MCPC

AI PCs are not just faster laptops. They are part of a broader shift in how enterprise AI gets delivered. And that shift has implications for device strategy that go well beyond performance specs.

Hardware selection, endpoint management, workload distribution, governance, lifecycle planning — the edge vs. cloud AI decision touches all of it.

Our latest article is a practical read for IT leaders working through what this shift means for their device programs.

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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.

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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.

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