Modern software delivery depends on the reliability, integrity, and security of container images. As organisations migrate to microservices, automated CI/CD pipelines, and multi-cloud architectures, the container image becomes more than a packaging mechanism, it becomes a security boundary. A single vulnerability embedded in an image can replicate in clusters, environments, and deployments, creating widespread risk for applications that rely on speed and repeatability.
A recent industry report [PDF] argues that Britain’s railway network could carry an extra billion journeys by the mid-2030s, building on the 1.6 billion passenger rail journeys recorded to year-end March 2024. The next decade will involve a combination of complexity and control, as more digital systems, data, and interconnected suppliers create the potential for more points of failure.
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Arm Holdings has positioned itself at the centre of AI transformation. In a wide-ranging podcast interview, Vince Jesaitis, head of global government affairs at Arm, offered enterprise decision-makers look into the company’s international strategy, the evolution of AI as the company sees it, and what lies ahead for the industry.
For large retailers, the challenge with AI isn’t whether it can be useful, but how it fits into everyday work. A new three-year AI partnership by Tesco points to how one of the UK’s biggest supermarket groups is trying to achieve just that.
Of all the many industries, it’s marketing where AI is no longer an “innovation lab” side project but embedded in briefs, production pipelines, approvals, and media optimisation. A WPP iQ post published in December, based on a webinar with WPP and Stability AI, shows what AI deployment in daily operations looks like.
Zara is testing how far generative AI can be pushed into everyday retail operations, starting with a part of the business that rarely gets attention in technology discussions: product imagery.
Human Resources is an area in many organisations where AI can have significant operational impact. The technology is now being embedded into day-to-day operations, in activities like answering employees’ questions and supporting training. The clearest impact appears where organisations can measure the tech’s outcomes, typically in time saved and the numbers of queries successfully resolved.
By December 2025, AI adoption on Wall Street had moved past experiments inside large US banks and into everyday operations. Speaking at a Goldman Sachs financial-services conference in New York on 9 December, bank executives described AI—particularly generative AI—as an operational upgrade already lifting productivity across engineering, operations, and customer service.
Roblox is often seen as a games platform, but its day-to-day reality looks closer to a production studio. Small teams release new experiences on a rolling basis and then monetise them at scale. That pace creates two persistent problems: time lost to repeatable production work, and friction when moving outputs between tools. Roblox’s 2025 updates point to how AI can reduce both, without drifting away from clear business outcomes.
The construction industry generates colossal amounts of data, with much of it unused or locked in spreadsheets. AI is now changing this, enabling teams to accelerate decision-making, enhance margins, and improve project outcomes. According to new research from Dodge Construction Network (Dodge) and CMiC, the true transformative impact of AI is highlighted by contractors, with 87% believing AI will “meaningfully transform their business,” despite current low adoption rates.
Walmart’s December 9 transfer to Nasdaq wasn’t just a symbolic gesture. The US$905 billion retailer is making its boldest claim yet: that it’s no longer a traditional discount chain, but a tech-powered enterprise using AI to fundamentally rewire its retail operations.
Generative AI’s experimental phase is concluding, making way for truly autonomous systems in 2026 that act rather than merely summarise.
BBVA is embedding AI into core banking workflows using ChatGPT Enterprise to overhaul risk and service in the sector.
Many companies are still working out how to use AI in a steady and practical way, but a small group is already pulling ahead. New research from NTT DATA outlines a playbook that shows how these “AI leaders” set themselves apart through strong plans, firm decisions, and a disciplined approach to building and using AI across their organisations.
Artificial intelligence is transforming the way information is created, summarised, and delivered. For publishers, the shift is already visible. Search engines provide AI-generated overviews, users get answers without clicking, and content is scraped by large language models that train on decades of journalism.
According to OpenAI, enterprise AI has graduated from the sandbox and is now being used for daily operations with deep workflow integrations.
ByteDance’s December 2 launch of an agentic AI smartphone prototype with ZTE sparked immediate consumer enthusiasm – and just as quickly triggered privacy concerns that forced the company to dial back capabilities. But beneath the headline-grabbing sell-out and subsequent controversy lies a more significant story: the enterprise implications of operating-system-level AI agents that can autonomously execute complex, multi-step tasks in device ecosystems.
Manufacturing executives are wagering nearly half their modernisation budgets on AI, betting these systems will boost profit within two years.
AI is moving deeper into the physical world, and EY is laying out a more structured way for companies to work with robots, drones, and other smart devices. The organisation is introducing a physical AI platform built with NVIDIA tools, opening a new EY.ai Lab in Georgia, and adding new leadership to guide its work in this field.
Since the surge in AI adoption over the last couple of months, it’s become clear there isn’t enough computational horsepower to go around (something that has become painfully obvious as cloud providers have accrued months-long waitlists for high-end GPU instances). Unlike the brief cryptocurrency-mining GPU craze a few years ago, today’s crunch is driven by real demand from AI research and deployments.
Chip stacking strategy is emerging as China’s innovative response to US semiconductor restrictions, but can this approach truly close the performance gap with Nvidia’s advanced GPUs? As Washington tightens export controls on cutting-edge chipmaking technology, Chinese researchers are proposing a bold workaround: stack older, domestically-producible chips together to match the performance of chips they can no longer access.
North American enterprises are now actively deploying agentic AI systems intended to reason, adapt, and act with complete autonomy.
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