According to SAP, enterprise AI governance secures profit margins by replacing statistical guesses with deterministic control. Ask a consumer-grade model to count the words in a document, and it will often miss the mark by ten percent. Manos Raptopoulos, Global President of Customer Success Europe, APAC, Middle East & Africa at SAP, observes that the The post SAP: How enterprise AI governance secures profit margins appeared first on AI News.

As of 1st June 2026, GitHub Copilot will charge its users on the basis of the tokens they use, rather than a flat rate subscription model. The model that’s seeing the shutters closed on it is, or rather was, simple to understand and use. Users were given a set number of ‘Premium Requests’ according to The post Per-token AI charges come to GitHub Copilot appeared first on AI News.

LG is currently engaged in exploratory discussions with NVIDIA concerning physical AI, data centres, and mobility. Following a meeting in Seoul between LG CEO Ryu Jae-cheol and Madison Huang, Senior Director of Product Marketing for Omniverse and Robotics at NVIDIA, the core operational dependencies required to run complex automated systems are becoming apparent. While the The post What LG and NVIDIA’s talks reveal about the future of physical AI appeared first on AI News.

APIs and MCPs are often mentioned in the same breath as ways that systems can exchange information, but they are designed differently and have different purposes. This article hopes to explain the differences and how software developers and users should approach interaction with each. An API is mainly found in software applications, while an MCP The post A guide to APIs, MCPs, and MCP Gateways appeared first on AI News.

Every cloud beat. Every capex forecast rose. That is the two-sentence summary of the biggest earnings day of 2026, and it tells you almost everything you need to know about where Big Tech’s AI infrastructure spending actually stands right now. Microsoft, Alphabet, Meta, and Amazon collectively committed somewhere between US$630 billion and US$650 billion in The post Big Tech just proved AI infrastructure spending works. Then it raised the bill anyway appeared first on AI News.

Australia’s financial regulator has warned financial firms that AI agent governance and assurance practices are poorly governed. The warning comes as banks and superannuation trustees expand AI in internal and customer-facing operations. The Australian Prudential Regulation Authority said it conducted a targeted review of selected large regulated entities in late 2025 to assess AI adoption The post AI agent governance takes focus as regulators flag control gaps appeared first on AI News.

Getting stalled enterprise AI rollouts in the EMEA region moving again will require CIOs to aggressively audit their systems. Over the past 18 months, AI deployments across Europe advanced far beyond initial testing. Companies poured capital into large language models and machine learning, expecting heavy operational upgrades. IDC research reveals that boards are slowing down, The post IDC: How EMEA CIOs can jumpstart AI rollouts appeared first on AI News.

OpenAI launched GPT-5.5 on April 23 as what it calls “a new class of intelligence for real work and powering agents,” and the framing is deliberate. OpenAI says it’s the most capable agentic AI model to date, built from the ground up to plan, use tools, check its own output, and work through tasks independently. The post GPT-5.5 is OpenAI’s most capable agentic AI model yet appeared first on AI News.

To regulate software delivery costs and SDLC governance, IBM is launching Bob, an AI platform built to anchor enterprise engineering. Accumulated technical debt, hybrid cloud structures, and rigid compliance requirements clash with the raw speed of coding assistants. Without boundaries, they generate unmanaged liabilities rather than functional progress. Dinesh Nirmal, SVP at IBM Software, explained: The post IBM launches AI platform Bob to regulate SDLC costs appeared first on AI News.

When people talk about artificial intelligence, they usually focus on what it produces: Human-like text, stunning images, or eerily accurate recommendations. What rarely gets attention is how AI understands anything in the first place. That understanding begins with encoders. Think of an encoder as a translator that converts messy, real-world information into a structured language The post The evolution of encoders: From simple models to multimodal AI appeared first on AI News.