Why manufacturing needs a cloud-to-edge strategy
As AI and data analytics adoption accelerate, manufacturers are running into the limits of cloud-only approaches. That’s why many are moving toward cloud-to-edge strategies. Here’s how MSPs are helping them.
- As AI becomes pervasive in business operations, cloud-only infrastructure adds cost, latency, and security concerns to organizations such as manufacturing that are already
- As a result, organizations, such as manufacturers, are shifting to cloud-to-edge strategies that process data closer to the source, enabling faster insights, better performance, and stronger compliance for real-time, data-intensive AI workloads.
- But managing cloud and edge together is complex. Managed service providers (MSPs) design hybrid architectures, optimize workload placement, control costs, manage vendors, and secure every layer.
Artificial intelligence (AI) is not just a corporate pilot project anymore. AI is becoming central to how organizations operate and compete.
From predictive analytics and workflow automation to real-time business insight, AI is reshaping industries. But with that shift comes an important challenge: how to build the infrastructure to support data-intensive AI workloads.
Public clouds have been the default choice for scalable compute and storage. Yet as AI adoption accelerates, companies are running up against the limits of public cloud-only approaches.
Cloud processing can be costly, introduce latency, and complicate data security and compliance.
That’s why many organizations, manufacturers among them, are moving toward cloud-to-edge strategies—distributing compute power between centralized cloud platforms and local “edge” systems. By using edge networks, manufacturers are bringing the processing closer to where the users are– and where the data is generated. To make these strategies effective, businesses are increasingly turning to managed service providers (MSPs). MSPs bring the expertise needed to balance cloud and edge, creating secure, cost-effective architectures that support AI at scale.
What is edge computing? And why it matters for AI
Edge computing is a distributed architecture in which data processing, storage, and network connectivity occur closer to the data source (the “edge”) instead of relying solely on centralized cloud servers.
Think of Internet of Things (IoT) sensors, robotics in factories, smart medical devices, or mobile phones. With edge computing, much of the data from these devices is processed locally rather than transmitted across long distances to a cloud data center. The result? Faster response times, reduced network strain, improved resilience, and better security.
This matters in the era of AI and other data-intensive processes. A self-driving car can’t wait for cloud instructions when milliseconds determine safety. A manufacturer monitoring machinery can’t afford seconds of delay that might cause production downtime. Healthcare providers can’t risk delays when life-saving equipment depends on real-time insights.
The market reflects this shift. Statista projects global edge computing revenue will reach $350 billion by 2027.. Gartner predicts 75% of enterprise-managed data will be created and processed outside centralized data centers by 2025. And Accenture reports that 83% of executives believe edge computing is essential for future competitiveness.
“By moving data processing and artificial intelligence closer to the data source, edge AI is improving real-time insights, lowering latency, and revolutionizing industries,” noted Claire Costa in “Edge AI in 2025: Moving Intelligence Closer to the Source.”
Why MSPs are critical for successful cloud-to-edge adoption
But deploying edge computing isn’t as easy flipping a switch.Unlike cloud platforms, which are relatively standardized, edge environments are fragmented, industry-specific, and deeply tied to unique business processes. Companies must determine:
- Which workloads to run at the edge vs. in the cloud
- How to design secure architecture across distributed locations
- How to manage vendor contracts and integrate multiple providers
- How to control costs as AI workloads grow
That’s why, according to Hanover Research, 68% of respondents are relying on MSPs to manage and coordinate their cloud strategy.
- Design tailored cloud architecture. Aligning infrastructure with industry-specific AI use cases.
- Integrate edge and cloud. Unifying systems that would otherwise operate in silos.
- Secure sensitive data. Building layered defenses that extend from edge devices to central cloud.
- Manage complexity and costs. Monitoring usage, eliminating inefficiencies, and continuously optimizing IT spending.
In short, MSPs turn edge computing from an experimental project into a scalable, secure, and cost-effective foundation for AI.
The benefits of edge computing for AI
Before we explore how MSPs enable these benefits, let’s look at what edge computing brings to the table:
- Reduced latency. By processing data locally, organizations gain near-instant responses—critical for real-time AI applications.
- Lower bandwidth use. Less raw data is sent to the cloud, reducing congestion and cloud costs.
- Improved security. Sensitive data can be kept and processed locally, minimizing exposure risks.
- Greater reliability. Edge systems can continue operating even if cloud connectivity drops.
- Operational efficiency. According to Claire Costa, edge AI can cut unplanned downtime by 20% to 30%, reducing costly production delays.
These benefits explain why more companies are pushing compute toward the edge. But achieving them consistently requires careful design and management—areas where MSPs add real value.
How MSPs help organizations capture the benefits of cloud-to-edge strategy
Here’s how MSPs transform theoretical benefits of edge computing into real-world results:
- Security at every layer. MSPs build and monitor layered defenses across devices, networks, and cloud platforms. Many deploy automated threat detection and response, especially important for IoT-heavy environments.
- Workload optimization. MSPs help organizations decide which workloads should run where: edge for latency-sensitive tasks, cloud for deep analytics and model training.
- Vendor management and cost control. MSPs help businesses select the right mix of edge and cloud vendors, ensuring compliance with industry and security requirements. They continuously monitor usage to identify inefficiencies and align IT spend with business goals.
- Eliminating waste. By filtering and pre-processing data at the source, MSPs prevent unnecessary data transmission to the cloud—reducing bandwidth and storage costs.
- Holistic management. Instead of siloed edge systems and cloud environments, MSPs provide a unified management framework. This simplifies operations for IT teams, freeing them to focus on innovation.
- Enhanced customer and employee experience. With real-time AI insights at the edge, businesses can respond faster to customer needs and improve operational decision-making.
“Edge computing complements cloud computing, extending digital transformation to the edge,” said Gartner’s Thomas Bittman in “Computing and storage are moving to the edge, and IT needs to be ready.” MSPs enable this complementarity possible by stitching together cloud and edge into a cohesive strategy.
The hybrid advantage: Cloud and edge together
The future isn’t all-cloud or all-edge. It’s hybrid.
A hybrid cloud-to-edge strategy distributes workloads intelligently across public cloud, private cloud, and edge environments. The benefits include the following:
- Tiered bandwidth usage. Edge pre-processes and compresses data, sending only high-value insights to the cloud.
- Model training in the cloud, inference at the edge. Powerful cloud servers train models, while optimized versions run locally for real-time decisions.
- Stronger data security. Sensitive workloads stay in private or on-prem environments, while less sensitive processes leverage public cloud scale.
- Dynamic workload distribution. Work is routed based on latency, security, and bandwidth requirements.
- Resilience. Edge devices generate and process critical data in real time, while cloud environments provide redundancy and long-term storage.
Unsurprisingly, 65% of companies plan to merge edge and cloud environments within the next 12 months. The hybrid model reflects reality: cloud and edge are stronger together than apart.
Why MSPs are partners in your cloud-to-edge journey
AI is driving organizations to rethink their infrastructure. Public cloud architecture alone can’t meet the demands of latency-sensitive, data-intensive, and compliance-heavy workloads. Edge computing fills those gaps—but it requires thoughtful deployment and ongoing management.
That’s where MSPs come in. From workload placement and vendor management to layered security and cost optimization, MSPs can make a cloud-to-edge strategy viable and scalable.
Hybrid architectures—spanning cloud, private infrastructure, and edge—give businesses the balance of performance, cost control, and security they need. And with MSPs leading the way, organizations can unlock the full potential of AI while maintaining agility, compliance, and customer trust.
Check out the complete ebook on cloud-to-edge architecture here.
