Where Should AI Workloads Live? AI is everywhere—powering everything from self-checkout machines to advanced data analysis. But where should AI workloads actually run? Some perform best in a centralized data center, while others need to be processed right at the edge. So, what’s the answer? There isn’t one “right” answer—not exactly. Where AI runs depends on latency, bandwidth, security, and computing power. Get it right, and AI delivers speed, efficiency, and cost savings. Get it wrong, and you end up with bottlenecks, unnecessary expenses, and performance issues. The Case for AI at the Edge Some AI workloads can’t afford to wait. When milliseconds matter—like in manufacturing, healthcare, or security applications—AI needs to process data on-site, in real time. Instead of sending everything to a central data center, edge AI processes data where it’s created, reducing delays and keeping operations running even if connectivity is spotty. It also cuts bandwidth costs by limiting the amount of data that needs to be transmitted. That said, while AI at the edge offers lower latency, bandwidth efficiency, and more reliable operations, it comes with trade-offs. Storage, processing power, and security controls are often more constrained than in a traditional data center. Because of these limitations, organizations must be selective about which AI workloads run at the edge and ensure they have the right infrastructure to support them. The Role of the Data Center in AI Processing Not every AI workload belongs at the edge. Training AI models, running large-scale analytics, and handling massive datasets require the kind of high-performance computing that only a data center can provide. A centralized environment offers more processing power, stronger security, and the ability to scale AI workloads as they grow. Beyond raw computing power, data centers provide consistency. AI models rely on vast amounts of data, and a centralized infrastructure ensures better data management, compliance, and long-term storage. With IT teams under pressure to balance efficiency and cost, modern data centers are evolving to handle AI workloads without excessive power consumption or resource waste. But data centers come with challenges of their own. Latency can be an issue for AI that needs instant decision-making, and transmitting large amounts of data back and forth adds bandwidth strain. Most Businesses Need Both For many organizations, it’s not about choosing one or the other—it’s about making them work together. Some AI workloads need immediate processing on-site, while others require the scalability and power of a data center. The key is knowing which tasks belong where and ensuring your AI infrastructure is built to handle both environments seamlessly. The Right Infrastructure for AI—Wherever It Runs Balancing AI workloads across the edge and data center requires the right compute foundation—one that delivers scalability, security, and performance wherever AI runs. Hewlett Packard Enterprise Compute solutions provide the flexibility to support real-time edge processing, high-performance AI training in the data center, and everything in between. But knowing which workloads belong where—and how to deploy them effectively—isn’t always straightforward. ProCern can help you assess your AI infrastructure, determine the best workload placement, and implement the right HPE Compute solutions to ensure AI operates at peak performance—wherever it’s needed most.
Cyber threats don’t break down the door anymore—they walk right in. Stolen credentials, rogue devices, and unseen security gaps make it easier than ever for attackers to slip past defenses. IT teams try to keep up with access controls, but with more devices, users, and remote connections than ever, the old ways of securing a network fall short. The problem isn’t that businesses lack security tools—it’s that traditional access control wasn’t built for the scale and complexity of modern networks. Artificial intelligence is changing that. With the ability to analyze patterns, detect anomalies, and make split-second decisions, AI is helping IT teams stay ahead of threats that would otherwise go unnoticed. AI’s Role in Network Access Control AI makes access control faster and smarter. While traditional security systems rely on static rules—like usernames, passwords, and predefined permissions—AI adapts in real time, adjusting security based on user behavior, device activity, and changing risk levels. Here’s what AI brings to the table:• Identifies and classifies devices automatically: AI-powered recognition ensures that every device—whether it’s a personal phone, IoT sensor, or guest laptop—gets the right level of access without manual intervention.• Instantly spots suspicious activity: AI monitors login attempts, device behavior, and network traffic. If an employee suddenly logs in from an unusual location or a device behaves abnormally, AI flags the activity and triggers security protocols.• Adjusts access based on context: Instead of blocking legitimate users or letting in potential threats, AI evaluates risk dynamically. If a known employee logs in from an unrecognized device, AI might prompt multi-factor authentication rather than deny access outright.• Dynamically enforces security policies: AI updates security rules based on real-time network activity. If new risks emerge, AI modifies access permissions immediately, keeping threats out without causing unnecessary disruptions. Best Practices for Implementing AI AI is reshaping how access decisions are made—but putting it to work effectively takes more than flipping a switch. Here are some key considerations to keep in mind: • Maintain control of automation: While AI can automate your security decisions, your IT team still needs visibility and oversight. Choose solutions that let admins review AI-driven actions and adjust policies as needed.• Integrate with existing security tools: Access control powered by AI works best when it fits cleanly into your existing setup. Look for options that work seamlessly with your identity platforms, monitoring tools, and endpoint security—without adding extra complexity.• Train it with quality data inputs: AI learns from what’s happening on your network, but how well it adapts depends on the data feeding it. Make sure your systems are capturing accurate, relevant activity, so AI can keep up with shifting threats.• Balance security with usability: Security should be strong, but not at the cost of usability. Use tools that minimize unnecessary prompts or delays for trusted users while still responding to risky behavior when it appears. AI is changing how we think about network access control. With Aruba ClearPass, a network access control solution that uses AI to identify devices, enforce policies, and adapt to risk in real time, ProCern can help you stay ahead of threats, streamline access, and protect every device on your network
Early exploratory AI projects are one of the top 2024 IT trends tracked by our team. Recent announcements from IBM have shed light on their vision for AI-assisted coding for IBM i, a valuable roadmap for understanding where new capabilities can drive the most value for businesses that rely on RPG-based applications.
Recent announcements from IBM revealed that AI is coming to the IBM i platform.
In this blog, we explore the upcoming official AI-powered features from IBM, consider their implications for IT departments in manufacturing and distribution, and explain a framework for businesses ready to begin exploring early-stage AI applications capable of generating ROI without extensive investments or development.
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