Enterprise AI Daily Briefing

The company uses machine learning to streamline production workflows and tailor content delivery. In studio production, data-driven systems enable visual effects teams to complete complex sequences faster and with lower production overhead by connecting intended designs with actual footage.

Creative teams no longer have to queue technical requests with central IT, as tools are embedded directly into daily workflows so staff can resolve issues on the spot. Visual effects such as crowd size can be adjusted with AI.

In terms of content delivery, machine learning algorithms optimize streaming quality by compressing videos by each frame, all while predicting traffic surges in advance to prevent playback delays across global networks. To keep pace with this technical evolution, Netflix’s executive leadership is actively reshaping how the company manages its workforce. The company is restructuring teams and closing non-core operations, including internal gaming studios like Night School Studio and Moonloot Games.

Why it mattersFuturiom makes the control boundary visible: In terms of content delivery, machine learning algorithms optimize streaming quality by compressing videos by each frame, all while predicting traffic surges in advance to prevent playback delays across global networks. To keep pace… The leadership implication for Enterprise AI is to connect Enterprise AI Profile: Netflix Embeds AI Throughout Infrastructure - Futuriom to a named owner, a measurable baseline, and a review path for exceptions.

IT industry experts and analysts are still trying to piece together Nvidia’s surprise plan to pay $12.9 billion for open-source AI company Hugging Face . Nvidia dominates AI with its GPUs, and the company generates billions of dollars in revenue through a proprietary approach to the fast-moving technology.

Hugging Face, on the other hand, hosts open models and has been a neutral player between chip vendors and model labs. “This is about Nvidia having more say in how the stack gets built,” said Stephanie Walter, analyst at Hyperframe Research.

Hugging Face is wildly popular with developers, and Nvidia is buying early influence with that crowd. “You have a better chance of being part of the production environment later,” Walter said, adding that she wasn’t sure how Nvidia reached a nearly $13 billion price tag for the acquisition. “Hugging Face has near-uncontested market primacy over where developers go for open-weight model releases.

Why it mattersThe enterprise ai implication is concrete because Computerworld ties the capability to an operating choice: Hugging Face is wildly popular with developers, and Nvidia is buying early influence with that crowd. “You have a better chance of being part of the production environment… The leadership implication for Enterprise AI is to connect Nvidia-Hugging Face deal could require an enterprise AI rethink to a named owner, a measurable baseline, and a review path for exceptions.

FC Bayern reports the development described in "FC Bayern and Wonderful Announce Enterprise AI Partnership - FC Bayern". The capability is tied to the enterprise ai workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. FC Bayern describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersFC Bayern changes the risk calculation for this workflow. The upside is linked to The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. FC Bayern describes the capability as part of its enterprise offering.; the… The leadership implication for Enterprise AI is to connect FC Bayern and Wonderful Announce Enterprise AI Partnership - FC Bayern to a named owner, a measurable baseline, and a review path for exceptions.

Two new reports show how AI adoption is spreading across firms and workers - and what frontier organizations are doing differently. Organizations are expanding both where they use AI and what they ask it to do.

Enterprise AI is moving from assistance to execution, yet not all firms are making that transition at the same pace. Frontier firms - those in the top 10% of AI usage each month - now generate 8.3× as many output tokens per active user as typical firms.

The measure is a proxy for depth of use, and the widening gap appears alongside greater adoption of capabilities that connect agents to company context, tools, and repeatable workflows. Today we are publishing two complementary studies that examine this shift. Enterprise Signals leads with a practical view of agentic AI across OpenAI’s enterprise customer base, including what frontier firms are doing differently and where agentic work is spreading.

Why it mattersWhat matters for operators is the constraint behind the announcement: The measure is a proxy for depth of use, and the widening gap appears alongside greater adoption of capabilities that connect agents to company context, tools, and repeatable workflows. Today we are publishing… The leadership implication for Enterprise AI is to connect From assistance to execution: How enterprises put AI to work to a named owner, a measurable baseline, and a review path for exceptions.

Fasten your seatbelt and empty that bladder: AI investment is rising, but reported enterprise earnings impact remains stubbornly flat Four years into the generative AI revolution, consulting giant McKinsey reckons we've finally started the engine and are officially "on the road to ROI." Whether that road leads to actual profit-making and how long it takes to travel is anyone's guess, because the firm's data suggests most respondents still aren't reporting an enterprise-level earnings contribution from AI. McKinsey surveyed 1,719 professionals and business leaders from around the world and across industries for its report on the State of AI in 2026, and what it found sounds a lot like what similar studies have determined in the past couple of years.

According to the report, more businesses are deploying more AI in the belief that their investments will start paying off, but the number of people reporting an actual earnings boost from their AI initiatives has remained flat. According to the survey data, 37 percent of respondents “attribute at least some EBIT [earnings before interest and taxes] impact to AI use,” which is “about the same” share as respondents to its 2025 survey.

The word "some" is doing a lot of heavy lifting there, because only a small minority of respondents qualify as McKinsey’s AI high performers. McKinsey considers AI high performers to be respondents who attribute at least 5 percent of their organizations’ EBIT to AI use and describe the technology’s impact as “significant.” The number of high performers has remained flat since last year - just 6 percent of survey respondents met both criteria. Despite the face-slapping reality of hard-to-find benefits, companies are plowing ahead with their AI investments - at least for now.

Why it mattersThis is a testable market signal for enterprise ai: Register reports The word "some" is doing a lot of heavy lifting there, because only a small minority of respondents qualify as McKinsey’s AI high performers. McKinsey considers AI high performers to be respondents who… The leadership implication for Enterprise AI is to connect McKinsey says enterprise AI is finally 'on the road to ROI' - The Register to a named owner, a measurable baseline, and a review path for exceptions.

Disrupt 2026: OpenAI, Anthropic, Replit, and more take over 6 industry stages. 25% off tickets now Back by popular demand: Save up to $300 on Disrupt IBM on Thursday announced its partnership with OpenAI to bring the AI company’s models and tools to more enterprise customers, opening another avenue for OpenAI to connect with some of the world’s largest companies through IBM’s global consulting business as competition for corporate AI spending intensifies.

The deal, terms of which were not disclosed, comes less than a year after IBM announced a similar alliance with Anthropic. OpenAI and IBM will jointly market AI offerings and develop industry-specific solutions for sectors including financial services, government, telecommunications, and retail, IBM said.

Under the agreement, IBM will establish a dedicated OpenAI practice within IBM Consulting and train and certify tens of thousands of consultants - primarily retraining existing employees - on OpenAI’s technologies over the next several months, Mike Healy, managing partner at IBM Consulting, told TechCrunch. The training will focus on OpenAI’s Codex, API, cybersecurity, and consultative solution credentials. IBM will also create a group of specialized “Forward Deployed Experts” trained through OpenAI’s Partner Network, Healy said.

Why it mattersTechCrunch makes the control boundary visible: Under the agreement, IBM will establish a dedicated OpenAI practice within IBM Consulting and train and certify tens of thousands of consultants - primarily retraining existing employees - on OpenAI’s technologies over the next… The leadership implication for Enterprise AI is to connect IBM partners with OpenAI to bolster enterprise AI push - TechCrunch to a named owner, a measurable baseline, and a review path for exceptions.

Teradata reports that despite increased AI spending, organizations struggle to achieve enterprise-wide ROI due to misaligned data and measurement structures. (Ball SivaPhoto // Shutterstock/Ball SivaPhoto // Shutterstock) According to new data from autonomous AI knowledge platform Teradata , a persistent tension remains in enterprise agentic AI adoption. Despite continuous and aggressive investment, many organizations are failing to move from experimentation to enterprise-wide adoption.

Based on a survey of 1,000 senior technology and data leaders, Teradata's 2026 report, Arrested Automation: Why Agentic AI Stalls at the Enterprise Level , identifies misaligned data and measurement structures as a root cause of this ROI gap and offers guidance for enterprises to shift their strategy to maximize returns on their AI investments. The report found that although 90% of senior technology leaders expect to increase agentic AI investments over the next 12 months, only 37% of organizations report measurable business impact.

Sixty-three percent say they have seen no more than a small or emerging positive return on their AI investments to date. To show where organizations are on this journey, the report categorizes them into an agentic AI maturity index. About a quarter of organizations (28%) are in the experimenting stage, exploring localized pilot projects that often lead to personal productivity gains.

Why it mattersKIRO 7 News Seattle makes the control boundary visible: Sixty-three percent say they have seen no more than a small or emerging positive return on their AI investments to date. To show where organizations are on this journey, the report categorizes them into an agentic AI… The leadership implication for AI in Executive & Strategy is to connect Companies keep spending on AI despite roadblocks on returns - KIRO 7 News Seattle to a named owner, a measurable baseline, and a review path for exceptions.

The Government will help 10,000 enterprises over the next three years to use AI meaningfully. The Government is seeking to broaden adoption of artificial intelligence among Singapore-based small and medium-sized enterprises by supporting 10,000 firms over the next three years to move from experimentation to operational integration.

“We will help 10,000 enterprises use AI meaningfully,” said Minister for Digital Development and Information Josephine Teo at the ATxSummit on 20 May, referring to the National AI Impact Programme that aims to broaden the base of enterprise users. This will be part of 10 refreshed priorities under the newly updated Singapore’s National AI Strategy (NAIS) to harness AI for the public good.

Hosted by the Infocomm Media Development Authority at Capella Singapore, ATxSummit covers a range of topics such as agentic and embodied AI, AI safety and governance, space satellites and communications, and quantum compute through a series of plenary sessions. Teo was delivering the opening keynote at the event. In her address, the minister covered Singapore’s AI priorities and ambitions, including how the Republic will deepen adoption across key sectors, partner industry to solve real-world problems, and strengthen its position as a trusted AI hub.

Why it mattersThe ai in executive & strategy implication is concrete because Singapore Economic Development Board (EDB) ties the capability to an operating choice: Hosted by the Infocomm Media Development Authority at Capella Singapore, ATxSummit covers a range of topics such as agentic and… The leadership implication for AI in Executive & Strategy is to connect Singapore updates national AI strategy, partners Google and OpenAI - Singapore Economic Development Board (EDB) to a named owner, a measurable baseline, and a review path for exceptions.

Artificial intelligence is already changing how organizations hire, develop talent, write software, serve customers, and improve productivity. But its deeper impact is on the enterprise itself.

Drawing on his experience as CEO and Co-Founder of Eightfold AI, Ashutosh Garg argues that as AI becomes embedded into how organizations make decisions, apply knowledge, and execute work, leaders must rethink not only the operating model of the enterprise, but the assumptions on which it was built. Every enterprise reflects the realities of the environment in which it was built.

The structures that define modern organizations, from functional departments and management hierarchies to planning cycles and governance processes, did not emerge because they were the ideal way to organize work. They evolved because they solved the practical challenges of growth. As businesses grew larger and more complex, human bandwidth became the defining constraint.

Why it mattersEuropean Business Review changes the risk calculation for this workflow. The upside is linked to Drawing on his experience as CEO and Co-Founder of Eightfold AI, Ashutosh Garg argues that as AI becomes embedded into how organizations make decisions, apply knowledge, and execute… The leadership implication for AI in Executive & Strategy is to connect AI Didn’t Just Change Work. It Changed the Enterprise - The European Business Review to a named owner, a measurable baseline, and a review path for exceptions.

Hershey is revamping marketing mix modeling by working with Mutinex and Tracer to make the slow, backward-looking measurement process faster and more frequent. Mutinex, underpinned by Claude and Gemini, gives Hershey an always-on system that supports monthly decisions across media and trade spend.

The confectionery company is using agents to connect spending decisions with sales outcomes rather than waiting for an annual analysis. Adweek describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersAdweek makes the control boundary visible: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence. The buyer question is whether that boundary is strong enough for… The leadership implication for AI in Marketing is to connect Hershey Bets on Agentic AI to Rethink $2B in Marketing Spend to a named owner, a measurable baseline, and a review path for exceptions.

Keurig Dr Pepper launched the ninth season of Fansville and uses artificial intelligence, first- and third-party data, and optimized workflows for precision targeting and personalization. The prior iteration included more than 2,500 marketing permutations, producing more than double the sales lift and roughly 30% higher incremental return on ad spend than the national campaign average, according to KDP.

The campaign will run across television, digital, radio, out-of-home and social channels, with more than 200 pieces of localized content in 50 markets. Marketing Dive describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersThe ai in marketing implication is concrete because Marketing Dive ties the capability to an operating choice: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence. The leadership implication for AI in Marketing is to connect How Dr Pepper’s Fansville campaign stays relevant and drives outcomes to a named owner, a measurable baseline, and a review path for exceptions.

Adobe acquired Indian AI startup Rilo, its six-person team and licenses to technology for natural-language marketing and go-to-market workflows. Rilo can create workflows for competitor intelligence, content repurposing, sales-call analysis and lead generation; financial terms were not disclosed and the standalone product will shut down.

Adobe said the team will work on enterprise marketer AI, following Adobe’s acquisition of Semrush. MarTech describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersMarTech changes the risk calculation for this workflow. The upside is linked to Adobe said the team will work on enterprise marketer AI, following Adobe’s acquisition of Semrush. MarTech describes the capability as part of its enterprise offering.; the evidence still requires… The leadership implication for AI in Marketing is to connect Adobe acquires Rilo to add natural-language marketing workflow orchestration to a named owner, a measurable baseline, and a review path for exceptions.

Cohere’s enterprise sales guide covers prospecting, CRM administration, forecasting, workflow automation, governance and adoption. It positions retrieval-grounded generation as a way to use account, opportunity and product context without asking sellers to move between disconnected systems.

The guide emphasizes permissioning, evaluation and human review for customer-facing outputs rather than treating automated outreach as a standalone content task. Cohere describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersCohere makes the control boundary visible: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence. The buyer question is whether that boundary is strong enough for… The leadership implication for AI in Sales is to connect AI for Sales: the 2026 enterprise guide to a named owner, a measurable baseline, and a review path for exceptions.

Barchart.com reports the development described in "Kwati AI Develops AI-Native ERP Platform to Unify Enterprise Workflows, Data and Decision-Making - Barchart.com". The capability is tied to the ai in sales workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Barchart.com describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersThe ai in sales implication is concrete because Barchart.com ties the capability to an operating choice: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence. The leadership implication for AI in Sales is to connect Kwati AI Develops AI-Native ERP Platform to Unify Enterprise Workflows, Data and Decision-Making - Barchart.com to a named owner, a measurable baseline, and a review path for exceptions.

GlobeNewswire reports the development described in "AI Workflow Orchestration Market Size to Hit $95.10 Billion by 2035 | SNS Insider - GlobeNewswire". The capability is tied to the ai in sales workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. GlobeNewswire describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersGlobeNewswire changes the risk calculation for this workflow. The upside is linked to The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. GlobeNewswire describes the capability as part of its enterprise offering.… The leadership implication for AI in Sales is to connect AI Workflow Orchestration Market Size to Hit $95.10 Billion by 2035 | SNS Insider - GlobeNewswire to a named owner, a measurable baseline, and a review path for exceptions.

Coworker describes an enterprise agent built around Organizational Memory, a dynamic model of teams, projects, customers and relationships. The platform handles research, planning and execution across a technology ecosystem and lists customer-service, sales-pipeline and knowledge-management use cases.

Its comparison of service platforms separates rapid self-service, multilingual outsourcing, call intelligence and persistent enterprise context, emphasizing that the system of record and resolution agent can be different choices. Coworker AI describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersCoworker AI makes the control boundary visible: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence. The buyer question is whether that boundary is strong enough… The leadership implication for AI in Customer Service is to connect AI customer service companies and platforms to a named owner, a measurable baseline, and a review path for exceptions.

While deploying intelligent software is a crucial first step, AI agents are only the beginning of a much broader path towards full agentic transformation across the enterprise. According to a recent research from Deloitte, realising this potential will require an overhaul of traditional operating models.

As AI shifts from initial experimentation to enterprise-wide execution, global businesses face the critical challenge of adapting their operations to an agentic future. To explore how organisations can bridge this gap between ambition and operational readiness, The Future of Enterprise AI forum at the AI LIVE: The London Summit will bring together industry leaders to map out the forthcoming transformation on 20 October at Olympia London.

Click here to secure your tickets to AI LIVE: The London Summit 2026. Deloitte’s findings from “AI agents are only the beginning: The path to agentic transformation” project indicate dramatic operational shifts over the next four years as organisations move beyond initial pilot phases toward fully agentic enterprise structures. The firms research indicates that 74% of leaders expect nearly half of their business processes to be rebuilt or redesigned around AI agents, while 61% anticipate processes running continuously powered by real-time agent decisions.

Why it mattersThe ai in customer service implication is concrete because AI Magazine ties the capability to an operating choice: Click here to secure your tickets to AI LIVE: The London Summit 2026. Deloitte’s findings from “AI agents are only the beginning: The path to agentic… The leadership implication for AI in Customer Service is to connect AI LIVE: Rebuilding Workflows for the Future of Enterprise - AI Magazine to a named owner, a measurable baseline, and a review path for exceptions.

Manila Times reports the development described in "Compunnel Digital Earns Frost & Sullivan's 2026 Global Company of the Year Recognition for AI-led Digital Customer Experience Enablement - The Manila Times". The capability is tied to the ai in customer service workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Manila Times describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersManila Times changes the risk calculation for this workflow. The upside is linked to The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Manila Times describes the capability as part of its enterprise offering.; the… The leadership implication for AI in Customer Service is to connect Compunnel Digital Earns Frost & Sullivan's 2026 Global Company of the Year Recognition for AI-led Digital Customer Experience Enablement - The Manila Times to a named owner, a measurable baseline, and a review path for exceptions.

Every technology company now has an AI sentence. Trimble has something more useful: five years of financial restructuring that might allow the sentence to become a business model.

The positioning, construction and industrial-technology company reported second-quarter 2026 revenue of $972 million, up 11% year over year. Adjusted earnings reached $0.86 per share, while annualized recurring revenue rose to $2.51 billion, up 14%, according to Trimble’s quarterly announcement .

Trimble raised its full-year outlook and said it achieved a 30% adjusted EBITDA margin a year earlier than planned. The measures are company-defined and adjusted, but the quarter gives real financial weight to its claim that Trimble can become an “AI-native intelligence and execution layer for the physical world.” The more interesting question is not whether Trimble uses AI. It is whether the company’s mix of hardware, software and field data gives it an advantage that Autodesk, Bentley, Hexagon, Procore and specialist AI vendors cannot easily copy.

Why it mattersGeoawesome makes the control boundary visible: Trimble raised its full-year outlook and said it achieved a 30% adjusted EBITDA margin a year earlier than planned. The measures are company-defined and adjusted, but the quarter gives real financial weight to its claim that Trimble… The leadership implication for AI in Product & Innovation is to connect Trimble’s Q2 Results Show the Business Behind Its ‘AI-Native’ Ambition - Geoawesome to a named owner, a measurable baseline, and a review path for exceptions.

Home » Physical AI Leaves the Lab: Are Factories Ready? AI is acquiring eyes , arms and wheels .

As intelligent machines move from controlled demonstrations onto factory floors, physical AI promises to transform industrial automation. But manufacturers still face major hurdles in turning impressive technology into reliable, scalable value.

For decades, industrial robots have excelled at doing exactly what they are told. Programmed to weld the same seam, move the same component or repeat the same assembly operation thousands of times, they have delivered huge productivity gains, provided their environment remains predictable. Physical AI promises something different.

Why it mattersThe ai in product & innovation implication is concrete because DirectIndustry e-Magazine ties the capability to an operating choice: For decades, industrial robots have excelled at doing exactly what they are told. Programmed to weld the same seam, move the same component or… The leadership implication for AI in Product & Innovation is to connect Physical AI Leaves the Lab: Are Factories Ready? - DirectIndustry e-Magazine to a named owner, a measurable baseline, and a review path for exceptions.

Visitors to AMB in Stuttgart will be able to experience the latest developments in the fields of control and drive technology, robotics and machine tools. At the Fanuc stand, there will be a particular focus on artificial intelligence and digital technologies, which are becoming increasingly important for industrial applications.

At AMB in Stuttgart (15 - 19 September 2026), Fanuc will be presenting the latest developments in the fields of control and drive technology, robotics and machine tools. There will be a particular focus on artificial intelligence and digital technologies, which are becoming increasingly important for industrial applications.

These include new possibilities in the fields of simulation, digital twins and Physical AI, which Fanuc is driving forward in collaboration with partners such as Nvidia and Google. A dedicated area of the Fanuc stand (Hall 6, Stand D10) is devoted to such innovation partnerships. Visitors will learn how the integration of the Roboguide simulation software into Nvidia’s open reference framework, ‘Nvidia Isaac Sim’, enables high-precision digital twins.

Why it mattersETMM changes the risk calculation for this workflow. The upside is linked to At AMB in Stuttgart (15 - 19 September 2026), Fanuc will be presenting the latest developments in the fields of control and drive technology, robotics and machine tools. There will be a particular focus… The leadership implication for AI in Product & Innovation is to connect Fanuc at AMB 2026: AI, Digital Twins and New CNC 500i-A - ETMM to a named owner, a measurable baseline, and a review path for exceptions.

Battalion invests cash in AI partner Collide, gaining priority access to its operations system and advancing a broader AI and data center strategy. Battalion Oil Corporation (BATL) announced an equity investment in Collide Industrial Technologies and its designation as a Strategic Partner for Collide’s AI-native operations system for oil and gas.

The partnership gives Battalion priority access to the Collide Operations System, product advisory rights, and a subscription-based deployment across its upstream operations. The rollout starts by unifying more than 100 terabytes of well files, land records, contracts, and production history into a single queryable model of the business and is expected to deliver Riggs, Collide’s AI work environment, to Battalion’s production engineers at about day 90.

The investment is funded from balance sheet cash and aligns with Battalion’s strategy to lower operating costs, improve capital efficiency, and support its multi-year drilling and M&A programs. Battalion is also exploring a large-scale data center on company-owned acreage in West Texas as part of its broader AI strategy. Battalion’s announced Collide investment and partnership now include an executed subscription agreement for deployment across its upstream operations; the investment is funded from balance-sheet cash, but its financial terms are undisclosed.

Why it mattersStock Titan makes the control boundary visible: The investment is funded from balance sheet cash and aligns with Battalion’s strategy to lower operating costs, improve capital efficiency, and support its multi-year drilling and M&A programs. Battalion is also exploring a… The leadership implication for AI in Operations is to connect Battalion Oil invests in AI and plans to combine more than 100 terabytes of records into one system - Stock Titan to a named owner, a measurable baseline, and a review path for exceptions.

Shared enterprise AI operating model accelerates deployment, strengthens operating leverage and supports scalable expansion across businesses BEIJING, Aug. 18, 2026 /PRNewswire/ -- Yiren Digital Ltd. (NYSE: YRD) ("Yiren Digital" or the "Company"), a leading company specializing in financial technology and artificial intelligence innovation across multiple industries in China and global markets, today announced continued progress in upgrading AI capabilities across core enterprise functions, establishing a shared operating model that enables AI capabilities developed within one business to be rapidly deployed across additional functions.

This progress advances the Company's transition toward an AI-native, multi-industry operating platform. Beyond developing AI independently for individual use cases, Yiren Digital has built a common enterprise AI framework that standardizes models, agents, workflows and governance.

This approach fosters modularity, resource sharing and model reusability, shortening development cycles and creating a scalable operating model capable of supporting long-term expansion into additional AI-enabled verticals. As a result, AI capabilities developed for one business function can be adapted to additional businesses without rebuilding core models, workflows or governance, reducing implementation time while improving consistency across the organization. Deploying technology into production across the Company's credit and insurance businesses has enabled Yiren Digital to develop, validate and standardize enterprise AI capabilities before expanding them across the broader organization.

Why it mattersThe ai in operations implication is concrete because Yahoo Finance ties the capability to an operating choice: This approach fosters modularity, resource sharing and model reusability, shortening development cycles and creating a scalable operating model capable of supporting… The leadership implication for AI in Operations is to connect Yiren Digital Upgrades Enterprise AI Across Core Business Functions to a named owner, a measurable baseline, and a review path for exceptions.

Forbes reports the development described in "Serval Wants To Replace ServiceNow With AI That Builds Enterprise Automation - Forbes". The capability is tied to the ai in operations workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Forbes describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersForbes changes the risk calculation for this workflow. The upside is linked to The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Forbes describes the capability as part of its enterprise offering.; the evidence… The leadership implication for AI in Operations is to connect Serval Wants To Replace ServiceNow With AI That Builds Enterprise Automation - Forbes to a named owner, a measurable baseline, and a review path for exceptions.

DATE: Thursday, September 10, 2026 TIME: 2:00 PM EDT/ 11:00 AM PDT Growing warehouses face a familiar bind: customer expectations keep rising while headcount, space and IT resources stay flat. Many small and medium-sized businesses assume the only way to fix warehouse inefficiency is a costly, developer-heavy customization project - one most teams don't have the staff or budget to support.

Join Infios, Supplysoft and a guest customer for a candid conversation on how a configurable WMS lets SMB operations adapt to unique workflows, integrate with ERP systems and support growth - all without writing a line of code or waiting on a development queue. Why configuration (not customization) is the faster, lower-risk path to warehouse efficiency How to evaluate whether your current WMS can flex with you as you grow What it really takes to become "cloud-ready" without an in-house IT team Real results from an SMB warehouse that scaled throughput and accuracy without adding headcount or infrastructure Whether you're managing rising return rates, seasonal demand swings or the leap to next-day fulfillment, this webinar will show you how to get more out of your WMS investment starting on day one.

Featuring: Justin Velthoen, Director of Product Management, Infios and Humberto “Bert” Rodriguez, Co-founder and the CEO, Supplysoft Supply Chain Management Review describes the capability as part of its enterprise offering. The source does not disclose an independent production benchmark.

Why it mattersSupply Chain Management Review makes the control boundary visible: Featuring: Justin Velthoen, Director of Product Management, Infios and Humberto “Bert” Rodriguez, Co-founder and the CEO, Supplysoft Supply Chain Management Review describes the capability as part of its… The leadership implication for AI in Supply Chain & Procurement is to connect Configure, Don’t Customize: A Smarter Path to Warehouse Efficiency for Growing Businesses - Supply Chain Management Review to a named owner, a measurable baseline, and a review path for exceptions.

Offering increased integration options and AI enhancements, warehouse automation systems give workers an even greater assist. Amazon’s next-generation autonomous Proteus robot acts on natural language commands to take on more tasks across its operations.

The new technology builds on the original autonomous robot and expands its ability to assist employees with their daily tasks. Using advances in artificial intelligence, the new Proteus is designed to understand natural language.

Employees will now be able to direct Proteus in the same way they would communicate with a colleague - using plain, conversational language, with no technical commands and no programming interface. The new Proteus is currently being piloted in Amazon’s labs, with deployment in Europe planned for the first half of 2027. Like its predecessor, the new Proteus is designed to take on physically demanding tasks - moving heavy carts and covering long distances - so employees can focus on higher-skilled work like managing inventory flow and ensuring quality control.

Why it mattersThe ai in supply chain & procurement implication is concrete because Inbound Logistics ties the capability to an operating choice: Employees will now be able to direct Proteus in the same way they would communicate with a colleague - using plain, conversational language, with no… The leadership implication for AI in Supply Chain & Procurement is to connect Warehouse Robots At Your Service - Inbound Logistics to a named owner, a measurable baseline, and a review path for exceptions.

Business Wire reports the development described in "NextSmartShip Releases New Fulfillment Integration for Temu U.S. Sellers - Business Wire". The capability is tied to the ai in supply chain & procurement workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Business Wire describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersBusiness Wire changes the risk calculation for this workflow. The upside is linked to The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Business Wire describes the capability as part of its enterprise offering.… The leadership implication for AI in Supply Chain & Procurement is to connect NextSmartShip Releases New Fulfillment Integration for Temu U.S. Sellers - Business Wire to a named owner, a measurable baseline, and a review path for exceptions.

Fortune Business Insights reports the development described in "AI Automation Market Size, Share, Growth Forecast, 2034 - Fortune Business Insights". The capability is tied to the ai in finance workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Fortune Business Insights describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersFortune Business Insights makes the control boundary visible: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence. The buyer question is whether that boundary is… The leadership implication for AI in Finance is to connect AI Automation Market Size, Share, Growth Forecast, 2034 - Fortune Business Insights to a named owner, a measurable baseline, and a review path for exceptions.

Rillet has raised a $100 million Series C at a $1 billion valuation as the AI-native enterprise resource planning company accelerates development of what it calls “Accounting Superintelligence,” where AI agents perform increasingly complex finance work directly inside a real-time general ledger. The round was led by ICONIQ, with participation from Sequoia, Andreessen Horowitz, Sequoia Global Equities, Bain Capital Ventures, Oak HC/FT, Battery Ventures, FirstMark, Scale Venture Partners and Creandum.

The financing represents Rillet’s third fundraising round in approximately 14 months and brings total funding to more than $200 million. Rillet plans to use the new capital to expand its agentic finance platform, which is designed to enable finance professionals and AI agents to work together using the same accounting data, policies, controls and audit infrastructure.

The financing follows a period of rapid commercial growth. Rillet said new annual recurring revenue doubled during the last three months, while its customer base has grown to more than 600 companies. The platform is being used by publicly traded companies and fast-growing technology businesses, including Mercor, Function Health and Temporal.

Why it mattersThe ai in finance implication is concrete because Pulse 2.0 ties the capability to an operating choice: The financing follows a period of rapid commercial growth. Rillet said new annual recurring revenue doubled during the last three months, while its customer base has grown to… The leadership implication for AI in Finance is to connect Rillet Raises $100 Million Series C At $1 Billion Valuation As AI-Native ERP Tops 600 Customers - Pulse 2.0 to a named owner, a measurable baseline, and a review path for exceptions.

Business and technology leaders need no convincing that the time of agentic AI is here. Organizations are rapidly adopting agents , and few executives doubt the technology’s potential to transform work.

But many organizations find that realizing the desired return on investment (ROI) from AI hinges on having the right foundation, with inadequate infrastructure and data being major blockers . Agentic AI places considerable new demands on enterprise data systems.

The shift from answering questions to taking actions means AI agents need data from across the enterprise, in all its structured and unstructured forms, and with the right business context. To make decisions and act in real time, agents also need frictionless access to the organization’s operational systems - for example, those storing its supply chain, point-of-sale, or human resources data. Legacy data systems, even those updated just a few years ago, struggle to meet these demands.

Why it mattersMIT Technology Review changes the risk calculation for this workflow. The upside is linked to But many organizations find that realizing the desired return on investment (ROI) from AI hinges on having the right foundation, with inadequate infrastructure and data being major… The leadership implication for AI in Finance is to connect Scaling AI agents with trustworthy data - MIT Technology Review to a named owner, a measurable baseline, and a review path for exceptions.

Ema launched HR, IT and Finance Hub, with purpose-built AI Employees for routine work across systems of record. The hubs cover onboarding, benefits, coaching, headcount planning, asset and identity management, ticketing, payroll, timesheets and expenses.

Ema says its agents plan steps, execute them, check work, route approvals and confirm completion with role-based permissions, audit trails and human oversight, integrating with more than 250 business applications. Markets Insider describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersMarkets Insider makes the control boundary visible: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence. The buyer question is whether that boundary is strong… The leadership implication for AI in People / HR is to connect Ema launches HR, IT and Finance Hub for enterprise AI employees to a named owner, a measurable baseline, and a review path for exceptions.

The AI Weekly library tracks seven named HR and recruiting deployments, three in production or with results, three with a reported outcome and two halted or reversed. UBS is making AI literacy an explicit criterion for its 2027 junior investment-banking intake.

McKinsey plans agents to match nearly 40,000 consultants to engagements, while Providence reported a 90% reduction in hiring-step time and a 70% improvement in job-request accuracy using IBM’s platform. The library also records halted or controversial cases, including emotion-recognition monitoring at MetLife.

AI Weekly describes the capability as part of its enterprise offering. The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersThe ai in people / hr implication is concrete because AI Weekly ties the capability to an operating choice: AI Weekly describes the capability as part of its enterprise offering. The source does not disclose an independent production benchmark. The operational result therefore… The leadership implication for AI in People / HR is to connect AI in HR and recruiting: seven real deployments to a named owner, a measurable baseline, and a review path for exceptions.

Right now, successful organizations are shifting from experimental AI pilots to enterprise-wide integration, fundamentally rethinking operating models, rather than simply adding AI capabilities to existing processes. As Jerry Haar and Veronika Cieslak recount, these are processes that, perhaps like never before, require strategic leadership from the very top.

The integration of artificial intelligence across business operations has accelerated dramatically, with worker access to AI rising by 50 percent in 2025 alone. Unlike previous waves of digital transformation focused primarily on infrastructure modernization, the current AI revolution requires organizations to fundamentally rethink their entire operating models, governance structures, and competitive strategies.

The strategic landscape for AI adoption has shifted decisively in 2026 from isolated pilot projects to comprehensive enterprise integration. Organizations are moving beyond treating AI as an experimental technology and instead embedding it as a core component of business strategy and operations. This transition requires fundamental changes in how companies conceptualize competitive advantage in digitally disrupted environments.

Why it mattersEuropean Business Review changes the risk calculation for this workflow. The upside is linked to The integration of artificial intelligence across business operations has accelerated dramatically, with worker access to AI rising by 50 percent in 2025 alone. Unlike previous waves… The leadership implication for AI in People / HR is to connect How Are Corporations Adapting to AI and Digital Transformation? - The European Business Review to a named owner, a measurable baseline, and a review path for exceptions.

Broadcom reports the development described in "Broadcom Announces VMware AI Factory, Enabling Faster Time to Production AI and Greater Control Over AI Tokenomics - Broadcom". The capability is tied to the ai in technology workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Broadcom describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersBroadcom makes the control boundary visible: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence. The buyer question is whether that boundary is strong enough for… The leadership implication for AI in Technology is to connect Broadcom Announces VMware AI Factory, Enabling Faster Time to Production AI and Greater Control Over AI Tokenomics - Broadcom to a named owner, a measurable baseline, and a review path for exceptions.

13, 2026 (GLOBE NEWSWIRE) -- Rackspace Technology ® (NASDAQ: RXT), a global enterprise AI infrastructure and solutions provider, today announced the appointment of Chetan Gupta, Ph.D., as Chief AI Officer. Gupta will lead the Office of AI, with responsibility for Rackspace's AI strategy, research, innovation, governance, and adoption across the company and its customers.

His appointment strengthens Rackspace's ability to help institutions deploy AI safely, economically, and at scale in environments where the stakes are highest: regulated industries, sovereign jurisdictions, and mission-critical operations. Gupta joins Rackspace after nearly a decade leading global AI research at Hitachi, where he served as General Manager of the Advanced AI Center in Japan, Vice President of the Industrial AI Lab in North America, and head of Hitachi's Global AI Center of Excellence.

His work was instrumental in Hitachi's evolution toward Physical AI, building systems for railways, energy grids, factories, and buildings, environments where AI must operate under institutional accountability, jurisdictional requirements, and zero tolerance for failure. Earlier in his career, he spent seven years at HP Labs translating advanced AI research into deployed solutions across logistics, manufacturing, energy, and mobility. He has authored nearly 300 papers and patents and mentored a generation of AI researchers and engineers.

Why it mattersThe ai in technology implication is concrete because Yahoo Finance ties the capability to an operating choice: His work was instrumental in Hitachi's evolution toward Physical AI, building systems for railways, energy grids, factories, and buildings, environments where AI must… The leadership implication for AI in Technology is to connect Rackspace Technology Appoints Chetan Gupta, Ph.D., as Chief AI Officer to Advance Enterprise and Sovereign AI to a named owner, a measurable baseline, and a review path for exceptions.

AI security startup Mindgard has closed a $30 million Series A funding round, pushing the company’s total funding to nearly $42 million. The milestone reflects the growing urgency among enterprise organizations seeking to secure rapidly expanding artificial intelligence deployments.

The round was led by Album VC, with Karma Ventures joining as a new backer alongside returning investors .406 Ventures, Atlantic Bridge, IQ Capital, and Lakestar. Mindgard plans to allocate the fresh capital toward scaling its product, engineering, sales, and marketing teams to meet surging customer demand.

Spun out of Lancaster University, home to what Mindgard describes as the world’s largest AI security lab, the company has converted over a decade of research expertise into a commercial platform. Its core innovation centers on Dynamic Application Security Testing (DAST) for AI, a continuous, automated red-teaming framework. Rather than relying on theoretical risk assessments, the platform probes AI models, agents, and applications for vulnerabilities using real-world attack techniques.

Why it mattersCyberSecurityNews changes the risk calculation for this workflow. The upside is linked to The round was led by Album VC, with Karma Ventures joining as a new backer alongside returning investors .406 Ventures, Atlantic Bridge, IQ Capital, and Lakestar. Mindgard plans to allocate… The leadership implication for AI in Technology is to connect Mindgard Raises $30 Million to Tackle AI's Fastest-Growing Attack Surface - CyberSecurityNews to a named owner, a measurable baseline, and a review path for exceptions.

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By Michelle Smith August 24, 2026 • 7 minute read To keep pace with modern business, data strategy is shifting toward more autonomous real-time systems that deliver intelligence at the moment decisions are made. Driven by agentic AI, modern data teams are moving beyond simply looking at what happened.

Now they’re automating complex workflows that analyze what’s happening, anticipate what might happen next, and recommend or take action. In this article, I’ll define some of the top trends defining this era, from data agents and semantic layers to hybrid data architectures and next-generation data governance. Data agents are AI-powered software agents that access governed enterprise data and tools to answer questions and perform defined tasks.

Why it mattersO'Reilly Media makes the control boundary visible: Now they’re automating complex workflows that analyze what’s happening, anticipate what might happen next, and recommend or take action. In this article, I’ll define some of the top trends defining this era, from data agents and… The leadership implication for AI in Data & AI is to connect Data Intelligence: Building Your Competitive Advantage in the Era of AI - O'Reilly Media to a named owner, a measurable baseline, and a review path for exceptions.

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To deliver the outcomes you want it to deliver, AI needs more than data. It also needs meaning, context, relationships, business rules and trusted knowledge.

This is driving interest in ontologies, knowledge graphs and semantic layers. So far, so good - but the technology works best when it is grounded in a clear understanding of what your business actually needs to know. To build an ontology, you need to understand what knowledge matters most.

Why it mattersThe ai in data & ai implication is concrete because NTT Data ties the capability to an operating choice: This is driving interest in ontologies, knowledge graphs and semantic layers. So far, so good - but the technology works best when it is grounded in a clear understanding of… The leadership implication for AI in Data & AI is to connect What AI-ready knowledge really requires - NTT Data to a named owner, a measurable baseline, and a review path for exceptions.

SAP SE ’s SAP Business Data Cloud is emerging as an important pillar of the company’s AI strategy as enterprises look to bring together business data and provide AI agents with the context required to automate processes. The solution featured prominently in the second quarter, with AI and SAP Business Data Cloud serving as key pillars in more than 90% of SAP’s 50 largest deals.

This strong adoption gives management confidence about business momentum in the second half of the year. SAP’s current cloud backlog increased 26%, while cloud revenues grew 24% to €6.3 billion in the quarter.

SAP Business Data Cloud forms the data foundation of the context and reason pillar of SAP’s new Business AI platform. It provides agents with broad access to enterprise data. SAP is strengthening this foundation through Dremio, whose Apache Iceberg-native technology allows mission-critical SAP and non-SAP data to be analyzed together in real time without first moving or copying the information.

Why it mattersGlobe and Mail changes the risk calculation for this workflow. The upside is linked to This strong adoption gives management confidence about business momentum in the second half of the year. SAP’s current cloud backlog increased 26%, while cloud revenues grew 24% to €6.3… The leadership implication for AI in Data & AI is to connect Can SAP Business Data Cloud Become Its Next Major Growth Engine? - The Globe and Mail to a named owner, a measurable baseline, and a review path for exceptions.

Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly. This brief assesses data broker compliance with California privacy laws and calls for more stringent consumer protections.

Data brokers are third parties that buy and sell consumers’ data without their specific knowledge or consent. In so doing, they expose consumers and the public to potential risks such as data breaches, political violence, and national security threats.

Despite these risks, few states regulate this opaque ecosystem. Widely lauded as the most comprehensive state consumer privacy statute, the California Consumer Privacy Act (CCPA) and the Delete Act require data brokers to allow consumers to exercise their privacy rights (e.g., by deleting their data or opting out of data collection) and mandate that brokers disclose the number of requests they receive each year. We assess data broker compliance with the CCPA and the Delete Act, finding that many brokers obstruct consumers from properly exercising their requests and ignore disclosure requirements, leaving consumers to navigate a complex system with no direct remedy for harm.

Why it mattersStanford HAI makes the control boundary visible: Despite these risks, few states regulate this opaque ecosystem. Widely lauded as the most comprehensive state consumer privacy statute, the California Consumer Privacy Act (CCPA) and the Delete Act require data brokers to allow… The leadership implication for AI in Risk, Legal & Compliance is to connect Regulating Data Brokers in the Age of AI: A California Case Study - Stanford HAI to a named owner, a measurable baseline, and a review path for exceptions.

The California Legislature passed a significant number of privacy-and AI-related bills during the latest legislative session, which ended August 31, 2026. The measures - which span AI transparency, employment decisions, privacy practices, use of chatbots, health care services, children’s online protections, and more - will have wide-ranging effects on companies doing business in California, if and when they are signed into law by the Governor.

The California Legislature’s 2026 session underscores the state’s continued focus on privacy and AI regulation. The AI package reflects a growing focus on high-impact use cases, including employment decisions and consumer-facing interactions involving chatbots and digital replicas, while reinforcing the importance of human oversight in consequential decision-making.

On the privacy front, lawmakers added to the state’s already expansive frameworks. For example, they approved heightened regulation of businesses that sell or share sensitive personal data, as well as new measures addressing children’s online privacy and safety - including a replacement framework for the Age-Appropriate Design Code Act and new restrictions on addictive platform features directed at minors. At the same time, the Legislature adopted a CIPA reform measure aimed at curbing the onslaught of certain private claims under the state’s wiretapping act.

Why it mattersThe ai in risk, legal & compliance implication is concrete because Wiley Rein ties the capability to an operating choice: On the privacy front, lawmakers added to the state’s already expansive frameworks. For example, they approved heightened regulation of businesses that sell… The leadership implication for AI in Risk, Legal & Compliance is to connect California Closes Legislative Session with Significant AI and Privacy Developments - Wiley Rein to a named owner, a measurable baseline, and a review path for exceptions.

Workday proposes five safeguards for enterprise agents: persistent identity and authority, rigorous human oversight, sensitive-data protections, auditable logs, and assurance before deployment. The company says agents working across employment, payroll, benefits and financial approvals need explicit owners, permissions, escalation paths and mechanisms to review, override or stop actions.

Workday also urges NIST to develop an agentic AI profile and a standardized assurance schema. Workday describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersWorkday changes the risk calculation for this workflow. The upside is linked to Workday also urges NIST to develop an agentic AI profile and a standardized assurance schema. Workday describes the capability as part of its enterprise offering.; the evidence still requires local… The leadership implication for AI in Risk, Legal & Compliance is to connect Workday’s Vision for Governing AI in the Enterprise to a named owner, a measurable baseline, and a review path for exceptions.

Capgemini to provide engineering, integration, and industry expertise to support Ambarella's next phase of growth SANTA CLARA, Calif. and NEW YORK, Sept. 03, 2026 (GLOBE NEWSWIRE) -- Ambarella, Inc. (NASDAQ: AMBA), an edge AI semiconductor company, and Capgemini (Euronext Paris: CAP), the global AI-driven business and technology transformation company, today announced their engagement to accelerate the development and deployment of Edge and Physical AI solutions across smart infrastructure, retail and logistics, industrial automation, healthcare, and automotive sectors.

Under the agreement, Capgemini will provide engineering, systems integration, and industry expertise to help accelerate customer adoption of Ambarella's Edge and Physical AI technologies. The work will focus on developing solutions that enable AI processing closer to where data is generated, including in cameras, vehicles, industrial equipment, robotics systems, and other intelligent devices.

Capgemini will provide services to help Ambarella ideate and establish a dedicated global Edge and Physical AI Center of Excellence designed to accelerate solution development, proof-of-concept initiatives, technology validation, and deployment readiness for enterprise customers. Capgemini's experts will also work with relevant technology providers and ecosystem participants to help deliver complete solutions suited to enterprise deployment requirements. "Edge and Physical AI represent an important opportunity to extend AI from the data center into cameras, robots, vehicles, machines and other systems that perceive and interact directly with the physical world," said Fermi Wang, President and Chief Executive Officer of Ambarella.

Why it mattersYahoo Finance makes the control boundary visible: Capgemini will provide services to help Ambarella ideate and establish a dedicated global Edge and Physical AI Center of Excellence designed to accelerate solution development, proof-of-concept initiatives, technology validation… The leadership implication for Enterprise AI Labs is to connect Ambarella Engages Capgemini to Help Accelerate Enterprise Adoption of Edge and Physical AI to a named owner, a measurable baseline, and a review path for exceptions.

We are now at the stage where digital leaders have some experience in how to begin the cultural adoption of Artificial Intelligence (AI). At major bank BNP Paribas Fortis, Chief Data Scientist Manuel Piette is using communities, Domino data technology, and Europe’s frontier AI technology Mistral to improve data management and speed the adoption and usage of AI.

Piette was in London and shared his CoE approach with us. BNP Paribas Fortis was created in spring 2009 following the acquisition of Fortis Bank in Belgium by BNP Paribas.

It is the largest retail bank in Belgium, offering a full range of services to retail customers, as well as business banking to both small firms and enterprises. Piette has been with the organization for 21 years in a variety of data analytics roles supporting marketing, retail and private banking, and now is the bank’s Chief Data Scientist, leading the Data Science Chapter within the AI Tribe. A centre of excellence has been developed by Piette to help teams across the bank learn and adopt AI, especially as his team has developed and deployed the BNP Paribas Fortis generative AI platform, a secure large language model (LLM) for the 11,000 employees.

Why it mattersThe enterprise ai labs implication is concrete because diginomica ties the capability to an operating choice: It is the largest retail bank in Belgium, offering a full range of services to retail customers, as well as business banking to both small firms and enterprises. Piette… The leadership implication for Enterprise AI Labs is to connect BNP Paribas Fortis scales AI with a CoE and Mistral - chief data scientist Manuel Piette explains - diginomica to a named owner, a measurable baseline, and a review path for exceptions.

CPI's integrated infrastructure solutions help organizations validate AI, high-density computing, and hybrid cloud deployments before production. 2, 2026 /PRNewswire/ -- Chatsworth Products Inc. (CPI), a global manufacturer of IT infrastructure solutions, today announced it has joined Digital Realty Innovation Lab (DRIL) in London , a collaborative testing environment where organizations test, validate, and optimize AI and hybrid cloud infrastructure before production deployment.

As a vendor partner, CPI showcases its industry-leading ZetaFrame ® Cabinet System integrated with eConnect ® PDUs, cable management, and thermal management solutions that enable customers to design, test, and optimize high-density AI infrastructure in a production-grade environment. "AI infrastructure only earns its keep once it's proven under real conditions, not just on paper.

Bringing CPI's cabinet, power, and thermal expertise into the Digital Realty Innovation Lab means our customers in London can pressure-test high-density AI deployments before they ever touch production, de-risking decisions that used to be made largely on faith," said Séamus Dunne, Managing Director, UK & Ireland , Digital Realty. The lab gives enterprises access to a production-grade data center to test AI and hybrid cloud architectures using real workloads. By combining Digital Realty's infrastructure with partner technologies, organizations can reduce deployment risk, improve performance, and accelerate value.

Why it mattersStreetInsider changes the risk calculation for this workflow. The upside is linked to As a vendor partner, CPI showcases its industry-leading ZetaFrame ® Cabinet System integrated with eConnect ® PDUs, cable management, and thermal management solutions that enable customers to… The leadership implication for Enterprise AI Labs is to connect Chatsworth Products (CPI) Joins Digital Realty Innovation Lab in London to Advance AI Infrastructure Validation - StreetInsider to a named owner, a measurable baseline, and a review path for exceptions.

Today’s companies run on a growing web of applications, data platforms, cloud environments, workflows, and purpose-built business systems. The development of technology has opened new possibilities for automation and intelligence, and it has also created a lot of fragmentation.

HR may be working with one set of applications, finance another, and sales, marketing, IT, operations, and customer service all have their own data environments and technology stacks. Therefore, valuable information is often trapped in organizational and technological silos.

These siloed systems create challenges that are so much more than just integrating data. Critical information is spread across several platforms, which may make it difficult for business leaders to get a complete picture of how the organization is performing. Sales teams may not have access to relevant customer-service insights, finance teams may not have real-time visibility of operational changes, and HR leaders might find it difficult to connect workforce capabilities with changing business requirements.

Why it mattersAiThority makes the control boundary visible: These siloed systems create challenges that are so much more than just integrating data. Critical information is spread across several platforms, which may make it difficult for business leaders to get a complete picture of how the… The leadership implication for AI Operating Models is to connect AI Fabric - Connecting Every Business Function Through Seamless Enterprise Intelligence - AiThority to a named owner, a measurable baseline, and a review path for exceptions.

thestreet.com reports the development described in "AI agents create new problem for enterprise software - thestreet.com". The capability is tied to the ai operating models workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. thestreet.com describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersThe ai operating models implication is concrete because thestreet.com ties the capability to an operating choice: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow… The leadership implication for AI Operating Models is to connect AI agents create new problem for enterprise software - thestreet.com to a named owner, a measurable baseline, and a review path for exceptions.

McKinsey & Company reports the development described in "Lessons from our alliances: What AWS’s agentic journey can teach CEOs about rewiring for AI - McKinsey & Company". The capability is tied to the ai operating models workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. McKinsey & Company describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersMcKinsey & Company changes the risk calculation for this workflow. The upside is linked to The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. McKinsey & Company describes the capability as part of its enterprise… The leadership implication for AI Operating Models is to connect Lessons from our alliances: What AWS’s agentic journey can teach CEOs about rewiring for AI - McKinsey & Company to a named owner, a measurable baseline, and a review path for exceptions.

Adnan Masood is an Engineer, Thought Leader, Author, AI/ML PhD, Stanford Scholar, Harvard Alum, Microsoft Regional Director, and STEM Robotics Coach. McKinsey’s The State of AI in 2026: On the Road to ROI contains a gap worth taking seriously: 80% of respondents say AI improves their productivity, while 37% attribute a positive contribution to EBIT - earnings before interest and taxes - to AI.

That second figure is essentially unchanged from 2025 [1, p. Neither percentage measures return on investment , the net financial benefit relative to what was invested.

One captures perceived individual improvement; the other captures reported earnings contribution. An unchanged share reporting benefits also tells us little about whether those benefits have grown larger. The report identifies a credible management problem, while leaving much of the investment case unproven.

Why it mattersMedium makes the control boundary visible: One captures perceived individual improvement; the other captures reported earnings contribution. An unchanged share reporting benefits also tells us little about whether those benefits have grown larger. The report identifies a… The leadership implication for Enterprise AI-ROI & Value Maxing is to connect You Can’t Book a Saved Hour as Profit - Medium to a named owner, a measurable baseline, and a review path for exceptions.

Enterprises are accelerating their AI investments and deploying agents, budgets are ballooning out of control, and leaders are being asked to justify the cost. Yet insight into the return on investment (ROI) can be opaque.

Tempo says its new Workforce Intelligence (WFI) offering can help product managers make the case for, and optimize, their AI spend. The collaborative workspace platform provider says that WFI is the first Atlassian Marketplace app that automatically connects AI tool activity directly to Jira work items, tasks, epics, and initiatives to help leaders understand AI use, cost, its productivity impacts, and where the tools actually deliver ROI.

“The amount of money people are spending on AI is enormous, and a very large percentage of it is wasted,” said Tempo CEO Vic Chynoweth . “Being able to orient your investment toward outcomes you know are working is going to be a big lift for organizations.” According to IBM, only 29% of executives can confidently measure AI ROI, and just 25% of AI initiatives actually deliver expected ROI. And pressure is only increasing; Kyndryl reported that 61% of senior business leaders feel more burdened to prove AI ROI than they did just a year ago.

Why it mattersThe enterprise ai-roi & value maxing implication is concrete because InfoWorld ties the capability to an operating choice: “The amount of money people are spending on AI is enormous, and a very large percentage of it is wasted,” said Tempo CEO Vic Chynoweth . “Being able to… The leadership implication for Enterprise AI-ROI & Value Maxing is to connect Enterprises can measure AI usage, but the hard part is proving that it actually delivered value - InfoWorld to a named owner, a measurable baseline, and a review path for exceptions.

Seeking Alpha reports the development described in "Enterprises focused on ROI, but AI spending remains strong: UBS - Seeking Alpha". The capability is tied to the enterprise ai-roi & value maxing workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Seeking Alpha describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersSeeking Alpha changes the risk calculation for this workflow. The upside is linked to The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Seeking Alpha describes the capability as part of its enterprise offering.… The leadership implication for Enterprise AI-ROI & Value Maxing is to connect Enterprises focused on ROI, but AI spending remains strong: UBS - Seeking Alpha to a named owner, a measurable baseline, and a review path for exceptions.

Wonderful announced a $550 million Series C at a $5 billion valuation led by Insight Partners, with Salesforce and existing investors participating. Since March it says it expanded to more than 35 markets and 650 employees.

Its AI OS coordinates agents, workflows, AI-native applications, enterprise context, integrations and governed execution; the company uses forward-deployed engineers to co-build a first use case and transfer capability to customers. Wonderful describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersWonderful makes the control boundary visible: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence. The buyer question is whether that boundary is strong enough… The leadership implication for AI Operating Systems (AIOS) is to connect AI operating systems need a shared orchestration layer to a named owner, a measurable baseline, and a review path for exceptions.

Orchestra formally launched an agentic control plane after platform usage grew more than tenfold and reported $4.6 million in funding. Its serverless engine can orchestrate millions of tasks and agents in parallel, while more than 100 integrations connect existing data infrastructure.

Orchestra Runtime isolates agents, limits resource access and supports incident investigation and failure triage; its Context Layer organizes enterprise systems and workflows for model-agnostic execution. SD Times describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersThe ai operating systems (aios) implication is concrete because SD Times ties the capability to an operating choice: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow… The leadership implication for AI Operating Systems (AIOS) is to connect Orchestra launches agentic control plane for enterprise data and AI to a named owner, a measurable baseline, and a review path for exceptions.

The Real Bottleneck in Enterprise AI Isn’t the Technology At Worth's exclusive fireside chat, IBM's Sunil Murthy reveals why closing AI's ROI gap depends less on smarter models and more on redesigning the business around them. One year ago, enterprise AI was defined by experimentation.

Companies were building pilots, testing large language models, and trying to determine where generative AI fit inside their organizations. Success was measured by whether the technology worked.

At Worth’s second annual AI reception with IBM during the Ai4 conference in Las Vegas, I sat down with Sunil Murthy, IBM’s AI Field CTO, to discuss what has changed over the past twelve months. “The rate and pace of innovation is pretty rapid,” Murthy said. Organizations have moved from pilots into production much faster than many expected.

Why it mattersworth.com changes the risk calculation for this workflow. The upside is linked to Companies were building pilots, testing large language models, and trying to determine where generative AI fit inside their organizations. Success was measured by whether the technology worked.… The leadership implication for AI Operating Systems (AIOS) is to connect The Real Bottleneck in Enterprise AI Isn’t the Technology - worth.com to a named owner, a measurable baseline, and a review path for exceptions.

IBM and OpenAI are launching a broad enterprise partnership aimed at putting artificial intelligence to work across core business operations. The partnership will combine OpenAI models and products with IBM Consulting technology and expertise.

The companies say they plan to help enterprises transform with AI more securely across core operations while defending against cyber threats accelerated by AI, with a focus on workflow automation, application modernization and AI risk management. “Enterprises no longer need convincing that the models are powerful,” Michael Healy , Managing Partner of Offerings, Assets and Gen AI at IBM Consulting, told IBM Think in an interview.

“The challenge now is turning that intelligence into agentic workflows that actually run the business.” Under the partnership, OpenAI frontier models such as GPT-5.6, along with Codex and ChatGPT Work, will be embedded into IBM Consulting Advantage , IBM’s AI platform for delivering consulting services, according to the announcement. Initial areas of focus will include financial services, government, telecommunications and retail, as well as finance, procurement, customer operations and human resources. A dedicated OpenAI Practice will also be created, with thousands of IBM consultants and engineers obtaining expert-level certifications through the OpenAI Partner Network.

Why it mattersIBM makes the control boundary visible: “The challenge now is turning that intelligence into agentic workflows that actually run the business.” Under the partnership, OpenAI frontier models such as GPT-5.6, along with Codex and ChatGPT Work, will be embedded into IBM Consulting… The leadership implication for AI Automation is to connect IBM and OpenAI team up to bring AI deeper into the enterprise - IBM to a named owner, a measurable baseline, and a review path for exceptions.

World Economic Forum reports the development described in "The AI workplace revolution: Are organizations ready or not? - The World Economic Forum". The capability is tied to the ai automation workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. World Economic Forum describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersThe ai automation implication is concrete because World Economic Forum ties the capability to an operating choice: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow… The leadership implication for AI Automation is to connect The AI workplace revolution: Are organizations ready or not? - The World Economic Forum to a named owner, a measurable baseline, and a review path for exceptions.

Business Wire reports the development described in "Coforge Expands Strategic Partnership with Pega to Accelerate Enterprise AI Transformation - Business Wire". The capability is tied to the ai automation workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Business Wire describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersBusiness Wire changes the risk calculation for this workflow. The upside is linked to The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Business Wire describes the capability as part of its enterprise offering.… The leadership implication for AI Automation is to connect Coforge Expands Strategic Partnership with Pega to Accelerate Enterprise AI Transformation - Business Wire to a named owner, a measurable baseline, and a review path for exceptions.

Nutanix and ChronoScale plan to integrate their platforms so enterprises can extend into ChronoScale GPU-as-a-Service, pre-paid inference tokens through ChronoScale Token Factory, and a locally-deployed ChronoScale Foundry for enterprise agentic AI workflows ChronoScale to leverage Nutanix Agentic AI software solution for neoclouds to deliver broad portfolio of accelerated compute and AI services Collaboration extends to go-to-market, joint solution development, technical integration, and customer engagement programs SAN JOSE, Calif. and Menlo Park, Calif., Aug. 18, 2026 (GLOBE NEWSWIRE) -- Nutanix (NASDAQ: NTNX), a hybrid cloud leader and AI innovator, and ChronoScale Holdings Corporation (NASDAQ: CHRN), an accelerated compute platform purpose-built to support demanding artificial intelligence workloads, today announced a strategic partnership to jointly deliver enterprise-ready AI infrastructure and help accelerate adoption of AI services across global markets.

The partnership brings together complementary capabilities enterprise customers have historically had to assemble themselves - combining Nutanix's full portfolio of agentic AI solutions with ChronoScale's accelerated compute, enterprise AI foundry, and outcome-driven delivery model. ChronoScale plans to leverage Nutanix software within its AI infrastructure platform to help deliver a broad portfolio of accelerated compute and AI services.

The parties expect Nutanix software to help support customer onboarding, tenant management, service automation, virtualized infrastructure, managed Kubernetes environments, and advanced AI service offerings. ChronoScale's platform is designed to support a broad ecosystem of technology partners. The companies also intend to jointly maintain demonstration and proof-of-concept en

Why it mattersYahoo Finance makes the control boundary visible: The parties expect Nutanix software to help support customer onboarding, tenant management, service automation, virtualized infrastructure, managed Kubernetes environments, and advanced AI service offerings. ChronoScale's… The leadership implication for AI adoption is to connect Nutanix and ChronoScale Announce Strategic Partnership to Accelerate Enterprise AI Adoption to a named owner, a measurable baseline, and a review path for exceptions.

Kyndryl’s 2026 People Readiness Report, based on 1,100 senior leaders in eight countries, says 57% of enterprises have broadly deployed AI, up from 35% a year earlier, but only 11% achieved both top objectives. Only 23% of business leaders say their workforce is fully prepared, while 81% expect agents to make impactful decisions within a year and 25% fully trust unsupervised AI today.

Kyndryl identifies role redesign, change management and deliberate workforce readiness as traits of a roughly 9% Pacesetter cohort. MarketScale describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersThe ai adoption implication is concrete because MarketScale ties the capability to an operating choice: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence. The leadership implication for AI adoption is to connect AI is deployed in 57% of enterprises, but only 11% have hit their top two goals to a named owner, a measurable baseline, and a review path for exceptions.

New research from Rubrik Zero Labs indicates that the rapid adoption of AI agents within enterprises is creating a significant security gap, as organizations deploy these autonomous systems without adequate governance and control mechanisms, according to reports by TahawulTech. The report, based on a survey of over 1,600 IT and security leaders, reveals that 86% anticipate AI agents will surpass their organization's security guardrails within the next year.

Compounding this issue, only 23% claim full visibility into the AI agents operating in their environments, leading to an inability to secure identities that are actively making decisions and interacting with critical data. This "shadow workforce" of non-human identities is proliferating faster than enterprises can track, creating new avenues for compromise.

Furthermore, over 80% of respondents find that AI agents require more manual oversight than they provide in efficiency, and 88% lack the ability to roll back agent actions without disrupting systems. With nearly half of respondents expecting agentic systems to drive the majority of attacks in the coming year, the implications for boards and executive teams are clear: AI strategy must be integrated with resilience strategy to maintain operational safety in an increasingly autonomous landscape.

Why it mattersMSSP Alert changes the risk calculation for this workflow. The upside is linked to Compounding this issue, only 23% claim full visibility into the AI agents operating in their environments, leading to an inability to secure identities that are actively making decisions and… The leadership implication for AI adoption is to connect Enterprise AI agent adoption outpaces security controls, report finds - MSSP Alert to a named owner, a measurable baseline, and a review path for exceptions.

GlobeNewswire reports the development described in "Parksy.com Reveals AI-Native Operating Model for Free International Parking Marketplace - GlobeNewswire". The capability is tied to the ai-enabled, ai-first, and ai-native product and operating model shifts workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. GlobeNewswire describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersGlobeNewswire makes the control boundary visible: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence. The buyer question is whether that boundary is strong… The leadership implication for AI-enabled, AI-first, and AI-native product and operating model shifts is to connect Parksy.com Reveals AI-Native Operating Model for Free International Parking Marketplace - GlobeNewswire to a named owner, a measurable baseline, and a review path for exceptions.

Business Wire reports the development described in "Cognida Acquires the Automate Platform to Advance AI-Native Accounting and Operations - Business Wire". The capability is tied to the ai-enabled, ai-first, and ai-native product and operating model shifts workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Business Wire describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersThe ai-enabled, ai-first, and ai-native product and operating model shifts implication is concrete because Business Wire ties the capability to an operating choice: The source does not disclose an independent production benchmark. The operational result therefore depends on… The leadership implication for AI-enabled, AI-first, and AI-native product and operating model shifts is to connect Cognida Acquires the Automate Platform to Advance AI-Native Accounting and Operations - Business Wire to a named owner, a measurable baseline, and a review path for exceptions.

With ChatGPT Work and Codex, RingCentral builds AI product features faster and centralizes operational intelligence. With nearly three decades of innovation in business communications, RingCentral has grown into a global company generating more than $2.6 billion in annual revenue, with thousands of employees worldwide.

Today, the company is extending its tradition of innovation by embracing AI-native ways of working. By giving every employee room to experiment with ChatGPT Work and Codex, RingCentral has ensured that anyone at the company, regardless of engineering experience, can build transformative products and infrastructure.

To encourage AI fluency across a global engineering organization, RingCentral’s Office of the CEO sponsored an AI-Native Challenge. Every participant was given ChatGPT Work and Codex, and asked to build a complete, end-to-end project - with no mandated workflow or other constraints. More than a coding exercise, the challenge immersed employees in the full AI-native development lifecycle, from planning and implementation to testing, documentation, CI/CD, and iteration.

Why it mattersOpenAI changes the risk calculation for this workflow. The upside is linked to Today, the company is extending its tradition of innovation by embracing AI-native ways of working. By giving every employee room to experiment with ChatGPT Work and Codex, RingCentral has ensured… The leadership implication for AI-enabled, AI-first, and AI-native product and operating model shifts is to connect How RingCentral builds AI-native work from engineering to ops to a named owner, a measurable baseline, and a review path for exceptions.

NEW YORK and SUNNYVALE, Calif; Sept. 8, 2026 - Accenture (NYSE: ACN) and Google Cloud today launched the Accenture Gemini Enterprise Business Group, a global group designed to help clients scale Gemini Enterprise outcomes in the agentic AI era.

Part of the Accenture Google Business Group, the new group represents a significant joint investment and brings together Accenture’s Gemini Enterprise-certified professionals, FDEs, specialized Google Cloud engineering talent and Accenture's industry and functional experience to help clients realize measurable business value from their agentic AI and data investments. “The companies seeing the greatest outcomes from AI are unlocking new growth, increasing productivity and resilience, and creating better experiences for their customers and employees,” said Julie Sweet, chair and CEO, Accenture.

“The Accenture Gemini Enterprise Business Group will help clients achieve these outcomes faster, bringing together the talent, advanced AI and data capabilities, and industry expertise needed to reinvent with confidence and create value at scale.” "Deploying agentic AI is a top priority for enterprises today, and the Accenture Gemini Enterprise Business Group significantly expands the expertise and resources available to help our customers deliver real business value," said Thomas Kurian, CEO, Google Cloud. “Building on the success we’ve seen with world’s leading brands, we’re combining Google Cloud’s full-stack AI capabilities with Accenture's deep industry expertise to deliver transformation at scale.” Introducing the Accenture Gemini Enterprise Business Group As organizations shift from traditional software development lifecycles to agentic AI, the Accenture Gemini Enterprise Business Group is designed to meet clients wherever they are i

Why it mattersAccenture makes the control boundary visible: “The Accenture Gemini Enterprise Business Group will help clients achieve these outcomes faster, bringing together the talent, advanced AI and data capabilities, and industry expertise needed to reinvent with confidence and create… The leadership implication for Agentic AI is to connect Accenture and Google Cloud Deepen Partnership with Formation of New Accenture Gemini Enterprise Business Group - Accenture to a named owner, a measurable baseline, and a review path for exceptions.

Nutanix Inc. today introduced new capabilities intended to help enterprises run agentic artificial intelligence applications alongside existing virtual machines and containerized workloads without splitting their infrastructure into separate management silos. The updates include the general availability of Nutanix Enterprise AI 2.8 and a forthcoming release of Nutanix Kubernetes Platform 2.19.

Nutanix also made its Service Provider Central program for cloud partners generally available and detailed a partner program for building cloud, Kubernetes, AI and virtual machine migration services. The company calls its approach “dual-native” because its platform treats VMs and containers as first-class infrastructure.

That means customers can run Kubernetes on Nutanix’s Acropolis Hypervisor virtualization platform when isolation and operational consistency are priorities or deploy Kubernetes directly on bare-metal systems for workloads that require different performance or resource profiles. The distinction is not simply the ability to support both architectures, said Thomas Cornely, executive vice president of product management at Nutanix.. “It’s not about getting containers working; it’s about how you operate and manage those containers,” he said.

Why it mattersThe agentic ai implication is concrete because SiliconANGLE ties the capability to an operating choice: That means customers can run Kubernetes on Nutanix’s Acropolis Hypervisor virtualization platform when isolation and operational consistency are priorities or deploy… The leadership implication for Agentic AI is to connect Nutanix expands cloud platform with controls for agentic AI - SiliconANGLE to a named owner, a measurable baseline, and a review path for exceptions.

There are two prevailing schools of thought regarding the AI-agent workforce. One says organizations should prepare for agentic AI , in which the human-in-the-middle role is largely transitional and serves to buy time to improve agents’ accuracy and build trust in their decision-making.

Others say AI agents will largely augment humans , but expect workflows to change drastically from task-based processes to more asynchronous, choreographed operations. Businesses will likely have a mix of agentic and human-augmented AI agents, with many more in pilot stages.

As part of this transformation, CIOs need to consider how to evolve the IT organization and upskill IT employees for this future. According to Deloitte’s 2026 Global Technology Leadership Survey , 75% of IT leaders agree their operating models and processes must change within the next 12 to 18 months to drive greater value. “Upskilling IT for an AI-agent workforce requires more than training; it requires behavior change because as AI takes on more routine development activities, technology professionals increasingly focus on validating, governing, and directing AI-generated outputs,” says Doug Vargo, VP of consulting services and head of the national AI and alliances team at CGI.

Why it matterscio.com changes the risk calculation for this workflow. The upside is linked to Others say AI agents will largely augment humans , but expect workflows to change drastically from task-based processes to more asynchronous, choreographed operations. Businesses will likely have a… The leadership implication for Agentic AI is to connect How to upskill IT for agentic AI: 7 pathways to success - cio.com to a named owner, a measurable baseline, and a review path for exceptions.

Golden Technology’s architecture playbook argues that AI-ready modernization should preserve reliable legacy systems while adding modular integration, governed data, automation and generative AI. It recommends mapping business capabilities, classifying systems to retain, integrate, refactor or retire, and establishing owners, quality thresholds, access rules and lineage for priority data products.

It treats the AI pipeline as a production system with identity, evaluation, observability, human review and incident response. Golden Technology describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersGolden Technology makes the control boundary visible: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence. The buyer question is whether that boundary is strong… The leadership implication for AI Enablement, AI Solutions, and AI Architecture is to connect Scaling modern enterprise architecture in 2026 to a named owner, a measurable baseline, and a review path for exceptions.

Oracle Blogs reports the development described in "Private Agent Factory: Accelerate Enterprise AI with Simpler Deployment, Stronger Governance, and Greater Choice - Oracle Blogs". The capability is tied to the ai enablement, ai solutions, and ai architecture workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Oracle Blogs describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersThe ai enablement, ai solutions, and ai architecture implication is concrete because Oracle Blogs ties the capability to an operating choice: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope… The leadership implication for AI Enablement, AI Solutions, and AI Architecture is to connect Private Agent Factory: Accelerate Enterprise AI with Simpler Deployment, Stronger Governance, and Greater Choice - Oracle Blogs to a named owner, a measurable baseline, and a review path for exceptions.

#FrontPageLIVE 🔴 AI, chips, pricing and India’s next wave of innovation are all in focus on the latest edition of AIM Front Page. From a massive new US research investment to a robotics IPO that surged on debut, the global AI landscape is moving fast.

Here’s what we’re tracking: - Micron Technology announces a $10 billion investment in a new research institution, Micron Research Labs. - OpenAI , Anthropic and Palantir Technologies introduce zero data retention policies as enterprise privacy concerns grow. - Chinese robotics company Unitree Robotics raises $905 million in its IPO and surges 460% on day one. - Indian IT firms face pressure to cut costs by 25 - 30%, pushing the industry towards AI-driven, outcome-based pricing. - Shaadi.com founder and Shark Tank India judge Anupam Mittal sparks debate over how AI companies price for the Indian market. - Tamil Nadu gets its first Anthropic Claude Innovation Lab in Hosur. - MWire Labs founder Badal Nyalang will soon join Front Page live to discuss Lemka, building foundational AI models for Northeast Indian languages, and driving indigenous linguistic sovereignty from Shillong. Catch the full stories and what they mean on AIM Front Page LIVE at 1 PM: https://lnkd.in/geJyCFpn #AI #ArtificialIntelligence #IndiaTech #TechNews #AIIndia #Innovation #FrontPageLIVE To view or add a comment, sign in India's AI ecosystem is earning its place on the world stage.

As per the Stanford Global AI Vibrancy Report, India ranks third globally in AI competitiveness and ecosystem vibrancy and is the second-largest contributor to AI projects on GitHub. #IndiaAI #GlobalRecognition #DigitalIndia #ProudMoment 🔗 Read here: https://lnkd.in/gxnDcE3B Ashwini Vaishnaw | Jitin Prasada | S Krishnan | Sudeep Shrivastava | Ministry of Electronics and Informat LinkedIn describes the capability as part of its enterprise offering. The source does not disclose an independent production benchmark.

Why it mattersLinkedIn changes the risk calculation for this workflow. The upside is linked to Here’s what we’re tracking: - Micron Technology announces a $10 billion investment in a new research institution, Micron Research Labs. - OpenAI , Anthropic and Palantir Technologies introduce zero… The leadership implication for AI Enablement, AI Solutions, and AI Architecture is to connect #FrontPageLIVE 🔴 AI, chips, pricing and India’s next wave of innovation are all in focus on the latest edition of AIM Front Page. From a massive new US research investment to a rob to a named owner, a measurable baseline, and a review path for exceptions.

You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer).

In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. The convergence of synthetic biology, artificial intelligence (AI), and automation (SynBioxAI) creates research activities that simultaneously engage biosecurity, AI governance, export control, and data sovereignty frameworks - none of which are designed for convergent science.

We conduct a comprehensive cross-jurisdictional analysis of this regulatory landscape across sixteen nations, identifying critical ambiguities where novel SynBioxAI objects fall between established regulatory categories. Through seven realistic collaboration scenarios, we demonstrate that regulatory friction is multiplicative rather than additive. We propose the SynBioxAI Regulatory Interoperability Toolkit (RIOT): a practical, seven-lens institutional framework that gives research institutions the capacity to navigate regulatory divergence efficiently and transparently.

Why it mattersNature makes the control boundary visible: We conduct a comprehensive cross-jurisdictional analysis of this regulatory landscape across sixteen nations, identifying critical ambiguities where novel SynBioxAI objects fall between established regulatory categories. Through seven… The leadership implication for AI Governance, policy, safety, and compliance, AI Risk is to connect Navigating regulatory fragmentation in the convergence of synthetic biology, artificial intelligence, and automation - Nature to a named owner, a measurable baseline, and a review path for exceptions.

Ask any CEOs or boards how they oversee AI risk, and you'll almost always get a version of the same answer: legal owns the compliance review, IT owns the systems, the CISO owns security. But since AI governance is a multidisciplinary priority for AI-native leaders, it requires a new approach to leadership and responsibility.

Traditional cybersecurity governance was built to protect data: prevent unauthorized access, lock the perimeter, keep the records clean. That model worked when systems stored and processed information.

But it breaks down when systems generate intelligence from information - and that's exactly what AI does. When a machine learning model trains on a dataset, it doesn't store that data. It absorbs its statistical patterns into millions of parameters.

Why it mattersThe ai governance, policy, safety, and compliance, ai risk implication is concrete because Bessemer Venture Partners ties the capability to an operating choice: But it breaks down when systems generate intelligence from information - and that's exactly what AI does. When a… The leadership implication for AI Governance, policy, safety, and compliance, AI Risk is to connect What every CEO needs to know about AI governance - Bessemer Venture Partners to a named owner, a measurable baseline, and a review path for exceptions.

Nearly three out of four financial institutions cannot say which of their vendors use artificial intelligence; Risk and compliance firm Ncontracts's 2026 State of Third-Party Risk Management Survey found that 72 percent of institutions remained only partially aware of vendor AI use, and 9 percent had not assessed it at all. That blind spot now poses the sharper third-party risk for insurance carriers than the AI itself, says John Romano (pictured), a principal at Baker Tilly in Philadelphia who leads the accounting and advisory firm's insurance regulatory practice.

A managing general agent (MGA) that uses generative AI to summarize claims files sits at the low end of any risk scale, said Romano. Meanwhile, a partner that runs proprietary models to: select risks, set pricing, triage claims, score severity, or refer fraud, sits far higher.

“If it affects price, if it affects coverage, if it affects claims outcomes, if it affects fraud [or] any customer communications that are regulatory bound, then it deserves heightened oversight,” Romano said. One commercial insurers AI chatbot approved a claim at the wrong figure, ten-times too large. “It was supposed to be $50,000, but the chatbot said $500,000,” he said.

Why it mattersinsurancebusinessmag.com changes the risk calculation for this workflow. The upside is linked to A managing general agent (MGA) that uses generative AI to summarize claims files sits at the low end of any risk scale, said Romano. Meanwhile, a partner that runs proprietary models… The leadership implication for AI Governance, policy, safety, and compliance, AI Risk is to connect TPA governance risk hides in vendors' AI control, requires carrier strategy: Baker Tilly - insurancebusinessmag.com to a named owner, a measurable baseline, and a review path for exceptions.

AI is Improving Productivity and Quality of Work, while Cultural Barriers and Gaps in Work Redesign Could Limit AI Success ARLINGTON, Va. , Sept. 8, 2026 /PRNewswire/ -- Artificial intelligence (AI) has moved beyond experimentation and isolated technology applications and is increasingly embedded in the core operations of organizations.

But new research from Eagle Hill Consulting finds that management practices, workforce strategies, and organizational cultures are not evolving at the same pace. A new Eagle Hill Consulting AI Capabilities survey among senior business decision makers finds that organizations are using AI at nearly equal rates for business operations (73 percent of respondents), decision support and analytics (72 percent), and employee productivity and knowledge work (71 percent).

At the same time, those leaders report that AI is delivering its strongest value in improving how work gets done: 66 percent report improved employee productivity, 59 percent report improved operational efficiency, 55 percent report improved quality of work, and 53 percent report improved customer experience. "AI is no longer just a technology implementation or a collection of productivity tools. It is part of how organizations operate, make decisions, and get work done," said Melissa Jezior , president and chief executive officer of Eagle Hill Consulting.

Why it mattersPR Newswire makes the control boundary visible: At the same time, those leaders report that AI is delivering its strongest value in improving how work gets done: 66 percent report improved employee productivity, 59 percent report improved operational efficiency, 55 percent… The leadership implication for Enterprise AI People and Culture is to connect New Eagle Hill Consulting Research Finds AI Is Reshaping How Organizations Work, But Leadership and Culture Lag Behind to a named owner, a measurable baseline, and a review path for exceptions.

Global consulting firm honored for embedding innovation, AI training and employee-driven problem-solving into the workplace experience MENLO PARK, Calif., Sept. 9, 2026 /PRNewswire/ -- Global consulting firm Protiviti has been named to Fast Company 's Best Workplaces for Innovators in North America 2026 list , underscoring the firm's commitment to making innovation a practical, employee-driven part of how people learn, collaborate and deliver value for clients.

This recognition reflects Protiviti's investment in a workplace culture that helps employees turn promising ideas into scalable solutions, better ways of working and measurable business impact. Across the firm, employees have access to structured programs, innovation communities, advanced artificial intelligence tools, generative AI training, design thinking resources and opportunities to submit, test and advance new ideas.

Innovation training and AI enablement: All employees firmwide participate in a core innovation curriculum including design thinking, agile principles and experiential learning as well as generative AI training. Employee-led ideas: Team members submit use cases, join internal innovation challenges and contribute to global communities focused on improving business processes and client outcomes. Innovation ambassadors: Employee ambassadors support the exchange of ideas across geographies, roles and teams.

Why it mattersThe enterprise ai people and culture implication is concrete because Yahoo! Finance Canada ties the capability to an operating choice: Innovation training and AI enablement: All employees firmwide participate in a core innovation curriculum including design thinking, agile… The leadership implication for Enterprise AI People and Culture is to connect Protiviti Named to Fast Company Best Workplaces for Innovators 2026 List - Yahoo! Finance Canada to a named owner, a measurable baseline, and a review path for exceptions.

AI Transformation, Workforce Upskilling, Learning Excellence, Innovation, Operational Efficiency As advances in artificial intelligence accelerated across industries, Bausch + Lomb recognized an opportunity to build AI capabilities at scale while improving productivity, innovation, and operational performance. Leadership identified a gap between employee awareness of AI and the ability to apply it meaningfully in day-to-day work.

To address that challenge, the company launched its AI Academy powered by Coursera, making foundational AI learning a core expectation across the enterprise. The initiative was designed to do more than increase AI literacy.

It aimed to create a workforce capable of identifying opportunities, solving business problems, and generating measurable value through AI-powered solutions. By embedding AI learning into performance management and innovation programs, Bausch + Lomb positioned AI capability as a strategic business priority rather than a standalone training initiative. Bausch + Lomb faced a common challenge confronting many organizations: employees understood the potential of AI, but lacked the confidence, practical skills, and structured pathways needed to apply it effectively.

Why it matterscoursera.org changes the risk calculation for this workflow. The upside is linked to To address that challenge, the company launched its AI Academy powered by Coursera, making foundational AI learning a core expectation across the enterprise. The initiative was designed to do… The leadership implication for Enterprise AI People and Culture is to connect Coursera helps Bausch + Lomb save 32,000+ hours - coursera.org to a named owner, a measurable baseline, and a review path for exceptions.

Caterpillar Inc reports the development described in "Caterpillar and FieldAI Advance AI-Powered Industrial Innovation - Caterpillar Inc". The capability is tied to the digital twins and industrial simulation workflow and its responsible operating team.

The source provides a basis for testing ownership, controls, measurable results and limits before wider deployment. Caterpillar Inc describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersCaterpillar Inc makes the control boundary visible: The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence. The buyer question is whether that boundary is strong… The leadership implication for Digital twins and industrial simulation is to connect Caterpillar and FieldAI Advance AI-Powered Industrial Innovation - Caterpillar Inc to a named owner, a measurable baseline, and a review path for exceptions.

Roche has launched one of the largest artificial intelligence infrastructures in the pharmaceutical industry, powered by over 3,500 NVIDIA Blackwell graphics processing units. The platform integrates real-world experimental data into model training through a “Lab-in-the-Loop” approach to accelerate drug discovery and development.

The pharmaceutical industry is entering a new computational era with Roche’s deployment of its global artificial intelligence factory , a hybrid-cloud infrastructure designed to integrate advanced modeling across the full drug development lifecycle. Powered by more than 3,500 NVIDIA Blackwell graphics processing units (GPUs), this system represents one of the largest computational investments in pharmaceutical research.

By leveraging NVIDIA’s healthcare artificial intelligence platform, Roche aims to scale biological modeling in areas such as genomics, molecular design, and diagnostics. The platform is designed to accelerate target identification, molecule optimization, and process development in parallel. A central component is the “Lab-in-the-Loop” system, where experimental results continuously refine predictive models in near real time.

Why it mattersThe digital twins and industrial simulation implication is concrete because 2 Minute Medicine ties the capability to an operating choice: By leveraging NVIDIA’s healthcare artificial intelligence platform, Roche aims to scale biological modeling in areas such as genomics… The leadership implication for Digital twins and industrial simulation is to connect Roche and NVIDIA deploy the pharmaceutical industry’s largest artificial intelligence factory - 2 Minute Medicine to a named owner, a measurable baseline, and a review path for exceptions.

When Unilever developed the new FIFA World Cup - branded body washes, soaps, and deodorants, the consumer-products giant relied on digital twin technology to ensure the limited-edition line was match-ready. Deodorant stick manufacturing at the company’s Raeford site in North Carolina relied on a virtual replica of physical systems, known as a digital twin, to continuously monitor critical parameters such as temperature, pressure, and flow.

Digital twins can be used to observe, analyze, and optimize physical manufacturing equipment and even entire factory floors, enabling manufacturers to simulate production scenarios, identify inefficiencies, and improve their operations. According to Vicky Cuthbert, Chief Product Supply Chain Officer for personal care products at Unilever, this virtual environment identified abnormal patterns earlier, helping engineers understand potential root causes.

“It enables faster decision-making, reduces troubleshooting time, and supports more efficient and reliable production,” Cuthbert told BeautyMatter. “This digital twin has enabled Raeford to efficiently support the production of limited-edition Dove products for the FIFA World Cup 2026.” As global supply chain pressures persist amid geopolitical conflicts in the Middle East and tariffs that have increased input costs, beauty makers have sought to improve their physical operations with digital twins, Internet of Things (IoT) sensors, and artificial intelligence. These upgrades allow manufacturers to get a real-time view of factory operations, detect issues earlier, conduct more virtual packaging and product innovation to lower costs and reduce waste, improve product quality and consistency, and make workers more productive.

Why it mattersBeautyMatter changes the risk calculation for this workflow. The upside is linked to Digital twins can be used to observe, analyze, and optimize physical manufacturing equipment and even entire factory floors, enabling manufacturers to simulate production scenarios, identify… The leadership implication for Digital twins and industrial simulation is to connect How AI Digital Twins Are Transforming Beauty Manufacturing and Product Innovation - BeautyMatter to a named owner, a measurable baseline, and a review path for exceptions.

AI agents are being widely deployed inside businesses today, but do they actually know how to get results? This is the fundamental question being asked in many boardrooms as enterprises implement AI strategies tied to agents performing key tasks.

Impetus Technologies Inc. has built its value proposition on the belief that it can bridge the “context gap,” the space between what AI models know and the unique attributes of the organizations they serve. “We were in this world, and we knew what data is and where it sits,” said Deepak Khosla (pictured), chief growth officer and head of AI at Impetus.

“We figured out the gap is not the large language models. The gap is the context and that’s why we want to fill that gap.” Khosla spoke with John Furrier during an exclusive conversation on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how Impetus builds an operational framework to bring context to agentic AI. (* Disclosure below.) To generate the necessary context, Impetus employs an enterprise AI operational framework to test and govern the data that agents use.

Why it mattersSiliconANGLE makes the control boundary visible: “We figured out the gap is not the large language models. The gap is the context and that’s why we want to fill that gap.” Khosla spoke with John Furrier during an exclusive conversation on theCUBE, SiliconANGLE Media’s… The leadership implication for Ontology, knowledge graph, and semantic layer developments is to connect AI agents get smarter with context engineering - SiliconANGLE to a named owner, a measurable baseline, and a review path for exceptions.

The insurer built TravelersLLM to handle insurance-specific queries, while broad reasoning, research and coding queries are saved for costlier frontier models. AI cost management has become a primary concern for executives.

Some companies, such as Travelers Insurance, are adding flexibility to their model selection - while also building their own to mitigate costs. In June, the insurer, which generated $49 billion in revenues in 2025 and employs 30,000, unveiled a proprietary large language model called TravelersLLM .

The internal model delivers better results than commercially available AI models when it comes to insurance-related questions and is cheaper to run than frontier models, according to Mojgan Lefebvre, EVP, chief technology and operations officer at Travelers . Although the company worked to build and incorporate a lower-cost internal model into its ecosystem, Lefebvre said the model works alongside frontier models and is not a replacement for the innovation and advancements offered by frontier developers. When Travelers’ applications handle a query, it’s either directed to TravelersLLM or a frontier model depending on the task, Lefebvre said.

Why it mattersThe ontology, knowledge graph, and semantic layer developments implication is concrete because CIO Dive ties the capability to an operating choice: The internal model delivers better results than commercially available AI models when it comes to insurance-related questions and… The leadership implication for Ontology, knowledge graph, and semantic layer developments is to connect Travelers builds its own LLM, cutting AI costs to a named owner, a measurable baseline, and a review path for exceptions.

Enterprise Knowledge distinguishes an ontology as a formal model of domain entities, attributes, relationships and constraints from a knowledge graph that instantiates those concepts with linked organizational data. It explains that ontology injection anchors model reasoning in domain semantics, while Graph RAG retrieves relevant connected data at query time.

The combination can constrain generative AI, standardize integration across systems and support reasoning across connected domains rather than isolated text fragments. Enterprise Knowledge describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersEnterprise Knowledge changes the risk calculation for this workflow. The upside is linked to The combination can constrain generative AI, standardize integration across systems and support reasoning across connected domains rather than isolated text fragments. Enterprise… The leadership implication for Ontology, knowledge graph, and semantic layer developments is to connect Ontology and Knowledge Graph in the Age of AI and Agents to a named owner, a measurable baseline, and a review path for exceptions.

Large construction projects still finish about 20 percent late and spend up to 80 percent more than budgeted (McKinsey). Those overruns shred margins, strain cash flow, and bruise reputations.

AI-powered platforms blend historical data with live field inputs to flag issues weeks in advance - one engine even averted a 250-day delay on a £4.1 billion London rail tunnel (nPlan). We scored seven standout tools on predictive accuracy, data integration, usability, ecosystem fit, implementation effort, and value.

The rundown reveals who dominates enterprise-grade analytics, who wins with mobile-first field design, and why 54 percent of owners already rely on connected data to stay on time and on budget (Dodge Construction Network). You might expect decades of lessons to tame runaway budgets, yet overruns remain the rule rather than the exception. Modern builds combine thousands of inter-dependent tasks, specialty trades, and regulatory checkpoints.

Why it mattersBBN Times makes the control boundary visible: The rundown reveals who dominates enterprise-grade analytics, who wins with mobile-first field design, and why 54 percent of owners already rely on connected data to stay on time and on budget (Dodge Construction Network). You might… The leadership implication for AI in Construction is to connect Best Construction Project Management Software: 7 Tools That Forecast Overruns - BBN Times to a named owner, a measurable baseline, and a review path for exceptions.

PlanRadar AI Agents automate routine construction tasks, including RFI responses, using project data while tracking actions and maintaining user access controls. PlanRadar has introduced AI Agents, a new feature designed to automate routine tasks across construction, real estate and facility management projects.

Users can create agents with natural-language prompts or choose from prebuilt options. The agents can complete assigned actions without an approval step.

One example, the Response agent, reviews incoming requests for information (RFIs), searches project documents and drafts a response with the relevant source. PlanRadar said the process can reduce initial response times from hours to minutes. Agent activity is logged and attributed, and users can test an agent before deploying it.

Why it mattersThe ai in construction implication is concrete because For Construction Pros ties the capability to an operating choice: One example, the Response agent, reviews incoming requests for information (RFIs), searches project documents and drafts a response with the relevant source.… The leadership implication for AI in Construction is to connect PlanRadar Launches AI Agents for Construction Workflows - For Construction Pros to a named owner, a measurable baseline, and a review path for exceptions.

Mastt’s March-to-June 2026 survey says 9.3% of construction project managers use AI agents today and 46.3% plan to start. Reporting was the leading perceived value area at 84.3%, followed by document management at 69.4%, cost management and forecasting at 65.7%, contract administration at 63.9% and scheduling at 62%.

The survey says 75.9% see AI speeding at least 11% of their workday, while reporting automation was the most requested tool. Mastt describes the capability as part of its enterprise offering.

The source does not disclose an independent production benchmark. The operational result therefore depends on implementation scope, controls and local workflow evidence.

Why it mattersMastt changes the risk calculation for this workflow. The upside is linked to The survey says 75.9% see AI speeding at least 11% of their workday, while reporting automation was the most requested tool. Mastt describes the capability as part of its enterprise offering.; the… The leadership implication for AI in Construction is to connect State of AI in Construction Project Management 2026 to a named owner, a measurable baseline, and a review path for exceptions.

Verisk launched Fraud Discovery, a comprehensive fraud prevention platform for the insurance industry, enhancing detection and investigation capabilities. Verisk has launched Verisk Fraud Discovery, a comprehensive fraud prevention platform designed to combat the increasing complexity of insurance fraud, which has become more sophisticated due to economic pressures and technological advancements.

The platform integrates fraud intelligence, analytics, digital forensics, and case management into a single solution, enabling insurers to uncover hidden relationships and patterns in fraudulent claims effectively. With £1.16 billion in fraud detected in the UK in 2024 alone, the need for improved intelligence and investigative capabilities is crucial.

The modular design of Verisk Fraud Discovery allows organizations to tailor its functionalities based on their fraud maturity and operational needs. Early adopters include Hiscox, Allianz, and law firm Weightmans, who emphasize the importance of advanced technology in enhancing fraud detection efforts. Verisk continues to support insurers in strengthening their fraud prevention strategies using advanced analytics and connected intelligence.

Why it mattersQuiver Quantitative makes the control boundary visible: The modular design of Verisk Fraud Discovery allows organizations to tailor its functionalities based on their fraud maturity and operational needs. Early adopters include Hiscox, Allianz, and law firm Weightmans, who… The leadership implication for AI in Insurance is to connect Verisk Launches Fraud Discovery Platform to Unify Insurance Fraud Intelligence, Analytics and Case Management - Quiver Quantitative to a named owner, a measurable baseline, and a review path for exceptions.

8th September 2026 - ’ Accenture surveyed 263 senior insurance executives with direct accountability for AI, data, technology, and business transformation across the Americas, Europe, and Asia-Pacific. The survey involved conducting in-depth interviews with 15 executives from leading global carriers to understand the current state of AI transformation across Property & Casualty and Life insurance.

The report revealed that more than four in five (81%) insurers are seeing real revenue gains from AI, achieving at least a 5% improvement in gross written premiums from AI and data initiatives, driven by better pricing, personalisation and cross-selling. Additional data from Accenture’s latest 2026 Pulse of Change survey reflects this momentum with 86% of insurance employees stating that AI tools have increased their overall productivity.

At the same time, only 23% of insurers have achieved true enterprise-wide integration, which highlights the need for a combined AI, business and people strategy, analysts state. Other findings include legacy and data barriers, with legacy integration at 50% and data quality/accessibility at 45% being the top barriers to scaling AI. Moreover, although 70% of insurers run targeted AI skills initiatives, only 14% have scaled these programs enterprise-wide, keeping capabilities concentrated in specialist groups.

Why it mattersThe ai in insurance implication is concrete because Reinsurance News ties the capability to an operating choice: At the same time, only 23% of insurers have achieved true enterprise-wide integration, which highlights the need for a combined AI, business and people strategy… The leadership implication for AI in Insurance is to connect When it comes to AI adoption most re/insurers are leaving value on the table: Accenture - Reinsurance News to a named owner, a measurable baseline, and a review path for exceptions.

Insurers are automating claims and underwriting faster than their verification systems are developing, according to new research commissioned by Clearspeed. The study found a widening gap between AI adoption and insurers’ ability to validate information used in automated decisions.

Researchers examined how insurers handle evidence as AI takes on more decisions, customer interactions and workflow handoffs. The research covered 76 public filings from 49 insurers and reinsurers alongside 31 insurance studies.

Researchers also conducted 16 interviews with claims and underwriting executives at insurance companies in the US and UK. Insurers are increasingly automating evidence reviews and customer interactions as generative AI makes manipulated information easier to produce. Photos and documents now require greater scrutiny, and synthetic voices or identities create another source of fraud exposure.

Why it mattersbeinsure.com changes the risk calculation for this workflow. The upside is linked to Researchers examined how insurers handle evidence as AI takes on more decisions, customer interactions and workflow handoffs. The research covered 76 public filings from 49 insurers and… The leadership implication for AI in Insurance is to connect Insurance AI adoption exposes verification gap, study finds - beinsure.com to a named owner, a measurable baseline, and a review path for exceptions.

Amazon Logistics dominates U.S. e-commerce with 40,000+ trucks and 110 aircraft, but its limited international reach pushes shippers toward global specialists. UPS - Best for worldwide delivery across 220+ countries C.H.

Robinson - Best for asset-light brokerage across four continents Kuehne + Nagel - Best for large-scale warehousing across 100 countries J.B. Hunt - Best for North American truckload and intermodal freight FedEx - Best for air-heavy express shipping globally DHL Group - Best for high-volume parcel delivery at global scale XPO Logistics - Best for LTL freight in North America and Europe Ryder Supply Chain - Best for dedicated fleet and warehouse management This guide ranks the top 10 logistics companies in the USA for 2026 - covering their services, fleet sizes, global reach, revenue, ratings, and what each is genuinely best for - so you can make an informed decision.

The global logistics market was worth approximately $9.41 trillion in 2023 and is projected to exceed $14.08 trillion by 2028 - growing at an implied CAGR of ~8.4%, driven by e-commerce expansion, supply chain digitization , and rising consumer delivery expectations. For U.S. brands, retailers, and e-commerce operators, choosing the right logistics partner is one of the most consequential operational decisions you will make. It affects delivery speed, customer satisfaction, RTO rates, cost structure, and your ability to scale.

Why it mattersClickPost makes the control boundary visible: The global logistics market was worth approximately $9.41 trillion in 2023 and is projected to exceed $14.08 trillion by 2028 - growing at an implied CAGR of ~8.4%, driven by e-commerce expansion, supply chain digitization , and… The leadership implication for AI in Logistics & Warehousing is to connect Top 10 Logistics Companies in the USA 2026: Ranked & Reviewed - ClickPost to a named owner, a measurable baseline, and a review path for exceptions.

NVIDIA’s agreement to acquire Hugging Face for approximately $12.9 billion looks, at first, like another large transaction in an AI market already full of large numbers. Look more closely, however, and this is considerably more interesting than a semiconductor company buying a software company.

NVIDIA already dominates one of the most important layers of artificial intelligence: accelerated computing. Hugging Face occupies a different position.

It has become one of the principal places where developers discover models, evaluate them, modify them, and decide how and where those models should run. NVIDIA is therefore not simply acquiring another AI asset. It is moving toward the interchange where models, applications, developers, and computing infrastructure meet.

Why it mattersThe ai in logistics & warehousing implication is concrete because Logistics Viewpoints ties the capability to an operating choice: It has become one of the principal places where developers discover models, evaluate them, modify them, and decide how and where those models should… The leadership implication for AI in Logistics & Warehousing is to connect NVIDIA Is Buying the Distribution Layer of AI - Logistics Viewpoints to a named owner, a measurable baseline, and a review path for exceptions.

Artificial intelligence is becoming one of the biggest talking points in logistics, with technology providers promising smarter decision-making, greater efficiency and increasingly autonomous warehouse operations. But there is a fundamental problem: AI can only make good decisions if the data behind those decisions accurately reflects what is happening on the warehouse floor.

In the latest episode of Logistics Business Conversations , host Peter MacLeod is joined by Oana Jinga, Chief Commercial and Product Officer at Dexory , to explore why accurate physical data could be the missing ingredient in many warehouse AI strategies. The discussion looks at the gap that can exist between what a Warehouse Management System says is happening and the physical reality inside the building.

Jinga explains that warehouse data accuracy can sometimes be significantly lower than operators believe, creating problems that can ripple through picking, fulfilment, productivity and customer service. Inventory accuracy is only part of the picture. Effective AI also needs to understand the physical environment around the stock - including warehouse space, rack structures, movement, machinery and the shape and size of goods.

Why it mattersLogistics Business changes the risk calculation for this workflow. The upside is linked to In the latest episode of Logistics Business Conversations , host Peter MacLeod is joined by Oana Jinga, Chief Commercial and Product Officer at Dexory , to explore why accurate physical… The leadership implication for AI in Logistics & Warehousing is to connect Why Warehouse AI Fails Without Accurate Physical Data - Podcast - Logistics Business to a named owner, a measurable baseline, and a review path for exceptions.

But the technology should be viewed as nothing more than a tool, and one needing a policy to use effectively and safely In a first of its kind, STN EXPO West introduced a new session series focused on the use of artificial intelligence in school transportation. Held July 14, the track focused on AI in dispatch, personnel productivity, budgeting, fleet management, risk mitigation and bell schedules.

Prior to the six breakout sessions, a general session “Beyond ChatGPT: The AI Revolution in Student Transportation,” consisted of all AI session speakers and served as an opening panel, helping transportation leaders cut through the hype to define what AI actually is - and isn’t - today. Moderated by STN Editor-in-Chief Ryan Gray, the panel consisted of Richard Jimenez, director of transportation for Placentia-Yorba Linda Unified School District; Timothy Purvis of Pupil Transportation Information; GP Singh, the founder of Bytecurve and a strategic advisor to Transit Technologies after selling his company last year; and Rosalyn Vann-Jackson, Ph.D., the executive director of enrollment and student services for Broken Arrow Public Schools in Oklahoma and founder/CEO of Bloom Bigger Coaching.

The panel dissected AI’s transformative impact, referencing tools like ChatGPT, Google Gemini and Claude. It spotlighted Broken Arrow’s integration of AI for daily operations, policy analysis, comparative studies on sick leave and overtime (notably for special education aides), and public records requests. Jimenez shared how AI use at Placentia-Yorba Linda streamlines compliance, special education (IEP/IDEA) processes, legal messaging to parents, and multi-services bid response summarization, reducing manual workloads and enhancing communication.

Why it mattersSchool Transportation News makes the control boundary visible: The panel dissected AI’s transformative impact, referencing tools like ChatGPT, Google Gemini and Claude. It spotlighted Broken Arrow’s integration of AI for daily operations, policy analysis, comparative studies on… The leadership implication for AI in Fleet Management is to connect AI Takes a Larger Role in School Transportation Management - School Transportation News to a named owner, a measurable baseline, and a review path for exceptions.

Check out the latest Ford Pro software updates, including Google Maps integration, Remote Vehicle Alarm integration, Motor Pool for easier management of shared pool vehicles, and more. Ford Pro's latest software updates give fleet managers better vehicle insights, improved telematics, and new tools to manage drivers and fleet vehicles.

Every month, Ford Pro releases software updates to make fleet management easier. The latest enhancements give fleet managers quicker access to critical information, more visibility across vehicles, and smarter tools that reduce daily friction.

In August 2026, Ford Pro introduced several new features and enhancements, including an expansion of Ford Pro AI, integration of Google Maps, Remote Vehicle Alarm integration, a new Dashcam settings tab, and more. Ford Pro said fleet managers spend, on average, over 23 hours a week juggling routine tasks, from scheduling service to managing drivers and tracking costs. Now, Ford Pro AI is bringing relief to even more fleets.

Why it mattersThe ai in fleet management implication is concrete because Work Truck Online ties the capability to an operating choice: In August 2026, Ford Pro introduced several new features and enhancements, including an expansion of Ford Pro AI, integration of Google Maps, Remote Vehicle… The leadership implication for AI in Fleet Management is to connect Ford Pro Software Updates: August 26 - Work Truck Online to a named owner, a measurable baseline, and a review path for exceptions.

Brigade Electronics demoed its AI360 camera system in VR at WasteExpo. Waste Management has explored VR to cut time technicians lose walking away from repairs to look up information.

For fleet and MRF operators, VR is becoming a procurement tool for training, maintenance and safety validation. This story was produced through MarketScale .

See how Transportation teams put it to work with Partner & Channel Enablement . Key facts, context, and what it means, in one minute. VR in waste fleets is starting to justify itself on technician time, not novelty, Waste Dive’s Waste Management example frames the ROI conversation around wrench time lost to information lookups.

Why it mattersMarketScale changes the risk calculation for this workflow. The upside is linked to For fleet and MRF operators, VR is becoming a procurement tool for training, maintenance and safety validation. This story was produced through MarketScale .; the evidence still requires local… The leadership implication for AI in Fleet Management is to connect VR is moving from trade-show demo to maintenance tool in waste fleets - MarketScale to a named owner, a measurable baseline, and a review path for exceptions.