Why manufacturing requires a cloud-to-edge strategy

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As manufacturing begins to take AI and automation onboard, manufacturers may discover the limits of relying solely on public clouds. Training and running AI models in the cloud can be costly, bandwidth-intensive, and risky when sensitive data is involved.

The answer? A cloud-to-edge strategy—where processing moves closer to the devices and users generating the data. By combining the scalability of cloud with the immediacy of the edge, organizations gain the speed, security, and efficiency of today’s AI-driven business demands.

But there’s no denying that designing and operating a cloud-to-edge architecture requires deep expertise. That’s where managed service providers (MSPs) come in.

An informational booklet titled "Building a cloud-to-edge strategy for AI" with interior pages about edge computing and AI strategies, featuring graphics and statistics.

An MSP can help your organization

Optimize workloads

Decide which data should stay at the edge for speed, and which belongs in the cloud for deeper analysis.

Streamline vendor management

Select the right mix of cloud and edge technologies while controlling costs.

Unify operations

Replace siloed systems with an integrated, secure, cloud-to-edge platform.

Strengthen security

Implement layered protections across cloud and edge environments.

Boost customer experience

Deliver faster, smarter services powered by real-time insights.

It’s no surprise that 68% of organizations already rely on MSPs to manage and coordinate their cloud strategies.

Take the next step

The future of AI is not in the cloud or the edge—it’s in both. A well-architected cloud-to-edge strategy unlocks real-time intelligence, operational efficiency, and competitive advantage.

With the right MSP partner, your business can design, deploy, and manage a cloud-to-edge AI strategy tailored to your industry and goals.

👉 Download the ebook to learn how to work with an MSP to make cloud-to-edge work for you.