NHS England is giving more than 505,000 clinicians and support staff access to Microsoft 365 Copilot in what will be the largest AI deployment in healthcare globally. The rollout follows a pilot across 90 NHS organisations in which 30,000 workers used the tool for administrative tasks. NHS England says the average participant saved 43 minutes per day, roughly equivalent to five working weeks per year.

The contract is valued at approximately £120 million and covers Microsoft 365 Copilot, Copilot Studio, and agent-governance tooling. NHS England plans to onboard 200,000 users within the first six months and the full 505,000 within a year. The subscription includes Copilot Studio, a platform for building AI agents without requiring technical expertise.

“By rolling out Microsoft Copilot across the NHS, we can reduce that burden, free up clinicians’ time and help staff focus on what they do best, caring for patients,” UK Health Innovation and Safety Minister Preet Kaur Gill said. The deployment is framed as part of the Government’s 10 Year Health Plan for England. Rob Thompson, NHS England’s Chief Digital, Data and Technology Officer, described the potential to save clinical staff “nearly a day’s worth of admin time every fortnight” as a “gamechanger for patients.”

The administrative burden the rollout aims to address is substantial. A 2026 UK study published in the National Library of Medicine found that resident doctors spend four hours on administrative work for every hour of direct patient contact, with 73% of their time consumed by non-patient-facing tasks. Separately, research from the Health Education and Training Trust found clinicians spend an average of 13.5 hours per week on clinical documentation, a 25% increase over seven years and more than a third of their working hours.

Microsoft identified five job roles set to benefit most: clinical administration, ward clerks, medical secretaries, core services, and management. The tool will handle writing, information retrieval, summarisation, and analysis. NHS England’s internal estimates suggest the 43-minute daily saving could translate to roughly 3,600 full-time-equivalent roles freed for direct patient care once the deployment reaches full scale.

That 43-minute figure, however, has not been independently verified. It comes from NHS England’s own pilot data, and the methodology behind it has not been published. Microsoft’s consumer Copilot terms of service label the product “for entertainment purposes only,“ a clause that applies to consumer products rather than the enterprise M365 tier NHS England is deploying.

The distinction matters, but the broader adoption picture is unflattering. Only 3% of Microsoft’s 450 million M365 enterprise users currently pay for the $30-per-month Copilot add-on, and accuracy surveys have shown negative net promoter scores. Lapsed users have cited distrust of answers as their primary reason for stopping.

Accenture deployed Copilot to all 743,000 employees earlier this year in the largest enterprise rollout to date, reporting 89% monthly active usage among a 200,000-person cohort. But Accenture invested heavily in structured change management, one-on-one training, and internal communities to drive that adoption. NHS England’s ability to replicate that approach across a workforce spanning hundreds of hospitals, clinics, and GP practices, with widely varying levels of digital literacy, is an open question.

Staff training and governance are acknowledged risks. NHS England has committed to an “extensive training and adoption programme,” and experience from Welsh councils running a similar Microsoft 365 rollout suggests that internal AI champions, practitioners teaching other practitioners, are critical to uptake. The NHS will need to get its governance, data protection policies, and usage strategies in place before the tool can deliver its promised value at scale.

For Microsoft, the NHS deal is commercially significant. At an estimated £120 million, it is one of the largest single Copilot contracts in the public sector and provides the company with a flagship healthcare reference at a time when enterprise AI adoption has lagged projections. “Bringing AI safely into the flow of healthcare will help ease pressures, improve productivity, and support better decision-making across the health service,” Microsoft UK and Ireland CEO Darren Hardman said.

The deployment is a bet that an AI tool with a mixed enterprise track record can deliver measurable results in one of the world’s largest and most complex public healthcare systems. If the 43-minute daily saving holds at scale, the NHS will have demonstrated a use case that every national health service in Europe will want to study. If adoption stalls, staff resist, or the time savings prove overstated, it will be £120 million of public money spent on a product whose own maker struggled to sell to the private sector.

For the better part of a year, Microsoft has been telling us that the future of AI on Windows belongs to Copilot+ PCs. If you wanted Microsoft’s most advanced local AI features, you needed a machine with a dedicated Neural Processing Unit (NPU). That was the deal. Now, Microsoft appears to be rewriting the rules.

According to updated documentation, Windows 11’s local Language Model APIs can now run on non-Copilot+ PCs, provided they have an Nvidia GeForce RTX 30-series GPU (or newer) with at least 6GB of VRAM. On the surface, this sounds like a developer-focused update. In reality, it could be one of the most significant shifts in Microsoft’s AI PC strategy since Copilot+ PCs launched last year. More importantly, it raises a question that has been lingering ever since the AI PC era began: Did we really need NPUs for all of this in the first place?

The CoPilot+ exclusivity era was always a little awkward

When Copilot+ PCs debuted in June 2024, Microsoft positioned them as the gateway to local AI experiences on Windows. To qualify, a device needed 16GB of RAM, SSD storage, and an NPU capable of delivering at least 40 TOPS of AI performance. The messaging suggested that these specialized chips were essential for running AI workloads locally. While that’s true in terms of efficiency, it never told the full story.

Anyone familiar with AI hardware already knew that GPUs were more than capable of handling these workloads. In fact, modern graphics cards are often significantly more powerful than NPUs for running language models and generative AI applications. That’s why most enthusiasts experimenting with local AI tools, from small language models to image generators, have been relying on GPUs for years. Yet Windows’ native AI experiences remained locked behind the Copilot+ badge.

That created an odd situation. A gaming PC with an RTX 4070 had more than enough horsepower to run AI models locally, but it couldn’t access Microsoft’s native AI framework because it lacked an NPU. Meanwhile, a thinner laptop with a qualifying NPU could. This latest change doesn’t completely erase that divide, but it certainly makes it look thinner than ever.

Microsoft may be laying the groundwork for AI beyond NPUs

The newly expanded Language Model APIs allow developers to tap into local AI capabilities on supported Nvidia hardware. Microsoft says these APIs can now run on non-Copilot+ systems equipped with RTX 30-series GPUs or newer, provided they have at least 6GB of VRAM. These APIs are powered by Phi Silica, Microsoft’s compact on-device language model. Applications can use it for tasks such as summarizing text, rewriting content, converting text into tables, formatting information, and generating responses from prompts.

Think of it as a lightweight, local version of the AI features people typically associate with services like ChatGPT. The difference is that everything runs directly on the device rather than in the cloud. That’s important for two reasons. First, privacy — if AI processing stays on your PC, sensitive documents, notes, emails, and drafts don’t have to leave the machine. Second, performance — local AI features can run instantly without waiting for cloud servers, subscriptions, or an internet connection.

The interesting part is how Microsoft plans to distribute these capabilities. If an app needs Phi Silica, Windows can download the required model through Windows Update and run it locally using supported hardware. So, the operating system is beginning to treat AI models like another Windows component rather than a premium feature reserved for a specific class of PCs. That’s a notable philosophical shift.

The beginning of the end for CoPilot+ exclusives?

Before you get too excited, this doesn’t mean every AI feature is suddenly coming to older Windows machines. Features such as Recall, Click to Do, and some of Microsoft’s AI-powered creative tools still appear tied to systems with NPUs. The newly expanded support currently applies to Language Model APIs, which are primarily focused on text-based AI experiences.

Still, history suggests these walls rarely stay up forever. Once Microsoft demonstrates that local AI can run effectively on mainstream RTX hardware, it becomes harder to justify why certain AI experiences must remain exclusive to NPUs. Developers won’t care whether the AI workload is running on an NPU or a GPU as long as the experience works well. Consumers certainly won’t. That’s why this update feels more significant than the documentation change might suggest.

For now, it’s just one API. But it also represents Microsoft’s first meaningful step toward acknowledging something many PC enthusiasts have been saying all along: capable GPUs were never the problem. And if local AI can run perfectly well on millions of existing RTX-powered PCs, the distinction between a “Copilot+ PC” and a regular Windows PC may start to matter a lot less than Microsoft originally hoped.

The Swiss post-quantum-crypto firm has lifted its WeCan stake from 28% to majority and committed CHF 5M to accelerate AI-compliance tooling for Pictet, Lombard Odier and Barclays.

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Geneva-based SEALSQ has acquired a majority equity stake in WeCan Group, lifting its position from the 28% taken in October 2025 and committing a further CHF 5m ($6.1m) to accelerate the joint development of an AI-powered compliance co-pilot built on post-quantum cryptography for the global financial industry.

The investment is being made through SEALSQ’s dedicated Quantum Fund, an internal strategic vehicle the company set up to back early-stage quantum-computing and quantum-security companies.

The customer base WeCan brings to the platform is the part that makes the announcement substantive. The company already counts Pictet, Lombard Odier, Edmond de Rothschild, Syz and Barclays among its private-banking clients for its existing know-your-customer infrastructure, which has been operational across several Swiss tier-one wealth managers since 2020.

The WeCan compliance stack uses blockchain-based document verification to handle the KYC and AML data-sharing that private banks have historically struggled to coordinate, even when serving overlapping client books. SEALSQ’s majority position now puts that customer base alongside SEALSQ’s own quantum-resistant Hardware Security Modules (HSMs) and post-quantum-cryptography (PQC) IP.

The product the two companies are now building is the more strategically interesting half. The joint platform will use SEALSQ’s PQC primitives to handle the cryptographic layer, WeCan’s existing private-bank customer integration to handle the operational layer, and an AI compliance co-pilot to absorb the rules-based-to-judgment work that private-bank compliance teams currently do by hand.

The pitch, on the company’s own framing, is a compliance system that survives both the EU AI Act’s 2027 enforcement window and the anticipated post-quantum cryptographic transition that NIST’s August 2024 PQC standards have started to make mandatory.

The post-quantum dimension is the part most likely to be under-priced by readers outside the cryptography community. Existing public-key cryptography, the RSA and elliptic-curve protocols underlying almost every banking-and-government secure-communications system in use today, will be broken by sufficiently capable quantum computers.

The estimates of when that breaking becomes practical range from five to fifteen years, but the “harvest now, decrypt later” threat, in which adversaries capture encrypted financial traffic today and decrypt it once quantum hardware matures, means the transition needs to start now rather than then.

NIST finalised its first set of post-quantum standards (CRYSTALS-Kyber, CRYSTALS-Dilithium) in 2024, and the financial-services sector is reportedly the first commercial vertical where mandatory PQC migration is being seriously planned.

SEALSQ’s positioning sits cleanly inside that timeline. The company, listed on Nasdaq under LAES, has built a portfolio of quantum-resistant HSMs and is currently constructing what it calls a fully integrated quantum vertical-sovereign stack through its Quantum Fund investments in carefully selected quantum-computing companies across the US and Europe.

The WeCan deal is the most concrete commercial test of whether the SEALSQ stack can actually be deployed inside operational banking systems rather than remaining a regulatory-aspiration story.

The European banking sector is visibly building out a multi-layered AI-compliance-and-security stack in parallel, with SEALSQ-WeCan focused on the cryptographic-integrity layer, Bayshore on the legal-rule layer, and the BNP-Mistral programme on the AI-attacker-defence layer.

What the SEALSQ-WeCan announcement leaves under-specified is timing. Neither company has disclosed a launch date for the compliance co-pilot or specified which of the named banks will be the first deployment customer.

The CHF 5m commitment, set against the cumulative engineering work the post-quantum-AI integration will require, is best read as Series-A-equivalent runway rather than the cost of the full build-out. Whether further capital follows from SEALSQ’s public-equity base or from outside investors is the next concrete question.

SEALSQ’s shares were modestly higher in pre-market trading on the announcement.