In brief:
NVIDIA’s RTX Spark announcement adds momentum to the shift toward more powerful AI at the endpoint, but for most organizations, the bigger issue is readiness. Success will depend on the infrastructure, governance, support, and lifecycle planning needed to scale AI effectively.
NVIDIA and Microsoft have introduced RTX Spark, a new Windows PC platform designed to support more demanding AI models and personal AI agents directly on laptops and desktops.
Glancing at the press release, it sounds impressive:
- It powers the world’s first Windows PCs purpose-built for personal agents.
- Features one petaflop of AI performance.
- Offers industry-leading power efficiency.
- Has full-stack NVIDIA AI and graphics technology and up to 128GB of unified memory.
But what the announcement really shows is the growing momentum in a trend already well underway: that the personal computer is being re-architected for the AI era. The PC is evolving from a place where users access AI, to a place where more of that intelligence runs, responds, and acts locally. For IT leaders, the question is no longer whether endpoint AI is coming, but rather how to support it in a way that is secure, cost-effective, and operationally sustainable.
Endpoint AI has huge benefits but is evolving faster than operational readiness
It’s easy to understand the appeal of running AI locally because when more processing happens on the device, organizations can:
- Reduce latency and get faster response times.
- Have greater control over data and privacy.
- Support more personalized, always-on user experiences.
- Reduce token costs by moving suitable AI workloads onto the device.
The challenge is that endpoint AI capability is advancing faster than most operating models are being implemented.
Our data reinforces this
At SHI’s 2026 Spring Summit, the disconnect between AI demand and IT readiness was clear. Polls with the 330+ attendees showed that:
- 78% of IT leaders already have multiple AI apps on their phone.
- Only 13% say they are fully leveraging AI at the endpoint in production.
- 53% describe their IT approach as overly reactive.
- 41% say endpoints are the biggest source of digital friction.
At the same time, cost pressure is intensifying:
- 47% cite budget alignment as their top concern.
Taken together, these responses point to a widening readiness gap between growing AI expectations and the operational reality inside most IT environments. AI is already shaping how individuals work, but many organizations are still figuring out how to support it at scale across the wider environment, from infrastructure and governance through to endpoint performance and user experience.
As endpoint AI grows, IT planning gets more complex
AI at the endpoint is no longer a standalone device conversation. It’s part of an integrated AI infrastructure strategy, where performance depends on having the right mix of device, network, and backend infrastructure working together seamlessly to support:
- Better employee experience – performance, responsiveness, and usability of AI tools.
- Security and governance – how AI accesses data, systems, and workflows.
- Cost management – hardware refresh cycles, memory requirements, energy use, and the balance between local and cloud AI spend.
- Operational efficiency – support models, automation, and device lifecycle management.
AI readiness cannot be treated as an infrastructure decision alone. Many organizations are focused on private cloud, backend capacity, and centralized AI platforms, but value still depends on how well AI performs, integrates, and can be supported at the endpoint.
What organizations need next for endpoint AI readiness
AI at the endpoint will continue to evolve quickly. Hardware innovation is accelerating. Software ecosystems are adapting. And user expectations are shifting just as fast. But success will not be defined by access to the latest technology. It will be defined by how well organizations can translate that capability into a sustainable, secure, and cost-effective operating model.
That is the gap most organizations are now trying to close. And as a new generation of AI-capable devices reaches the market, that gap is becoming harder to postpone.
Our take on what NVIDIA’s announcement means for your endpoint AI strategy
NVIDIA’s announcement adds momentum and visibility to a direction the market is already heading in, and while it does not mean an immediate refresh for every organization, it does make AI-ready endpoint strategy a planning issue now.
The issue is not simply whether AI-ready devices are available. It is whether the wider environment is ready to support them. Organizations need a joined-up strategy from the data center to the end user, rather than treating AI PCs as a standalone refresh decision. We help customers align infrastructure, data, security, networking, endpoint readiness, governance, support, and lifecycle strategy so AI investments deliver value in the real world.
What we’re seeing across customers is that the groundwork for AI at the endpoint is often incomplete, especially in terms of use-case clarity, governance, support readiness, and lifecycle planning. Before investing in the next generation of devices, organizations should focus on:
- Understanding real AI use cases – where local AI will deliver measurable value, not just novelty. We offer this through our AI & Cyber Labs.
- Assessing endpoint readiness – whether current devices, memory profiles, and performance baselines can support emerging workloads, in our Next-Gen Device Lab.
- Establishing governance early – defining how AI agents access data, systems, and workflows.
- Shifting support models – moving from reactive IT to proactive, AI-aware operations.
- Creating a cost strategy – aligning refresh cycles and investment to business outcomes, not hype. We give you clarity on this with our Intelligent Refresh Program and FinOps/ITAM services.
AI-driven endpoint and infrastructure demand is also colliding with a constrained hardware market. Memory-intensive workloads are increasing device requirements just as supply pressures drive up costs, forcing organizations to rethink refresh cycles and move toward more intelligent, data-driven lifecycle strategies.
The organizations that gain the most won’t necessarily be the ones that refresh first. They will be the ones that build a clear operating model for AI at the endpoint, so when new devices arrive, they are ready to scale them with confidence.
NEXT STEPS:
Speak to an SHI expert about how we help organizations operationalize AI across the full environment, including through our Intelligent Refresh Program.
Learn more about how AI-enabled endpoints transform work in this blog.
Still have questions? Here’s what IT leaders need to know about NVIDIA’s announcement and the shift to AI PCs.
Frequently asked questions
What is an AI PC?
An AI PC is a device designed to run artificial intelligence workloads directly on the hardware, rather than relying solely on cloud-based processing. This allows for faster performance, lower latency, and greater control over data.
What did NVIDIA announce with RTX Spark, and why does it matter?
NVIDIA announced RTX Spark, a new Windows PC platform designed to run more demanding AI workloads and personal AI agents directly on laptops and desktops.
What makes this significant is the shift beyond lighter, NPU-led AI PCs toward higher-performance local AI execution. More work is moving onto the device instead of the cloud.
For organizations, that raises important questions around endpoint strategy, governance, support, cost management, and where AI workloads should run.
Is NVIDIA the first to enable high-performance AI on laptops?
No. Other platforms, including AMD Strix Halo-based systems, have already pushed toward larger unified memory and stronger on-device AI performance.
What NVIDIA adds is greater visibility and likely broader market momentum, which could accelerate adoption and decision-making across the enterprise.
Why is AI moving to the endpoint instead of the cloud?
Running AI locally improves performance, reduces latency, and gives organizations more control over data and privacy. It can also help reduce token-based cloud costs for certain workloads.
However, not all workloads belong on the endpoint, which is why most organizations will need a balanced approach.
Do AI PCs replace cloud AI?
No. AI PCs complement cloud AI rather than replacing it.
Most organizations will operate a hybrid model, where some workloads run locally on devices and others remain in the data center or cloud.
The real challenge is designing that environment so performance, governance, security, and cost all work together.
What challenges do organizations face when adopting AI PCs?
Most organizations are not yet set up to manage AI workloads at scale across endpoints.
Common gaps include governance, endpoint support models, lifecycle planning, and cost visibility across devices, cloud, and software layers.
Do we need to refresh devices now for AI PCs?
Not necessarily. While new AI-capable devices are entering the market, most organizations are not yet ready to fully utilize them.
The priority should be understanding use cases, assessing current endpoint readiness, and putting governance, support, and cost models in place.
Refreshing devices without that foundation is unlikely to deliver meaningful value.
What should organizations do next?
Start by identifying where AI workloads will deliver real value, then assess whether your current endpoints, infrastructure, and governance models are ready to support them.
From there, organizations can take a more strategic approach to refresh cycles, rather than reacting to new hardware announcements.
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