The age of AI isn’t coming—it’s here, reshaping the way organizations think, operate, and win. At the heart of this transformation? Edge infrastructure. A significant surge in AI inference workloads is now a critical driver, necessitating the re-architecture of edge systems to power innovative new business applications built on AI. And this isn’t just about processing data faster. Transformation at the edge is about delivering unforgettable customer experiences, locking down sensitive data, and unlocking operational superpowers that process real-time applications and AI workloads as near to the user as possible.
Edge infrastructure and the AI advantage
What is edge infrastructure? It’s the technology that brings compute power out from distant, centralized data centers and puts it right up close to where data is born and used. There’s no single edge location. Rather, edge computing comprises a continuum of computing resources. In addition to on-premises edge locations such as office and industrial server closets, the edge may refer to regional service provider colocation facilities, as well as network cell towers and smaller data centers that serve wider geographic areas. Across this spectrum, the shift to edge computing can deliver blazing-fast insights and instant action to underpin a range of critical initiatives, from stopping fraud in its tracks to powering predictive maintenance and revolutionizing the retail experience.
AI is fueling this edge explosion. Real-time AI workloads, driven by inferencing, demand ultra-responsive, resilient edge systems. And it’s not just about performance. Cost savings, regulatory compliance, and data sovereignty are all key consideration factors. As the edge is fast becoming the launchpad for next-generation business insights and operations, the need for secure, high-performance infrastructure at the edge is non-negotiable. According to IDC’s 2025 EdgeView survey, a whopping 53% of organizations plan to upgrade their edge compute for AI. And with edge data volumes expected to hit 1.6 petabytes per organization by 2027, the time to build robust edge infrastructure is now.
The legacy trap: Why yesterday’s infrastructure can’t keep up
AI-ready edge systems are game-changers, but deploying and managing them isn’t easy. Imagine the challenge: deploying one server at 100 locations has very different requirements than deploying 100 servers at only one location. Traditional edge strategies struggle to keep up, creating headaches at every turn.
- Performance constraints: Legacy systems are often rigid and disconnected, unable to flex for modern edge workloads like inferencing, which can result in performance bottlenecks. This is compounded by physical limitations with regard to power and space.
- Operational complexity: Legacy systems often lack centralized visibility and management, as well, which creates operational complexity that, at AI-era scale, can result in “truck rolls” and configuration chaos that drive many edge projects well over budget.
- Security risks: Traditional approaches also fall short when it comes to managing security risks that increase as AI operations shift to the edge and expose models, applications, and devices to tampering and evolving physical and cyber threats.
- Talent gaps: Scarce IT staff at edge sites can lead to critical skills gaps, rising costs, and even safety risks.
- Solution fragmentation: Disconnected compute, storage, and security systems, along with integration challenges this creates for IT and OT, drain productivity.
How modern edge infrastructure accelerates innovation
To conquer these challenges, you need edge systems built for today and ready for tomorrow. Here’s what sets winners apart:
- Full-stack systems: Purpose-built for traditional and demanding new AI workloads, integrating compute, storage, networking, and security for effortless management.
- Centralized management: SaaS-driven, policy-based control with zero-touch provisioning, user-defined planned updates, and global visibility.
- Designed-in security: From physical tamper protection to AI model defense, every layer is locked down.
- Future-proof flexibility: Modular designs that let you upgrade what you need, when you need it.
- Tested reliability: Pre-validated, industry-specific solutions mean quicker, smoother rollouts you can trust.
The result? Better performance, greater efficiency, and fortified data security right where you need it.
Edge computing: the backbone of digital business
Edge computing isn’t a trend. It’s the foundation of modern business. As data volumes skyrocket, only edge systems deliver the real-time insights and agility needed to thrive. Yes, distributed IT brings complexity, but the answer to addressing this is simple: infrastructure designed for deployment ease, use case flexibility, and airtight security.
A successful edge strategy means understanding your unique needs and choosing systems that protect both your data and your bottom line. Unified edge solutions cut the management burden and unleash the full power of your data, especially when fueling advanced AI models and the new business applications they enable.
Ready to seize your competitive edge? Download IDC research on unified edge infrastructure to dive deeper into these critical insights and start optimizing your edge strategy today.
