Enterprise AI Daily Briefing

Expanded collaboration can help organizations build intelligent enterprises through scaled enterprise AI, M&A transformation, and ERP modernization NEW YORK , Sept 3, 2026 /PRNewswire/ -- PwC US and Palantir Technologies Inc. (NASDAQ: PLTR ) today announced an expansion of their strategic alliance to help organizations use data and AI to transform critical business operations and deliver measurable enterprise value.

The alliance will initially focus on three priority transformation areas: scaling enterprise AI, transforming mergers and acquisitions, and modernizing enterprise resource planning (ERP) systems. The expanded collaboration combines Palantir's artificial intelligence and data platforms with PwC's industry, engineering, and business transformation experience. Together, PwC and Palantir will bring AI deeper into their clients' enterprise - transforming how decisions are made, how work gets done and how organizations address complex business challenges.

The investment reflects a renewed focus by PwC and Palantir on areas where AI is helping reshape how complex transformations are delivered, including data migrations, agentic workforce solutions, and technology integrations and separations. PwC is also investing in expanding its technical and functional talent across these areas. "AI's greatest opportunity isn't in isolated use cases - it's in fundamentally changing how enterprises operate," said Patrick Pugh, Global Alliances & Ecosystem Leader, PwC.

Why it mattersPR Newswire reports 3, 2026 /PRNewswire/ -- PwC US and Palantir Technologies Inc. (NASDAQ: PLTR ) today announced an expansion of their strategic alliance to help organizations use data and AI to transform critical business operations and deliver measurable enterprise value.. That matters for enterprise portfolio review because enterprise AI portfolio leader must decide whether PwC and Palantir Expand Strategic Alliance to Help Organizations Scale can improve time to value and control coverage without weakening accountability; The investment reflects a renewed focus by PwC and Palantir on areas where AI is helping reshape how is the boundary for the claim. The leadership test is to connect this evidence to enterprise ai, a responsible owner, and a measurable production outcome.

The expanded collaboration targets enterprise AI deployment, M&A transformation and ERP modernization, giving Palantir Technologies Inc. (NASDAQ:PLTR) a broader route for embedding its platforms into complex corporate workflows Palantir Technologies Inc. (NASDAQ:PLTR) and PwC US are expanding their strategic alliance around three areas: enterprise AI, M&A transformation and ERP modernization.

The collaboration combines Palantir Foundry and AIP with PwC's engineering, industry and transformation capabilities, potentially extending Palantir technology deeper into enterprise operations. The companies are introducing an AI-native deals IT platform designed to help clients execute transactions up to 50% faster and cut one-time transaction costs by up to 45%. PwC and Palantir will also target SAP and ERP transformation, using AI to improve data quality and identify process inefficiencies before implementation.

No new contract value or revenue contribution for Palantir was disclosed, making adoption and resulting commercial activity important measures of the alliance's impact. Palantir and PwC are broadening their relationship beyond individual AI deployments to address large-scale enterprise transformation projects. The expanded alliance will initially concentrate on scaling AI into production, transforming M&A processes and modernizing ERP systems.

Why it mattersThe evidence combines Palantir Technologies Inc. (NASDAQ:PLTR) and PwC US are expanding their strategic alliance around three areas: enterprise AI, M&A transformation and ERP modernization. with The companies are introducing an AI-native deals IT platform designed to help clients execute transactions up to 50% faster and cut one-time transaction costs by up to 45%.. In enterprise portfolio review, that gives enterprise AI portfolio leader a concrete question about time to value and control coverage, not a reason to assume that No new contract value or revenue contribution for Palantir was disclosed making adoption and resulting commercial activity important has been solved. The leadership test is to connect this evidence to enterprise ai, a responsible owner, and a measurable production outcome.

Fundraising Tracker '26: Epsilon Health nabs $27.6M; Implicity secures $40M At , we keep track of all the venture capital being funneled into the health tech and digital health industries Our fundraising tracker provides updated coverage of noteworthy digital health and health tech funding rounds, though we'll still profile exciting new companies and larger rounds that catch our eye in depth.

Sept.10-Epsilon Health Precision healthcare AI Series: stealth Amount: $27.6 million Investors: AlleyCorp, with participation from Uncork Capital, Renegade Partners, SemperVirens, and Jack Altman. AI-native radiology practice Epsilon Health emerged from stealth to speed up medical imaging interpretation. Epsilon’s model combines AI with physician oversight to accelerate clinical workflows while reducing administrative burden.

The funding will accelerate Epsilon’s market expansion and support practices through hiring, expanded clinical partnerships and new infrastructure investments. “Epsilon has built an entirely new kind of radiology practice, which I think makes a great blueprint for how healthcare will be done in the future,” said Jack Altman, who previously invested through Alt Capital, in a statement . “In less than 10 months, they’ve gone from nothing to processing thousands of studies a day for some of the largest imaging providers in the country. I couldn’t be more excited about what they’re doing.” Sept. 9-Viv Patented tampon technology Series: undisclosed Amount: $2 million Investors: Launchpad Venture Group, Shelly Berkowitz of SSB Next Chapter Holdings, Westchester Angels and individual investors Period care brand Viv is continuing its expansion across the U.S.

Why it mattersThe operational significance is in Our fundraising tracker provides updated coverage of noteworthy digital health and health tech funding rounds, though we'll still profile exciting new companies and larger rounds that catch our eye in depth.. It changes the enterprise portfolio review decision for enterprise AI portfolio leader, while The funding will accelerate Epsilon s market expansion and support practices through hiring expanded clinical partnerships and new keeps the reported result from being treated as universal. The leadership test is to connect this evidence to enterprise ai, a responsible owner, and a measurable production outcome.

There are plenty of new chapters to celebrate in this month’s CxOs On the Move Our latest roundup features 66 technology executives stepping into CIO, CTO, CISO, AI, data, and digital roles across a wide range of industries.

Each brings a unique career path to their new role, making for another impressive group of technology leaders to recognize this month. Chandhu Nair - Senior Vice President and Chief AI Officer at Target Chandhu Nair has been named Senior Vice President and Chief AI Officer at Target, becoming the company’s first Chief AI Officer. In the newly created role, he will lead efforts to strengthen and coordinate the use of artificial intelligence across the enterprise.

Most recently, Nair served as Senior Vice President, Stores, Data, AI and Innovation at Lowe’s, where he spent more than six years in roles spanning data, AI, innovation, product and technology. At Target, Nair sees opportunities to use AI to better anticipate changing guest needs, improve inventory management, simplify work for employees and enable faster, more informed decisions. His initial focus will be on listening and learning across the organization to identify the areas where AI can deliver the greatest impact.

Why it mattersThe National CIO Review connects the development to a practical control question: Chandhu Nair - Senior Vice President and Chief AI Officer at Target Chandhu Nair has been named Senior Vice President and Chief AI Officer at Target, becoming the company’s first Chief AI Officer.. For enterprise AI portfolio leader, the implication is a test of time to value and control coverage under the constraint that Most recently Nair served as Senior Vice President Stores Data AI and Innovation at Lowe s where he. The leadership test is to connect this evidence to enterprise ai, a responsible owner, and a measurable production outcome.

STMicroelectronics and NUS launch Corporate Lab to power the future of Edge AI in Singapore STMicroelectronics (NYSE: STM) and the National University of Singapore have launched the four-year ST-NUS HELIX Corporate Lab in Singapore to advance next-generation edge AI hardware through system-to-silicon research HELIX (Hardware for Embodied Low-power Intelligent Xcceleration) will focus on memory-centric architectures, in-memory computing, scalable compute-and-memory systems, and advanced silicon and embedded-memory technologies, leveraging ST’s P18 18nm FD-SOI and embedded Phase Change Memory.

The initiative aims to enable generative and embodied AI use cases at the edge and strengthen Singapore’s semiconductor R&D capabilities and talent pipeline. The lab commits ST’s design chassis and engineering support to research; industrialization is described as a path, not a completed product. On August 24, 2026 , STMicroelectronics and NUS officially launched the four-year HELIX lab, with ST committing a dedicated P18 18nm FD-SOI design chassis and technology and engineering support for the research.

The chassis is described as an industrial-grade foundation for developing, integrating, and validating AI accelerator concepts, with industrialization presented as a future path rather than a completed product. Researchers from NUS and ST will jointly undertake research work packages, talent development, IP creation, and demonstration activities. In the Aug 24 session, STM declined 2.37% , reflecting a moderate negative market reaction.

Why it mattersThis is more than a category signal because The chassis is described as an industrial-grade foundation for developing, integrating, and validating AI accelerator concepts, with industrialization presented as a future path rather than a completed product.. In enterprise portfolio review, enterprise AI portfolio leader can use it to examine time to value and control coverage; the gating issue remains The chassis is described as an industrial-grade foundation for developing integrating and validating AI accelerator concepts with industrialization. The leadership test is to connect this evidence to enterprise ai, a responsible owner, and a measurable production outcome.

More than 70 AI centres of excellence have been established as companies accelerate adoption across key sectors Anthropic is the latest major artificial intelligence laboratory to plan a presence in Singapore, following similar moves by rivals OpenAI and Google DeepMind.

Over the past two years, many firms have also set up AI centres of excellence in Singapore to promote the use of the technology in various sectors. There are more than 70 of such centres of excellence to date. These add to a S$1 billion five-year national plan to boost AI research in public institutions.

The five-year plan, slated to last until 2030, will see the setup of research centres of excellence, which will complement the current network of more than 70 AI centres of excellence. These centre openings are part of the National AI Strategy 2.0 to position Singapore as a global hub where real-world uses of AI are showcased. In May, ChatGPT creator OpenAI committed more than S$300 million to establish an Applied AI Lab in Singapore, its first outside the United States.

Why it mattersThe development changes the control question for enterprise AI portfolio leader: The five-year plan, slated to last until 2030, will see the setup of research centres of excellence, which will complement the current network of more than 70 AI centres of excellence.. If the team applies it to enterprise portfolio review, it must reconcile Anthropic is the latest major artificial intelligence laboratory to plan a presence in Singapore following similar moves by rivals OpenAI and Google DeepMind. with The five-year plan slated to last until 2030 will see the setup of research centres of excellence which before claiming movement in time to value and control coverage. The leadership test is to connect this evidence to enterprise ai, a responsible owner, and a measurable production outcome.

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Why it mattersGlobal Banking & Finance Review reports The platform covers a diverse range of topics, including banking, insurance, investment, wealth management, fintech, and regulatory issues.. That matters for strategy and capital planning because CEO and strategy office must decide whether Enterprise AI Is Shifting From Models to Systems Architecture Global can improve profit-pool exposure without weakening accountability; We cannot guarantee the accuracy or applicability of any information provided with respect to your individual or personal is the boundary for the claim. The leadership test is to connect this evidence to ai in executive & strategy, a responsible owner, and a measurable production outcome.

The bigger challenge is competing with businesses designed around AI from day one For many established companies, the AI conversation starts with tools: Where can we deploy AI pilots? For more than two centuries, companies have been designed around assumptions inherited from the industrial age. As organizations grow, they add specialization, management layers, processes, controls, budgets, and systems intended to make performance more predictable.

Meanwhile, a new generation of companies is starting with a different question: If we use AI from the ground up, how would we design this business? Incumbents are largely using AI to improve organizations built for an earlier era. AI-native competitors can rethink the organization itself: its workflows, staffing, management layers, products, and cost structure.

An established company might use AI to make an existing process more efficient. An AI-native company can ask whether that process, or the organizational structure around it, needs to exist at all. This raises a much harder question than how to adopt AI: How do you keep running the business that works today while simultaneously building the one that might replace it tomorrow?

Why it mattersThe evidence combines For many established companies, the AI conversation starts with tools: Where can we deploy AI pilots? with Incumbents are largely using AI to improve organizations built for an earlier era.. In strategy and capital planning, that gives CEO and strategy office a concrete question about profit-pool exposure, not a reason to assume that An established company might use AI to make an existing process more efficient. has been solved. The leadership test is to connect this evidence to ai in executive & strategy, a responsible owner, and a measurable production outcome.

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 AI Tribe 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. He describes the approach of an AI Tribe as: We focus on four main areas: deployment of AI, improving the customer experience, improving employee productivity and the optimization and automation of processes, such as fighting fraud and customer protection.

Why it mattersThe operational significance is in 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.. It changes the strategy and capital planning decision for CEO and strategy office, while Piette has been with the organization for 21 years in a variety of data analytics roles supporting marketing keeps the reported result from being treated as universal. The leadership test is to connect this evidence to ai in executive & strategy, a responsible owner, and a measurable production outcome.

17th edition of the longest-running GCC and technology awards recognizes 18 organizations and leaders defining the next era of value creation from India BENGALURU, India, Aug 19, 2026 /PRNewswire/ -- The 17th edition of the Zinnov Awards, held on Day 1 of Zinnov Confluence 2026, recognized 18 organizations and leaders across 10 categories for their impact on innovation, AI, leadership, talent, culture, and enterprise value creation.

One of the most coveted recognitions in the GCC ecosystem, the Zinnov Awards celebrate the Titans in Tech building world-class capabilities from India for the world. This year's Awards come as India's GCCs undergo a fundamental reset - moving beyond labor and cost arbitrage to value arbitrage and evolving into high-maturity nerve centers that increasingly own products, platforms, innovation, and global business outcomes. Anchored in the theme of Winning the AI Race, this edition reflects how quickly GCC transformation is accelerating.

According to the Nasscom-Zinnov GCC Landscape Report 2026, more than 1,200 India GCCs have AI/ML capabilities, supported by over 250,000 AI/ML professionals. Further, 96% of GCCs established post-FY2021 entered with a product or portfolio mandate, while 49% were AI-first from day one. Three new categories - AI Excellence, Ecosystem Synergy Award, and AI Innovation Vanguard - were introduced this year, recognizing the growing importance of enterprise AI impact, ecosystem-led innovation, and technology leadership.

Why it mattersThe Wire India connects the development to a practical control question: This year's Awards come as India's GCCs undergo a fundamental reset - moving beyond labor and cost arbitrage to value arbitrage and evolving into high-maturity nerve centers that increasingly own products, platforms, innovation, and global business outcomes.. For chief marketing officer, the implication is a test of conversion lift under the constraint that According to the Nasscom-Zinnov GCC Landscape Report 2026 more than 1 200 India GCCs have AI/ML capabilities supported. The leadership test is to connect this evidence to ai in marketing, a responsible owner, and a measurable production outcome.

Tech Mahindra Launches AWS Agentic Process Transformation CoE to Redefine AI-Led Business Operations Tech Mahindra (NSE: TECHM), a leading global provider of technology consulting and digital solutions to enterprises across industries, announced the launch of its Amazon Web Services (AWS) Agentic Process Transformation (APT) Center of Excellence (CoE), a strategic initiative designed to accelerate enterprise adoption of Agentic AI through scalable, outcome-driven business transformation The AWS APT CoE will deliver scalable AI solutions that drive measurable results for customers across industries.

The CoE combines Tech Mahindra BPS’ deep process expertise with AWS cloud and Agentic AI capabilities to help organizations move from AI experimentation to measurable business impact. Built as a scalable AI execution engine, the AWS APT CoE will allow enterprises to deploy industry-specific AI solutions that improve operational efficiency, reduce costs, enhance decision-making, and accelerate pilot-to-production cycles. The initiative reinforces Tech Mahindra’s collaboration with AWS while strengthening its ability to deliver governed, enterprise-scale AI transformation across industries including telecom, healthcare, banking and financial services, retail, and manufacturing. “Enterprises are moving quickly on AI, but many still struggle to scale beyond pilots and fragmented use cases,” said Birendra Sen, President - Business Process Services, Tech Mahindra . “With the AWS APT CoE, we are bringing together Tech Mahindra BPS’ process expertise and AWS-native AI capabilities to help customers operationalize Agentic AI with stronger governance, faster execution, and measurable business impact.” Katie Pender, Chief Operating Officer, Target Group , said, “Since introducing the Collections Guru agent, we're seeing encouraging early results, including anticipated efficiency gains of around 40% in the areas where it's been rolled out.

It's been a valuable step in how we're modernising our operations.” Chandra Pinapala, GSI Director, AWS, said, “In a time of rapid technological change, a Center of Excellence becomes the anchor that helps partners and customers learn together, deliver value faster, and reimagine business processes with confidence.” The APT CoE is already delivering measurable business impact through its first jointly developed solution. Collections Guru - an agentic AI-powered collections agent co-developed by Tech Mahindra and AWS as part of the APT CoE - was deployed by Target Group, a leading UK-based financial services outsourcing provider, to transform arrears management operations. Built on AWS cloud and AI infrastructure, the solution delivered approximately 40% efficiency gains by autonomously optimizing collection strategies through agentic AI, representing the type of production-grade, jointly engineered offering the CoE is designed to scale across industries.

Why it mattersThis is more than a category signal because It's been a valuable step in how we're modernising our operations.” Chandra Pinapala, GSI Director, AWS, said, “In a time of rapid technological change, a Center of Excellence becomes the anchor that helps partners and customers learn together, deliver value faster, and reimagine business processes with confidence.” The APT CoE is already delivering measurable business impact through its first jointly developed solution.. In campaign and content planning, chief marketing officer can use it to examine conversion lift; the gating issue remains It's been a valuable step in how we're modernising our operations. Chandra Pinapala GSI Director AWS said In. The leadership test is to connect this evidence to ai in marketing, a responsible owner, and a measurable production outcome.

PwC’s Rima Safari explains how OpenAI, agentic AI, governance and data strategy are reshaping enterprise AI adoption and production SHI’s Shane Cronin explains AI tokenomics, FinOps, AI ROI and how businesses can make smarter decisions about managing growing AI costs.

Logicalis VP Anita Swann explains how Microsoft partners can help enterprises move AI from experimentation to secure, measurable business outcomes. Kaseya’s JV Varma explains how MSPs can modernize security models, strengthen cyber resilience, and move beyond reactive security practices. Xentegra CTO Phillip Sellers explains how partners can turn AI hype into customer value while addressing security, data governance and adoption.

Arcova’s Joseph Perry discusses AI security hype, emerging risks and how channel partners can become trusted strategic advisors for customers. Channel Business Channel Business Related Topics Channel Analysis Channel Careers Helpdesk, ITSM & Other Tools Mergers & Acquisitions Running an MSP SIs, VARs, Advisors & MSSP News Vendor Leadership & Partner Programs Top Articles View All Hover to load posts Security Security Related Topics Managed Services Next-Gen Solutions Resiliency, Backup & Recovery Tools & Platforms Top Articles View All Hover to load posts AI AI Related Topics Building Channel Revenue Emerging Tech LLMs, Chatbots, and Agents MSP Automation Solutions Top Articles View All Hover to load posts Infrastructure Infrastructure Related Topics Cloud & Hybrid On-Premises Virtualization Top Articles View All Hover to load posts Lists & Awards Lists & Awards Top Articles Link to AI 50 List AI 50 List Channel Insider's editorial team spotlights the top AI leaders from MSPs, vendors, and channel businesses delivering measurable outcomes. Link to CML 100 Honorees CML 100 Honorees Check out our CML 100 List to discover the top channel marketing individuals who are transforming channel marketing for their organizations.

Why it mattersThe development changes the control question for chief marketing officer: Arcova’s Joseph Perry discusses AI security hype, emerging risks and how channel partners can become trusted strategic advisors for customers.. If the team applies it to campaign and content planning, it must reconcile SHI s Shane Cronin explains AI tokenomics FinOps AI ROI and how businesses can make smarter decisions about managing growing AI costs. with Arcova s Joseph Perry discusses AI security hype emerging risks and how channel partners can become trusted strategic before claiming movement in conversion lift. The leadership test is to connect this evidence to ai in marketing, a responsible owner, and a measurable production outcome.

Enterprise AI employs artificial intelligence and machine learning technology to solve problems faced by large-scale companies and organizations Common use cases for enterprise AI include process automation, supply chain analytics, marketing and customer service Instead of following explicit, mathematical instructions, these computational systems identify patterns from analyzed data via algorithms and statistical models , imitating intelligent human behavior.

They’re able to “teach” themselves by drawing inferences from the information sets in a sort of cognitive processing procedure. Enterprise AI are solutions that apply artificial intelligence and machine learning to solve problems faced by large-scale companies and organizations. It's commonly used for process automation, supply chain analytics and customer service.

Enterprise AI solutions further distribute the power of data science , processing complex amounts of information and presenting it across simple interfaces for practical use by the people and teams running large-scale organizations. While boosting employee productivity , cost reduction and optimizing business operations are constant variables for executives to consider, enterprise AI offers an array of solutions to common operational hangups. Smart personal assistants, such as Siri, Cortana and Alexa, as well as automated online customer support chatbots are some examples of enterprise AI used to benefit performance metrics like customer relationship management.

Why it mattersBuilt In reports Common use cases for enterprise AI include process automation, supply chain analytics, marketing and customer service Instead of following explicit, mathematical instructions, these computational systems identify patterns from analyzed data via algorithms and statistical models , imitating intelligent human behavior.. That matters for pipeline and account review because chief revenue officer must decide whether Enterprise AI Definition Platforms and More Built In can improve pipeline conversion without weakening accountability; Enterprise AI solutions further distribute the power of data science processing complex amounts of information and presenting it is the boundary for the claim. The leadership test is to connect this evidence to ai in sales, a responsible owner, and a measurable production outcome.

Enterprise software vendors are rapidly embedding AI agents and intelligent automation into ERP, HR, CRM, IT service management, collaboration and other enterprise platforms While these capabilities promise greater efficiency by automating routine decisions and orchestrating workflows, they also raise an important governance question: How can organizations design AI-enabled enterprise workflows so that automation improves efficiency without weakening the human expertise needed to evaluate exceptions, correct errors and maintain operations?

From both corporate and legal perspectives, governance means that the business is accountable to its key stakeholders: employees, customers, shareholders and the broader community. The historical role of governance has been to reduce corporate risk. This risk was managed by maintaining the privacy of customer data, ensuring that data and other IT assets were secure, and working with users to set guardrails defining which systems and assets employees across functions are authorized to use.

However, with the introduction of AI, AI agents and greater business process automation, the enterprise risk management plane has broadened. How, for example, can enterprises maintain business resilience by ensuring there is no erosion of human skill sets and know-how as more AI and automation are introduced? "We view this as an important topic that must be actively managed," said Christophe Theys, global head of AI, Data & Analytics for DHL Supply Chain.

Why it mattersThe evidence combines While these capabilities promise greater efficiency by automating routine decisions and orchestrating workflows, they also raise an important governance question: How can organizations design AI-enabled enterprise workflows so that automation improves efficiency without weakening the human expertise needed to evaluate exceptions, correct errors and maintain operations? with The historical role of governance has been to reduce corporate risk.. In pipeline and account review, that gives chief revenue officer a concrete question about pipeline conversion, not a reason to assume that However with the introduction of AI AI agents and greater business process automation the enterprise risk management plane has been solved. The leadership test is to connect this evidence to ai in sales, a responsible owner, and a measurable production outcome.

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. These deployments demonstrate how AI can evolve from solving isolated business problems to becoming an integrated enterprise capability.

Why it mattersThe operational significance is in 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.. It changes the pipeline and account review decision for chief revenue officer, while As a result AI capabilities developed for one business function can be adapted to additional businesses without rebuilding keeps the reported result from being treated as universal. The leadership test is to connect this evidence to ai in sales, a responsible owner, and a measurable production outcome.

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. Additionally, 61% expect AI agents to operate largely autonomously with humans serving in supervisory oversight roles and 58% predict agents will autonomously coordinate across functional boundaries to execute complex tasks.

Why it mattersAI Magazine connects the development to a practical control question: 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.. For chief customer officer, the implication is a test of resolution rate under the constraint that Deloitte s findings from AI agents are only the beginning The path to agentic transformation project indicate dramatic. The leadership test is to connect this evidence to ai in customer service, a responsible owner, and a measurable production outcome.

How to upskill IT for agentic AI: 7 pathways to success 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. “The cognitive habits that define experienced engineers are deeply ingrained, so they need to develop new ways of working focused on reviewing outputs, framing intent, and curating the context that keeps those outputs accurate, secure, and aligned with business objectives.” How CIOs upskill their organizations will follow several career tracks. Developing business acumen and AI literacy for IT leaders AI is requiring more IT professionals to shift left into transformational leadership and change-agent roles. These leaders will advise business managers on when to use AI versus other technologies to automate tasks, and when to consider top-down re-engineering workflows based on AI capabilities. “Leaders need to help their teams understand how work flows across the business, where AI fits into that process, and where humans need to stay accountable,” says Jamie Lyon, chief product and strategy officer at Lucid Software. “As AI agents take on more of the execution, critical thinking becomes even more important because people still need to provide the context, define the process, and make the decisions AI can’t.” One of the top barriers in delivering value from AI is employee adoption.

Why it mattersThis is more than a category signal because 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. “The cognitive habits that define experienced engineers are deeply ingrained, so they need to develop new ways of working focused on reviewing outputs, framing intent, and curating the context that keeps those outputs accurate, secure, and aligned with business objectives.” How CIOs upskill their organizations will follow several career tracks.. In service resolution, chief customer officer can use it to examine resolution rate; the gating issue remains According to Deloitte s 2026 Global Technology Leadership Survey 75% of IT leaders agree their operating models and. The leadership test is to connect this evidence to ai in customer service, a responsible owner, and a measurable production outcome.

Co-located with Southeast Asia’s first Google DeepMind research lab, the Singapore Engineering Center translates frontier AI research into production-grade cloud and AI solutions tailored to the needs of Singapore-based companies targeting high-growth global markets SINGAPORE, September 15, 2026 - Google Cloud today inaugurated the Singapore Engineering Center (SEC), its flagship product development hub in Southeast Asia Bringing together specialized software engineers across AI, AI Infrastructure, Data, Compute, Machine Learning, Core Networking, Storage as well as Frontline Support and more, the Google Cloud SEC partners directly with enterprises to translate foundational technical breakthroughs into production-ready cloud systems tuned to the needs of Singapore enterprises going global.

By building solutions in Singapore for worldwide deployment, the Google Cloud SEC breaks the mold of conventional regional support outposts. This establishes a unique model in enterprise tech-surpassing pure-play AI labs constrained by scale and traditional hyperscalers confined to post-sales maintenance. Strengthening Singapore's Deep Tech and National AI Ecosystem Google Cloud shared its plans to launch the SEC at Google for Singapore in February 2026, which deepens the company’s commitment to growing an AI-ready workforce and driving regional innovation.

Supported by the Singapore Economic Development Board (EDB), the Google Cloud SEC mandate includes developing: Next-Generation Agentic Cloud: Architecting scalable, secure data engines and resilient cloud infrastructure built for low-latency, mission-critical enterprise and agentic workloads. Frontier Models to Enterprise Systems: Integrating foundational model and agentic platform breakthroughs into Google's comprehensive cloud solutions, optimized for localized contexts, and global export. Developer Platforms and Automation: Delivering secure API frameworks and autonomous agent orchestration tooling to accelerate software delivery across hybrid and multicloud environments, including Open Source leadership and ecosystem development and contribution. “Singapore is proud to host Google Cloud’s first Engineering Center in Southeast Asia.

Why it mattersThe development changes the control question for chief customer officer: Supported by the Singapore Economic Development Board (EDB), the Google Cloud SEC mandate includes developing: Next-Generation Agentic Cloud: Architecting scalable, secure data engines and resilient cloud infrastructure built for low-latency, mission-critical enterprise and agentic workloads.. If the team applies it to service resolution, it must reconcile Bringing together specialized software engineers across AI AI Infrastructure Data Compute Machine Learning Core Networking Storage as well as Frontline Support and more the with Supported by the Singapore Economic Development Board EDB the Google Cloud SEC mandate includes developing Next-Generation Agentic Cloud before claiming movement in resolution rate. The leadership test is to connect this evidence to ai in customer service, a responsible owner, and a measurable production outcome.

They made the comments at a transnational investment trends conference during the 26th China International Fair for Investment and Trade in Xiamen, Fujian province The event featured the release of the Statistical Bulletin of FDI in China 2026 and the Chinese edition of the 2026 World Investment Report.

These structural shifts come amid an uneven global investment recovery. Global FDI rose 6 percent to $1.6 trillion in 2025, but growth remained highly concentrated across specific destinations and industries, said Li Nan, director of the Division on Investment and Enterprise at the United Nations Trade and Development. Strategic industries, including artificial intelligence infrastructure, semiconductors, critical minerals, and energy transition technologies and services, accounted for 44 percent of global greenfield investment project value in 2025, up from 16 percent in 2020, Li said, adding that China remains a major destination and source of international investment, with foreign investment increasingly directed toward advanced manufacturing, technological innovation and modern services.

The Ministry of Commerce said China's utilized foreign investment fell 6.2 percent year-on-year to 438.33 billion yuan ($65.34 billion) in the first seven months. However, inflows into high-tech industries rose 32.7 percent to 182.31 billion yuan, accounting for 41.6 percent of the total. Meanwhile, 37,711 new foreign-invested enterprises were established nationwide during the same period, up 4.4 percent year-on-year.

Why it mattersChina Daily Global Edition reports The event featured the release of the Statistical Bulletin of FDI in China 2026 and the Chinese edition of the 2026 World Investment Report.. That matters for product discovery because chief product officer must decide whether Innovation tech major draws for FDI China Daily Global Edition can improve time to launch without weakening accountability; The Ministry of Commerce said China's utilized foreign investment fell 6.2 percent year-on-year to 438.33 billion yuan 65.34 is the boundary for the claim. The leadership test is to connect this evidence to ai in product & innovation, a responsible owner, and a measurable production outcome.

Leading AI in product lifecycle management market providers such as Siemens Digital Industries Software, PTC, Dassault Systèmes, Autodesk are expanding their capabilities across segments including By Component, By Deployment Mode, By Technology, By Application, By End-Use Industry What is the AI in Product Lifecycle Management Market Size in 2026?

The global AI in product lifecycle management market size accounted for USD 8.60 billion in 2025 and is predicted to increase from USD 10.69 billion in 2026 to approximately USD 75.72 billion by 2035, expanding at a CAGR of 24.30% from 2026 to 2035. The market is driven by the rising adoption of AI PLM software, the growth of Industry 4.0, and digital transformation across industries. North America led the AI in product lifecycle management market in 2025 with a 38% share.

Asia Pacific is expected to grow at the fastest CAGR of 29.6% between 2026 and 2035. By component, the software segment led the market with a 76% share in 2025. By component, the services segment is expected to grow at the fastest CAGR of 27.1% in the upcoming period.

Why it mattersThe evidence combines What is the AI in Product Lifecycle Management Market Size in 2026? with The market is driven by the rising adoption of AI PLM software, the growth of Industry 4.0, and digital transformation across industries.. In product discovery, that gives chief product officer a concrete question about time to launch, not a reason to assume that Asia Pacific is expected to grow at the fastest CAGR of 29.6% between 2026 and 2035. has been solved. The leadership test is to connect this evidence to ai in product & innovation, a responsible owner, and a measurable production outcome.

Digital Twin as a Service (DTaaS) for SMEs The Digital Twin Market was valued at USD 39.45 billion in 2025 and is projected to reach USD 53.60 billion in 2026 before climbing to USD 1,085.20 billion by 2035, registering a CAGR of 38.70% during the 2026-2035 forecast window This acceleration is anchored in two converging forces: widespread industrial IoT platform maturation and a wave of government mandates requiring a real-time digital twin for energy grid management across safety-critical infrastructure in the US, EU, and China.

The US Department of Energy's 2024 allocation of USD 1.2 billion toward grid modernization programs specifically earmarked digital-replica capabilities for transmission monitoring [2] . Physics-informed, cloud-native simulation environments combining sensor data, AI inference and 3D visualization replace legacy siloed SCADA and CAD-based design procedures. Global spending on enterprise IoT-based digital twin for smart manufacturing surpassed USD 8 billion in 2024, led by automotive OEMs and semiconductor fabs seeking 12-18% yield gains through virtual process optimization [3] .

Industrial digital twin for predictive maintenance already underlies more than 40% of new condition monitoring contracts signed by Tier-1 equipment vendors [4] . North America holds over 41.0% of the Digital Twin Market, driven by defense-sector digital-thread programs and hyperscaler cloud spending. Asia-Pacific is the fastest expanding area at an expected 28.10% CAGR, fueled by China’s “Digital China 2035” vision and India’s Smart Cities Mission.

Why it mattersThe operational significance is in This acceleration is anchored in two converging forces: widespread industrial IoT platform maturation and a wave of government mandates requiring a real-time digital twin for energy grid management across safety-critical infrastructure in the US, EU, and China.. It changes the product discovery decision for chief product officer, while Industrial digital twin for predictive maintenance already underlies more than 40% of new condition monitoring contracts signed by keeps the reported result from being treated as universal. The leadership test is to connect this evidence to ai in product & innovation, a responsible owner, and a measurable production outcome.

Technology Sensors and Instrumentation Devices Cloud and Platforms Research and Development Governance, Standards and Regulations Application and Middleware Security Big Data and Analytics AI and Machine Learning Industry Manufacturing Transportation and Logistics Retail and E-commerce Banking and Financial Services Government, Healthcare and Education Industrial Application Smart Cities Future Workplace Commercial Smart Home Customer Engagement Human-AI collaboration hiring accelerates as industries redesign work for agentic AI Hiring for roles centred on human-AI collaboration is accelerating across industries as companies redesign work for agentic AI, says GlobalData Jobs referencing human-AI collaboration continued to grow in the second quarter of 2026, underscoring the transformative impact of agentic AI on traditional business models, the intelligence and productivity platform reveals. “The focus is on not merely about automation but about creating synergies between human intelligence and AI capabilities with the objective of enhancing productivity, improving decision-making, and driving innovation,” says Sherla Sriprada , business fundamentals Analyst at GlobalData.

In automotive, Canadian Tire Corp ’s posting for “director, Engineering Performance & AI Metrics” points to the establishment of an Agile and AI performance measurement practice. The initiative aims to augment traditional agile metrics with new KPIs that reflect total system health, including human-AI collaboration and automation effectiveness. Meanwhile, Faraday & Future ’s “AI Corporate Strategy director” role is leading the transition from human-driven processes to those driven by AI agents, emphasising integration of AI products into existing tool stacks.

Chubb ’s “VP, AI Operating Model and Execution” defines what work is retained by employees versus delegated to AI agents, and builds feedback loops, design principles, and responsible AI guardrails into day-to-day execution. JPMorgan ’s “Human-AI Conversational Designer, vice president” focuses on designing end-to-end human-AI experiences across client and advisor journeys, including interaction patterns, service flows, wireframes, and prototypes for agentic AI systems. Manulife ’s “director, Forward Deployed Organization Architect” and State Street’s “Head of process Re-engineering and AI-led operating Model redesign, Managing Director” underscore a shift from isolated AI pilots toward enterprise-wide process architectures that specify handoffs, controls, and measurable interaction points between automation/AI agents and human judgment.

Why it mattersFutureIOT connects the development to a practical control question: The initiative aims to augment traditional agile metrics with new KPIs that reflect total system health, including human-AI collaboration and automation effectiveness.. For chief operating officer, the implication is a test of process cycle time under the constraint that Chubb s VP AI Operating Model and Execution defines what work is retained by employees versus delegated to. The leadership test is to connect this evidence to ai in operations, a responsible owner, and a measurable production outcome.

New delivery capability bridges business strategy and AI engineering, pairing real-world software execution with a client-owned operating system 4, 2026 /PRNewswire/ -- Proxet , a leader in data science and AI engineering, today announced the commercial launch of its Intent-Driven Lifecycle (IDLC) transformation offering.

Built to bridge the gap between business strategy and AI execution, Proxet applies IDLC directly to real-world software project streams. The result delivers immediate project outcomes while establishing a client-owned operating system that keeps human engineering talent focused on architecture, intent, and verification. While traditional software methodologies treat AI as an isolated developer copilot, Proxet's IDLC offering integrates AI across the entire delivery lifecycle.

By establishing a "shared second brain"-a persistent context system connecting business stakeholders, product managers, QA specialists, and engineers-IDLC ensures every AI-assisted session builds on identical organizational knowledge. "When code generation becomes trivial, the real bottleneck in software delivery becomes human clarity and validation," said Vlad Medvedovsky, Founder and CEO of Proxet. "IDLC isn't a software tool or a static playbook; it's a cultural and operational transformation.

Why it mattersThis is more than a category signal because By establishing a "shared second brain"-a persistent context system connecting business stakeholders, product managers, QA specialists, and engineers-IDLC ensures every AI-assisted session builds on identical organizational knowledge.. In operational planning, chief operating officer can use it to examine process cycle time; the gating issue remains By establishing a shared second brain a persistent context system connecting business stakeholders product managers QA specialists and. The leadership test is to connect this evidence to ai in operations, a responsible owner, and a measurable production outcome.

Why AI TCO is so tricky - and how to start calculating it Achieving return on investment is impossible without knowing the total cost of ownership (TCO) of an initiative - and when it comes to AI, CIOs are finding cost calculations anything but straightforward Subscription and token costs are a big part of the calculus, but several other factors go into the cost of AI projects, says Ben Schein , chief AI and analytics officer at AI data platform provider Domo.

Chief among those are cloud infrastructure costs and the human time involved in guiding or correcting AI outputs, he notes. In addition, many organizations have multiple divisions using different AI tools for vastly different purposes. “There’s not like a single ledger,” Schein says. “Right now, and maybe for the foreseeable future, there’s sort of like a multiple ledger approach to how all this works.” While token costs have dropped significantly in the past two years, costs vary wildly between models and AI providers, and the price drops are often offset by increased usage . And AI providers have also explored other kinds of consumption-based pricing, including API calls, compute time, or documents processed.

All this makes it difficult to measure TCO, Schein says. “You have sort of these subscriptions, you have the consumption and the tokenization, you have some of the infrastructure you might be paying for,” he says. “There’s also a human tax that introduces new time for verification and review, and if the AI is sloppy or creating slop, you might be inadvertently adding to your costs without knowing it.” It’s difficult to measure TCO because AI doesn’t have a single cost center, agrees Shane Cronin , head of FinOps and ITAM services at systems integrator SHI. “By the time you’re looking at the bill, you’re dealing with token consumption, cloud infrastructure, multiple AI models, governance tooling, integration work and, increasingly, autonomous agents making decisions across systems,” he says. IT leaders at many organizations still define AI success through narrow technical metrics instead of prioritizing business outcomes, Cronin adds. “Calculating token costs is relatively straightforward,” he adds. “Calculating whether those tokens actually created measurable business value is much harder. That’s where most CIOs are today.” Michael Moran , chief technology and information officer at contact center outsourcing provider NQX, sees several other factors leading to further unpredictability over AI costs.

Why it mattersThe development changes the control question for chief operating officer: All this makes it difficult to measure TCO, Schein says. “You have sort of these subscriptions, you have the consumption and the tokenization, you have some of the infrastructure you might be paying for,” he says. “There’s also a human tax that introduces new time for verification and review, and if the AI is sloppy or creating slop, you might be inadvertently adding to your costs without knowing it.” It’s difficult to measure TCO because AI doesn’t have a single cost center, agrees Shane Cronin , head of FinOps and ITAM services at systems integrator SHI. “By the time you’re looking at the bill, you’re dealing with token consumption, cloud infrastructure, multiple AI models, governance tooling, integration work and, increasingly, autonomous agents making decisions across systems,” he says.. If the team applies it to operational planning, it must reconcile Subscription and token costs are a big part of the calculus but several other factors go into the cost of AI projects says Ben with All this makes it difficult to measure TCO Schein says. You have sort of these subscriptions you have before claiming movement in process cycle time. The leadership test is to connect this evidence to ai in operations, a responsible owner, and a measurable production outcome.

Europe Automated Storage and Retrieval System Market Size, Share, Trends, & Growth Forecast Report By Function, Type, Industry, and Country (UK, France, Spain, Germany, Italy, Russia, Sweden, Denmark, Switzerland, Netherlands, Turkey, Czech Republic, and Rest of Europe), Industry Analysis From 2026 to 2034 Executive Summary: Europe Automated Storage and Retrieval System Market Market Scope: Comprehensive European automated storage and retrieval system (ASRS) market analysis covering equipment types, operational functions, industry verticals, country-level adoption frameworks, and industrial logistics metrics Market Valuation: Valued at USD 32.70 billion in 2025 , estimated at USD 34.75 billion in 2026 , and projected to reach USD 56.57 billion by 2034 , registering a steady CAGR of 6.28% from 2026 to 2034.

Primary Growth Drivers: Exponential e-commerce growth demanding high-speed order fulfillment, persistent workforce shortages and rising hourly labor costs across European logistics, and the continuous push toward Industry 4.0 automation and green energy-efficient warehouse operations. Major Europe ASRS Market Players & Industry Structure Market Structure: Highly competitive European intralogistics and warehouse automation landscape featuring global system integrators and specialized technology providers competing on AI-driven software coordination, modular scalability, energy efficiency, and localized engineering networks. Key Companies: SSI Schaefer, Swisslog Holding AG, Dematic, Kardex Group, Knapp AG, TGW Logistics Group, Mecalux S.A., Vanderlande Industries, and Beumer Group.

Europe Automated Storage and Retrieval System Market Size The Europe automated storage and retrieval system market size will reach USD 32.70 billion in 2025 and is anticipated to reach USD 34.75 billion in 2026 to reach USD 56.57 billion by 2034, growing at a CAGR of 6.28% during the forecast period from 2026 to 2034. An Automated Storage and Retrieval System (ASRS) refers to a combination of computer-controlled systems that automatically place and retrieve items from defined storage locations. These systems are widely used in manufacturing, warehousing, logistics, and distribution centers to enhance operational efficiency, reduce labour costs, and optimize space utilization.

Why it mattersMarket Data Forecast reports Market Valuation: Valued at USD 32.70 billion in 2025 , estimated at USD 34.75 billion in 2026 , and projected to reach USD 56.57 billion by 2034 , registering a steady CAGR of 6.28% from 2026 to 2034.. That matters for supplier and fulfillment review because chief supply chain officer must decide whether Europe Automated Storage And Retrieval System Market Report Market Data can improve supplier lead time without weakening accountability; Europe Automated Storage and Retrieval System Market Size The Europe automated storage and retrieval system market size will is the boundary for the claim. The leadership test is to connect this evidence to ai in supply chain & procurement, a responsible owner, and a measurable production outcome.

The Warehouse Management System Market reached an estimated USD 4.32 Billion in 2025 and is projected to climb to USD 5.04 Billion in 2026 before expanding to USD 20.24 Billion by 2035, reflecting a 16.7% CAGR across the 2026-2035 forecast window Two forces underpin this trajectory: the sustained expansion of global e-commerce-cross-border online retail alone grew 26% year-over-year in 2024 [1] -and a structural labor deficit in distribution operations that pushes companies toward software-orchestrated workflows.

Government digitization mandates, such as the EU Digital Product Passport regulation slated for phased implementation from 2027, add regulatory urgency to adoption timelines [2] . Legacy spreadsheet-based picking lists and siloed enterprise resource planning modules are giving way to cloud-native, AI-augmented platforms capable of real-time slot optimization and demand-sensing replenishment. Capital investment in the Warehouse Management System Market accelerated sharply after 2022; BloombergNEF tracked over USD 8.7 Billion in logistics -tech venture funding during 2023-2024, a sizable share of which targeted inventory orchestration and warehouse automation software [3] .

Predictive analytics engines embedded within modern platforms can lift inventory accuracy by roughly 30%, cutting carrying costs and improving fill rates simultaneously [4] . North America commands the largest regional share at 38.1% of the Warehouse Management System Market, driven by mature third-party logistics networks and early cloud adoption. Asia-Pacific is the fastest-growing region with a projected 20.1% CAGR through 2035, fueled by China's smart-logistics corridors and India's expanding organized retail footprint.

Why it mattersThe evidence combines Two forces underpin this trajectory: the sustained expansion of global e-commerce-cross-border online retail alone grew 26% year-over-year in 2024 [1] -and a structural labor deficit in distribution operations that pushes companies toward software-orchestrated workflows. with Legacy spreadsheet-based picking lists and siloed enterprise resource planning modules are giving way to cloud-native, AI-augmented platforms capable of real-time slot optimization and demand-sensing replenishment.. In supplier and fulfillment review, that gives chief supply chain officer a concrete question about supplier lead time, not a reason to assume that Predictive analytics engines embedded within modern platforms can lift inventory accuracy by roughly 30% cutting carrying costs and has been solved. The leadership test is to connect this evidence to ai in supply chain & procurement, a responsible owner, and a measurable production outcome.

SAS study links trustworthy AI practices to higher enterprise ROI Organizations that enforce data quality and system explainability are 15 times more likely to achieve strong returns on their artificial intelligence projects A new SAS report with research insights by IDC uncovers what’s powering the organizations winning the race to profit from their AI investments: embracing trustworthy AI measures Organizations applying trustworthy AI practices were 15 times more likely to report strong return on investment (ROI) from their AI projects.

As identified in the second annual Data and AI Impact Report: The New Economics of Trust , organizations with the strongest governance, data quality and auditability practices - a comparatively small market segment - consistently outperformed peers, reporting at least double the ROI from AI deployments. Fewer than one in 20 trustworthy AI ‘laggard’ organizations reported the same. “When AI works, it’s incredibly impactful,” said Bryan Harris, CTO at SAS. “However, it is well documented that state-of-the-art agents can have error rates that exceed 25% on complex tasks- which is unacceptable in high-stakes decision-making. In order to achieve accuracy and repeatability, organizations must embed domain expertise into agentic workflows, while keeping people at the center of governance and oversight.

Organizations that do this successfully will close the trust gap and gain a competitive advantage in the market with AI." “As AI becomes more autonomous, organizations face a new challenge: maintaining confidence in systems people don't fully understand,” said Chris Marshall, Vice President at IDC. “Our findings show that stronger oversight, explainability, accountability and data foundations are becoming prerequisites for scaling AI successfully.” AI that can't explain itself is a major business liability Researchers found that at many organizations, employees are increasingly hesitant to rely on systems that may or may not be able to offer correct output or explain how AI arrived at a final decision. As AI gains autonomy, this liability grows, making explainability crucial for success. The report also explored a major hurdle to success in AI adoption: when employees' lack of trust in AI decisions leads them to override and make manual corrections.

Why it mattersThe operational significance is in Organizations applying trustworthy AI practices were 15 times more likely to report strong return on investment (ROI) from their AI projects.. It changes the supplier and fulfillment review decision for chief supply chain officer, while Organizations that do this successfully will close the trust gap and gain a competitive advantage in the market keeps the reported result from being treated as universal. The leadership test is to connect this evidence to ai in supply chain & procurement, a responsible owner, and a measurable production outcome.

A new study by SAP SE (NYSE: SAP) and Oxford Economics has revealed that Thai businesses are seeing growing returns from AI as investment and adoption accelerate, with companies expecting AI ROI to nearly double over the next two years BANGKOK, THAILAND, [15 September 2026] - A new study by SAP SE (NYSE: SAP) and Oxford Economics has revealed that the average company in Thailand expects to spend US$18.3 million (THB602.9 million) on AI this year, below the global average of US$28 million (THB922.5 million).

However, AI investment is expected to grow by 44% in the next two years. The average Thai company expects to drive 18% ROI this year (US$3.5 million / THB115.3 million), a figure that is expected to grow to 35% in two years’ time (US$8.8 million / THB289.9 million). Agentic AI is also emerging as an important source of future value.

Nearly 8 in ten (78%) Thai businesses see agentic AI as having moderate to very high potential to transform their organizations, while expected ROI from agentic AI is projected to reach US$8.5 million (THB280.0 million / 13%) over the next two years. These insights have been revealed in new global research, The SAP Value of AI Report 2026 , which surveyed 2,600 business leaders across 13 countries, including 200 from Thailand. Commenting on the research, Kulwipa Piyawattanametha, Managing Director, SAP Indochina, noted, “Thai businesses are moving from AI experimentation toward execution, and we are beginning to see that momentum reflected in growing returns.

Why it mattersSAP News Center connects the development to a practical control question: The average Thai company expects to drive 18% ROI this year (US$3.5 million / THB115.3 million), a figure that is expected to grow to 35% in two years’ time (US$8.8 million / THB289.9 million).. For chief financial officer, the implication is a test of close-cycle time under the constraint that Nearly 8 in ten 78% Thai businesses see agentic AI as having moderate to very high potential to. The leadership test is to connect this evidence to ai in finance, a responsible owner, and a measurable production outcome.

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. Enterprise AI investment doesn’t automatically lead to enterprise-wide ROI 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. The 40% of enterprises in the developing stage have some successful models and automations but haven’t figured out how to connect knowledge outside of individual team silos.

Why it mattersThis is more than a category signal because To show where organizations are on this journey, the report categorizes them into an agentic AI maturity index.. In financial analysis and control, chief financial officer can use it to examine close-cycle time; the gating issue remains To show where organizations are on this journey the report categorizes them into an agentic AI maturity index.. The leadership test is to connect this evidence to ai in finance, a responsible owner, and a measurable production outcome.

This story was originally published on CX Dive Only one-quarter of AI use cases in customer service produce a return on investment , according to a Gartner analysis of 432 use cases released last month.

Another one-quarter deliver negative returns, and 42% have unclear ROI in which support leaders say they simply don't know the value produced. Despite such unclear returns, more than three-quarters of leaders are planning to increase investment in AI in 2026. Although executives at major companies from Verizon to Airbnb tout the success of their AI chatbots, cost savings from AI investments in customer service remain elusive for most companies.

Among business functions, customer service leads AI adoption in the enterprise. Gartner found that customer service and support teams are pursuing on average nearly five AI use cases and committing about 13% of their functional budget to AI. However, customer support leaders are increasingly being graded on something they can't often prove.

Why it mattersThe development changes the control question for chief financial officer: Among business functions, customer service leads AI adoption in the enterprise.. If the team applies it to financial analysis and control, it must reconcile Only one-quarter of AI use cases in customer service produce a return on investment according to a Gartner analysis of 432 use cases released with Among business functions customer service leads AI adoption in the enterprise. before claiming movement in close-cycle time. The leadership test is to connect this evidence to ai in finance, a responsible owner, and a measurable production outcome.

A top-tier smart glasses brand has secured nearly RMB 1 billion in Series C financing and officially initiated its IPO preparation process | HardKr Exclusive Hard Krypton learned that INMO Technology, a global smart glasses brand, has recently completed its Series C3 financing round, led by Sichuan Revitalization Science and Technology Innovation Fund, with follow-on investments from Jing'an Capital, Shibei Hi-Tech, Guangzhou Industrial Investment, Sichuan Pilot Test Platform, Meishan Pilot Test Platform and Dongpo State-owned Investment Following the completion of Series C1 and C2 financing rounds earlier this year, the total amount of the company's Series C financing has reached nearly RMB 1 billion.

The funds from this round will be mainly used for the R&D and implementation of the new generation of spatial intelligent hardware product lines, as well as the continuous iterative upgrading of the INMO AIOS system, to strengthen the construction of underlying core capabilities of hardware and software. Meanwhile, the company will increase brand building and omni-channel layout, improve commercialization capabilities, promote product breakthroughs to wider user groups and accelerate business growth. At present, INMO Technology has officially launched the preparation for listing, and its revenue growth rate has remained above 200% for consecutive years in recent years.

Smart glasses are being pushed to the most prominent position in the consumer electronics sector. IDC data shows that the global shipment of AI smart glasses in 2025 increased by more than 200% year-on-year; the growth rate in the Chinese market is even more significant. Statistics from RUNTO show that the omni-channel sales volume of domestic smart glasses in the first half of 2026 has reached 909,000 units, a year-on-year increase of 85.5%, with corresponding sales revenue of RMB 1.88 billion, a year-on-year increase of 98.7%.

Why it matterseu.36kr.com reports Following the completion of Series C1 and C2 financing rounds earlier this year, the total amount of the company's Series C financing has reached nearly RMB 1 billion.. That matters for workforce planning because chief people officer must decide whether Top Smart Glasses Brand Secures Nearly RMB 1 Billion Series can improve time to competency without weakening accountability; Smart glasses are being pushed to the most prominent position in the consumer electronics sector. is the boundary for the claim. The leadership test is to connect this evidence to ai in people / hr, a responsible owner, and a measurable production outcome.

Members of get exclusive access to the full range of products and services that deliver Trusted Insights for What's Ahead ® including webcasts, publications, data and analysis, plus discounts to conferences and events AI and the Labor Force: Scenarios for Stakeholders AI is spreading through US workplaces more quickly than previous technologies, yet its effects on productivity, employment, and wages remain difficult to discern.

To help leaders navigate this uncertainty, this report examines four potential labor-force impact scenarios and identifies steps policymakers, business leaders, and educators can take to prepare for possible disruptions. Trusted Insights for What’s Ahead ® Through the end of 2025, about 18% of US firms and 41% of US workers reported using AI, with adoption particularly high among larger firms and in knowledge-intensive sectors such as professional services and finance. Despite this rapid diffusion, individual worker productivity gains and employment effects have been slower to materialize and remain difficult to measure.

AI has demonstrated significant productivity gains in specific contexts, such as customer support and software development. However, AI’s capabilities remain uneven and, in some cases, can lead to worse employee performance. This emphasizes the need for CEOs to understand how AI could make workers more productive and for collaboration with educators to ensure employees have the skills needed to succeed in an AI-driven economy.

Why it mattersThe evidence combines AI and the Labor Force: Scenarios for Stakeholders AI is spreading through US workplaces more quickly than previous technologies, yet its effects on productivity, employment, and wages remain difficult to discern. with Trusted Insights for What’s Ahead ® Through the end of 2025, about 18% of US firms and 41% of US workers reported using AI, with adoption particularly high among larger firms and in knowledge-intensive sectors such as professional services and finance.. In workforce planning, that gives chief people officer a concrete question about time to competency, not a reason to assume that AI has demonstrated significant productivity gains in specific contexts such as customer support and software development. has been solved. The leadership test is to connect this evidence to ai in people / hr, a responsible owner, and a measurable production outcome.

AI has accelerated delivery, yet why is the organization still treading water? A report that would originally take two to three weeks to complete was generated by AI in just two hours. With no other options, she spent another two weeks digesting and verifying the report generated in just a few hours. This is a microcosm of the current application of AI in enterprise management.

When the person in charge was about to prepare for the board of directors presentation, they applied for a two-week extension. At the site of the Bosshui event hosted by Fudan University School of Management, Zhang Qi, Head of North Asia at Top Employers Institute, shared a real experience from a friend of his. As an HR business partner at a foreign-funded enterprise, this friend needed to collect data from frontline business staff and provide analysis reports for the management.

In the past, sorting out materials and producing presentation materials took a lot of time, but the emergence of AI agents has greatly shortened this process. However, the initial excitement soon turned into new confusion: AI generated a detailed and excellent report in a very short time, but many of the analysis conclusions in it were difficult for her to fully understand. After all, it is not AI that stands in front of the board of directors in the end, nor is it AI that explains the data and responds to follow-up questions to the executives, but the HR herself.

Why it mattersThe operational significance is in A report that would originally take two to three weeks to complete was generated by AI in just two hours.. It changes the workforce planning decision for chief people officer, while In the past sorting out materials and producing presentation materials took a lot of time but the emergence keeps the reported result from being treated as universal. The leadership test is to connect this evidence to ai in people / hr, a responsible owner, and a measurable production outcome.

Altimetrik Named to Constellation Research ShortLists™ for AI Services and Digital Transformation Services DETROIT--(BUSINESS WIRE)-- Altimetrik , an AI engineering company, has been named to Q3 2026 Constellation Research ShortLists™ for AI Services: Global and Digital Transformation Services (DTX): Global The AI Services ShortList recognizes firms with the specialized technical depth to help enterprises design, build, train, manage and operate AI capabilities, spanning programming, data engineering and analysis, model development and training, AI and ML operations, responsible AI and security.

The DTX ShortList evaluates firms that combine business strategy, creative design, innovative delivery models, exponential technology expertise and rigorous testing to reimagine business models, co-create future solutions and operate them at scale. This distinction is increasingly relevant as organizations move beyond isolated AI initiatives and seek partners capable of modernizing the data, platforms and processes required for enterprise scale adoption. Constellation Research projects the global AI services market to grow from $252 billion in 2024 to $1.42 trillion by 2031, highlighting the accelerating demand for providers that can connect specialized AI engineering with strategy, design and scaled execution. “Being named to both ShortLists is meaningful because it validates the challenge we have set out to solve for our clients,” said Raj Sundaresan, CEO of Altimetrik. “AI does not scale by simply adding another layer to fragmented data, legacy platforms, and outdated processes.

It scales when strategy, data, models, platforms, security, and governance are engineered to work together as one system. That is the foundation of ALTi AIOS™ and the core of our practitioner-driven approach. This recognition reinforces our belief that successful AI transformation is not about experimentation alone.

Why it mattersThe National Law Review connects the development to a practical control question: This distinction is increasingly relevant as organizations move beyond isolated AI initiatives and seek partners capable of modernizing the data, platforms and processes required for enterprise scale adoption.. For chief technology officer, the implication is a test of deployment lead time under the constraint that It scales when strategy data models platforms security and governance are engineered to work together as one system.. The leadership test is to connect this evidence to ai in technology, a responsible owner, and a measurable production outcome.

Samsung SDS Leads Enterprise AI Transformation(AX) Beyond AI Adoption, Unveiling Its Multi-Dimensional AI Full-Stack Strategy at Real Summit 2026 □ Presenting a roadmap for successful AX that delivers business results - June-hee Lee, President and CEO of Samsung SDS, emphasizes, “AI changes tasks, but AX changes the business.” - Unveiled seven core elements for a successful AX □ Strengthening the execution system of 'multi-dimensional AI full-stack' to support clients throughout their AX journey - Extending AX capabilities proven through the ‘Client Zero’ strategy - Training approximately 200 embedded Forward Deployed Engineers (FDEs) by year-end to rapidly deliver innovation - Combining AI infrastructure, platforms and solutions with industry expertise and end-to-end capabilities to present a multi-dimensional AI full-stack strategy □ Expanding cooperation with global frontier AI companies to strengthen the AI ecosystem - First Korean company to join OpenAI’s ‘Daybreak’ program, driving cyber-defense innovation with security-specialized models - First in Korea to jointly explore new businesses through a strategic partnership with Anthropic □ Extending AX beyond the digital domain into 'physical AI' driving its Robotics Transformation (RX) business in earnest - Driving autonomous manufacturing, beyond automated manufacturing, by launching a robotic orchestration platform in 2027 - Extending AX into the physical domain, building on experience with approximately 430 manufacturing-automation clients On September 8th, Samsung SDS held Real Summit 2026, an enterprise AX conference, at the COEX Convention Center in Seoul The company presented specific execution strategies and a roadmap for narrowing the gap between AI adoption and real-world business results based on its multi-dimensional AI full-stack strategy.

Approximately 15,000 industry stakeholders attended Real Summit 2026 (approximately 8,000 on-site with the rest online), demonstrating strong interest in the event. □ Beyond Operational Efficiency to Business Redesign: Seven Core AX Elements for Real-World Results June-hee Lee, President and CEO of Samsung SDS, said during his keynote speech, “AI Transformation (AX) is a fundamental shift going beyond adopting AI to merely save time and money. AX is about completely redesigning the business and and the way work is done from scratch.” He emphasized, “Companies that have achieved AX anticipate clients’ needs before they ask and respond to the market in real time, rather than analyzing the market and responding in turn. AI changes tasks, but AX changes the business.” Citing data from the global market research firm McKinsey & Company, CEO Lee stated, "While 88% of companies have adopted AI in at least one business function, only 6% say AI is making a meaningful contribution to their profits.

There is still a gap between AI adoption and business results." He went on to suggest seven key elements for successful AX: Process Re-Design (redesigning processes in consideration of how AI and people work together); AI-Ready Data (structuring data into a format that can be utilized by AI); Agent Ops (an efficient operating framework for multiple AI agents); AI Governance (policy setting and controlling AI behavior); AI Security; AI Organization (building an AI-friendly organization); and People & Culture (changing corporate culture). □ Strengthening AX Execution at Client Sites Through ‘Client Zero’ and ‘FDE’ Samsung SDS is pursuing a 'Client Zero' strategy, whereby Samsung SDS applies these core AX elements to its own internal operations first to verify business outcomes. The company continuously evaluates AX performance in real-world environments across its seven internal mega-processes: development, procurement, quality, management support, project delivery, marketing, and sales. The know-how accumulated through this process is then applied to client operations.

Why it mattersThis is more than a category signal because There is still a gap between AI adoption and business results." He went on to suggest seven key elements for successful AX: Process Re-Design (redesigning processes in consideration of how AI and people work together); AI-Ready Data (structuring data into a format that can be utilized by AI); Agent Ops (an efficient operating framework for multiple AI agents); AI Governance (policy setting and controlling AI behavior); AI Security; AI Organization (building an AI-friendly organization); and People & Culture (changing corporate culture). □ Strengthening AX Execution at Client Sites Through ‘Client Zero’ and ‘FDE’ Samsung SDS is pursuing a 'Client Zero' strategy, whereby Samsung SDS applies these core AX elements to its own internal operations first to verify business outcomes.. In platform delivery, chief technology officer can use it to examine deployment lead time; the gating issue remains There is still a gap between AI adoption and business results. He went on to suggest seven key. The leadership test is to connect this evidence to ai in technology, a responsible owner, and a measurable production outcome.

Securing AI agents in the enterprise: Learnings from our journey at Microsoft As AI agents become more sophisticated and autonomous, large enterprises like ours face a fundamental challenge: How do you enable powerful new AI experiences among your employees without compromising security, governance, or operational control? That was the guiding mantra behind an ambitious cross-company effort involving our team in Microsoft Digital-the company’s IT organization-and a number of our product teams.

The effort, internally referred to as the Securing AI Agents initiative, brought together teams from Microsoft Digital, Windows, Entra, Intune, Defender, Purview, and Microsoft Security to validate secure AI agent scenarios inside Microsoft’s corporate tenant. Together, we set out to prove that AI agents could operate safely inside a real enterprise environment, not just in a controlled demonstration. “Securing AI in the enterprise at pace requires an integrated, full-stack approach. By validating these capabilities together at enterprise scale, we’re generating the real-world learning to strengthen Microsoft’s products and give customers a trusted blueprint for secure AI adoption.” Ragini Singh, partner group engineering manager, Microsoft Digital “Securing AI in the enterprise at pace requires an integrated, full-stack approach.

By validating these capabilities together at enterprise scale, we’re generating the real-world learning to strengthen Microsoft’s products and give customers a trusted blueprint for secure AI adoption.” As part of our role as the company’s Customer Zero , this work was recently showcased by Samantha Song and Scott Hanselman at the Microsoft Build 2026 conference . “Securing AI in the enterprise at pace requires an integrated, full-stack approach,” says Ragini Singh, a partner group engineering manager in Microsoft Digital. “By validating these capabilities together at enterprise scale, we’re generating the real-world learning to strengthen Microsoft’s products and give customers a trusted blueprint for secure AI adoption. Looking ahead, our vision is to make this integrated security foundation the standard for every enterprise, so organizations can scale autonomous agents with speed, confidence, and trust.” The Secure AI Agents initiative was the result of an all-hands-on-deck project behind the scenes here at Microsoft: Months of testing, coordination, validation, and refinement that transformed an emerging concept into a governable enterprise pattern. “All of these teams came together and not only enabled the environment in our IT tenant but also ensured that all the right policies were deployed to make it so that these agents can run safely. That meant not bypassing any of the security parameters that we’ve already deployed.” Shyam Sunder Gogi, technical program manager, Microsoft Digital “All of these teams came together and not only enabled the environment in our IT tenant but also ensured that all the right policies were deployed to make it so that these agents can run safely.

Why it mattersThe development changes the control question for chief technology officer: By validating these capabilities together at enterprise scale, we’re generating the real-world learning to strengthen Microsoft’s products and give customers a trusted blueprint for secure AI adoption.” As part of our role as the company’s Customer Zero , this work was recently showcased by Samantha Song and Scott Hanselman at the Microsoft Build 2026 conference . “Securing AI in the enterprise at pace requires an integrated, full-stack approach,” says Ragini Singh, a partner group engineering manager in Microsoft Digital. “By validating these capabilities together at enterprise scale, we’re generating the real-world learning to strengthen Microsoft’s products and give customers a trusted blueprint for secure AI adoption.. If the team applies it to platform delivery, it must reconcile That was the guiding mantra behind an ambitious cross-company effort involving our team in Microsoft Digital-the company s IT organization-and a number of our with By validating these capabilities together at enterprise scale we re generating the real-world learning to strengthen Microsoft s before claiming movement in deployment lead time. The leadership test is to connect this evidence to ai in technology, a responsible owner, and a measurable production outcome.

The Knowledge Management Software Market reached an estimated USD 14.56 billion in 2025 and is projected to grow from USD 17.15 billion in 2026 to USD 70.01 billion by 2035, registering a CAGR of 16.92% across the forecast period Two forces are converging to drive this expansion: enterprise-wide mandates to retain institutional expertise amid workforce turnover, and governments tightening data governance standards - the EU's Data Governance Act and the U.S.

Executive Order on AI (October 2023) both compel organizations to formalize how intellectual assets are captured, stored, and retrieved [1] [2] . Combined, these catalysts are pushing annual corporate spending on knowledge infrastructure past traditional IT budget thresholds. Legacy intranets and static document repositories are being dismantled in favor of AI-augmented platforms that integrate retrieval-augmented generation, semantic search, and automated taxonomy creation.

Microsoft alone channeled over USD 13 billion into OpenAI partnerships through 2024, embedding generative capabilities directly into SharePoint and Viva [3] . IBM's watsonx platform and ServiceNow's Now Assist similarly reflect a vendor race to fuse large language models with enterprise knowledge sharing systems, converting passive content libraries into dynamic decision-support engines. North America commands approximately 41.05% of the Knowledge Management Software Market, underpinned by early cloud adoption and a dense SaaS vendor ecosystem.

Why it mattersMarket Research Future reports Two forces are converging to drive this expansion: enterprise-wide mandates to retain institutional expertise amid workforce turnover, and governments tightening data governance standards - the EU's Data Governance Act and the U.S.. That matters for data-product delivery because chief data officer must decide whether Knowledge Management Software Market Size Share Growth Report MRFR Market can improve data quality without weakening accountability; Microsoft alone channeled over USD 13 billion into OpenAI partnerships through 2024 embedding generative capabilities directly into SharePoint is the boundary for the claim. The leadership test is to connect this evidence to ai in data & ai, a responsible owner, and a measurable production outcome.

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Data Intelligence: Building Your Competitive Advantage in the Era of AI 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. Instead of navigating reports and filters, a user can now ask, “Why did sales decline last quarter?” and receive an analysis directly.

Why it mattersThe evidence combines Join a live online event on the O’Reilly platform to learn from the experts shaping tech. with Driven by agentic AI, modern data teams are moving beyond simply looking at what happened.. In data-product delivery, that gives chief data officer a concrete question about data quality, not a reason to assume that In this article I ll define some of the top trends defining this era from data agents and has been solved. The leadership test is to connect this evidence to ai in data & ai, a responsible owner, and a measurable production outcome.

Lakehouse Business Data Models for Retail & Consumer Goods Production-ready, governed Silver-layer business data models for Retail & Consumer Goods that deploy directly into Unity Catalog as the analytical foundation of a lakehouse-consistent on day one A library of forty production-ready Silver-layer business data models, one per industry, that deploy directly into Unity Catalog as the analytical foundation of a Databricks lakehouse.

Each model is complete, governed, and internally consistent on day one. Every domain, table, column, foreign key, classification tag, and metric view is already defined. Generic industry templates average every business in a sector, leaving customers months of trimming work.

A Lakehouse Business Data Model is shaped like a single organization in its industry, with the terminology and divisions it actually uses. Every model ships in two scopes, MVM (Minimum Viable Model) and ECM (Expanded Coverage Model), supports three Unity Catalog layouts (cataloging styles), and includes the same complete artifact bundle. Each model is published as a complete bundle. `model.json` is the logical model that captures every domain, subdomain, product, attribute, foreign key, classification tag, and metric view definition.

Why it mattersThe operational significance is in A library of forty production-ready Silver-layer business data models, one per industry, that deploy directly into Unity Catalog as the analytical foundation of a Databricks lakehouse.. It changes the data-product delivery decision for chief data officer, while A Lakehouse Business Data Model is shaped like a single organization in its industry with the terminology and keeps the reported result from being treated as universal. The leadership test is to connect this evidence to ai in data & ai, a responsible owner, and a measurable production outcome.

The evolving AI compliance landscape: governance, risk and regulatory uncertainty This is an Insight article, written by a selected contributor as part of GIR's co-published content The increasing integration of AI across various sectors has spurred governments and organisations worldwide to grapple with novel legal and ethical challenges presented by this technology.

Key developments include the European Union’s Artificial Intelligence Act (EU AI Act) moving from adoption into staged application, [1] as well as continued legislative activity at state and federal levels in the United States. Organisations seeking to remain compliant amid a fragmented compliance landscape face ever-growing challenges. In the midst of this activity, businesses continue to recognise the need for robust AI governance policies, but these policies are becoming more focused, shifting to investing in compliance measures that will mature and evolve their AI programmes (eg, training and governance structures to assign explicit accountability for AI risks associated with bias, data privacy, intellectual property and algorithmic accountability). [2] This proactive approach is crucial for navigating the evolving regulatory landscape, though security and risk concerns remain the top barrier to scaling AI. [3] Looking ahead, it is becoming clear that organisations focused on building AI compliance foundations will be most successful in building trust and ensuring the safe and ethical use of AI. [4] The Trump presidential administration has focused on rapid AI adoption connected to market and infrastructure growth (as opposed to President Biden’s approach, which focused on safety testing and risk management).

On 23 January 2025, the Trump administration issued an Executive Order (EO) adopting an AI governance strategy centred on fostering US leadership and innovation in the field of AI. A primary objective is to ensure the United States maintains global dominance in AI, particularly in the face of increasing international competition. [5] This approach is expected to involve a significant rollback of regulations implemented by the previous administration, which were viewed as potential impediments to rapid technological advancement. [6] Since then, President Trump signed the “Ensuring a National Policy Framework for Artificial Intelligence” EO, which directs the Department of Justice and the Department of Commerce to challenge and potentially restrict state-level AI regulations (such as Colorado’s sweeping AI law) that are deemed overly burdensome to technology companies. [7] Specifically, the EO established an AI Litigation Task Force to challenge US state AI laws; further, within 90 days of the date of the EO, the Secretary of Commerce was instructed to publish an evaluation of existing state AI laws that identify “onerous laws” as well as laws that should be referred to the AI Litigation Task Force. [8] The evaluation of state AI laws “shall, at a minimum, identify laws that require AI models to alter their truthful outputs, or that may compel AI developers or deployers to disclose or report information in a manner that would violate the First Amendment or any other provision of the Constitution”. [9] As expected, the current administration’s policies have largely prioritised economic competitiveness and national security, with a particular focus on outpacing global rivals, most notably China, in the development and deployment of AI technologies. [10] Interestingly, however, on 2 June 2026, President Trump announced a new EO that pivots towards heightened US government interest and oversight in risk implications associated with the evolution of some AI models. [11] That EO describes how the federal government will collaborate with frontier AI models to “modernize government and private sector information systems and harden them against external threats”. [12] Through the EO, the administration has created a voluntary framework for AI developers to engage with the federal government to evaluate frontier AI models for cybersecurity controls. [13] Although the current framework is voluntary, unlike China’s mandatory approach, it will be important to watch how this EO is implemented - and how future initiatives develop - as a potential indicator of broader regulatory trends. Another significant trend continues to be the growing emphasis on AI governance and the development of internal AI policies within organisations.

Why it mattersGlobal Investigations Review connects the development to a practical control question: Organisations seeking to remain compliant amid a fragmented compliance landscape face ever-growing challenges.. For general counsel and risk leader, the implication is a test of control coverage under the constraint that On 23 January 2025 the Trump administration issued an Executive Order EO adopting an AI governance strategy centred. The leadership test is to connect this evidence to ai in risk, legal & compliance, a responsible owner, and a measurable production outcome.

It is increasingly embedded in core business processes, supporting decisions, automating workflows, generating content, detecting anomalies, predicting outcomes and, in some cases, acting with growing autonomy Linked to this, regulatory developments and public scrutiny are converging toward the same conclusion: high-impact AI must be demonstrably trustworthy.

Organizations that cannot explain how AI decisions are produced, or how risks are controlled in practice, will struggle to maintain stakeholder trust. Traditional audit approaches were often built around relatively stable IT systems, defined business processes, and clear control points. AI systems can rely on large and dynamic datasets, evolve over time, produce probabilistic outputs, and interact with business users in ways that are difficult to predict fully upfront.

Generative AI and agentic AI further increase this complexity by enabling systems to interpret prompts, generate content, recommend actions, or even orchestrate tasks across systems. It may include custom-built models, vendor-specific tools, AI features embedded in enterprise platforms, online AI tools, and unregistered “shadow AI,” all of which can be used in different ways (decision AI, generative AI and, increasingly, agentic AI). Risks differ depending on how AI is accessed and used.

Why it mattersThis is more than a category signal because Generative AI and agentic AI further increase this complexity by enabling systems to interpret prompts, generate content, recommend actions, or even orchestrate tasks across systems.. In risk review and evidence production, general counsel and risk leader can use it to examine control coverage; the gating issue remains Generative AI and agentic AI further increase this complexity by enabling systems to interpret prompts generate content recommend. The leadership test is to connect this evidence to ai in risk, legal & compliance, a responsible owner, and a measurable production outcome.

How to Audit AI Compliance from Both Sides of the Table The tricky thing about AI compliance is that most organizations are going to experience it from both sides You need to be able to explain how AI is being used inside your own organization, what it can access, and how you're managing the risk.

At the same time, you need to understand how your vendors are using AI and whether that introduces new risk into your environment. This may sound simple, but it's actually a bit convoluted. AI is increasingly appearing in approved enterprise tools, existing SaaS products, developer workflows, agents, and tools employees adopt on their own.

Model Context Protocol (MCP), a tool that makes it possible for AI models to connect with outside tools and data, is making it easier to connect AI applications to external data and systems. And behind any one AI-enabled product there may be several other providers, models, APIs, or subprocessors involved. There also isn't one universal "AI compliance audit." Requirements will vary depending on the laws, frameworks, contracts, and use cases that apply to an organization.

Why it mattersThe development changes the control question for general counsel and risk leader: Model Context Protocol (MCP), a tool that makes it possible for AI models to connect with outside tools and data, is making it easier to connect AI applications to external data and systems.. If the team applies it to risk review and evidence production, it must reconcile You need to be able to explain how AI is being used inside your own organization what it can access and how you're managing with Model Context Protocol MCP a tool that makes it possible for AI models to connect with outside tools before claiming movement in control coverage. The leadership test is to connect this evidence to ai in risk, legal & compliance, a responsible owner, and a measurable production outcome.

Why AI adoption is making Saas visibility a priority for enterprises Opinions expressed by contributors are their own As employees begin adopting cloud tools and AI agents on their own, organizations are realizing they cannot govern what they cannot see.

Enterprise software environments have never been more complex or difficult to track. The same agility that allows teams to spin up a new project before the end of the day is quietly creating a monitoring issue that IT leaders are still scrambling to solve. SaaS Management has moved from being a procurement headache to a security and governance necessity, driven largely by the speed at which employees, not IT departments, are now adopting applications, including a fast-growing network of AI tools.

The scale of the problem is greater than many realize Torii’s 2026 SaaS Benchmark Report found that the average organization now runs more than 830 applications. A number that would likely be hard to keep track of. And more than half of those are not formally managed by IT.

Why it mattersDigital Journal reports As employees begin adopting cloud tools and AI agents on their own, organizations are realizing they cannot govern what they cannot see.. That matters for lab-to-production transfer because chief innovation officer must decide whether Why AI adoption is making Saas visibility a priority for can improve pilot-to-production rate without weakening accountability; The scale of the problem is greater than many realize Torii s 2026 SaaS Benchmark Report found that is the boundary for the claim. The leadership test is to connect this evidence to enterprise ai labs, a responsible owner, and a measurable production outcome.

Better intent hasn't produced more adoption - that's the uncomfortable finding from inside enterprise AI rollouts right now, and it's the opposite of what most leaders expect Intent engineering shifts the focus from features to outcomes: users describe the outcome they want in natural language - e.g.

I need to install a water filtering system in my kitchen tap. I am not very handy, need tools, parts, instructions with a budget of $300 - and the system translates the intent into actions. The reason why is that the chain from intent to adoption is only as strong as its most human link.

AI creates value only when people trust it enough to change how they work. The last mile is human and it has to be led, not installed. Projects stall not because the tools failed, but because the transformation was run as a technology program when at its core it's a human one.

Why it mattersThe evidence combines Intent engineering shifts the focus from features to outcomes: users describe the outcome they want in natural language - e.g. with I am not very handy, need tools, parts, instructions with a budget of $300 - and the system translates the intent into actions.. In lab-to-production transfer, that gives chief innovation officer a concrete question about pilot-to-production rate, not a reason to assume that AI creates value only when people trust it enough to change how they work. has been solved. The leadership test is to connect this evidence to enterprise ai labs, a responsible owner, and a measurable production outcome.

Palo Alto Networks PANW believes the shift toward enterprise AI adoption is creating new cybersecurity needs across networks, applications, identities and security operations In the third quarter of fiscal 2026, management said AI is increasing network traffic, creating more machine and AI-agent identities, and allowing attackers to find vulnerabilities and launch attacks faster.

PANW is directly benefiting from this trend of strong demand for enterprise AI adoption, which should help the company strengthen its position against cybersecurity rivals, such as CrowdStrike CRWD and Zscaler ZS . The company's Network Security business is already benefiting from higher AI-related traffic. Next-generation firewall bookings grew nearly 40% year over year, while hardware had its best quarter in a decade.

Software firewall annual recurring revenues (ARR) also increased 25% year over year as customers expanded capacity to inspect traffic between cloud and AI workloads. PANW reported early wins in AI data centers, including an $80 million deal with a U.S. power producer that selected next-generation firewalls and SASE. Enterprise AI adoption is also creating opportunities in other parts of PANW's portfolio.

Why it mattersThe operational significance is in In the third quarter of fiscal 2026, management said AI is increasing network traffic, creating more machine and AI-agent identities, and allowing attackers to find vulnerabilities and launch attacks faster.. It changes the lab-to-production transfer decision for chief innovation officer, while Software firewall annual recurring revenues ARR also increased 25% year over year as customers expanded capacity to inspect keeps the reported result from being treated as universal. The leadership test is to connect this evidence to enterprise ai labs, a responsible owner, and a measurable production outcome.

Abacus builds on partnerships with OpenAI, Google, and Microsoft for managed, governance-first enterprise AI solutions; executes Anthropic model deployments NEW YORK , Sept 14, 2026 /PRNewswire/ -- Abacus , a global managed IT and cybersecurity service provider built for highly regulated industries, announced the launch of AI³ (Abacus AI Acceleration) , a strategic initiative designed to help organizations adopt, manage, and scale artificial intelligence through a governance-first, continuously managed approach.

Built on partnerships with some of the world's leading AI innovators, including OpenAI, Google, and Microsoft, AI³ positions Abacus as the trusted partner organizations need to navigate an increasingly complex AI landscape. By combining best-of-breed AI technologies with deep expertise in infrastructure, cybersecurity, compliance, and operations, Abacus helps clients transform AI from isolated experimentation into measurable business outcomes. "The future of AI will belong to organizations that can pair innovation with governance, ambition with accountability, and strategy with execution," said Anthony J.

"At Abacus, our AI 3 initiative is helping clients turn disruption into direction, bringing clarity and confidence to an increasingly complex AI landscape. By combining decades of experience operating and securing complex environments, with a deep compliance focus, we are now helping clients adopt AI in ways that are reliable and scalable to drive a lasting competitive advantage." The AI³ initiative includes a new lineup of product offerings as well as add-ons to existing core services. The firm has proven expertise in deploying Anthropic models, executing over a dozen Claude deployments, and offers an AI Risk and Readiness Assessment designed to help healthcare and financial services firms understand and grow their AI maturity.

Why it mattersPR Newswire connects the development to a practical control question: By combining best-of-breed AI technologies with deep expertise in infrastructure, cybersecurity, compliance, and operations, Abacus helps clients transform AI from isolated experimentation into measurable business outcomes.. For transformation leader, the implication is a test of decision latency under the constraint that At Abacus our AI 3 initiative is helping clients turn disruption into direction bringing clarity and confidence to. The leadership test is to connect this evidence to ai operating models, a responsible owner, and a measurable production outcome.

Artificial intelligence (AI) agents are quickly moving from experimental tools to active participants in enterprise workflows For chief information security officers (CISOs), the immediate priority should be gaining visibility of agents and establishing deterministic controls over what existing agents can access and do.

Unlike traditional generative AI applications that primarily produce content, agentic AI systems can interact with tools, call application programming interfaces (APIs), retrieve corporate information, and make changes to enterprise systems. This creates significant opportunities for automation, but it also means AI-generated decisions can translate directly into real-world impacts. In response, a compelling cyber security proposition has emerged: use AI agents to secure other AI agents.

If enterprises deploy autonomous systems at a scale and speed human security teams cannot match, an equally autonomous defensive layer may appear to be the logical answer. However, this may actually risk unnecessarily expanding one’s attack surface. Why the concept of “agents securing agents” remains largely aspirational right now Large language models (LLMs) are probabilistic systems.

Why it mattersThis is more than a category signal because If enterprises deploy autonomous systems at a scale and speed human security teams cannot match, an equally autonomous defensive layer may appear to be the logical answer.. In operating-model redesign, transformation leader can use it to examine decision latency; the gating issue remains If enterprises deploy autonomous systems at a scale and speed human security teams cannot match an equally autonomous. The leadership test is to connect this evidence to ai operating models, a responsible owner, and a measurable production outcome.

Mobisoft Infotech Introduces Enterprise Agentic AI Engineering and Integration Services Mobisoft Infotech today announced the introduction of its enterprise agentic AI engineering and integration services, designed to help organizations move beyond standalone generative AI applications and deploy AI agents capable of executing multi-step workflows across enterprise systems Enterprises want AI that understands objectives and acts within real business processes.

The real challenge is engineering agents that operate reliably within enterprise security and governance.” - Ritesh Patil, Founder and Chief Delivery Officer at Mobisoft Infotech Enterprises want AI that understands objectives and acts within real business processes. The real challenge is engineering agents that operate reliably within enterprise security and governance.” The new services address a growing enterprise priority. Enterprises want to turn advances in large language models and autonomous AI into reliable, governed systems that operate within existing technology, security, data, and compliance environments.

Organizations are experimenting with AI agents for operations, customer service, knowledge management, software engineering, analytics, and other functions. As they do, the engineering challenge is shifting from model access to production readiness. Enterprise deployments increasingly require agents to securely access business applications and interpret organizational data.

Why it mattersThe development changes the control question for transformation leader: Organizations are experimenting with AI agents for operations, customer service, knowledge management, software engineering, analytics, and other functions.. If the team applies it to operating-model redesign, it must reconcile Enterprises want AI that understands objectives and acts within real business processes. with Organizations are experimenting with AI agents for operations customer service knowledge management software engineering analytics and other functions. before claiming movement in decision latency. The leadership test is to connect this evidence to ai operating models, a responsible owner, and a measurable production outcome.

Grand View Research puts the enterprise agentic AI market at $24.5 billion by 2030 On the other hand, MarketsandMarkets estimates the AI agents’ market size at $52.62 billion over the same horizon. Whether shared infrastructure genuinely removes rebuild cost is testable, and the test is uncomfortable for the category. If the claim holds, the share of agent budgets going into integration work should be falling.

Both start from a mid-single-digit-billion base and both land on a compound growth rate near 46%. When two credible forecasters agree that closely on the slope and differ by more than a factor of two on the destination, the interesting information is in what each of them is counting. The Forecasts Agree on the Slope and Not on the Size A forecast range is a decent proxy for how well a category has been defined, and agentic AI is currently defined differently by everyone measuring it.

The firm projects this enterprise deployment’s growth from $2.6 billion in 2024 to $24.5 billion by 2030 at a 46.2% compound rate. MarketsandMarkets draws a wider boundary around agent software generally that runs from $5.26 billion in 2024 to $52.62 billion in four years at 46.3%. Absolute market sizes are hostage to definitions, while growth rates tend to survive them.

Why it mattersthenextweb.com reports On the other hand, MarketsandMarkets estimates the AI agents’ market size at $52.62 billion over the same horizon.. That matters for value realization review because CFO and CIO must decide whether Auditing the agent economy what the forecasts actually say thenextweb.com can improve realized savings without weakening accountability; The firm projects this enterprise deployment s growth from 2.6 billion in 2024 to 24.5 billion by 2030 is the boundary for the claim. The leadership test is to connect this evidence to enterprise ai-roi & value maxing, a responsible owner, and a measurable production outcome.

New delivery model helps reduce migration timelines from months to weeks combining specialized AI agents with human integration experts for secure, governed API-led connectivity at scale MuleSoft AI Pod is accessible through Glob.AI , Globant's AI-native technology service model for delivering enterprise AI at scale.

3, 2026 /PRNewswire/ -- Globant , a global company focused on driving enterprise reinvention through AI, today introduced Salesforce's MuleSoft AI Pod combining specialized AI agents with human experts to accelerate API-led connectivity and delivery. As AI initiatives become more complex, organizations need faster, more governed ways to connect the applications, data and workflows that power enterprise transformation. Enterprises are under pressure to move agentic AI from experimentation to production, but disconnected systems and fragmented data remain a major barrier.

MuleSoft AI Pod helps address that integration bottleneck by accelerating secure API-led connectivity, integration delivery and governed execution across initiatives of varying size and complexity. This AI Pod is accessible through Glob.AI , the new AI delivery model by Globant. "As organizations look to scale Agentforce and other AI initiatives across the enterprise, integration and data readiness are becoming essential," said Roland Berthelot, Global Head of Salesforce Studio at Globant.

Why it mattersThe evidence combines MuleSoft AI Pod is accessible through Glob.AI , Globant's AI-native technology service model for delivering enterprise AI at scale. with As AI initiatives become more complex, organizations need faster, more governed ways to connect the applications, data and workflows that power enterprise transformation.. In value realization review, that gives CFO and CIO a concrete question about realized savings, not a reason to assume that MuleSoft AI Pod helps address that integration bottleneck by accelerating secure API-led connectivity integration delivery and governed execution has been solved. The leadership test is to connect this evidence to enterprise ai-roi & value maxing, a responsible owner, and a measurable production outcome.

Airrived adds Agentic Observability to track AI agent actions and risks Airrived will reveal Agentic Observability, a major expansion of its enterprise Agentic OS built to give organizations end-to-end visibility into how AI agents behave, from the moment enterprise data enters the platform, through agent reasoning and execution, to the final business outcome Traditional application monitoring was designed to answer a simple question: is the software running?

And ultimately, what did it decide, and what did that decision cost? Airrived Agentic Observability brings all of these answers into a single control plane. “Enterprises are moving from software that executes instructions to agents that reason, decide, and act,” said Anurag Gurtu , CEO of Airrived. “You cannot govern what you cannot see. Agentic Observability gives enterprises visibility from data, to decision, to action, to outcome.” Airrived delivers visibility across the entire agentic lifecycle: Enterprise integration → context lake → agentic app → agent → action → outcome.

Organizations can trace data as it moves from enterprise integrations into Airrived’s Context Lake, through agentic applications and individual agents, and ultimately to the actions and outcomes those agents produce. Rather than simply confirming that an agent executed, Airrived surfaces the full environment surrounding that execution. For each agent and agentic application, Airrived surfaces its creator, owner, associated users, roles, permissions, and permitted actions, along with whether human-in-the-loop approval is required before it acts.

Why it mattersThe operational significance is in Traditional application monitoring was designed to answer a simple question: is the software running?. It changes the value realization review decision for CFO and CIO, while Organizations can trace data as it moves from enterprise integrations into Airrived s Context Lake through agentic applications keeps the reported result from being treated as universal. The leadership test is to connect this evidence to enterprise ai-roi & value maxing, a responsible owner, and a measurable production outcome.

Integrating AI Agent governance, multi-model routing, and on-premises AI infrastructure, NeuroWatt also opens complimentary Agent application consultations TAIPEI, Sept 9, 2026 /PRNewswire/ -- NeuroWatt announced the launch of NeuroTeam, an enterprise-grade Agentic AI Workforce designed to help organizations connect enterprise knowledge, existing systems, and business workflows into coordinated teams of AI Agents capable of executing real-world tasks.

NeuroTeam unifies Agent reasoning, tool execution, identity and access management, policy controls, human approvals, auditability, and monitoring in a single enterprise-grade architecture. As enterprises begin connecting AI Agents to CRM, ERP, customer service, project management, and SaaS platforms, the challenge is no longer just model performance. Organizations also need to ensure that AI Agents can operate securely within enterprise permissions, governance policies, and data protection requirements.

From Standalone AI Tools to Governed AI Workforces NeuroTeam supports SSO, RBAC, ABAC, Agent Identity, Human-in-the-loop approvals, API controls, and tool-level permissions, helping enterprises establish a governed framework for AI Agent deployment. At the model layer, NeuroTeam uses an Internal LLM Gateway to support multi-model routing, cost management, and data masking across private models, on-premises LLMs, and external large language models. This allows enterprises to select the most appropriate AI model and deployment environment based on data sensitivity, latency, cost, security, and governance requirements.

Why it mattersYahoo Finance connects the development to a practical control question: As enterprises begin connecting AI Agents to CRM, ERP, customer service, project management, and SaaS platforms, the challenge is no longer just model performance.. For enterprise architect, the implication is a test of traceability under the constraint that From Standalone AI Tools to Governed AI Workforces NeuroTeam supports SSO RBAC ABAC Agent Identity Human-in-the-loop approvals API. The leadership test is to connect this evidence to ai operating systems (aios), a responsible owner, and a measurable production outcome.

As agentic AI moves from experimentation toward enterprise deployment, the challenge is figuring out how agents can work together, connect to the systems and data they need, and operate safely across the workflows that run a business Although agentic AI has been adopted by some 80% of Fortune 500 companies, progress toward meaningful scale remains uneven, with many organizations still working through isolated pilots.

For Arun Chandra, chief operating officer at NiCE, the first step is moving beyond experimentation for its own sake. “Everybody's trying to figure out what can we do with this technology?” he says. But scaling requires a clearer connection to business strategy: Organizations need to define whether they are trying to increase revenue, reduce costs, or pursue another strategic or financial objective. From there, they need to rethink the workflows where agents will operate instead of just layering AI onto existing processes. “The last thing you want to do is to apply AI on an outdated or an inefficient workflow,” Chandra says.

That shift requires organizations to treat agentic AI as a cohesive system. Agents need access to the data, knowledge, and context required to make effective decisions, as well as connections to back-end systems if they are expected to take action. Fragmented information can undermine those capabilities: “The efficacy of these AI agents is purely a function of the context, the knowledge, and the data they can ingest and use,” Chandra says.

Why it mattersThis is more than a category signal because That shift requires organizations to treat agentic AI as a cohesive system.. In AI platform control, enterprise architect can use it to examine traceability; the gating issue remains That shift requires organizations to treat agentic AI as a cohesive system.. The leadership test is to connect this evidence to ai operating systems (aios), a responsible owner, and a measurable production outcome.

AI governance is shifting from periodic compliance review to a critical component that’s embedded in the architectural design of an organization and operationalized at runtime As autonomous agents execute business processes in real time, the distance between a decision and its consequences shrinks, pushing governance out of the compliance calendar and into daily operations.

"Applying traditional strategic governance to AI, the way you would with applications and systems, just doesn't work for AI agents," says Philipp Herzig, CTO of SAP. The agent acts on your behalf, at times without your explicit approval. With proactive real-time operational governance, you are preventing issues rather than chasing them." Continuous AI governance requires enterprises to answer four questions at all times: Which AI agents exist across the enterprise, and what purpose does each serve?

Financial services, healthcare, pharmaceutical, and public sector organizations face the greatest urgency around these questions, with regulators already expecting documented accountability - and the AI governance capabilities these organizations build will quickly become standard in other industries. Regulated industries are hitting the limits of traditional AI governance The limits of traditional approaches to technology governance become most apparent in regulated industries when AI agents begin operating within existing accountability and compliance requirements. Banks apply model risk management guidance such as SR 11-7 and SR 26-2.

Why it mattersThe development changes the control question for enterprise architect: Financial services, healthcare, pharmaceutical, and public sector organizations face the greatest urgency around these questions, with regulators already expecting documented accountability - and the AI governance capabilities these organizations build will quickly become standard in other industries.. If the team applies it to AI platform control, it must reconcile As autonomous agents execute business processes in real time the distance between a decision and its consequences shrinks pushing governance out of the compliance with Financial services healthcare pharmaceutical and public sector organizations face the greatest urgency around these questions with regulators already before claiming movement in traceability. The leadership test is to connect this evidence to ai operating systems (aios), a responsible owner, and a measurable production outcome.

If we have selected the wrong experience for you, please change it above Governing Agentic AI Through an Agent Action Enforcement Layer: AI Observability Framework for Controllable, Trustworthy Multiagent Systems As organizations accelerate their use of autonomous and semi-autonomous artificial intelligence (AI) agents, they face a widening gap between what these agents can do and what enterprises can safely permit them to do.

Agents today can generate content, orchestrate workflows, call application programming interfaces (APIs), and interact with systems on behalf of users or business processes. Yet most enterprises lack a unified way to evaluate or govern an agent’s intended actions before those actions touch critical systems or sensitive data. A new architectural pattern is emerging: the Agent Action Enforcement Layer (AAEL).

This layer provides a structured, pre-execution mechanism for understanding, evaluating, and controlling the actions that AI agents propose to take across applications, cloud environments, and multiagent ecosystems. By introducing action plans, policy-aware evaluation, and non-bypass execution controls, AAEL gives organizations the ability to guide agentic AI toward compliant, secure, and predictable behavior-at enterprise scale. This paper outlines how an AAEL can help enterprises adopt agentic AI with confidence.

Why it mattersDeloitte reports Governing Agentic AI Through an Agent Action Enforcement Layer: AI Observability Framework for Controllable, Trustworthy Multiagent Systems As organizations accelerate their use of autonomous and semi-autonomous artificial intelligence (AI) agents, they face a widening gap between what these agents can do and what enterprises can safely permit them to do.. That matters for process automation because automation leader must decide whether Governing Agentic AI Through an Agent Action Enforcement Layer Deloitte can improve touchless processing rate without weakening accountability; This layer provides a structured pre-execution mechanism for understanding evaluating and controlling the actions that AI agents propose is the boundary for the claim. The leadership test is to connect this evidence to ai automation, a responsible owner, and a measurable production outcome.

Plenty of organizations have AI policies promising human review and responsible use Attorney and CPA Justin Kavalir argues those statements are only assertions and a recent federal case shows what happens when one is tested and no evidence of the promised oversight can be produced.

An increasing number of organizations have policies on AI . Many contain some version of a statement calling for responsible AI use and claiming AI systems are subject to human oversight. Often, this includes language that human review or verification of AI output is required.

These policy statements are assertions, but they are only the beginning of governance . What record is created when this review or verification occurs? A recent Rule 11 federal sanctions order from the Western District of Tennessee is instructive here, highlighting the type of evidence of responsible AI use that organizations may be asked to produce.

Why it mattersThe evidence combines Attorney and CPA Justin Kavalir argues those statements are only assertions and a recent federal case shows what happens when one is tested and no evidence of the promised oversight can be produced. with Many contain some version of a statement calling for responsible AI use and claiming AI systems are subject to human oversight.. In process automation, that gives automation leader a concrete question about touchless processing rate, not a reason to assume that These policy statements are assertions but they are only the beginning of governance has been solved. The leadership test is to connect this evidence to ai automation, a responsible owner, and a measurable production outcome.

Artificial Intelligence (“AI”) is embedded across higher education, from student research and writing to faculty assessment to administrative operations As AI adoption accelerates, colleges and universities face pressure to set clear expectations that balance innovation, academic integrity, and institutional risk.

Institutions that take proactive steps now to establish expectations, review policies, engage governance bodies, and educate campus communities will be better positioned to navigate this landscape and remain effective in an evolving technological landscape. Act now to clarify what is required versus recommended, strengthen oversight, and train stakeholders. From Restriction to Responsibility: Managing AI on Campus Many institutions are moving away from blanket prohibitions on AI and instead incorporating frameworks that emphasize responsible use, transparency, and accountability.

Existing academic integrity policies often predate generative AI and may not clearly address when AI assistance is permissible, when disclosure is required, or how AI-related misconduct will be evaluated. As a result, institutions are increasingly revisiting policies and guidance to provide greater clarity for students, parents and faculty. Required elements should include clear definitions, permitted uses with disclosure expectations, prohibited conduct, and procedures for evaluation and enforcement.

Why it mattersThe operational significance is in As AI adoption accelerates, colleges and universities face pressure to set clear expectations that balance innovation, academic integrity, and institutional risk.. It changes the process automation decision for automation leader, while Existing academic integrity policies often predate generative AI and may not clearly address when AI assistance is permissible keeps the reported result from being treated as universal. The leadership test is to connect this evidence to ai automation, a responsible owner, and a measurable production outcome.

Many organizations remain unprepared for the rapid growth of AI-generated content and synthetic datasets across enterprise environments, leaving them equally unprepared to govern that data effectively and within compliance boundaries Governance frameworks are lagging behind AI, even as AI-generated and other algorithmically generated synthetic data permeate across business functions.

Governance is now a strategic business issue, not just an IT concern. Executives should establish governance best practices before operational and regulatory complexity increases. IT leaders investing in AI need to construct an AI data lifecycle policy and establish scalable governance.

AI-generated data and synthetic data differ fundamentally from traditional data. Traditional data originates from real-world business activities, customer interactions, transactions, sensors or human-created content. It is the data generated by years of doing business. AI-generated data is produced by machine learning models, including text, images, code, audio or analytics. For enterprises, AI-generated data accelerates content creation, software development, customer support and decision-making.

Why it mattersTechTarget connects the development to a practical control question: Executives should establish governance best practices before operational and regulatory complexity increases.. For CIO and change leader, the implication is a test of active usage under the constraint that AI-generated data and synthetic data differ fundamentally from traditional data.. The leadership test is to connect this evidence to ai adoption, a responsible owner, and a measurable production outcome.

Geering brings two decades of privacy, risk and public policy experience to spearhead global data privacy and AI regulatory compliance SALT LAKE CITY , Sept 14, 2026 /PRNewswire/ -- Instructure , the leading learning ecosystem and maker of Canvas LMS, today announced Stephan Geering as chief privacy officer.

In this role, Geering will lead Instructure's global data privacy programs, working across legal, security and product teams to guide how the company builds and governs technology for educators and learners worldwide. He will define Instructure's data privacy strategy and ensure compliance with evolving AI regulations worldwide, overseeing how Instructure evaluates AI features for data privacy and safety risks before they reach customers, setting requirements for how new products handle learner and educator data, and leading the company's response to emerging AI regulation. Geering will also serve as a trusted advisor to Instructure's leadership team on data privacy and AI compliance.

He will work directly with product and engineering teams to build data privacy and AI governance requirements into the company's roadmap from the earliest stages of development. Two Decades in Data Privacy and Risk Leadership Geering joins Instructure after more than 20 years in data privacy, risk management and public policy roles spanning the technology, financial services and government sectors. Most recently, he led the data privacy, AI governance, and compliance programs at a major education technology firm, where he helped secure key ISO certifications.

Why it mattersThis is more than a category signal because He will work directly with product and engineering teams to build data privacy and AI governance requirements into the company's roadmap from the earliest stages of development.. In adoption planning, CIO and change leader can use it to examine active usage; the gating issue remains He will work directly with product and engineering teams to build data privacy and AI governance requirements into. The leadership test is to connect this evidence to ai adoption, a responsible owner, and a measurable production outcome.

Why AI Governance is Now a Board-Level Business Priority Sustaining AI innovation beyond its initial advantage requires effective governance Across every sector, AI is transitioning rapidly from isolated experimentation to enterprise-wide rollout, yet strategic oversight and workforce capabilities continue to lag behind.

As reliance on automated systems deepens, long-term commercial success increasingly relies on managing technology responsibly rather than simply scaling it quickly. Corporate boards now treat AI management as a core business imperative rather than an isolated IT concern. Although regulatory approaches differ between the UK and the EU, global organisations encounter rising expectations surrounding corporate accountability, operational resilience and digital trust.

The International Monetary Fund highlights AI-driven cyber threats as a potential systemic risk to global financial stability, underscoring how deeply software vulnerabilities affect overall business continuity. According to Chris Dimitriadis , Chief Global Strategy Officer at ISACA , organisations need to move beyond “minimal compliance” and towards demonstrable governance and accountability. Fresh insights from the ISACA 2026 AI Pulse Poll reveal a growing divide between technology integration and managerial control.

Why it mattersThe development changes the control question for CIO and change leader: The International Monetary Fund highlights AI-driven cyber threats as a potential systemic risk to global financial stability, underscoring how deeply software vulnerabilities affect overall business continuity.. If the team applies it to adoption planning, it must reconcile Across every sector AI is transitioning rapidly from isolated experimentation to enterprise-wide rollout yet strategic oversight and workforce capabilities continue to lag behind. with The International Monetary Fund highlights AI-driven cyber threats as a potential systemic risk to global financial stability underscoring before claiming movement in active usage. The leadership test is to connect this evidence to ai adoption, a responsible owner, and a measurable production outcome.

Company Insights Working at Atlassian Culture & Values Inclusion & Diversity Career Growth & Development Compensation & Benefits Work-Life Balance & Wellbeing Leadership & Management Innovation, Technology & Agility Mission, Purpose & Impact Stability & Growth What practices at Atlassian support employee job satisfaction? Atlassian’s culture is collaborative, distributed-first and deeply rooted in empowering teams to move quickly, solve meaningful problems and build products used by hundreds of thousands of organizations globally.

The company frames its mission around “unleashing the potential of every team,” and that philosophy shapes how employees work, communicate and build products. Team-first and mission-driven culture: Atlassian’s culture is centered around teamwork, transparency and customer impact. Its products - including Jira, Confluence, Trello, Loom and Rovo - are designed to help teams collaborate more effectively, and employees consistently describe that same collaborative mindset internally.

Atlassian serves more than 350,000 customers globally, including a large majority of Fortune 500 companies, giving employees exposure to large-scale technical and enterprise challenges. Distributed work built intentionally: One of Atlassian’s strongest cultural differentiators is Team Anywhere, its distributed work philosophy. Rather than treating remote work as temporary flexibility, Atlassian built systems, communication norms and collaboration processes around async-first work.

Why it mattersBuilt In reports Atlassian’s culture is collaborative, distributed-first and deeply rooted in empowering teams to move quickly, solve meaningful problems and build products used by hundreds of thousands of organizations globally.. That matters for business-model design because business-unit president must decide whether What's It Like to Work at Atlassian 2026 Built In can improve gross margin without weakening accountability; Atlassian serves more than 350 000 customers globally including a large majority of Fortune 500 companies giving employees is the boundary for the claim. The leadership test is to connect this evidence to ai-enabled, ai-first, and ai-native product and operating model shifts, a responsible owner, and a measurable production outcome.

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. "That shift requires leaders to think much more broadly about AI transformation.

Why it mattersThe evidence combines 8, 2026 /PRNewswire/ -- Artificial intelligence (AI) has moved beyond experimentation and isolated technology applications and is increasingly embedded in the core operations of organizations. with 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).. In business-model design, that gives business-unit president a concrete question about gross margin, not a reason to assume that AI is no longer just a technology implementation or a collection of productivity tools. has been solved. The leadership test is to connect this evidence to ai-enabled, ai-first, and ai-native product and operating model shifts, a responsible owner, and a measurable production outcome.

Organizations shift focus from experimentation to deployment, exposing workforce, governance and data readiness gaps DOWNERS GROVE, Ill. , Aug 25, 2026 /PRNewswire/ -- Nearly six in 10 organizations now prioritize integrating artificial intelligence (AI) into their technology stack, signaling a shift from AI experimentation to enterprise deployment, according to new research from CompTIA , the leading global provider of vendor-neutral technology training and certifications.

CompTIA's inaugural "Corporate AI Adoption" report finds that organizations are increasingly focused on embedding AI into core business operations rather than simply expanding employee use of AI tools. The findings suggest that AI success increasingly depends on integration, workforce readiness, governance and data management. AI success increasingly depends on integration, workforce readiness, governance and data management.

"Organizations are discovering that buying AI tools is the easy part," said Seth Robinson, vice president of research at CompTIA. "Creating an organization capable of deploying AI securely, responsibly and effectively is a much bigger challenge. The next leaders in AI won't necessarily be the organizations spending the most on technology, but the ones investing in people, processes and data readiness." The research also highlights the growing importance of workforce readiness as organizations scale AI initiatives.

Why it mattersThe operational significance is in 25, 2026 /PRNewswire/ -- Nearly six in 10 organizations now prioritize integrating artificial intelligence (AI) into their technology stack, signaling a shift from AI experimentation to enterprise deployment, according to new research from CompTIA , the leading global provider of vendor-neutral technology training and certifications.. It changes the business-model design decision for business-unit president, while Organizations are discovering that buying AI tools is the easy part said Seth Robinson vice president of research keeps the reported result from being treated as universal. The leadership test is to connect this evidence to ai-enabled, ai-first, and ai-native product and operating model shifts, a responsible owner, and a measurable production outcome.

The Air Force is embarking on an aggressive push to use artificial intelligence across its training pipelines to shorten technical training timelines, accelerate pilot training, and teach basic AI skills to airmen across the force These ambitions are outlined in a new guidance document released Wednesday by Air Education and Training Command, which oversees Air Force training, from recruit training and foundational military job training to advanced schools.

The coming changes could reshape not only how airmen are trained, but also how instructors teach and how the service manages its people. The push follows Defense Secretary Pete Hegseth's January directive to make the military an "AI-first" force and aggressively eliminate bureaucratic barriers to adopting the technology. "Every leader from wing commanders to line instructors are expected to overcome cultural resistance , enforce enterprise consolidation, and drive this shift across their organizations," Air Force Lt.

Clark Quinn, who oversees AETC, wrote in a foreword for the new strategic planning. Subordinate commands have three months to figure out how they'll implement Quinn's directive. The resistance mentioned in the new guidance document isn't unique to the military.

Why it mattersBusiness Insider connects the development to a practical control question: The push follows Defense Secretary Pete Hegseth's January directive to make the military an "AI-first" force and aggressively eliminate bureaucratic barriers to adopting the technology.. For CISO and AI platform owner, the implication is a test of authorized task completion under the constraint that Clark Quinn who oversees AETC wrote in a foreword for the new strategic planning.. The leadership test is to connect this evidence to agentic ai, a responsible owner, and a measurable production outcome.

Trainocate Malaysia Launches AI Training Roadmap 2026, a 7-Level Framework to Close the Enterprise AI Skills Gap The seven-level enterprise upskilling framework will take organisations from foundational AI literacy to geo deployment and governance Kuala Lumpur, Malaysia, September 14, 2026 -- Trainocate Malaysia today announced the launch of its AI Training Roadmap 2026 , a structured, seven-level enterprise upskilling framework designed to take organizations from foundational AI literacy to agentic AI deployment and governance.

The program spans over 100 training hours and is customizable across 17 industries, delivered by practitioners with hands-on enterprise AI experience. According to Gartner, worldwide AI spending is projected to reach $2.59 trillion in 2026, a 47% increase over the previous year. Separately, McKinsey's State of AI research finds that 88% of organizations now use AI in at least one business function, with generative AI adoption climbing to 72% of organizations, up from just 33% in 2024.

McKinsey's own data shows only about a third of organizations have scaled AI enterprise-wide, and just 37-39% report any measurable profit impact from their AI investments. Trainocate's roadmap is built to address that gap directly, moving employees and leadership alike from basic tool use to production-grade, governed AI deployment. The roadmap's seven levels are structured to serve every stakeholder group, with each program tied to measurable KPIs rather than attendance alone: AI for Developers: ML & AI engineering for technical teams AI for Automation: Workflow automation for technical and business teams AI for Leaders: Strategic vision for executives & leaders AI Ethics & Governance: Responsible AI for all stakeholders "Organizations today are not short on AI enthusiasm, they are short on structured capability.

Why it mattersThis is more than a category signal because McKinsey's own data shows only about a third of organizations have scaled AI enterprise-wide, and just 37-39% report any measurable profit impact from their AI investments.. In agent authorization and execution, CISO and AI platform owner can use it to examine authorized task completion; the gating issue remains McKinsey's own data shows only about a third of organizations have scaled AI enterprise-wide and just 37-39% report. The leadership test is to connect this evidence to agentic ai, a responsible owner, and a measurable production outcome.

NASA's NASTRAN software pioneered structural analysis, transforming engineering design globally Modern simulation tools and digital twins now help companies virtually test complex physical systems. COSMIC maintains a library of computer programs from NASA and other government agencies and offers them for sale at a fraction of the cost of developing a new program. Computerized Structural Analysis and Research (CSAR) Corporation, located in Agoura Hills, California, is a leading producer of mechanical computer-aided engineering software.

Emerging AI and cloud platforms further accelerate product development and design optimization. NASA software engineers have created thousands of computer programs over the decades. These computer tools can design, test, and analyze a broad assortment of aerospace parts and structures.

Considered one of the most successful and widely used NASA software programs is the NASA Structural Analysis Program, called for short, NASTRAN ® . Originally created by Goddard Space Flight Center for spacecraft design, NASTRAN has been employed in a host of non-aerospace applications. NASTRAN is available to industry through NASA’s Computer Software Management and Information Center (COSMIC), located at the University of Georgia.

Why it mattersThe development changes the control question for CISO and AI platform owner: Considered one of the most successful and widely used NASA software programs is the NASA Structural Analysis Program, called for short, NASTRAN ® .. If the team applies it to agent authorization and execution, it must reconcile Modern simulation tools and digital twins now help companies virtually test complex physical systems. with Considered one of the most successful and widely used NASA software programs is the NASA Structural Analysis Program before claiming movement in authorized task completion. The leadership test is to connect this evidence to agentic ai, a responsible owner, and a measurable production outcome.

The partnership aims to make jobsites and factories safer, smarter and more productive by turning real-time observations into actionable AI operational insights 2, 2026 to advance AI-powered autonomy, robotics and “physical AI” for industrial operations.

The partnership targets safer, smarter and more productive jobsites and factories by converting real-time observations into actionable operational insights. The collaboration combines Caterpillar’s industry expertise, engineering capabilities and large operational data sets with FieldAI’s robot-agnostic autonomy and AI-enabled robot foundation models. Early applications include autonomous inspections, jobsite and facility digital twins , enhanced situational awareness to identify risks sooner and operational optimization using simulation, automation and AI-driven insights.

Leveraging NVIDIA accelerated computing and NVIDIA Omniverse technologies, the partners aim to improve site visibility, accelerate decision-making and support the next generation of industrial operations within Caterpillar’s manufacturing modernization and “jobsite of the future” initiatives. In the Sep 2 session, CAT gained 1.65% , reflecting a mild positive market reaction. Data tracked by StockTitan Argus on the day of publication.

Why it mattersStock Titan reports 2, 2026 to advance AI-powered autonomy, robotics and “physical AI” for industrial operations.. That matters for AI platform enablement because AI platform architect must decide whether Caterpillar teams up on AI-powered robots for jobsite inspections Stock can improve latency and reliability without weakening accountability; Leveraging NVIDIA accelerated computing and NVIDIA Omniverse technologies the partners aim to improve site visibility accelerate decision-making and is the boundary for the claim. The leadership test is to connect this evidence to ai enablement, ai solutions, and ai architecture, a responsible owner, and a measurable production outcome.

Unified industrial automation platform combines two complementary layers of intelligence: Physical AI for robotics and machines, and Agentic AI for the system Live at Booth 236860 (Sept 14-19): Experience the new MachineAgent agentic cell, delivering an interactive, end-to-end automated workflow for machine design, programming, deployment, and troubleshooting.

Live Physical AI demonstrations include Rapid Operator AI deep bin picking and goal-driven collision-free path planning, showcasing autonomous perception, grasping, and robotic motion powered by GRIIP. 10, 2026 /PRNewswire/ -- At IMTS 2026, Vention will unveil new Physical AI and Agentic AI capabilities together for the first time on a single automation platform. Combining both levels of intelligence makes automation faster to deploy, easier to operate, and simpler to scale.

"AI is changing what manufacturers should expect from automation," said Etienne Lacroix, founder and CEO of Vention. "Physical AI gives machines the ability to understand and adapt to the factory floor. Agentic AI brings that same intelligence to the people designing, programming, and operating automation.

Why it mattersThe evidence combines Live at Booth 236860 (Sept 14-19): Experience the new MachineAgent agentic cell, delivering an interactive, end-to-end automated workflow for machine design, programming, deployment, and troubleshooting. with 10, 2026 /PRNewswire/ -- At IMTS 2026, Vention will unveil new Physical AI and Agentic AI capabilities together for the first time on a single automation platform.. In AI platform enablement, that gives AI platform architect a concrete question about latency and reliability, not a reason to assume that AI is changing what manufacturers should expect from automation said Etienne Lacroix founder and CEO of Vention. has been solved. The leadership test is to connect this evidence to ai enablement, ai solutions, and ai architecture, a responsible owner, and a measurable production outcome.

Caterpillar is collaborating with FieldAI to develop and deploy physical AI, autonomous systems, robotics, and digital-twin technology for industrial jobsites and manufacturing environments The collaboration is aimed at improving safety, productivity, and operational efficiency as industrial companies face labor shortages and increasing productivity requirements.

Caterpillar and FieldAI plan to combine Caterpillar’s industrial expertise, engineering capabilities, and operational data with FieldAI’s AI-enabled robot foundation models, which are designed to operate across complex and dynamic industrial environments. Initial applications include autonomous inspections designed to increase safety and operational visibility, along with digital twins of jobsites and facilities that can provide real-time information about equipment, infrastructure, and operations. The companies are also targeting enhanced situational awareness that can identify risks earlier and AI-driven operational optimization using simulation, automation, and real-time insights.

FieldAI’s robot-agnostic autonomy platform and foundation models are designed for industrial environments where conventional automation can struggle. Its technology can process large volumes of operational and jobsite data and transform that information into actionable insights. The collaboration will also use NVIDIA accelerated computing, NVIDIA Omniverse technology, and high-fidelity digital twins developed from operational data.

Why it mattersThe operational significance is in The collaboration is aimed at improving safety, productivity, and operational efficiency as industrial companies face labor shortages and increasing productivity requirements.. It changes the AI platform enablement decision for AI platform architect, while FieldAI s robot-agnostic autonomy platform and foundation models are designed for industrial environments where conventional automation can struggle. keeps the reported result from being treated as universal. The leadership test is to connect this evidence to ai enablement, ai solutions, and ai architecture, a responsible owner, and a measurable production outcome.

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. This closed feedback loop enables more adaptive model training and reduces reliance on static datasets.

Why it matters2 Minute Medicine connects the development to a practical control question: Powered by more than 3,500 NVIDIA Blackwell graphics processing units (GPUs), this system represents one of the largest computational investments in pharmaceutical research.. For chief risk officer, the implication is a test of auditability under the constraint that The platform is designed to accelerate target identification molecule optimization and process development in parallel.. The leadership test is to connect this evidence to ai governance, policy, safety, and compliance, ai risk, a responsible owner, and a measurable production outcome.

UBS is telling clients that artificial intelligence is translating into real, growing spend on Snowflake, and the bank remains Buy-rated on the stock heading into its fiscal second-quarter results on September 2 The bank's analysts spoke with seven enterprise partners and customers to gauge demand trends, adoption of Snowflake's Cortex Code and Coco tools, and the risk that large language models could eat into spending on established data software vendors.

The checks came back strong, according to UBS, with customers and partners largely expecting their Snowflake spend to accelerate, helped by continued Coco adoption. Companies are increasingly focused on their data layer as new AI applications and agents need access to corporate data, UBS said, a dynamic that is making Snowflake, along with Databricks, Microsoft and others, more essential to enterprise infrastructure. On competition, UBS said Databricks came up most often as the company taking share, with Microsoft also mentioned.

UBS also flagged a growing push among enterprises to better operationalize their data with AI models to improve returns, which typically requires a data ontology layer such as a semantic layer or knowledge graph. One investor worry UBS tested directly: that frontier AI models are getting good enough at data tasks that companies could bypass data software vendors altogether and use the models on their own. UBS said its checks found little evidence of this happening, concluding that very few enterprises are using LLMs' data capabilities in a way that is cutting into spending on Snowflake, Palantir or Databricks.

Why it mattersThis is more than a category signal because UBS also flagged a growing push among enterprises to better operationalize their data with AI models to improve returns, which typically requires a data ontology layer such as a semantic layer or knowledge graph.. In governance control testing, chief risk officer can use it to examine auditability; the gating issue remains UBS also flagged a growing push among enterprises to better operationalize their data with AI models to improve. The leadership test is to connect this evidence to ai governance, policy, safety, and compliance, ai risk, a responsible owner, and a measurable production outcome.

How AI Is Transforming the Australian Insurance Industry in 2026: Opportunities, Challenges, and Future Trends 01 The State of AI Adoption in the Australian Insurance Industry in 2026 02 7 Opportunities for AI in the Insurance Industry in Australia, Transforming the Value Chain 03 What Are The Business Benefits of AI in Insurance 04 What Are the Key Challenges Slowing AI Adoption Across Australian Insurers and Their Solutions 05 The 2026 Regulatory Horizon: Preparing for the Transparency Deadline 06 What is the Future of AI in the Insurance Industry? 07 How to Implement AI in Insurance for Australian Enterprises?

08 How Appinventiv Can Help Insurers Build Responsible and Scalable AI Solutions? AI in the insurance industry in Australia has crossed from experimentation into operational deployment, with claims automation, fraud detection, and dynamic pricing delivering measurable returns. APRA’s April 2026 letter is a direct instruction to boards and executive management: AI governance, lifecycle ownership, and explainability are current compliance obligations enforced under existing prudential standards, not future expectations.

The December 2026 transparency deadline for Automated Decision-Making will require every insurer using AI in pricing or claims decisions to document and explain algorithmic reasoning. Agentic AI represents the next material capability shift for the sector. The transition from generative AI to agentic systems that orchestrate complete workflows will compress operational timelines.

Why it mattersThe development changes the control question for chief risk officer: The December 2026 transparency deadline for Automated Decision-Making will require every insurer using AI in pricing or claims decisions to document and explain algorithmic reasoning.. If the team applies it to governance control testing, it must reconcile 07 How to Implement AI in Insurance for Australian Enterprises with The December 2026 transparency deadline for Automated Decision-Making will require every insurer using AI in pricing or claims before claiming movement in auditability. The leadership test is to connect this evidence to ai governance, policy, safety, and compliance, ai risk, a responsible owner, and a measurable production outcome.

Artificial Intelligence (AI) in Insurance Market Report is Segmented by Types (Software,Platform), Application (Life Insurance,Car Insurance,Property Insurance,Other), and Geography (North America, Europe, Asia-Pacific, South America, and Middle East and Africa) Summary Market Overview Key Findings Latest Trends Market Dynamics Segmentation Analysis Regional Outlook Top Companies Report Coverage Frequently Asked Questions The global artificial intelligence (AI) in insurance market is likely to grow from approximately USD 718.9 million in 2026 to USD 2288.58 million in 2035, with an average CAGR of 15.3% during the forecast period.

The Artificial Intelligence (AI) in Insurance Market is advancing rapidly as insurers embed machine learning, generative AI, predictive analytics, natural language processing, computer vision, and intelligent automation across underwriting, claims, fraud detection, customer service, policy administration, and risk assessment. Approximately 82% of leading insurers have already deployed or are piloting machine-learning capabilities, while predictive analytics influences around 74% of selected underwriting decisions. Software represents approximately 62.4% of market activity as carriers increasingly implement modular solutions for document extraction, claims triage, fraud scoring, customer communication, and automated decision support.

Generative AI adoption has also accelerated, enabling insurers to process large volumes of policies, images, emails, claims documents, medical records, and inspection information while maintaining human oversight for complex or high-risk decisions. The United States remains the largest national adoption center and is responsible for the majority of North America's approximately 36% global market share. Around 65% of US insurers are investing in cloud-native AI environments, while approximately 71% of American policyholders prefer digital-first interactions.

Why it mattersMarket Growth Reports reports Summary Market Overview Key Findings Latest Trends Market Dynamics Segmentation Analysis Regional Outlook Top Companies Report Coverage Frequently Asked Questions The global artificial intelligence (AI) in insurance market is likely to grow from approximately USD 718.9 million in 2026 to USD 2288.58 million in 2035, with an average CAGR of 15.3% during the forecast period.. That matters for workforce change because CHRO must decide whether Artificial Intelligence AI in Insurance Market Size 2035 Market Growth can improve skill proficiency without weakening accountability; Generative AI adoption has also accelerated enabling insurers to process large volumes of policies images emails claims documents is the boundary for the claim. The leadership test is to connect this evidence to enterprise ai people and culture, a responsible owner, and a measurable production outcome.

Artificial intelligence is beginning to reshape far more than the speed at which insurance software can be developed AI agents are increasingly being introduced across claims, underwriting, fraud detection, payments and customer service, creating new questions about how these systems should be tested, governed and monitored once they enter production.

The QA Insurance Forum London 2026 will address those questions through a full day of expert panels, case studies, technology presentations and networking on Wednesday, 25 November. Taking place at Cavendish Venues, 44 Hallam Street , London W1W 6JJ ( right ), the Forum is designed specifically for quality engineering, testing, DevOps, automation, technology risk and digital resilience professionals working across life, health, property and casualty insurance. Rather than treating AI as a general innovation topic, the programme will concentrate on the practical engineering questions now facing insurers: how to validate autonomous applications, measure the return on AI investment, provide secure test data, modernise core platforms and prevent faster software delivery from creating greater operational risk.

Confirmed panellists include Bogdan Grigorescu, Senior Technical Lead, Engineering and Automation at Direct Line Group; Michael Daniels, Risk Officer at Aviva; and Valentina Smirnova, formerly Head of Transformation, Finance at AXA Partners . The opening panel, The AI revolution in quality engineering: What is the return on investment for InsurTech?, will set the direction for the day. AI copilots, large language models and autonomous agents are already being incorporated into development and testing tools.

Why it mattersThe evidence combines AI agents are increasingly being introduced across claims, underwriting, fraud detection, payments and customer service, creating new questions about how these systems should be tested, governed and monitored once they enter production. with Taking place at Cavendish Venues, 44 Hallam Street , London W1W 6JJ ( right ), the Forum is designed specifically for quality engineering, testing, DevOps, automation, technology risk and digital resilience professionals working across life, health, property and casualty insurance.. In workforce change, that gives CHRO a concrete question about skill proficiency, not a reason to assume that Confirmed panellists include Bogdan Grigorescu Senior Technical Lead Engineering and Automation at Direct Line Group Michael Daniels Risk has been solved. The leadership test is to connect this evidence to enterprise ai people and culture, a responsible owner, and a measurable production outcome.

Analysis of 76 insurer and reinsurer financial and regulatory filings finds AI-generated and manipulated evidence absent from discussions of claims and underwriting CHEVY CHASE, Md. , Sept 3, 2026 /PRNewswire/ -- Clearspeed , the global leader in voice-based risk assessment, today released new research examining the growing challenge for insurers to move faster with AI and automation while determining whether the information those systems act on can be trusted.

The Speed of Trust: Building the Trust Intelligence Layer for Insurance in the Age of Agentic AI , commissioned by Clearspeed and independently authored by insurance innovation strategist Sabine VanderLinden , CEO of Alchemy Crew Ventures, draws on a review of 76 public filings from 49 insurers and reinsurers, 31 industry studies, and 16 interviews with claims and underwriting leaders at insurance companies across the United States and United Kingdom. The research identifies a paradox emerging as insurers rapidly adopt AI and automation: the industry is automating decisions, handoffs, evidence review, and customer interactions faster than it is building the infrastructure needed to clear those interactions confidently. Simultaneously, AI is making it faster and easier to create convincing false or manipulated photos, documents, voices, and identities that can enter insurance workflows.

"Industry research published in March 2026, based on a survey of 300 U.S. insurance claims professionals, found that 98% agree AI editing tools are driving a rise in digital media fraud , while just 32% say they are very confident they could identify a deepfake." "That is the verification gap: the distance between what the industry can see coming and what it can currently detect," said VanderLinden. "Insurance is automating decisions faster than it can verify the information behind them." Alchemy Crew Ventures researchers searched 76 annual reports, 10-K filings, proxy statements, and statutory returns from 49 insurers and reinsurers for a dozen terms related to AI-generated and manipulated evidence, media, and imagery. The analysis found: Zero mentions of synthetic media, synthetic identity, or voice cloning across all 76 filings Just six of 49 companies mention deepfakes - and only as a cybersecurity concern, never in connection with evidence used in claims or underwriting decisions Five of the world's top 10 reinsurers were analyzed; none mention deepfakes, synthetic media, or AI-generated evidence in their most recent annual reporting "There is a striking gap between where this risk is discussed and where capital is committed," said VanderLinden.

Why it mattersThe operational significance is in 3, 2026 /PRNewswire/ -- Clearspeed , the global leader in voice-based risk assessment, today released new research examining the growing challenge for insurers to move faster with AI and automation while determining whether the information those systems act on can be trusted.. It changes the workforce change decision for CHRO, while Industry research published in March 2026 based on a survey of 300 U.S. insurance claims professionals found that keeps the reported result from being treated as universal. The leadership test is to connect this evidence to enterprise ai people and culture, a responsible owner, and a measurable production outcome.

Artificial intelligence is becoming increasingly important to the U.S. insurance industry as insurers look for ways to improve underwriting, claims processing, pricing and customer service By automating repetitive, data-intensive tasks, the technology can allow employees to handle greater workloads while potentially reducing operating costs and improving underwriting profitability.

As a result, AI is rapidly becoming an important growth and efficiency driver for industry players. KNSL and The Allstate Corporation ALL stand out because they combine significant data and technology capabilities with established insurance franchises and strong operating performance. According to Deloitte, 76% of insurance executives surveyed have implemented generative AI in at least one business function, highlighting the technology's rapid adoption.

AI is also gaining traction in fraud detection, claims management and underwriting, where faster data analysis can help insurers make better decisions. Deloitte estimates that AI-driven, real-time fraud analytics could help P&C insurers save as much as $160 billion by 2032 by reducing fraudulent claims. As insurance pricing moderates across several markets, technology could become an increasingly important competitive advantage.

Why it mattersYahoo Finance connects the development to a practical control question: KNSL and The Allstate Corporation ALL stand out because they combine significant data and technology capabilities with established insurance franchises and strong operating performance.. For chief engineer, the implication is a test of asset downtime under the constraint that AI is also gaining traction in fraud detection claims management and underwriting where faster data analysis can help. The leadership test is to connect this evidence to digital twins and industrial simulation, a responsible owner, and a measurable production outcome.

Unmasking Fraud: Verisk Releases Fraud Discovery, connecting intelligence, analytics and investigations in one powerful fraud platform Verisk introduces a unified, modular platform to help insurers tackle increasingly complex and interconnected fraud activity Verisk (VRSK) has launched Verisk Fraud Discovery, a modular fraud prevention platform that unifies intelligence, advanced analytics, network analysis, digital media forensics and case management in a single solution.

The platform is designed for insurers to detect, investigate and disrupt complex, evolving fraud patterns across underwriting, claims and investigations. In the Sep 8 session, VRSK declined 5.54% , reflecting a notable negative market reaction. Our momentum scanner triggered 39 alerts that day, indicating elevated trading interest and price volatility.

Data tracked by StockTitan Argus on the day of publication. VRSK was down 2.52% before the announcement; the Sep 4 CargoNet fraud-related update also preceded a 2.52% decline, providing relevant but separate context for this platform launch. Sep 04 Fraud exposure report 24h Move -2.5% CargoNet reported higher Labor Day cargo theft and growing identity-based fraud exposure.

Why it mattersThis is more than a category signal because Data tracked by StockTitan Argus on the day of publication.. In asset and simulation planning, chief engineer can use it to examine asset downtime; the gating issue remains Data tracked by StockTitan Argus on the day of publication.. The leadership test is to connect this evidence to digital twins and industrial simulation, a responsible owner, and a measurable production outcome.

Some thoughts on the potential of AI, making savings and what insurers should spend those savings on, from Gleb Tsipursky, PhD, a behavioral scientist, CEO of Disaster Avoidance Experts - full bio below; Insurance is an apprenticeship business disguised as a data business Junior claims handlers and underwriters learn by seeing ordinary cases, then discovering the details that make some of them extraordinary.

Stanford’s August 12 employment update analysed U.S. payroll data through June 2026. Employment among workers ages 22-25 in highly AI-exposed occupations was about 19% below the path it would have followed if it had kept pace with similarly aged workers in less-exposed occupations. The comparable gap was 15% in the July 2025 data vintage.

The adjustment appears mainly through reduced hiring, especially in occupations where AI tends to automate human tasks. Insurers have plenty of work that looks ideal for automation: first-pass claim summaries, policy comparisons, document extraction, fraud flags, customer correspondence and routine underwriting preparation. A new claims professional who never builds a first chronology can miss how facts change meaning as a file develops.

Why it mattersThe development changes the control question for chief engineer: The adjustment appears mainly through reduced hiring, especially in occupations where AI tends to automate human tasks.. If the team applies it to asset and simulation planning, it must reconcile Junior claims handlers and underwriters learn by seeing ordinary cases then discovering the details that make some of them extraordinary. with The adjustment appears mainly through reduced hiring especially in occupations where AI tends to automate human tasks. before claiming movement in asset downtime. The leadership test is to connect this evidence to digital twins and industrial simulation, a responsible owner, and a measurable production outcome.

When it comes to AI adoption most re/insurers are leaving value on the table: Accenture 8th September 2026 - Author: Kassandra Jimenez-Sanchez - While most insurers are using artificial intelligence (AI) to improve today’s performance, many are “leaving value on the table,” according to Accenture’s new report, ‘How Insurers Drive Revenue by Deploying AI with Intent.’ 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. 83% of respondents reported moderate-to-severe gaps in linking AI capabilities to measurable outcomes, and only 32% of leaders are prioritising revenue growth as their main goal, “leaving significant value on the table.” The report also found that 68% of respondents expect AI agents to transform core workflows.

Why it mattersreinsurancene.ws reports 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.. That matters for semantic data design because chief data architect must decide whether When it comes to AI adoption most re/insurers are leaving can improve data consistency without weakening accountability; Other findings include legacy and data barriers with legacy integration at 50% and data quality/accessibility at 45% being is the boundary for the claim. The leadership test is to connect this evidence to ontology, knowledge graph, and semantic layer developments, a responsible owner, and a measurable production outcome.

Only 23% of insurers scale AI across the enterprise, Accenture finds More than four in five insurers report measurable premium growth from AI and data initiatives, but fewer than one-quarter have deployed the technology across their entire organization, according to Accenture The findings show a wide gap between successful individual AI projects and adoption across insurance operations.

Accenture surveyed 263 senior insurance executives responsible for AI, data, technology or business transformation across the Americas, Europe and Asia-Pacific. The research also included interviews with 15 executives from major global insurers. Accenture sells technology and transformation services to insurers, giving it a commercial interest in the subject examined by the report.

Eighty-one percent of surveyed insurers said data and AI initiatives had increased gross written premiums by at least 5%. Another 7% reported improvements above 20%, with better pricing, personalization and cross-selling cited among the sources of growth. Despite those results, only 23% said they had achieved enterprise-wide AI adoption.

Why it mattersThe evidence combines The findings show a wide gap between successful individual AI projects and adoption across insurance operations. with The research also included interviews with 15 executives from major global insurers.. In semantic data design, that gives chief data architect a concrete question about data consistency, not a reason to assume that Eighty-one percent of surveyed insurers said data and AI initiatives had increased gross written premiums by at least has been solved. The leadership test is to connect this evidence to ontology, knowledge graph, and semantic layer developments, a responsible owner, and a measurable production outcome.

Scammers pivot as business email fraud claims surge: Travelers Business email compromise (BEC) claims were 57% higher in the second quarter of 2026 compared with the same period last year, according to the Travelers' Q2 2026 Cyber Threat Report Ransomware claims have seen no significant rise this year, and even declined 5% from the first quarter, as fraudsters have shifted their attention to authentication tokens to enable account takeovers, according to the same study.

"Business email compromise scams drove a big increase in claims during the first six months of 2026, with account takeovers surging due to a variety of factors, including a shift in tactics by attackers, who are stealing authentication tokens instead of passwords ," Lauren Winchester, head of Cyber Risk Services at Travelers, told Digital Insurance. A token is proof of authentication that allows the user to bypass required passwords or other multi-factor authentication controls. There has also been a shift to use phishing kits that are augmented by AI tools to control an enterprise workspace like Microsoft 265 or Google Workspace, which includes email, file storage, collaboration tools and cloud applications.

The report has several suggestions to defend against token theft including: Monitor beyond passwords and endpoints like unexpected token issuance and new application consents and sign-ins. Build token response into the incident plan instead of just a password reset. Fraudsters are also using deep fake videos and phone calls in social engineering, and AI in the process of ransomware negotiations, according to the report.

Why it mattersThe operational significance is in Ransomware claims have seen no significant rise this year, and even declined 5% from the first quarter, as fraudsters have shifted their attention to authentication tokens to enable account takeovers, according to the same study.. It changes the semantic data design decision for chief data architect, while The report has several suggestions to defend against token theft including Monitor beyond passwords and endpoints like unexpected keeps the reported result from being treated as universal. The leadership test is to connect this evidence to ontology, knowledge graph, and semantic layer developments, a responsible owner, and a measurable production outcome.

Offering increased integration options and AI enhancements, warehouse automation systems give workers an even greater assist As You Wish: Amazon’s New Warehouse Robot Follows Natural Language Commands 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. The original Proteus operates in dock areas within fulfillment centers, navigating safely around people and transporting carts that can weigh around 900 pounds.

Why it mattersInbound Logistics connects the development to a practical control question: Using advances in artificial intelligence, the new Proteus is designed to understand natural language.. For construction operations leader, the implication is a test of schedule variance under the constraint that The new Proteus is currently being piloted in Amazon s labs with deployment in Europe planned for the. The leadership test is to connect this evidence to ai in construction, a responsible owner, and a measurable production outcome.

NextGen 2026 Keynotes: Eli Lilly, Tractor Supply and Wayfair Register today Podcast: Talking Supply Chain: Why worker voice belongs in supply chain risk management Webinar: Closing the Execution Gap: How Agentic AI Drives Faster Supply Chain Decisions News: Automating the mess: What a million warehouse robots can teach smaller operators News: First Shift: AI demand tightens supply while manufacturers deepen regional sourcing Artificial Intelligence: First Shift: AI demand tightens supply while manufacturers deepen regional sourcing NextGen Supply Chain Conference: First Shift: AI demand tightens supply while manufacturers deepen regional sourcing Logistics and 3PL leaders bring fulfillment innovation to NextGen 2026 Logistics, fulfillment and 3PL operations will be a major focus of the 2026 NextGen Supply Chain Conference, with sessions spanning healthcare logistics, home delivery, warehouse intelligence, omnichannel fulfillment and carrier performance Ryder and BJC HealthCare will receive the Partnership in Execution Award and explain how a 3PL-healthcare collaboration improved order fulfillment, inventory visibility, costs and service to clinicians.

Small Group Sessions featuring Vitti Logistics, ODW Logistics and DHL Supply Chain will give attendees practical looks at computer vision, autonomous inventory intelligence and the human role in automated warehouses. Main-stage speakers from Wayfair, Penske Logistics, DP World, GXO Logistics and Amazon will address home delivery, transformation, omnichannel execution and predictive carrier-risk management. Logistics providers are being asked to do more than move and store products.

Customers increasingly expect their 3PL partners to help redesign networks, deploy automation, improve inventory accuracy, manage risk and create the visibility needed to make faster decisions. Fulfillment operations face a similar mandate as companies balance speed and service with cost, labor constraints and rising operational complexity. Those pressures and the strategies logistics leaders are using to address them will be a major focus of the 2026 NextGen Supply Chain Conference , taking place Oct.

Why it mattersThis is more than a category signal because Customers increasingly expect their 3PL partners to help redesign networks, deploy automation, improve inventory accuracy, manage risk and create the visibility needed to make faster decisions.. In project controls, construction operations leader can use it to examine schedule variance; the gating issue remains Customers increasingly expect their 3PL partners to help redesign networks deploy automation improve inventory accuracy manage risk and. The leadership test is to connect this evidence to ai in construction, a responsible owner, and a measurable production outcome.

The Rest Of Asean Smart Warehousing Market was valued at $311.9 Million in 2025 and projected to reach to $509.2 Million by 2030 , representing a compound annual growth rate of CAGR 10.3% Rest Of Asean's smart warehousing market is positioned for accelerated growth as emerging economies in the region embrace digital logistics solutions.

Rest Of Asean Smart Warehousing Market Trends and Insights This expansion reflects Rest Of Asean's increasing adoption of automation, IoT integration, and AI-driven logistics solutions across emerging economies in the region. Rest Of Asean is capitalizing on rising e-commerce demand, supply chain digitalization, and labor cost pressures that are driving investment in intelligent warehouse management systems. The 10.3% compound annual growth rate (CAGR) positions Rest Of Asean as one of the fastest-growing smart warehousing markets globally, outpacing the global average of 8.3% .

Rest Of Asean's growth is underpinned by infrastructure modernization, government initiatives supporting digital transformation, and the entry of multinational logistics providers establishing regional hubs. Rest Of Asean's competitive advantage lies in its lower implementation costs and high labor availability, making automation investments increasingly attractive to both domestic and international operators. Rest Of Asean's market trajectory through 2030 reflects structural shifts in supply chain management, with cloud-based warehouse management systems, robotics, and real-time visibility platforms becoming standard.

Why it mattersThe development changes the control question for construction operations leader: Rest Of Asean's growth is underpinned by infrastructure modernization, government initiatives supporting digital transformation, and the entry of multinational logistics providers establishing regional hubs.. If the team applies it to project controls, it must reconcile Rest Of Asean's smart warehousing market is positioned for accelerated growth as emerging economies in the region embrace digital logistics solutions. with Rest Of Asean's growth is underpinned by infrastructure modernization government initiatives supporting digital transformation and the entry of before claiming movement in schedule variance. The leadership test is to connect this evidence to ai in construction, a responsible owner, and a measurable production outcome.

The Asean Smart Warehousing Market was valued at $1537.1 Million in 2025 and projected to reach to $2461.5 Million by 2030 , representing a compound annual growth rate of CAGR 9.9% Asean's smart warehousing market is poised for exceptional growth as the region capitalizes on its strategic position in global supply chains and rising e-commerce penetration.

Asean Smart Warehousing Market Trends and Insights Asean's rapid e-commerce adoption and logistics modernization are driving demand for automated warehouse solutions across the region. The 9.9% compound annual growth rate reflects Asean's accelerating digital transformation in supply chain operations, outpacing the global average of 8.3% . Asean's strategic position as a manufacturing and trade hub amplifies investment in smart warehousing technologies.

Between 2025 and 2030, Asean is expected to witness significant deployment of IoT-enabled systems, robotics, and AI-driven inventory management. The region's growing middle class and expanding cross-border e-commerce are key catalysts propelling Asean's smart warehousing adoption. Major economies within Asean are prioritizing warehouse automation to enhance operational efficiency and reduce logistics costs..

Why it mattersMarketsandMarkets reports Asean's smart warehousing market is poised for exceptional growth as the region capitalizes on its strategic position in global supply chains and rising e-commerce penetration.. That matters for claims or underwriting operations because chief claims or underwriting officer must decide whether Asean Smart Warehousing Market Size Share Trends Growth Analysis Report can improve claims cycle time without weakening accountability; Between 2025 and 2030 Asean is expected to witness significant deployment of IoT-enabled systems robotics and AI-driven inventory is the boundary for the claim. The leadership test is to connect this evidence to ai in insurance, a responsible owner, and a measurable production outcome.

Descartes Systems Group (Nasdaq: DSGX) (TSX: DSG) announced it has acquired Extensiv, a California-based provider of AI-enabled warehouse management and omnichannel fulfillment solutions for third-party logistics providers (3PLs) and ecommerce brands Descartes Systems Group (Nasdaq: DSGX) (TSX: DSG) announced it has acquired Extensiv , a California-based provider of AI-enabled warehouse management and omnichannel fulfillment solutions for third-party logistics providers (3PLs) and ecommerce brands.

The deal, valued at approximately US $120 million , was funded from cash on hand. Extensiv’s platform helps 3PLs manage inventory, orders, B2B/B2C fulfillment, and billing across connected sales channels, ecommerce platforms, marketplaces, and carriers, generating rich operational data to support AI-driven insights. According to Descartes, the acquisition extends its warehouse and inventory management capabilities, deepens its presence in the 3PL and ecommerce fulfillment markets, and adds more participants and data to the Descartes Global Logistics Network.

The move follows Descartes’ August 24, 2026 acquisition of Tai, which provides AI-powered transportation management solutions for freight brokers. Acquires Extensiv for approximately US $120 million in cash, expanding 3PL WMS and fulfillment capabilities Adds AI-enabled omnichannel fulfillment data and intelligence to the Descartes Global Logistics Network Deepens presence in 3PL and ecommerce fulfillment markets with additional customers, partners, and operational data Builds on August 24, 2026 acquisition of Tai to broaden AI-powered logistics solutions portfolio Uses approximately US $120 million of cash on hand to fund the Extensiv acquisition In the Sep 1 session, DSGX declined 0.26% , reflecting a mild negative market reaction. Data tracked by StockTitan Argus on the day of publication.

Why it mattersThe evidence combines Descartes Systems Group (Nasdaq: DSGX) (TSX: DSG) announced it has acquired Extensiv , a California-based provider of AI-enabled warehouse management and omnichannel fulfillment solutions for third-party logistics providers (3PLs) and ecommerce brands. with Extensiv’s platform helps 3PLs manage inventory, orders, B2B/B2C fulfillment, and billing across connected sales channels, ecommerce platforms, marketplaces, and carriers, generating rich operational data to support AI-driven insights.. In claims or underwriting operations, that gives chief claims or underwriting officer a concrete question about claims cycle time, not a reason to assume that The move follows Descartes August 24 2026 acquisition of Tai which provides AI-powered transportation management solutions for freight has been solved. The leadership test is to connect this evidence to ai in insurance, a responsible owner, and a measurable production outcome.

Why Warehouse AI Fails Without Accurate Physical Data 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.

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. It is this combination of digital and physical information that is helping drive the development of Physical AI in logistics. The episode also explores whether AI could eventually capture the instinctive knowledge of experienced warehouse managers.

Why it mattersThe operational significance is in 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.. It changes the claims or underwriting operations decision for chief claims or underwriting officer, while Effective AI also needs to understand the physical environment around the stock including warehouse space rack structures movement keeps the reported result from being treated as universal. The leadership test is to connect this evidence to ai in insurance, a responsible owner, and a measurable production outcome.

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Link to The Value of Vehicle Electrification The Value of Vehicle Electrification Electric vehicles (EVs) present automakers with many design, engineering, and manufactu ring challenges. Link to Accelerating Manufacturing Digital Transformation with Industrial Connectivity and IoT Accelerating Manufacturing Digital Transformation with Industrial Connectivity and IoT Digital transformation is empowering industrial organizations to deliver sustainable innovation, disruption-proof products and services, and continuous operational improvement. Link to Smart Manufacturing for Automotive Smart Manufacturing for Automotive Leading a transportation revolution in autonomous, electric, shared mobility and connectivity with the next generation of design and development tools.

Link to Center for Data Pipeline Automation Center for Data Pipeline Automation As businesses become data-driven and rely more heavily on analytics to operate, getting high-quality, trusted data to the right data user at the right time is essential. Link to Center for Automated Integration Center for Automated Integration The goal of automated integration is to enable applications and systems that were built separately to easily share data and work together, resulting in new capabilities and efficiencies that cut costs, uncover insights, and much more. Link to Continuous Intelligence: Insights Continuous Intelligence: Insights Digital transformation requires continuous intelligence (CI).

Why it mattersRT Insights connects the development to a practical control question: Link to Accelerating Manufacturing Digital Transformation with Industrial Connectivity and IoT Accelerating Manufacturing Digital Transformation with Industrial Connectivity and IoT Digital transformation is empowering industrial organizations to deliver sustainable innovation, disruption-proof products and services, and continuous operational improvement.. For chief logistics officer, the implication is a test of order accuracy under the constraint that Link to Center for Data Pipeline Automation Center for Data Pipeline Automation As businesses become data-driven and rely. The leadership test is to connect this evidence to ai in logistics & warehousing, a responsible owner, and a measurable production outcome.

The Fleet Management Market reached USD 35.18 Billion in 2025 and enters the forecast window at USD 40.21 Billion in 2026, climbing to USD 133.88 Billion by 2035 at a 14.3% CAGR Environmental Protection Agency's Phase 3 greenhouse gas standards for heavy-duty vehicles, finalized in March 2024, force commercial operators to measure fuel burn at the vehicle level rather than the depot level [2] .

Alongside it, the European Union's Mobility Package I retrofit deadline for second-generation smart tachographs pulled roughly 900,000 international haulage vehicles into mandatory digital compliance during 2024-2025 [4] . Legacy black-box trackers that reported position at 15-minute intervals are giving way to multi-sensor edge platforms that fuse CAN-bus diagnostics, dashcam video, and driver identity into a single telemetry stream. Carriers now buy outcomes - collision reduction, idle elimination, uptime - rather than dots on a map.

The International Energy Agency estimates that commercial vehicle electrification and digital efficiency programs together attracted more than USD 45 billion in fleet-level capital deployment during 2024 [5] , and that spending flows directly into the Fleet Management Market through connected platform subscriptions. North America holds 33.5% of 2025 revenue, sustained by federal hours-of-service enforcement and dense third-party logistics networks. Asia-Pacific grows fastest at a 16.5% CAGR, propelled by China's smart logistics build-out and India's formalizing trucking sector.

Why it mattersThis is more than a category signal because The International Energy Agency estimates that commercial vehicle electrification and digital efficiency programs together attracted more than USD 45 billion in fleet-level capital deployment during 2024 [5] , and that spending flows directly into the Fleet Management Market through connected platform subscriptions.. In warehouse and fulfillment operations, chief logistics officer can use it to examine order accuracy; the gating issue remains The International Energy Agency estimates that commercial vehicle electrification and digital efficiency programs together attracted more than USD. The leadership test is to connect this evidence to ai in logistics & warehousing, a responsible owner, and a measurable production outcome.

The Telematics Market reached USD 56.60 billion in 2025 and is projected to grow from USD 62.60 billion in 2026 to USD 155.03 billion by 2035, registering a CAGR of 10.6% during the forecast period Regulatory mandates are the primary accelerant - Europe's eCall requirement now compels every new passenger vehicle to carry an embedded connectivity module, while India's AIS 140 standard is forcing public transport operators to retrofit GPS-based tracking systems across hundreds of thousands of buses [1] .

These mandates create a factory-level demand floor that insulates the Telematics Market from discretionary spending cycles. Legacy standalone GPS trackers and manual vehicle logging are giving way to cloud-connected, AI-driven platforms capable of predictive maintenance , driver behavior scoring, and vehicle-to-everything communication. Semiconductor content per vehicle is on track to double by 2030, raising hardware bills but also unlocking richer data streams that power usage-based insurance and advanced fleet analytics [2] .

The rollout of 5G and multi-access edge computing is transforming what was once a simple location-tracking exercise into a real-time decision engine. North America commands the largest share of the Telematics Market at 34.0% of 2025 revenue, anchored by mature fleet management adoption and regulatory requirements around electronic logging devices. Europe follows closely at 29.7%, driven by eCall mandates and stringent emissions monitoring.

Why it mattersThe development changes the control question for chief logistics officer: The rollout of 5G and multi-access edge computing is transforming what was once a simple location-tracking exercise into a real-time decision engine.. If the team applies it to warehouse and fulfillment operations, it must reconcile Regulatory mandates are the primary accelerant Europe's eCall requirement now compels every new passenger vehicle to carry an embedded connectivity module while India's AIS with The rollout of 5G and multi-access edge computing is transforming what was once a simple location-tracking exercise into before claiming movement in order accuracy. The leadership test is to connect this evidence to ai in logistics & warehousing, a responsible owner, and a measurable production outcome.

Top 5 fleet management platforms for commercial fleets Running a commercial vehicle fleet without the right technology can quickly become a costly exercise Fleet operators need to keep vehicles moving, driver compliance, maintenance under control and customers informed, while also managing fuel, safety and increasingly complex reporting requirements.

This article from US-based TenTrucks , compares five of the best management platforms and what they offer to fleet management. Depending on the vehicles and journeys involved, fleets may need to manage tachograph records, drivers’ hours, working-time requirements, vehicle defects and maintenance records, as well as the wider responsibilities associated with operating a compliant commercial fleet. GOV.UK guidance states that tachographs record driving time, breaks, rest periods, other work, availability, speed and distance, making effective tachograph management an important part of fleet operations.

At the same time, many fleet operators work across borders or use platforms originally designed for international markets. That makes it important to look beyond a platform’s country of origin and assess how well it supports the specific operational and regulatory requirements of your fleet. After reviewing a range of fleet management and transportation platforms, this guide compares five options worth considering in 2026, with particular attention to their relevance for UK operators and their ability to support wider international operations.

Why it mattersfleetpoint.org reports Fleet operators need to keep vehicles moving, driver compliance, maintenance under control and customers informed, while also managing fuel, safety and increasingly complex reporting requirements.. That matters for fleet maintenance and dispatch because fleet operations director must decide whether Top 5 fleet management platforms for commercial fleets fleetpoint.org can improve unplanned downtime without weakening accountability; At the same time many fleet operators work across borders or use platforms originally designed for international markets. is the boundary for the claim. The leadership test is to connect this evidence to ai in fleet management, a responsible owner, and a measurable production outcome.

aims to help business owners make informed decisions to support and grow their companies We research and recommend products and services suitable for various business types, investing thousands of hours each year in this process.

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Azuga's intuitive desktop and app control dashboards are packed with information that's easy to access and analyze. Businesses can receive 24/7 customer service via phone, email and ticketing. Azuga has a growing app marketplace, with vendors offering enhanced routing and scheduling services, fuel card integration and improved fleet management.

Why it mattersThe evidence combines We research and recommend products and services suitable for various business types, investing thousands of hours each year in this process. with We have financial relationships with some companies we cover, earning commissions when readers purchase from our partners or share information about their needs.. In fleet maintenance and dispatch, that gives fleet operations director a concrete question about unplanned downtime, not a reason to assume that Azuga's intuitive desktop and app control dashboards are packed with information that's easy to access and analyze. has been solved. The leadership test is to connect this evidence to ai in fleet management, a responsible owner, and a measurable production outcome.

Smith System Wants Work Truck Fleets to Look Beyond the Driver Alert Smith System’s new Driver Risk Management program is built to help mixed work truck fleets turn telematics alerts and observations into specific behaviors, coaching actions, and measurable follow-up Smith System’s Driver Risk Management program brings driver data, training, coaching, and analytics into one system.

For mixed work truck fleets, the approach focuses on common driver behaviors even as vehicles, routes, and operating environments change. A driver can spend one day in a pickup running between job sites and another behind the wheel of a heavier vocational truck in a crowded work zone. Then the vehicle changes, the route changes, the load changes, and the traffic environment changes.

However, the behaviors fleets should manage do not change, according to Smith System CEO Derek Dunaway. That idea is at the center of the company’s new Driver Risk Management program, a fully managed safety program that connects driver training, monitoring, coaching, corrective actions, and analytics through the Smith5Keys behavioral framework. Dunaway told Work Truck that fleets with a wide mix of pickups, vans, and vocational trucks should separate what changes in the job from what remains consistent behind the wheel. “A driver who reads the road far enough ahead, keeps a space cushion, and maintains eye movement is managing risk the same way in a half-ton pickup on a rural two-lane as in a bucket truck in a downtown work zone,” Dunaway said.

Why it mattersThe operational significance is in Smith System’s Driver Risk Management program brings driver data, training, coaching, and analytics into one system.. It changes the fleet maintenance and dispatch decision for fleet operations director, while However the behaviors fleets should manage do not change according to Smith System CEO Derek Dunaway. keeps the reported result from being treated as universal. The leadership test is to connect this evidence to ai in fleet management, a responsible owner, and a measurable production outcome.