The Business Case for Integrated Compute Solutions: Why Buy Usually Outperforms Build

Adopting Artificial Intelligence (AI) is an essential product feature and an operational asset. For many organizations, using AI becomes a reality when deployed at the edge – limiting data loss risk, lowering costs, and helping accelerate results. But where do you begin? For many original equipment manufacturers (OEMs), the journey starts by seeking clear answers to a few big questions: What data do you have now? What problems are you trying to solve with AI? And ultimately, how can you deliver and monetize those solutions? To compete in sectors like healthcare, manufacturing, and retail, OEMs need to approach AI from both sides—integrating it into their products and building it into their business processes. This article covers key elements in crafting an edge strategy that lets AI work for your products and your operations. A Purposeful Approach to AI for OEMs Deploying AI at the edge isn’t about seeking quick fixes for your problems. AI is about creating long-term, focused solutions that drive actual results. This purposeful approach to AI is what sets successful AI adoption apart. It starts with understanding what problem you’re solving and why it matters to your market. OEMs must identify their goals, knowing that successful AI strategies evolve and adapt over time. Arrow’s Intelligent Solutions team provides OEMs with a roadmap to success, offering purpose-built hardware and an end-to-end approach for deploying AI at the edge in real-world applications. From design and validation to supply chain management, Arrow helps align each step with your strategic goals. But even with the proper hardware, OEMs benefit most when they develop a holistic edge strategy that reduces complexity, manages costs, and accelerates time-to-market. Picking the Right Technology and Platforms The best AI platform does not necessarily mean the one with the latest hardware (although hardware performance matters). You also want to make sure you avoid obsolescence and have access to long-term support. Every AI solution requires a solid foundation—robust hardware and the right software ecosystem. Arrow’s role as an expert in providing purpose-built hardware means we can help organizations navigate a tricky marketplace and make the right decision for their specific needs. Leveraging relationships with leading technology suppliers is essential and allows you to stay current with hardware updates and shifts in the tech landscape. Arrow’s Intelligent Solutions team has spent decades building this network, helping OEMs create reliable connections with the right hardware suppliers and aligning them with the latest industry developments. What Is Needed to Deliver an Effective AI Solution? When delivering AI solutions, it’s important to remember that most products need careful hardware and software integration before they are customer-ready. With companies like Amazon and Roku leading the way in usability, today’s customers have higher expectations than ever before. For OEMs, a solution that delivers a consistent, user-friendly experience is the key to success. Arrow supports OEMs through every integration phase so complex AI systems function smoothly and are optimized to create value. This consistency and post-sales support are critical: if a system isn’t performing correctly, it’s not paying for itself. Our approach provides the infrastructure for OEMs to meet these expectations reliably, helping them build brand trust and operational efficiency. Capabilities Required for an OEM-Focused Strategy To maximize the value of AI, OEMs must focus on three essential capabilities: 1. Software Deployment and Integration Effective AI solutions aren’t just "plug and play"; they require thoughtful integration into existing systems. Software deployment must align with industry standards, optimizing performance without adding unnecessary complexity. For example, by handling deployment internally, OEMs can avoid the delays and costs of piecemeal integration and help launch immediate operational products. 2. Post-Sales Support and Risk Management The job continues once a product reaches the customer. OEMs need a dedicated support framework to ensure customers have a seamless experience. Post-sales support should extend throughout the lifecycle and offer troubleshooting, updates, and guidance when challenges arise, thus enhancing stability for OEM operations. 3. Customer Interfaces From order tracking to budget reviews, OEMs need straightforward tools to manage business processes efficiently. Leveraging user-friendly digital interfaces can empower OEMs, helping them focus on growth instead of navigating complex customer systems. These capabilities are foundational for any OEM looking to maximize the impact of their AI investments and maintain a competitive advantage. Putting AI to Work in Operations: "Don’t Just Make AI, Use It" For OEMs, adopting AI continues beyond integrating it into products. The real value comes when building AI into your operations. One example is Arrow’s internal AI chatbot powered by natural language processing. This tool will offer real-time information to team members across departments, streamlining workflows and enabling faster decision-making. A recent study from Stanford and MIT showed that similar AI chatbots improved productivity by around 14%. AI tools can help streamline internal operations for research or sales support. By utilizing AI within your processes, you can enhance productivity and improve your competitive position. Conclusion: Moving Beyond AI as a Buzzword It’s more apparent than ever that AI is more than a trend—it’s a transformative technology that can elevate an entire business when deployed thoughtfully. For OEMs, crafting an edge strategy to deploy AI that aligns with specific goals, maximizes operational efficiency, and supports long-term growth is essential and inspiring. Companies can turn this incredible potential into practical and meaningful gains by strategically integrating and aligning AI with evolving business needs.

The landscape of data processing is undergoing a massive shift. As artificial intelligence (AI) and particularly Generative (Gen) AI continue the relentless march forward, original equipment manufacturers (OEMs) face a critical question: where will the data be processed and stored? Let’s delve into the evolving world of far edge, near edge, and on-premise computing, exploring the considerations for OEMs seeking to build Gen AI solutions. Gen AI, while powerful at the edge, introduces some unique challenges to edge infrastructure, including: Massive datasets: Gen AI algorithms often require vast amounts of training data, which can strain far edge and near edge resources. Limited processing power: Gen AI models are computationally intensive. Edge devices, often with limited processing power compared to on-premise servers, can struggle to run complex Gen AI models efficiently. Real-time or near real-time needs: Certain Gen AI applications, like personalized recommendations or anomaly detection, might necessitate real-time or near real-time processing, pushing the boundaries of latency tolerance. The traditional centralized data processing model in the cloud is giving way to a more distributed approach. Leveraging a combination of far edge, near edge, and cloud resources can distribute the processing load. Far edge devices can perform initial data collection and pre-processing, while near edge nodes handle preliminary training and filtering. Complex Gen AI computations can then be offloaded to on-premise data centers or the cloud. Figure 1: Distributed computing: From the Far Edge to the Core/Cloud Far Edge Computing: Extending the Boundaries Far edge computing refers to processing data at the outermost edge of the network, close to where data is generated, which is particularly beneficial in scenarios where low latency and real-time processing are crucial. Applications in Gen AI Advantages of Far-Edge Processing Smart Surveillance and Security: Gen AI models at the far edge can analyze video feeds in real time to detect threats and recognize faces, reducing the need for data transmission to central servers. Autonomous Robots or Vehicles Robots and vehicles equipped with edge devices can instantaneously process sensor and camera data, enabling safer navigation and obstacle avoidance. Reduced Latency: 5 to 20 ms. Immediate data processing near the source. Bandwidth Efficiency: Less data goes to centralized cloud systems. Enhanced Privacy: Local data processing mitigates the risk of data breaches.   Near Edge Computing: Bridging the Gap Acting as a bridge between far edge devices and the cloud, near edge devices offer more processing power than their far edge counterparts. Gateways, local servers, and fog computing nodes typically reside at the near edge, handling tasks like data aggregation, filtering, and preliminary analytics. Applications in Gen AI Advantages of Near-Edge Processing Retail: Near edge devices can analyze customer behavior in real time to provide personalized shopping experiences and manage inventory efficiently. Smart Cities: Environmental monitoring systems at the near edge can swiftly process and respond to data on air quality, noise levels, and traffic patterns. Balanced Latency: Up to 50 ms. Quicker than centralized processing, suitable for applications that require timely responses but are not instantaneous. Scalability: Easier to scale across multiple locations compared to far edge devices. Cost-Effective: Reduces the need for extensive infrastructure at the far edge.   On-Premise/Cloud Computing: Control and Customization On-premise computing involves deploying and managing computing resources within the organization’s physical location. This approach offers high levels of control and security but can be less scalable than cloud-based solutions. Applications in Gen AI Advantages of On-Premise/Cloud Processing Healthcare: Hospitals can use on-premise Gen AI systems to analyze medical imaging and patient data, ensuring compliance with stringent data privacy regulations. Industrial IoT (IIoT): Manufacturing plants can deploy AI models to monitor machinery and predict maintenance needs, reducing downtime and improving productivity. Complete Control: Full data and infrastructure ownership allows for tailored solutions. Enhanced Security: Data remains within the organization's premises, reducing exposure to external threats. Customization: Flexibility to customize hardware and software to specific requirements.   6 Considerations for OEMs: Selecting the Right Infrastructure The choice between far edge, near edge, and on-premise processing depends on several factors for OEMs: Data volume and complexity: For applications generating large volumes of complex data, near edge or on-premise solutions might be necessary. Conversely, low-volume sensor data might be well-suited for far edge processing. Latency requirements: Real-time decision-making often necessitates on-site processing. Applications requiring minimal delay might favor near edge or on-premise solutions over the cloud. Security and privacy: Far or near edge processing can offer greater control and security for sensitive data. Regulatory compliance might also dictate processing location. Cost and scalability: Cloud-based solutions provide scalability and eliminate the need for upfront hardware investment. However, on-premise solutions can provide greater cost control over time. Power availability: Far edge devices often operate on limited power, necessitating efficient processing with minimal resource consumption. Operating environments: Temperature variations, dust, noise, and humidity are factors when determining where to deploy the infrastructure. Summary: The Future is Distributed The future of data processing lies in a distributed approach, with far edge, near edge, and on-premise solutions working in concert with the cloud. As Gen AI evolves, OEMs must adopt flexible and scalable processing strategies. By understanding the strengths and limitations of each processing platform and implementing a hybrid approach when necessary, OEMs can ensure their solutions effectively leverage the power of AI at the edge. Companies looking to leverage Gen AI at the edge can benefit from the combined expertise of Arrow and Dell OEM Solutions. Dell's robust portfolio provides the ideal building blocks for far edge, near edge, and on-premise deployments. Arrow can guide companies through the process, from selecting the optimal hardware for their Gen AI application to helping ensure a smooth supply chain and offering implementation support. Their expertise in solution orchestration enables companies to deploy and manage their Gen AI solutions at the edge with greater efficiency and reduced risk. Related Resources: eBook: Infrastructure Considerations for Rugged Edge Applications Product brief: Storage Portfolio At-a-Glance Infographic    

AI adoption permeates all industries, revolutionizing traditional processes, unlocking unprecedented use case applications, and opening new, unimaginable possibilities. According to GlobalData's forecast, the overall AI market will be worth $909B by 2030. The healthcare industry is in a transformative era with the adoption of AI technology. From pharmaceutical research and development to remote patient monitoring and in-home care, we are witnessing unprecedented leaps in robotic surgical procedures, precision imaging, and early disease detection, diagnosis, and treatment. In healthcare, AI innovation often happens at the edge, where data is created and handled close to where it occurs. The amount of data and the specific needs of medical applications require accurate and quick processing with minimal latency. The sharing and size of the data, as well as the quick response of edge medical devices, show how important it is to have a strong edge infrastructure. This requires an update or the creation of new designs to handle the power, capacity, and size needed to run many of these new AI-powered medical devices. The increasing rack densities and power requirements of high-performing chips and the higher heat loads from boosted compute power and super-sized servers highlight the critical need for efficient and rapid cooling. Modern imaging equipment like MRI machines, CT scanners, and X-ray devices demand extensive storage, high-speed processing capabilities, advanced computing, and operating systems. However, this doesn't necessarily mandate immediate radical overhauls. Initially, placing compact server units adjacent to machines near the source can support AI medical devices. Beyond the device build and embedded components, server support installs can go directly into the room. For larger installations and to support multiple devices, on-premise rack systems, micro data centers, or backup power sources such as UPS, complete with cooling and monitoring, can be installed in dedicated server room closets. Additional considerations include the number of devices, data volume, and real-time responses. One thing is certain: downtime is not an option. Arrow helps companies navigate the complexities of AI deployment. We work closely with leading technology and infrastructure providers such as Supermicro, APC, and Microsoft to develop certified, high-performing AI-ready devices, from board-level compute and operation systems to software, servers, and micro data centers. Whether you need to modify, expand, or design new architecture for your existing infrastructure, our integrated solutions help pave the way for a successful edge AI deployment in healthcare. Learn more about the impact of edge AI in healthcare in our brochure or speak to one of our experts for more guidance toward a seamless AI deployment.