AI News Archive: June 10, 2026 — Part 5
Sourced from 500+ daily AI sources, scored by relevance.
- Maryland’s New AI Leader Will Support State Deployment
The new senior adviser for responsible AI will work across sectors to support the technology’s responsible use in strengthening services and economic development. He arrives from the U.S. Digital Response.
Score: 66🌐 MovesJun 10, 2026https://www.govtech.com/workforce/marylands-new-ai-leader-will-support-state-deployment - Waymo built a virtual driver to study how humans react to surprises on the road
The new Reference Driver model works as a behavioral crash test dummy to determine how to better respond to surprises on the road.
Score: 66🌐 MovesJun 10, 2026https://www.theverge.com/transportation/947178/waymo-reference-driver-model-surprise-avoid-collision - Citi Singapore to expand offerings for wealth clients, tap AI as competition heats up
Citi Singapore to expand offerings for wealth clients, tap AI as competition heats up The Straits Times
- Agentic AI Is Breaking Your ROI Model. Here’s How to Fix It
There is a well-established playbook for calculating the return on an IT investment. Define a baseline, model the efficiency gains, project the cost savings, and present a number to the CFO. For most technology investments, that approach works. Agentic AI breaks it. Unlike conventional software deployments, agentic AI (systems that execute multi-step tasks autonomously, make […] The post Agentic AI Is Breaking Your ROI Model. Here’s How to Fix It appeared first on IDC .
Score: 66🌐 MovesJun 10, 2026https://www.idc.com/resource-center/blog/agentic-ai-is-breaking-your-roi-model-heres-how-to-fix-it/ - Welcome to the new DeepL API experience: More capabilities, more control and more ways of building with DeepL
Discover the new DeepL API updates for building real-time voice translation experiences
- 'Reading relationships, crunching stats'—184-times faster data analysis
A research team at POSTECH led by Professor Wook-Shin Han of the Department of Computer Science and Engineering and the Graduate School of Artificial Intelligence, along with Ph.D. candidates Taesung Lee and Jaehyun Ha, has developed TurboLynx, an engine capable of analyzing complex, highly interconnected data up to 184 times faster than existing systems.
Score: 66🌐 MovesJun 10, 2026https://techxplore.com/news/2026-06-relationships-crunching-stats-faster-analysis.html - Taiwan’s 50 Richest 2026: AI Demand Drives Up Tycoons’ Wealth By 56% To A Record $308 Billion
Jason and Richard Chang, the siblings behind semiconductor packaging and testing company ASE Technology Holding, take the No. 1 spot for the first time.
- Struggling German auto supplier Bosch pivots to robots
German industrial giant Bosch said Wednesday it will step up efforts in the field of humanoid robotics as its traditional auto parts business comes under increasing pressure.
Score: 65🌐 MovesJun 10, 2026https://techxplore.com/news/2026-06-struggling-german-auto-supplier-bosch.html - Path traversal flaw in AI dev platform Langflow exploited in attacks
Attackers are actively exploiting CVE-2026-5027, a high-severity path traversal vulnerability in the AI development platform Langflow, to write arbitrary files on exposed servers. [...]
- China's next AI trade is robots: Toss Securities
China is rapidly turning humanoid robots into its next AI growth story, using the same manufacturing playbook that helped it dominate the smartphone and electric vehicle sectors, according to Toss Securities. In a report titled "Back from Shenzhen" released Wednesday, the brokerage's research center said robotics is emerging as the next major growth driver for China's artificial intelligence industry, following a visit to the southern Chinese technology hub. "Robotics is the sector drawing the m
- China’s Lifelike Emotional Companion Bots Go on Sale
The life-size robot is billed as an emotional companion, employing both affective AI and encrypted memory storage.
Score: 65🌐 MovesJun 10, 2026https://www.sixthtone.com/news/1018634/China’s Lifelike Emotional Companion Bots Go on Sale - Xcode 27 expands agentic coding toolset with Gemini integration
Starting with Xcode 27, developers will be able to natively use Google Gemini, in addition to Claude Code and OpenAI Codex, to plan, write, and review code. Here are the details. more…
Score: 65🌐 MovesJun 10, 2026https://9to5mac.com/2026/06/10/xcode-27-expands-agentic-coding-toolset-with-gemini-integration/ - AI Data Centers Put Australia’s Power Grid Under New Pressure
Australia’s energy operator warned that fast-growing AI data centers could create new power grid stability risks as compute demand rises across APAC. The post AI Data Centers Put Australia’s Power Grid Under New Pressure appeared first on TechRepublic .
Score: 65🌐 MovesJun 10, 2026https://www.techrepublic.com/article/news-ai-data-centers-grid-risk-apac-australia/ - Martin Scorsese’s promotion of AI company called a ‘betrayal’ by directors union
A letter from the Art Director’s Guild accused the acclaimed filmmaker of “turning his back” on the artists who helped him create some of his best work.
- Sam Altman’s eye-scanning startup reportedly lays off employees
Sam Altman’s eye-scanning startup reportedly lays off employees San Francisco Chronicle
Score: 65🌐 MovesJun 10, 2026https://www.sfchronicle.com/tech/article/tools-humanity-layoffs-altman-22298435.php - HPE’s Unleash AI takes aim at the ‘AI pilot trap’
For all the excitement artificial intelligence has generated, success is still eluding many companies. A recent PricewaterhouseCoopers LLP study found that just 20% of enterprises are achieving at least three-quarters of the revenue and efficiency gains AI promises. Gartner estimates that at least half of generative AI projects were abandoned last year, and other estimates […] The post HPE’s Unleash AI takes aim at the ‘AI pilot trap’ appeared first on SiliconANGLE .
Score: 65🌐 MovesJun 10, 2026https://siliconangle.com/2026/06/10/hpe-unleash-ai-targets-stalled-ai-pilots-hpeaimomentum/ - When Your AI Agent’s Memory Becomes a Security Liability
Check Point Research discovered how a single overlooked API in LangGraph, one of the world’s most widely used AI agent frameworks, can hand an attacker complete control of your AI infrastructure. LangGraph is not a niche tool. With close to 46.5 million downloads last month alone, it powers AI agents across thousands of production environments, from customer support automation […] The post When Your AI Agent’s Memory Becomes a Security Liability appeared first on CXOToday.com .
- How Fable 5 And Mythos 5 Change AI Security, Data Retention, And Vendor Risk
Anthropic’s Fable 5 and Mythos 5 is the most 2026 product launch you’ll read this year. The same model can find nation-state zero days, design novel drug candidates, and play FireRed on a Gameboy Advance with nothing but screenshots. And for the gaming fans out there, yes, we got Fable 5 before Fable 4. These […]
Score: 65🌐 MovesJun 10, 2026https://www.forrester.com/blogs/how-fable-5-and-mythos-5-change-ai-security-data-retention-and-vendor-risk/ - This California city just approved the use of Flock drones as first responders, but residents are worried about 'militarization and surveillance'
Stockton, California, just invested millions of dollars in Flock drones to act as first responders.
- Walmart's AI-powered warehouses are slashing the time it takes store employees to unload trucks
Walmart's AI-powered warehouses are slashing the time it takes store employees to unload trucks Business Insider
Score: 65🌐 MovesJun 10, 2026https://www.businessinsider.com/walmarts-ai-warehouses-slash-time-for-workers-to-unload-trucks-2026-6 - Markets dumped India for AI stars. BlackRock says that’s a mistake
BlackRock believes India's equity market is unfairly penalized for its limited direct AI exposure and oil price sensitivity. Despite record foreign outflows and a challenging macro environment, the asset manager remains constructive on India's medium- to long-term prospects, citing strong demographics, infrastructure, and financial sector strength. They see potential for indirect AI-related opportunities and favor sectors like financials.
- Uncovr raises $7M to build AI infrastructure for surgery
Uncovr, a surgical AI startup focused onclinical documentation and workflow intelligence, has raised $7 million in seedfunding. The round was led by Index Ventures, with participation from Seedcamp,Fr...
Score: 65💰 MoneyJun 10, 2026https://tech.eu/2026/06/10/uncovr-raises-7m-to-build-ai-infrastructure-for-surgery/ - Timing Trick Cuts Energy Used in LLM Training by Up to 14 Percent
Adjusting clocking frequency during computation can save energy without affecting performance
- Factory Robot Startup Mujin Raising Funds Ahead of IPO by 2030
Japanese robot software developer Mujin plans to go public by 2030, building up funds and momentum to capture fast-growing demand for factory-use AI.
- Trump Muses About Government Taking a Piece of A.I. Companies
President Trump touched on what is an increasingly hot topic in Washington: how average Americans can get a piece of the tech industry’s A.I. windfall.
Score: 65🌐 MovesJun 10, 2026https://www.nytimes.com/2026/06/10/technology/president-trump-americans-sharing-ai-wealth.html - North Carolina treasurer passes on SpaceX citing valuation concerns; favors OpenAI, Anthropic
North Carolina has invested heavily in AI startups OpenAI and Anthropic while avoiding a direct stake in SpaceX.
- Apple's New AI Photo Tool Wants to Rewrite Your Memories
Apple's New AI Photo Tool Wants to Rewrite Your Memories PCMag
Score: 65🌐 MovesJun 10, 2026https://www.pcmag.com/opinions/apples-new-ai-photo-tool-wants-to-rewrite-your-memories - IBM’s consulting chief warns AI will ‘implode’ unprepared rivals
IBM Consulting, under Mohamad Ali, claims to have surged ahead in AI by pioneering a human-plus-digital workforce and transforming its own operations before selling innovative solutions to clients, leaving less-prepared consulting rivals scrambling to keep up, writes Maria Ward-Brennan. IBM’s consulting chief, Mohamad Ali, warns that AI will “implode” unprepared rivals, claiming the group has [...]
Score: 64🌐 MovesJun 10, 2026https://www.cityam.com/ibms-consulting-chief-warns-ai-will-implode-unprepared-rivals/ - Why your most AI-savvy employees are driving shadow AI
Last year, an engineer working for a messaging app posted a question on TeamBlind, the anonymous forum for verified tech workers: Did every company restrict ChatGPT, Claude, and Gemini — or was it just his? When the company he worked for banned these tools, it offered an internal alternative built on ChatGPT, but the engineer didn’t like it because it slowed him down. “It was kinda useless,” he said. The TeamBlind thread quickly filled with responses from techies at other organizations who joined him in his frustration that company-approved AI tools were heavily restricted or stripped of many useful features. A week later, the same engineer returned to the forum with a workaround. Using a WebAssembly-based LLM engine, he managed to run a coding model entirely inside his browser, with conversations stored locally and no outbound network traffic for his employer to detect. “Happy coding,” he wrote on the forum. “DM me for features.” Often, the employees who best understand the capabilities of gen AI are also the most likely to bend or break organizational rules governing its use. Engineers and, perhaps counterintuitively, other workers who have undergone mandatory AI training often see official guardrails less as strict boundaries and more as hurdles to overcome in the name of speed. A recent LexisNexis report found that 74% of AI-trained employees use unauthorized AI tools , versus only 17% of untrained employees. “The issue is the gap between employee capability and enterprise-ready tooling,” says Dani McCormick, VP of product at Nexis Solutions. “Those with greater awareness of AI tools are more likely to experiment and incorporate them into their workflows.” Training appears to remove some of the hesitation employees may initially feel toward gen AI, which can act as a barrier to adoption. “The takeaway isn’t that training creates risk, but that it surfaces demand faster than many organizations are prepared to meet,” McCormick adds. Given all these, CIOs need to walk a fine line between encouraging AI adoption and controlling how these tools are used. That’s a difficult task that requires a rethink. As employees grow more comfortable with gen AI, traditional approaches, including blanket bans, may no longer work and can even prove counterproductive. A more productive approach would be to capitalize on shadow AI’s silver lining. Using restricted AI tools can also be a sign that employees see value and are trying to move faster, says Seth Cohen, CIO at P&G. “The opportunity is to bring that learning into a system that’s right for the company and can scale,” he says. But figuring out how to create that system can be a challenge for many CIOs under pressure to encourage experimentation while also protecting sensitive data and maintaining control over an increasingly fragmented AI landscape. Build better trainings One of the biggest challenges organizations face with AI use is how uneven it can be across the business. While some teams have integrated AI deeply into their daily workflows, others remain hesitant or disengaged. “That imbalance is often where unsanctioned usage is most visible, and where there’s the greatest opportunity for better alignment,” says McCormick. One way to close that divide is through hands-on AI training programs that address both the technical and ethical dimensions of AI use. These programs should teach employees how to integrate authorized AI tools into their daily work while explaining why using those authorized platforms matters, from protecting sensitive data and ensuring compliance, to maintaining transparency and accountability across the organization. “Training is most effective when employees can apply it in their day-to-day roles, whether that’s improving decisions, accelerating innovation, or strengthening execution,” says Cohen. These trainings should include everyone, not just tech workers, because gen AI tools are becoming mainstream, and employees with little formal technical background are increasingly experimenting with them on their own — a trend many CIOs have noticed. “I’d say around 30% of untrained staff are more curious and exploring capabilities,” says Art Thompson, CIO at the City of Detroit. The real focus, he adds, should be empowering people to use technology responsibly. “If not, the shadow ecosystem will grow and we’ll have less visibility than we do today,” he adds. A strong AI training program needs to address judgment, governance, and trust all at once, while also giving employees a broader understanding of the organization, its partners, and the wider ecosystem in which their AI tools operate. Workers need to understand how their choices can affect data security, customer trust, regulatory compliance, and business relationships. Thompson saw that many employees still fail to understand how AI vendors source information or how outputs should be verified. “Having rules is a great start, but people need to understand the guidance to use the tools responsibly,” he says. “Having business units buy into the governance piece and be a part of the IT culture is a great way to help shape that.” Several CIOs argue that rule enforcement should be done carefully. “If employees fear they’ll be disciplined for experimenting with AI, they won’t stop using it, they’ll just hide it,” says Matt Kunkel, CEO and co-founder of AI GRC platform LogicGate. “Instead, organizations should create an environment where employees feel comfortable disclosing AI use without fear of retribution, and reward employees who flag potential AI risks.” Addressing employee AI needs Designing better training programs and stronger governance frameworks is only half the challenge. Organizations also need to address the underlying reason employees turn to shadow AI in the first place, which is looking for tools that help them work faster and reduce friction in their daily tasks. In many cases, if someone is willing to pay out of their own pocket for an AI tool, it may mean they’re not getting what they need from the organization’s official systems. “That’s both a risk issue and a missed opportunity,” says Prakash Kota, CIO at UKG. “Shadow AI grows in the gap between what employees are ready to do and what the organization enables them to do.” According to Kota, this should be seen as an opportunity to better understand what employees are trying to accomplish and where official tools fall short. Richard Amos, CIO of IT services provider Blue Mantis, agrees with this approach. “In general, I’d seek to first understand if approved tools are hard to use, limited, or slow to provision,” he says. “If they are, employees will find alternatives. People naturally look for ways to get their work done better.” Amos adds that, in most cases, employees who use unauthorized AI tools don’t act with malicious intent. Often, shadow AI doesn’t emerge from defiance but from curiosity, frustration, or a desire to work faster. “Once the use case is understood, it may be an opportunity to review it at the AI governance committee and consider it for the backlog,” Amos adds. Paying attention to the AI tools employees use covertly can also help CIOs spot emerging trends before they become larger governance or security issues. Organizations with visibility into employee experimentation are often better positioned to understand which tools workers actually find useful. “You’ll also catch new tools the moment they show up,” says Ryan Fritts, CIO of security provider Everon Solutions. Picking up the pace Several organizations have upgraded their AI tools after realizing that employees were turning to unauthorized alternatives to fill gaps in existing systems. But simply offering approved tools isn’t enough. Those platforms also need to remain flexible and adaptable enough to evolve alongside employees’ needs, and the rapid pace of AI innovation. “No one will be excited to build digital solutions on a platform that uses outdated models,” says Cohen. Some organizations even give employees more flexibility by allowing them to choose among several of the latest commercially available foundation models. Others provide secure AI environments where workers can safely experiment. “Allow for freedom within a framework and ensure active learnings are captured to improve the overall platform offerings,” Cohen says. The underlying challenge is finding the right balance between rules and freedom, giving employees enough space to integrate AI effectively into their work while still maintaining oversight. CIOs need to build systems and cultures that allow teams to learn without exposing the organization to unnecessary risk. And instead of playing an endless guessing game with new AI apps, organizations may gain more by focusing on their data. “The work that actually moves the needle is on the data side, getting clear on what data sits where, and being able to enforce policy on it in real time,” Fritts says. “Get the data posture right and most of the shadow AI panic quiets down on its own.”
Score: 64🌐 MovesJun 10, 2026https://www.cio.com/article/4178359/why-your-most-ai-savvy-employees-are-driving-shadow-ai.html - Private AI school approved by Boston School Committee
Private AI school approved by Boston School Committee The Boston Globe
Score: 64🌐 MovesJun 10, 2026https://www.bostonglobe.com/2026/06/10/metro/private-ai-school-alpha-approved-in-boston/ - Huawei Cloud shifts the AI cloud fight beyond token prices
Huawei is focusing on domestic computing power and enterprise use cases as rivals compete on cheaper AI inference.
Score: 64🌐 MovesJun 10, 2026https://kr-asia.com/huawei-cloud-shifts-the-ai-cloud-fight-beyond-token-prices - AI is becoming a single point of failure — and most companies don’t see it
Artificial intelligence doesn’t exist in a vacuum. It runs on infrastructure that is increasingly constrained, contested and, in many cases, outside a company’s control. That reality is starting to surface in subtle ways. Vendors are adjusting access to AI capabilities, introducing tiered usage models and quietly reshaping what customers can expect from their tools. Microsoft, for example, has already shifted features and access within its Copilot ecosystem, signaling that capacity is not unlimited. This isn’t new. In the early days of the internet, service providers could throttle access based on demand or pricing tiers until regulation stepped in to standardize availability. AI is beginning to follow a similar trajectory but with a more complex set of constraints: power availability, data center capacity, geopolitical risk and vendor concentration. What makes this different is how quickly AI is being embedded into core business workflows. Nearly three-quarters of organizations are already using AI to automate processes across multiple business functions. Yet most have done little to account for the business interruption risk that creates. Many enterprises treat AI as always-available infrastructure. In reality, it is capacity-constrained, vendor-dependent and vulnerable to disruption. The next phase of AI maturity isn’t about adoption. It will be about resilience, continuity and dependency management. Business continuity in an AI-dependent operating model The question is no longer whether work can get done without AI. It is whether businesses can operate at the speed and volume they have already committed to without it. Many organizations have redesigned workflows around AI-enabled efficiency. Tasks that once took hours now take minutes. Teams have been streamlined, and service-level commitments have been tightened. In many cases, entire operating models assume continuous AI availability. In practice, it doesn’t hold. Even short disruptions can expose the gap. During a recent Microsoft services outage, some organizations lost access to AI models embedded in their workflows. Employees had to manually process tasks that had been automated — slowing operations and creating backlogs almost immediately. At a small scale, that’s manageable. At the enterprise scale, it becomes a continuity risk. Planning for AI disruption starts with a mindset shift. Most continuity planning assumes degradation: systems slow down but still function. However, AI introduces scenarios where capabilities are unavailable altogether. When building out a business continuity plan, three things are key: Know what breaks. Most organizations don’t have a clear inventory of where AI is embedded across their workflows, including dependencies on specific vendors, models and infrastructure. Without that visibility, it’s difficult to understand failure points or build a mitigation plan around them. Plan for absence, not degradation. If an AI system goes offline, what happens next? In many cases, there is no fallback. Skills have atrophied, staffing models have changed and processes have been optimized around automation. This is where risk management needs a seat at the table, not just IT. Reintroduce operational buffers. Resilience requires redundancy — whether that’s retaining institutional knowledge, maintaining alternative workflows or diversifying providers. These investments rarely show immediate returns, which is why they are often deferred. This is not fundamentally different from how organizations approached cybersecurity a decade ago. What once felt optional is now baseline. Insurance and the emerging business interruption gap As AI becomes embedded in core operations, the financial exposure tied to its disruption is becoming harder to ignore. This exposure does not fit neatly into existing insurance frameworks. There are parallels to the early days of cyber risk. Before stand-alone cyber policies existed, losses were often absorbed — or disputed — across general liability, crime and fraud coverage. Insurers responded by introducing exclusions and, eventually, dedicated cyber policies. AI risk is following a similar path, but with additional complexity. Events like the CrowdStrike outage, which affected systems globally, raised questions about business interruption coverage , with organizations pursuing claims tied to financial losses. In that case, cyber coverage was a likely entry point. AI introduces a different layer. A disruption may not be a cyber event at all. It could be tied to power grid constraints affecting data centers, vendor-driven capacity limits, regulatory restrictions or geopolitical events. The failure is external and not necessarily malicious, which raises a fundamental question: Where does the loss sit? For most organizations today, the answer is unclear. That uncertainty is driving early conversations around stand-alone AI coverage. While those products are still evolving, the more immediate priority is understanding where exposure exists and where it may be underinsured. That requires translating AI dependency into financial terms. What revenue is tied to AI-enabled workflows? What contractual obligations depend on those outputs? What happens if those capabilities are unavailable? Until those questions are addressed, the risk remains largely unquantified. Your AI partners are your risk model Much of this exposure is concentrated in a small number of providers. The companies building and operating large-scale AI systems, such as OpenAI and Anthropic, are making real-time decisions about how their platforms operate under constraints. Those decisions shape how every dependent organization experiences performance, access and disruption. That includes how capacity is allocated when demand exceeds supply, which features are available at different pricing tiers, how models are trained and governed and how infrastructure is expanded or limited based on power and regulatory conditions. These are not purely technical considerations. They are business decisions made by third parties that directly affect your operations. As a result, choosing an AI partner is a dependency decision that will shape how your business operates under both normal conditions and disruption. Three areas to consider: Trust and responsibility. How models are trained, how bias is managed and how outputs are governed all have downstream implications. Operational reliability. Service levels now involve more than uptime. They include which capabilities you can access, at what price tier and whether performance holds when demand is high. Alignment of priorities. When constraints emerge, vendors decide where resources go. Enterprises need to understand where they sit in that hierarchy. There is no universal framework for making these decisions yet. Organizations are building their approach in real time. A different kind of infrastructure risk AI is often framed as a competitive advantage. In many cases, it is. But as it shifts from a capability to core infrastructure that is shared, constrained and subject to forces beyond any single organization’s control, the risk profile changes fundamentally. That dependency is the risk. Enterprises don’t need to slow down adoption. The pressure to move forward is real. But they do need a clearer view of what they are building on and what happens when that foundation is under strain. This article is published as part of the Foundry Expert Contributor Network. Want to join?
Score: 64🌐 MovesJun 10, 2026https://www.cio.com/article/4183037/ai-is-becoming-a-single-point-of-failure-and-most-companies-dont-see-it.html - Xpeng boss to head robot unit with humanoid mass production imminent
The announcement follows market talk that Shi Xiaoxin, a core executive involved in the IRON project, had left the company earlier this month. Xpeng confirmed on Wednesday that Shi had resigned as senior director of robotics product planning, without giving further details.
- Two-hour learning? AI-powered Alpha School lands in Seattle region
Alpha School, an AI-powered private school chain that has students complete core academics in two hours a day, plans to open a campus in Kirkland this fall and will run summer programs on Microsoft's Redmond campus. Read More
Score: 63🌐 MovesJun 10, 2026https://www.geekwire.com/2026/two-hour-learning-ai-powered-alpha-school-lands-in-seattle-region/ - A jazz label covered an AI-generated hit to make a point the music industry has been avoiding
A jazz label turned a faceless AI song into a statement about human creativity and built a tool to back it up.
- AI Infrastructure Short-Circuits But Now Sparks Not One, But Two Buy Points
Riding demand for AI data centers, AI infrastructure leader Amphenol looks to connect with a fresh breakout. The post AI Infrastructure Short-Circuits But Now Sparks Not One, But Two Buy Points appeared first on Investor's Business Daily .
- AI Boosting Productivity but Fueling Job Anxiety Among Canadian Workers
AI Boosting Productivity but Fueling Job Anxiety Among Canadian Workers Toronto Star
- Greg Abbott tells PUC, ERCOT not to pass new data center costs to customers
Greg Abbott tells PUC, ERCOT not to pass new data center costs to customers Houston Chronicle
- AWS and Databricks at Data + AI Summit 2026: Accelerating real-world AI innovation
AWS and Databricks continue to simplify complex data challenges, enabling organizations...
Score: 63🌐 MovesJun 10, 2026https://www.databricks.com/blog/aws-and-databricks-data-ai-summit-2026-accelerating-real-world-ai-innovation - Hey Siri, You're the Framework for Apple's Smart Glasses Now
WWDC was all about Siri AI, but it certainly looks like a piece in the smart glasses puzzle. Vision Pro's new features are another big sign.
Score: 62🌐 MovesJun 10, 2026https://www.cnet.com/tech/computing/hey-siri-youre-the-framework-for-apples-smart-glasses-now/ - New flak for govt AI scheme
The government is pressing ahead with gathering public input on the controversial TH-AI Passport project amid criticism that the move is an attempt to "whitewash" the scheme.
Score: 62🌐 MovesJun 10, 2026https://www.bangkokpost.com/thailand/general/3268953/new-flak-for-govt-ai-scheme - FinOps discipline finds its footing in managing AI spend as token economics reshape enterprise budgets
As generative AI accelerates from a product experiment into a core enterprise operating cost, the discipline of FinOps is evolving rapidly around managing AI spend, introducing a layer of complexity that traditional cloud budgets never fully prepared practitioners to handle. Token economics are forcing organizations to rethink not just how they measure spend, but what […] The post FinOps discipline finds its footing in managing AI spend as token economics reshape enterprise budgets appeared first on SiliconANGLE .
Score: 62🌐 MovesJun 10, 2026https://siliconangle.com/2026/06/10/managing-ai-spend-effectively-generative-ai-era-finopsx/ - LinkedIn’s AI Content Boom Is Creating a Credibility Problem That’s Hurting Founders
When everyone uses AI, no one stands out.
- From automated to autonomous: Why your ERP needs agentic AI for demand-supply balancing
If you walk into the control room of a modern Tier-1 auto components manufacturer, you likely will not see piles of spreadsheets anymore. You will see massive screens displaying SAP HANA dashboards, Oracle Demantra forecasts, and SAP Ariba supplier portals. Many supply chain leaders believe that because they have implemented these heavy-hitting enterprise systems, they have achieved “digital transformation”. But there is a hidden trap: automation is not intelligence. Let us look at a “typical day” at NexGear Auto Systems. It has top-tier enterprise resource planning (ERP) and advanced planning systems (APS). But let us see what happens when volatility hits, and why transitioning from automated to agentic AI is the real game-changer. The automated but rigid reality: the exception management trap The trigger: Tuesday, 09:00. A major original equipment manufacturer (OEM) customer sends an automated Electronic Data Interchange (EDI) 830 Planning Schedule directly into NexGear’s SAP HANA system. It needs a 20% increase in sensor clusters for next month and is delaying its sedan wiring harnesses due to low demand. The automated process and its flaws: Vikram, VP of Sales: The EDI flows seamlessly into the system. Oracle Demantra updates the statistical forecast. The flaw? Demantra relies on historical data and rigid algorithms. It updates the numbers, but it does not understand the context of why the OEM changed the order, nor can it negotiate the shift. Ananya, Head of Supply Chain: The work: At 02:00 on Wednesday, the SAP system runs its massive overnight Material Requirements Planning (MRP) batch job. The reality: When Ananya logs in on Wednesday morning, she does not have a solution; she has a dashboard blinking with 450 red “exception alerts”. The delay: SAP Ariba automatically sent updated purchase orders (POs) to the Tier-2 suppliers through the supplier portal. However, the microchip supplier rejected the sudden 20% increase due to a shortage. The system simply flagged this as a “stockout risk”. Ananya now has to spend the next 48 hours manually calling suppliers, expediting freight, and overriding system parameters. Rahul, Plant Manager: His scheduling module flags a capacity constraint. The system just says “overload”. Rahul still has to pull his supervisors into a room to manually determine how to resequence the machines. The result: The system processed the data faster than a spreadsheet, but the heavy lifting of decision-making and problem-solving still fell entirely on human shoulders. The ERP is a system of record; it is not a system of intelligence and action. The paradigm shift: the agentic AI layer Now, let us inject a workforce of digital workers, or agentic AI, into NexGear’s existing technology stack. The AI does not replace SAP or Oracle; it sits on top of them as a “system of intelligence”, acting as the ultimate orchestrator. Here is that exact same Tuesday morning, supercharged by agentic AI: The trigger: Tuesday, 09:00. The OEM customer sends the EDI into SAP. The autonomous resolution: 09:01: The market intelligence agent for demand The agent intercepts the EDI. Instead of waiting for Demantra’s batch run, it instantly validates the change against external unstructured data, such as market news about surging SUV sales. It automatically updates the demand signal in SAP HANA in real time. 09:03: The supply risk agent The agent does not wait for the overnight MRP run. It instantly simulates the bill of materials explosion. It proactively queries the SAP Ariba supplier portal network. It sees that the primary microchip supplier has rejected the 20% surge. Instead of just flashing a red alert on Ananya’s dashboard, the AI acts. It autonomously scans the Ariba network for secondary, pre-approved suppliers with available inventory. Here, the AI agent mimics human intelligence and, therefore, works autonomously to find the next-best alternative supplier. 09:05: The balancing orchestrator The AI calculates that buying from Supplier B incurs a 4% material cost premium but prevents an OEM line stoppage. The action: Without human intervention, the AI drafts the new PO, routes it through SAP Ariba, obtains Supplier B’s confirmation through the portal, and updates the inbound logistics tracking. It then updates Rahul’s factory scheduling module with a dynamically optimised sequence that minimises changeover times. The human-in-the-loop action 09:10: Ananya logs in. Instead of 450 red alerts, she sees a clean dashboard. The AI presents a summary: “OEM demand shifted. Microchip shortfall resolved through Supplier B at a 4% premium, within your 5% approved guardrails. System updated.” The AI then escalates one high-level strategic issue: “Supplier B is located near a port facing potential labour strikes next week. Do you want me to split the order and use air freight to mitigate the risk?” Ananya clicks “Yes”, and the AI executes the complex split shipment in SAP. The true value of AI in an automated enterprise If you already have SAP, Oracle, and Ariba, agentic AI is the bridge that takes you from merely recording supply chain events to autonomously resolving them. Eradicating exception fatigue: Human planners stop acting as “ERP babysitters”, clearing hundreds of low-level alerts, which frees them to handle complex, strategic supplier relationships. Continuous versus batch planning: Moving away from rigid, overnight MRP runs to real-time, continuous demand-supply synchronisation. Smarter ROI on existing technology: AI maximises the significant investments already made in ERPs by finally utilising data lakes to execute autonomous decisions. In the modern auto components industry, the winner is not the company with the most data in its ERP. It is the company whose systems can think, balance, and act on that data before the competition even finishes its morning coffee. The rise of digital workers: institutionalising AI agents in SCM AI agents mimic human intelligence across various operational roles by utilising cognitive abilities such as perception, reasoning, planning, execution, reflection, adaptation, metacognition, and memory. Because of these advanced capabilities, AI agents function as “digital workers”, capable of executing complex tasks traditionally performed by people in roles such as demand planners or supply chain managers. Therefore, organisations must begin initiating proofs of concept (PoCs) to develop their own strategic playbooks, allowing them to successfully institutionalise digital workers throughout the broader supply chain management (SCM) ecosystem. However, integrating AI agents into an existing SCM ecosystem is a complex undertaking that requires robust change management. It necessitates a significant shift in human roles and responsibilities to effectively supervise and co-ordinate with these digital counterparts. Furthermore, strict governance is essential: AI agents must be observable; their decision-making outputs must be explainable; and they must strictly safeguard data security and privacy. Finally, robust business continuity measures must be established to mitigate risks and maintain operations in the event of unexpected algorithmic errors or system failures. ------ By V. Srinivasa Rao (VSR) Chief Digital and Agentic AI Advisor and Consultant Former Chief Digital Officer, Tech Mahindra (Disclaimer: The views expressed in this article are those of the authors and do not necessarily reflect the views or editorial position of Dataquest or CyberMedia.)
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