AI Applications

A major development in the world of finance has taken place with Scalable Capital announcing that it is now open to all major AI assistants. The firm’s platform, which allows clients to manage their investments using simple prompts in natural language, can be connected to popular AI tools such as ChatGPT and Claude.

The move marks a significant shift towards integrating human and artificial intelligence capabilities in the financial sector. Scalable Capital claims that Agentic Investing represents ‘the greatest technological shift in financial technology since internet banking’, setting a new benchmark for how humans, AI, and capital markets interact.

Erik Podzuweit, Founder and Co-CEO of Scalable Capital, highlights the potential benefits of this integration: ‘By opening our platform, we are enabling clients to manage their investments with unprecedented ease and efficiency.’ The firm’s ambition is clear – it wants to be at the forefront of innovation in the international financial world.

Agentic Investing offers a range of features that can be accessed directly through AI assistants. These include trading, setting up savings plans, managing watchlists, and setting price alerts. Clients also have access to powerful tools from Scalable Insights: investors and AI assistants can tap into deep portfolio analyses such as diversification health checks, scenario analyses, sector or region breakdowns, and risk assessments.

News, real-time quotes, and historical price data are available free of charge through Agentic Investing. This comprehensive platform is designed to streamline the investment process for clients, making it easier for them to manage their portfolios using simple natural language prompts.

The potential applications of this technology are vast. For example, investors can create a personalised newsletter based on the securities in their portfolio and watchlist every market day at 7:00 a.m., or compare their portfolio with model portfolios such as the All-Weather Portfolio on justETF.

Preparation for analyses or tax returns is also simplified through Agentic Investing. Clients can export transactions in crypto ETPs from specific years, prepare savings plans based on projected returns, and even adjust limit orders for specific stocks. The platform’s capabilities extend to building custom tools – clients can create interactive dashboards for their portfolios, complete with holdings, performance, transactions, price charts, and news tickers.

Scalable Capital provides both a CLI application and an MCP server as part of Agentic Investing. The CLI application allows users to install the tool directly on their device, making it suitable for use with locally hosted AI assistants and models. MCP is a standard developed by Anthropic that enables connection between external providers and major AI assistants.

A step-by-step setup guide for all major AI assistants is available at scalable.capital/agentic-investing. The process of activating Agentic Investing involves logging in with Scalable credentials, confirming via two-factor authentication upfront and at regular intervals, and connecting the account to a preferred AI assistant.

Scalable Capital emphasizes that users are required to log in with their own credentials and confirm transactions through 2FA. This ensures that all activities can be monitored in real-time within the client area. Deposits and withdrawals remain possible only via the web or app.

The launch of Agentic Investing underscores Scalable Capital’s commitment as an innovation driver in the international financial world. The new features complement the company’s established AI ecosystem, which includes Insights – an AI-powered chatbot introduced by Scalable Capital in August 2025 that answers financial questions and provides real-time analysis directly within the app.

Scalable Capital will continue to expand Agentic Investing and integrate AI across various areas of its platform. The firm aims to best support clients in building their wealth through this integration, marking a significant step forward for human-AI collaboration in finance.

Thomson Reuters has made its proprietary large language model (LLM) official with the launch of Thomson. The company emphasizes that this achievement was accomplished at a fraction of the cost of other frontier models like ChatGPT and Claude, which it fully owns and controls.

The development of Thomson is significant not only because of its impressive performance but also due to its unique training data set. Unlike other LLMs, Thomson was trained on proprietary content built up over decades by Thomson Reuters, including Westlaw and Practical Law. This domain expertise no other company can match has been a crucial factor in the model’s success.

Joel Hron, chief technology officer at Thomson Reuters, believes that this achievement could change the economics of professional AI. He notes that for years, the industry has focused on scale as the answer: bigger models, more compute, and more money. However, with Thomson, there is another path – starting with a strong foundation, specializing it deeply for the work that matters, and building intelligence that is highly capable, far more efficient, and entirely under your control.

Thomson Reuters has invested around $40 million in developing this model over the past two years, covering both talent and compute. The final training run for the version launching today cost just $450,000. This number indicates what Thomson Reuters was able to achieve with its data and content on top of world-class open-source models.

The first deployment of Thomson will be in an upcoming release of CoCounsel Legal, driving its Tabular Analysis functionality. Thomson will be the default model for this feature, although administrators can switch it to another model through CoCounsel Legal’s admin settings. The company emphasizes that CoCounsel will remain multi-model, tapping into whichever LLM is best suited for the task at hand.

Over time, Hron expects Thomson to take a bigger and bigger share of the tokens and work done by CoCounsel. Thomson Reuters plans eventually to extend its models across its portfolio of legal and tax products. The company believes that Thomson does not need to keep pace with the frontier of general intelligence across all dimensions but should instead set the frontier for legal.

Beyond its own products, Thomson Reuters has begun early conversations with large law firms and corporations about direct access to the model. This includes firms interested in fine-tuning it with their own data as they look to bring greater sovereignty to their knowledge and expertise. The company is open to working with these organizations and licensing Thomson directly.

The development of Thomson involved starting with an existing open-source model, training it to become a legal specialist, rather than building from scratch. Although the models used changed over the course of the project, the most recent was Qwen 3.5. Central to this process were subject-matter experts – the armies of lawyers and tax professionals employed by Thomson Reuters.

Andrew Bean, who heads TR’s evaluations team, said hundreds of experts helped decide what the model should be trained to do in the first place, created many thousands of examples of high-quality answers, and then judged the model’s outputs in blind, head-to-head comparisons against frontier models. None of this training data comes from customers; instead, Thomson Reuters replicates relevant tasks with its own experts.

Thomson Reuters has engaged several third-party security firms to validate the hosting, serving, and security controls around the model. Because open-source models keep improving, the company built a process that is repeatable – swapping out starting points close to half a dozen times over the life of the project as better models emerged.

The result is a model that gained deep capability in legal and tax work without losing general abilities such as writing, math, and reasoning over long documents. This achievement marks a significant milestone for Thomson Reuters, which now owns not only its content but also the intelligence powering it.

Thomson Reuters’ internal testing of Thomson has shown strong results on various benchmarks. One evaluation described during the briefing was an in-house benchmark called Deep Research, built around legal research queries drafted by experts based on their work in practice. For each query, they supplied criteria for a good answer to contain.

The answers were scored on two dimensions: completeness and factuality – whether the citations provided actually supported the claims being made. Thomson Reuters compared its model against GPT 5.4 and Claude Sonnet 5 using this benchmark, scoring each twice – once with access only to web content and once with access to TR’s proprietary content.

While Thomson performed respectably in the web-only test, it did much better when trained on TR’s own data, outscoring both of the other LLMs. This demonstrates that there is a significant uplift from training on one’s own tools – a key advantage of having an in-house model like Thomson Reuters’ proprietary LLM.

Thomson Reuters will publish a technical report this week with comprehensive evaluations, including results on standard public AI benchmarks and human testing. The company has also begun making the model available to legal and AI academics for direct evaluation and is releasing a ‘small’ version as an open-weight model on Hugging Face under a non-commercial academic license.

One of those academics, Jonathan H. Choi at Washington University School of Law, tested Thomson against ChatGPT and Claude using challenging questions from his Corporate Tax class. He preferred Thomson’s responses overall, especially appreciating the links to treatises that made them more transparent and useful for legal work.

Thomson Reuters is building a developer portal that will eventually allow outside parties to access the model directly with API keys, configurable parameters, and sample documentation. The company emphasizes that this marks a new chapter – Thomson Reuters no longer only integrates content but also builds intelligence powering professional work.

A new study has introduced an artificial intelligence (AI) system that streamlines wind farm maintenance planning, reducing costs and improving efficiency. Researchers developed a domain-tailored large language model named LLM4M to optimize maintenance decision-making in the complex process of wind turbine upkeep. The team’s work addresses long-standing limitations of conventional maintenance planning tools, which often consume substantial computation time and lack flexibility when adapting to changing operational scenarios with diverse failure modes and variable expenses.

Wind farm operation and maintenance costs account for a significant 30% of onshore wind turbines’ total life cycle cost. As the global demand for renewable energy continues to grow, there is an increasing need for resource-efficient scheduling frameworks that can minimize costs while ensuring reliable turbine performance. The research frames wind turbine maintenance as a multi-team collaborative route optimization problem, which involves complex considerations such as spatial travel costs, heterogeneous repair team capabilities, and distinct failure mode repair expenses.

The researchers built LLM4M by fine-tuning the open-source Qwen2.5-7B-Instruct base model using labeled-data supervised learning techniques. They adopted low-rank adaptation (LoRA) to adjust model weights without erasing pre-trained general knowledge. The workflow relies on custom structured prompt templates that convert wind farm spatial layouts, fault information, and crew specifications into standardized text inputs for the model’s reasoning process.

The full five-step framework covers demand clarification, operational data extraction, structured prompt interaction, LLM deductive reasoning, and executable maintenance plan delivery. Benchmark tests compare LLM4M against unmodified base models, mainstream general large-parameter language models (LLMs), and traditional heuristic optimization algorithms such as genetic algorithms and particle swarm optimization.

In fixed scenario testing with three maintenance teams and five faulty turbines, the fine-tuned LLM4M generates fully implementable maintenance plans in all test samples. In contrast, the unfine-tuned base model only produces valid schedules for 83.40% of test cases. The model achieves a mean absolute percentage error (MAPE) of 0.0200 relative to mathematically derived optimal cost solutions, outperforming all evaluated general LLMs whose MAPE values range between 0.1378 and 0.1580.

Additional tests across nonfixed scenarios with variable team counts and fault node volumes verify the model’s generalization capacity. Researchers identify clear phase transition behavior within model performance, showing qualitative shifts in solving ability once training datasets include at least three maintenance teams. This observation offers actionable guidance for curating training corpora for maintenance-focused domain LLMs.

Trials simulating dynamic real-time maintenance record implementable plan generation rates of 97.90% to 99.40% for adjusted test conditions. In deployment terms, LLM4M supports local offline operation with consumer-grade graphics hardware, removing recurring API access fees and network dependence that cloud-based general LLMs require.

The study outlines future research directions including dataset expansion covering weather constraints, integration of predictive and preventive maintenance logic, interpretability analysis for model decision pathways, and embedding physical engineering rules into fine-tuning pipelines. The paper’s findings have significant implications for the wind energy industry, where efficient maintenance planning can lead to substantial cost savings and improved turbine performance.

A new multi-agent system, AgentAD, has been developed to automate the design of algorithms for Earth Observation Satellite Scheduling Problem (EOSSP). This complex task requires matching satellite observation windows, task priorities, onboard power and storage limits, and satellite attitude transition time constraints to maximize completed observation tasks. The traditional workflow involves aerospace and optimization experts manually building, testing, and revising standalone algorithms for every unique satellite-target scenario, a process marked by heavy time and resource costs.

The research team constructed AgentAD as a collaborative multi-agent system with five specialized roles: Algorithm Designer (AD), Algorithm Programmer (AP), Algorithm Optimizer (AO), Code Tester (CT), and Project Manager (PM). Each agent operates under tailored prompting paradigms that define fixed roles, task boundaries, input-output standards, and cross-agent communication protocols. The overall workflow mirrors real-world software development split into four sequential phases: design, optimization, testing, and documentation.

The system converts unstructured natural language descriptions of EOSSP scenarios into functional Python code through repeated atomic dialogue cycles between paired agents. Three core supporting mechanisms underpin stable collaboration: role specialization that restricts each agent to narrow professional duties, a permanent memory stream that archives all historical dialogue to avoid repeated errors, and chain-of-coding that breaks code writing and debugging into sequential subtasks to mitigate LLM hallucinations.

The AD first interprets raw scenario input and drafts initial algorithm frameworks including greedy, dynamic programming, and genetic structures. The PM coordinates agent communication and passes preliminary algorithm outlines to the AO, which proposes structural and parametric improvements to raise task completion performance. The AP translates revised algorithm logic into runnable annotated code, which the CT validates on EOSSP datasets and reports bugs or performance feedback back to the AO for iterative refinement.

This optimization-test loop repeats until convergence or predefined iteration limits, after which the PM compiles all dialogue records and algorithm iteration trails into standardized documentation. Ablation experiments confirm that the AO serves as the most indispensable component for performance improvement, followed by the CT for accurate feedback provision, while the memory stream delivers greater performance gains than the chain-of-coding module.

The team ran comparative trials across basic EOSSP settings with static constraints and complex scenarios augmented by dynamic task priorities, satellite forbidden time windows, and minimum imaging quality thresholds. The results show that algorithms autonomously evolved by AgentAD achieve higher task completion rates across most test environments, though the performance margin narrows as satellite and task scales expand due to increased contextual comprehension burdens on LLMs.

Convergence tracking shows AgentAD’s fitness metric Fi(a) rises steadily through iterative modification after identifying viable optimization trajectories. Trials across different large language models (LLMs), including GPT-3.5-Turbo, GPT-4o, DeepSeekV3 and Llama3.3, reveal more capable LLMs cut total atomic dialogue counts and token consumption without substantial shifts in final code length.

The authors outline plans to integrate multimodal satellite cloud data, introduce human expert feedback loops, and build visual interactive dashboards to enhance real-world operability of the automated algorithm generation pipeline. The research demonstrates the potential for large language models as a judge or decision-maker in complex optimization tasks, leveraging their capabilities to streamline data analysis tools and automate routine tasks.

A team of researchers has developed an innovative framework for industrial design automation, leveraging large language models (LLMs) to minimize manual intervention. The new system, called LLM-IDA, addresses the limitations of existing LLM-assisted computer-automated design pipelines by providing a structured workflow that mitigates reliability gaps.

The research team notes that general-purpose LLMs often lack consistent explainability and robustness when deployed for iterative industrial design tasks. These black-box characteristics can lead to hallucination risks and disrupt domain-specific API calls in standard retrieval-augmented generation workflows.

To counter these issues, the researchers constructed a ‘Task Analysis–Code Generation–Code Feedback’ closed-loop logic within LLM-IDA. This mechanism maps high-dimensional natural language problem spaces onto low-dimensional structured intent spaces, reducing information entropy and applying iterative code validation with execution error feedback to lift the probability of generating fully functional engineering scripts.

The framework’s modular structure maintains domain agnosticism, allowing for adaptation to various use cases such as electronic design, energy system, and materials engineering. The researchers also outline two primary current limitations: weak spatial geometry perception for complex CAD assemblies and long-chain reasoning instability in general-purpose LLMs.

To address these challenges, the team plans to fine-tune open-source language models on CAD and CAE datasets, alongside developing a unified engineering-focused world model to strengthen physical reasoning capacity. This follow-up work aims to further enhance the capabilities of LLM-IDA for industrial design automation tasks.

The new framework’s performance was benchmarked against conventional RAG workflows using the pass@10 evaluation metric across a curated dataset of industrial design cases. Ablation tests verified the individual contributions of built-in templates, task analysis logic, and code testing feedback modules across multiple mainstream LLMs.

LLM-IDA operates within three vertical functional layers corresponding to distinct design phases: conceptual drafting, knowledge-driven refinement, and digital prototyping. The bottom layer relies on multimodal foundational black-box models for user natural language input processing, requirement analysis, and initial conceptual design generation through multimodal perception and long-sequence content creation.

The middle grey-box layer embeds specialized CAE engineering knowledge graphs and optimization algorithms to translate abstract concepts into constraint-compliant design schematics. This narrows the search space for subsequent simulation-based tuning by encoding physical manufacturing and functional rules into agent decision logic.

The top white-box layer establishes direct connections with CAD and CAE software application programming interfaces, deploying LLM code generation capabilities to build parametric digital prototypes and run finite element analysis performance evaluations with automated feedback loops. The full automation pipeline is split into two core modules: a specification module and a code-generation module that incorporates quantification, surrogate, and prototyping sub-units.

The specification module formalizes unstructured natural language user requests into standardized optimization problem tuples containing defined design variables, solution boundaries, objective functions, and constraint sets. This distributes segmented tasks to downstream code-generation agents, which operate in sequential coordination: the quantification module generates executable simulation scripts via iterative coding and visual testing loops based on PyAnsys libraries.

The surrogate module builds neural network approximation models through uniform sampling and adaptive data normalization to cut repeated high-fidelity simulation overhead. The prototyping module integrates pre-verified simulation and surrogate interfaces with multi-objective optimization templates such as NSGA-II to produce converged Pareto front solutions.

A major development in the field of artificial intelligence has taken place at UC Santa Barbara, with the university receiving a four-year, $20 million federal grant to establish an AI-programmable cloud laboratory. This cutting-edge facility will be housed within OASIS, a technology and science hub for students and startup companies on campus.

The new lab will allow researchers from anywhere in the country to access equipment remotely, marking a significant advancement in collaborative research efforts. According to Javier Read de Alaniz, a professor of chemistry and biochemistry at UCSB, ‘You can run research from anywhere at any time.’

Read de Alaniz is also co-director of BioPACIFIC MIP, a federally funded program for synthetic biology, chemistry, and materials science research that has partnered with UCSB. The university was selected to receive the AI grant due in part to its existing partnership with this program.

The OASIS facility, which opened last fall, already serves as a hub for student researchers and private/public companies. It features an open office space surrounded by laboratories where students are actively working on projects.

Universities such as Stanford, Cal Tech, and Scripps are also operating similar AI-programmable cloud labs. The National Science Foundation awarded the $20 million grant to UCSB under its umbrella, with a four-year term for the project.

A vetting process is required for researchers who apply to work remotely at OASIS, limiting access to U.S.-based organizations only. According to Rachel Segalman, vice chancellor for research at UCSB, ‘The application is limited to U.S. organizations.’

Representative Salud Carbajal announced the grant on Friday to a group of UCSB administrators and faculty outside the OASIS facility in Goleta. He described the development as ‘a big freaking deal,’ highlighting its potential impact on research capabilities nationwide.

The AI-programmable cloud laboratory is expected to streamline collaborative efforts between researchers across different institutions, making it easier for them to share resources and expertise.

A South Korean company, SpeedFloor, is working on a solution to automate what has long been considered one of the most labor-intensive and time-consuming steps in warehousing: loading cargo onto trucks. The process typically involves workers climbing into trailers, stacking boxes, pushing pallets, and waiting for vehicles to be ready. This manual handoff can be costly, especially if vehicles or robotic systems are idle while someone stacks freight inside them.

SpeedFloor’s proposed solution is a powered conveyor system that turns the floor of a truck or trailer into a surface that can move cargo in or out. Instead of sending workers, forklifts, or robots into the cargo bay, the system pushes or pulls prepared loads horizontally. The company argues that this approach not only streamlines operations but also reduces labor costs and improves safety.

The key insight behind SpeedFloor’s solution is recognizing that warehouse automation can still leave a costly manual process at the dock. Even with sophisticated facilities featuring robots, automated storage systems, and autonomous trucks, workers may need to enter trailers to stack freight before vehicles depart. This is where SpeedFloor comes in – its system eliminates this step by preparing loads outside the vehicle using people, robot arms, or other warehouse equipment.

The company’s solution involves installing a low-profile conveyor belt inside a truck or trailer. For palletized cargo, the system can transfer loads into or out of the vehicle. For loose boxes, SpeedFloor proposes pre-stacking: boxes are assembled in loading order outside the vehicle and then pushed in when the truck arrives. This approach allows for faster turnaround times and increased efficiency.

SpeedFloor’s product is not just about making trucks autonomous; it’s about making them available sooner. Faster turnaround could mean more trips per shift for delivery operators and drivers paid by distance, weight, or boxes moved rather than time spent at a dock. For warehouses, this means using fewer vehicles to move the same daily volume.

The company says its system can be installed in various types of vehicles, from small box trucks to trailers and containers. SpeedFloor describes it as modular: head, body, and tail sections can be assembled to match a truck’s length while following standardized cargo vehicle widths. Smaller configurations use a 12-volt electrical system; heavier applications require dedicated battery packs beneath the trailer.

SpeedFloor acknowledges that its biggest proposed applications – handling loads of roughly 25 to 30 tons – come with a meaningful constraint: the system itself can weigh about two tons, which eats into payload capacity. The company is working to make the hardware lighter and thinner while maintaining strength for heavy cargo over long distances and rough roads.

Safety is also part of SpeedFloor’s pitch. Loading docks combine moving vehicles, forklifts, heavy cargo, and confined spaces – a hazardous mix that OSHA specifically identifies as high-risk areas in warehouses. Reducing the need for workers to enter trailers could lower exposure to some of these risks; however, the conveyor itself needs guarding, maintenance, lockout procedures, and safe integration with other equipment.

SpeedFloor claims it has deployed more than 100 core units worldwide, including systems for Korea’s Ministry of National Defense and Korea Post. Its customer list also includes Kurly, Hanwha Aerospace, and LG CNS as customers or partners in particular deployments. The company cites a Kurly implementation that reduced loading time from over an hour to under 10 minutes.

SpeedFloor has some external validation: South Korea’s Ministry of Land, Infrastructure and Transport recognized its small-vehicle automated loading and unloading technology in 2024. The ministry described the chain-and-belt design as a way to move cargo without requiring drivers to enter the loading space – an improvement for efficiency and safety.

SpeedFloor is now looking to prove that its idea can travel beyond South Korea’s borders. It began selling in the U.S. through MyTrucker Pro in February 2026, with installation and dealer partners intended to support regional expansion. The company also aspires to apply its technology to air-cargo applications where standardized unit load devices could make replication easier.

SpeedFloor acknowledges that its proposed moat – a full dock-automation stack including vehicle hardware, docking stations, software coordination of warehouse and truck conveyors, integration with AMRs and forklifts, installation expertise, and reliability under real freight conditions – is the test ahead. A convincing demonstration is one thing; repeatable deployment, service, payload economics, and verified customer outcomes are another.

If SpeedFloor can prove that it makes automated warehouses and trucks work together without creating another bottleneck, its conveyor floor could become a surprisingly important piece of logistics infrastructure.

Researchers at Chinese academic institutions have developed a new framework to boost the performance of large language models when answering technical questions.

In industries like manufacturing, equipment maintenance, and fault diagnosis, experts rely heavily on knowledge-based inquiries. However, current LLMs often produce fluent but incorrect outputs when handling specialized content – a problem these researchers set out to solve.

The new framework is designed specifically for accurately answering industrial-related questions. To achieve this goal, it’s built around four core technical contributions: creating an industry-specific knowledge base, improving text classification, enhancing knowledge retrieval, and refining question-answering capabilities.

One key component of the framework involves building a massive database from various sources like journal articles, dissertations, patents, and production documents. This data is cleaned up, processed, and organized to retain its meaning in segments no longer than 256 characters – long enough for most search queries.

The unified knowledge base is then split into five independent sub-databases for chemical engineering, petroleum, metallurgy, electricity, and building materials sectors. By partitioning the data this way, searches can focus on relevant areas, leading to more accurate results in industries that need precision.

A BERT-based text classifier acts as a filter within the framework’s modules. It performs two classification tasks: screening out non-industrial questions and assigning valid ones to their matching sub-domains based on semantic features extracted from token embeddings – essentially categorizing queries by relevance.

Tests show that this BERT classifier delivers consistent performance with low result variance across repeated training runs, which is crucial for knowledge-based applications like question answering in industry settings. Its strength lies in its ability to handle diverse industrial queries effectively and efficiently.

For knowledge retrieval, the researchers employ a GTE-DPR dual-encoder dense retrieval architecture using the GTE-large-zh embedding model. This system generates 1024-dimensional text vectors that allow it to match query embeddings with precomputed chunk embeddings quickly – making relevant information easier to locate within large databases.

According to metrics, the Domain + GTE–DPR setup outperforms general-purpose DPR and domain-only DPR alternatives in semantic matching precision for industrial text. Its consistent results across test trials demonstrate its effectiveness in retrieving accurate knowledge chunks from a large-scale industrial base – making it stand out among competitors.

After filtering and ranking relevant knowledge chunks, the framework combines them with original user prompts through prompt engineering before passing the information to two open-source LLMs: ChatGLM2-6B and LLaMA2-7B. These models generate domain-grounded answers based on validated documents, reducing reliance on incomplete or inaccurate information – a critical improvement in AI tools for businesses.

Experiments have shown that the modular RAG system outperforms standalone LLM inference in question answering tasks when properly aligned with human evaluation standards of answer quality. This advancement comes from carefully integrating various components rather than relying solely on one model’s capabilities.

Despite its improvements, however, the framework still faces challenges posed by niche industrial terminology and misalignment between current metrics and the nuanced understanding humans use to judge the accuracy of an AI-generated response – a limitation that future versions aim to address.

The Women, Peace, and Security (WPS) agenda has long recognized the importance of prevention in conflict resolution. Now, a new approach is emerging that combines WPS principles with artificial intelligence (AI). This fusion aims to shift the focus from reactive measures to proactive ones, anticipating conflicts before they escalate.

A key challenge lies in how AI systems are currently used within security frameworks. These implementations tend to be focused on operational or post-crisis response rather than prevention. Initiatives that develop early warning indicators or build social cohesion and community resilience are limited in scope and implementation.

The WPS agenda provides a proven model for conflict prevention, drawing on humanitarian principles such as the protection of civilians and respect for international law. Applying these principles to AI governance and policy structures would facilitate more comprehensive and context-sensitive approaches to conflict prevention.

Reframing AI from a reactive solution to a preventive instrument across government, civil society, and the private sector offers a critical opportunity to reduce both digital and physical pathways to conflict while advancing more inclusive and sustainable security outcomes. This requires deliberate choices and understanding about how AI is used and the broader security paradigms that determine which risks are prioritized.

Current approaches to AI governance and policy globally remain fragmented, largely voluntary, and primarily oriented toward innovation, risk management, or post-harm mitigation rather than prevention. Existing frameworks establish important norms around transparency, accountability, and safety but do not systematically address early warning, conflict prevention, or the protection of individual and community security as core objectives.

No comprehensive governance framework integrates gender analysis, conflict-prevention research, and the WPS agenda into the life cycle and application of AI systems for security. This gap between the capabilities of AI and its application in preventing conflict and mitigating risks is increasing.

Addressing this gap requires coordinated action across sectors and issue areas to move beyond reactive governance toward a prevention-focused approach. For instance, departments and agencies must establish and enforce requirements for gender-responsive and conflict-informed risk assessments for AI systems deployed (and being developed) in governance, security, and information environments.

The U.S. Government and Congressional Initiatives can play a crucial role in this effort by embedding prevention as a core objective of AI use and national security. This involves using existing laws and strategies such as the Elie Wiesel Genocide and Atrocities Prevention Act of 2018 and the U.S. Women, Peace and Security Act of 2017 to ensure that risks are identified and mitigated before harm occurs.

AI governance should build upon these structures rather than entirely separate processes. This requires mandating the integration of WPS prevention principles when using AI as a tool for diplomacy, foreign policy engagements, and security planning. Federal funding should be directed toward targeted research that examines how AI shapes early warning indicators, individual security, and democratic resilience.

Think tanks, academic institutions, and nonprofits can advance applied, interdisciplinary research that directly links the developing AI governance structures with WPS and conflict prevention outcomes to bridge the persistent gap between technical development and policy implementation. Organizations and leaders should translate technical risks into actionable tools that policymakers can implement.

The Institute of Strategic Dialogue’s (ISD) work on misogynistic radicalization pathways offers a strong example of this dynamic, translating technical risks into specific policy asks such as standardized transparency reporting requirements, survivor-centered standards, and mandated intersectional analysis in AI risk assessments. Think tanks, academic institutions, and nonprofits can serve as conveners, bringing together tech leaders, security practitioners, and WPS experts to share insights and coordinate responses.

The private sector must also take an active role in integrating prevention into the full life cycle of AI systems. This includes embedding gender analysis and conflict-prevention metrics into design, testing, and deployment processes rather than treating them as secondary considerations. Companies should invest in education initiatives and improve efforts for AI literacy for policymaking and public resilience.

Digital gender-based violence and targeted attacks must be recognized as core national and international security concerns. Investments in AI literacy are necessary to strengthen both policymaking and public resilience while reinforcing that AI supports, rather than replaces, human judgment and expertise.

Healthcare organizations have spent billions of dollars attempting to force disparate systems to communicate with each other through custom APIs and shared data standards. Despite these efforts, administrative staff continue to spend their days manually copying data across disjointed software screens. This raises the question: what if solving interoperability doesn’t require complex data integration at all?

PocketHealth’s CEO, Rishi Nayyar, sat down with Healthcare IT Today to discuss how agentic AI is transforming health system operations. The company has pioneered a fresh approach to operational automation with its new agentic operations platform, Conductor, which tackles longitudinal workflows across disconnected systems.

Agentic AI offers a direct workaround to traditional interoperability bottlenecks. Rather than waiting for universal standards or API adoption, intelligent agents can work across the entire system at the interface layer, not just the parts a vendor exposes through an API. This approach bypasses the need for complex data integration and allows organizations to automate cross-platform workflows immediately.

EHRs are built for data consistency, not dynamic process orchestration. Electronic health records excel at serving as systems of record, but managing complex, longitudinal operational workflows that span service lines and systems requires a dedicated automation layer. Organizations rely on human staff members to serve as makeshift bridges between systems that don’t talk to each other.

Nayyar pointed out that intelligent software can replicate these exact human workflows across graphical user interfaces. ‘Agentic AI can do the interoperability work that humans are doing now,’ Nayyar explained. ‘Right now, if you have two systems that don’t speak to each other, a human plays that role [the bridge]. I look at a work list, then I open up MEDITECH or Cerner, and then I do X, Y, Z task based on the work list.’

Agentic AI can navigate software screens, input data, and execute tasks across disparate systems just as a human staff member would. This allows health systems to automate cross-platform workflows immediately without waiting for underlying software architectures to unify. By leveraging this technology, organizations can streamline operations and improve patient satisfaction.

Nayyar believes that EHRs should stick with what they excel at: preserving ground-truth clinical data at specific points in time. They lack the architectural flexibility required to orchestrate complex, multi-step workflows that span across different service lines, third-party systems, and patient communication channels. Using purpose-built automation layers on top of the EHR allows each system to perform its primary function effectively.

The need for separate and dedicated coordination layer to automate cross-departmental workflows while using the EHR’s data stores is crucial. This approach enables organizations to manage complex care journeys across departments without relying solely on human staff members. By automating these tasks, health systems can reduce manual labor from front-desk staff and improve patient satisfaction.

For Nayyar, hesitating to automate administrative tasks carries measurable risk for health systems. Patient expectations have shifted dramatically toward instant access and digital convenience. Staying attached to legacy operational methods will slowly erode patient and staff confidence which leads to organizational decline.

Nayyar went further and shared his opinion that patient retention and referral patterns will reflect this operational divide sooner than many executives think. ‘We are moving into a world where these things will very quickly become table stakes,’ Nayyar cautioned. ‘Patients will expect to call and be able to talk to someone immediately, not stay on hold or print off and then fax a form in order to get a referral.’

Health IT leaders cannot afford to wait for total data standardization before streamlining operations. By deploying agentic AI to handle cross-system tasks, organizations can immediately remove manual labor from front-desk staff while improving patient satisfaction. PocketHealth demonstrates that the path forward lies in pairing trusted systems of record with smart automation tools built specifically for process orchestration.

Agentic AI addresses health system interoperability by interacting with software through graphical user interfaces rather than relying solely on custom APIs or shared data standards. This approach allows organizations to automate cross-platform workflows immediately without waiting for underlying software architectures to unify. By leveraging this technology, health systems can improve patient satisfaction and reduce manual labor from front-desk staff.

The operational risks of postponing administrative automation are severe. Delaying operational automation exposes health systems to degradation in patient experience, lower retention rates, and reduced referral volumes. Today’s patients expect immediate response times and digital self-service options. Organizations that maintain manual phone lines, paper forms, and slow scheduling processes will rapidly lose market share to competitors offering frictionless access.

Nayyar emphasized the importance of adopting agentic AI technology to automate administrative tasks. ‘We are moving into a world where these things will very quickly become table stakes,’ Nayyar cautioned. ‘Patients will expect to call and be able to talk to someone immediately, not stay on hold or print off and then fax a form in order to get a referral.’

Health IT leaders must recognize the benefits of agentic AI technology in automating cross-system tasks. By streamlining operations and improving patient satisfaction, organizations can reduce manual labor from front-desk staff and improve overall efficiency.

A new integration between Sunbound and PointClickCare is set to streamline revenue cycle management for senior living operators. The partnership brings together AI-powered solutions that automate billing processes, freeing up staff from manual work.

The integration allows operators who use PointClickCare’s electronic health record (EHR) system to connect seamlessly with Sunbound’s Revenue Operating System. This enables automated billing across Medicaid, Medicare, and resident private pay, starting from the census and resident data already stored in their EHR.

According to Sunbound, its AI-powered platform improves cash flow by automating revenue workflows, including admissions, payments, and end-to-end revenue cycle management. The company’s Revenue Operating System is specifically designed for senior living, unifying financial operations and working capital to put revenue on autopilot.

With this integration, PointClickCare customers can now access Sunbound’s solutions through the PointClickCare Marketplace. This means that census, resident, and payer data flow from PointClickCare into Sunbound, eliminating duplicate entry and spreadsheets.

Two main solutions are available: Sunbound RCM delivers a comprehensive revenue cycle management solution, including eligibility and insurance verification, prior authorization, claim validation, submission, denial management, appeals, and payment posting. The AI-powered platform flags potential denials before claims submission and diagnoses the root cause of any denials.

Ssunbound Private Payments automates private-pay operations with AI-powered billing QA, automated invoicing, timely reminders, mobile check deposit, and a family portal that answers questions before they reach staff. This solution is designed to streamline manual work and free up staff for more critical tasks.

The integration was built through the PointClickCare Marketplace Partner Program using PointClickCare’s APIs. Sunbound pairs its software with a dedicated billing team, providing full visibility into every claim backed by people accountable for results.

Senior living operators can now find and evaluate authorized third-party solutions that extend their existing technology through the PointClickCare Marketplace. With this integration, operators can automate revenue cycle management and private payment automation without adding extra complexity to their systems.

The benefits of using Sunbound’s solutions are clear: operators see a 99% net collection rate on claims and a 95%+ on-time resident payment rate. This is made possible by the company’s AI-powered platform that automates financial operations and provides expert support for complex exceptions.

Sunbound was founded to address the manual work burden faced by senior living teams, which can lose hours to billing and follow-up tasks. The company aims to clear this repetitive workload so staff can focus on residents’ needs.

The integration with PointClickCare marks a significant step forward in streamlining revenue cycle management for senior living operators. By automating billing processes and providing expert support, Sunbound is helping operators get paid accurately and on time.

Travel industry network Virtuoso has introduced an artificial intelligence-powered foundation that aims to simplify complexity and enhance the human connection in luxury travel. The new capability, which was unveiled at Travel Week 2026, is designed to connect data, systems, and security across the Virtuoso network, making it easier for advisors, member agencies, and partners to access and act on expert knowledge and thousands of interconnected data points.

The foundation builds on Virtuoso’s existing technology philosophy, ‘automate the predictable so that you can humanize the exceptional.’ This approach has guided the development of various tech solutions aimed at preserving and enhancing the human connection in luxury travel. The new intelligence capability is a key component of this strategy, which seeks to streamline complexity and unlock collective advantage within the Virtuoso network.

Virtuoso’s advisors have been quick to adopt AI technology, with 74% now using it in their business. This adoption rate far surpasses the industry average, reported by Phocuswright at 59%. However, despite its benefits, AI-generated travel content has also revealed some limitations. Much of this content overlaps due to similar sources being used, resulting in repetitive and untailored itineraries that contribute to overcrowding at popular destinations.

Human expertise remains a crucial aspect of luxury travel, with three-quarters of Virtuoso clients citing their advisor’s accommodation and destination expertise as a key benefit. In contrast, fewer than half of travelers advised by others share this sentiment. Additionally, 60% of Virtuoso clients point to an advisor’s ability to curate experiences tailored to them, whereas only one-third of those advised elsewhere can say the same.

The luxury travel market is showing interest in using advisors for future trips, with 79% of high-end travelers expressing openness to this approach. Furthermore, half of Virtuoso advisors report clients steering away from over-touristed destinations, highlighting a growing desire for unique and exclusive experiences.

As AI technology continues to evolve, it’s clear that algorithmic inspiration is giving way to analog escapes in luxury travel. Time, privacy, and the ability to disconnect are emerging as the ultimate luxuries, with Virtuoso positioning itself at the forefront of this trend.

The new intelligence capability is built on Virtuoso’s proprietary global knowledge, which has been cultivated over decades through member and advisor expertise, partner relationships, and institutional experience. This collective knowledge was previously scattered across people, conversations, and systems that couldn’t be universally accessed. The foundation will simplify complexity by connecting data, systems, and security across the network.

According to Virtuoso Chairman/CEO Matthew Upchurch, ‘What we’ve built is a foundational digital capability that is ever evolving with infinite possibilities.’ This new capability brings together expertise, insights, data, and relationships within the Virtuoso community – advisors, member agencies, preferred partners – making it easier to access, combine, and act on them.

The foundation will allow the network to tap into collective intelligence in a greater way while also helping Virtuoso learn from how the network uses this capability. This feedback loop will enable the organization to identify gaps and build solutions to close them. Rather than introducing a standalone product, this is an intelligence foundation that will continue to grow and expand based on network engagement.

Future capabilities are intended to help member agencies, advisors, and partners discover opportunities, make informed business decisions, and draw upon trusted network intelligence in new ways. The tool also aims to enable Virtuoso’s own teams to better serve the network with more precise support. As Virtuoso President Brad Bourland noted, ‘AI is at the core of how it works, but what we’re really talking about is Virtuoso collective intelligence, powered by AI.’

The project’s next horizon builds on this foundation by combining Virtuoso’s proprietary insights with vetted external market, economic, and behavioral data. This will create a richer understanding of what Virtuoso knows and the forces shaping the world around it. The aim is to deliver intelligence with a level of context, relevance, and precision that meets the unique needs of each member and advisor.

The new foundation marks a significant step forward in Virtuoso’s technology strategy, which focuses on adding value to existing platforms, nurturing an ecosystem of solution providers, and creating unique value within the network. As Upchurch emphasized, ‘The future of this organization is not a one-size-fits-all system that flattens the network into sameness.’ Instead, it’s a foundation built to enhance each member’s and advisor’s own unique expression while unlocking collective advantage.

A growing number of consumer goods companies are leveraging the power of Amazon Web Services (AWS) to drive innovation, increase agility, and boost operational efficiency. By tapping into industry-specific solutions in AWS Marketplace, these organizations can build stronger brands, make data-driven decisions, and stay ahead of market trends.

The key to success lies in harnessing the same cloud capabilities that have enabled Amazon.com to create a seamless shopping experience for its customers over 25 years. This integrated view of the consumer allows companies to personalize interactions, ensure product availability, and unify the shopping experience – all critical factors in building brands consumers love.

Organizational agility is another major benefit of using AWS Marketplace solutions. By leveraging unmatched compute capabilities, machine learning (ML), and artificial intelligence (AI) tools for business, consumer goods companies can draw key insights from vast amounts of data to predict opportunities and risks in real-time. This enables them to act swiftly and make informed decisions.

Today, more than 100,000 customers use AWS services across the value chain to accelerate decision-making. By connecting disparate data sources and taking advantage of automation, AI, and ML capabilities, companies can streamline their operations, reduce costs, and innovate key enterprise applications without putting critical systems at risk.

Some notable examples of consumer goods companies that have successfully implemented AWS Marketplace solutions include Danone Indonesia, Stanley Black & Decker, Georgia-Pacific, and ONNIT. These organizations have reported significant improvements in efficiency, productivity, and innovation – all essential for staying competitive in today’s fast-paced market.

According to Epsilon Analisa Akbar, Digital Analytics Manager at Danone Indonesia, ‘Treasure Data makes it possible to collect several data sources into a single database and normalize that data across different variables.’ This has enabled the company to enrich customer profiles and make more informed decisions. Similarly, Stanley Black & Decker’s Business Innovation Director, Orlando Gadea Ros, notes that ‘we need to constantly look for ways to disrupt ourselves’ – a sentiment echoed by other companies in the industry.

Steven Bakalar, Vice President of IT Digital Transformation at Georgia-Pacific, highlights the flexibility and scalability provided by combining SAS with AWS: ‘this gives us the necessary flexibility and scalability to put analytic capabilities in the hands of users across the organization.’ Meanwhile, ONNIT’s Marketing Project Manager, Vanessa Escobedo Barba, praises the efficiency gains from using Tenovos DAM: ‘our team’s efficiency has skyrocketed because they can now find and process content instantly’.

The South Korean military is taking steps to automate more of its field maintenance and logistics operations, leveraging artificial intelligence, robotics, and autonomous systems. This move comes as the army prepares for a sustained decline in available personnel due to demographic changes.

As part of this effort, Army Headquarters has commissioned a study on converting key maintenance functions to automated systems. The project is tied to ArmyTiger L, which aims to transform its future force structure through advanced technologies. Specifically, it seeks to build what the army calls a ‘smart field maintenance support system’.

The need for automation in logistics and maintenance duties is pressing due to demographic decline. As personnel numbers steadily decrease, the military must make more efficient use of its workforce. Moreover, there’s a widening gap between advances in combat platforms and the systems used to maintain them.

While newer tanks, self-propelled howitzers, and other weapons systems require fewer crew members to operate, maintenance and support operations have not undergone a corresponding reduction in staffing requirements. This discrepancy is driving the army’s push for automation.

The study will examine AI-based predictive maintenance using small language models and machine learning. Additionally, it will assess ways to automate maintenance information management. The military also plans to evaluate civilian technologies such as maintenance robots, automated tools, and wearable strength-assist suits designed to reduce physical burdens on personnel.

For logistics operations, the army is looking at automated storage and handling systems, drone delivery, autonomous transport vehicles, and palletized load systems. Technologies will be evaluated according to unit type and echelon, including infantry, armored, mechanized infantry, and artillery units.

Some of these technologies are already being tested in various capacities. The Army’s 1st Supply Group unveiled a smart logistics center last week that has automated about 70 percent of its logistics processes. This system reduced on-site workforce requirements by approximately 16 percent while cutting average processing time from eight days to five.

Trials involving wearable robotic suits have been underway at ammunition units since July, and transport drones and palletized load systems are being examined for broader military logistics applications.

A military official stated that the study aims to distinguish between facilities and equipment that can be introduced immediately and capabilities that should be pursued over the medium and long term. The army also plans to draw up short- and mid-term implementation plans by 2040, outlining what a fully developed smart field maintenance unit should look like.

Researchers have found that AI mortgage assistants are prone to errors when it comes to identifying foreign deposits in bank statements. In a recent study, three leading general-use AI models were tested on their ability to flag transactions of foreign origin. The results showed that the models struggled significantly with this task, highlighting concerns about the accuracy and bias of these systems.

When researchers asked the AI models which deposits could be of foreign origin, they found that transactions connected to English names were flagged only 13.3% of the time. However, when the deposit came from a non-English name, the number jumped to 77%. This disparity suggests that the models are relying too heavily on linguistic cues rather than actual evidence.

This issue is not just about AI’s ability to provide accurate information; it also raises concerns about bias and fairness in mortgage lending. Matthew Toles, a Columbia University doctoral student and co-author of the study, notes that ‘you aren’t determined whether you’re foreign-based on how your name sounds.’ This means that borrowers from non-English speaking countries may be unfairly flagged as high-risk or denied loans.

The study’s findings are particularly concerning given the widespread adoption of AI mortgage assistants in the industry. More than 80% of lenders were evaluating the technology as of June, according to a survey by The Mortgage Collaborative, while 17% had already deployed it in live production workflows. This rapid adoption has raised concerns about the lack of standardization and testing for these systems.

Diane Yu, co-founder and CEO of Tidalwave, a mortgage technology company that collaborated with Columbia researchers on the work, notes that ‘everybody is using AI, but nobody really understands how to use AI in compliance and with the correct guardrail.’ This highlights the need for standardized testing and evaluation methods to ensure that these systems are used correctly.

The study introduces MortarBench, an open-source benchmark designed to measure the accuracy of AI mortgage assistants. The benchmark includes a set of common and useful tasks in loan origination, such as identifying payroll deposits, large transactions, and jointly held accounts. By using this benchmark, lenders can compare the performance of different models and identify areas for improvement.

The researchers tested three leading general-use AI models - Gemini 3.1 Pro, GPT-5.5, and Claude Sonnet 4.6 - on a set of tasks from MortarBench. The results showed that even these strong models struggled with accuracy, particularly when it came to identifying specific transactions in bank statements.

Zhou Yu, an associate professor at Columbia and study co-author, compares the task of finding specific transactions to ‘finding the needle in the haystack.’ However, the researchers found that the AI models often made the opposite mistake, pulling in transactions that didn’t belong. This highlights the need for more accurate and reliable methods for identifying relevant information.

The most common problem with the AI models was misclassification, where they incorrectly identified a transaction as belonging to a certain category. For example, Gemini counted a personal loan as a buy-now-pay-later transaction, while GPT-5.5 assumed all wire transfers were international. These errors demonstrate that simply finding relevant information is not enough; the systems must also accurately classify and interpret it.

Matthew Toles notes that these scores are for ‘naive use of foundational models,’ which means they are using a basic model without any additional training or fine-tuning. He cautions that major industry players typically develop their own proprietary models, which perform better on these tasks. For example, Tidalwave scored 95% on yes or no questions in a separate benchmark test.

The study’s findings have significant implications for the mortgage lending industry, particularly when it comes to AI adoption and standardization. As lenders look to automate processes and reduce costs, they must also ensure that their systems are accurate and reliable. The MortarBench study provides a much-needed tool for evaluating these systems and identifying areas for improvement.

The results of this study could have far-reaching consequences beyond the mortgage lending industry. As users increasingly rely on AI models for financial decision-making, errors and biases can carry higher stakes. Matthew Toles warns that ‘we don’t have visibility into what these models are doing, what companies are doing with them, and what the outcomes are.’ This highlights the need for greater transparency and accountability in AI development and deployment.

The study’s authors emphasize the importance of asking lenders about their use of AI mortgage assistants and whether they have undergone independent evaluation. Borrowers should also be aware that these systems may not always provide accurate or unbiased information. As Diane Yu notes, ‘you should ask those questions’ and ‘be very careful.’

The MortarBench study provides a valuable resource for evaluating the accuracy and reliability of AI mortgage assistants. By using this benchmark, lenders can compare their models to industry standards and identify areas for improvement. This could help mitigate systemic risks associated with widespread adoption of these systems.

In conclusion, the findings of this study highlight the need for greater standardization and testing in the development and deployment of AI mortgage assistants. As the industry continues to adopt these technologies, it is essential that we prioritize accuracy, reliability, and fairness.

The field of mathematics is undergoing significant changes as artificial intelligence (AI) increasingly tackles complex problems once thought beyond its reach. This shift has mathematicians reevaluating their role in the discipline and considering how it will evolve alongside rapidly advancing technology.

At the recent International Congress of Mathematicians in Philadelphia, 2006 Fields Medalist Terence Tao reflected on this pivotal moment for mathematics during a talk titled ‘Mathematics in the Age of AI.’ While many conversations focus on the capabilities of future AI systems, Tao argued that mathematicians need to ask more fundamental questions about their goals and values.

Tao assumed that soon, AI will be able to perform a reasonable fraction of mathematical tasks successfully. He then posed a crucial question: If this is the case, what does it mean for our understanding of successful mathematics? In other words, why do we do mathematics in the first place?

Mathematicians have long focused on problem-solving as a core aspect of their work. However, with AI now impacting this area significantly, Tao chose to focus on this facet during his talk. He identified several stages involved in mathematical problem-solving: providing and verifying proofs, evaluating and explaining these proofs, and making them accessible to the broader community.

AI has made significant strides in generating candidate proofs or crucial ideas, as well as rigorously checking very long or intricate proofs. However, Tao emphasized that AI’s limitations become apparent when it comes to human evaluation and explanation of these proofs. He noted that even large models can produce 100,000-line proofs that are difficult for humans to understand.

The problem lies in the fact that if no one understands a proof, its likelihood of being accepted by the community becomes slim. A successful result is not just about generating a correct answer but also about making it valuable and useful to others. AI might be good at finding results, but it often fails to provide context that makes people care.

Tao argued that without human evaluation and explanation, a proof cannot reach its full potential – namely, becoming canonicalized or widely accepted as part of the mathematical canon. This process involves not just generating correct answers but also providing context, describing how one arrived at those results, and making them accessible to others.

The value of mathematics is often measured by its applications in other fields such as engineering and physics. However, these applications are typically built on top of canonicalized knowledge – the kind found in textbooks rather than recent research papers. Tao noted that AI itself relies heavily on this existing body of work for its success.

So where do we go from here? Tao encourages mathematicians to engage in discussions about their goals and values regarding problem-solving, education, publication, and community culture. He challenges them to take the initiative in integrating AI into mathematics rather than passively reacting to it.

The integration of AI will require mathematicians to set clear rules on what is acceptable or not, rather than letting external actors define these standards for us. This involves considering how we can use AI tools effectively while maintaining our values and goals as a community.

A $7 billion technology company based in Silicon Valley is aggressively promoting its AI-powered tools for automating administrative tasks in healthcare, with some customers reporting significant financial losses and negative outcomes. According to internal documents and communications obtained by STAT, Commure offers substantial compensation to medical clinics and other parties willing to refer its products to new business prospects. This referral program is part of the company’s strategy to rapidly sell its AI tools into diverse clinical settings with customizable financial incentives.

Commure was founded in 2020 on a vision to build a new operating system for healthcare, leveraging artificial intelligence to take power and money from massive insurers and give it back to doctors. The company’s executives have framed their mission as a kind of Robin Hood story: using AI to redistribute wealth from industry giants to medical professionals. However, Commure isn’t just promising to enrich doctors with money from insurers; the company also offers compensation to those who promote its products.

STAT has found that Commure offers thousands of dollars in various forms to customers and other parties willing to refer its products to new business prospects. This is part of a broader effort by the company to swiftly sell AI tools it claims can unburden clinicians burned out by healthcare’s bureaucratic morass. The compensation is seen as crucial for the company’s rapid growth, with Commure claiming that its tools are used by more than 500 health care organizations across the country.

Many of Commure’s customers speak highly of the company and its products, testifying in marketing videos and podcast interviews that they have restored joy and reliable revenue to their practices. The company claims that its AI-powered tools for billing, scheduling, and clinical documentation are used by ‘130 of the nation’s largest health systems,’ including giants like HCA Healthcare and Tenet Healthcare.

However, some customers report suffering steep financial losses and other negative outcomes after implementing Commure’s products. Despite these concerns, the company maintains that its referral programs are industry standard and that the vast majority of its hundreds of customers are happy with its services. The effort to rapidly sell AI into diverse clinical settings with customizable financial incentives magnifies the difficulty of evaluating products that directly impact the cost and quality of health services received by millions of Americans.

Commure’s CEO, Tanay Tandon, has expressed his vision for a world where doctors are millionaires while industry giants like UnitedHealth have significantly reduced market value. However, this goal remains elusive as Commure continues to face challenges in balancing its business model with the needs and concerns of its customers.

The Defense Intelligence Agency (DIA) is working towards a future where artificial intelligence tools support military operations through seamless interactions between digital agents. According to Maj. Gen. Robert Kinney, the DIA’s chief AI officer, this vision involves ‘agent-to-agents’ interactions that enable data analysis and decision-making across combatant commands.

Kinney shared his insights during a panel at the DODIIS Worldwide conference in Tampa, Florida, where he discussed the agency’s ongoing efforts to build an AI enterprise platform service. This 90-day ‘sprint’ aims to establish a comprehensive framework for integrating AI tools and data sources within the military intelligence community.

One key component of this effort is the model context protocol (MCP), which will provide a standardized way to access intel data from various external systems and data sources. MCP serves as the technical foundation that enables AI tools to connect with these external systems, facilitating more efficient data exchange and analysis.

Looking ahead to the next year, Kinney emphasized the importance of building digital agents that can interact with each other in a secure and compliant manner. This involves developing ‘agents-with-agents’ capabilities, which will allow for more sophisticated decision-making and task execution within military operations.

The potential benefits of this approach are significant, as it could enable real-time data sharing and analysis between different directorates and commands. For instance, an agent responsible for collection management might communicate with agents in the 3 Directorate (operations) or the 4 Directorate (contested logistics), facilitating more informed decision-making across the board.

However, officials must also consider the risks associated with these emerging technologies. Kinney noted that setting up guardrails to mitigate potential risks is crucial, particularly when it comes to irreversible mission areas such as fires and planning. In such cases, human oversight will be essential to ensure responsible use of AI capabilities.

The DIA is currently laying the digital foundation for this vision by establishing ‘pipes’ that support data access and agent development. Additionally, officials are exploring the ‘tradecraft piece’ of using agents interacting with each other, focusing on responsible parameters for operating within these systems.

Real estate technology company Inside Real Estate has announced two significant advancements in its artificial intelligence (AI) strategy: the launch of ComplianceAI, an automated compliance-review capability built in partnership with SetWave, and early support for MCP capabilities that enable direct integration between AI assistants and BoldTrail BackOffice. These moves mark a major shift towards creating intelligent back offices that can handle complex tasks efficiently.

The new autonomous compliance capability, powered by SetWave’s technology, is designed to streamline the review process of transaction files. According to data from SetWave, a substantial portion of submitted files contain errors or omissions, and reviews often take days or weeks after the file has closed. ComplianceAI aims to move this review to the moment of submission.

Compliance remains one of the most persistent operational burdens for brokerages. The introduction of ComplianceAI changes how brokerages approach compliance by shifting it from a post-factum check to an integral part of every transaction from the start. Throughout the transaction, ComplianceAI runs full compliance checks, validating required fields and signatures, flagging missing or inconsistent items, and cross-referencing the entire packet.

Agent visual compliance reports are generated automatically, with results updating in BoldTrail BackOffice without requiring a separate tool or login. This integration ensures that brokerages can catch issues before closing instead of after, reducing the risk of errors and omissions.

As brokers themselves experienced this problem firsthand, SetWave’s Co-Founder Dalton Brewer notes: ‘Partnering with Inside Real Estate lets us put that solution directly in front of the brokerages who need it most.’ This collaboration enables real estate companies to leverage AI assistants within their existing workflow, streamlining compliance checks and reducing administrative burdens.

Inside Real Estate has also confirmed early access for MCP capabilities. An open standard, MCP gives AI assistants a secure direct line into transaction and back office operations. With the broader shift towards agentic AI in 2026, Inside Real Estate worked closely with customers to develop this capability, allowing brokers, staff, and agents to surface workflows most important to them.

Key features of MCP capabilities include generating, validating, and presenting offers with embedded forms and eSignature; flagging issues before a deal is at risk; eliminating double entry by enriching transaction data automatically; strengthening the consumer experience through transaction timelines and post-close follow-ups; opening new revenue streams by tracking ancillary services; and making every agent and staff member sharper with bespoke pipeline reporting.

According to Joe Skousen, CEO of Inside Real Estate: ‘Brokerages shouldn’t have to choose between a powerful platform and compliance they can trust. ComplianceAI brings that compliance directly into the workflow our customers already use.’ The integration of MCP capabilities takes this further by enabling agents to open transactions, prepare paperwork, and get it signed just by asking – all within their existing system.

This move is part of a larger shift underway at Inside Real Estate. Their agentic AI platform, Streams Studio, connects brokerages’ entire tech stack, not just BoldTrail BackOffice. Paired together, brokers, teams, and agents can enjoy fully AI-enabled real estate operations from compliance and transactions to lead generation, marketing, and beyond.

Inside Real Estate serves nearly 400,000 top brokerages, agents, and teams as a trusted technology partner. Their branded portfolio, BoldTrail, delivers growth engines for real estate businesses with tools for lead generation, marketing automation, client relationship management, back office solutions, and best-in-class integrations.

Trimble has introduced a new artificial intelligence (AI) solution designed to automate manual workflows and streamline operations for global supply chains. The company’s latest innovation is called Trimble Arc Agent, which aims to simplify tasks and reduce reliance on multiple agents.

The single AI agent can handle complex tasks with ease, moving beyond the limitations of multi-agent setups that often hinder productivity in logistics teams. By automating office work and eliminating manual processes, Trimble Arc Agent enables organizations to focus on high-level strategy where human decision-making is crucial.

According to a recent survey conducted by Trimble and FreightWaves, 70% of transportation and logistics companies struggle with manual, repetitive tasks as their leading pain point. These tasks not only consume staff time but also stifle productivity.

The solution connects directly to existing transportation management systems (TMS) and third-party applications like Gmail and Outlook. By securely extracting and validating data from unstructured channels such as emails, PDFs, and spreadsheets, Trimble Arc Agent allows teams to turn real-world inputs into actionable system-of-record data.

Trimble Arc Agent is built with core agentic safety features that minimize the risk of AI hallucinations. This ensures organizations can drive faster throughput and reduce operational costs without compromising service levels or margins. The solution addresses industry concerns regarding AI reliability, making automation a viable option for companies looking to improve efficiency.

The skills catalog at the heart of Trimble Arc Agent is designed with governance controls and risk review workflows in mind. By combining a library of ready-to-use skills with custom skill-building capabilities, organizations can address their biggest challenges through simple natural language interactions.

Example skills include data entry, market insights, customer support, and personal assistant functions. These features allow teams to automate tasks such as order entry and contract intake, extract full truckload rate intelligence, provide contextual troubleshooting assistance, or consolidate daily workflows into a single overview.

The solution is available for Trimble customers worldwide on a single-tier subscription model with no seat limits or feature gates. Users receive 10 hours of agent working time (with additional overage hours available) and automatic access to all new skills and features as they launch.

Trimble Arc Agent’s design prioritizes safety, reliability, and explainability. The solution is built on enterprise-grade AI guardrails and human-in-the-loop controls that ensure AI-driven decisions are auditable and supportable. This approach addresses industry concerns regarding AI reliability so automation does not come at the cost of service commitments or compliance.

The Trimble Arc Agent skills catalog will continue to expand as new skills are added over time, allowing organizations to adapt their workflows according to changing needs. The solution’s intuitive interface enables users to easily build custom skills without requiring engineering resources.

Trimble is a global technology company that connects the physical and digital worlds, transforming the ways work gets done. With relentless innovation in precise positioning, modeling, and data analytics, Trimble enables essential industries including construction, geospatial, and transportation.

The introduction of Trimble Arc Agent marks another step forward for Trimble’s efforts to streamline operations and improve efficiency across various sectors. By providing a comprehensive solution that addresses the challenges faced by logistics teams, Trimble is helping organizations automate manual workflows and unlock new levels of productivity.