Building Infrastructure Confidence in an Uncertain Supply Chain Climate

Finalizing a new product can often be more complex than developing the initial prototype. While a prototype only needs to be functional, a product ready for the market must consider component lifecycle, cost, and supply chain management factors. In the medical equipment industry, optimizing the bill of materials (BOM) for a device’s computing aspect is crucial; this helps ensure cost-effectiveness while maintaining the desired quality and performance of the end product.

As businesses race to deploy AI, edge computing, and real-time analytics, product developers must make a critical decision: Build from scratch with discrete components for compute and storage, or Adopt pre-integrated, commercial hardware platforms that offer tested, scalable solutions. While the DIY approach might seem appealing at first glance, compelling evidence suggests that integrated solutions deliver superior value across multiple dimensions for most businesses. The Real Cost Equation: Beyond Initial Purchase Price A comprehensive VDC Research study has demonstrated that organizations leveraging commercial hardware platforms cut their total development costs by 20% compared to those sourcing only component-level technology. This significant cost advantage stems from multiple factors that companies may overlook in initial budget comparisons. When organizations build from scratch, they incur substantial hidden costs: Engineering time spent on low-level integration challenges Extended development cycles Troubleshooting compatibility issues Ongoing maintenance and support requirements These costs compound over time, creating a total cost of ownership that far exceeds initial expectations. Accelerating Time to Market: The Hidden Competitive Advantage Integrated solutions reduce costs and significantly accelerate time to market. Organizations that outsource development tasks are more likely to complete projects ahead of schedule than those managing everything in-house. Project delays often stem from technical complexity, integration challenges, and shifting specifications—issues amplified by talent shortages and evolving skill demands. Certified, pre-integrated third-party solutions help mitigate these risks by streamlining development, reducing engineering bottlenecks, and easing internal workloads. Leveraging commercial technology platforms enables faster deployment, minimizes disruptions, and provides a scalable foundation for future product generations. [caption id="attachment_12806" align="alignnone" width="1080"] Figure: Attributions of Delays for Current Projects (Source: VDC Research)[/caption] In one compelling case study highlighted in the same VDC report, a leading medical device manufacturer achieved a 20% reduction in project schedule by using commercially integrated systems for their patient monitoring solution. This acceleration meant: Earlier revenue generation Faster response to market demands Improved customer satisfaction Enhanced competitive positioning Focusing on What Truly Differentiates Your Business The most strategic advantage of integrated solutions is the ability to reallocate resources toward genuine innovation and differentiation. Organizations sourcing complete commercial hardware platforms can devote more development resources to developing differentiating intellectual property than those building from components. [caption id="attachment_12807" align="alignnone" width="1080"] Figure: Estimated Percent of Development Costs Spent on Specific Tasks, Segmented by Hardware Sourcing (Source: VDC Research)[/caption] This shift in focus has a significant impact: Engineering teams spend more of their time on advanced analytics and AI Development resources target customer-facing features • Organizations can explore new business models and revenue streams Technical teams build expertise in high-value domains rather than routine integration tasks One industrial automation company featured in the VDC study saw a remarkable 50% increase in profitability after shifting to commercial technology solutions. The tremendous savings in software development time and the ability to focus on their core business value outweighed their initial hesitation about hardware costs. Driving Business Transformation with Integrated Commercial Solutions Integrated solutions extend beyond hardware, providing complementary technologies, software, and lifecycle services that streamline development, reduce risk, and accelerate innovation. Key benefits of integrated commercial solution providers include: Pre-certified configurations for seamless regulatory compliance Seamless interoperability with software platforms and services Reliable supply chains ensure consistent component availability Global support networks for troubleshooting and technical assistance Regular updates and security patches to enhance reliability and security A transportation control system manufacturer highlighted in the VDC report leveraged commercial platforms to avoid expensive certification failures and delays. By selecting pre-certified hardware with bundled operating system and application-level firmware, they essentially transformed into a software company focused solely on their unique value proposition. Making the Right Choice for Your Organization While the advantages of integrated solutions are clear, making the appropriate selection requires careful consideration of several factors: Evaluate your core competencies: If hardware integration isn't a key differentiator, integrated commercial platforms offer clear advantages. Consider the entire lifecycle: Factor in development time, maintenance requirements, upgrade paths, and long-term support needs. Assess customization needs: Many commercial solutions offer significant customization options without the overhead of building from scratch. Calculate the innovation dividend: Consider how much more your teams could accomplish if freed from low-level integration work. Value supply chain resilience: Commercial platforms typically have established supply chains and contingency plans.   Arrow’s Integration Services Arrow, working closely with Dell Technologies OEM Solutions, helps businesses deliver high-quality integrated products at scale by providing hardware configuration, software imaging, system testing, and logistics across a global network of facilities. With end-to-end integration capabilities, including custom OS deployment, full rack enclosures, and prototype development, Arrow helps enable companies to optimize costs, reduce complexity, and accelerate time to market while maintaining the highest quality standards. Conclusion: Strategic Technology Decisions Drive Business Success As the research demonstrates, organizations that use integrated commercial platforms consistently outperform competitors by delivering innovative solutions faster and at lower total cost. Contact our solutions team today to discuss how Arrow can help transform your technology development and deployment approach. Download the VDC Research’s report Reduce Cost, Drive Innovation and Shorten Time to Market with Commercial Hardware Platforms.

Artificial Intelligence (AI) is rapidly reshaping industries, transforming how businesses operate, make decisions, and interact with customers. For original equipment manufacturers (OEMs) and independent software vendors (ISVs), understanding AI’s capabilities is crucial for staying competitive and unlocking new opportunities. However, many businesses struggle to harness AI’s full potential. This article explores the different types of AI, their applications, the challenges AI can address, and how OEMs and ISVs can successfully integrate AI into their solutions. Understanding Artificial Intelligence AI is the development of computer systems that can perform tasks traditionally requiring human intelligence. These systems process vast amounts of data, recognize patterns, and make informed decisions, often with minimal human intervention. AI is not a singular technology but a broad spectrum of capabilities ranging from rule-based automation to advanced deep learning algorithms that can generate new content, recognize speech, and predict outcomes. AI’s applications extend across industries, enhancing automation, optimizing efficiency, and enabling new ways of interacting with technology. While AI has been around for decades, recent advancements in computational power and data availability have accelerated its adoption, making it an essential tool for businesses seeking to innovate and remain competitive. The Different Types of AI Natural Language Processing Natural language processing (NLP) enables machines to understand, interpret, and generate human language. This AI discipline has led to the development of chatbots, virtual assistants, automated customer service tools, and sophisticated translation software. Businesses leverage NLP to enhance customer interactions, analyze large volumes of text data, and improve application search and discovery functions. For OEMs and ISVs, NLP provides a way to integrate voice recognition, sentiment analysis, and automated support into their products. For example, voice-controlled interfaces in smart devices rely heavily on NLP to enable seamless interaction between users and technology. Additionally, businesses can use NLP-driven analytics tools to extract insights from unstructured data sources such as emails, customer reviews, and social media. Machine Learning and Predictive Analytics Machine learning (ML) is one of the most widely implemented forms of AI. It involves training algorithms to recognize patterns in data and make predictions without being explicitly programmed for each scenario. Predictive analytics, a branch of ML, allows businesses to anticipate future trends, detect anomalies, and optimize operations. Manufacturers and software vendors are incorporating ML into their systems to improve efficiency, reduce costs, and drive innovation. Predictive maintenance, for example, uses AI to monitor industrial equipment and forecast potential failures before they occur, reducing downtime and repair costs. In the cybersecurity domain, ML algorithms analyze network traffic patterns to detect potential threats in real-time, allowing businesses to respond proactively to security risks. Generative AI Generative AI represents the next frontier in artificial intelligence. Unlike traditional ML models that analyze and classify data, generative AI creates new content based on learned patterns. This capability has vast implications across industries, from automating content creation to designing novel products. For OEMs and ISVs, generative AI can streamline processes such as automated documentation, AI-generated design prototypes, and enhanced user experience customization, allowing opportunities for companies to automate tasks while maintaining quality and coherence. Addressing Challenges with AI Despite AI’s potential, implementing these technologies comes with challenges. One of the most significant obstacles is helping ensure that AI systems operate transparently and fairly. AI models often rely on vast datasets for training, and biases in these datasets can lead to skewed or unethical decision-making. For businesses deploying AI, maintaining accountability and implementing fairness in AI-driven outcomes is critical. Another challenge is integrating AI into existing workflows and infrastructure. Many organizations face difficulties managing the large volumes of data required to train AI models and ensuring seamless integration with legacy systems. Additionally, AI models require continuous refinement to adapt to changing business environments and emerging data patterns. Security and privacy concerns also play a major role in AI adoption. AI systems often process sensitive customer and business data, making them potential targets for cyber threats. Implementing robust security measures and complying with data privacy regulations are essential for businesses leveraging AI. Deploying AI for OEMs and ISVs Businesses need a clear strategy that aligns with their operational goals to deploy AI effectively. Successful AI integration involves more than simply adopting the latest technology; it requires thoughtful planning and alignment with business needs. OEMs and ISVs can start by assessing how AI can enhance their offerings, whether by improving automation, streamlining workflows, or enhancing customer interactions. Cloud-based AI services have made AI more accessible, allowing companies to integrate machine learning models and NLP capabilities without needing in-house expertise in AI development. By leveraging AI-as-a-Service solutions, businesses can reduce the complexity of AI adoption and scale AI capabilities as needed. Ensuring data readiness is another crucial factor. AI systems are only as good as the data they process. High-quality, structured datasets enable AI models to generate accurate and meaningful insights. Businesses must prioritize data collection, storage, and management strategies to maximize AI’s effectiveness. Collaborating with AI technology providers can also accelerate AI deployment. Experts who specialize in AI solutions can help OEMs and ISVs navigate the complexities of AI implementation, from selecting the right algorithms to optimizing AI models for their specific industry needs. Following AI deployment, continuous monitoring and refinement are necessary. AI systems require ongoing updates to remain relevant and effective. Performance evaluations and model retraining help ensure that AI solutions continue to deliver value as business needs evolve. The Future of AI in Business AI is rapidly evolving, and its impact will continue to expand across industries. Advances in explainable AI (XAI) make AI systems more transparent, allowing businesses to understand and trust AI-driven decisions. Edge AI enables AI processing closer to data sources and enhances real-time decision-making capabilities, particularly in IoT applications and remote deployments. For OEMs and ISVs, investing in AI today means staying ahead in an increasingly digital and automated world. AI adoption will continue accelerating, driving innovation in product development, customer engagement, and operational efficiency. Companies strategically integrating AI will position themselves as industry leaders, gaining a competitive edge in the market. Conclusion Understanding AI’s different forms, from NLP to predictive analytics and generative AI, allows businesses to harness its potential effectively. While challenges exist, a well-planned AI strategy can unlock new opportunities, improve efficiency, and drive business growth. At Arrow, we help businesses build a comprehensive AI strategy that balances performance, cost, and scalability. By leveraging advanced AI solutions from our cutting-edge ecosystem, we help ensure your AI deployments remain efficient, reliable, and sustainable. Contact our experts to learn how we can enable your next AI-driven technology.

Healthcare is entering a decade of unprecedented transformation. Costs are expected to rise at roughly 8.5% annually, according to PwC1. At the same time, care is shifting out of hospitals and into distributed environments supported by virtual delivery, automation, AI, and continuous data flows. Healthcare leaders are no longer asking how technology fits into their ecosystem; they are asking how quickly they can modernize and how deeply compute needs to move closer to where care happens. "By 2035, healthcare will be proactive, automated, robot-enabled, and accessible wherever life happens" – PwC Companies developing imaging solutions, care-coordination platforms, analytics tools, and diagnostic devices face new pressures. They must support richer data workloads, higher image fidelity, and AI-powered insights while reducing operational complexity. They need compute that is powerful, long-lasting, secure, and flexible enough to support the rapidly growing range of care settings from traditional radiology suites to mobile carts to in-home monitoring. This is where HP Z Workstations, supported by Arrow’s intelligent solutions business’s engineering and lifecycle services, provide value. The New Compute Reality in Healthcare By 2035, PwC projects that more than $1T1 of annual spending will shift from infrastructure-heavy environments to virtual, intelligent, and distributed care models. This shift requires high-performing, reliable, and easy-to-integrate compute at the edge. Three trends are reshaping what healthcare enterprises expect from their hardware partners: 1. Care is moving closer to the patient From hospital-at-home programs to connected diagnostics, compute must support real-time decision-making at the point of care. This includes low-latency inferencing, 3D imaging, data acquisition, and analytics where bandwidth or uptime cannot be compromised. 2. AI workloads are expanding AI algorithms support early risk detection, triage, image enhancement, and operational automation. AI will become a core driver of diagnosis, navigation, and workflow efficiency. This requires dependable GPU-accelerated systems and scalable architecture. 3. Infrastructure must shrink Facilities are evolving into high-speed care nodes. In many environments, equipment must fit into carts, cabinets, mini-data-closets, or small clinical rooms. In these space-constrained environments, compute density is just as critical as performance. HP and Arrow directly address these needs with a combined foundation of long-lifecycle hardware and specialized engineering support. Why HP Z Workstations Fit the Needs of Healthcare HP Z Workstations have become the trusted backbone for imaging, diagnostics, clinical informatics, life sciences, and emerging AI-enabled healthcare devices. Their value is clear: Proven platform stability Z platforms support long, predictable lifecycles that reduce redesign and validation. This is critical for imaging systems, analytics platforms, and medical device OEMs that require consistent configurations across years of production. Flexible deployments from cart to data closet The Z portfolio spans small-form-factor systems, mini workstations, full-tower compute, and the Z4R 1U rack workstation. At the same time, healthcare enterprises can standardize workflows while deploying purpose-built form factors to support: Mobile carts Ultrasound and point-of-care systems Distributed radiology and visualization workflows Hybrid on-prem and cloud-connected AI systems Enterprise-grade performance for clinical workloads HP Z systems deliver the compute needed for 3D imaging, multimodal diagnostics, real-time rendering, and advanced analytics. With support for NVIDIA, AMD, and Intel GPUs, organizations can match the right accelerator to each workload from visualization to generative AI. Optimized for edge inferencing Z platforms support containerized AI workloads, local inferencing, and low-latency processing. They excel in: Imaging reconstruction Segmentation and detection models Real-time triage Video analytics in clinical settings Clinical decision-support applications Security is built into every layer Healthcare IT teams benefit from features such as HP self-healing BIOS and HP Wolf Security, which provides hardware-enforced security. These protections help safeguard sensitive clinical data and maintain endpoint trust in distributed environments. The Role of Arrow Intelligent Solutions Building on HP’s compute foundation, Arrow engineers, integrates, and delivers platforms designed to remove deployment friction for OEMs, ISVs, and system integrators. Arrow’s teams support the full lifecycle of healthcare solutions: Engineering and validation Arrow provides engineering support to help ensure each workstation performs consistently across healthcare applications. This includes optimization, testing, and configuration services that reduce variability and improve workload stability. Thermal and mechanical testing BIOS configuration and tuning GPU and workload optimization Application validation and stress testing Integration and manufacturing With Arrow, we deliver complete system integration so healthcare solutions arrive deployment-ready. Systems are built, tested, and packaged consistently across global facilities. Custom imaging and assembly Branding and labeling Cabling, racking, and full system integration Global manufacturing and logistics at scale Supply chain stability Predictable component availability is essential as care models expand beyond traditional facilities. Arrow manages long-term planning and coordination to keep healthcare platforms consistent year after year. Multi-year forecasting Inventory management Global fulfillment Lifecycle continuity Arrow and HP: Supporting the Demands of Modern Healthcare Healthcare is becoming more distributed, data-intensive, and AI-enabled. Rising costs, workforce challenges, and the shift toward virtual and connected care models are pushing organizations to rely on technology that can operate reliably across many clinical environments. HP Z Workstations and Arrow’s intelligent solutions business provide the computing foundation required for this evolution. HP delivers long-life, high-performance platforms that support imaging, diagnostics, analytics, and AI at the point of care. Arrow contributes engineering expertise, global integration, and lifecycle support that help healthcare organizations deploy and maintain these platforms consistently at scale. Together, HP and Arrow give healthcare enterprises confidence that their compute infrastructure will remain stable, responsive, and ready to grow with emerging demands.

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.