ChatGPT

Indonesia’s push into AI-led growth is gaining momentum as more local organisations look for ways to build their own applications, update their systems, and strengthen data oversight. The country now has broader access to cloud and AI tools after Microsoft expanded the services available in the Indonesia Central cloud region, which first went live six months ago. The expansion gives businesses, public bodies, and developers more options to run AI workloads inside the country instead of relying on overseas data centres. The update was shared at the Cloud & AI Innovation Summit in Jakarta, where business and government leaders met to discuss how Indonesia can advance its AI ambitions. Speakers included Mike Chan, who leads Azure AI Apps & Agents in Asia, and Dharma Simorangkir, President Director of Microsoft Indonesia. Their message was consistent: local capacity is only useful if organisations put it to work. During the event, Dharma said the new services “open the door for every organisation to innovate in Indonesia, for Indonesia,” calling on teams across sectors to build solutions that tackle national needs. A shift toward building, not just adopting Many Indonesian enterprises are moving beyond basic AI trials and are now designing tools that solve problems unique to their operations. Microsoft describes these kinds of organisations as Frontier Firms — teams that treat AI as a core part of how they work rather than an optional add-on. These firms tend to focus on building applications that make tasks easier for customers, improve internal processes, or modernise old workflows. To support this shift, the Indonesia Central region now hosts a range of Azure services that help teams design and deploy software. These include tools for building data-connected applications, services for storing and managing structured data, and a set of AI-ready virtual machines that can train and run advanced models. The machines, built for heavy computing work, allow teams to keep data inside the country while working with complex AI workloads. The region now supports Microsoft 365 Copilot as well, bringing AI features to common work tools. Developers also have access to GitHub Copilot, which suggests code and speeds up software development. These services form a connected stack that helps teams move past small pilots and into production, where reliability and cost control matter more. Early Microsoft cloud projects emerging across Indonesia The expansion of the region follows steady demand since its launch in May 2025. Companies across mining, travel, and digital services are already using local cloud infrastructure to refresh legacy systems and meet stricter data governance needs. Petrosea and Vale Indonesia are among the firms using the region to support technical upgrades and secure local data storage. Digital-first players are also experimenting with more direct AI engagement. One example is tiket.com, which built its own AI travel assistant using the Azure OpenAI Service. The assistant lets customers interact with the platform in everyday language, from checking flight updates to adding extra services after a booking. “Our advancements in artificial intelligence are designed to deliver the best possible experience for our customers,” said Irvan Bastian Arief, PhD, Vice President of Technology GRAND, Data & AI at tiket.com. The company sees conversational AI as a way to make travel planning simpler while reducing friction in customer support. Bringing scattered data into one system A major theme at the Summit was the need to get data in order before adopting AI at scale. To support this, Microsoft introduced Microsoft Fabric to the Indonesian market. Fabric is a single environment that brings together data engineering, integration, warehousing, analytics, and business intelligence. It includes Copilot features that help teams prepare data and build insights without juggling multiple tools. For many organisations, data sits across different internal systems and cloud providers. Fabric gives teams one place to bring these sources together, which may help improve governance, speed up reporting, and control costs. The platform is designed for teams that want structure without building their own data foundation from scratch. Preparing Indonesia’s workforce for practical AI with Microsoft tools The day’s focus was not limited to infrastructure. Microsoft also highlighted its AI training program, Microsoft Elevate, which is now entering its second year. The program has already reached more than 1.2 million learners and aims to certify 500,000 people in AI skills by 2026. The next phase will focus on hands-on use, encouraging participants to apply AI in real settings rather than only learning concepts in theory. Training covers a wide range of groups — teachers, nonprofit workers, community leaders, and people looking to improve their digital skills. Participants learn through tools such as Microsoft Copilot, Learning Accelerator, Minecraft Education, and modules designed to explain how AI can support practical tasks. During the Summit, Dharma said that cloud and AI “are the backbone of national competitiveness” and stressed that infrastructure only matters if people are prepared to use it. Building a long-term ecosystem These efforts sit within a broader commitment of US$1.7 billion that Microsoft has pledged for Indonesia from 2024 to 2028. The investment spans infrastructure, partner support, and talent development. The company is also preparing to host GitHub Universe Jakarta on 3 December 2025, a developer-focused gathering meant to encourage collaboration among software teams, startups, and researchers. Indonesia is aiming to position itself as a centre for secure and inclusive AI development in the region. With the expansion of the Indonesia Central cloud region, new data and AI tools, and growing attention on workforce training, the country is taking steps to build the foundations needed for long-term digital growth. Companies now have the option to build AI systems closer to home, developers have more resources, and workers have more pathways to gain practical skills. The coming years will show how these pieces fit together as organisations move from experimentation to long-term use. (Photo by Simon Ray) See also: Microsoft, NVIDIA, and Anthropic forge AI compute alliance Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post New Microsoft cloud updates support Indonesia’s long-term AI goals appeared first on AI News. View the full article

Manufacturers today are working against rising input costs, labour shortages, supply-chain fragility, and pressure to offer more customised products. AI is becoming an important part of a response to those pressures. When enterprise strategy depends on AI Most manufacturers seek to reduce cost while improving throughput and quality. AI supports these aims by predicting equipment failures, adjusting production schedules, and analysing supply-chain signals. A Google Cloud survey found that more than half of manufacturing executives are using AI agents in back-office areas like planning and quality. ([Hidden Content]) The shift matters because the use of AI links directly to measurable business outcomes. Reduced downtime, lower scrap, better OEE (overall equipment effectiveness), and improved customer responsiveness all contribute to positive enterprise strategy and overall competitiveness in the market. What recent industry experience reveals Motherson Technology Services reported major gains – 25-30% maintenance-cost reduction, 35-45% downtime reduction, and 20-35% higher production efficiency after adopting agent-based AI, data-platform consolidation, and workforce-enablement initiatives. ServiceNow has described how manufacturers unify workflows, data, and AI on common platforms. It reported that just over half of advanced manufacturers have formal data-governance programmes in support of their AI initiatives. These instances show the direction of travel: AI is being deployed inside operations – not in pilots, but in workflows. What cloud and IT leaders should consider Data architecture Manufacturing systems depend on low-latency decisions, especially for maintenance and quality. Leaders must work out how to combine edge devices (often OT systems with supporting IT infrastructure) with cloud services. Microsoft’s maturity-path guidance highlights that data silos and legacy equipment remain a barrier, so standardising how data is collected, stored, and shared is often the first step for many future-facing manufacturing and engineering businesses. Use-case sequencing ServiceNow advises starting small and scaling AI roll-outs gradually. Focusing on two or three high-value use-cases helps teams avoid the “pilot trap”. Predictive maintenance, energy optimisation, and quality inspection are strong starting points because benefits are relatively easy to measure. Governance and security Connecting operational technology equipment with IT and cloud systems increases cyber-risk, as some OT systems were not designed to be exposed to the wider internet. Leaders should define data-access rules and monitoring requirements carefully. In general, AI governance should not wait until later phases, but begin in the first pilot. Workforce and skills The human factor remains important. Operators’ trust AI-supported systems goes without saying and there needs to be confidence using systems underpinned by AI. According to Automation.com, manufacturing faces persistent skilled-labour shortages, making upskilling programmes an integral part of modern deployments. Vendor-ecosystem neutrality The ecosystem of many manufacturing environments includes IoT sensors, industrial networks, cloud platforms, and workflow tools operating in the back office and on the facility floor. Leaders should prioritise interoperability and avoid lock-in to any one provider. The aim is not to adopt a single vendor’s approach but to build an architecture that supports long-term flexibility, honed to the individual organisation’s workflows. Measuring impact Manufacturers should define metrics, which may include downtime hours, maintenance-cost reduction, throughput, yield, and these metrics should be monitored continuously. The Motherson results provide realistic benchmarks and show the outcomes possible from careful measurement. The realities: beyond the hype Despite rapid progress, challenges remain. Skills shortages slow deployment, legacy machinery produces fragmented data, and costs are sometimes difficult to forecast. Sensors, connectivity, integration work, and data-platform upgrades all add up. Additionally, security issues grow as production systems become more connected. Finally, AI should coexist with human expertise; operators, engineers, and data scientists behind the scenes need to work together, not in parallel. However, recent publications show these challenges are manageable with the right management and operational structures. Clear governance, cross-functional teams, and scalable architectures make AI easier to deploy and sustain. Strategic recommendations for leaders Tie AI initiatives to business goals. Link work to KPIs like downtime, scrap, and cost per unit. Adopt a careful hybrid edge-cloud mix. Keep real-time inference close to machines while using cloud platforms for training and analytics. Invest in people. Mixed teams of domain experts and data scientists are important, and training should be offered for operators and management. Embed security early. Treat OT and IT as a unified environment, assuming zero-trust. Scale gradually. Prove value in one plant, then expand. Choose open ecosystem components. Open standards allow a company to remain flexible and avoid vendor lock-in. Monitor performance. Adjust models and workflows as conditions change, according to results measured against pre-defined metrics. Conclusion Internal AI deployment is now an important part of manufacturing strategy. Recent blog posts from Motherson, Microsoft, and ServiceNow show that manufacturers are gaining measurable benefits by combining data, people, workflows, and technology. The path is not simple, but with clear governance, the right architecture, an eye to security, business-focussed projects, and a strong focus on people, AI becomes a practical lever for competitiveness. (Image source: “Jelly Belly Factory Floor” by el frijole is licensed under CC BY-NC-SA 2.0. ) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Manufacturing’s pivot: AI as a strategic driver appeared first on AI News. View the full article

The ability to execute adversarial learning for real-time AI security offers a decisive advantage over static defence mechanisms. The emergence of AI-driven attacks – utilising reinforcement learning (RL) and Large Language Model (LLM) capabilities – has created a class of “vibe hacking” and adaptive threats that mutate faster than human teams can respond. This represents a governance and operational risk for enterprise leaders that policy alone cannot mitigate. Attackers now employ multi-step reasoning and automated code generation to bypass established defences. Consequently, the industry is observing a necessary migration toward “autonomic defence” (i.e. systems capable of learning, anticipating, and responding intelligently without human intervention.) Transitioning to these sophisticated defence models, though, has historically hit a hard operational ceiling: latency. Applying adversarial learning, where threat and defence models are trained continuously against one another, offers a method for countering malicious AI security threats. Yet, deploying the necessary transformer-based architectures into a live production environment creates a bottleneck. Abe Starosta, Principal Applied Research Manager at Microsoft NEXT.ai, said: “Adversarial learning only works in production when latency, throughput, and accuracy move together. Computational costs associated with running these dense models previously forced leaders to choose between high-accuracy detection (which is slow) and high-throughput heuristics (which are less accurate). Engineering collaboration between Microsoft and NVIDIA shows how hardware acceleration and kernel-level optimisation remove this barrier, making real-time adversarial defence viable at enterprise scale. Operationalising transformer models for live traffic required the engineering teams to target the inherent limitations of CPU-based inference. Standard processing units struggle to handle the volume and velocity of production workloads when burdened with complex neural networks. In baseline tests conducted by the research teams, a CPU-based setup yielded an end-to-end latency of 1239.67ms with a throughput of just 0.81req/s. For a financial institution or global e-commerce platform, a one-second delay on every request is operationally untenable. By transitioning to a GPU-accelerated architecture (specifically utilising NVIDIA H100 units), the baseline latency dropped to 17.8ms. Hardware upgrades alone, though, proved insufficient to meet the strict requirements of real-time AI security. Through further optimisation of the inference engine and tokenisation processes, the teams achieved a final end-to-end latency of 7.67ms—a 160x performance speedup compared to the CPU baseline. Such a reduction brings the system well within the acceptable thresholds for inline traffic analysis, enabling the deployment of detection models with greater than 95 percent accuracy on adversarial learning benchmarks. One operational hurdle identified during this project offers valuable insight for CTOs overseeing AI integration. While the classifier model itself is computationally heavy, the data pre-processing pipeline – specifically tokenisation – emerged as a secondary bottleneck. Standard tokenisation techniques, often relying on whitespace segmentation, are designed for natural language processing (e.g. articles and documentation). They prove inadequate for cybersecurity data, which consists of densely packed request strings and machine-generated payloads that lack natural breaks. To address this, the engineering teams developed a domain-specific tokeniser. By integrating security-specific segmentation points tailored to the structural nuances of machine data, they enabled finer-grained parallelism. This bespoke approach for security delivered a 3.5x reduction in tokenisation latency, highlighting that off-the-shelf AI components often require domain-specific re-engineering to function effectively in niche environments. Achieving these results required a cohesive inference stack rather than isolated upgrades. The architecture utilised NVIDIA Dynamo and Triton Inference Server for serving, coupled with a TensorRT implementation of Microsoft’s threat classifier. The optimisation process involved fusing key operations – such as normalisation, embedding, and activation functions – into single custom CUDA kernels. This fusion minimises memory traffic and launch overhead, which are frequent silent killers of performance in high-frequency trading or security applications. TensorRT automatically fused normalisation operations into preceding kernels, while developers built custom kernels for sliding window attention. The result of these specific inference optimisations was a reduction in forward-pass latency from 9.45ms to 3.39ms, a 2.8x speedup that contributed the majority of the latency reduction seen in the final metrics. Rachel Allen, Cybersecurity Manager at NVIDIA, explained: “Securing enterprises means matching the volume and velocity of cybersecurity data and adapting to the innovation speed of adversaries. “Defensive models need the ultra-low latency to run at line-rate and the adaptability to protect against the latest threats. The combination of adversarial learning with NVIDIA TensorRT accelerated transformer-based detection models does just that.” Success here points to a broader requirement for enterprise infrastructure. As threat actors leverage AI to mutate attacks in real-time, security mechanisms must possess the computational headroom to run complex inference models without introducing latency. Reliance on CPU compute for advanced threat detection is becoming a liability. Just as graphics rendering moved to GPUs, real-time security inference requires specialised hardware to maintain throughput >130 req/s while ensuring robust coverage. Furthermore, generic AI models and tokenisers often fail on specialised data. The “vibe hacking” and complex payloads of modern threats require models trained specifically on malicious patterns and input segmentations that reflect the reality of machine data. Looking ahead, the roadmap for future security involves training models and architectures specifically for adversarial robustness, potentially using techniques like quantisation to further enhance speed. By continuously training threat and defence models in tandem, organisations can build a foundation for real-time AI protection that scales with the complexity of evolving security threats. The adversarial learning breakthrough demonstrates the technology to achieve this – balancing latency, throughput, and accuracy – is now capable of being deployed today. See also: ZAYA1: AI model using AMD GPUs for training hits milestone Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Adversarial learning breakthrough enables real-time AI security appeared first on AI News. View the full article

Malaysia has captured 32% of Southeast Asia’s total AI funding—equivalent to US$759 million—between H2 2024 and H1 2025, establishing itself as the region’s dominant destination for artificial intelligence investment as massive infrastructure expansion and high consumer adoption converge to reshape the country’s technology landscape, according to the e-Conomy SEA 2025 report released by Google, Temasek, and Bain & Company. The Malaysia AI investment surge is underpinned by a dramatic expansion in physical infrastructure that sets the country apart from regional competitors. Data centre capacity exploded from 120 megawatts in 2024 to 690 MW in the first half of 2025, with plans reported to further increase capacity by 350%—representing half of all planned regional capacity. This infrastructure-first approach appears to be working. Google has committed US$2 billion in investment, including the development of its first Google data centre and Google Cloud region in Malaysia, specifically to meet growing demand for AI-ready cloud services both locally and globally. The funding reality: concentration and opportunity While the headline US$759 million figure positions Malaysia as a regional leader in Malaysia AI investment, the composition reveals both strengths and vulnerabilities. The funding was supported primarily by major digital financial services deals, particularly a significant private equity transaction in H2 2024 that elevated the overall numbers. Private funding across Malaysia’s broader digital economy tells a more nuanced story. The deal count in H1 2025 stood at just 23 deals, significantly below the 2021 peak of 236 deals, indicating that while individual transaction sizes have increased, the breadth of investment activity has narrowed considerably. Digital financial services accounted for 84% of H1 2024 funding, raising questions about whether Malaysia’s AI investment ecosystem has sufficient diversification to sustain momentum if fintech consolidation slows or regulatory headwinds emerge. However, investor sentiment remains optimistic. Nearly two-thirds (64%) of surveyed investors expect funding activity in Malaysia to rise through 2030, particularly in software, services, AI and deep tech—categories that extend beyond the current fintech concentration. Malaysia also led Southeast Asia in IPO activity over the past 12 months, contributing roughly half of the region’s total listings. This exit activity signals that investors see viable pathways to liquidity, a critical factor for sustaining long-term AI investment flows. Consumer adoption: rapid uptake with emerging commercial validation If infrastructure investment represents Malaysia’s strategic bet on AI, consumer behaviour suggests the market is responding. Some 74% of Malaysian digital consumers report interacting with AI tools and features daily—a penetration rate that positions the country among the region’s most engaged AI user bases. The nature of engagement extends beyond passive consumption. According to the report, 68% of consumers have conversations with and ask questions of AI chatbots, indicating comfort with conversational AI interfaces that go beyond simple task automation. More significantly for commercial AI development, 55% of Malaysian consumers expect AI to make decisions faster and with less mental effort. This trust signal suggests readiness for agentic AI applications that operate with greater autonomy. This consumer readiness is translating into measurable commercial outcomes. Revenue growth for apps with marketed AI features surged 103% in H1 2025 compared to H1 2024, providing concrete evidence that AI functionality drives monetisation beyond experimentation or novelty value. “With three in four Malaysian digital consumers having used GenAI tools, this strong daily engagement is laying a solid foundation for the next phase of AI-powered growth,” said Ben King, Managing Director of Google Malaysia & Singapore. “In line with the nation’s goal of becoming a regional digital leader by 2030, Google remains fully committed to supporting Malaysia’s ambition to build an inclusive, innovative, and AI-ready digital economy.” The trust equation: data sharing versus privacy concerns One of the most striking findings in Malaysia’s AI adoption profile is consumer willingness to share data access with AI agents. Some 92% of respondents indicated they would share data such as shopping and viewing history, and social connections with AI systems—a figure that significantly exceeds comfort levels seen in more privacy-conscious markets. For context, privacy and data security concerns around agentic AI in Malaysia stand at 60%, which is actually 10 percentage points higher than the ASEAN-10 average of 50%. This apparent contradiction—high willingness to share data coupled with elevated privacy concerns—suggests Malaysian consumers recognise both the utility and the risks of AI systems, rather than exhibiting naive enthusiasm. This nuanced trust profile creates both opportunities and responsibilities for AI developers. The willingness to share data enables more sophisticated personalisation and AI agent capabilities, but the parallel privacy concerns indicate that consumers expect robust data governance in return. Top motivations for using or paying for AI features reveal a pragmatic consumer base. Saving time on research and comparisons ranks highest at 51%, followed by saving money through better deals or price tracking at 39%, and exclusive access to products and 24/7 customer support at 30%. These priorities suggest AI adoption in Malaysia is driven by functional value rather than technological curiosity. Infrastructure scale meets strategic questions The planned 350% increase in data centre capacity positions Malaysia to host not just domestic AI workloads but regional and potentially global operations. Half of all planned Southeast Asian data centre capacity being located in Malaysia represents a concentration that could drive network effects and talent clustering. However, several strategic questions remain unresolved. Can Malaysia move beyond hosting infrastructure to developing proprietary AI capabilities? The emergence of ILMU, Malaysia’s first home-grown large language model now being deployed by digital banks, suggests domestic AI development is beginning, but scale remains limited. Will the infrastructure investments translate into high-value job creation, or will Malaysia primarily provide the physical substrate while control and value accrue elsewhere? The country’s 80% AI awareness rate—indicating most users have learned about AI through various approaches—suggests potential for workforce development, but awareness alone doesn’t guarantee technical capability. The regulatory environment also faces testing. The new Consumer Credit Act, requiring buy-now-pay-later providers and non-bank lenders to be licensed, indicates authorities are introducing structure to previously loosely governed digital sectors. How regulators approach AI governance—balancing innovation enablement with consumer protection—will significantly impact whether Malaysia’s AI investment sustains its current trajectory. Regional implications and competitive dynamics Malaysia’s infrastructure and funding concentration create both collaboration and competition dynamics across Southeast Asia. The interoperability of the DuitNow QR standard across an increasing number of regional markets, now including Cambodia, demonstrates Malaysia’s capacity for cross-border digital integration that could extend to AI services. However, as neighbouring countries observe Malaysia’s AI momentum, competitive infrastructure buildouts are likely. The sustainability of Malaysia’s current leadership position depends on translating first-mover advantages into durable capabilities—technical talent, regulatory frameworks, and commercial ecosystems that compound rather than commoditise. “The real opportunity now lies in how businesses harness AI as a catalyst for impact while building on Malaysia’s strong digital foundations,” said Amanda Chin, Partner at Bain & Company. This framing acknowledges that infrastructure and funding, while necessary, are insufficient without execution. As Malaysia’s AI investment reaches significant scale, the critical test shifts from capital attraction to value creation—whether the US$759 million in funding and massive infrastructure expansion generate genuinely innovative AI applications or primarily replicate capabilities developed elsewhere. The data confirms Malaysia has secured a leadership position in Southeast Asia’s AI landscape. Converting that position into sustained technological advantage requires moving beyond infrastructure provision into invention, a transition that remains very much in progress. (Photo by Luiz Cent) See also: Huawei commits to training 30,000 Malaysian AI professionals as local tech ecosystem expands Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post e-Conomy SEA 2025: Malaysia takes 32% of regional AI funding appeared first on AI News. View the full article

Zyphra, AMD, and IBM spent a year testing whether AMD’s GPUs and platform can support large-scale AI model training, and the result is ZAYA1. In partnership, the three companies trained ZAYA1 – described as the first major Mixture-of-Experts foundation model built entirely on AMD GPUs and networking – which they see as proof that the market doesn’t have to depend on NVIDIA to scale AI. The model was trained on AMD’s Instinct MI300X chips, Pensando networking, and ROCm software, all running across IBM Cloud’s infrastructure. What’s notable is how conventional the setup looks. Instead of experimental hardware or obscure configurations, Zyphra built the system much like any enterprise cluster—just without NVIDIA’s components. Zyphra says ZAYA1 performs on par with, and in some areas ahead of, well-established open models in reasoning, maths, and code. For businesses frustrated by supply constraints or spiralling GPU pricing, it amounts to something rare: a second option that doesn’t require compromising on capability. How Zyphra used AMD GPUs to cut costs without gutting AI training performance Most organisations follow the same logic when planning training budgets: memory capacity, communication speed, and predictable iteration times matter more than raw theoretical throughput. MI300X’s 192GB of high-bandwidth memory per GPU gives engineers some breathing room, allowing early training runs without immediately resorting to heavy parallelism. That tends to simplify projects that are otherwise fragile and time-consuming to tune. Zyphra built each node with eight MI300X GPUs connected over InfinityFabric and paired each one with its own Pollara network card. A separate network handles dataset reads and checkpointing. It’s an unfussy design, but that seems to be the point; the simpler the wiring and network layout, the lower the switch costs and the easier it is to keep iteration times steady. ZAYA1: An AI model that punches above its weight ZAYA1-base activates 760 million parameters out of a total 8.3 billion and was trained on 12 trillion tokens in three stages. The architecture leans on compressed attention, a refined routing system to steer tokens to the right experts, and lighter-touch residual scaling to keep deeper layers stable. The model uses a mix of Muon and AdamW. To make Muon efficient on AMD hardware, Zyphra fused kernels and trimmed unnecessary memory traffic so the optimiser wouldn’t dominate each iteration. Batch sizes were increased over time, but that depends heavily on having storage pipelines that can deliver tokens quickly enough. All of this leads to an AI model trained on AMD hardware that competes with larger peers such as Qwen3-4B, Gemma3-12B, Llama-3-8B, and OLMoE. One advantage of the MoE structure is that only a sliver of the model runs at once, which helps manage inference memory and reduces serving cost. A bank, for example, could train a domain-specific model for investigations without needing convoluted parallelism early on. The MI300X’s memory headroom gives engineers space to iterate, while ZAYA1’s compressed attention cuts prefill time during evaluation. Making ROCm behave with AMD GPUs Zyphra didn’t hide the fact that moving a mature NVIDIA-based workflow onto ROCm took work. Instead of porting components blindly, the team spent time measuring how AMD hardware behaved and reshaping model dimensions, GEMM patterns, and microbatch sizes to suit MI300X’s preferred compute ranges. InfinityFabric operates best when all eight GPUs in a node participate in collectives, and Pollara tends to reach peak throughput with larger messages, so Zyphra sized fusion buffers accordingly. Long-context training, from 4k up to 32k tokens, relied on ring attention for sharded sequences and tree attention during decoding to avoid bottlenecks. Storage considerations were equally practical. Smaller models hammer IOPS; larger ones need sustained bandwidth. Zyphra bundled dataset shards to reduce scattered reads and increased per-node page caches to speed checkpoint recovery, which is vital during long runs where rewinds are inevitable. Keeping clusters on their feet Training jobs that run for weeks rarely behave perfectly. Zyphra’s Aegis service monitors logs and system metrics, identifies failures such as NIC glitches or ECC blips, and takes straightforward corrective actions automatically. The team also increased RCCL timeouts to keep short network interruptions from killing entire jobs. Checkpointing is distributed across all GPUs rather than forced through a single chokepoint. Zyphra reports more than ten-fold faster saves compared with naïve approaches, which directly improves uptime and cuts operator workload. What the ZAYA1 AMD training milestone means for AI procurement The report draws a clean line between NVIDIA’s ecosystem and AMD’s equivalents: NVLINK vs InfinityFabric, NCCL vs RCCL, cuBLASLt vs hipBLASLt, and so on. The authors argue the AMD stack is now mature enough for serious large-scale model development. None of this suggests enterprises should tear out existing NVIDIA clusters. A more realistic path is to keep NVIDIA for production while using AMD for stages that benefit from the memory capacity of MI300X GPUs and ROCm’s openness. It spreads supplier risk and increases total training volume without major disruption. This all leads us to a set of recommendations: treat model shape as adjustable, not fixed; design networks around the collective operations your training will actually use; build fault tolerance that protects GPU hours rather than merely logging failures; and modernise checkpointing so it no longer derails training rhythm. It’s not a manifesto, just our practical takeaway from what Zyphra, AMD, and IBM learned by training a large MoE AI model on AMD GPUs. For organisations looking to expand AI capacity without relying solely on one vendor, it’s a potentially useful blueprint. See also: Google commits to 1000x more AI infrastructure in next 4-5 years Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post ZAYA1: AI model using AMD GPUs for training hits milestone appeared first on AI News. View the full article

In order to meet the massive demand for AI, Google wants to double the overall size of its servers every six months, a growth rate that would create a 1000x greater capacity in the next four or five years. The statement came from the head of Google’s AI infrastructure, Amin Vahdat, during an all-hands meeting on November 6, according to CNBC. Alphabet, Google’s parent company is certainly performing well, so such a requirement may be within its financial capabilities. It reported good Q3 figures at the end of October, and has raised its capital expenditure forecast to $93 billion, up from $91 billion. Vahdat addressed one employee’s question about the company’s future amid talk of an ‘AI bubble’ by re-stating the risks of not investing aggressively enough. In its cloud operations, such investment in infrastructure has paid off. “The risk of under-investing is pretty high […] the cloud numbers would have been much better if we had more compute.” Google’s cloud business continues to grow at around a 33% per year, creating an income stream that enables the company to be “better positioned to withstand misses than other companies,” he said. With better infrastructure running more efficient hardware such as the seventh-gen Tensor Processing Unit and more efficient LLM models, Google is confident that it can continue to create value for its enterprise users’ increased implementation of AI technologies. According to Markus Nispel of Extreme Networks, writing on techradar.com in September, it’s IT infrastructure that’s making companies’ AI vision falter. He places the blame for any failure of AI projects on the high demands AI workloads place on legacy systems, the need for real-time and edge facilities (often lacking in current enterprises), and the continuing presence of data silos. “Even when projects do launch, they’re often hampered by delays caused by poor data availability or fragmented systems. If clean, real-time data can’t flow freely across the organisation, AI models can’t operate effectively, and the insights they produce arrive too late or lack impact,” he said. “With 80% of AI projects struggling to deliver on expectations globally, primarily due to infrastructure limitations rather than the AI technology itself, what matters now is how we respond.” His views are shared by decision-makers at the large technology providers: Capital expenditure by Google, Microsoft, Amazon, and Meta is expected to top $380 billion this year, the majority of which is focused on AI infrastructure. The message from the hyperscalers is clear: If we build it, they will come. Addressing the infrastructure challenges that organisations experience is the key component to successful implementation of AI-based projects. Agile infrastructure as close as possible to the point of compute and data sets that are unified are seen as important parts of the recipe for getting full value from next-generation AI projects. Although some market realignment is expected across the AI sector in the next six months, companies like Google are among those expected to be able to consolidate on the market and continue to offer game-changing technologies based on AI as it evolves. (Image source: “Construction site” by tomavim is licensed under CC BY-NC 2.0.) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Google commits to 1000x more AI infrastructure in next 4-5 years appeared first on AI News. View the full article

A €1.2 trillion AI prize sits on the table for Europe’s economy, and the region has the talent and raw ingredients to claim it. While the global narrative often focuses on competition with the US and China, the view from the ground in Europe is a region of untapped potential, world-class talent, and deep infrastructure investment. Debbie Weinstein, President of Google EMEA, sees a “new generation of visionary founders” ready to drive the region’s future. The opportunity is built on a foundation of scientific excellence and a workforce that is “as bright as anywhere else in the world.” The task now is to leverage Europe’s strengths to close the AI adoption gap and accelerate growth. A foundation of innovation Europe is already a powerhouse of scientific breakthrough. The Google DeepMind team – which includes Nobel prize winners – drives discovery from London, while nearly one million researchers across EMEA use AlphaFold to solve biological problems. Europe isn’t starting from scratch; it is a hub of high-level R&D. That intellectual capital is being matched by hard investment. Just last week, Google announced a €5.5 billion investment in Germany to support connectivity and infrastructure. The choice to base ‘Security Operations Centres’ in Munich, Dublin, and Malaga also highlights Europe’s specific strength: a deep, culturally ingrained commitment to privacy and security. For businesses, this signals that Europe offers a stable and secure environment for building long-term digital strategies. The potential of AI in Europe Currently, only 14 percent of European businesses use AI. While some see this as a lag, optimists see it as massive headroom for growth. The businesses that do adopt these tools are seeing powerful results. Weinstein points to Spanish startup Idoven as a prime example of Europe’s potential. They are using AI to help doctors detect heart disease earlier, proving that when European founders get access to the right tools, they build world-changing solutions. The operational gains are equally tangible in traditional sectors. In automotive, upgrading from basic voice assistants to AI co-pilots can prevent accidents by detecting driver fatigue. In cybersecurity, modern tools allow teams to stay ahead of sophisticated threats. The technology acts as a force multiplier, giving businesses the “most powerful toolbox they’ve ever had.” To fully realise this €1.2 trillion potential, Europe’s businesses need access to the same high-performance AI models as their global peers. The latest models are 300 times more powerful than those from two years ago, offering a massive productivity boost to those who can deploy them. There is positive momentum on the regulatory front. Weinstein notes that the release of the Commission’s Digital Omnibus is a “step in the right direction” to help businesses compete globally. The goal now is harmonisation; creating a clearer and simpler regime that allows companies to train models responsibly and launch products faster. A unified market with clear and sensible rules will be the catalyst that turns potential into GDP. Investing in the workforce The final piece of the puzzle is people. Seizing this moment requires a workforce confident in using it. Weinstein stresses that we need leaders who can identify opportunities and managers who are AI-literate. This is happening through partnership. Google has already helped over 15 million Europeans learn digital skills and is now rolling out a €15 million AI Opportunity Fund to support vulnerable workers. For enterprise leaders, the message is clear: investing in skills today builds the confidence to take risks and grow tomorrow. Europe has the talent, the values, and the infrastructure. With the right focus on skills and a push for harmonised access to tools, Europe is well-positioned to lead the way and capture the full value of the AI era. See also: How the Royal Navy is using AI to cut its recruitment workload Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post How Europe’s talent can secure a trillion-euro AI economic injection appeared first on AI News. View the full article

AI spending in Asia Pacific continues to rise, yet many companies still struggle to get value from their AI projects. Much of this comes down to the infrastructure that supports AI, as most systems are not built to run inference at the speed or scale real applications need. Industry studies show many projects miss their ROI goals even after heavy investment in GenAI tools because of the issue. The gap shows how much AI infrastructure influences performance, cost, and the ability to scale real-world deployments in the region. Akamai is trying to address this challenge with Inference Cloud, built with NVIDIA and powered by the latest Blackwell GPUs. The idea is simple: if most AI applications need to make decisions in real time, then those decisions should be made close to users rather than in distant data centres. That shift, Akamai claims, can help companies manage cost, reduce delays, and support AI services that depend on split-second responses. Jay Jenkins, CTO of Cloud Computing at Akamai, explained to AI News why this moment is forcing enterprises to rethink how they deploy AI and why inference, not training, has become the real bottleneck. Why AI projects struggle without the right infrastructure Jenkins says the gap between experimentation and full-scale deployment is much wider than many organisations expect. “Many AI initiatives fail to deliver on expected business value because enterprises often underestimate the gap between experimentation and production,” he says. Even with strong interest in GenAI, large infrastructure bills, high latency, and the difficulty of running models at scale often block progress. Jay Jenkins, CTO of Cloud Computing at Akamai. Most companies still rely on centralised clouds and large GPU clusters. But as use grows, these setups become too expensive, especially in regions far from major cloud zones. Latency also becomes a major issue when models have to run multiple steps of inference over long distances. “AI is only as powerful as the infrastructure and architecture it runs on,” Jenkins says, adding that latency often weakens the user experience and the value the business hoped to deliver. He also points to multi-cloud setups, complex data rules, and growing compliance needs as common hurdles that slow the move from pilot projects to production. Why inference now demands more attention than training Across Asia Pacific, AI adoption is shifting from small pilots to real deployments in apps and services. Jenkins notes that as this happens, day-to-day inference – not the occasional training cycle – is what consumes most computing power. With many organisations rolling out language, vision, and multimodal models in multiple markets, the demand for fast and reliable inference is rising faster than expected. This is why inference has become the main constraint in the region. Models now need to operate in different languages, regulations, and data environments, often in real time. That puts enormous pressure on centralised systems that were never designed for this level of responsiveness. How edge infrastructure improves AI performance and cost Jenkins says moving inference closer to users, devices, or agents can reshape the cost equation. Doing so shortens the distance data must travel and allows models to respond faster. It also avoids the cost of routing huge volumes of data between major cloud hubs. Physical AI systems – robots, autonomous machines, or smart city tools – depend on decisions made in milliseconds. When inference runs distantly, these systems don’t work as expected. The savings from more localised deployments can also be substantial. Jenkins says Akamai analysis shows enterprises in India and Vietnam see large reductions in the cost of running image-generation models when workloads are placed at the edge, rather than centralised clouds. Better GPU use and lower egress fees played a major role in those savings. Where edge-based AI is gaining traction Early demand for edge inference is strongest from industries where even small delays can affect revenue, safety, or user engagement. Retail and e-commerce are among the first adopters because shoppers often abandon slow experiences. Personalised recommendations, search, and multimodal shopping tools all perform better when inference is local and fast. Finance is another area where latency directly affects value. Jenkins says workloads like fraud checks, payment approval, and transaction scoring rely on chains of AI decisions that should happen in milliseconds. Running inference closer to where data is created helps financial firms move faster and keeps data inside regulatory borders. Why cloud and GPU partnerships matter more now As AI workloads grow, companies need infrastructure that can keep up. Jenkins says this has pushed cloud providers and GPU makers into closer collaboration. Akamai’s work with NVIDIA is one example, with GPUs, DPUs, and AI software deployed in thousands of edge locations. The idea is to build an “AI delivery network” that spreads inference across many sites instead of concentrating everything in a few regions. This helps with performance, but it also supports compliance. Jenkins notes that almost half of large APAC organisations struggle with differing data rules across markets, which makes local processing more important. Emerging partnerships are now shaping the next phase of AI infrastructure in the region, especially for workloads that depend on low-latency responses. Security is built into these systems from the start, Jenkins says. Zero-trust controls, data-aware routing, and protections against fraud and bots are becoming standard parts of the technology stacks on offer. The infrastructure needed to support agentic AI and automation Running agentic systems – which make many decisions in sequence – needs infrastructure that can operate at millisecond speeds. Jenkins believes the region’s diversity makes this harder but not impossible. Countries differ widely in connectivity, rules, and technical readiness, so AI workloads must be flexible enough to run where it makes the most sense. He points to research showing that most enterprises in the region already use public cloud in production, but many expect to rely on edge services by 2027. That shift will require infrastructure that can hold data in-country, route tasks to the closest suitable location, and keep functioning when networks are unstable. What companies need to prepare for next As inference moves to the edge, companies will need new ways to manage operations. Jenkins says organisations should expect a more distributed AI lifecycle, where models are updated across many sites. This requires better orchestration and strong visibility into performance, cost, and errors in core and edge systems. Data governance becomes more complex but also more manageable when processing stays local. Half of the region’s large enterprises already struggle with the variance in regulations, so placing inference closer to where data is generated can help. Security also needs more attention. While spreading inference to the edge can improve resilience, it also means every site must be secured. Firms need to protect APIs, data pipelines, and guard against fraud or bot attacks. Jenkins notes that many financial institutions already rely on Akamai’s controls in these areas. (Photo by Igor Omilaev) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post APAC enterprises move AI infrastructure to edge as inference costs rise appeared first on AI News. View the full article

We’ve all seen the headlines: a third of US college students say they use ChatGPT for writing tasks at least once a month. The share of US teens turning to the same tool for schoolwork doubled between 2023 and 2024. Generative AI tools overall are a fixture of life for seven out of ten teens. The advent of ChatGPT and its competitors was supposed to put even the best essay writing services out of business. After all, generative AI can create an essay in seconds. So, why pay a professional to take care of it? Yet, three years after the launch of ChatGPT, academic help services are still going strong. Here’s why US students continue to choose expert help over AI-generated content, and the four services they trust with their assignments. How students actually use AI tools When it first made the news, ChatGPT was called “the death of the English essay.” Now, that kind of language seems like a promise of an apocalypse that (predictably, in hindsight) never came. Today, students don’t use generative AI tools to generate whole essays. Across multiple surveys, brainstorming, outlining, research, and test prep emerge as the main use cases for AI. For example, the survey from University of California Irvine found that: 66% use AI to learn more on a specific topic/subject 56% use it to prepare for tests 55% use it to find academic sources 46% use it for note-taking Only a third of respondents (31%) reported turning to AI tools to write essays. The percentage went even lower for scholarship and college application essays (21%). Why students still opt for top essay writing services While AI tools are great at generating long texts in a blink of an eye for free, that’s where their benefits typically end. Unlike professional writers, AI simply can’t: Grasp all the intricacies and subtleties of the expectations toward an essay, especially if it’s meant for a college or scholarship application Capture the customer’s authentic voice based on samples of their previous writing Write an essay that’s truly distinct and memorable: AI tools regurgitate cliché narratives and generic statements Come up with qualitatively new ideas and arguments: AI can only repeat the opinions already out there Verify the essay is 100% factually correct: AI tools can hallucinate facts, and many don’t even include precise sources of information Potential AI checks are another concern that pushes some students to hire a top essay writing service instead of using AI. For one, Turnitin automatically checks all assignments for both plagiarism and AI content now. Some educators take it on themselves to run AI content scans, too. An essay written by a professional will pass those checks without a hitch, which can’t be said about an AI-generated one. 4 best online essay writing services students trust Which platforms score the highest among the best online essay writing services trusted by US students? Here’s your snapshot of four such platforms: ServiceBest forRating (Sitejabber)EssayProOne-stop help4.4/5 based on 31,122 reviewsWritePaperIn-depth research5.0/5 based on 1,019 reviewsMyPaperHelpPersonalised writing4.8/5 based on 364 reviewsPaperWriterCollaborative approach4.9/5 based on 848 reviews EssayPro: Best for one-stop help EssayPro is the essay writing service online with the most extensive track record on this list. As of writing, it has over 30,000 reviews on Sitejabber alone and completes 300,000+ assignments annually. But that’s not what makes it the best essay writing service. Based on customer reviews, students prefer EssayPro to AI because its essay help remains affordable, all while being more in-depth, insightful, and creative than AI content. The fact that its writers specialise in 140+ subjects and 50+ paper types helped EssayPro secure its popularity, too. Pricing Custom writing: Starts at $10.80/page Rewriting: Starts at $7.56/page Editing: Starts at $7.56/page Proofreading: Starts at $5.40/page Pros 350+ writers specialising in 140+ subjects Good price-quality ratio with transparent pricing and no surprise fees Solid track record of on-time delivery Free plagiarism and AI reports Full control over who works on your essay Cons Not all orders can be completed in 3 hours No over-the-phone customer support WritePaper: Best for in-depth research AI tools can’t do the kind of research, analysis, and synthesis that experts at WritePaper do every day. That’s what makes it the best college essay writing service for essays and other papers that have to be insightful and present advanced, nuanced arguments on a complex topic. According to the best essay writing service reviews, WritePaper’s experts are especially good at delivering in-depth essays in research-intensive disciplines that require advanced reasoning. Those include nursing, philosophy, psychology, and history. Pricing Custom writing: Starts at $10.80/page Rewriting: Starts at $7.56/page Editing: Starts at $7.56/page Proofreading: Starts at $5.40/page Pros Solid argumentation and research skills among writers 115+ subjects covered Around-the-clock support and help Thoroughly researched essays with advanced reasoning Diverse formatting options (MLA, APA, Chicago, etc.) Cons Potentially overwhelming writer selection process Graphs and tables cost extra MyPaperHelp: Best for personalised writing While all services on this list provide custom writing services, MyPaperHelp is a paper writing service frequently praised for its personalised approach to orders. Its experts readily work with samples and adapt the style and tone of voice to the essay’s context and purpose. They also build on the ideas, suggestions, and whole outlines added to the order form. This makes MyPaperHelp the best essay writing website for any essay that has to be highly personal in nature. Think scholarship and college application essays or creative writing assignments that focus on personal experiences rather than academic research. Pricing Custom writing: Starts at $10.80/page Rewriting: Starts at $7.56/page Editing: Starts at $7.56/page Proofreading: Starts at $5.40/page Pros Wide range of writing styles and tones of voice supported Essays fully adapted to their context and purpose High-quality creative writing assignments Possible to attach samples to the order form that writers build on Unique, authentic writing that doesn’t rehash generic ideas or clichés Cons You have to be very precise with your instructions to leave no room for misunderstandings Originality reports aren’t provided by default; you have to request one (although they are free) PaperWriter: Best for collaborative approach Yes, US students turn to PaperWriter for many reasons, but direct writer communication is the most frequently cited one. So, if two-way communication with the writer is important, PaperWriter is definitely worth considering. PaperWriter’s experts routinely reach out to customers via direct chat whenever they need to clarify the requirements or ask for additional information. That makes PaperWriter the best essay writing service for students who want their essays to reflect their thoughts, ideas, and opinions to the letter. Pricing Custom writing: Starts at $10.80/page Rewriting: Starts at $7.56/page Editing: Starts at $7.56/page Proofreading: Starts at $5.40/page Pros Direct writer chat with end-to-end encryption Strict privacy policy that protects confidentiality Responsive writers who proactively communicate with customers Unlimited free revisions without mandatory waiting time Review work services available Cons Writer selection may be a bit time-consuming A collaborative approach is, by definition, also somewhat time-consuming Final thoughts: AI can’t rival human creativity & expertise AI tools may be becoming more ingrained in the learning process, but that doesn’t mean they’re ready to replace human creativity and expertise altogether. Yes, they can help you outline an essay or brainstorm ideas. But only professionals can come up with truly fresh ideas or develop a complex argument on a topic that requires hours of research. So, it’s safe to say that essay writing services aren’t going anywhere any time soon. They will continue supporting students in their studies, more so than AI tools. If you’re looking for the best essay writing service, Reddit and other social media platforms are a good place to start. Independent review platforms like Sitejabber and Reviews.io can also come in handy. Image source: Unsplash The post 4 best essay writing websites students choose over AI appeared first on AI News. View the full article

Alibaba’s recently launched Qwen AI app has demonstrated remarkable market traction, accumulating 10 million downloads within seven days of its public beta release—a velocity that exceeds the early adoption rates of ChatGPT, Sora, and DeepSeek. The application’s rapid uptake reflects a strategic shift in how technology giants are approaching AI commercialisation. While international competitors like OpenAI and Anthropic have built their businesses around subscription models, Alibaba’s free-access approach challenges this framework by integrating AI capabilities directly into existing consumer and enterprise ecosystems. According to the South China Morning Post, the Qwen app serves as more than a chatbot, functioning as “a comprehensive AI tool designed to meet user needs in both professional and personal contexts.” Available on Apple’s App Store and Google Play since mid-November, the application integrates with Alibaba’s e-commerce platforms, mapping services, and local business tools—demonstrating what industry analysts term “agentic AI” capabilities that execute cross-scenario tasks rather than simply generating content. 10,000,000 users creating with Qwen Chat — and we’re just getting started. From here, let’s begin — [Hidden Content] pic.twitter.com/xnW7FU3Kdo — Qwen (@Alibaba_Qwen) November 17, 2025 Enterprise adoption drives momentum The technical foundation underpinning the Qwen AI app’s consumer success has been building since 2023, when Alibaba fully open-sourced its Qwen model series. This decision has resulted in cumulative global downloads exceeding 600 million, establishing Qwen as one of the world’s most widely adopted open-source large language models. For enterprises evaluating AI deployment strategies, this adoption pattern offers instructive insights. The recently released Qwen3-Max model now ranks among the top three globally in performance benchmarks, with notable traction in Silicon Valley. Airbnb CEO Brian Chesky has stated publicly that his company “heavily relies on Qwen” for speed and quality, whileNVIDIA CEO Jensen Huang acknowledged Qwen’s growing dominance in the global open-source model market. These enterprise endorsements signal practical business value rather than speculative potential. Companies implementing AI solutions face persistent challenges around cost management, integration complexity, and demonstrable return on investment. Alibaba’s strategy addresses these pain points by offering capable models without licensing fees while providing integration pathways through its broader ecosystem. Competitive implications for business leaders Su Lian Jye, chief analyst at consultancy Omdia, told SCMP that increased user adoption generates valuable feedback loops: “More users mean more feedback, which would allow Alibaba to further fine-tune its models.” This observation highlights a competitive advantage for cloud service providers with substantial capital reserves and existing user data infrastructure. The timing of Qwen’s launch carries strategic significance. ******** AI startups Moonshot AI and Zhipu AI recently introduced subscription fees for their Kimi and ChatGLM services, respectively, creating an opening for Alibaba’s free-access positioning. Su noted that AI startups might struggle to compete with this approach, which “will only work for cloud service providers that have large capital reserves and can monetise user data.” For enterprise decision-makers, this competitive dynamic presents both opportunities and considerations. Free-access models reduce initial deployment costs but raise questions about long-term sustainability, data privacy frameworks, and vendor lock-in risks. Organisations adopting AI tools must evaluate whether immediate cost savings align with their governance requirements and strategic independence. Navigating geopolitical complexity The Qwen app’s success unfolds against a backdrop of intensifying US-China technology competition. Some US observers have expressed concerns about Alibaba’s advancement rate and investment scale. Marketing specialist Tulsi Soni remarked on social media that “we’re witnessing a full-blown Qwen panic” in Silicon Valley—a comment reflecting anxiety about competitive positioning rather than technical assessment. Alibaba has also faced scrutiny, including unsubstantiated allegations from the Financial Times regarding ******** military applications, which the company rejected. For multinational enterprises operating across these geopolitical boundaries, such tensions complicate AI procurement decisions and require careful risk assessment. What this means for enterprise AI strategy The Qwen AI app’s trajectory offers several practical takeaways for business leaders navigating AI adoption. First, open-source models have matured to competitive parity with proprietary alternatives in many use cases, potentially reducing dependency on subscription-based providers. Second, ecosystem integration—connecting AI capabilities with existing business tools—delivers more immediate value than standalone chatbot functionality. Third, the bifurcation between free-access and subscription models will likely intensify, requiring organisations to evaluate the total cost of ownership beyond initial licensing fees. As Alibaba positions Qwen for evolution into what industry observers describe as “a national-level application,” enterprises worldwide face strategic choices about AI infrastructure. The question is no longer whether to adopt AI tools, but which deployment models align with specific business requirements, risk tolerances, and competitive positioning. The coming months will reveal whether Alibaba’s strategy successfully monetises its massive user base while maintaining the technical performance that attracted enterprise adopters. For now, the Qwen AI app’s early success demonstrates that alternative business models can compete effectively against established subscription frameworks—a development that should inform enterprise planning across industries. See also: Alibaba rolls out revamped Qwen chatbot as model pricing drops Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Qwen AI assistant surpasses 10 million downloads as Alibaba disrupts the enterprise AI market appeared first on AI News. View the full article

With its WorldGen system, Meta is shifting the use of generative AI for 3D worlds from creating static imagery to fully interactive assets. The main bottleneck in creating immersive spatial computing experiences – whether for consumer gaming, industrial digital twins, or employee training simulations – has long been the labour-intensive nature of 3D modelling. The production of an interactive environment typically requires teams of specialised artists working for weeks. WorldGen, according to a new technical report from Meta’s Reality Labs, is capable of generating traversable and interactive 3D worlds from a single text prompt in approximately five minutes. While the technology is currently research-grade, the WorldGen architecture addresses specific pain points that have prevented generative AI from being useful in professional workflows: functional interactivity, engine compatibility, and editorial control. Generative AI environments become truly interactive 3D worlds The primary failing of many existing text-to-3D models is that they prioritise visual fidelity over function. Approaches such as gaussian splatting create photorealistic scenes that look impressive in a video but often lack the underlying physical structure required for a user to interact with the environment. Assets lacking collision data or ramp physics hold little-to-no value for simulation or gaming. WorldGen diverges from this path by prioritising “traversability”. The system generates a navigation mesh (navmesh) – a simplified polygon mesh that defines walkable surfaces – alongside the visual geometry. This ensures that a prompt such as “medieval village” produces not just a collection of houses, but a spatially-coherent layout where streets are clear of obstructions and open spaces are accessible. For enterprises, this distinction is vital. A digital twin of a factory floor or a safety training simulation for hazardous environments requires valid physics and navigation data. Meta’s approach ensures the output is “game engine-ready,” meaning the assets can be exported directly into standard platforms like Unity or Unreal Engine. This compatibility allows technical teams to integrate generative workflows into existing pipelines without needing specialised rendering hardware that other methods, such as radiance fields, often demand. The four-stage production line of WorldGen Meta’s researchers have structured WorldGen as a modular AI pipeline that mirrors traditional development workflows for creating 3D worlds. The process begins with scene planning. A LLM acts as a structural engineer, parsing the user’s text prompt to generate a logical layout. It determines the placement of key structures and terrain features, producing a “blockout” – a rough 3D sketch – that guarantees the scene makes physical sense. The subsequent “scene reconstruction” phase builds the initial geometry. The system conditions the generation on the navmesh, ensuring that as the AI “hallucinates” details, it does not inadvertently place a boulder in a doorway or block a fire exit path. “Scene decomposition,” the third stage, is perhaps the most relevant for operational flexibility. The system uses a method called AutoPartGen to identify and separate individual objects within the scene—distinguishing a tree from the ground, or a crate from a warehouse floor. In many “single-shot” generative models, the scene is a single fused lump of geometry. By separating components, WorldGen allows human editors to move, delete, or modify specific assets post-generation without breaking the entire world. For the last step, “scene enhancement” polishes the assets. The system generates high-resolution textures and refines the geometry of individual objects to ensure visual quality holds up when close. Operational realism of using generative AI to create 3D worlds Implementing such technology requires an assessment of current infrastructure. WorldGen’s outputs are standard textured meshes. This choice avoids the vendor lock-in associated with proprietary rendering techniques. It means that a logistics firm building a VR training module could theoretically use this tool to prototype layouts rapidly, then hand them over to human developers for refinement. Creating a fully textured, navigable scene takes roughly five minutes on sufficient hardware. For studios or departments accustomed to multi-day turnaround times for basic environment blocking, this efficiency gain is quite literally world-changing. However, the technology does have limitations. The current iteration relies on generating a single reference view, which restricts the scale of the worlds it can produce. It cannot yet natively generate sprawling open worlds spanning kilometres without stitching multiple regions together, which risks visual inconsistencies. The system also currently represents each object independently without reuse, which could lead to memory inefficiencies in very large scenes compared to hand-optimised assets where a single chair model is repeated fifty times. Future iterations aim to address larger world sizes and lower latency. Comparing WorldGen against other emerging technologies Evaluating this approach against other emerging AI technologies for creating 3D worlds offers clarity. World Labs, a competitor in the space, employs a system called Marble that uses Gaussian splats to achieve high photorealism. While visually striking, these splat-based scenes often degrade in quality when the camera moves away from the centre and can drop in fidelity just 3-5 metres from the viewpoint. Meta’s choice to output mesh-based geometry positions WorldGen as a tool for functional application development rather than just visual content creation. It supports physics, collisions, and navigation natively—features that are non-negotiable for interactive software. Consequently, WorldGen can generate scenes spanning 50×50 metres that maintain geometric integrity throughout. For leaders in the technology and creative sectors, the arrival of systems like WorldGen brings exciting new possibilities. Organisations should audit their current 3D workflows to identify where “blockout” and prototyping absorb the most resources. Generative tools are best deployed here to accelerate iteration, rather than attempting to replace final-quality production immediately. Concurrently, technical artists and level designers will need to transition from placing every vertex manually to prompting and curating AI outputs. Training programmes should focus on “prompt engineering for spatial layout” and editing AI-generated assets for 3D worlds. Finally, while the output is standard, the generation process requires plenty of compute. Assessing on-premise versus cloud rendering capabilities will be necessary for adoption. Generative 3D serves best as a force multiplier for structural layout and asset population rather than a total replacement for human creativity. By automating the foundational work of building a world, enterprise teams can focus their budgets on the interactions and logic that drive business value. See also: How the Royal Navy is using AI to cut its recruitment workload Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post WorldGen: Meta reveals generative AI for interactive 3D worlds appeared first on AI News. View the full article

OpenAI has introduced group chats inside ChatGPT, giving people a way to bring up to 20 others into a shared conversation with the chatbot. The feature is now available to all logged-in users after a short pilot earlier this month, and it shifts ChatGPT from a mostly one-on-one tool to something that supports small-group collaboration. OpenAI frames the update as a simple way to plan daily tasks with friends, family members, or coworkers, such as setting up a dinner, preparing a trip, or drafting an outline together. But the feature may have broader value for work teams that already use ChatGPT for brainstorming, research, and early project discussions. How the feature works A group chat begins when you select the “people” icon in the top-right corner of the ChatGPT app. The app creates a new shared space by copying your current conversation, and you can invite others by sending a link. That link can be shared again, allowing people to bring more participants into the discussion. The first time a user joins or creates a group chat, ChatGPT asks them to set a name, username, and profile photo so the group can easily identify who is talking. OpenAI says ChatGPT has been trained to “go along with the flow of the conversation,” deciding when to respond and when to stay silent. If someone wants ChatGPT to add something directly, they can mention “ChatGPT” in their message. The model can react with emojis and use profile photos when creating personalised images. A settings panel in the top-right corner of the screen lets users add or remove people, mute notifications, or provide custom instructions to ChatGPT. OpenAI notes that the model will not use memories from personal chats inside group conversations and will not create new memories based on group activity. Group chats run on GPT-5.1 Auto, which chooses the response model based on the prompt and the options available to the user. Rate limits only apply when ChatGPT sends a message. The rollout follows the recent release of GPT-5.1 Instant and Thinking models, and earlier launches such as Sora, a social app for creating short videos. How group chats may support real collaboration While the consumer pitch focuses on casual planning, many of the challenges companies face stem from how people share ideas, review drafts, and coordinate across different roles. Group chats may help reduce some of that friction by giving teams a single space to talk with ChatGPT in the loop. Aligning cross-functional teams Large organisations often struggle to keep product, design, engineering, and marketing teams aligned, especially at the start of a project. Early ideas often get scattered across email threads and chat apps. In a group chat, everyone can contribute in one place. If someone joins late or misses part of the discussion, ChatGPT can summarise the thread, identify open questions, or help turn the group’s notes into a structured plan. This may help teams move from early debate to action without losing context. Smoother review cycles Drafts usually go through long review loops that involve different people using different channels. Comments come in at different times, and it becomes hard to track which version is the current one. In a group chat, the team can react to the same draft together. ChatGPT can rewrite passages, compare alternate versions, or help clarify feedback. This may speed up work for teams dealing with tight deadlines or frequent updates. Faster onboarding for new teammates New team members often join projects that have months of history behind them. They must spend time tracking down old messages and files to understand how decisions were made. A manager could add a new teammate to an existing group chat and ask ChatGPT to summarise past discussions, highlight key choices, and show which tasks remain open. This may reduce the time and effort needed for onboarding. Coordinating shared tasks Routine coordination—such as preparing an internal workshop, drafting a customer email, or planning an event—often gets slowed down by back-and-forth messages that stretch across days. In a group chat, anyone can ask ChatGPT to create a schedule, rewrite a message, build a checklist, or compare options. The group can then adjust the details together without starting from scratch each time. Organising creative feedback Creative work can stall when feedback comes in messy or conflicting forms. Designers, writers, and analysts often get comments spread across different channels. Group chats keep all feedback in one place. ChatGPT can group comments into themes, point out contradictions, or propose drafts that reflect what the team wants. This can help reduce rework and steer the group toward a shared direction. A broader shift in how teams use ChatGPT The introduction of group chats arrives during a ******* when many companies are testing ways to bring AI deeper into their workflows. ChatGPT already helps many users draft, summarise, and revise work. Giving teams a shared space may change how early project conversations take shape, especially for organisations experimenting with AI-supported planning and reviews. The feature does not replace human coordination, but it introduces a shared surface where people can talk to each other and bring ChatGPT into the discussion when they need it. For teams dealing with scattered inputs, slow reviews, or rapid context-switching, group chats may offer a simpler way to keep projects moving. (Photo by Solen Feyissa) See also: Lightweight LLM powers Japanese enterprise AI deployments Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post ChatGPT group chats may help teams bring AI into daily planning appeared first on AI News. View the full article

The Royal Navy is handing the first line of its recruitment operations to a real-time AI avatar called Atlas. Atlas is powered by a large language model and has been deployed to field questions from prospective submariners. The deployment shows how AI can support a shift from slow text-based triage to fast and immersive automated support. Public sector IT projects often suffer from bloated timelines and vague deliverables, but the Navy’s latest deployment is grounded in hard operational metrics. The launch of Atlas follows a specific business case: the need to filter and support candidates for one of the service’s most demanding roles while reducing the administrative burden on human staff. The data behind the deployment The Royal Navy, working with WPP Media’s Wavemaker, has spent years refining its automated entry points. Before the avatar, there was a text-based assistant. That initial system, which was recently upgraded to a full LLM and retrieval-augmented generation (RAG) solution, proved the efficacy of the model. It fielded over 460,000 queries from more than 165,000 users and logged a 93 percent satisfaction rate. More importantly for the bottom line, the text-based system slashed the workload for live-agent teams by 76 percent. It also generated 89,000 expressions of interest, proving that automation could widen the funnel without overwhelming the recruiting officers. Atlas is effectively the visual evolution of those successes, designed to arrest the attention of a younger demographic that engages differently with digital channels. Under the hood of the AI recruitment avatar The architecture relies on a multi-vendor ecosystem rather than a single-source solution. Wavemaker led the strategic direction and conversational design, ensuring the “brain” of the operation was trained on the correct knowledge base. Voxly Digital built the front and back end, supported by Great State, the Navy’s digital agency. Functionally, Atlas does more than recite policy. It uses a conversational interface that is multimedia-enabled. If a candidate asks about life on a submarine – a notorious pain point for recruitment conversion due to the unique lifestyle – Atlas can respond with spoken answers, on-screen captions, and relevant videos or quotes from serving personnel. The goal is to keep the user in the ecosystem longer. Atlas will be trialled at events and linked directly to the NavyReady app and the Enterprise Customer Relationship Management (e-CRM) programme, ensuring data continuity. Augmentation, not replacement Despite the high degree of automation, the Royal Navy frames this AI avatar as a workforce augmentation tool for recruitment. Paul Colley, Head of Marketing at the Royal Navy, was explicit about the boundaries of the technology: “When it comes to AI, our focus is on how we can use it responsibly and strategically to better arm the teams we have. It’s not about replacing human support. It’s about giving the best support we can wherever and whenever candidates need it.. “We’re excited to launch Atlas and see if it can provide a new, different kind of support for those who would be considering the submarine service but need some more time to explore and discuss.” Caroline Scott, Head of e-CRM and Innovation, added: “By trialling new interfaces and adopting a test-and-learn mindset, the Royal Navy can be better equipped to understand how these technologies can transform the way people connect, apply for roles, and engage with us, while also creating more meaningful digital experiences.” For business leaders, the Atlas pilot illustrates a mature approach to generative AI adoption. The Navy didn’t start with the avatar; they started with the data and a simpler text interface. Only after securing a 76 percent efficiency gain did they scale up to the more complex and resource-intensive visual medium. The end result is an AI-assisted recruitment system that filters low-value queries at scale, allowing human recruiters to focus on the serious candidates. See also: Lightweight LLM powers Japanese enterprise AI deployments Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post How the Royal Navy is using AI to cut its recruitment workload appeared first on AI News. View the full article

Choosing the right thermal binoculars is essential for security professionals and outdoor specialists who need reliable long-range detection. Many users who previously relied on the market’s best night vision binoculars now seek advanced thermal imaging for superior clarity, extended range, and weather-independent performance. In 2026, ATN continues to lead the market with cutting-edge thermal binoculars designed for precision and durability. Understanding ATN thermal binocular technology Why ATN thermal binoculars stand out ATN’s thermal binoculars, like the ATN BinoX 4T and BinoX 4T Pro Series, deliver top-tier detection capabilities by combining high-resolution sensors, variable magnification, and intelligent onboard software. These binoculars do not rely on light; instead, they detect heat signatures, making them effective in fog, total darkness, heavy brush, and complex terrain. Long-range detection performance ATN thermal binoculars are engineered for extended-range identification. With ultra-sensitive sensors, powerful optical zoom, and built-in image enhancement algorithms, models like the ATN BinoX 4T 640 offer detection ranges stretching several thousand yards. For 2026, ATN has focused on improving temperature sensitivity, refresh rates, and enhanced object recognition to support longer detection distances with greater precision. Buyer’s guide: How to choose the right thermal binoculars for 2026 Sensor resolution Resolution is the most important factor in long-range performance. ATN offers two main categories: 384×288 sensors for budget-friendly, mid-range detection 640×480 sensors for maximum clarity and long-range detail Higher resolution provides sharper images, better object identification, and more accurate heat differentiation at extended distances. Optical and digital magnification ATN’s BinoX 4T models offer variable zoom that maintains image stability and clarity. For long-range detection, selecting a model with higher native magnification is essential. ATN’s 640 series typically provides the best combination of zoom and image quality without excessive pixelation. Refresh rate and image processing ATN integrates advanced thermal processors and high refresh rates that minimise image lag, especially during fast movement. A 60 Hz refresh rate provides smoother scanning and more accurate target tracking. Detection, recognition, and identification ranges Before buying, compare ATN’s DRI specifications: Detection: seeing a heat signature at extreme distance Recognition: determining object classification (animal, human, vehicle) Identification: positive ID at long range ATN’s latest 640 sensors significantly enhance all three. Battery performance ATN’s thermal binoculars feature extended battery life suitable for long hunts or overnight surveillance. In 2026, ATN upgrades include improved energy efficiency and faster charging through USB-C. Smart features ATN leads the industry with integrated intelligent features, including: Laser rangefinder Ballistic Information Exchange Video recording and streaming Built-in compass and gyroscope The features support long-range accuracy and situational awareness. One-time list: Top ATN features to look for in 2026 High-resolution 640 sensor Variable optical zoom Extended DRI ranges Laser rangefinder integration Smart video recording and streaming Long battery life Rugged, weather-resistant construction Durability and build quality ATN thermal binoculars are built for harsh environments, featuring reinforced housings, weatherproof design, and ergonomic grip options. Durable construction is essential for long-range users who rely on their optics in extreme temperatures, rough terrain, and unpredictable weather. Price and warranty support ATN provides competitive pricing for premium thermal gear, along with strong warranty coverage and US-based customer support. Higher-end models may cost more, but they deliver unmatched long-range clarity and reliability. Final thoughts on choosing ATN thermal binoculars in 2026 Selecting the right thermal binoculars for long-range detection involves understanding sensor quality, magnification, DRI ranges, and overall performance. ATN remains the leading choice for professionals and hunters in 2026 due to its combination of high-resolution sensors, precise zoom systems, and smart onboard technologies. Whether you’re observing wildlife at extreme distances or ensuring perimeter security, ATN’s BinoX 4T and 4T Pro models offer exceptional reliability and long-range clarity. Image source: Unsplash The post How to choose the best thermal binoculars for long-range detection in 2026 appeared first on AI News. View the full article

Many organisations are trying to update their infrastructure to improve efficiency and manage rising costs. But the path is rarely simple. Hybrid setups, legacy systems, and new demands from AI in the enterprise often create trade-offs for IT teams. Recent moves by Microsoft and several storage and data-platform vendors highlight how enterprises are trying to deal with these issues, and what other companies can learn from them as they plan their own enterprise AI strategies. Modernisation often stalls when costs rise Many businesses want the flexibility of cloud computing but still depend on systems built on virtual machines and years of internal processes. A common problem is that older applications were never built for the cloud. Rewriting them can take time and create new risks. But a simple “lift and shift” move often leads to higher bills, especially when teams do not change how the workloads run. Some vendors are trying to address this by offering ways to move virtual machines to Azure without major changes. Early users say the draw is the chance to test cloud migration without reworking applications on day one. For some, this early testing is tied to preparing systems that will later support enterprise AI workloads. They also point to lower storage costs when managed through Azure’s own tools, which helps keep the move predictable. The key lesson for other companies is to look for migration paths that match their existing operations instead of forcing a full rebuild from the start. Data protection and control remain top concerns in hybrid environments The risk of data loss or long outages still keeps many leaders cautious about large modernisation plans. Some organisations are now building stronger recovery systems in on-premises, edge, and cloud locations. Standard planning now includes features like immutable snapshots, replication, and better visibility of compromised data. A recent integration between Microsoft Azure and several storage systems seeks to give companies a way to manage data in on-premises hardware and Azure services. Interest has grown among organisations that need local data residency or strict compliance rules. These setups let them keep sensitive data in-country while still working with Azure tools, which is increasingly important as enterprise AI applications depend on reliable and well-governed data. For businesses facing similar pressures, the main takeaway is that hybrid models can support compliance needs when the control layer is unified. Preparing for AI often requires stronger data foundations, not a full rebuild Many companies want to support AI projects but don’t want to overhaul their entire infrastructure. Microsoft’s SQL Server 2025 adds vector database features that let teams build AI-driven applications without switching platforms. Some enterprises have paired SQL Server with high-performance storage arrays to improve throughput and reduce the size of AI-related data sets. The improvements are becoming part of broader enterprise AI planning. Teams working with these setups say the attraction is the chance to run early AI workloads without committing to a new stack. They also report that more predictable performance helps them scale when teams begin to train or test new models. The larger lesson is that AI readiness often starts with improving the systems that already hold business data instead of adopting a separate platform. Managing Kubernetes alongside older systems introduces new complexity Many enterprises now run a mix of containers and virtual machines. Keeping both in sync can strain teams, especially when workloads run in more than one cloud. Some companies are turning to unified data-management tools that allow Kubernetes environments to sit alongside legacy applications. One example is the growing use of Portworx with Azure Kubernetes Service and Azure Red Hat OpenShift. Some teams use it to move VMs into Kubernetes through KubeVirt while keeping familiar workflows for automation. The approach aims to reduce overprovisioning and make capacity easier to plan. For others, it is part of a broader effort to make their infrastructure ready to support enterprise AI initiatives. It also gives companies a slower, safer path to container adoption. The broader lesson is that hybrid container strategies work best when they respect existing skills rather than forcing dramatic shifts. A clearer path is emerging for companies planning modernisation Across these examples, a common theme stands out: most enterprises are not trying to rebuild everything at once. They want predictable migration plans, stronger data protection, and practical ways to support early AI projects. The tools and partnerships now forming around Azure suggest that modernisation is becoming less about replacing systems and more about improving what is already in place. Companies that approach modernisation in small, steady steps – while keeping cost, security, and data needs in view – may find it easier to move forward without taking on unnecessary risk. See also: Bain & Company issues AI Guide for CEOs, opens Singapore hub Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Pure Storage and Azure’s role in AI-ready data for enterprises appeared first on AI News. View the full article

Enterprise AI deployment has been facing a fundamental tension: organisations need sophisticated language models but baulk at the infrastructure costs and energy consumption of frontier systems. NTT Inc.’s recent launch of tsuzumi 2, a lightweight large language model (LLM) running on a single GPU, demonstrates how businesses are resolving this constraint—with early deployments showing performance matching larger models at a fraction of the operational cost. The business case is straightforward. Traditional large language models require dozens or hundreds of GPUs, creating electricity consumption and operational cost barriers that make AI deployment impractical for many organisations. (GPU Cost Comparison) For enterprises operating in markets with constrained power infrastructure or tight operational budgets, these requirements eliminate AI as a viable option. The company’s press release illustrates the practical considerations driving lightweight LLM adoption with Tokyo Online University’s deployment. The university operates an on-premise platform keeping student and staff data within its campus network—a data sovereignty requirement common across educational institutions and regulated industries. After validating that tsuzumi 2 handles complex context understanding and long-document processing at production-ready levels, the university deployed it for course Q&A enhancement, teaching material creation support, and personalised student guidance. The single-GPU operation means the university avoids both capital expenditure for GPU clusters and ongoing electricity costs. More significantly, on-premise deployment addresses data privacy concerns that prevent many educational institutions from using cloud-based AI services that process sensitive student information. Performance without scale: The technical economics NTT’s internal evaluation for financial-system inquiry handling showed tsuzumi 2 matching or exceeding leading external models despite dramatically smaller infrastructure requirements. This performance-to-resource ratio determines AI adoption feasibility for enterprises where the total cost of ownership drives decisions. The model delivers what NTT characterises as “world-top results among models of comparable size” in Japanese language performance, with particular strength in business domains prioritising knowledge, analysis, instruction-following, and safety. For enterprises operating primarily in Japanese markets, this language optimisation reduces the need to deploy larger multilingual models requiring significantly more computational resources. Reinforced knowledge in financial, medical, and public sectors—developed based on customer demand—enables domain-specific deployments without extensive fine-tuning. The model’s RAG (Retrieval-Augmented Generation) and fine-tuning capabilities allow efficient development of specialised applications for enterprises with proprietary knowledge bases or industry-specific terminology where generic models underperform. Data sovereignty and security as business drivers Beyond cost considerations, data sovereignty drives lightweight LLM adoption across regulated industries. Organisations handling confidential information face risk exposure when processing data through external AI services subject to foreign jurisdiction. In fact, NTT positions tsuzumi 2 as a “purely domestic model” developed from scratch in Japan, operating on-premises or in private clouds. This addresses concerns prevalent across Asia-Pacific markets about data residency, regulatory compliance, and information security. FUJIFILM Business Innovation’s partnership with NTT DOCOMO BUSINESS demonstrates how enterprises combine lightweight models with existing data infrastructure. FUJIFILM’s REiLI technology converts unstructured corporate data—contracts, proposals, mixed text and images—into structured information. Integrating tsuzumi 2’s generative capabilities enables advanced document analysis without transmitting sensitive corporate information to external AI providers. This architectural approach—combining lightweight models with on-premise data processing—represents a practical enterprise AI strategy balancing capability requirements with security, compliance, and cost constraints. Multimodal capabilities and enterprise workflows tsuzumi 2 includes built-in multimodal support handling text, images, and voice within enterprise applications. Thismatters for business workflows requiring AI to process multiple data types without deploying separate specialised models. Manufacturing quality control, customer service operations, and document processing workflows typically involve text, images, and sometimes voice inputs. Single models handling all three reduce integration complexity compared to managing multiple specialised systems with different operational requirements. Market context and implementation considerations NTT’s lightweight approach contrasts with hyperscaler strategies emphasising massive models with broad capabilities. For enterprises with substantial AI budgets and advanced technical teams, frontier models from OpenAI, Anthropic, and Google provide cutting-edge performance. However, this approach excludes organisations lacking these resources—a significant portion of the enterprise market, particularly across Asia-Pacific regions with varying infrastructure quality. Regional considerations matter. Power reliability, internet connectivity, data centre availability, and regulatory frameworks vary significantly across markets. Lightweight models enabling on-premise deployment accommodate these variations better than approaches requiring consistent cloud infrastructure access. Organisations evaluating lightweight LLM deployment should consider several factors: Domain specialisation: tsuzumi 2’s reinforced knowledge in financial, medical, and public sectors addresses specific domains, but organisations in other industries should evaluate whether available domain knowledge meets their requirements. Language considerations: Optimisation for Japanese language processing benefits Japanese-market operations but may not suit multilingual enterprises requiring consistent cross-language performance. Integration complexity: On-premise deployment requires internal technical capabilities for installation, maintenance, and updates. Organisations lacking these capabilities may find cloud-based alternatives operationally simpler despite higher costs. Performance tradeoffs: While tsuzumi 2 matches larger models in specific domains, frontier models may outperform in edge cases or novel applications. Organisations should evaluate whether domain-specific performance suffices or whether broader capabilities justify higher infrastructure costs. The practical path forward? NTT’s tsuzumi 2 deployment demonstrates that sophisticated AI implementation doesn’t require hyperscale infrastructure—at least for organisations whose requirements align with lightweight model capabilities. Early enterprise adoptions show practical business value: reduced operational costs, improved data sovereignty, and production-ready performance for specific domains. As enterprises navigate AI adoption, the tension between capability requirements and operational constraints increasingly drives demand for efficient, specialised solutions rather than general-purpose systems requiring extensive infrastructure. For organisations evaluating AI deployment strategies, the question isn’t whether lightweight models are “better” than frontier systems—it’s whether they’re sufficient for specific business requirements while addressing cost, security, and operational constraints that make alternative approaches impractical. The answer, as Tokyo Online University and FUJIFILM Business Innovation deployments demonstrate, is increasingly yes. See also: How Levi Strauss is using AI for its DTC-first business model Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Lightweight LLM powers Japanese enterprise AI deployments appeared first on AI News. View the full article

We’re all chasing talent. It’s become as crucial to success as building amazing products, and a lot of businesses are feeling the squeeze. The problem is that demand for people with AI skills is skyrocketing, but the supply isn’t keeping up. The OECD points this out – lots of us need AI expertise, but very few job postings actually require it. But there’s a promising trend emerging, and it’s happening in Europe. On the continent and in the ***, some things are happening in AI education – experiments that use AI to change how people learn. These are glimpses into the future workforce, showing us how the next generation will approach problem-solving and collaboration in a world increasingly using AI. Let’s take a look at a few examples, and examine how they can help businesses rethink their approach to talent. Training teachers to work with AI – the Manchester story The University of Manchester is integrating generative AI into how it prepares future educators, using the tools critically, creatively, and thoughtfully, combining AI’s suggestions with their students’ knowledge and experience. That suggests a future where employees aren’t consumers of training but are comfortable co-creating with AI. Future generations will expect AI assistance in their day-to-day tasks, and the real competitive edge won’t be whether people use AI, but how they use it responsibly and ethically. UNESCO’s take is spot-on, highlighting the enhancing of human capabilities, not replacing them. Building AI skills from the ground up: AI-ENTR4YOUTH AI-ENTR4YOUTH is a programme bringing together Junior Achievement Europe and partners in ten European countries. Here AI is embedded in entrepreneurship education, where students use AI tools to tackle real-world problems, with a focus on innovation and European values. This develops practical AI literacy early on, linking AI with the entrepreneurial mindset; the ability to spot opportunities and test new ideas. Importantly, it’s broadening the pool of AI talent by reaching students who might chose business, not technical degrees. The skills gap can be solved. Companies that complain about a lack of AI talent should ask: How can we actively support or emulate programmes like AI-ENTR4YOUTH to build the workforce we need? Personalised learning & impact: The Social Tides perspective Social Tides champions education innovators in Europe. Its work highlights projects that use AI to create more tailored learning experiences, particularly for students who need extra support or have diverse learning styles. AI is helping personalise content, act as mentor, and build communities around students. The common thread is human oversight. AI gives recommendations and insight, but humans are still very much in the loop, making judgements and offering support. This aligns with best AI business practice, as leaders try to make learning an integral part of the working day. Key questions for leaders What does this mean for decision-makers? Here are a few questions to consider: Learning architecture: Are we embracing AI-assisted, personalised learning paths internally? Talent & pipeline: Are we shaping the future talent pool through partnerships with local schools and universities? Governance & ethics: Do we have clear guidelines for AI use in training, ensuring fairness and transparency? Vendor choices: Are we selecting AI tools that align with our values and pertinent regulations? Although these educational programmes could be termed experiments, they are a signal of how the future of work might be shaped. Companies that pay attention now will be the ones to secure better talent and build more adaptable, learning-driven organisations. (Image source: “Laboratory” by ♔ Georgie R is licensed under CC BY-ND 2.0. T) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post What Europe’s AI education experiments can teach a business appeared first on AI News. View the full article

By 2027, half of all business decisions will be augmented or automated by AI agents for decision intelligence. This seismic shift is changing how organisations operate, and AI leaders are under pressure to adapt, innovate, and guide their teams through complexity. Gartner Data & Analytics Summit 2026 is designed to help these leaders meet their biggest challenges head-on. This year, Gartner is expanding its AI content to ensure attendees have the resources and insights they need to succeed. The summit’s agenda places AI at the forefront, with a track dedicated entirely to artificial intelligence. Sessions will cover everything from AI strategy and responsible AI to risk management, governance, generative AI, large language models, retrieval augmented generation, prompt engineering, and machine learning. Attendees will have the opportunity to learn from experts who understand the nuances of scaling AI, building resilient architectures, and navigating the ethical considerations that come with advanced technologies. In addition to technical deep dives, the summit introduces a spotlight track focused on AI leadership. The programme is crafted for executives tasked with building world-class AI organisations. It explores real-world use cases, robust delivery models, and the importance of strong governance to ensure safe and scalable operations. The conference recognises that AI agents are not infallible, but with the right knowledge and collaboration, leaders can empower their organisations to make smarter, more confident decisions. The Gartner Data & Analytics Summit 2026 will take place from March 9 to 11 in Orlando, Florida. Attendees will join a global community of data, analytics, and AI professionals, gaining exclusive access to Gartner’s trusted research, expert insights, and networking opportunities with peers and thought leaders. For AI leaders ready to shape the future, this event offers the clarity, confidence, and connections needed to accelerate innovation and drive impact. Learn more about the Gartner Data & Analytics Summit and how you can advance your AI strategy here. The post Gartner Data & Analytics Summit unveils expanded AI agenda for 2026 appeared first on AI News. View the full article

Microsoft, Anthropic, and NVIDIA are setting a bar for cloud infrastructure investment and AI model availability with a new compute alliance. This agreement signals a divergence from single-model dependency toward a diversified, hardware-optimised ecosystem, altering the governance landscape for senior technology leaders. Microsoft CEO Satya Nadella says the relationship is a reciprocal integration where the companies are “increasingly going to be customers of each other”. While Anthropic leverages Azure infrastructure, Microsoft will incorporate Anthropic models across its product stack. Anthropic has committed to purchasing $30 billion of Azure compute capacity. This figure shows the immense computational requirements necessary to train and deploy the next generation of frontier models. The collaboration involves a specific hardware trajectory, beginning with NVIDIA’s Grace Blackwell systems and progressing to the Vera Rubin architecture. NVIDIA CEO Jensen Huang expects the Grace Blackwell architecture with NVLink to deliver an “order of magnitude speed up,” a necessary leap for driving down token economics. For those overseeing infrastructure strategy, Huang’s description of a “shift-left” engineering approach – where NVIDIA technology appears on Azure immediately upon release – suggests that enterprises running Claude on Azure will access performance characteristics distinct from standard instances. This deep integration may influence architectural decisions regarding latency-sensitive applications or high-throughput batch processing. Financial planning must now account for what Huang identifies as three simultaneous scaling laws: pre-training, post-training, and inference-time scaling. Traditionally, AI compute costs were weighted heavily toward training. However, Huang notes that with test-time scaling – where the model “thinks” longer to produce higher quality answers – inference costs are rising. Consequently, AI operational expenditure (OpEx) will not be a flat rate per token but will correlate with the complexity of the reasoning required. Budget forecasting for agentic workflows must therefore become more dynamic. Integration into existing enterprise workflows remains a primary hurdle for adoption. To address this, Microsoft has committed to continuing access for Claude across the Copilot family. Operational emphasis falls heavily on agentic capabilities. Huang highlighted Anthropic’s Model Context Protocol (MCP) as a development that has “revolutionised the agentic AI landscape”. Software engineering leaders should note that NVIDIA engineers are already utilising Claude Code to refactor legacy codebases. From a security perspective, this integration simplifies the perimeter. Security leaders vetting third-party API endpoints can now provision Claude capabilities within the existing Microsoft 365 compliance boundary. This streamlines data governance, as the interaction logs and data handling remain within the established Microsoft tenant agreements. Vendor lock-in persists as a friction point for CDOs and risk officers. This AI compute partnership alleviates that concern by making Claude the only frontier model available across all three prominent global cloud services. Nadella emphasised that this multi-model approach builds upon, rather than replaces, Microsoft’s existing partnership with OpenAI, which remains a core component of their strategy. For Anthropic, the alliance resolves the “enterprise go-to-market” challenge. Huang noted that building an enterprise sales motion takes decades. By piggybacking on Microsoft’s established channels, Anthropic bypasses this adoption curve. This trilateral agreement alters the procurement landscape. Nadella urges the industry to move beyond a “zero-sum narrative,” suggesting a future of broad and durable capabilities. Organisations should review their current model portfolios. The availability of Claude Sonnet 4.5 and Opus 4.1 on Azure warrants a comparative TCO analysis against existing deployments. Furthermore, the “gigawatt of capacity” commitment signals that capacity constraints for these specific models may be less severe than in previous hardware cycles. Following this AI compute partnership, the focus for enterprises must now turn from access to optimisation; matching the right model version to the specific business process to maximise the return on this expanded infrastructure. See also: How Levi Strauss is using AI for its DTC-first business model Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Microsoft, NVIDIA, and Anthropic forge AI compute alliance appeared first on AI News. View the full article

Firms in the asset management industry are turning increasingly to generative and agentic AI to streamline operations, improve decision-making, and uncover new sources of alpha (the measure of an investment strategy’s ability to outperform the market after accounting for risk). The trend is continuing with the latest partnership between Franklin Templeton and Wand AI, marking a shift toward more autonomous, data-driven investment processes. Franklin Resources, operating as Franklin Templeton, has entered into a strategic partnership with enterprise AI platform, Wand AI, to begin the enterprise deployment of agentic AI in Franklin Templeton’s worldwide platform. Wand’s Autonomous Workforce and Agent Management technologies have enabled Franklin to implement agentic AI at scale, accelerating data-driven decision-making in its investment processes. The collaboration has moved from initial small-scale pilot programmes to fully operational AI systems, strengthening the partnership between the two companies. The first implementations concentrated on high-value applications of AI in Franklin Templeton’s investment teams, but now both have plans to mass-deploy intelligent agents in various departments. The company plans to extend the use of Wand AI’s intelligent agent in 2026, a move designed to drive digital transformation in the organisation and enhance investment research. Franklin hopes to ensure AI systems are managed responsibly under strict oversight, compliance, and risk control, therefore maintaining trust and transparency. Vasundhara Chetluru, Head of AI Platform at Franklin Templeton, said, “With strong governance in place, we are demonstrating that AI can deliver secure, scalable, and measurable value.” Rotem Alaluf, CEO of Wand AI, commented on the company’s AI vision, saying its mission is to “elevate AI from experimental technology to a fully integrated, adaptive workforce that drives enterprise-wide transformation and delivers significant business impact.” Alaluf said AI agents can “seamlessly collaborate with human teams and operate at scale in complex, highly regulated environments to achieve transformative results,” but only when these are “properly governed, orchestrated, and deployed as a unified agentic workforce.” AI takes centre stage in asset management Other companies in the sector are also going all-in on AI. Goldman Sachs has implemented AI at scale, with CEO, David Solomon, pinpointing the technology as a key force in economic growth. He is on the record as saying the opportunity presented by AI is “enormous.” According to the Goldman Sachs report, “AI: In a Bubble?”, the company estimates that generative AI could create US $20 trillion of economic value in the long term. The report suggests AI has the capacity to create up to a 15% uplift in US labour productivity, if adopted at scale. In June 2025, Goldman Sachs (GS) expanded its use of AI by launching a generative-AI assistant inside the firm, joining an increasing list of big banks that were already using the technology for operations. The GS AI assistant was designed to help with tasks including drafting initial content, completing data analysis, and summarising complex documents. This has improved productivity in teams, freeing thousands of employees to prioritise higher-value strategic work, the bank says. Such moves signal a shift away from AI niche-use cases and pilot projects to border enterprise deployments in major institutions, aimed at enhancing productivity and operational support. While David Solomon acknowledges that AI presents an “enormous” opportunity, he has emphasised that there will be “winners and losers.” Some capital investments will not yield return, according to Solomon, which is why he says clients must be diligent in their AI investments. Solomon has also noted how technology has already transformed the composition of the GS workforce make-up over the last twenty-five years. Today, the bank employs 13,000 engineers, illustrating the change in job functions over time. Rather than roles disappearing with technological advancement, Solomon believes economies and workforces adapt to change. “At the end of the day, we have an incredibly flexible, nimble economy. We have a great ability to adapt and adjust,” he said. “Yes, there will be job functions that shift and change, but I’m excited about it. If you take a three-to-five-year view, it’s giving us more capacity to invest in our business,” he said. Goldman Sachs and Franklin Temleton are part of a wider trend of financial institutions accelerating AI adoption. Solomon said, “I can’t find a CEO that I’m talking to, in any industry, that is not focused on how they can re-imagine and automate processes in their business to create operating efficiency and productivity.” (Image source: “Trading Floor at the New York Stock Exchange during the Zendesk IPO” by Scott Beale is licensed under CC BY-NC-ND 2.0) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Franklin Templeton & Wand AI bring agentic AI to asset management appeared first on AI News. View the full article

Over half of us now use AI to search the web, yet the stubbornly low data accuracy of common tools creates new business risks. While generative AI (GenAI) offers undeniable efficiency gains, a new investigation highlights a disparity between user trust and technical accuracy that poses specific risks to corporate compliance, legal standing, and financial planning. For the C-suite, the adoption of these tools represents a classic ‘shadow IT’ challenge. According to a survey of 4,189 *** adults conducted in September 2025, around a third of users believe AI is already more important to them than standard web searching. If employees trust these tools for personal queries, they are almost certainly employing them for business research. The investigation, conducted by Which?, suggests that unverified reliance on these platforms could be costly. Around half of AI users report trusting the information they receive to a ‘reasonable’ or ‘great’ extent. Yet, looking at the granularity of the responses provided by AI models, that trust is often misplaced. The accuracy gap when using AI to search the web The study tested six major tools – ChatGPT, Google Gemini (both standard and ‘AI Overviews’), Microsoft Copilot, Meta AI, and Perplexity – across 40 common questions spanning finance, law, and consumer rights. Perplexity achieved the highest total score at 71 percent, closely followed by Google Gemini AI Overviews at 70 percent. In contrast, Meta scored the lowest at 55 percent. ChatGPT, despite its widespread adoption, received a total score of 64 percent, making it the second-lowest performer among the tools tested. This disconnect between market dominance and reliable output underlines the danger of assuming popularity equals performance in the GenAI space. However, the investigation revealed that all of these AI tools frequently misread information or provided incomplete advice that could pose serious business risks. For financial officers and legal departments, the nature of these errors is particularly concerning. When asked how to invest a £25,000 annual ISA allowance, both ChatGPT and Copilot failed to identify a deliberate error in the prompt regarding the statutory limit. Instead of correcting the figure, they offered advice that potentially risked breaching HMRC rules. While Gemini, Meta, and Perplexity successfully identified the error, the inconsistency across platforms necessitates a rigorous “human-in-the-loop” protocol for any business process involving AI to ensure accuracy. For legal teams, the tendency of AI to generalise regional regulations when using it for web search presents a distinct business risk. The testing found it common for tools to misunderstand that legal statutes often differ between *** regions, such as Scotland versus England and Wales. Furthermore, the investigation highlighted an ethical gap in how these models handle high-stakes queries. On legal and financial matters, the tools infrequently advised users to consult a registered professional. For example, when queried about a dispute with a builder, Gemini advised withholding payment; a tactic that experts noted could place a user in breach of contract and weaken their legal position. This “overconfident advice” creates operational hazards. If an employee relies on an AI for preliminary compliance checks or contract review without verifying the jurisdiction or legal nuance, the organisation could face regulatory exposure. Source transparency issues A primary concern for enterprise data governance is the lineage of information. The investigation found that AI search tools often bear a high responsibility to be transparent, yet frequently cited sources that were vague, non-existent, or have dubious accuracy, such as old forum threads. This opacity can lead to financial inefficiency. In one test regarding tax codes, ChatGPT and Perplexity presented links to premium tax-refund companies rather than directing the user to the free official HMRC tool. These third-party services are often characterised by high fees. In a business procurement context, such algorithmic bias from AI tools when using them for web search could lead to unnecessary vendor spend or engagement with service providers that pose a high risk due to not meeting corporate due diligence standards. The major technology providers acknowledge these limitations, placing the burden of verification firmly on the user—and, by extension, the enterprise. A Microsoft spokesperson emphasised that their tool acts as a synthesiser rather than an authoritative source. “Copilot answers questions by distilling information from multiple web sources into a single response,” the company noted, adding that they “encourage people to verify the accuracy of content.” OpenAI, responding to the findings, said: “Improving accuracy is something the whole industry’s working on. We’re making good progress and our latest default model, GPT-5, is the smartest and most accurate we’ve built.” Mitigating AI business risk through policy and workflow For business leaders, the path forward is not to ban AI tools – which often increases by driving usage further into the shadows – but to implement robust governance frameworks to ensure the accuracy of their output when bring used for web search: Enforce specificity in prompts: The investigation notes that AI is still learning to interpret prompts. Corporate training should emphasise that vague queries yield risky data. If an employee is researching regulations, they must specify the jurisdiction (e.g., “legal rules for England and Wales”) rather than assuming the tool will infer the context. Mandate source verification: Trusting a single output is operationally unsound. Employees must demand to see sources and check them manually. The study suggests that for high-risk topics, users should verify findings across multiple AI tools or “double source” the information. Tools like Google’s Gemini AI Overviews, which allow users to review presented web links directly, performed slightly better in scoring because they facilitated this verification process. Operationalise the “second opinion”: At this stage of technical maturity, GenAI outputs should be viewed as just one opinion among many. For complex issues involving finance, law, or medical data, AI lacks the ability to fully comprehend nuance. Enterprise policy must dictate that professional human advice remains the final arbiter for decisions with real-world consequences. The AI tools are evolving and their web search accuracy is gradually improving, but as the investigation concludes, relying on them too much right now could prove costly. For the enterprise, the difference between a business efficiency gain from AI and a compliance failure risk lies in the verification process. See also: How Levi Strauss is using AI for its DTC-first business model Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AI web search risks: Mitigating business data accuracy threats appeared first on AI News. View the full article

A new Bain & Company report says many organisations in Southeast Asia are still stuck in early testing because they treat AI as a set of tools rather than a change in how the business works. In The Southeast Asia CEO’s Guide to AI Transformation, the authors say leaders should first look at how AI could reshape their industry and revenue plans, then put money into areas where they expect clear and measurable results. The region’s mix of cultures, income levels, and market sizes makes AI adoption harder than in places with more uniform conditions. People shop and behave differently across countries, wages are still low, and many firms don’t have the scale to run long and costly trials. These factors mean that simple efficiency gains rarely deliver strong returns. Real gains come when AI is used to rethink how the business runs, make decisions faster, or increase capacity without growing the team. Bain’s analysis shows that wages in Southeast Asia are about 7 per cent of US levels, which limits how much companies can save from labour cuts. The report also notes that only 40 per cent of the region’s market value comes from large-cap firms, compared with 60 per cent in India. With fewer large firms able to absorb early AI costs, leaders need to aim for speed, scale, and new processes instead of relying on cost savings alone. How AI is helping today Some organisations in the region are already seeing clear gains by linking their AI plans to business goals. The AI guide highlights early moves such as using AI to shorten product launch times or reduce supply chain issues, opening new chances for revenue. A factory might use predictive models to reduce machine downtime and lift output. A financial institution could use large language models to support compliance work, cutting the time needed to process and respond to requests. Bain senior partner Aadarsh Baijal says impact depends on how leaders think about their market. He believes many still see AI “as a rollout of software rather than a redesign of how the business competes.” When leaders understand how AI changes demand, pricing, operations, or customer needs, they can decide where to focus their efforts. What the guide says about data, culture, and people in AI The report also stresses that AI transformation relies on people, habits, and skills, not only technology. Many organisations think scaling AI is a hiring problem, but Bain argues that the talent often already exists in the business. The real issue is getting teams to work together and helping staff understand how to use AI in their jobs. The authors describe two groups involved in successful change. The “Lab” is made up of technical teams who rebuild processes and create the first versions of new tools. The “Crowd” includes employees across the business who need enough AI awareness to use those tools day to day. Without both groups, projects stall. Senior partner Mohan Jayaraman says the strongest results appear when existing teams lead the work. In his view, impact increases when companies match small expert groups with wider training so new systems become part of normal workflows rather than one-off trials. Leaders also need to fix ongoing issues such as data quality, how data is tracked, governance, and links to current systems. They also need to decide how their AI plans connect with major platforms such as AWS Bedrock, Azure AI Foundry, Google Vertex AI, or IBM watsonx. Without this groundwork, early gains are hard to repeat at scale. A regional push to support enterprise AI Bain is setting up an AI Innovation Hub in Singapore with support from the Singapore Economic Development Board (EDB). The hub’s goal is to help companies move beyond trials by building AI systems that can run at scale. It will work across advanced manufacturing, energy and resources, financial services, healthcare, and consumer goods. The hub sits within a growing AI community in Singapore, which has more than a thousand startups and is expected to generate about S$198.3 billion in economic value from AI by 2030. Its work will cover production-ready systems such as predictive maintenance for factories, AI support for regulatory tasks in finance, and personalisation tools for retail. It will also help companies build internal teams and engineering skills so they can run AI programmes on their own. As competition in Southeast Asia increases, firms that treat AI as a shift in how they operate — a central theme in Bain’s AI guide — will be better positioned to turn pilots into long-term results. See also: Is AI in a bubble? Succeed despite a market correction Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Bain & Company issues AI guide for CEOs and opens Singapore hub appeared first on AI News. View the full article

At SC25, Dell Technologies and NVIDIA introduced new updates to their joint AI platform, aiming to make it easier for organisations to run a wider range of AI workloads, from older models to newer agent-style systems. As more companies scale their AI plans, many run into the same issues. They need to manage a growing mix of hardware and software, keep control of their data, and make sure their systems can grow over time. Recent research shows that most organisations feel safer working with a trusted partner when adopting new technology, and many see more value when AI can operate closer to their own data. The Dell AI Factory with NVIDIA is built around that idea. It combines Dell’s full stack of infrastructure with NVIDIA’s AI tools, supported by Dell’s professional services team. The goal is to help companies move from ideas to real results while keeping technical complexity in check. Faster deployment through integrated platforms Dell is expanding its storage and AI capabilities to help organisations automate setup, improve performance, and run real-time AI tasks with more consistency. ObjectScale and PowerScale, the storage engines behind the Dell AI Data Platform, now work with the NVIDIA NIXL library from NVIDIA Dynamo. This integration supports scalable KV Cache storage and sharing, enabling a one-second Time to First Token at a 131K-token context window, while helping reduce costs and ease pressure on GPU memory. The Dell AI Factory with NVIDIA also adds support for Dell PowerEdge XE7740 and XE7745 systems equipped with the NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs and NVIDIA Hopper GPUs. According to Dell, these systems give organisations more room to run larger multimodal models, agent-style workloads, training tasks, and enterprise inferencing with stronger performance. Dell says the addition of the Dell Automation Platform is meant to remove guesswork by delivering tuned and validated deployments through a secure setup. The platform aims to produce repeatable results and give teams a clearer path to building AI workflows. Alongside this, software tools such as the AI code assistant with Tabnine and the agentic AI platform with Cohere North are becoming automated, helping teams move workloads into production faster and keep operations manageable as they scale. Beyond core data-centre systems, Dell’s AI PC ecosystem now supports devices with NVIDIA RTX Blackwell GPUs and NVIDIA RTX Ada GPUs, giving organisations more hardware options across Dell laptops and desktops. Dell Professional Services is also offering interactive pilots that use a customer’s own data to test AI ideas before large investments. These pilots focus on clear metrics and outcomes so teams can judge business value with more certainty. Next-generation infrastructure for stronger AI performance Dell is updating its infrastructure portfolio to support more complex AI and HPC workloads, with an emphasis on performance, scale, and easier management. The Dell PowerEdge XE8712, arriving next month, supports up to 144 NVIDIA Blackwell GPUs in a standard rack. This makes rack-scale AI and HPC more accessible, backed by unified monitoring and automation through iDRAC, OpenManage Enterprise, and the Integrated Rack Controller. Enterprise SONiC Distribution by Dell Technologies now supports NVIDIA Spectrum-X platforms along with NVIDIA’s Cumulus OS. This helps organisations build open, standards-based AI networks that can operate across different vendors. The latest SmartFabric Manager release also extends support to Dell’s Enterprise SONiC on NVIDIA Spectrum-X platforms, aiming to reduce deployment time and setup errors through guided automation. More choice through an expanded AI ecosystem Organisations continue to adjust their AI budgets and plans, and many want flexibility in the tools they choose. Red Hat OpenShift for the Dell AI Factory with NVIDIA is now validated on more Dell PowerEdge systems, giving teams more ways to run AI workloads at scale. Support now includes both the Dell PowerEdge R760xa and the Dell PowerEdge XE9680 with NVIDIA H100 and H200 Tensor Core GPUs. This pairing brings together Red Hat’s controls and governance tools with Dell’s secure infrastructure, offering a clearer path for companies that need to scale AI. Dell executives say the updates are meant to help organisations move from small pilots to real deployment. Jeff Clarke, vice chairman and chief operating officer at Dell Technologies, said the Dell AI Factory with NVIDIA addresses a core challenge for many teams: “how to move from AI pilots to production without rebuilding their infrastructure.” He added that Dell has “done the integration work so customers don’t have to,” which he believes will help organisations deploy and scale with more confidence. NVIDIA sees the shift as part of a broader change in how companies use AI. Justin Boitano, vice president of Enterprise AI products, described the moment as one where enterprise AI is moving from experimentation to transformation, advancing at a speed that is “redefining how businesses operate.” He said Dell and NVIDIA aim to support this transition with a unified platform that brings together infrastructure, automation, and data tools to help organisations “deploy AI at scale and realise measurable impact.” Industry analysts see similar demand for integrated systems. Ashish Nadkarni, group vice president and general manager for Infrastructure Systems, Platforms and Technologies at IDC, said many teams want AI-ready systems that are powerful but also easier to run. He noted that the combination of Dell’s AI portfolio with NVIDIA’s technology represents “a significant step forward in delivering enterprise-ready AI.” (Image by Dell Technologies) See also: 10% of Nvidia’s cost: Why Tesla-Intel chip partnership demands attention Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post SC25 showcases the next phase of Dell and NVIDIA’s AI partnership appeared first on AI News. View the full article

Author: Olga Zharuk, CPO, Teqblaze When it comes to applying AI in programmatic, two things matter most: performance and data security. I’ve seen too many internal security audits flag third-party AI services as exposure points. Granting third-party AI agents access to proprietary bidstream data introduces unnecessary exposure that many organisations are no longer willing to accept. That’s why many teams shift to embedded AI agents: local models that operate entirely in your environment. No data leaves your perimeter. No blind spots in the audit trail. You retain full control over how models behave – and more importantly, what they see. Risks associated with external AI use Every time performance or user-level data leaves your infrastructure for inference, you introduce risk. Not theoretical – operational. In recent security audits, we’ve seen cases where external AI vendors log request-level signals under the pretext of optimisation. That includes proprietary bid strategies, contextual targeting signals, and in some cases, metadata with identifiable traces. The isn’t just a privacy concern – it’s a loss of control. Public bid requests are one thing. However, any performance data, tuning variables, and internal outcomes you share is proprietary data. Sharing it with third-party models, especially those hosted in extra-EEA cloud environments, creates gaps in both visibility and compliance. Under regulations like GDPR and CPRA/CCPA, even “pseudonymous” data can trigger legal exposure if transferred improperly or used beyond its declared purpose. For example, a model hosted on an external endpoint receives a call to assess a bid opportunity. Alongside the call, payloads may include price floors, win/loss outcomes, or tuning variables. The values, often embedded in headers or JSON payloads, may be logged for debugging or model improvement and retained beyond a single session, depending on vendor policy. ******-box AI models compound the issue. When vendors don’t disclose inference logic or model behaviour, you’re left without the ability to audit, debug, or even explain how decisions are made. That’s a liability – both technically and legally. Local AI: A strategic shift for programmatic control The shift toward local AI is not merely a defensive move to address privacy regulations – it is an opportunity to redesign how data workflows and decisioning logic are controlled in programmatic platforms. Embedded inference keeps both input and output logic fully controlled – something centralised AI models take away. Control over data Owning the stack means having full control over the data workflow – from deciding which bidstream fields are exposed to models, to setting TTL for training datasets, and defining retention or deletion rules. The enables teams to run AI models without external constraints and experiment with advanced setups tailored to specific business needs. For example, a DSP can restrict sensitive geolocation data while still using generalized insights for campaign optimisation. Selective control is harder to guarantee once data leaves the platform’s boundary. Auditable model behaviour External AI models often offer limited visibility into how bidding decisions are made. Using a local model allows organisations to audit their behaviour, test its accuracy against their own KPIs, and fine-tune its parameters to meet specific yield, pacing, or performance targets. The level of auditability strengthens trust in the supply chain. Publishers can verify and demonstrate that inventory enrichment follows consistent, verifiable standards. The gives buyers higher confidence in inventory quality, reduces spend on invalid traffic, and minimises fraud exposure. Alignment with data privacy requirements Local inference keeps all data in your infrastructure, under your governance. That control is essential for complying with any local laws and privacy requirements in regions. Signals like IP addresses or device IDs can be processed on-site, without ever leaving your environment – reducing exposure while preserving signal quality with appropriate legal basis and safeguards. Practical applications of local AI in programmatic In addition to protecting bidstream data, local AI improves decisioning efficiency and quality in the programmatic chain without increasing data exposure. Bidstream enrichment Local AI can classify page or app taxonomy, analyse referrer signals, and enrich bid requests with contextual metadata in real time. For example, models can calculate visit frequency or recency scores and pass them as additional request parameters for DSP optimisation. The accelerates decision latency and improves contextual accuracy – without exposing raw user data to third parties. Pricing optimisation Since ad tech is dynamic, pricing models must continuously adapt to short-term shifts in demand and supply. Rule-based approaches often react more slowly to changes compared to ML-driven repricing models. Local AI can detect emerging traffic patterns and adjust the bid floor or dynamic price recommendations accordingly. Fraud detection Local AI detects anomalies pre-auction – like randomized IP pools, suspicious user agent patterns, or sudden deviations in win rate – and flags them for mitigation. For example, it can flag mismatches between request volume and impression rate, or abrupt win-rate drops inconsistent with supply or demand shifts.The does not replace dedicated fraud scanners, but augments them with local anomaly detection and monitoring, without requiring external data sharing. The are just a few of the most visible applications – local AI also enables tasks like signals deduplication, ID bridging, frequency modeling, inventory quality scoring, and supply path analysis, all benefiting from secure, real-time execution at the edge. Balancing control and performance with local AI Running AI models in your own infrastructure ensures privacy and governance without sacrificing optimisation potential. local AI moves decision-making closer to the data layer, making it auditable, region-compliant, and fully under platform control. Competitive advantage isn’t about the fastest models, but about models that balance speed with data stewardship and transparency. The approach defines the next phase of programmatic evolution – intelligence that remains close to the data, aligned with business KPIs and regulatory frameworks. Author: Olga Zharuk, CPO, Teqblaze Image source: Unsplash The post Local AI models: How to keep control of the bidstream without losing your data appeared first on AI News. View the full article

In its pursuit of a direct-to-consumer (DTC) first business model, Levi Strauss is weaving AI and cloud platforms into its core operations. The nearly 175-year-old apparel company is leveraging Microsoft technologies to modernise its consumer experiences and improve internal productivity. Levi Straus’ approach provides a case study for other enterprises in using a unified technology stack to address a specific commercial objective. AI ‘superagent’: A unified front-end for operations at Levi Strauss A central component of this initiative is the development of agentic AI solutions. Levi Strauss is deploying an Azure-native “orchestrator agent” embedded within Microsoft Teams, which functions as a “superagent”. This agent serves as a single conversational portal for employees across corporate, retail, and warehouse environments. Operationally, it fields employee questions and routes them to specialist behind-the-scenes subagents, some of which are already deployed. This points toward a consolidation of employee-facing tools; instead of training staff on multiple applications, the agent provides a single interface to streamline workflows. This AI-centric business model is part of a wider goal outlined by Jason Gowans, Chief Digital and Technology Officer at Levi Strauss & Co. “We’re rewiring Levi Strauss & Co to be a DTC-first, fan-obsessed retailer making every interaction faster, smarter, and more personal,” said Gowans. “AI sits at the center of that pivot—fueling innovation, elevating employee creativity, unlocking productivity, and helping us deliver the connected, memorable experiences that keep our fans returning again and again.” This focus on productivity extends to the developer side. Teams are using GitHub Copilot for key projects involving quality engineering and release management. At the same time, other employees are being equipped with Microsoft Surface Copilot+ PCs. Employee feedback indicates these devices have led to improvements in speed and data handling, with features like the Copilot key reducing time spent searching for information. The foundational cloud and security posture Levi Strauss’ AI implementation rests on prerequisite infrastructure work. For business leaders, this highlights that advanced AI models require a consolidated cloud environment. As part of its broader digital efforts, Levi Strauss is relying on Microsoft Azure, having moved application workloads from its on-premises data centres. The company used Azure Migrate and GitHub Copilot to plan and execute the consolidation of its private data centre environment. This cloud foundation is also central to the company’s security posture. Levi Strauss is leveraging Azure AI Foundry and Semantic Kernel to build intelligent automation capabilities. Security is being integrated into the AI framework itself and the tools are being used to power security agents and policy orchestration; a method that allows Levi Strauss to maintain a zero-trust security model while continuing to scale its AI-driven initiatives across global operations. To manage the new hardware endpoints, the company is also using Microsoft Intune for zero-touch device onboarding and application deployment. Leveraging the full AI ecosystem to pivot business model Levi Strauss’ initiative demonstrates an ecosystem-based approach to adopting AI. Rather than a piecemeal rollout of individual tools, the company is integrating AI agents, developer tools, and new hardware on top of a common cloud platform. Keith Mercier, VP of Worldwide Retail and Consumer Goods Industry at Microsoft, noted that Levi Strauss “exemplifies how iconic brands can reinvent themselves with cloud and AI technologies.” For other enterprises, the Levi Strauss case study serves as a blueprint for linking AI and foundational cloud migration directly to high-value outcomes like the pivot to a DTC business model. See also: IBM: Data silos are holding back enterprise AI Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post How Levi Strauss is using AI for its DTC-first business model appeared first on AI News. View the full article