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Enterprise leaders are entering 2026 with an uncomfortable mix of volatility, optimism, and pressure to move faster on AI and quantum computing, according to a paper published by the IBM Institute for Business Value. Its findings are based on more than 1,000 C-suite executives and 8,500 employees and consumers. While only around a third of executives are optimistic about the global economy, more than four in five are confident about their own organisation’s performance in the year ahead. Executives expect to make faster decisions and are willing to redesign operating models, while employees are broadly positive about AI in their working lives. Customers, in turn, are ready to reward (or punish) brands based on how companies use their data. Trend 1: agentic AI a strategic asset Agentic AI is emerging as one of the main tools leaders expect to use in the coming year, and most execs say AI agents are already helping them. However, for agentic AI to succeed, the expressed opinions state: Data architecture needs to support near real-time insight, not periodic reporting. AI agents’ success will depend on access to core systems (ERP, CRM, supply chain platforms). Agentic AI shifts from experimental to operational. Leaders feel they must decide which decisions can be delegated to AI agents, which require human review, and should must remain human-led. Trend 2: employees will ask for more training and AI is okay Most employees say the pace of technology change in their roles is sustainable, and that they’re confident about keeping up with new tools. Twice as many employees say they would embrace, not resist, greater use of AI in the workplace, seeing the technology as a way to remove repetitive tasks and learn new skills. This aligns with findings in research by KPMG. Executives expect a significant re-skilling requirement from their employees, so leaders should anticipate that at least half their workforce will need some form of re-skilling by the end of 2026, thanks to AI automation. Other surveys concur with IBM, and state the skills needed most are problem-solving, creativity, and innovation. Employees say they are willing to change employers to access better training opportunities, meaning skills development now plays a direct role reducing employee churn. Trend 3: customers will hold data policies to account The executives surveyed agreed that consumer trust in a brand’s use of AI will define the success of new products and services. Consumers are willing to tolerate occasional errors, but not opacity. Customers want explanations of how their data is used, knowledge of when AI is involved in interactions with them, and simple ways to opt in or out. The studies by Deloitte and KPMG (see above) reinforce this picture. Implications for leaders include treating transparency as a product feature and selecting models that support explainability. Trend 4: AI and cloud will need local provision AI sovereignty—an organisation’s ability to control and govern its AI systems, data, and infrastructure—has moved to the centre of resilience planning. Almost all executives surveyed said they will factor AI sovereignty into their 2026 strategy. In the light of concerns about data residency and cloud jurisdiction, leaders are rethinking where models run and where data lives. Studies from *** and European IT leaders show rising concern about over-reliance on foreign (read, ‘US-based’ cloud services in the latter case). Advisory firm Accenture also urges leaders [PDF] to develop sovereign AI strategies that prioritise control, transparency, and choice. Key takeaways include the need for portable AI platforms, monitoring for data compliance, and a heavy emphasis on the physical location of data. AI resilience is ultimately about continuity and transparency. It requires ensuring the organisation can adapt and operate openly, even when the global technological and geopolitical landscapes shift. Trend 5: planning on quantum advantage The report’s findings say quantum is moving towards experimentation in the near term. IBM’s own research on quantum readiness (in line with its monetisation of quantum services) suggests that early quantum advantage is likely in targeted domains such as optimisation and materials science. The report urges the identification of small numbers of high-impact quantum uses in the enterprise, and the joining of ecosystems early. “Identify big bets to win with emerging technologies, including quantum, and partner on innovation to share costs,” the report states. (Image source: “California Perfect” by moonjazz is licensed under CC BY-SA 2.0.) See also: How the MCP spec update boosts security as infrastructure scales 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 IBM cites agentic AI, data policies, and quantum as 2026 trends appeared first on AI News. View the full article

While tech giants pour billions into computational power to train frontier AI models, China’s DeepSeek has achieved comparable results by working smarter, not harder. The DeepSeek V3.2 AI model matches OpenAI’s GPT-5 in reasoning benchmarks despite using ‘fewer total training FLOPs’ – a breakthrough that could reshape how the industry thinks about building advanced artificial intelligence. For enterprises, the release demonstrates that frontier AI capabilities need not require frontier-scale computing budgets. The open-source availability of DeepSeek V3.2 lets organisations evaluate advanced reasoning and agentic capabilities while maintaining control over deployment architecture – a practical consideration as cost-efficiency becomes increasingly central to AI adoption strategies. The Hangzhou-based laboratory released two versions on Monday: the base DeepSeek V3.2 and DeepSeek-V3.2-Speciale, with the latter achieving gold-medal performance on the 2025 International Mathematical Olympiad and International Olympiad in Informatics – benchmarks previously reached only by unreleased internal models from leading US AI companies. The accomplishment is particularly significant given DeepSeek’s limited access to advanced semiconductor chips due to export restrictions. Resource efficiency as a competitive advantage DeepSeek’s achievement contradicts the prevailing industry assumption that frontier AI performance requires greatly scaling computational resources. The company attributes this efficiency to architectural innovations, particularly DeepSeek Sparse Attention (DSA), which substantially reduces computational complexity while preserving model performance. The base DeepSeek V3.2 AI model achieved 93.1% accuracy on AIME 2025 mathematics problems and a Codeforces rating of 2386, placing it alongside GPT-5 in reasoning benchmarks. The Speciale variant was even more successful, scoring 96.0% on the American Invitational Mathematics Examination (AIME) 2025, 99.2% on the Harvard-MIT Mathematics Tournament (HMMT) February 2025, and achieving gold-medal performance on both the 2025 International Mathematical Olympiad and International Olympiad in Informatics. The results are particularly significant given DeepSeek’s limited access to the raft of tariffs and export restrictions affecting China. The technical report reveals that the company allocated a post-training computational budget exceeding 10% of pre-training costs – a substantial investment that enabled advanced abilities through reinforcement learning optimisation rather than brute-force scaling. Technical innovation driving efficiency The DSA mechanism represents a departure from traditional attention architectures. Instead of processing all tokens with equal computational intensity, DSA employs a “lightning indexer” and a fine-grained token selection mechanism that identifies and processes only the most relevant information for each query. The approach reduces core attention complexity from O(L²) to O(Lk), where k represents the number of selected tokens – a fraction of the total sequence length L. During continued pre-training from the DeepSeek-V3.1-Terminus checkpoint, the company trained DSA in 943.7 billion tokens using 480 sequences of 128K tokens per training step. The architecture also introduces context management tailored for tool-calling scenarios. Unlike previous reasoning models that discarded thinking content after each user message, the DeepSeek V3.2 AI model retains reasoning traces when only tool-related messages are appended, improving token efficiency in multi-turn agent workflows by eliminating redundant re-reasoning. Enterprise applications and practical performance For organisations evaluating AI implementation, DeepSeek’s approach offers concrete advantages beyond benchmark scores. On Terminal Bench 2.0, which evaluates coding workflow capabilities, DeepSeek V3.2 achieved 46.4% accuracy. The model scored 73.1% on SWE-Verified, a software engineering problem-solving benchmark, and 70.2% on SWE Multilingual, demonstrating practical utility in development environments. In agentic tasks requiring autonomous tool use and multi-step reasoning, the model showed significant improvements over previous open-source systems. The company developed a large-scale agentic task synthesis pipeline that generated over 1,800 distinct environments and 85,000 complex prompts, enabling the model to generalise reasoning strategies to unfamiliar tool-use scenarios. DeepSeek has open-sourced the base V3.2 model on Hugging Face, letting enterprises implement and customise it without vendor dependencies. The Speciale variant remains accessible only through API due to higher token use requirements – a trade-off between maximum performance and deployment efficiency. Industry implications and acknowledgement The release has generated substantial discussion in the AI research community. Susan Zhang, principal research engineer at Google DeepMind, praised DeepSeek’s detailed technical documentation, specifically highlighting the company’s work stabilising models post-training and enhancing agentic capabilities. The timing ahead of the Conference on Neural Information Processing Systems has amplified attention. Florian Brand, an expert on China’s open-source AI ecosystem attending NeurIPS in San Diego, noted the immediate reaction: “All the group chats today were full after DeepSeek’s announcement.” Acknowledged limitations and development path DeepSeek’s technical report addresses current gaps compared to frontier models. Token efficiency remains challenging – the DeepSeek V3.2 AI model typically requires longer generation trajectories to match the output quality of systems like Gemini 3 Pro. The company also acknowledges that the breadth of world knowledge lags behind leading proprietary models due to lower total training compute. Future development priorities include scaling pre-training computational resources to expand world knowledge, optimising reasoning chain efficiency to improve token use, and refining the foundation architecture for complex problem-solving tasks. See also: AI business reality – what enterprise leaders need to know 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 China’s DeepSeek V3.2 AI model achieves frontier performance on a fraction of the computing budget appeared first on AI News. View the full article

Thrive Holdings’ push to modernise accounting and IT services is entering a new stage, as OpenAI prepares to take an ownership stake in the company and place its own specialists inside Thrive’s businesses. In doing so, OpenAI is testing an AI-driven model that pairs capital, sector expertise, and embedded technical teams. Thrive started its holding company earlier this year to buy and manage firms in day-to-day service industries. Its aim has been to rebuild these companies with more efficient processes, new data practices, and practical uses of AI. OpenAI’s deeper involvement now turns that idea into a real-time experiment in how traditional providers can update their work without relying only on off-the-shelf tools. A test case for bringing AI into core operational work While most enterprise discussions about AI tend to revolve around pilots and proof-of-concepts, Thrive is taking a different approach: buying companies outright and redesigning how they run. Its two current businesses – Crete Professionals Alliance (accounting) and Shield Technology Partners (IT services) – employ more than 1,000 people. Thrive has committed $500 million to Crete and, together with ZBS Partners, more than $100 million to Shield. For companies watching from the outside, the appeal is clear. These industries carry heavy workloads, manual tasks, and tight margins. They also handle sensitive data and operate under strict deadlines. Any AI system introduced into that environment needs domain context, training, and adjustments that fit local processes – not generic automation. Crete has already begun using AI to cut down routine tasks like data entry and early-stage tax workflows. Shield is on track to complete 10 acquisitions by the end of the year, giving Thrive a base of IT operations which it intends to redesign with new tools and methods. What OpenAI gains OpenAI is under pressure to find real, enterprise-scale use cases for its models. Investors value the company at roughly $500 billion, and its long-term commitments include about $1.4 trillion in infrastructure spending through 2033. To justify those figures, it is betting that businesses will spend heavily on tools that help them work faster and handle complex tasks at volume. By taking a stake in Thrive Holdings, OpenAI gains something it cannot produce on its own: access to companies where it can experience models in day-to-day working, and training specialists on real operations. The more Thrive’s companies grow, the more OpenAI’s stake may expand, according to a person familiar with the deal. Joshua Kushner, founder of both Thrive Capital and Thrive Holdings, said, “We are excited to extend our partnership with OpenAI to embed their frontier models, products, and services into sectors we believe have tremendous potential to benefit from technological innovation and adoption.” The partnership also gives OpenAI a path to collect value from the engineering support it provides. Its team will develop custom models for Thrive’s companies and embed researchers and engineers on site, according to partner Anuj Mehndiratta, who oversees product and technology strategy at Thrive Holdings. What enterprises can learn from this approach For many companies, the hardest part of using AI is not the model but the redesign of existing work. Thrive’s strategy reflects a shift toward deeper integration, where AI teams sit inside the business units they support rather than acting as external advisers. The model lets companies: Build tools shaped around real workflows, not abstract use cases Train models on controlled, high-quality data Reduce the gap between engineering teams and front-line employees Test changes faster, with direct feedback from staff It also surfaces the real cost of AI adoption. Custom work requires engineering time, domain knowledge, and long-term alignment between owners and model developers. Thrive’s partnership with OpenAI formalises that alignment in a way that may become more common as enterprises look for results rather than demonstrations. Brad Lightcap, OpenAI’s COO, said, “The partnership with Thrive Holdings is about demonstrating what’s possible when frontier AI research and deployment are rapidly deployed in entire organisations to revolutionise how businesses work and engage with customers.” The wider competitive landscape The deal lands at a time when AI companies are trying to anchor themselves inside major enterprise accounts. Anthropic is reaching more businesses through Microsoft partnerships, and. Google is drawing interest with its latest model and has seen its market value rise as companies explore new AI options. OpenAI, meanwhile, has taken stakes in partners like AMD and CoreWeave to support its long-term infrastructure needs. OpenAI also expanded its reach on Monday this week, announcing a separate agreement with Accenture. Its ChatGPT Enterprise product will be rolled out to “tens of thousands” of Accenture employees, giving OpenAI another route into large-scale corporate use. A possible blueprint If Thrive’s companies show meaningful improvement in how they operate, the model could influence how other enterprises think about AI transformation. Rather than layering tools on top of old processes, some may move toward deeper restructuring, guided by technical teams that understand both the model and the business. For now, Thrive Holdings serves as a live case study of what that approach looks like when applied to industries that rarely make tech headlines but form the backbone of day-to-day business operations. See also: AI business reality – what enterprise leaders need to know 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 How OpenAI and Thrive are testing a new enterprise AI model appeared first on AI News. View the full article

North American enterprises are now actively deploying agentic AI systems intended to reason, adapt, and act with complete autonomy. Data from Digitate’s three-year global programme indicates that, while adoption is universal across the board, regional maturity paths are diverging. North American firms are scaling toward full autonomy, whereas their European counterparts are prioritising governance frameworks and data stewardship to build long-term resilience. From utility to profitability The story of enterprise automation has changed. In 2023, the primary objective for most IT leaders was cost reduction and the streamlining of routine tasks. By 2025, the focus has expanded. AI is no longer viewed solely as an operational utility but as a capability enabling profit. Data supports this change in perspective. The report indicates that North American organisations are seeing a median return on investment (ROI) of $175 million from their implementations. Interestingly, this financial validation is not unique to the fast-moving North American market. European enterprises, despite a more measured and governance-heavy approach, report a comparable median ROI of approximately $170 million. This consistency suggests that while deployment strategies differ, with Europe focusing on risk management and North America on speed, the financial outcomes are similar. Every organisation surveyed confirmed implementing AI within the last two years, utilising an average of five distinct tools. While generative AI remains the most widely deployed at 74 percent, there is a notable rise in “agentic” capabilities. Over 40 percent of enterprises have introduced agentic or agent-based AI, advancing beyond static automation toward systems that can manage goal-oriented workflows. IT operations autonomy becomes the proving ground for agentic AI While marketing and customer service often dominate public discourse regarding AI, the IT function itself has emerged as the primary laboratory for these deployments. IT environments are inherently data-rich and structured, creating ideal conditions for models to learn, yet they remain dynamic enough to require the adaptive reasoning that agentic AI systems promise. This explains why 78 percent of respondents have deployed AI within IT operations, the highest rate of any business function. Cloud visibility and cost optimisation lead the adoption curve at 52 percent, followed closely by event management at 48 percent. In these scenarios, the technology is not alerting humans to problems so much as actively interpreting telemetry data to provide a unified view of spending across hybrid environments. Teams leveraging these tools report improvements in decision accuracy (44%) and efficiency (43%), allowing them to handle higher workloads without a corresponding increase in escalations. The cost-human conundrum Despite the optimism surrounding ROI, the report highlights a “cost-human conundrum” that threatens to stall progress. The paradox is straightforward: enterprises deploy AI to reduce reliance on human labour and operational costs, yet those exact factors act as the primary inhibitors to growth. 47 percent of respondents cite the continued need for human intervention as a major drawback. Far from achieving the complete autonomy of “set and forget” solutions, these agentic AI systems require ongoing oversight, tuning, and exception management. Simultaneously, the cost of implementation ranks as the second-highest concern at 42 percent, driven by the expenses associated with model retraining, integration, and cloud infrastructure. The talent required to manage these costs is in short supply. A lack of technical skills remains the primary obstacle to further adoption for 33 percent of organisations. Demand for professionals capable of developing, monitoring, and governing these complex systems exceeds current supply, creating a self-reinforcing loop where investment increases operational capacity but simultaneously raises human and financial dependencies. Trust and perception gap A divergence in perspective exists between executive leadership and operational practitioners. While 94 percent of total respondents express trust in AI, this confidence is not distributed evenly. C-suite leaders are markedly more optimistic, with 61 percent classifying AI as “very trustworthy” and viewing it primarily as a financial lever. Only 46 percent of non-C-suite practitioners share this high level of trust. Those closer to the daily operation of these models are more acutely aware of reliability issues, transparency deficits, and the necessity for human oversight. This gap suggests that while leadership focuses on long-term overhaul and autonomy, teams on the ground are grappling with pragmatic delivery and governance challenges. There is also a mixed view on how these agents will function. 61 percent of IT leaders view agentic systems not as replacements, but as collaborators that augment human capability. However, the expectation of automation varies by industry. In retail and transport, 67 percent believe agentic AI will alter the essential tasks of their roles, while in manufacturing, the same percentage views these agents primarily as personal assistants. Complete agentic AI autonomy is rapidly approaching The industry anticipates a rapid progression toward reduced human involvement in routine processes. Currently, 45 percent of organisations operate as semi- to fully-autonomous enterprises. Projections indicate this figure will rise to 74 percent by 2030. This evolution implies a change in the role of IT. As capabilities mature, IT departments are expected to transition from being operational enablers to acting as orchestrators. In this model, the IT function manages the “system of systems,” ensuring that various intelligent agents interact correctly while humans focus on creativity, interpretation, and governance rather than execution. “Agentic AI is the bridge between human ingenuity and autonomous intelligence that marks the dawn of IT as a profit-driving, strategic capability,” notes Avi Bhagtani, CMO at Digitate. “Enterprises have moved from experimenting with automation to scaling AI for measurable impact.” The transition to agentic AI requires more than just software procurement; it demands an organisational philosophy that balances automation with human augmentation. Policies alone are insufficient; governance must be integrated directly into system design to ensure transparency and ethical oversight in every decision loop. European organisations are currently leading in this area, prioritising ethical deployment and strong oversight frameworks as a foundation for resilience. Furthermore, the shortage of technical talent cannot be solved by hiring alone. Organisations must invest in upskilling existing teams, combining operations expertise with data science and compliance literacy. Finally, reliable autonomy depends on high-quality data. Investments in data integration and observability platforms are necessary to provide agents with the context required to act independently. The era of experimental AI has passed. The current phase is defined by the pursuit of autonomy, where value is derived not from novelty, but from the ability to scale agentic AI sustainably across the enterprise. “As organisations balance autonomy with accountability, those that embed trust, transparency, and human engagement into their AI strategy will shape the future of digital business,” Bhagtani concludes. See also: How the MCP spec update boosts security as infrastructure scales 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 Agentic AI autonomy grows in North American enterprises appeared first on AI News. View the full article

When JPMorgan Asset Management reported that AI spending accounted for two-thirds of US GDP growth in the first half of 2025, it wasn’t just a statistic – it was a signal. Enterprise leaders are making trillion-dollar bets on AI transformation, even as market observers debate whether we might be witnessing bubble-era exuberance. The conversation reached a turning point recently when OpenAI CEO Sam Altman, Amazon’s Jeff Bezos, and Goldman Sachs CEO David Solomon each acknowledged market froth within days of each other. But here’s what matters for enterprise decision-makers: acknowledging overheated markets isn’t the same as dismissing AI’s enterprise value. Corporate AI investment reached US$252.3 billion in 2024, with private investment climbing 44.5%, according to Stanford University. The question isn’t whether to invest in AI – it’s how to invest strategically while others – specifically, an organisation’s competitors – overspend on infrastructure and solutions that may never deliver returns. What separates AI winners from the 95% who fail An MIT study found that 95% of businesses invested in AI have failed to make money off the technology, according to ABC News. But that statistic masks a more important truth: 5% succeed – and they’re doing things fundamentally differently. High-performing organisations are investing more in AI capabilities, with more than one-third committing over 20% of their digital budgets to AI technologies, a McKinsey report shows. But they’re not just spending more – they’re spending smarter. The McKinsey research reveals what separates winners from the pack. About three-quarters of high performers say their organisations are scaling or have scaled AI, compared with one-third of other organisations. The leaders share common characteristics: they push for transformative innovation rather than incremental improvements, redesign workflows around AI capabilities, and implement rigorous governance frameworks. The infrastructure investment dilemma Enterprise leaders face a genuine dilemma. Google’s Gemini Ultra cost US$191 million to train, while OpenAI’s GPT-4 required US$78 million in hardware costs alone. For most enterprises, building proprietary large language models isn’t viable – and that makes vendor selection and partnership strategy important. Despite surging demand, CoreWeave slashed its 2025 capital expenditure guidance by up to 40%, citing delayed power infrastructure delivery. Oracle is “still waving off customers” due to capacity shortages, CEO Safra Catz confirmed, as per a Euronews report. This creates risk and opportunity. Enterprises that diversify their AI infrastructure strategies – building relationships with multiple providers, validating alternative architectures, and stress-testing for supply constraints – position themselves better than those betting everything on a single hyperscaler. Strategic AI investment in a frothy market Goldman Sachs equity analyst Peter Oppenheimer points out that “unlike speculative companies of the early 2000s, today’s AI giants are delivering real profits. While AI stock prices have appreciated strongly, this has been matched by sustained earnings growth.” The enterprise takeaway isn’t to avoid AI investment – it’s to avoid the mistakes that plague the 95% who see no returns: Focus on specific use cases with measurable ROI: High performers are more than three times more likely than others to say their organisation intends to use AI to bring about transformative change to their businesses, data from McKinsey shows. They’re not deploying AI for AI’s sake – they’re targeting specific business problems where AI delivers quantifiable value. Invest in organisational readiness, not just technology: Having an agile product delivery organisation is strongly correlated with achieving value. Establishing robust talent strategies and implementing technology and data infrastructure show meaningful contributions to AI success. Build governance frameworks now: The share of respondents reporting mitigation efforts for risks like personal and individual privacy, explainability, organisational reputation, and regulatory compliance has grown since 2022. As regulations tighten globally, early governance investment becomes a competitive advantage. Learning from market concentration In late 2025, 30% of the US S&P 500 was held up by just five companies – the greatest concentration in half a century. For enterprises, this concentration creates dependencies worth managing. The successful five percent diversify their AI vendors and their strategic approaches. They’re combining cloud-based AI services with edge computing, partnering with multiple model providers, and building internal capabilities for the workflows most important to competitive advantage. The real AI investment strategy Google’s Sundar Pichai captured the nuance enterprises must navigate: “We can look back at the internet right now. There was clearly a lot of excess investment, but none of us would question whether the internet was profound. I expect AI to be the same.” OpenAI’s ChatGPT has about 700 million weekly users, making it one of the fastest-growing consumer products in history. The enterprise challenge is deploying it effectively, leaving others waste billions on vanity projects. The enterprises winning at AI share a common approach: they treat AI as a business transformation initiative, not a technology project. They establish clear success metrics before deployment. They invest in change management as much as infrastructure. And they maintain healthy scepticism about vendor promises and remain committed to the technology’s potential. What this means for enterprise strategy Whether we’re in an AI bubble matters less to enterprise leaders than building sustainable AI capabilities. The market will correct itself – it always does. But businesses that develop genuine AI competencies during this investment surge will emerge stronger regardless of market dynamics. In 2024, the proportion of survey respondents reporting AI use by their organisations jumped to 78% from 55% in 2023, as per the Stanford data. AI adoption is accelerating, and enterprises that wait for perfect market conditions risk falling behind competitors building capabilities today. The strategic imperative isn’t to predict when the bubble bursts – it’s to ensure your AI investments deliver measurable business value regardless of market sentiment. Focus on practical deployments, measurable outcomes, and organisational readiness. Let others chase inflated valuations while you build sustainable competitive advantage. (Image source:Jasper Campbell) Want to experience the full spectrum of enterprise technology innovation? Join TechEx in Amsterdam, California, and London. Covering AI, Big Data, Cyber Security, IoT, Digital Transformation, Intelligent Automation, Edge Computing, and Data Centres, TechEx brings together global leaders to share real-world use cases and in-depth insights. Click here for more information. TechHQ is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AI business reality – what enterprise leaders need to know appeared first on AI News. View the full article

If you asked most enterprise leaders which AI tools are delivering ROI, many would point to front-end chatbots or customer support automation. That’s the wrong door. The most value-generating AI systems today aren’t loud, customer-facing marvels. They’re tucked away in backend operations. They work silently, flagging irregularities in real-time, automating risk reviews, mapping data lineage, or helping compliance teams detect anomalies before regulators do. The tools don’t ask for credit, but are saving millions. Operational resilience no longer comes from having the loudest AI tool. It comes from having the smartest one, placed where it quietly does the work of five teams before lunch. The machines that spot what humans don’t Take the case of a global logistics company that integrated a background AI system for monitoring procurement contracts. The tool scanned thousands of PDFs, email chains, and invoice patterns per hour. No flashy dashboard. No alerts that interrupt workflow. Just continuous monitoring. In the first six months, it flagged multiple vendor inconsistencies that, if left unchecked, would have resulted in regulatory audits. The system didn’t just detect anomalies. It interpreted patterns. It noticed a vendor whose delivery timelines were always one day off compared to logged timestamps. Humans had seen those reports for months. But the AI noticed that the error always occurred near quarter-end. The conclusion? Inventory padding. That insight led to a contract renegotiation that saved millions. This isn’t hypothetical. One similar real-world use case reported a seven-figure operational loss prevented through a near-identical approach. That’s the kind of ROI that doesn’t need a flashy pitch deck. Why advanced education still matters in the age of AI It’s easy to fall into the trap of thinking AI tools are replacing human expertise. But smart organisations aren’t replacing but reinforcing. People with advanced academic backgrounds are helping enterprises integrate AI with strategic precision. Specifically, those with a doctorate of business administration in business intelligence bring an irreplaceable level of systems thinking and contextual insight. The professionals understand the complexity behind data ecosystems, from governance models to algorithmic biases, and can assess which tools serve long-term resilience versus short-term automation hype. When AI models are trained on historical data, it takes educated leadership to spot where historical bias may become a future liability. And when AI starts making high-stakes decisions, you need someone who can ask better questions about risk exposure, model explainability, and ethics in decision-making. This is where doctorates aren’t just nice to have – they’re essential. Invisible doesn’t mean simple Too often, companies install AI as if it were antivirus software. Set it, forget it, hope it works. That’s how you get ******-box risk. Invisible tools must still be transparent internally. It’s not enough to say, “AI flagged it.” The teams relying on these tools – risk officers, auditors, operations leads – must understand the decision-making logic or at least the signals that drive the alert. The requires not just technical documentation, but collaboration between engineers and business units. Enterprises that win with background AI systems build what could be called “decision-ready infrastructure.” The are workflows where data ingestion, validation, risk detection, and notification are all stitched together. Not in silos. Not in parallel systems. But in one loop that feeds actionable insight straight to the team responsible. That’s resilience. Where operational AI works best Here’s where invisible AI is already proving its worth in industries: Compliance Monitoring: Automatically detecting early signs of non-compliance in internal logs, transactional data, and communication channels without triggering false positives. Data Integrity: Identifying stale, duplicate, or inconsistent data in business units to prevent decision errors and reporting flaws. Fraud Detection: Recognising pattern shifts in transactions before losses occur. Not reactive alerts after the fact. Supply Chain Optimisation: Mapping supplier dependencies and predicting bottlenecks based on third-party risk signals or external disruptions. In all these cases, the key isn’t automation for automation’s sake. It’s precision. AI models that are well-calibrated, integrated with domain knowledge, and fine-tuned by experts – not simply deployed off the shelf. What makes the systems resilient? Operational resilience isn’t built in a sprint. It’s the result of smart layering. One layer catches data inconsistencies. Another tracks compliance drift. Another layer analyses behavioural signals in departments. And yet another feeds all of that into a risk model trained on historical issues. The resilience depends on: Human supervision with domain expertise, especially from those trained in business intelligence. Cross-functional transparency, so that audit, tech, and business teams are aligned. The ability to adapt models over time as the business evolves, not just retrain when performance dips. Systems that get this wrong often create alert fatigue or over-correct with rigid rule-based models. That’s not AI. That’s bureaucracy in disguise. Real ROI doesn’t scream Most ROI-focused teams chase visibility. Dashboards, reports, charts. But the most valuable AI tools don’t scream. They tap a shoulder. They point out a loose thread. They suggest a second look. That’s where the money is. Quiet detection. Small interventions. Avoided disasters. The companies that treat AI as a quiet partner – not a front-row magician – are already ahead. They’re using it to build internal resilience, not just customer-facing shine. They’re integrating it with human intelligence, not replacing it. And most of all, they’re measuring ROI not by how cool the tech looks, but by how quietly it works. That’s the future. Invisible AI agents and assistants. Visible outcomes. Real, measurable resilience. The post How background AI builds operational resilience & visible ROI appeared first on AI News. View the full article

SAP is moving its sovereignty plans forward with EU AI Cloud, a setup meant to bring its past efforts under one approach. The goal is simple: give organisations in Europe more choice and more control over how they run AI and cloud services. Some may prefer SAP’s own data centres, some may use trusted European providers, and others may want everything managed on-site. EU AI Cloud is built to support those different needs while keeping data inside the region and in line with EU rules. Strengthening AI sovereignty across Europe SAP is also working with Cohere to bring new agent-style and multimodal AI tools to customers through Cohere North. These models will be available through SAP Business Technology Platform (SAP BTP), giving industries with strict data residency needs a way to build production-ready AI into everyday operations. The two companies say the goal is to help enterprises find better insights, improve decision support, and automate complex tasks without giving up control over compliance or performance. As Cohere’s team put it, their work with SAP is meant to keep advanced AI accessible to organisations that cannot move data outside Europe. SAP is building EU AI Cloud with help from a range of European and global partners. Models and applications from Cohere, Mistral AI, OpenAI, and others are integrated directly into SAP BTP, giving customers a clearer path to build, deploy, and scale AI applications. Companies can access partner tools as SaaS, PaaS, or IaaS and choose where to run them: on SAP infrastructure or on approved European partners. The aim is to give enterprises and public sector groups access to modern AI tools while staying within European standards for security, data protection, and sovereignty. Deployment choices tied to different security needs EU AI Cloud works through SAP Sovereign Cloud, which lets customers pick the level of control they want across the stack—from infrastructure to applications. AI models run on SAP’s cloud infrastructure and SAP BTP in European data centres, which keeps operations separate from US hyperscalers. Here are the deployment options: SAP Sovereign Cloud on SAP Cloud Infrastructure (EU) SAP’s IaaS is based on open-source tools and runs inside SAP’s European data centre network. Data stays within the EU to support compliance with regional data protection rules. SAP Sovereign Cloud On-Site Infrastructure is managed by SAP but housed in a customer’s chosen data centre. This setup offers the highest level of control over data, operations, and legal requirements while keeping access to SAP’s cloud architecture. Selected hyperscalers by market Some customers may still run SAP commercial SaaS on global cloud providers. When they do, they can add sovereignty features based on regional needs. Delos Cloud A sovereign cloud service in Germany designed for the public sector. It supports local rules and is built to help government organisations modernise their digital systems. EU AI Cloud gives organisations in Europe more choice in how they run AI and cloud workloads while keeping control of their data. The mix of deployment options, partner models, and sovereign design aims to support companies that face strict rules around privacy, storage, and operational oversight. For enterprises and public bodies that need AI systems built around local requirements, SAP’s approach offers a way to use advanced tools without giving up the safeguards they rely on. (Photo by Antoine Schibler) See also: Adversarial learning breakthrough enables real-time AI security 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 SAP outlines new approach to European AI and cloud sovereignty appeared first on AI News. View the full article

The latest MCP spec update fortifies enterprise infrastructure with tighter security, moving AI agents from pilot to production. Marking its first year, the Anthropic-created open-source project released a revised spec this week aimed at the operational headaches keeping generative AI agents stuck in pilot mode. Backed by Amazon Web Services (AWS), Microsoft, and Google Cloud, the update adds support for long-running workflows and tighter security controls. The market is drifting away from fragile, bespoke integrations. For enterprises, this is a chance to deploy agentic AI that can read and write to corporate data stores without incurring massive technical debt. MCP advances from ‘developer curiosity’ to practical infrastructure The narrative has shifted from experimental chatbots to structural integration. Since September, the registry has expanded by 407 percent, now housing nearly two thousand servers. “A year on from Anthropic’s launch of the Model Context Protocol, MCP has gone from a developer curiosity to a practical way to connect AI to the systems where work and data live,” says Satyajith Mundakkal, Global CTO at Hexaware, following this latest spec update. Microsoft has already “signaled the shift by adding native MCP support to Windows 11,” effectively moving the standard directly into the operating system layer. This software standardisation arrives alongside an aggressive hardware scale-up. Mundakkal highlights the “unprecedented infrastructure build-out,” citing OpenAI’s multi-gigawatt ‘Stargate’ programme. “These are clear signals that AI capabilities, and the data they depend on, are scaling fast,” he says. MCP is the plumbing feeding these massive compute resources. As Mundakkal puts it: “AI is only as good as the data it can reach safely.” Until now, hooking an LLM into a database was mostly synchronous. That works for a chatbot checking the weather, but it fails when migrating a codebase or analysing healthcare records. The new ‘Tasks’ feature changes this (SEP-1686). It gives servers a standard way to track work, allowing clients to poll for status or cancel jobs if things go sideways. Ops teams automating infrastructure migration need agents that can run for hours without timing out. Supporting states like working or input_required finally brings resilience to agentic workflows. MCP spec update improves security For CISOs especially, AI agents often look like a massive and uncontrolled attack surface. The risks are already visible; “security researchers even found approximately 1,800 MCP servers exposed on the public internet by mid-2025,” implying that private infrastructure adoption is significantly wider. “Done poorly,” Mundakkal warns, “[MCP] becomes integration sprawl and a ******* attack surface.” To address this, the maintainers tackled the friction of Dynamic Client Registration (DCR). The fix is URL-based client registration (SEP-991), where clients provide a unique ID pointing to a self-managed metadata document to cut the admin bottleneck. Then there’s ‘URL Mode Elicitation’ (SEP-1036). It allows a server – handling payments, for instance – to bounce a user to a secure browser window for credentials. The agent never sees the password; it just gets the token. It keeps the core credentials isolated, a non-negotiable for PCI compliance. Harish Peri, SVP at Okta, believes this brings the “necessary oversight and access control to build a secure and open AI ecosystem.” One feature as part of the spec update for MCP infrastructure has somewhat flown under the radar: ‘Sampling with Tools’ (SEP-1577). Servers used to be passive data fetchers; now they can run their own loops using the client’s tokens. Imagine a “research server” spawning sub-agents to scour documents and synthesise a report. No custom client code required—it simply moves the reasoning closer to the data. However, wiring these connections is only step one. Mayur Upadhyaya, CEO at APIContext, argues that “the first year of MCP adoption has shown that enterprise AI doesn’t begin with rewrites, it begins with exposure.” But visibility is the next hurdle. “The next wave will be about visibility: enterprises will need to monitor MCP uptime and validate authentication flows just as rigorously as they monitor APIs today,” Upadhyaya explains. MCP’s roadmap reflects this, with updates targeting better “reliability and observability” for debugging. If you treat MCP servers as “set and forget,” you’re asking for trouble. Mundakkal agrees, noting the lesson from year one is to “pair MCP with strong identity, RBAC, and observability from day one.” Star-studded industry line-up adopting MCP for infrastructure A protocol is only as good as who uses it. In a year since the original spec’s release, MCP hit nearly two thousand servers. Microsoft is using it to bridge GitHub, Azure, and M365. AWS is baking it into Bedrock. Google Cloud supports it across Gemini. This reduces vendor lock-in. A Postgres connector built for MCP should theoretically work across Gemini, ChatGPT, or an internal Anthropic agent without a rewrite. The “plumbing” phase of Generative AI is settling down, and open standards are winning the debate on connectivity. Technology leaders should look to audit internal APIs for MCP readiness – focusing on exposure rather than rewrites – and verify that the new URL-based registration fits current IAM frameworks. Monitoring protocols must also be established immediately. While the latest MCP spec update is backward compatible with existing infrastructure; the new features are the only way to bring agents into regulated, mission-relevant workflows and ensure security. See also: Adversarial learning breakthrough enables real-time AI security 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 MCP spec update boosts security as infrastructure scales appeared first on AI News. View the full article

The next frontier for edge AI medical devices isn’t wearables or bedside monitors—it’s inside the human body itself. Cochlear’s newly launched Nucleus Nexa System represents the first cochlear implant capable of running machine learning algorithms while managing extreme power constraints, storing personalised data on-device, and receiving over-the-air firmware updates to improve its AI models over time. For AI practitioners, the technical challenge is staggering: build a decision-tree model that classifies five distinct auditory environments in real time, optimise it to run on a device with a minimal power budget that must last decades, and do it all while directly interfacing with human neural tissue. Decision trees meet ultra-low power computing At the core of the system’s intelligence lies SCAN 2, an environmental classifier that analyses incoming audio and categorises it as Speech, Speech in Noise, Noise, Music, or Quiet. “These classifications are then input to a decision tree, which is a type of machine learning model,” explains Jan Janssen, Cochlear’s Global CTO, in an exclusive interview with AI News. “This decision is used to adjust sound processing settings for that situation, which adapts the electrical signals sent to the implant.” The model runs on the external sound processor, but here’s where it gets interesting: the implant itself participates in the intelligence through Dynamic Power Management. Data and power are interleaved between the processor and implant via an enhanced RF link, allowing the chipset to optimise power efficiency based on the ML model’s environmental classifications. This isn’t just smart power management—it’s edge AI medical devices solving one of the hardest problems in implantable computing: how do you keep a device operational for 40+ years when you can’t replace its battery? The spatial intelligence layer Beyond environmental classification, the system employs ForwardFocus, a spatial noise algorithm that uses inputs from two omnidirectional microphones to create target and noise spatial patterns. The algorithm assumes target signals originate from the front while noise comes from the sides or behind, then applies spatial filtering to attenuate background interference. What makes this noteworthy from an AI perspective is the automation layer. ForwardFocus can operate autonomously, removing cognitive load from users navigating complex auditory scenes. The decision to activate spatial filtering happens algorithmically based on environmental analysis—no user intervention required. Upgradeability: The medical device AI paradigm shift Here’s the breakthrough that separates this from previous-generation implants: upgradeable firmware in the implanted device itself. Historically, once a cochlear implant was surgically placed, its capabilities were frozen. New signal processing algorithms, improved ML models, better noise reduction—none of it could benefit existing patients. Jan Janssen, Chief Technology Officer, Cochlear Limited The Nucleus Nexa Implant changes that equation. Using Cochlear’s proprietary short-range RF link, audiologists can deliver firmware updates through the external processor to the implant. Security relies on physical constraints—the limited transmission range and low power output require proximity during updates—combined with protocol-level safeguards. “With the smart implants, we actually keep a copy [of the user’s personalised hearing map] on the implant,” Janssen explained. “So you lose this [external processor], we can send you a blank processor and put it on—it retrieves the map from the implant.” The implant stores up to four unique maps in its internal memory. From an AI deployment perspective, this solves a critical challenge: how do you maintain personalised model parameters when hardware components fail or get replaced? From decision trees to deep neural networks Cochlear’s current implementation uses decision tree models for environmental classification—a pragmatic choice given power constraints and interpretability requirements for medical devices. But Janssen outlined where the technology is headed: “Artificial intelligence through deep neural networks—a complex form of machine learning—in the future may provide further improvement in hearing in noisy situations.” The company is also exploring AI applications beyond signal processing. “Cochlear is investigating the use of artificial intelligence and connectivity to automate routine check-ups and reduce lifetime care costs,” Janssen noted. This points to a broader trajectory for edge AI medical devices: from reactive signal processing to predictive health monitoring, from manual clinical adjustments to autonomous optimisation. The Edge AI constraint problem What makes this deployment fascinating from an ML engineering standpoint is the constraint stack: Power: The device must run for decades on minimal energy, with battery life measured in full days despite continuous audio processing and wireless transmission. Latency: Audio processing happens in real-time with imperceptible delay—users can’t tolerate lag between speech and neural stimulation. Safety: This is a life-critical medical device directly stimulating neural tissue. Model failures aren’t just inconvenient—they impact quality of life. Upgradeability: The implant must support model improvements over 40+ years without hardware replacement. Privacy: Health data processing happens on-device, with Cochlear applying rigorous de-identification before any data enters their Real-World Evidence program for model training across their 500,000+ patient dataset. These constraints force architectural decisions you don’t face when deploying ML models in the cloud or even on smartphones. Every milliwatt matters. Every algorithm must be validated for medical safety. Every firmware update must be bulletproof. Beyond Bluetooth: The connected implant future Looking ahead, Cochlear is implementing Bluetooth LE Audio and Auracast broadcast audio capabilities—both requiring future firmware updates to the implant. These protocols offer better audio quality than traditional Bluetooth while reducing power consumption, but more importantly, they position the implant as a node in broader assistive listening networks. Auracast broadcast audio allows direct connection to audio streams in public venues, airports, and gyms—transforming the implant from an isolated medical device into a connected edge AI medical device participating in ambient computing environments. The longer-term vision includes totally implantable devices with integrated microphones and batteries, eliminating external components entirely. At that point, you’re talking about fully autonomous AI systems operating inside the human body—adjusting to environments, optimising power, streaming connectivity, all without user interaction. The medical device AI blueprint Cochlear’s deployment offers a blueprint for edge AI medical devices facing similar constraints: start with interpretable models like decision trees, optimise aggressively for power, build in upgradeability from day one, and architect for the 40-year horizon rather than the typical 2-3 year consumer device cycle. As Janssen noted, the smart implant launching today “is actually the first step to an even smarter implant.” For an industry built on rapid iteration and continuous deployment, adapting to decade-long product lifecycles while maintaining AI advancement represents a fascinating engineering challenge. The question isn’t whether AI will transform medical devices—Cochlear’s deployment proves it already has. The question is how quickly other manufacturers can solve the constraint problem and bring similarly intelligent systems to market. For 546 million people with hearing loss in the Western Pacific Region alone, the pace of that innovation will determine whether AI in medicine remains a prototype story or becomes standard of care. (Photo by Cochlear) See also: FDA AI deployment: Innovation vs oversight in drug regulation 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 Edge AI inside the human body: Cochlear’s machine learning implant breakthrough appeared first on AI News. View the full article

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. 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