in short:
NVIDIA’s RTX Spark announcement adds momentum to the shift to more powerful AI on endpoints, but the bigger question for most organizations is readiness. Success will depend on the infrastructure, governance, support and lifecycle planning required to effectively scale AI.
NVIDIA and Microsoft have launched 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 pretty impressive:
- It powers the world’s first Windows PC built specifically for personal agents.
- With 1 petaflop of AI performance.
- Delivers industry-leading power efficiency.
- Featuring full-stack NVIDIA AI and graphics technology and up to 128GB of unified memory.
But what this announcement really signals is the growing momentum of a trend that’s already underway: Personal computers are being redesigned for the age of artificial intelligence. PCs are evolving from where users access artificial intelligence to where more intelligence runs, responds and acts locally. For IT leaders, the question is no longer whether endpoint AI is coming, but 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 on-premises because when more processing occurs on-device, organizations can:
- Reduce latency and get faster response times.
- Have greater control over your data and privacy.
- Enable a more personalized, always-on user experience.
- Reduce token costs by offloading the right AI workloads to devices.
The challenge is that endpoint AI capabilities are evolving faster than most operational models can be implemented.
Our data reinforces this
The disconnect between AI needs and IT readiness was evident at the SHI 2026 Spring Summit. A poll of more than 330 attendees revealed:
- 78% of IT leaders already have multiple AI applications installed on their phones.
- Only 13% of respondents said they are fully leveraging AI at the production endpoint.
- 53% of respondents believe their approach to IT is too reactive.
- 41% said endpoints are the biggest source of digital friction.
At the same time, cost pressures are also intensifying:
- 47% cited budget adjustments as their top concern.
Taken together, these responses indicate a widening readiness gap between growing AI expectations and operational realities in most IT environments. AI is already shaping the way individuals work, but many organizations are still figuring out how to support AI at scale across their broader environments, from infrastructure and governance to endpoint performance and user experience.
As endpoint AI evolves, IT planning becomes more complex
AI at the endpoint is no longer a stand-alone device conversation. It is part of an integrated AI infrastructure strategy, where performance depends on the right combination of devices, networks, 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 – The balance between hardware refresh cycles, memory requirements, energy usage, and on-premises and cloud AI spending.
- operational efficiency – Supports model, automation and device lifecycle management.
AI readiness cannot be viewed solely as an infrastructure decision. Many organizations are focusing on private clouds, back-end capacity, and centralized AI platforms, but the value still depends on how well the AI is executed, integrated, and supported at the endpoint.
What organizations need next to be endpoint AI ready
Endpoint AI will continue to evolve rapidly. Hardware innovation is accelerating. The software ecosystem is adapting. User expectations are also changing rapidly. But success does not depend on access to the latest technology. It will depend on how organizations translate this capability into a sustainable, safe and cost-effective operating model.
This is a gap that most organizations are now working to close. As new generations of AI devices enter the market, this gap becomes increasingly difficult to postpone.
Our take on what NVIDIA’s announcement means for your endpoint AI strategy
NVIDIA’s announcement adds momentum and visibility to the direction the market is already heading, and while it doesn’t mean every organization will update immediately, it does make AI-ready endpoint strategy a planning matter now.
The question is not just whether AI devices are available. but rather 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 stand-alone refresh decisions. We help clients align infrastructure, data, security, networking, endpoint readiness, governance, support and lifecycle strategies so that AI investments create value in the real world.
What we see among customers is that the foundation for endpoint AI is often incomplete, particularly around use case clarity, governance, support readiness and lifecycle planning. Before investing in next-generation equipment, organizations should focus on:
- Learn about real-life AI use cases – Local AI will provide measurable value, not just novelty. We provide this service through the Artificial Intelligence and Cyber Lab.
- Assess endpoint readiness – Whether current devices, memory profiles and performance benchmarks can support emerging workloads in our next-generation device labs.
- Establish governance early – Define how AI agents access data, systems and workflows.
- Shifting support models – Move from reactive IT to proactive, AI-aware operations.
- Develop cost strategy – Align refresh cycles and investments based on business results, not hype. We make this clear to you with our smart refresh plans and FinOps/ITAM services.
AI-driven endpoint and infrastructure needs also collide with a constrained hardware market. Memory-intensive workloads are increasing device demands as supply pressure drives up costs, forcing organizations to rethink refresh cycles and move toward smarter, data-driven lifecycle strategies.
The organizations that gain the most are not necessarily the ones that update first. They will build clear operating models for AI at the endpoint, so when new devices arrive, they are ready to scale them with confidence.
Next step:
Talk to SHI experts about how we help organizations implement AI across their entire environment, including through our Smart Refresh program.
Learn more about how AI endpoints are changing the way work is done in this blog.
Still have questions? Here’s what IT leaders need to know about NVIDIA’s announcement and the shift to AI PCs.
FAQ
What is an artificial intelligence computer?
An AI PC is a device designed to run artificial intelligence workloads directly on hardware, rather than relying solely on cloud-based processing. This enables faster performance, lower latency, and better control of your data.
What did NVIDIA announce with RTX Spark? Why is it important?
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.
The significance of this lies in the move from lighter, NPU-dominated AI PCs to higher-performance local AI execution. More work is moving to devices rather than the cloud.
For organizations, this raises important questions about endpoint policy, governance, support, cost management, and where AI workloads should run.
Is NVIDIA the first to enable high-performance AI on laptops?
Won’t. Other platforms, including AMD Strix Halo-based systems, have moved toward larger unified memory and stronger on-device AI performance.
What NVIDIA adds is greater visibility and potentially broader market momentum, which can accelerate adoption and decision-making across the enterprise.
Why is AI moving to endpoints instead of the cloud?
Running AI locally improves performance, reduces latency, and gives organizations greater control over data and privacy. It can also help reduce token-based cloud costs for certain workloads.
However, not all workloads belong to endpoints, which is why most organizations require a balanced approach.
Will AI PC replace cloud AI?
Won’t. AI computers complement cloud AI, not replace it.
Most organizations will employ a hybrid model where some workloads run locally on the device while other workloads remain in the data center or cloud.
The real challenge is to design the environment so that performance, governance, security and cost all work together.
What challenges do organizations face when adopting AI PCs?
Most organizations are not ready to manage AI workloads at scale across endpoints.
Common gaps include governance, endpoint support models, lifecycle planning, and cost visibility across device, cloud, and software tiers.
Do we now need to update our equipment for AI computers?
not necessarily. While new AI devices are entering the market, most organizations are not yet ready to take full advantage of them.
Priorities should be to understand the use cases, assess current endpoint readiness, and put governance, support, and cost models in place.
Newer devices without this foundation are unlikely to deliver meaningful value.
What should organizations do next?
Start by identifying where AI workloads will provide real value, then assess whether your current endpoints, infrastructure, and governance models are ready to support them.
From that point on, organizations can take a more strategic approach to refresh cycles rather than reacting to new hardware releases.
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