discussion at the FSTEC conference in Dallas, Texas, female technology leaders from Smoothie King and 7 Brew shared their insights on using AI tools for business to remove frictions while protecting hospitality. The key takeaway: automation should run seamlessly in the background, without distracting from the customer experience.

Smoothie King’s Chief Information Officer Jyoti Lynch emphasized that automation is not about replacing human interaction but rather making it easier. ‘Automate the task, not the moment,’ she said, highlighting the importance of striking a balance between technology and hospitality.

The panel discussion was led by Women’s Foodservice Forum (WFF) Chief Brand Officer Ellie Doty and featured 7 Brew’s Chief Technology Officer Danyel Bischof-Forsyth. The session focused on how automation can be used to streamline back-of-house tasks, freeing up staff to focus on customer service.

Lynch gave the example of a common challenge faced by franchisees: when the point-of-sale system’s end-of-day process doesn’t complete overnight, stores are unable to process sales the next morning. This creates chaos for team members when they arrive at work. Smoothie King has automated this process to run in the background while the store is closed, ensuring a fully functional system for baristas when they start their shift.

Some automation does take place closer to customer interaction, but Lynch stressed that it won’t detract from the hospitality experience. For instance, if the system recognizes loyalty members as soon as they walk through the door, it can alert team members, making their lives easier.

7 Brew also prioritizes balancing customer engagement when investing in new technology. The drive-thru coffee chain recently launched its first app and emphasized that it was designed to be additive to 7 Brew’s friendly service. ‘The app gives you time at home or in your car to navigate surprises and offers before you even get there,’ Bischof-Forsyth said.

The investment in new technology has added an opportunity for connection between customers and employees: technology troubleshooting. 7 Brew trained its baristas on the app so they could assist customers when needed, rather than just focusing on automation.

Primarily, though, both companies focus on automating back-of-house tasks that would be invisible to guests. Bischof-Forsyth highlighted bookkeeping and inventory predictions as areas where great business value can be achieved through automation.

Whitbread, the UK’s largest hotel chain and owner of Premier Inn, is embracing artificial intelligence (AI) to streamline its operations. The company has partnered closely with Microsoft to implement responsibly governed AI that can improve productivity and automate business processes, freeing up colleagues to focus on higher-value tasks such as serving guests.

Domenica Sabou, Senior Location Strategy and Data Manager at Whitbread, has been experimenting with Microsoft 365 Copilot to build an AI agent that assesses the potential success of new hotel locations. The agent combines factors like driving distances and local amenities to score them, providing a data-driven starting point for strategic business recommendations rather than relying on instinct or partial evidence.

Sabou emphasizes that while the AI provides valuable insights, human judgment is still essential in making decisions. ‘The judgement is still coming from you,’ she says. ‘You have to do that. And you’re just learning constantly about how to prompt, what not to prompt: the best way of doing things.’ This approach highlights the importance of balancing AI-driven data analysis with human critical thinking and oversight.

The project showcases Whitbread’s efforts to leverage agentic AI technology from Microsoft to automate data-heavy tasks. By using Copilot, Sabou has been able to build an agent that can assist her in making informed decisions about new hotel locations. This is just the beginning of Whitbread’s plans to scale its use of this technology.

Whitbread has established an AI Enablement Office and implemented a governance process for colleagues who want to develop agents beyond their own work and share them with others. The company considers factors like safety, ownership, and cost before allowing useful experiments to become shared services. This approach is crucial in ensuring the responsible adoption of AI across the organization.

Mark Smith, Whitbread’s Group CTO, sees this governance as a foundation for broader adoption. ‘When we get to the end of this, everybody will have the opportunity to build an agent,’ he says. The ambition is to drive more efficiency into their business while preserving frontline roles and ensuring that humans remain involved in critical decision-making.

The company’s agentic ambitions are built on a solid Copilot foundation, which has helped spread AI literacy throughout Whitbread. They began with a pilot involving 270 support-centre colleagues at the head office, amplified by a ‘Digital Heroes’ programme between September 2025 and March 2026. This initiative provided access to Copilot alongside training, practical sessions, and a community where people could share their experiences.

The Digital Heroes programme was designed to be broad, bringing together individuals from various functions such as commercial, finance, property, procurement, people, technology, and more. Smith notes that while the initial response to the new tool was positive, it wasn’t enough on its own. ‘We’re a budget hotel business; we need to justify every pound that we spend,’ he says.

Whitbread gathered feedback throughout the pilot, with colleagues reporting an average time savings of 24 minutes per day as a result of using Copilot. As a consequence, the company is now extending Copilot to 1,650 people across its support centre and multi-site hotel managers. The next phase focuses on fostering sustained use.

Emma Cluett, Head of Delivery Transformation in Whitbread’s Group Transformation function, has used Copilot to help her team build internal capability. Her group manages large programmes for the company, handling tasks like business cases, executive presentations, meeting organization, and stakeholder alignment. Cluett emphasizes that AI can never replace human oversight: ‘The decisions will never go away from that kind of human sense-check.’

However, through Copilot, her team has been able to avoid extra costs by leveraging the tool as an additional workforce support system. ‘We’ve been able to keep our team number quite static because we’ve used Copilot,’ she says.

Sabou also reports increased efficiency due to using Copilot, estimating that it has saved her a considerable amount of time over a year of experimentation. She describes her interaction style with the AI as ‘a backwards and forwards conversation between you and the AI.’ Sabou notes that asking questions is key in getting the most out of Copilot.

The Digital Heroes programme plays a crucial role in spreading confidence among colleagues when it comes to using Copilot. Christopher Adams, Business Change Manager in Technology and Data, and Harprit Takkar, Technical Product Specialist for Frontline and AI, have led group sessions, drop-ins, written guidance, and short recap videos alongside formal training.

Adams notes that the content must constantly change due to the rapid evolution of technology: ‘The material we used six months ago is out of date already.’ He and Takkar focus on providing colleagues with a platform for asking questions as well as demonstrating new features. This approach also enables the company to discover valuable ideas from those closest to the work.

During the pilot, colleagues developed over 60 role-specific use cases across eight functions. Some tasks were straightforward, such as summarizing meetings or finding information, while others used Copilot to research complex questions that previously required external advice.

As Whitbread continues to roll out Copilot for more people, Smith will be monitoring whether colleagues keep discovering new ways to utilize the tool and share their experiences. ‘I want to see more people grabbing me in the coffee shop to say it’s been fantastic and they’ve learned lots,’ he says.

Ultimately, the goal of AI is to accelerate business operations while driving value for guests and shareholders. Smith emphasizes that Whitbread aims to get its employees working better-quality tasks in a more productive manner.

Global brand growth, data, insights, and analytics company Kantar has undergone a significant transformation in its approach to advertising intelligence. Historically, the firm’s role was to measure effectiveness and predict outcomes for advertisers based on decades of proprietary data. However, with the rise of generative AI, marketers increasingly need help optimizing creative content before media investment is committed. This shift prompted Kantar to rethink its strategic direction and explore new opportunities in the field.

In response to this changing landscape, Kantar expanded its LINK AI platform from ad scoring into a comprehensive AI-powered system for evaluating, optimizing, and generating better content at scale. The re-architected platform was built with Microsoft Azure capabilities, enabling it to connect intelligence, AI models, and creative workflows in a seamless manner. This integration has enabled marketers to move from insight to action in minutes, not hours.

The transformation of the LINK AI platform has had a profound impact on Kantar’s customers. With the ability to optimize each asset using generative AI, advertisers can meaningfully improve ROI and business performance. The shift from simply evaluating ads for effectiveness to now optimizing and generating new content in response to scoring has opened up broader commercial opportunities for Kantar. According to Heath Greenfield, EVP of Solutions & Platforms at Kantar, ‘The work we’ve done with Microsoft on LINK AI will help us grow our creative solutions portfolio and drive incremental revenue—not just replace or cannibalize existing revenue.’

The collaboration between Kantar and Microsoft has been instrumental in driving this transformation. The two companies co-engineered a cloud-native platform that would scale securely and reliably, built on Microsoft Azure. This platform leverages connected intelligence and AI models to power LINK AI scoring as well as the new agentic platform with recommendation, optimization, and generation capabilities. Additionally, Azure OpenAI provides image generation, video generation, and reasoning models, while Azure Kubernetes Service orchestrates creative workflows with scalable compute layers.

The re-architected LINK AI platform has delivered significant results for Kantar’s customers. Batch-scoring performance has improved by 20x, platform operating costs have been reduced by 4x, and the speed of single-video scoring has increased by 6x. This enables advertisers to test and refine creative before launch, reducing rework and improving overall business performance. LINK AI has now scored over 100,000 video advertisements in production.

The future capability that Kantar is most excited about validating is real-time, in-flight optimization. According to Ashok Kalidas, Chief AI Scientist at Kantar, ‘The ability to use continuous market feedback to tweak and personalize ads on the fly’ will be a game-changer for advertisers. With the foundation built with Microsoft, this vision is within reach. The collaboration has brought together connected intelligence, scalable execution, and governed AI—enabling Kantar to move creative optimization from a pre-launch exercise toward a real-time capability.

The transformation of the LINK AI platform marks a significant shift in Kantar’s approach to advertising intelligence. By leveraging generative AI tools for business, advertisers can now optimize content before media investment is committed. This has opened up new opportunities for Kantar to drive incremental revenue and grow its creative solutions portfolio. The collaboration with Microsoft has been instrumental in driving this transformation, and the results are already delivering measurable impact for Kantar’s customers.

The re-architected LINK AI platform uses a combination of traditional ML models and LLMs (large language models) to augment content evaluation. This approach enables advertisers to personalize ads, adapt them to different platforms, and close the loop by showing that new versions actually improve scores. The use of generative AI in this process has enabled Kantar to move from insight to action in minutes, not hours.

The collaboration between Kantar and Microsoft has also helped accelerate the adoption of LLMs as key components of LINK AI. By understanding the value of the new architectures and using LLMs while adopting a scalable approach, Microsoft’s FDE team has shortened time to value for Kantar. According to Jonathan Adler, Director, Enterprise Architect at Kantar, ‘The Microsoft FDE team brought strong technical talent, but just as importantly, they helped win people over to a new architectural approach.’

The re-architected LINK AI platform is now capable of scoring more than 100,000 video advertisements in production. This has enabled advertisers to move from insight to action in minutes, not hours. The use of generative AI tools for business has opened up new opportunities for Kantar to drive incremental revenue and grow its creative solutions portfolio.

The future capability that Kantar is most excited about validating is real-time, in-flight optimization. According to Ashok Kalidas, Chief AI Scientist at Kantar, ‘The ability to use continuous market feedback to tweak and personalize ads on the fly’ will be a game-changer for advertisers. With the foundation built with Microsoft, this vision is within reach.

Fleets are increasingly turning to artificial intelligence (AI) to automate routine tasks and streamline operations. According to industry leaders who spoke at The AI Summit in Pittsburgh on September 23rd, carriers are using AI agents to handle customer service inquiries, analyze costs, schedule appointments, and perform other time-consuming work that previously required significant staff time. This shift is not only aimed at improving efficiency but also at freeing up employees from mundane tasks so they can focus on higher-value activities such as planning and analysis.

The technology is being deployed across various functions including dispatch, maintenance, and back-office operations. In the case of Pitt Ohio, a top 100 for-hire carrier in North America, AI has been used to automate email responses, which have seen a significant reduction from 15 minutes to just one second. The company’s AI agent, nicknamed ‘Nate’ or No-Touch Email, handles customer service inquiries from start to finish with an accuracy rate of over 99%.

Pitt Ohio also uses computer vision tools to support costing studies, which can now be completed in about 10 minutes compared to nearly an hour previously required. Analysts are essentially acting as auditors of AI, verifying the accuracy and reliability of its outputs. This shift is not only aimed at improving productivity but also at reducing costs associated with manual labor.

The same trend is observed in maintenance operations where AI can streamline workflows by interpreting fault codes, checking warranty status, verifying parts availability, and scheduling shop visits. According to Vered Mandelboum Josef, CEO of Questar Auto Technologies, this area presents a classic opportunity for AI to make a significant impact on efficiency.

However, industry leaders also emphasized the importance of addressing workforce concerns as fleets introduce automation. About 50% of employees are reportedly terrified by the prospect of being replaced or having their roles diminished. Mandelboum Josef stressed that companies must ensure that employees feel empowered and elevated in their professionalism rather than threatened by AI.

Ahmad Kakar, CEO of Euclid AI, noted that while carriers can gain a competitive advantage by moving early to adopt AI technology, they should do so with caution. He predicted new positions will emerge as a result of automation, including an ‘agent operations manager’ responsible for overseeing and tuning fleets of AI agents.

The panelists agreed that carriers should move quickly but deliberately in adopting the technology. Mark Wang, AI Director at Pitt Ohio, cautioned against going too slow, stating that it is worse than moving too fast. He encouraged fleets to begin with realistic use cases that can produce internal success stories and build organizational confidence.

Industry leaders also emphasized the importance of employee buy-in when introducing automation. As Kakar noted, ‘no company has a lock’ on AI yet, implying that there are still opportunities for innovation and differentiation in this space. The panelists urged carriers to pursue realistic projects while shifting employees toward oversight, planning, and exception management.

Automation is already changing the dispatcher’s role, reducing administrative work and allowing employees to spend more time on higher-value activities such as planning and analysis. As Mark Hill, CEO of PCS Software, noted, ‘we’ve been able to take a lot of their busy work off of their plate.’ This shift not only improves productivity but also reduces employee burnout.

The industry’s biggest transition is from AI copilots that assist workers to systems that execute complete workflows. Kakar pointed to appointment scheduling and document processing as areas increasingly suited for automation. However, he emphasized the importance of human judgment in these processes, predicting new positions will emerge to oversee and tune fleets of AI agents.

The panelists agreed on the need for a balanced approach when adopting AI technology. They encouraged carriers to move quickly but deliberately, starting with realistic use cases that can produce internal success stories and build organizational confidence.

A new partnership between Tabi Connect and Kleinschmidt is set to streamline the freight quoting process for transportation companies. The integration of their systems will enable brokers to automate freight quotes, reducing the time spent on responding to quote requests while maintaining consistency in pricing strategies across teams.

The collaboration brings together Tabi’s Pricing Logic Engine with Kleinschmidt’s Intelligence Network. This allows freight quote requests to be sent directly into Tabi, where a brokerage’s pricing rules are automatically applied before the quote is returned to the shipper. The integration aims to reduce manual effort and improve efficiency in the quoting process.

Tabi’s system enables brokerages to set rules based on various factors such as customer, lane, equipment type, and market conditions. These rules are then applied to incoming requests using Tabi’s Pricing Logic Engine, eliminating the need for brokers to manually determine each rate. This approach ensures consistency in pricing strategies across teams.

Ricky Gonzalez, CEO of Tabi Connect, emphasizes that speed and consistency are crucial differentiators in a crowded freight brokerage market where margins continue to thin. He notes that automation is key to success, allowing brokers to quote with more confidence rather than just faster.

The partnership also connects Tabi with Capacity IQ, Kleinschmidt’s predictive freight-capacity platform. This platform analyzes over 150,000 loads moving across North America each day to predict where trucks are likely to become available. By incorporating this capacity data into the pricing process, brokers can factor in expected carrier availability when setting rates.

Dan Heinen, president and CEO of Kleinschmidt, highlights that Tabi helps brokers consistently apply their own pricing strategy to every opportunity. Combined with the Intelligence Network, it can dramatically shorten the time between receiving a quote request and responding. The integration aims to provide brokers with another data point when setting rates, particularly in markets where available capacity can change quickly.

Tabi currently connects with over 88 shipper TMS platforms, bid boards, and freight marketplaces through its API connections or robotic process automation (RPA) integrations. These connections enable the platform to return quotes within three seconds for API connections and around 35-50 seconds for RPA connections. The company’s broader platform captures freight quote opportunities from various sources, applies a brokerage’s pricing rules, and returns a rate.

Tabi says its platform is used by more than 100 logistics companies, including over 20 of the Top 100 U.S. brokerages. Additionally, it processes over 1 million quotes per month. The company connects with freight rating and market-data providers such as DAT, Greenscreens, C4, Sonar, and Truckstop.

The partnership builds on Kleinschmidt’s existing role as an integration provider for transportation and other supply chain companies. The company provides managed EDI, API, and B2B integration services that connect businesses with their trading partners. This connection enables the flow of capacity data into pricing, allowing brokers to quote with more confidence rather than just faster.

The Tabi-Kleinschmidt partnership brings together automation tools for freight quoting and predictive capacity analysis. By integrating these functions, brokers can consider expected carrier availability alongside their own pricing rules when setting rates. This could provide another crucial data point in determining how difficult a load may be to cover, particularly in markets where available capacity changes quickly.

Kleinschmidt’s acquisition of eBase Solutions in May expanded its integration capabilities, combining external B2B connectivity with internal enterprise integration capabilities. The Tabi partnership brings these connectivity capabilities into the freight brokerage pricing process, linking incoming quote requests with automated pricing and predictive capacity information.

Cleveland Clinic recently hosted its AI Summit for Healthcare Professionals, a comprehensive event that brought together healthcare professionals from across the region and country to explore the rapidly expanding role of artificial intelligence in clinical care. The summit was held in collaboration with the College of Healthcare Information Management Executives (CHIME) and featured Cleveland Clinic leaders and nationally recognized experts who shared insights on how AI is transforming healthcare delivery in various settings, including critical care, surgery, nursing, pharmacy, and medical education.

The event drew over 900 registered attendees, both in person and virtually, to the InterContinental Hotel for a day-long discussion on the potential of AI-powered tools to support diagnostics, predictive analytics, and personalized treatment planning. These innovations also aim to improve patient-centered care through virtual health assistants, chatbots, and AI-driven monitoring.

The keynote speaker was Peter Clardy, M.D., director of the Clinical Enterprise Team at Google Health, who engaged in a conversation with Jame Abraham, M.D., chairman of the Department of Hematology & Medical Oncology at Cleveland Clinic. Dr. Abraham emphasized that ‘artificial intelligence has the potential to fundamentally change the way we care for patients, educate future clinicians, and advance research.’

The summit’s speakers explored various aspects of AI implementation in healthcare, including its ability to reduce bias and ensure responsible use. They also discussed the need for careful consideration of ethical and regulatory issues surrounding AI adoption. Dr. Abraham noted that this event provided an essential opportunity for healthcare professionals to learn from one another and discuss both the promise and challenges of AI.

Cleveland Clinic’s AI Summit was designed to provide practical insight into the future of AI in medicine, catering to clinicians, physicians, advanced practice providers, nurses, administrators, trainees, and other healthcare professionals. The event aimed to equip attendees with a comprehensive understanding of how AI can be used thoughtfully to improve outcomes and patient experience.

Cleveland Clinic is a nonprofit multispecialty academic medical center that has been at the forefront of medical innovation since its founding in 1921 by four renowned physicians. With a vision of providing outstanding patient care based on principles of cooperation, compassion, and innovation, Cleveland Clinic has pioneered numerous medical breakthroughs, including coronary artery bypass surgery and the first face transplant in the United States.

Cleveland Clinic is consistently recognized for its expertise and care worldwide. The institution boasts an impressive workforce of over 83,000 employees globally, with more than 6,600 salaried physicians and researchers, and 21,900 registered nurses and advanced practice providers representing 140 medical specialties and subspecialties. Cleveland Clinic operates a vast health system comprising 23 hospitals, 300 outpatient facilities across multiple locations in the United States, Canada, the UAE, and England.

The institution’s extensive reach is reflected in its statistics: in 2025, there were over 15.9 million outpatient encounters, 343,000 hospital admissions and observations, and 336,000 surgeries and procedures throughout Cleveland Clinic’s health system.

Ethereum’s infrastructure has been put to the test, and it hasn’t come out unscathed. Researchers at Black Hat Asia 2026 presented a comprehensive analysis of the network using language model-driven differential testing. The results are striking: 98 bugs were found across all 11 major Ethereum clients, from EVM crashes to API inconsistencies.

The researchers built a local testnet with 30 nodes, each running one of six consensus and five execution clients. They sent identical inputs to every client, exposing behavioral differences that would otherwise go unnoticed. This approach is reminiscent of an AI-generated image being used as a judge in a competition – it provides a neutral perspective on the performance of different systems.

The team’s use of differential analysis flagged discrepancies in return data gas usage and storage across clients. They also identified core virtual machine crashes and JSON-RPC inconsistencies, which were present in every client tested. This is not just a matter of minor issues; 26 EVM bugs and 72 API bugs were detected, with more than 85% confirmed or fixed.

The researchers used language models to create minimal valid programs targeting single EVM operations and all paths. They then extracted interprocedural control flow graphs to mutate arguments and cover all possible scenarios in the specification. This approach allowed them to pinpoint specific functions in codebases written in Go, Java, Rust, and other languages that were contributing to the bugs.

The majority of detected bugs have been validated and patched by client development teams. However, this raises questions about the robustness of Ethereum’s infrastructure as a whole. When it comes to data analysis tools like these language models, they can provide a comprehensive view of system performance – but only if developers are willing to listen.

LLM-generated differential testing has proven itself to be a valuable tool in identifying and fixing bugs in complex systems like Ethereum. By leveraging machine learning jobs that focus on code analysis, researchers may uncover even more issues hiding beneath the surface. The results of this study serve as a reminder that there is still much work to be done in ensuring the reliability and security of decentralized networks.

The findings of this research have significant implications for the development of Ethereum’s infrastructure. As the network continues to grow and evolve, it will require ongoing testing and maintenance to ensure its stability and integrity. By embracing AI-driven differential testing as a standard practice, developers can streamline their processes and catch potential issues before they become major problems.

The use of language models in this study is particularly noteworthy. These tools have been gaining traction in various fields, from image recognition to natural language processing. In the context of Ethereum development, LLMs can serve as an additional layer of quality control – helping developers identify areas where their code may be vulnerable to bugs or security breaches.

The comprehensive nature of this analysis has provided a wealth of information for researchers and developers alike. By examining the specific functions in Go, Java, Rust, and other languages that were contributing to the bugs, they can gain a deeper understanding of how these issues arise – and what steps need to be taken to prevent them in the future.

The study’s findings have sparked renewed interest in the potential applications of LLMs in Ethereum development. As researchers continue to explore this area, we may see even more innovative uses for language models in testing and maintenance processes.

Futuri, an AI technology company, has introduced a framework aimed at helping sales leaders avoid the consequences of automating human moments in customer interactions. The ‘Automate, Augment, Hold’ framework is designed to help companies differentiate between tasks that can be automated and those that require human presence and value addition.

The idea behind this framework stems from conversations with over 40 enterprise sales leaders who have grappled with the same question: what happens when we automate a task versus automating a moment? Futuri’s CEO, Daniel Anstandig, has been working on this concept for a year and is now sharing his insights in ‘Where AI Ends and the Seller Begins’.

The framework categorizes every step of the revenue process into three categories. Automate covers tasks where only the output matters and human presence adds no customer value. Examples include research, first-draft emails, CRM updates, competitive briefs, meeting summaries, and more. These are tasks that can be easily automated without affecting the relationship with customers.

Augment, on the other hand, refers to work where AI does the heavy lifting but a person makes the final call. This includes drafting proposals, scoring accounts, surfacing signals, refining ideas, prioritizing, and deciding on timing. In this category, human judgment is essential for making informed decisions that impact customer relationships.

The third category, Hold, covers moments where automation degrades the relationship with customers. These include discovery calls, negotiations, first meetings with a new champion, recovery conversations, and delivering unwelcome news. According to Anstandig, ‘there’s a difference between automating a task and automating a moment.’ The presence of humans is crucial in these situations.

The cost of getting this wrong is what Futuri refers to as the trust deficit – a decline that doesn’t show up on any dashboard but has significant consequences. Customers may not complain directly, but they quietly disengage, leading to softer win rates and reduced revenue. This ‘trust deficit’ is the bill that comes due 18 months after automation mandates are implemented.

Anstandig emphasizes that most leaders are willing to automate tasks but often overlook the sorting process – determining which tasks can be automated and which require human presence. He argues that two companies with identical technology stacks can have vastly different outcomes, highlighting the importance of this distinction.

The full framework is available on Futuri’s website, along with a step-by-step exercise for auditing live sales processes. This comprehensive approach aims to help customer-facing workflows in various industries beyond just sales, including marketing and AI leadership.

The complexities of the US health care system can be daunting for many people. Understanding terms like health insurance, in-network providers, and deductibles can feel overwhelming. To get help, some individuals are turning to online forums and websites where they can hear firsthand from others about their experiences with the health care system.

A PhD student at the University of Albany’s School of Social Welfare is analyzing these online posts to better understand the barriers people face within the health care system. Arissa Spitalny’s work uses Reddit, a popular discussion website that hosts communities called subreddits where users can post about specific topics and reply to others’ posts.

Spitalny notes that individuals who may not feel comfortable sharing their concerns in traditional person-to-person interactions have a space on Reddit to anonymously voice their worries, ask for advice, or simply vent. This anonymity allows them to connect with others who share similar experiences without fear of judgment.

The subreddit ‘AskDocs’ is being examined by Spitalny, which has over half a million weekly visitors and was created in 2013. With over one million posts from this community, Spitalny is focusing on those that specifically mention health insurance.

Spitalny’s mentor, Assistant Professor Cheng Ren, specializes in using advanced artificial intelligence techniques and open-access data. Under his guidance, Spitalny developed an AI prompt to review the Reddit posts and classify approximately 15,000 of them that mentioned health insurance. The AI was trained on a dataset of 300 manually reviewed sample posts by Spitalny.

The use of AI significantly streamlined the analysis process for Spitalny, allowing her to efficiently examine the full dataset. She is now in the analysis and writing stage of her study, which will interpret patterns in the data collected from Reddit, including factors such as race, gender, and demographic location.

Spitalny’s research aims to contribute to a better understanding of health care access barriers and the role social work can play in improving care navigation. She hopes that by examining the support individuals seek online, she can gain insight into the lived experiences behind these patterns.

In the future, Spitalny plans to expand her study beyond insurance posts to examine other health care barriers as well.

The tech company Anthropic is expanding its support for scientists, aiming to accelerate scientific discovery through the use of its language model, Claude. Building on previous efforts, including the launch of Claude Science in June, Anthropic is now offering free and discounted subscriptions to researchers worldwide.

In a significant move, Anthropic has opened 10,000 seats for scientists to access Claude at no cost or reduced rates for one year through its new Claude team plan for scientists. Standard seats will be available for free, while premium seats with increased usage limits can be obtained for $15 per month. This program is set to extend beyond the initial 10,000 seats in the coming months.

The expansion of Anthropic’s AI for Science program also includes offering credits to researchers working on high-impact projects across various scientific fields. Initially focused on biological sciences, the company now plans to support scientists in other areas as well, such as those conducting ambitious and computationally intensive research like solving complex mathematical problems or protein design.

By providing access to Claude through subscriptions and credits, Anthropic aims to significantly accelerate scientific discovery. The company believes that its tools can help researchers streamline their work and achieve breakthroughs more efficiently.

To register for the Claude team plan, principal investigators at academic or nonprofit institutions must complete a verification form. Once verified, they can add researchers from their lab to the plan. Researchers working in biology and chemistry will still be limited to Opus-class models due to potential dual-use risks associated with using higher-level models like Fable.

Anthropic is also collaborating with the US government on an access program for life sciences professionals to use Mythos-class models safely. The company has already enrolled its first participants, with plans to increase access soon. Additionally, Anthropic is working towards establishing a shared specification for AI agents to operate physical devices, known as the Model Hardware Standard (MHS), which will be available in a research preview to select scientific labs and manufacturers.

Anthropic’s efforts also include funding independent research into how AI impacts users’ wellbeing through a $5 million grant program. Furthermore, the company has published an article explaining its text watermarking method used in Claude, addressing concerns about its effects on outputs.

A breakthrough in quantum computing has been achieved by QuEra Computing, a leader in the field. The company has successfully automated a critical subsystem of its quantum computers using artificial intelligence (AI), enabling the acceleration of commercial-grade deployments. This development marks a significant milestone for the industry, as it addresses one of the major challenges facing the widespread adoption of quantum computing: the need for human expert intervention to maintain and operate these complex machines.

The AI agent used in this achievement is Anthropic’s Claude, which was developed and validated by QuEra’s team. Claude wrote its own control software, allowing it to recover from laser system failures in seconds, compared to the minutes required by a specialist. This automation not only saves time but also reduces the need for manual intervention, making quantum computing more practical and efficient.

Quantum computers rely on lasers held at precise frequencies to control atomic qubits. However, these lasers drift over time, causing the machine to malfunction unless corrected by an expert. The increased number of lasers in each generation of machines has made this a significant challenge, as every laser requires manual tuning and commissioning. This process can take weeks, tying up scarce human expertise.

QuEra’s team spent two to three weeks writing a recovery script for common laser disturbances by hand. However, this approach had limitations, as it only handled failures that the authors anticipated listing. To overcome these challenges, QuEra turned to Claude and the Model Hardware Standard (MHS), a new standard for AI agents to safely operate physical equipment in scientific research and advanced manufacturing.

The MHS allows AI agents like Claude to run their own experiments on dedicated testbeds, propose fixes, try them out, read results, and refine. This process is continuous, including overnight runs, covering hundreds of failure cases that no team of specialists could work through by hand. Engineers set the scope, reviewed every step, and decided what counted as proof of success.

The software produced by Claude is a conventional, fully inspectable program, not a model making decisions at runtime. Moreover, it operates within safety bounds declared in the standard itself, ensuring safe operation from the outset. This approach has been validated through rigorous testing, with results showing that Claude can recover reliably and quickly, even when faced with real faults.

The pilot project demonstrated several key benefits of this AI-driven automation: reliable recovery, rapid response times, handling of real faults rather than just test cases, improved performance over expert tuning, and the ability to transfer settings between lasers. These achievements have significant implications for the deployment of commercial-grade quantum computers.

Prior to this breakthrough, every laser used in a quantum computer required standing on scarce human expertise time. On-site recovery was necessary at any hour, taking up to half an hour per tuning session, and commissioning a new operating point took weeks. With Claude’s automation, these limitations are significantly reduced, making it possible for more systems to be deployed and properly supported.

Sergio H. Cantu, Vice President of Quantum Systems at QuEra Computing, highlighted the importance of this achievement: ‘For years, the hardest part of scaling quantum computers wasn’t the physics; it was the people driving at 2 am to fix a laser lock.’ He emphasized that QuEra has built a solution using the Model Hardware Standard to address this challenge.

QuEra’s participation in the MHS research preview is an early example of AI partnerships. The company works closely with strategic partners, including Amazon Web Services for cloud delivery and HPE for on-premises high-performance computing integration. This collaboration enables QuEra to leverage cutting-edge technologies like machine learning jobs and apply them to real-world challenges.

The Model Hardware Standard (MHS) is a new standard that allows AI agents to safely operate physical equipment in scientific research and advanced manufacturing. It started as a collaboration between Anthropic and HHMI Janelia Research Campus, with QuEra participating in the limited research preview. The MHS ensures safe operation by declaring bounds, interlocks, and emergency stops within its own standard.

QuEra Computing is at the forefront of neutral-atom quantum computing, helping enterprise innovators leverage this technology to gain competitive advantage. With a public, peer-reviewed path to fault tolerance, QuEra operates globally from Boston, New Mexico, Tokyo, Zurich, and the United Kingdom. As quantum computing moves from ‘one day’ to ‘Day One,’ QuEra delivers practical impact today while leading the way toward large-scale, fault-tolerant systems.

QuEra’s President, Takuya Kitagawa, emphasized that even for experienced companies like his own, maintaining these machines at peak performance is a significant challenge. He highlighted the importance of this breakthrough: ‘A customer expects the entire computer to hold itself together without a specialist in the room.’ This achievement makes it far easier and cheaper to keep QuEra’s computers running at their best.

The results from the MHS research preview, combined with Anthropic’s frontier AI models, are significant. They demonstrate that AI can be used not only for automation but also for improving performance and efficiency. As quantum computing continues to advance, we can expect more innovations like this one to emerge.

Home furnishings retailer Williams-Sonoma is leveraging artificial intelligence (AI) to enhance its customer service model, according to CEO Laura Alber. The company has been using AI assistants to improve the checkout experience, product discovery, and personalization across its brand portfolio.

In a recent earnings call, Alber highlighted the importance of customer service in driving business success. Despite a volatile macroeconomic backdrop, Williams-Sonoma delivered another quarter of profitability, with comparable sales rising 6.2% year over year and total revenue growing 6.7% to nearly $2 billion.

Williams-Sonoma’s AI-powered shopping assistants, Olive and Otto, are designed to provide customers with personalized product recommendations and enhance their overall experience. Alber noted that both assistants have increased consumer engagement with the company’s content, design tools, and free design service offerings.

The launch of Otto across the Pottery Barn family in August marked a significant milestone for Williams-Sonoma’s AI efforts. The assistant is equipped to help customers narrow down product options based on their specific needs and preferences, much like human sales associates. According to Sameer Hassan, chief technology and digital officer, over 70% of customer engagements with Otto are resolved without the need for human intervention.

Williams-Sonoma’s confidence in its AI tools stems from its extensive reserves of first-party data. The company has been collecting rich data on customer behavior and preferences through its proprietary tech platforms and decades of expertise in home furnishings. Hassan emphasized that this data is a key differentiator, allowing Williams-Sonoma to drive impactful results with its AI-powered assistants.

One notable example of the success of Williams-Sonoma’s AI efforts is the performance of Olive at Williams Sonoma. Customers who use Olive convert at three times the rate of those who do not. The company has taken a similar approach with Otto, which Hassan described as ‘helping you narrow down the right piece for your space.’

Early results from Otto are promising, with customers responding positively to its personalized product recommendations and design tools. Williams-Sonoma’s ability to scale personalization using AI has also yielded significant revenue gains. According to Hassan, a visit where the experience is personalized generates roughly nine times the revenue of an average visit, up from two times last year.

Alber attributed Williams-Sonoma’s success in part to its strong brands and focus on customer service. The company believes that its execution and commitment to delivering exceptional experiences will continue to drive business growth in 2026 and beyond.

Hospitals are increasingly turning to AI tools to ease administrative burdens and improve patient care. One area where these tools make a significant impact is in billing, coding, and documentation.

However, some unsubstantiated claims have suggested that the use of AI tools has increased coding intensity, leading to higher healthcare costs. These allegations ignore underlying drivers of changes in coding intensity, including an aging population and rising prevalence of chronic disease.

Hospitals are caring for patients with higher acuity due to demographic shifts and advances in medicine. The AHA analysis found that 19% of hospital expense growth from 2019 to 2024 reflects caring for sicker, more complex patients. This trend is also reflected in the rising case-mix index – a standard measure of patient acuity.

The shift of lower-acuity care to outpatient settings has left hospitals with an increasingly complex and resource-intensive inpatient population. AI tools can support accurate coding and documentation by enhancing precision within established frameworks, but human validation remains essential for ensuring coding integrity and regulatory compliance.

Assertions that providers are upcoding due to the use of AI tools are striking given evidence of insurer-driven coding practices. Lawsuits, investigations, and reports from MedPAC, DOJ, and Congress have documented upcoding among large insurers, including adding diagnosis codes not supported by clinical care.

In 2025, MedPAC reported that upcoding contributed to $40 billion in overpayments to Medicare Advantage plans. One payer settled a lawsuit with the DOJ on upcoding allegations in March 2026, while another insurer is facing a state-level investigation for potentially fraudulent practices.

The AHA has urged policymakers to prevent insurers from systematically reducing reimbursement under the pretense of widespread inappropriate upcoding. This would help alleviate administrative waste and burden created by overly burdensome appeals processes.

AI tools are transforming care delivery in countless ways, supporting increased access, improved outcomes, and reduced costs. They offer tremendous potential for reducing more than $1 trillion spent annually on administrative functions. However, their use is not without controversy – particularly when it comes to coding intensity.

Some commercial insurers have asserted that AI tools used by providers are resulting in higher coding intensity. These accusations ignore multiple factors contributing to growth in coding intensity, including an aging population and increasing prevalence of chronic disease.

Hospitals maintain rigorous auditing and coding compliance programs to ensure they capture patient acuity levels accurately and meet regulatory requirements. Coding has always been governed by established guidelines, conventions, and expectations that define a compliant approach to code assignment.

Updates to evaluation and management coding guidelines have altered coding patterns, leading to more specific diagnoses being reported for higher-acuity patients. As providers adapt and data stabilize, these changes contribute to shifts in coding intensity.

The AHA has responded to concerns about AI tools used by providers, emphasizing the need for accurate representation of patient complexity. They argue that hospitals are not engaging in upcoding practices but rather accurately capturing acuity levels due to demographic shifts and advances in medicine.

In contrast, some insurers have been accused of using automated edits to unilaterally reduce reimbursement without reviewing medical documentation – a practice known as downcoding or partial denial. This can lead to burdensome appeals processes for providers seeking reimbursement for medically necessary care.

The AHA has urged policymakers to prevent such practices and ensure that hospitals are fairly reimbursed for the care they provide. They argue that accurate coding is critical for ensuring patient safety, quality of care, and regulatory compliance.

AI tools have tremendous potential for supporting data analysis in healthcare – from streamlining administrative tasks to improving patient outcomes. However, their use must be carefully managed to ensure accuracy, consistency, and fairness in reimbursement practices.

A team of researchers has developed an AI-powered platform that generates recipes for synthesizing new materials. The platform, built by a collaboration between Sungkyunkwan University (SKKU) and Massachusetts Institute of Technology (MIT), uses large language models to propose synthesis conditions and procedures for complex new materials based on existing literature.

The research team, led by Professor Sung Beom Cho at SKKU, aimed to reduce the trial-and-error process involved in synthesizing new materials. This often requires extensive experimentation with different precursors, temperatures, and reaction times. To streamline this process, they created a database of synthesis information from 4,407 open-access solid-state synthesis papers published in academic journals.

The team applied a retrieval-augmented generation (RAG) method to search for similar existing synthesis cases based on the desired material’s properties and conditions. This allowed them to propose candidate recipes that could be used as starting points for further experimentation.

When compared against actual synthesis conditions reported in papers, these proposed recipes scored an average of around 4 out of 5 on key synthesis variables. The team then tested this AI-generated recipe by synthesizing a new oxy-selenide-based solid electrolyte material for all-solid-state batteries.

However, the initial attempt resulted in several impurity phases forming instead of the target material. To refine their approach, they fed these results back into the model, which proposed follow-up recipes that progressively lowered the synthesis temperature. This iterative process allowed them to eventually synthesize a single-phase new material with no detectable impurities.

The success of this AI-powered platform demonstrates its potential for reducing trial and error in materials design by combining human scientists’ experimental experience with AI’s ability to draw on vast amounts of literature. By leveraging existing knowledge, researchers can quickly incorporate experimental results and develop synthesis methods for a wide range of new materials.

Google Cloud has launched a tailored AI solution designed to automate complex legal workflows across various practice areas. This specialized platform is engineered specifically for the needs of law firms, addressing the unique requirements and challenges that general-purpose tools often struggle with.

The unveiling marks an important step in Google’s efforts to provide industry-specific solutions. Gemini Enterprise for Law Firms is part of a series of tailored packages built on top of the secure, fully-governed Gemini Enterprise platform. Each solution delivers domain-specific AI capabilities, including custom agents and data connectors.

Law firms have long been hampered by manual processes and fragmented point-solutions that create a steep integration tax. General-purpose tools often lack the necessary controls for confidentiality, ethical walls, verified legal authority, and complete data isolation. Gemini Enterprise for Law Firms addresses these challenges head-on with its purpose-built capabilities and custom connections.

Thomas Kurian, CEO of Google Cloud, emphasized how ‘agentic AI’ empowers law professionals to research across historical case law, build complex arguments, and automate mundane tasks that provide enormous value to clients. However, ensuring the accuracy, factual grounding, and adherence to legal authority in these workflows is critical. Gemini Enterprise for Law Firms helps manage modern legal work while providing essential governance capabilities.

The solution includes four core components: custom skills for law agents, connectors to specialized systems, a third-party agent ecosystem, and the secure Gemini Enterprise platform. The platform provides a centralized control plane with full visibility, risk management, audit logging, and native governance architecture.

Gemini Enterprise for Law Firms automates complex operations such as proactive regulatory horizon scanning, data discovery, DSAR response, contract review, negotiation, and motion to seal preparation. It also builds and updates contracting playbooks by extracting key terms from legacy agreement archives. The platform’s agentic AI capabilities enable rapid generation of non-disclosure agreements with complete formatting confidence.

The solution is built on Google’s end-to-end first-party AI stack, spanning global infrastructure, custom silicon, foundation models, and an AI-ready data platform. Security remains paramount throughout the stack: client data stays within the organization’s private cloud perimeter, base models are never trained on customer data, and existing permissions are inherited via direct connectors.

Leading law firms such as Cleary, Freshfields, Weil, and Williams & Connolly have already adopted Gemini Enterprise for Law Firms in preview. These early adopters highlight the practical value of this solution in streamlining legal operations and delivering high-quality work to clients.

Cleary’s Managing Partner Jeff Karpf noted that using Google’s Gemini Enterprise allows them ‘to unlock greater efficiencies’ for their teams while maintaining quality work. Freshfields Global Managing Partner Alan Mason emphasized the importance of combining frontier technology with expertise, governance, and institutional knowledge to create value for clients.

Weil’s incoming Executive Partner Ramona Nee expressed enthusiasm for collaborating with Google Cloud engineers and shaping the platform based on sophisticated legal practice realities. Williams & Connolly Chairman Joe Petrosinelli welcomed partnering with Google Cloud in integrating Gemini Enterprise for Law Firms to streamline workflow and support litigators in critical client outcomes.

Google Cloud offers a powerful AI stack that enables organizations to transform their business for the Agentic Era, providing customers worldwide with trusted technology partnerships. By harnessing agentic AI capabilities, law firms can deliver high-quality work to clients while maintaining quality control and adherence to legal authority.