MCPC

Name: MCPC
Address: 21500 Aerospace Pkwy, Cleveland, OH, 44142, US
Telephone: +18007777178

End-to-end IT lifecycle solutions that simplify operations, secure assets, and improve digital employee experience. Learn more at www.mcpc.com.

From device deployment to asset disposition, MCPC helps organizations reduce risk, drive efficiency, and scale with confidence. MCPc is a global data protection company that improves the security and financial well-being of our clients.

06/03/2026

Welcome to the era of Edge AI — where the devices your workforce uses every day are becoming part of your AI infrastructure.

That shift raises a question more IT leaders are grappling with: which AI workloads should run on the device, and which belong in the cloud?

Our latest article covers the differences between edge and cloud AI, the pros and cons of each, and a practical six-factor framework for deciding where enterprise AI workloads should run.

Link in the comments →

06/02/2026

The choice between edge AI and cloud AI is not just an architecture decision. It affects which devices you buy, how you manage them, how long they remain capable of supporting your AI tools, and how you govern AI processing across your fleet.

Our latest article is a practical look at how IT leaders should think through that decision — and what it means for enterprise device strategy.

Link in the comments →

06/01/2026

Enterprise AI strategy used to be a cloud decision. Today it is more nuanced than that.

As AI processing moves closer to the device, IT leaders are increasingly responsible for deciding which workloads run locally, which belong in the cloud, and how those choices connect to the devices they manage.

Our latest article breaks down both approaches and gives IT leaders a practical framework for making that call.

Link in the comments →

05/28/2026

The future of enterprise AI is not cloud-only or edge-only.

It is a balance.

Some workloads belong on the edge because they benefit from low latency, local processing, or continuous ex*****on. Others are better suited to the cloud because they are more compute-intensive or less time-sensitive.

The challenge for IT leaders is not choosing one model over the other. It is deciding which layer should handle which part of the workload.

That balance is becoming a core part of device and infrastructure strategy.
https://hubs.li/Q04h__470

05/27/2026

AI PCs are not just faster laptops. They represent a shift in where enterprise AI processing happens — and that shift has implications that go well beyond the device spec sheet.

Hardware selection, endpoint management, workload distribution, governance, sustainability — edge AI touches all of it.

Our latest article, The Rise of Edge AI: What Shifting Compute Closer to Users Means for Device Strategy, is a practical read for IT leaders working through what this shift means for their device programs.

https://hubs.li/Q04j0WnG0

05/26/2026

One of the most practical arguments for edge AI does not get enough attention: it works when the network doesn't.

Cloud-dependent AI tools degrade or stop functioning entirely when connectivity is limited — remote locations, travel, facilities with constrained bandwidth, hybrid environments where network quality varies.

Edge AI processing happens locally, on the device, regardless of network conditions. For organizations with distributed or mobile workforces, that reliability is not a nice-to-have. It is an operational requirement.

Our latest article covers why compute is moving closer to users and what that means for enterprise device programs.

https://hubs.li/Q04hX7fy0

05/20/2026

Edge AI has a sustainability dimension that belongs in device strategy conversations — and it cuts both ways.

On one side, on-device AI processing reduces cloud infrastructure load. For organizations with sustainability commitments, that reduction in data center energy consumption is a real and plannable benefit.

On the other side, higher-specification endpoint devices carry a larger environmental footprint — greater manufacturing energy, materials use, and end-of-life disposal obligations than standard business laptops.

The sustainability math also depends heavily on lifecycle duration. A device that remains capable of running current AI workloads for four or five years has a meaningfully different environmental profile than one replaced in two because its NPU performance fell below evolving application thresholds.

Both sides of this equation belong in the planning conversation from the start. Our latest article covers the sustainability implications of edge AI adoption.

https://hubs.li/Q04hh6RX0

05/13/2026

For organizations in regulated industries, one of the most compelling arguments for edge AI is also one of the least talked about.

When AI inference runs in the cloud, data leaves the device. For healthcare, financial services, legal, and government organizations — or any organization handling sensitive intellectual property — that data movement creates compliance complexity that has to be actively managed.

On-device processing changes that. Sensitive data can be processed locally without being transmitted externally. For many organizations, that distinction is becoming a primary driver of interest in AI PCs — not just a secondary benefit.

Our latest article covers the data governance implications of edge AI adoption - https://hubs.li/Q04gstJf0

05/06/2026

Four things are driving AI processing from the cloud to the device — and they are all converging at the same time.

Latency. Real-time AI applications cannot afford the round trip to a remote server. Local processing responds instantly.

Connectivity. Cloud AI assumes a reliable network. Edge AI works regardless of network conditions — a meaningful advantage for distributed and mobile workforces.

Data governance. When inference runs in the cloud, data leaves the endpoint. Local processing keeps sensitive data where it belongs.

Cost. AI inference at scale generates cloud compute costs that compound quickly. Shifting appropriate workloads to the edge reduces that dependency without reducing capability.

Our latest article covers what this shift means for an organization's device strategy - https://hubs.li/Q04fDXy-0

05/05/2026

Evaluating AI PCs on NPU performance alone misses two factors that matter just as much in practice.

Thermal design. AI workloads running continuously on-device generate sustained compute demand. A device without adequate thermal management will throttle performance over extended sessions — quietly undermining the user experience the hardware was selected to deliver.

Memory configuration. Local AI inference is memory-intensive. Insufficient memory creates constraints that limit capability regardless of how strong the NPU is.

Selecting the right AI PC means evaluating these as well. Our latest article covers what the shift to edge AI means for hardware design and selection.

https://hubs.li/Q04fqFB-0

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21500 Aerospace Pkwy
Cleveland, OH
44142

Telephone

+18007777178

Website

http://www.mcpc.com/

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