I’ve returned from NVIDIA’s GTC event in Paris. In just a matter of weeks, NVIDIA took over half of Porte de Versailles and transformed it into the epicenter of AI conversation. It was impressive not just in terms of scale but in how it laid out where this industry is heading. The fact that NVIDIA recognized the value of hosting a GTC event in Europe speaks volumes about the opportunities in the region.

In his keynote, CEO Jensen Huang outlined a vision touching on sovereign AI, industrial supercomputing, and agentic systems, as well as the convergence of AI and quantum technologies. He encouraged European enterprises not just to adopt AI but to build and run it locally, aligning with the EU’s broader ambitions to assert digital independence and develop homegrown AI infrastructure.

Let’s unpack a few of the highlights from the NVIDIA GTC Paris 2025 event:

The Spotlight on Agentic AI

One of Huang’s central themes was how we’ve moved from perception AI (sensors and computer vision) to generative AI (text, images, code) and now into agentic AI. Unlike traditional AI systems that passively respond to prompts, agentic AI can make decisions and take actions independently, transforming AI from a tool into a trusted digital collaborator.

NVIDIA introduced several key innovations in this space:

1. NeMo Agent: a modular, task-driven framework for building autonomous AI agents that can plan and reason
2. AI Blueprint: a production-ready framework for building, testing, and deploying agentic and generative AI workflows
3. Agentic AI Safety Framework: a framework for ensuring autonomous AI systems operate safely, ethically, and reliably

These tools can enable organizations to build AI systems that automatically collect, analyze, and feed back data into models for ongoing improvement and refinement. There is a clear shift from co-pilots to AI systems that act with purpose, handle operational tasks, and learn in real time, opening the door to more autonomous customer service, operations, and engineering workflows.

The notable shift from NVIDIA in the agentic AI conversation was that modern AI infrastructure isn't just about compute, but about building intelligence pipelines that continuously fuel and evolve AI systems in a secure, scalable, and compliant manner.

Industrial AI: NVIDIA’s AI Factory Vision and The Indisputable Edge

Another major highlight was the introduction of AI Factories, or rather, next-gen data centers powered by NVIDIA's Blackwell GPUs and Omniverse tools. These facilities serve as the digital engines of Europe's AI ecosystem, supporting industries like automotive, manufacturing, and healthcare. NVIDIA showcased collaborations with some of Europe’s top automotive manufacturers and healthcare providers.

With Factory AI comes a renewed focus on edge computing as a foundational layer in NVIDIA’s broader vision. While centralized data centers orchestrate training and coordination, the edge performs real-world inference. It acts as the first point of contact for data collection and decision-making on factory floors, in vehicles, and across field equipment.

For Arrow customers, this signals an accelerating need for robust, deployable edge infrastructure that bridges physical operations and digital intelligence. As enterprises invest in AI-driven automation, edge platforms remain essential for Industrial AI.

Photo: NVIDIA Industrial AI at GTC Paris 2025

AI and Quantum: A Converging Frontier

It's challenging to determine how close or distant the convergence of AI and quantum computing is; for some industries, this convergence is closer than for others. Nevertheless, we gained a glimpse of an early yet clear vision for integrating AI workloads and quantum technologies, enabling quantum processors to handle highly specialized computations while GPUs manage traditional workloads. These innovations are designed to speed the development of quantum error correction. With this news came the launch announcement of CUDA-Q on Blackwell systems and collaboration with Denmark’s Gefion supercomputer.

From Concept to Scale and Possibility to Practicality – AI with Arrow

We recognize that AI is rapidly advancing across industries, and yet, turning innovation into practical solutions remains a significant challenge.

Organizations face a growing array of options, including but not limited to NVIDIA. And while NVIDIA’s latest platforms unlock extraordinary capabilities, not every use case demands that level of investment. In many cases, modular, off-the-shelf edge solutions may be more appropriate and efficient.

That’s where Arrow comes in. Our team understands the full AI infrastructure stack and applies this expertise to help solution builders translate their IP into deployable, production-grade systems. Whether it’s choosing the right processor for an edge AI workload, integrating GPUs into a thermally constrained enclosure, or managing compliance testing and imaging, we handle the complex platform engineering that happens behind the scenes.

Whether you’re deploying agentic AI at scale or piloting smart edge devices, we work with you to deliver solutions that align with your goals, timelines, and investment strategy. Feel free to reach out to me or one of our experts to learn how we can help you advance your AI-driven technologies, powered by NVIDIA.

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.

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.