Despite Computex’s push toward areas like data center AI and smart factories, PCs still have a long history at the show, reflective of the industry in Taiwan. Last year in particular was a particularly notable surge with the big AI PC push from the likes of Intel, AMD and Qualcomm. But this year was seemingly more quiet – at least, in terms of keynote addresses. Sure, Jensen Huang kept up his rockstar status with paparazzi and autograph-seeking crowds following him around, but NVIDIA’s messaging was centered around datacenter AI instead of PCs. The big PC-centric keynotes from the likes of Intel, ASUS, Microsoft and others were noticeably absent this year.

Now, Intel’s case was understandable given their recent management changes. And to be clear, they were very much engaged with the industry and small groups of media at their usual booth and hotel meeting rooms. That included working samples of their next Panther Lake processor, which will ship via OEMs as we transition into the new year. ASUS did not run its large launch events this year, but its executives were on stage at multiple keynotes like Qualcomm and AMD, and released an array of products in smaller media sessions, similar to its crosstown rivals Acer and MSI. Qualcomm held a keynote focused on design wins with HP and ASUS for its entry-level 8-core Snapdragon X processors as well as talking up its progress with native apps. But it deferred details of its next PC processor to its Snapdragon Summit in September. AMD’s keynote was more focused on workstations and pivoting ROCm to GPUs rather than talking too much about Krackan Point and NPU-based workloads.

That though was just from a keynote perspective. On the ground, the show floor still buzzed with energy from the modding community and overclockers with giant liquid nitrogen tanks. And to be clear, this year’s show was a week or two earlier than usual, clashing with other key industry events in May including Microsoft Build, Dell Tech World, Google I/O, and even Huawei’s HarmonyOS PC launch. Huawei’s efforts are worth a dedicated discussion on its own, but the short summary is that application support will be a critical gating factor. In the case of Microsoft, there were indeed a number of developments around new Copilot+ features, MCP support in Windows 11, as well enabling cross-NPU AI through Windows AI Foundry. But the lack of big use cases does makes one further wonder whether the momentum around AI PCs has stalled.

To be sure, the industry has been distracted with the trade war lately. Indeed, many of my conversations this year have led with a tariff discussion rather than AI PCs. We think that AI PC adoption will hit some speedbumps due to the volatility around tariffs, especially if buyers are under pressure to buy cheaper products in light of economic uncertainty. But the industry is still moving in a forward direction. All three of the PC CPU providers quietly showed off more apps taking advantage of their integrated NPUs. At the AMD event, Lenovo briefly mentioned that a school in Hong Kong used on-device AI trained on the school’s approved content to save time for both students and teachers. Overall, the industry’s progress is slower than what one might hope for, but it is still moving forward.

Disappointingly, the ongoing rumors of a potential NVIDIA and MediaTek entry into the Windows on Arm ecosystem were never confirmed. I recall waiting for confirmation last year too, but it never materialized in either year. MediaTek briefly mentioned its already-announced Kompanio Ultra for Chromebooks instead of Windows, while Jensen Huang deflected his AI PC comments toward GeForce GPUs (in his words, “RTX equals AI”) as well as DGX Spark, which is more of a specialized product for researchers. To that end, the more notable development wasn’t even at Computex, but instead at Dell Tech World, where the Dell Pro Max Plus workstation was unveiled with a Qualcomm AI 100 PC Inference Card consisting of two of the company’s Cloud AI 100 data center processors.

The industry is nonetheless still hopeful for AI PCs as a driver over time, with the installed base of NPUs being built first and developers eventually finding more ways to light up that power-efficient and optimized part of the die later. The forecast that we refreshed last week puts 57% of PC shipments next year with an integrated NPU, which is a few points lower than what we published in our previous cycle but nonetheless is close to expectations from market players, especially as Intel pushes both Arrow Lake as well as its upcoming Panther Lake processors next year. Upside scenarios could develop with the emergence of new silicon providers as well as new use cases, but that has not materialized yet. Let’s cross our fingers that the trade war doesn’t rock the boat too much either.

Bryan Ma - Vice President - IDC

Bryan Ma is Vice President of Client Devices research, covering mobile phones, tablets, PCs, AR/VR headsets, wearables, thin clients, and monitors across Asia as well as worldwide. Based in Singapore, Bryan provides insights and advisory services for both vendors and users, and coordinates his team of analysts in building IDC's core market data, analysis, and forecasts in these sectors. Bryan has been quoted in a number of publications, including The Wall Street Journal, The Economist, The Financial Times, BusinessWeek, The South China Morning Post, and The New York Times. He has been a featured speaker at numerous industry conferences and appears frequently as a guest commentator on television networks such as CNBC, Bloomberg, and the BBC.

Despite the economic uncertainty caused by global conflicts, inflation, and shifting market dynamics, one trend remains clear: companies are not pulling back from digital transformation. While spending patterns have become more cautious and strategic, investments in digital capabilities continue at a steady pace. Why? Because digital transformation is no longer optional — it’s a core part of how businesses stay competitive, resilient, and future-ready.

Based on current forecasts, digital transformation (DX) investments are projected to reach almost $4 trillion by 2028, accounting for about 70% of total ITC spend. Organizations understand that digital maturity is directly tied to resilience, agility, and competitive advantage. Whether it’s AI-powered analytics, supply chain automation, or cloud-based operations, the message is clear: pause now, fall behind later.

Strategic Spending in a Costly Tech Landscape

The rising costs of technology, driven in part by tariffs on hardware and components, are impacting budgets. In the first quarter of 2025, the IT spend remains robust and CIOs continue to prioritize their original IT goals. Organizations continue to invest heavily in digital transformation, with hardware accounting for an estimated 40% of total digital transformation investment. IT budgets seem more resilient than in the past as more have been moved from capex to opex. Companies are getting smarter about how they spend. They are revisiting contracts, renegotiating terms, and shifting sourcing strategies to adapt to a more expensive tech landscape. However, extending tariffs to digital services will increase costs and complexity of managing IT. Together with the economic slowdown, that is likely to trigger cuts and delays in business and DX initiatives. On the other hand, previous disruptions have accelerated major technology shifts and leading companies will likely seize the opportunity to accelerate their transformation.

Hardware is undoubtedly the most impacted area in today’s digital economy. Semiconductors, edge devices, data center infrastructure, and networking equipment are all directly affected by tariffs, labor shortages, and material price spikes. Despite these cost climbs, organizations remain committed to their hardware roadmaps. Digital transformation can’t happen without hardware, whether it’s deploying AI models, migrating to hybrid cloud environments, enabling IoT ecosystems, or powering real-time edge processing. Infrastructure is the foundation.

AI: The Cornerstone of Digital Transformation

AI is quickly becoming a cornerstone of digital transformation, and hardware is the foundation enabling that shift. As organizations race to adopt AI technologies driven by talent shortages, rising labor costs, and the urgent need for efficiency, there is growing demand for systems that support autonomous decision-making at scale. From our data, we see that AI-related investments currently account for 17% of total digital transformation spend, a figure expected to rise significantly in the coming years.

At the same time, the fact that 40% of digital transformation budgets are dedicated to hardware reveals a clear pattern: companies are laying the infrastructure needed to support these AI-driven futures. This strategic emphasis on hardware isn’t about today’s needs — it’s a signal that organizations are preparing the groundwork for the next wave of intelligent, automated systems.

Regional Flavor of Global Priorities

Regions differ in terms of the maturity of their organizations’ digital transformation. The US and Western Europe are at the highest level of digital maturity, while others are catching up. Regardless of maturity level, the primary focus of digital initiatives is optimizing business operations and enhancing cyber resiliency. Modernizing infrastructure in data centers, as well as cyber recovery and resiliency, are the top drivers of increased IT spending in preparation for greater AI use in business. In the US and Western Europe, the modernization of applications is prioritized more than in other regions. Digital sovereignty influences technology strategies strongly in Europe.

The Strategic Continuum of Digital

Digital transformation is no longer a linear journey—it’s a strategic continuum. Success in 2025 will belong to the organizations that invest in strong foundations, leverage AI wisely, and adapt with intention—not hesitation.

Key Takeaways

  • Invest in scalable infrastructure: Build AI-ready, flexible hardware and cloud systems to support long-term growth.
  • Build for resilience: Diversify vendors, localize supply chains and prepare for ongoing disruptions.
  • Act fast, but strategically: Move decisively on transformation – optimize for adaptability and not perfection.

Mariya Yahnyuk - Research Analyst - IDC

Mariya Yahnyuk has been a research analyst in IDC's Worldwide Data and Analytics team since 2022. Yaknyuk supports the development of IDC's Spending Guide portfolio, assuring alignment with technology and market changes, relevancy, and business value for customers Mariya directly supports IDC's Worldwide Channel Partner Ecosystem product as well as IDC's Digital Transformation, Public Cloud Services and Line of Business Spending Guides. She is involved with data modelling and forecasting for verticals and use cases and takes part in IDC custom data projects.

I attended PwC’s Workday Tomorrow 2025 event, held in Frankfurt from March 25–27, as a speaker. The atmosphere crackled with knowledge-sharing and plans as the 50 attending HRIT leaders and professionals discussed how to leverage AI in terms of people processes.

IDC’s extensive interviews with HR leaders at the event revealed they are indeed keen to leverage AI for:

  • Recruitment: CV prioritization, interview scheduling, candidate communication
  • Performance Management: Summarization, feedback gathering, goal setting
  • HR Assistants: HR help desk, transactional assistance
  • Job Descriptions and Skills Management: Inference, automated skills surveys

As a Workday customer, you can gradually switch on the AI capabilities embedded in the functional areas to which you have subscribed. IDC, however, recommends that customers launch their AI journey with less complex use cases (e.g., “GenAI job description”) that can be switched on and tested for fit with your HRIT teams.

Each Workday client can turn on/off the AI functionality in their own tenants by way of configuration, including data contributions on a field level. Workday’s AI architecture allows customers to always retain control over their data within Workday.

To help customers adopt AI capabilities, Workday offers fact sheets for all available AI use cases in Workday Community. The vendor also offers a broad AI Masterclass to help organizations get started in adopting AI. The Masterclass aims to help HR and IT professionals deepen their understanding of AI technologies, including how to deploy and govern AI responsibly, and covers a range of concrete case studies.

The HR function is a business partner — but also a cost center. Some of the HR participants in Frankfurt discussed how HR can better establish its business value contribution to obtain resources and funding to work with AI. This requires HR to establish business cases with concrete financial ROI metrics to justify the investment. AI solutions that save significant time for employees and managers, for example, can have substantial benefits.

Comprehensive planning is required to execute such wide-ranging, transformational AI use cases. These are complex projects that demand organization, implementation, and funding. Successful project outcomes also require specialist skills to address legal topics, data security, change management, Workday configuration, and deep industry knowledge.

Workday Tomorrow 2025 offered attendees the opportunity to gain a better understanding of how consulting firms like PwC can support Workday customers to prepare, plan, and execute AI use cases within ongoing transformational programs.

How the AI Wave Will Impact the HR Function

Even if HR itself does nothing with AI, will HR be impacted by the AI deployed in the core business of organizations? (Hint: It will!)

A March 2025 IDC survey of 419 CEOs revealed that more than half (55%) believe AI will lead to fundamental business model changes in their organization in 3-5 years (IDC’s CEO Survey 2025; N = 419).

The survey showed that CEOs see a number of skills gaps impeding AI success in their organizations. Interestingly, the most important skills gap identified was teaching AI to regular business employees.

CEOs have turned or will turn to HR to help remedy this skills gap: IDC believes we will see extensive reskilling and upskilling efforts to create an AI-ready workforce. HR will also be tasked with recruiting, retaining, and developing scarce AI-related tech skills in security, AI governance, data management, development, and other areas.

Change management and communications skills will be much needed as organizations undergo difficult, tech-driven changes. Employees have a lot at stake: Some skills will lose value as AI agents take over certain tasks, and some job roles will change and result in new tasks. In Frankfurt, PwC expert Armin von Rohrscheidt talked about how – at least in a German context – involving workers’ councils early, fully, and transparently is the recommended approach.

Interesting HR Perspectives that Came to Light

  • How does an organization train its workforce to become “AI-ready”?
  • How can an organization prepare regular business users to work with conversational user interfaces, prompts, and agentic workflows?
  • Are new training methods needed?

IDC believes that traditional linear elearning approaches will not suffice to bring about such skills. Instead, collaborative, social, experimental, and hybrid approaches are called for (a mix of real-time interactions and individual learning). Furthermore, learner progress and proficiency levels must be monitored as opposed to simple pass/no-pass quizzes.

Another discussion concerned how AI will impact the career progression of junior employees. Organizations are in the process of implementing agentic workflows so that basic administrative processes, or even longer-running processes, can be automated, with humans supervising the process as opposed to just being in the loop.

These basic processes have typically been performed by junior employees to help them “get their hands dirty” and “learn the ropes” of the organization. But if these entry-level processes will be performed by AI agents, how will junior employees gain an understanding of the basic workings of an organization?

This has been a theme for IDC’s Future of Work team. One hypothesis is that AI will not only automate basic tasks but will also assume a mentor’s role, enabling junior employees to explore simulated, experimental workflows and use this as a path to insights into core business processes.

Reflecting on Workday’s Expanded Partnership with PwC

Workday’s partnership with a major partner like PwC goes far beyond the traditional applications vendor + global systems integrator setup. As a key partner, PwC has a large number of certified consultants in the various Workday solutions and cloud tools, co-sells the solutions with Workday, and markets services capabilities at Workday events.

Today, however, PwC sells its own branded solutions, certified by Workday and built natively on the Workday Extend platform. Furthermore, these PwC solutions are sold on the Workday Marketplace. PwC co-markets and co-brands events with Workday, and Workday involves PwC in its multiyear product road maps.

This implies that PwC’s customer relationships in the Workday ecosystem have become truly multifaceted, spanning strategic consulting, project services, managed services, as well as subscriptions to a range of software-based products.

Selling software products requires relatively long-term and in-depth collaboration between Workday and a partner like PwC. If PwC creates a new product — for example, Sickness and Recovery Management — it is important that Workday is not planning to add such capabilities to its own HCM solution (within the next 24 months at least). There is no perpetual guarantee of free play, of course, but a certain time window must be guaranteed.

Final Thoughts

AI is not just another wave of technology to manage and roll out. It has massive transformational potential. It will permeate the business world whether we like it or not.

Any AI initiative will receive serious scrutiny from employees, senior stakeholders, unions, and regulators. However, if HR and IT concentrate on the best practices outlined at the Frankfurt conference — especially related to internal communications and change management — now is the time to get started.

Think outside of the box. AI is not a traditional tool rollout. Knowledge must be shared internally and among peers in other organizations. Network and iterate often. The future of the HR function is — without a doubt — linked to AI and automation.

Bo Lykkegaard - Associate VP for Software Research Europe - IDC

Bo Lykkegaard is associate vice president for the enterprise-software-related expertise centers in Europe. His team focuses on the $172 billion European software market, specifically on business applications, customer experience, business analytics, and artificial intelligence. Specific research areas include market analysis, competitive analysis, end-user case studies and surveys, thought leadership, and custom market models.

At GTC 2025, NVIDIA introduced several new AI and computing solutions aimed at advancing workstation graphics and AI infrastructure. The RTX PRO Blackwell series brings updated workstation GPUs based on the Blackwell architecture, designed to enhance performance for professional workflows. NVIDIA also unveiled the DGX Spark and DGX Station, expanding AI computing capabilities with Grace Blackwell technology. Additionally, the company highlighted its ongoing ISV collaboration and application optimization efforts, aiming to improve software integration and performance across various AI-driven applications. These updates reflect NVIDIA’s continued focus on developing solutions that support AI and high-performance computing advancements. 

NVIDIA Blackwell RTX Pro

The NVIDIA RTX PRO Blackwell series are a new generation of workstation and server GPUs designed to advance workflows for AI, technical, creative, engineering, and design professionals. These GPUs should offer significant improvements in accelerated computing, AI inference, ray tracing, and neural rendering technologies, according to NVIDIA. The RTX PRO Blackwell series include data center GPUs, desktop GPUs, and laptop GPUs, providing professionals with powerful tools for tasks such as agentic AI, simulation, extended reality, 3D design, and complex visual effects.

NVIDIA RTX PRO Blackwell Workstations, source: NVIDIA, 2025

The RTX PRO Blackwell GPUs feature notable generational enhancements, including up to 1.5x faster throughput with new neural shaders, up to 2x the performance of previous RT Cores, and up to 4,000 AI trillion operations per second with fifth-generation Tensor Cores. They also offer larger, faster GDDR7 memory, enhanced video encoding and decoding capabilities, and support for fifth-generation PCIe and DisplayPort 2.1. These GPUs are designed to elevate productivity, performance, and speed for professionals across various industries, from healthcare and manufacturing to media and entertainment.

DGX Spark: Compact AI Supercomputer for Local and Cloud Integration

NVIDIA has introduced the DGX Spark, a highly compact desktop PC described as AI supercomputer tailored for developers, researchers, and students. This system is powered by the GB10 Grace Blackwell Superchip, which delivers up to 1,000 trillion operations per second (TOPS) of AI computing at FP4 precision. The architecture incorporates fifth-generation Tensor Cores, enabling efficient fine-tuning and inference of large-scale AI models. The DGX Spark is equipped with 128GB of unified LPDDR5x system memory, offering a bandwidth of 273 GB/s through a 256-bit memory interface.

NVIDIA DGX Spark — formerly Project DIGITS — source: NVIDIA, 2025

A key feature of the DGX Spark is its use of NVLink-C2C technology, which facilitates coherent memory sharing between the CPU and GPU, achieving bandwidth five times greater than traditional PCIe systems. This capability is particularly beneficial for memory-intensive workloads. The system supports AI models with up to 200 billion parameters locally and can scale further by connecting two units to handle models with up to 405 billion parameters. Additionally, the DGX Spark integrates seamlessly with cloud platforms, including NVIDIA DGX Cloud, allowing users to transition between local and cloud-based AI workflows without significant modifications.

The DGX Spark is designed to empower users with advanced AI capabilities in a desktop form factor, making it suitable for prototyping, fine-tuning, and inferencing tasks across various domains.

DGX Station: High-Performance AI Computing for Desktop Environments

NVIDIA also announced the DGX Station, a continuation in advancement in desktop AI computing, offering data-center-level performance in a workstation format. It is built around the GB300 Grace Blackwell Ultra Desktop Superchip, which combines the Grace 72 CPU cores with a Blackwell GPU, connected via NVLink-C2C interconnect technology. This design enables high-bandwidth coherent data transfers between the CPU and GPU, optimizing performance for large-scale AI training and inferencing tasks.

NVIDIA DGX Spark and DGX Station, source: NVIDIA, 2025

The system features 784GB of coherent memory, divided between 288GB for the GPU and 496GB for the CPU, making it capable of handling complex AI models and datasets. Networking capabilities are enhanced by the ConnectX-8 SuperNIC, which supports speeds of up to 800Gb/s, allowing for efficient data movement and the ability to link multiple DGX Station units for distributed workloads.

FeatureLatest GB300 DGX StationPrevious Gen. DGX Station A100
Platform / ArchitectureNVIDIA Grace Blackwell Ultra Desktop Superchip (GB300) – an integrated solution pairing a custom NVIDIA Grace CPU with an NVIDIA Blackwell Ultra GPUDGX Station A100 – built on a proven data-center-class design utilizing discrete components
CPUNVIDIA Grace CPU (custom ARM-based processor integrated into the superchip; optimized for AI workloads)1 × AMD 7742 (64 cores, 2.25 GHz base / up to 3.4 GHz boost)
GPUNVIDIA Blackwell Ultra GPU – equipped with fifth-generation Tensor Cores offering next-generation FP4 (4-bit floating point) support4 × NVIDIA A100 GPUs, each with 80 GB – based on the Ampere architecture and proven for large-scale deep learning workloads
GPU Memory/Unified MemoryUp to 784 GB of large coherent (unified) memory – a shared pool combining high-bandwidth on-package memory for both the integrated CPU and GPU320 GB total GPU memory (80 GB per GPU) alongside 512 GB of separate DDR4 system memory
InterconnectNVIDIA NVLink-C2C chip-to-chip interconnect – enabling high-bandwidth, coherent data transfers between the integrated CPU and GPU componentsStandard NVLink interconnect architecture used to efficiently link the four discrete A100 GPUs (though not the next-gen NVLink-C2C seen in Blackwell)
NetworkingNVIDIA ConnectX-8 SuperNIC – supports up to 800 Gb/s for high-speed connectivity and scalability for AI clustersDual 10GBASE-T (RJ45) networking – sufficient for desktop AI workloads and common office networking needs
StorageNot explicitly detailed in current public disclosures (likely to feature high-speed NVMe storage to complement the onboard AI processing capabilities)Dual-drive setup: 7.68 TB NVMe U.2 drive for data storage plus a separate Boot M.2 NVMe drive
Power ConsumptionNot specifically published; engineered for desktop-form-factor efficiency for AI training/inferencingUp to 1,500 W under heavy load (as specified in the DGX Station A100 hardware datasheet)
Software/OSRuns NVIDIA DGX OS with a full-stack AI software suite (including pre-configured drivers and optimized AI libraries)Runs NVIDIA DGX OS – pre-configured with the NVIDIA AI Software Stack and containerized deep learning frameworks for streamlined deployment across cloud or local environments
Form FactorDesktop AI supercomputer – purpose-built for on-premises development and rapid prototyping with a “coherent memory” design that minimizes data movement overheadDesktop workstation-class AI supercomputer – built to deliver data-center-level performance in an office-friendly chassis with defined dimensions and thermal specifications (518 mm D × 256 mm W × 639 mm H; 91 lbs)
Additional Features– Next-generation FP4 accuracy for training and inference via 5th-generation Tensor Cores

– Integrated, high-bandwidth coherent memory with NVLink-C2C interconnect
– Proven performance on deep learning and inferencing tasks with established A100 GPUs

– Comprehensive connectivity, storage, and environmental controls (operating temperature 10°C–35°C)

A comparison between the latest NVIDIA Grace Blackwell DGX Station and the 2021 DGX A100 built around Ampere GPUs. Source: IDC, 2025

The DGX Station is equipped with the NVIDIA AI software stack, providing tools and frameworks for developing, training, and deploying AI models. This integration ensures compatibility with cloud and data center infrastructures, enabling scalability and flexibility for AI developers and researchers.

ISV Collaboration and Application Optimization

NVIDIA’s Blackwell architecture introduces significant advancements in computer-aided engineering (CAE) software, enabling simulation tools to achieve up to 50 times faster performance according to NVIDIA. This acceleration is particularly impactful for real-time digital twin applications, which rely on high computational efficiency to model and analyze complex systems dynamically. By integrating Blackwell technologies, NVIDIA announced that leading CAE software providers, including Ansys, Altair, Cadence, Siemens, and Synopsys, have enhanced their capabilities to address challenges in industries such as aerospace, automotive, energy, and manufacturing.

The architecture leverages CUDA-X libraries and optimized blueprints to improve simulation accuracy, reduce development time, and lower costs while maintaining energy efficiency.

CUDA-X Microservices. Source: NVIDIA, 2025

CUDA-X is a comprehensive suite of software libraries and tools designed to accelerate computing across a wide range of applications, including AI, data analytics, and scientific computing. In the context of the DGX Station, CUDA-X plays a pivotal role by optimizing the performance of AI workloads. It provides developers with access to highly efficient libraries for deep learning, such as cuDNN and TensorRT, which are essential for training and inferencing large-scale models. Additionally, CUDA-X enables seamless integration with the DGX Station’s advanced hardware, including the Grace Blackwell Ultra Superchip and NVLink-C2C interconnect, ensuring efficient utilization of the system’s computational and memory resources. This synergy allows researchers and developers to achieve faster model development cycles and enhanced scalability, making the DGX Station a powerful platform for cutting-edge AI innovation.

For example, Cadence has demonstrated the ability to simulate multibillion-cell computational fluid dynamics models on a single NVIDIA GB200 NVL72 server within 24 hours—a task that previously required extensive CPU clusters and multiple days. This breakthrough highlights the potential of Blackwell-powered systems to transform engineering workflows, enabling more efficient design processes and reducing reliance on physical testing methods.

The collaboration between NVIDIA and ISVs underscores the growing importance of accelerated computing in addressing computationally intensive tasks, paving the way for innovations in digital twin technology and beyond.

IDC Opinion

The discontinuation of 32-bit OpenCL and CUDA support in the Blackwell architecture primarily affects legacy applications that haven’t transitioned to 64-bit, while modern, fully 64-bit productivity software remains largely unaffected and continues to benefit from Blackwell’s enhanced performance. For instance, many professional workflow applications, simulation tools, and AI development environments have long since moved to 64-bit, allowing them to take full advantage of new features like advanced Tensor Cores, FP4 precision, and high-bandwidth coherent memory without issue. However, legacy or specialized tools still dependent on 32-bit components might experience errors or fallback CPU processing, which can slow down specific tasks and temporarily impede productivity. In practice, organizations relying on such legacy applications will need to update or recompile their applications to fully harness the substantial benefits offered by the new Blackwell RTX Pro GPUs in workstation scenarios.

NVIDIA’s Blackwell architecture introduces a new generation of Tensor Cores that natively support FP4 arithmetic. But hardware alone isn’t enough to harness the full potential of FP4 precision. NVIDIA has co-developed an optimized software ecosystem built into its DGX systems that fine-tune the quantization and calibration processes for FP4 computations. These algorithmic enhancements ensure that the error margins introduced by reducing precision are minimized. While FP4 precision provides an effective way to significantly boost compute performance and energy efficiency, NVIDIA has to demonstrate those benefits in use cases, compared to formats like FP16 or FP32, especially when marketed heavily with products like the DGX Spark.

Nearly a decade ago, NVIDIA introduced the DGX desktop workstations as an entirely in-house developed AI systems to direct sales. Now, by opening up its latest GB platforms to OEM partners, NVIDIA is strategically broadening its distribution channels and market reach. This shift not only enriches desktop workstation solutions with cutting edge technology across a wider audience but also empowers partners to innovate and adapt NVIDIA’s advanced AI capabilities to diverse industrial needs, reinforcing the company’s leadership and expanding its influence.

The DGX Spark and DGX Station face cost-performance challenges compared to SFF and high-end tower workstations with discrete graphics cards. Students, startups and small software houses, who are key drivers of AI development, may find NVIDIA’s solutions costly and prefer more efficient options like gaming graphics cards.

In conclusion, the introduction of Blackwell architecture and advancements in DGX systems reflect NVIDIA’s commitment to delivering cutting edge solutions for professionals, researchers, and enterprises. As the AI landscape evolves, NVIDIA’s strategic approach to expanding access through OEM partnerships and optimizing performance across software ecosystems ensures that its technology remains at the forefront of AI driven workflows.

Mohamed Hakam Hefny - Senior Program Manager - IDC

Mohamed Hefny leads market research in EMEA on professional workstation PCs and solutions. He also reports on professional computing semiconductors, processors, and accelerators (CPUs and GPUs), as well as breakthroughs and trends related to the market. In addition, Mohamed is actively involved in AI PC taxonomy and research. He participates in business development projects, contributes to consulting activities, and provides IDC customers with analysis, opinions, and advice.

AI is reshaping more than operations — it’s redefining how organizations source software. CIOs now lead procurement strategies that demand speed, strategic alignment, and machine-augmented decision-making. In a world where every tool promises GenAI, smart sourcing is no longer tactical — it’s transformational.

Today’s software procurement hinges on using AI to reshape vendor consideration, governance dynamics, and the very definition of “fit.” With GenAI tools embedded in nearly every platform, and agentic systems increasingly capable of orchestrating tasks and negotiations, tech buying is changing at its core. Tech leaders need new tools, new evaluation frameworks, and a new mindset.

The good news: this next wave of IT sourcing can be smarter, faster, and more aligned — if you’re ready to lead it.

Procurement’s Strategic Power Play: Why CIOs Should Care Now

Present-day CIOs must be enterprise strategists. They bridge finance and operations, turn data into decision-making power, and guide tech investments that shape their organization’s future.

What you may not realize: procurement is one of the most strategic — and often overlooked — arenas for CIOs to make an impact.

Why? Because procurement sits at the intersection of cost control, risk mitigation, supplier intelligence and, increasingly, AI-powered transformation. AI platforms are evolving fast — and with them, so is the opportunity to drive meaningful change.

But the explosion of enterprise apps — particularly GenAI-enabled tools — has created a sprawling, competitive vendor maze. In the past, procurement often followed a lengthy RFP and negotiation process.

But, the game has changed.

This is where strategic procurement comes in. Instead of focusing solely on tactical execution, procurement is now a key contributor to IT strategy — helping align software vendor selection with business priorities, governance standards, and AI readiness.

AI-powered software procurement platforms like IDC TechMatch are emerging to support this shift. By combining objective analyst insights, AI-powered matching, and a structured prioritization framework, you can navigate the vendor sprawl and zero in on what fits your business best — faster and with more confidence.

AI Claims vs. AI Reality: What Tech Leaders Must Vet

Nearly every software vendor today is promoting some form of GenAI.
The key is understanding what kind of AI you’re dealing with. For instance, is it assistive, acting as a co-pilot that supports human decisions? Or is it something more advanced — truly agentic?

Many vendors claim “agentic” capabilities, but most current tools are really enhanced virtual assistants — helpful, yes, but not autonomous decision-makers. Understand what’s real today (assistive GenAI) and what’s on the horizon (multi-agent collaboration). Being able to evaluate emerging tech without being dazzled by marketing is a core leadership skill. That’s why it’s critical to evaluate these tools carefully:

• Does the solution align with your data governance policies and risk tolerance?

• Will your team be able to integrate and manage it realistically, or will it create more overhead?

Treat AI solutions like any other key software: look closely at how they’re built, what data they’re trained on, how well they work with your current systems, and whether the outputs are explainable. Instead of just asking, “What can this software do?”, ask “What will this do for us — in our environment, with our goals, and under our constraints?”

Your Governance Model May Be Slowing Down Your Sourcing

Your governance model doesn’t just determine who makes business decisions. It also determines how technology gets prioritized, which solutions get visibility, and how fast software sourcing can happen.

In centralized models, IT has the authority to set sourcing standards and vet vendors, which can reduce duplication and support better risk management. But, it may also slow innovation if lines of business feel disconnected.

Federated models strike a balance — allowing individual departments to identify needs and propose solutions while still aligning with central guidelines. This is becoming the most common approach among digitally mature organizations, especially those scaling AI.

Decentralized models allow for speed and flexibility, but often result in overlapping tools, shadow IT, and fractured procurement efforts — especially problematic in an AI-driven environment where data governance is crucial.

Understanding how your governance model interacts with software sourcing is critical. It informs not just who gets a seat at the table, but what criteria matter most when evaluating a vendor.

Why Governance Accelerates AI — Instead of Blocking It

Too often, AI governance is perceived as a bottleneck — the controls that says “no” to innovation. But effective governance ensures that AI systems are secure, explainable, and accountable.

A well-structured AI governance framework helps answer key questions before a vendor is selected. For instance:
• What data will this system use?
•   How is bias mitigated?
•   Can outputs be audited?
•   What human oversight is required?

IDC’s Unified AI Governance Model outlines how organizations can move from governance chaos to clarity — integrating architecture, strategy, and culture to achieve responsible AI outcomes.

By addressing these questions up front, organizations avoid rework, maintain trust, and accelerate time to value. Strong governance becomes a competitive advantage, enabling teams to deploy AI faster and with more confidence.

It follows that contractual clauses are critical when adopting AI solutions. By emphasizing data governance, ethical considerations, security, transparency, and oversight, contractual clauses provide a framework for responsible AI adoption for businesses and organizations. These terms should be a core part of any vendor diligence process as well — because when AI is involved, assumptions aren’t enough.

Want to benchmark your AI evaluation approach? Download our AI Governance Checklist — used by tech leaders to simplify decisions, avoid vendor regret, and move faster with confidence.

The Talent Shift in Procurement (And the CIO’s Role in Shaping It)

As AI transforms procurement, new roles (and new titles) are emerging that represent a critical upskilling imperative:

The Procurement Agent Optimizer is someone who designs, trains, and manages autonomous or semi-autonomous agents that handle procurement tasks. Instead of drafting RFPs themselves, they teach the system how to do it. They define objectives, set parameters, monitor performance, and intervene when needed.

The AI Orchestration Lead manages a network of AI tools across the organization. Their job isn’t just prioritizing and selecting software but choreographing how various AI components — from co-pilots to back-end agents — interact across functions like finance, HR, operations, and IT.

These roles mark a fundamental shift in how talent adds value in procurement. For CIOs, this means two things: invest in talent that can embrace both business growth and AI fluency, and equip them with the skills and tools to act decisively.

Strategic Software Sourcing Starts with TechMatch

In this next phase of digital maturity, success in software procurement depends less on “What’s the best tool?” and more on “What’s the right fit for our mission, model, and maturity?”

Agentic systems and AI platforms offer immense promise — but only if CIOs and procurement leaders adapt their approach. That means reframing priorities around governance, alignment, and orchestration. It means empowering new roles. And it means using smarter tools — like IDC TechMatch — to navigate the vendor ecosystem with ease.

With IDC’s trusted insights and AI-powered precision, IDC TechMatch empowers tech leaders to cut through vendor noise, accelerate sourcing, and choose the right-fit solutions faster. Great tech decisions start with great intelligence. Start with IDC TechMatch.

Want to see how TechMatch zeroes in on your best-fit vendors — fast?
Watch the full demo to see how AI and IDC insights work together to match your priorities, budget, and governance needs in seconds.

Philip Carter - Group Vice President, General Manager, Research AI - IDC

Philip Carter is General Manager and Group Vice President for AI, Data, and Automation research at IDC. In this role, he leads a global team of analysts focused on delivering IDC's research and insights at the intersection of AI, data platforms, and intelligent automation - three foundational areas shaping the future of technology and business. His work is centered on helping C-Suite executives make sense of the rapid innovation in the AI space, and drive meaningful transformation through data- and intelligence-led strategies. BACKGROUND Carter has held multiple senior roles at IDC across regions. Prior to his current position, he served as GVP and GM of IDC TechMatch, where he led a global team tasked to build and commercialize IDC's first AI-powered digital platform - focused on helping CIOs and procurement executives evaluate and source technology vendors leveraging IDC trusted intelligence. Earlier in his IDC career, Carter was the lead for IDC's Global Thought Leadership research function and was also Chief Analyst for IDC Europe, where he drove innovation in research related to digital transformation, emerging business models, and technology strategy at the C-suite level. Before that, he worked in IDC's Asia/Pacific region, covering software, services, and sustainability. Prior to joining IDC, he held various leadership roles at SAS Institute across EMEA and APAC in marketing strategy, product management, and business development. He is a recognized industry voice, regularly featured on platforms such as CNBC and Bloomberg, and quoted in leading publications including the New York Times. EDUCATION/INDUSTRY ACCOMPLISHMENTS: - Honors degree in Business Science, majoring in Economics and Law, University of Cape Town, South Africa.

With continuously rising healthcare costs, there is a fundamental need to adopt a more preemptive strategy towards healthcare. Many questions are being asked. What can one do to improve population health outcomes and reduce the burden of disease? Would preempting illness not be a better option than treating it?  Can the life sciences industry leverage innovative strategies to design personalized solutions to improve health and prevent disease? Will this create a paradigm shift in healthcare strategy and R&D innovation?

Preemptive health focuses on anticipating and preventing diseases before they manifest, leveraging technological advancements to enhance early detection and intervention.  Preemptive health not only calls for innovative solutions, but it calls for an innovative mindset as well.

Various innovative solutions are being developed across the industry. Etiome (a Flagship Pioneering company) recently launched its AI-powered Temporal Biodynamics platform to forecast how individuals are likely to progress along the disease continuum, confirm disease biostages with temporally relevant markers, potential disease stage–specific targets, and develop Biostaged Medicines to halt or reverse disease, and is focusing on metabolic, neurodegenerative, pre-cancerous and autoimmune diseases.

Twin Health is creating digital twins, digital replicas of a person’s metabolism   to address the root causes of metabolic conditions like obesity, prediabetes, and type 2 diabetes. The focus is on analyzing an individual’s gut microbiome to understand its impact on the person’s overall health. Harvard has highlighted the importance of ‘precision nutrition’, wherein an individual’s DNA, microbiome, and metabolic response to specific foods or dietary patterns are evaluated to develop tailored diets. The concept of ‘Food as a Medicine‘ is gaining importance. Companies such as Brightseed are using AI-driven platforms to identify the health benefits of plant-based bioactives which have the potential to influence the microbiome.

The Glucagon-like peptide-1 GLP-1 receptor agonists (GLP-1RAs) market is booming as managing obesity and reducing the risk of developing type 2 diabetes takes center stage. The US Preventive Services Task Force (USPSTF) is reportedly developing a draft research plan to determine whether to grade anti-obesity medications (AOMs) as preventive medications for chronic weight management.  

Eko Health has developed AI-enabled digital stethoscopes with FDA-cleared algorithms to detect cardiovascular and pulmonary diseases during routine physical exams, facilitating early intervention and improved patient outcomes.

Genomic, Inc. has developed a test called Health Insights, which carries out polygenic risk scoring for the early detection of conditions such as cardiovascular disease, diabetes, breast cancer and prostrate cancer, now offered by the healthcare provider Bupa in the UK.

Neko Health offers AI-enabled non-invasive body scans, mapping millions of health data points on and in an individual’s body in a few minutes enabling the early detection of potential health issues.

The reality is that we need to focus on increasing our healthspan (the period of life spent in good health, free from major diseases and disabilities), not just our lifespan. We need to live healthy lives, not just longer lives. Pharmas and biotechs are increasingly focusing on developing products that increase the healthspan, in addition to lifespan. Longevity research is an area of growing importance. Bioage, for example, has entered into a multi-year research collaboration worth up to $550 million with Novartis, to identify “molecular mediators of the benefits of exercise.” Exercise can influence gene expression, but one’s capacity to exercise decreases with age. BioAge is exploring the possibility of developing a pill using its AI platform that will have the same positive effect on gene expression as exercise does, a key element of its partnership with Novartis.

The Advanced Research Projects Agency for Health (ARPA-H) research funding agency launched in 2022, made pivotal investments in breakthrough transformative biomedical and health technologies and applicable platforms to provide health solutions for all.  Early-stage funding remains challenging for healthspan innovators, and ARPA-H could serve as a valuable non-dilutive funding opportunity for them. ARPA-H’s Proactive Health initiative has a simple goal. It will promote treatments and behaviors that will reduce the likelihood that people become patients. And that is what the life sciences industry needs to prioritize. Yet care should be taken to ensure that while forecasting the likelihood of developing a disease, racial and ethnic disparities, as well as socioeconomic status (SES) should be accounted for, test results should not create unnecessary panic in a lay person, and clinical oversight should always be maintained. Pharmas and biotechs should invest in R&D to build out a pipeline of innovative products / solutions focused on preemptive health. Policy makers should encourage investment in preemptive health. At the end of the day, predictive approaches and preemptive strategies must lead the way. 

Nimita Limaye - Research Vice President - IDC

Dr. Nimita Limaye is a Research VP with IDC Health Insights and provides research-based advisory and consulting services, as well as market analysis on key topics related to R&D Strategy and Technology in the life sciences industry. She addresses aspects such as the role of digital transformation in discovery research, e-clinical ecosystems, the role of NLP, AI, ML, DL, RPA, in transforming drug development, precision medicine, pharma R&D execution and strategic outsourcing models.

Identity and access management (IAM), and by extension, identity security, is one of the most pervasive and impactful challenges facing all European organizations today, from an operational and risk management perspective.

The targeting of users and credentials has been well documented through year after year of the major global threat reports, such as Verizon’s Data Breach Investigations Report (DBIR). Phishing attacks continue unabated as threat actors steal more and more credentials. Verizon’s 2025 DBIR report highlights compromised credentials as the most common initial access vector among non-error breaches.

The challenge for many organizations is dealing with the sheer volume, velocity, and variety of IAM-related events. Take a workforce of a few thousand permanent employees that need access to an estate of a few hundred applications. Add in a few hundred temporary workers, partners, and contractors that need access to specific systems and applications on a constrained basis. Then add in a range of entitlement levels for what all of those users — permanent, temporary, and external — can do in each application. Remember also that the workforce is in constant flux, with new joiners, movers, and leavers. To prevent exposure, any changes to the access rights and entitlements of those users must be put into effect immediately when a transition takes place.

The outcome is a volume of IAM events and processes that simply cannot be managed without automation.

And that’s just the humans.

Organizations have become aware that there is an even bigger and faster-growing set of identities that they need to manage as a matter of increasing urgency: the non-humans.

The Rise of Non-Human Identities

Some non-human identity (NHI) types have been around for years, such as service accounts. These are already a concern, since many of them are entitled to execute privileged actions, which typically need a higher level of control to safeguard data and processes. Furthermore, nested privileges enabled by multiple overlapping or intersecting service accounts can obfuscate over-provisioning of access, which can be a major security risk.

Service accounts are just one category of NHIs that merit attention, however. The growing list includes device identities, cloud workloads, bots, APIs, and, increasingly, AI agents. Some of these NHIs are relatively long-lived and fixed, others are fast moving and ephemeral. Visibility into the creation and provisioning of some NHIs can be extremely limited for the identity, IT, and security professionals tasked with managing them. So how should organizations address this growing challenge and contain the risk? Can existing IAM and identity security tools be co-opted to manage the NHI pool?

According to preliminary data from IDC’s EMEA Security Technologies and Strategies Survey, 2025, more than a third of EMEA organizations are already grappling with this challenge. The short answer to the questions above is that existing tools can probably address some of the requirements of NHI IAM and security (but to adequately manage the risk, a dedicated approach is going to be required).

AI Agents: A Complex Challenge

If we take AI agents as an example, these are probably one of the most complex and fastest-growing NHI categories. According to IDC’s March 2025 Future Enterprise Resiliency and Spending (FERS) Survey, 38% of European organizations are already investing in agentic AI, with a further 43% conducting initial testing and proofs of concept. IDC’s 2025 Worldwide Future of Work Predictions report projects that by 2027, agentic AI workflows will impact at least 40% of knowledge work in G2000 organizations.

Functionally, AI agents can act like service accounts in some aspects; at the same time, they share some behaviors with human identities. They can also be a force multiplier for risk. In an ordinary business process, a human user might conduct actions that call a handful of APIs (another at-risk NHI category, since API access is often unsecured). When we enable AI agents to act on our behalf, they may be calling hundreds of APIs, creating a flywheel effect that multiplies the risk.

This brings in a bigger topic of security by design, which is as relevant here as it is in any other sphere of security. As development teams build agentic AI services, it is critical that security is built in from the start. It’s far more complex and costly to add on once agents are live. This means building in seamless and secure authentication requirements before a user or an agent is able to do anything; ensuring secure and vaulted credentials for API tokens; and applying fine-grained and dynamically updated authorization for permissions that an agent needs to complete a task (and nothing more).

From an IAM perspective, these are some of the key building blocks to ensure that AI agents don’t become an NHI risk; however, further controls and guardrails will be needed. For other NHI categories, the requirements may be different, and organizations should conduct risk assessments for each category individually before taking the necessary measures to protect them.

Like all IAM challenges, the NHI issue is not insurmountable. However, organizations should avoid the historic IAM mistakes of siloed approaches and short-term fixes and make sure that appropriate security controls are built in, from the beginning, wherever NHIs are active within their systems. What’s required is a strategic, granular, and risk-based approach that addresses IAM for all NHIs before they become embedded in all our business processes.

Mark Child - Associate Research Director, European Security - IDC

Associate Research Director Mark Child of IDC’s European Security Group leads the group's Endpoint Security and Identity & Digital Trust (IDT) research for both Western Europe and Central & Eastern Europe. He monitors developments in security technologies and strategies as organizations address the challenges of evolving business models, IT infrastructure, and cyberthreats. Mark's coverage includes in-depth security market studies, end-user research, white papers, and custom consulting.

Following the September 2024 launch of the Core Ultra 200V Series ‘Lunar Lake’ mobile-focused CPUs aimed at extreme mobility and battery life use cases, Intel introduced the new Intel Core Ultra 200H ‘Arrow Lake’ processors aimed at mobile computing for businesses, creators, and gaming enthusiasts at CES in January 2025.

These latest Intel Core Ultra mobile processors boast advanced AI features, improved efficiency, and enhanced performance over previous generation mobile CPUs.
The Intel Core Ultra 288V CPU features four Performance cores and four Efficiency cores, a second generation neural processing unit (NPU) with up to 48 trillion operations per second (TOPS), and Intel Arc Graphics 140V with eight Xe2-cores delivering up to 67 TOPS, for over 100 total system TOPS.

The Intel Core Ultra 285H CPU includes six Performance cores, eight efficiency cores, and two low-power cores, an NPU with up to 13 TOPS, and Intel Arc 140T graphics with eight Xe Matrix Extension (XMX)-enhanced Xe-cores capable of up to 77 TOPS, for just under 100 system TOPS. Across the platform, these processors achieve up to 99 TOPS by utilizing the GPU, CPU, and NPU

Intel provided IDC with samples of the Lenovo IdeaPad Pro 5 (model 16IAH10) featuring the 16-core Intel Core Ultra 9 285H, and the Lenovo Yoga Slim 7 15 (model 15ILL9), featuring the eight-core Intel Core Ultra 9 288V. Both these thin and light notebooks are solid options for business, content creation, light gaming, and other demanding tasks, but target quite different performance, battery life, and user workload requirements.

The Notebook Specifications

The two Intel Core Ultra 9 200 series CPUs take quite different approaches in terms of design. The Intel Core Ultra 9 288V is composed of two chiplet tiles. The largest tile is the CPU, NPU, and GPU complex together with the memory controllers, while the smaller input/output (IO) tile handles functions such as USB and PCIe connectivity. The Intel Core Ultra 9 285H, on the other hand, features four tiles, with separate CPU, GPU, SOC with NPU and two low-power Efficient cores, and IO tiles.

One of other big differences between the two different CPUs is that the Intel Core Ultra 9 285H notebook comes with 32GB of LPDDR5-8533 RAM soldered to the motherboard, while the Intel Core Ultra 288V has 32GB of the same speed RAM directly integrated into the CPU package itself, allowing for lower power and latency memory operations (compared to having to go off package to the motherboard for memory transactions).

The displays are both high definition (HD) – the Lenovo IdeaPad Pro 5 features a 16-inch 2.8K OLED panel with a resolution of 2880×1800, offering 500 nits of typical brightness and up to 1100 nits of peak brightness. It covers 100% of the DCI-P3 color gamut, has a 120Hz refresh rate, and supports DisplayHDR True Black 1000. The Lenovo Yoga Slim 7 15 features a 15-inch IPS screen, also 2880×1800.

Connectivity options for both include Wi-Fi 7, Bluetooth 5.4, Thunderbolt 4, HDMI 2.1, USB-A, and USB-C; however, the Idea Pad Pro 5 also has an SD card reader. Audio is delivered through stereo speakers optimized with Dolby Atmos. Although both platforms support Thunderbolt 5, the feature is absent from both laptops. Lenovo’s reasoning behind this choice remains uncertain, but it misses the chance to leverage higher bandwidth for quicker data transfer, superior display capabilities, accelerated charging, and greater compatibility.
For video calls and security, the system features a Full HD 1080p camera with an infrared sensor, privacy shutter, and time-of-flight sensor.

The Laptop Look and Feel

Both notebooks are built for rugged mobility. They boast a sleek and durable design with an aluminum top and bottom, providing a premium and sturdy feel. The surfaces are anodized and sandblasted for a smooth finish, and their color is Luna Grey, giving them a modern and elegant look.

The keyboards are backlit, making it easy to type in low-light conditions. They feature a traditional layout with comfortable key travel and responsiveness. The touchpads are buttonless glass surface multi-touch touchpads, supporting Precision TouchPad technology.

The Lenovo Yoga Slim 7 15 weighs in at 1.46kg (3.2lbs) and is a true thin and light notebook. The Lenovo IdeaPad Pro 5 weighs 1.72kg (3.79lbs), which makes it relatively lightweight and portable, considering its powerful specifications and large display. This combination of robust chassis, comfortable keyboard, high-resolution screens, slim profiles, and light weight makes both a great choice for work and play — but the differences mean that the products are targeted at quite different use cases. The Lenovo Yoga Slim 7 15 with the Intel Core Ultra 0 288V is focused on all-day productivity and mobility, while the Lenovo IdeaPad Pro 5 with the Intel Core Ultra 285H is geared more toward supporting high-performance mobile workloads such as content creation or design and rendering activities.

The Intel Core Ultra 9 288V

Built on TSMC’s N3B process, the Intel Core Ultra 9 288V features four Performance cores and four low-power Efficiency cores coupled with 12MB of cache, and is targeted at all-day efficiency performance for sustained productivity. Single-thread performance is competitive, with the Performance cores reaching up to 5.1GHz and the Efficient cores up to 3.7GHz.

The Intel Core Ultra 9 266V with an integrated NPU at 48 peak TOPS supports Intel Deep Learning (DL) Boost and various AI software frameworks such as OpenVINO, WindowsML, ONNX RT, DirectML, and WebNN. This makes it an excellent choice for AI and machine learning workloads. The processor’s base power is 30W, with a maximum turbo power of 37W, allowing it to power the thinnest and lightest notebooks with efficient performance all day.

The Intel Arc 140V GPU

When it comes to graphics, the Intel Arc 140V graphics features eight Xe2 cores that boost up to 2.05GHz. Built on the second generation Xe graphics architecture that is also featured in the well-received Intel Arc B580 series of discrete GPUs, it is a major redesign compared with the original Xe +graphics cores.

The Intel Arc 140V GPU boasts deeper caches, overhauled Ray Tracing Units (RTUs), and two Render Slices, each containing four Xe2-cores (for eight in total). It fully supports DirectX 12 Ultimate, including hardware ray tracing and mesh shading, ensuring competitive graphics performance.

The Intel Core Ultra 9 285H

Built on TSMC’s N3B lithography, with a total of 16 cores, the Intel Core Ultra 9 Processor 285H is a powerhouse designed for high-performance mobile computing. With six Performance cores, eight Efficient cores, and two low-power Efficient cores, this processor is built to handle a wide range of tasks with ease. The Performance cores can reach a maximum turbo frequency of 5.4GHz, ensuring adequate performance for demanding applications, while the Efficient cores and low-power Efficient cores provide a balance of power and efficiency for regular tasks. The processor also boasts a 24MB cache, which helps to speed up data access and improve overall system responsiveness.

In addition to its impressive core configuration, the Intel Core Ultra 9 285H supports Intel DL Boost and various AI software frameworks such as OpenVINO, WindowsML, ONNX RT, DirectML, and WebNN. This makes it an excellent choice for AI and machine learning workloads. The processor’s base power is 45W with a maximum turbo power of 115W, ensuring that it can deliver high performance when needed while maintaining energy efficiency.

The Intel Arc 140T GPU

The Intel Core Ultra 285H processor, featuring the built-in Intel Arc 140T GPU, is a standout in this generation. Built on the enhanced Xe LPG+ architecture, it offers a significant 20% performance boost over its predecessor integrated in the Intel Core Ultra 100H series, according to Intel. This uplift is particularly noticeable in AI workloads, ray tracing, and gaming, whether you are at home or on the go.

The Intel Arc 140T GPU boasts a robust configuration with double the L2 cache (8MB dedicated to the GPU) and two Render Slices, each containing four Xe-cores (totaling eight Xe-cores). It fully supports DirectX 12 Ultimate, including hardware ray tracing and mesh shading, ensuring top-tier graphics performance.

Each Xe-core is equipped with 16 Xe Vector Engines for SIMD8 width execution. The LPG+ architecture introduces XMX, which supports INT4, INT8, FP16, and BF16 data types, delivering exceptional performance for AI workloads and Intel XE Super Sampling (XeSS) AI game upscaling. XeSS leverages these capabilities to provide high-fidelity frames close to native quality with significantly higher frames per second (FPS).

Additionally, the LPG+ architecture includes a specialized RTU designed for real-time ray tracing, offering realistic lighting and reflections. Each RTU features an enhanced traversal pipeline with double the performance and a ray-triangle intersection unit capable of performing 12 box intersection tests per clock cycle.

Media and Display

The Xe Media Engine supports various codecs, bit depths, and chroma subsampling. It even supports HEVC 4:2:2 encoding and decoding, a format commonly used in professional cameras. Additionally, it ensures reliable 4K video playback from platforms like YouTube and Netflix and supports the modern AV1 codec. The engine can handle up to 8K 10-bit high-dynamic range (HDR) video workloads at 30 FPS across dual Multi-Format Codec Engines (MFXs), and 8K 10-bit HDR playback at 60 FPS on a single MFX.
The Display Engine supports a single monitor with up to 8K resolution at 60Hz with HDR, utilizing the latest HDMI 2.1 or DisplayPort 2.1 standards. It can also manage up to four external monitors at 4K resolution with HDR at 60Hz, providing a high-quality viewing experience. The integrated panel supports a refresh rate of up to 120Hz at WQUXGA (3840×2400) resolution, thanks to its eDP1.4b support.

Benchmarks

PCMark 10

PCMark 10 is a comprehensive benchmarking tool that covers the wide variety of tasks performed in the modern workplace. Web browsing, videoconferencing, spreadsheet and word processing, photo and video editing, and rendering and visualization are some of the tasks tested by the tool.

The IdeaPad Pro 5 with the Intel Core Ultra 9 285H ‘Arrow Lake’ CPU achieved a score of 7,854, outperforming 91% of all results produced by PCMark 10. This is a strong performance that shows the productivity-focused performance advantage of the extra cores in the Intel Core Ultra 9 285H compared to the all-day efficient performance approach of the four Performance cores and the four Efficiency cores in the Intel Core Ultra 9 288V ‘Lunar Lake’ CPU, which scored a still very competitive 6,968 but at significantly lower power draw with much improved battery life.

The Procyon AI Text Generation Benchmark

The UL Procyon AI Text Generation Benchmark, developed with insights from top AI vendors, aims to simplify and standardize the evaluation of local AI performance, especially for large language models (LLMs). It assesses performance using models such as Phi-3.5-mini, Mistral-7B, Llama-3.1-8B, and Llama-2-13B23. This benchmark measures how effectively a device handles local LLM inference tasks, like utilizing an on-device AI assistant for routine office tasks.

We conducted the test using the Intel Arc 140T and ARC 140V powered with OpenVINO 2024.5.0 as the AI inference engine. The results demonstrate that, in the realm of AI, performance depends on more than just raw power. The 140V outperforms the 140T through its enhanced architecture, innovative features, and superior efficiency.

The Procyon AI Image Generation Benchmark

The UL Procyon AI Image Generation Benchmark, developed in collaboration with key industry members, provides a consistent and accurate workload for measuring the inference performance of on-device AI accelerators, such as high-end discrete GPUs. This benchmark ensures fair and comparable results across various hardware.

We conducted the demanding Stable Diffusion XL (FP16) test using OpenVINO as the AI inference engine to evaluate performance of the integrated GPUs given the minimum requirement of 16GB of RAM. The Intel Arc 140T integrated into the Core Ultra 9 285H achieved an overall AI image generation score of 363, with a total time of 1649.551 seconds and an image generation speed of 103.097 seconds per image. The Arc 140V graphics integrated into the Core Ultra 9 288V scored 345, achieving 95% of the performance of the Intel Arc 140T, but at significantly lower system power draw.

We also tested image generation using the integrated NPU. Here, the IdeaPad Pro 5 with Core Ultra 9 285H and the first generation Intel NPU architecture with 13 AI TOPS peak performance, scored 843 compared to the score of 2,713 achieved by the Yoga Slim 7 15 with the second generation Intel NPU architecture, with up to 48 peak AI TOPS. This increase of over 200% in inferencing performance highlights the rapid increase in NPU hardware and software performance that the second generation of NPUs are able to deliver in the Microsoft Copilot+ PC category.

Blender Benchmark

Blender Benchmark version 4.3.0 was used to assess the Intel Arc 140T GPU’s rendering performance. With a score of 761, the GPU’s performance ranked among the top 35% of benchmarks running the same workload.

Considering the Intel Arc 140T is an entry-level integrated GPU with just eight Xe-cores, its performance was unexpectedly impressive. It ranked nearly in the top third of all results, significantly surpassing our initial expectation of it being just above the bottom third.

With a higher socket power budget, the Xe Intel Arc 140T also outperformed the Xe2 Intel Arc 140V graphics integrated into the Intel Core Ultra 9 288V in the Lenovo Yoga Slim 7 15:

Cinebench Performance

Cinebench is a useful indicator of a highly parallel multi-thread workload that can stress system throughput and performance. We tested both notebooks using the well-established Cinebench R23 as well as the still fairly new Cinebench 2024.

For Cinebench R23, the productivity-focused Intel Core Ultra 9 285H in the Lenovo IdeaPad Pro 5, with 16 cores including six Performance cores, managed almost double the multi-thread performance of the Core Ultra 9 288V with eight cores in total and four Performance cores.

However, the Core Ultra 9 288V, with a design focus on efficient performance, managed 60% better performance per watt on the multi-threaded workload with a turbo boost power of 37W, compared to 115W for the Intel Core Ultra 9 285H.

For Cinebench 2024, the story was similar, with the Core Ulta 9 285H again outperforming the Core Ultra 9 288V by 80% in ultimate multi-threaded performance, but with the Core Ultra 9 288V again having the advantage in performance efficiency with 72% better performance per watt in this test at maximum turbo power.

The Procyon Battery Life Benchmark

The video playback test uses the Microsoft Films & TV app included with Windows to measure battery life. The benchmark plays a HD video file in full-screen mode until the battery is depleted.

In our test, the Lenovo IdeaPad Pro 5 achieved a video playback battery life of nine hours and three minutes. The battery level started at 100% and ended at 3%. The maximum detected brightness was 80%, while the minimum detected brightness was 56%. The power plan was ‘balanced.’

This test highlighted just how far Intel has progressed with battery life and efficiency, with the Intel Core Ultra 9 288V, based on the ‘Lunar Lake’ architecture, achieving 17 hours and 22 minutes of battery life.

Gaming Performance

The Lenovo IdeaPad Pro 5 showed entry-level gaming performance across various titles. In The Shadow of the Tomb Raider, it achieved an average of 53 FPS on medium settings with XeSS set to balanced, at 1920×1200 resolution.

Turning to the Lenovo Yoga Slim 7 15, the Intel Core Ultra 9 achieved 62 FPS on average, highlighting the advances that Intel has made with the Xe2-based iGPU.

Looking at some other games for the Lenovo IdeaPad Pro 5:

  • For The Callisto Protocol, the laptop managed 37 FPS on the lowest settings, with AMD FSR2 set to performance.
  • Fortnite ran smoothly at 90 FPS on the lowest graphical preset, with XeSS set to performance. Switching the rendering mode from DX12 to Performance Mode, generally used by competitive gamers, boosted the frame rate to 150 FPS on average.
  • In Cyberpunk 2077, the built-in benchmark showed 31 FPS on average with the low ray tracing preset and Intel XeSS 1.3 set to auto. Disabling ray tracing and using the low preset increased the frame rate to 51 FPS.

Enabling ray tracing again, but with AMD FSR 3 set to performance and FSR 3 frame generation enabled, resulted in 58 FPS on average.

IDC Opinion and Conclusion

Both these Lenovo notebooks are sleek, solid, and stylish. They offer a premium look and feel, combined with professional-grade performance, excellent graphics, and lightweight mobility. Features like fast charging, long-lasting battery, and consistent fast responsiveness further enhance their appeal, making them ideal choice for work, productivity, and even some play. However, they do this in different ways to suit different use cases and ways of working.

The Intel Core Ultra 9 288V in the Lenovo Yoga Slim 7 15 is optimized for efficient performance, with great single-thread performance provided by four Performance cores that enable snappy responsiveness with lightly threaded applications. Meanwhile, the four low-power Efficient cores greatly boost battery life for background or less demanding tasks and media playback. Salespeople will appreciate its lightweight and durable design, making it ideal for trade shows and showroom demos. Executives will value the long battery life, which allows them to check spreadsheets, browse the internet, or watch media during long flights without concern.

For more demanding applications featuring many threads, such as running complex calculations, managing heavy spreadsheets with numerous formulas, or designing marketing materials and presentations, the Intel Core Ultra 9 285H with six Performance cores, eight Efficient cores, and two low-power Efficient cores provides extra processing grunt at the expense of higher overall power consumption and shorter battery life.

The standout aspect of our personal experience was the impressive speed for everyday use of both systems. This is largely thanks to the new Performance Core architecture as well as the ultra-fast LPDDR5-8533 memory and well-designed memory controller, which enhance overall system performance. This level of engineering showcases Intel’s expertise and capabilities for mobile processors. Another positive point is that both notebooks operate nearly silent almost all the time, adding to the overall pleasant user experience. Even when stress-tested or running games, the fan noise was not overly intrusive, maintaining around 35dB.

The second generation of Intel Core Ultra processors shows marked improvements in graphics performance and efficiency. The improved Xe LPG+ architecture is evident, especially in productivity, as the Intel Arc 140T demonstrated nearly double the performance of its predecessor, which was integrated in the previous generation Intel Core Ultra 9 185H in various workloads. The Intel Arc 140V similarly shows major improvements in overall performance and responsiveness and, in many aspects, is now class leading for mobile x86 processors. Intel’s engineering innovation allows integrated GPUs to leverage up to 16GB of system memory, which is a smart move. Models like Stable Diffusion XL are making notable progress in productivity applications, especially in marketing, advertising, and content creation. Due to VRAM limitations, this model will not even run on many more powerful discrete GPUs that have 8 or 12GB of VRAM.

In terms of gaming, both these notebooks are capable of supporting entry-level gaming, particularly when optimizing settings and utilizing upscaling technologies. This should get even better in the near future when XeSS 2 gets more widely adopted, with two groundbreaking new features: XeSS Frame Generation and Xe Low Latency. Combined with XeSS Super Resolution, gaming on integrated graphics will run smoother and at higher frame rates.

Mohamed Hakam Hefny - Senior Program Manager - IDC

Mohamed Hefny leads market research in EMEA on professional workstation PCs and solutions. He also reports on professional computing semiconductors, processors, and accelerators (CPUs and GPUs), as well as breakthroughs and trends related to the market. In addition, Mohamed is actively involved in AI PC taxonomy and research. He participates in business development projects, contributes to consulting activities, and provides IDC customers with analysis, opinions, and advice.

Andrew Buss - Senior Research Director, European Enterprise Infrastructure - IDC

Andrew Buss is research director for IDC's European Enterprise Infrastructure program. Based in London, he is responsible for driving IDC's research covering present and future trends impacting servers, storage and networking and IT service delivery. Central to this is understanding how on-premises IT is evolving under the emergence of Open Source, Software-Defined Enterprise, multi-Cloud adoption and Cloud-native development practices, and how this will impact everything from the low level silicon underpinnings and system design, through to the design and integration of the different infrastructure components, up to the platform management and service delivery.

Technology partners help bridge ecosystems. IDC’s 2024 EMEA Partner Survey sheds light on some of the underlying dynamics. For instance, 2 in 5 partners surveyed (41%) said they have a relationship with AWS. Of those AWS partners, 84% said they also work with Microsoft, while 23% partner with Red Hat and 15% have a relationship with ServiceNow.

At a global level, IDC’s Channel Partner Ecosystem (CPE) Database shows how the ecosystems of major cloud and software providers overlap to differing degrees, highlighting the need for different approaches across players.

The chart below demonstrates partner network overlap. Each column represents the degree of overlap between the provider on the row and the provider on the column. The darker the color, the more the overlap. For example, the analysis of ServiceNow’s partner network (column 7) shows a greater overlap with Salesforce than with Workday.

Given the rise of connected ecosystems, insights like these aren’t just interesting stats but hold practical and increasingly strategic value in today’s tech landscape. Think about the potential of accessing the list of common partners across vendors in the map — and getting detailed information in seconds.

Links across technology portfolios and partner networks fuel opportunities for vendors and bring enhanced value to customers. The IT industry has seen a sustained rise of the “platform paradigm” that enables connectedness and modularity across infrastructure, applications, data, and more. This requires architectures that allow technologies to integrate with and layer on top of one another. This trend is fueling growth in alliances and ecosystem engagement.

Customers benefit by being able to run integrated tech stacks that match their needs and requirements. For example, an organization may choose to run SAP on Microsoft Cloud, or may leverage Red Hat OpenShift to manage hybrid environments. Approved partner solutions from SAP Store extend the functionality of the core application, while Microsoft Azure provides native integrations with data warehouse solutions like Snowflake and Databricks.

The value of an integrated technology stack based on customer choice will increase significantly with the growing adoption of AI, which depends on contextually relevant, high-quality data collected across different systems and workloads.

Vendors are supporting these increasingly connected, platform-based strategies through new technology alliances, by building out their own ecosystems, and by connecting to others. A key component in bringing these alliances and connected ecosystems to life is the integration of IT/technology partners such as systems integrators, managed service providers, and value-added resellers.

Understanding partner capabilities and relationships is key to managing the entire partner life cycle. IDC offers complementary support for partner engagement strategies, including:

• The IDC CPE Database consolidates information on more than 600,000 partners worldwide on their capabilities, business models, relationships with vendors, and more. The data-driven partner intelligence we provide enables companies to compress the time and effort needed to recruit new partners, evaluate their current ecosystem, and benchmark against competitors’ ecosystems.
• The syndicated and custom research of IDC’s Partnering Practice specializes in understanding the ecosystem of technology partners and their engagement with vendors and customers. Our team of dedicated analysts offers thought leadership and advice through quantitative and qualitative insights on the global and regional levels.

Andreas Storz - Senior Research Manager, EMEA Partnering Ecosystems - IDC

Andreas Storz is senior research manager for IDC’s Europe, Middle East & Africa (EMEA) Partnering Ecosystems program. Based in the US, Andreas focuses on the evolution of go-to-market models, new digital value chains and the wider impact on partner ecosystems, exploring how current and future trends will impact the vendor, distributor, and partner landscape.

Gabriele Roberti - Research Manager, European Industry Solutions, Customer Insights & Analysis - IDC

Gabriele Roberti is a director for IDC's European Data and Analytics team. In this role, he oversees IDC’s partner intelligence data. He manages all Channel Partner Ecosystem programs, providing an extensive view on partners’ and vendors’ capabilities and relationships. He joined IDC in 2012 with a focus on the Italian market, then moved into regional research coordinating the delivery of regional quantitative research on IT strategies for vertical markets.

I’ve been hosting the IDC Smart Cities Awards since 2018 and this year was the first time that I got choked up multiple times during the awards ceremony. And I wasn’t the only one!

IDC’s Smart City North America awards were hosted at Smart Cities Connect, held in beautiful San Antonio.  As the award winners receive their awards, each team has a chance to say a few words about their initiative and this year their messages were powerful, personal and showed the passion for their work.

The award recipient from the City of Charlotte, Jamar Davis, whose project “Access Charlotte” focused on housing as a means to drive access to broadband, was emotional as he talked about his connection to the places he was serving.  Amy Atchley from the City of Austin said, when describing their project with Austin Energy, “Smart cities is for dreamers.” She didn’t mean Smart Cities are hypothetical; she meant this is a group that is dreaming big and, slowly but surely, realizing those dreams. These were just two of the speakers that moved me and the rest of the attendees.

 We can see the practical application of technology to big ideas and big challenges in the agenda from Smart Cities Connect where we host the awards.  From Digital Transformation and urban operations to community engagement, cities and their tech partners came together to demonstrate how their use of technology has matured in service to the public. Our award winners and finalists are a microcosm of this.

Here are just a few examples of the innovative projects that made this year’s IDC Smart City North America winners and finalists so impressive, and the three key take-aways from the winners.

Deliver on What Your Community Needs and Wants

  • The city of Phoenix, AZ  – the hottest city in the US – discovered that their residents wanted access to chilled drinking water.  With ideas from the community, the city developed a custom-designed water station that features two drinking spouts at ADA-approved heights, a bottle filling station, an internal chiller, and smart meters for reporting live water usage data via a central dashboard. The initiative is tackling hydration and heat concerns by establishing a network of modern, chilled fountains that enhance resilience to extreme heat, reduce plastic waste, and support access to essential services like work and healthcare.
  • The City of Chandler, AZ developed an Instant Language Assistant (ILA) that tested real-time translation tools to improve resident communication across 250 languages. Custom devices, used at city service counters and events, the ILA supported over 560 face-to-face interactions and enabled communication through headsets, keyboards (including Braille), and ASL support. Success stories included a hearing-impaired resident renewing a passport and immediate language support in libraries and housing services. Following the pilot’s success, city leadership approved funding for 60 ILA units over three years, making Chandler a regional leader in inclusive, tech-driven public service.

Do the Hard Work to take Partnerships to a New Level to Achieve Scaled Results

  • The City of San Antonio worked with two utilities and their initiative showcases the power of inter-utility collaboration, maximizing shared infrastructure to improve service reliability, and empowering residents with real-time insights and enable more efficient operations. San Antonio Water System (SAWS) modernized its 600,000-endpoint water network by replacing manual meter reading with advanced metering infrastructure (AMI). Faced with labor challenges, rising costs, and billing delays, SAWS partnered with CPS Energy to use its existing industrial IoT (IIoT) network, avoiding the cost of building a new system. A pilot using 2,500 ultrasonic meters showed near-perfect read accuracy, real-time usage data, and early leak detection, improving billing, customer engagement, and conservation. The shared network supports future smart city uses, setting an example of how cross-utility collaboration can increase ROI, operational efficiency, and resident satisfaction.
  • South Bend, IN  launched an innovative grant program to expand the city’s Real-Time Crime Center (RTCC) by partnering with local organizations to enhance security infrastructure. Run by SBPD and the Department of Innovation & Technology, the program offers up to $4,000 for eligible investments like cameras and software by local businesses. In return, participants integrate their security systems into the RTCC via the FususCORE device. Since launch, 39 organizations have joined, adding 171 camera views—a 51% increase in RTCC coverage. Benefits include improved incident response, deterrence, and stronger community-police ties. The program prioritizes privacy and transparency and has already helped SBPD address 13 safety incidents.

Embed Resilience and Sustainability in Project Design

  • The Cincinnati/Northern Kentucky International Airport modernized its main garage by addressing poor lighting, high energy use, and inefficient space utilization. The outdated sodium vapor lighting was costly and limited future upgrades like EV charging. The project introduced LED lighting, IoT sensors, and a data platform to improve navigation, safety, and energy efficiency. Goals included enhancing passenger experience, expanding infrastructure, reducing costs, and achieving ROI within 3.5 years. The system enabled real-time parking guidance, supported scalable innovation, and created new revenue opportunities through better space management and pricing strategies. Overall, the project marked a transformative shift in airport parking operations through smart, sustainable technology.
  • Generation Park Generation Park, a 4,300-acre master-planned community in Northeast Houston, is a public-private partnership between McCord Development and the Generation Park Management District. Facing high water bills and unaccounted water loss due to aging infrastructure and lack of monitoring tools, McCord built MizuWatch, a digital twin IoT water monitoring platform, using the AWS Garnet Framework based on FIWARE open standards. MizuWatch enables real-time water usage analytics, leak detection, and system transparency. It helped reduce billing and improve efficiency by identifying leaks and enabling proactive collaboration with water operators. The Garnet Framework also prevents vendor lock-in and will serve as the data foundation for future smart city initiatives in Generation Park.

One project embodied all of these – Austin Energy’s EVs for Schools initiative which provides an educational living lab that is scaling country-wide. Austin Energy is supporting Austin’s goals to be net zero emissions by 2040 which requires 40% of all vehicle miles traveled to be electric by 2030. To achieve these goals, Austin Energy is educating  community members on transportation electrification via a sustainable transportation curriculum prioritizing students from disadvantaged communities. Teachers are provided with a digital tool kit and training highlighting STEM education, environmental justice, EVs, clean energy and sustainable mobility. Austin Energy conducts EV workshops introducing students to fun, leading-edge concepts around EV technology, an EV virtual reality experience, and field trip adventures. 

These are just a few of many amazing projects.  We had a very difficult time choosing the winners from the list of finalists. And I wish I could expand on all the projects in this blog, but that will come later in a more detailed write-up. 

The IDC Smart Cities Awards program isn’t just about recognizing the work communities are doing all around North America, it’s also to connect peers to learn from each other and share our continued vision for the future of our communities.  We hope more organizations and municipalities will join us next year!

To learn more about IDC’s work with cities and communities, and our 2026 Smart City Awards, please visit us at IDC.com.

Ruthbea Yesner - Program VP - IDC

Ruthbea Yesner is the Vice President of Government Insights at IDC. In this practice, Ms. Yesner manages the US Federal Government, Education, and the Worldwide Smart Cities and Communities Global practices. Ms. Yesner's research discusses the strategies and execution of relevant technologies and best practice areas, such as governance, innovation, partnerships and business models, essential for government and education transformation. Ms. Yesner's research includes analytics, artificial intelligence, Open data and data exchanges, digital twins, artificial intelligence, the Internet of Things, cloud computing, and mobile solutions in the areas of economic development and civic engagement, urban planning and administration, smart campus, transportation, and energy and infrastructure. Ms. Yesner contributes to consulting engagements to support K-12 and higher education institutions, state and local governments and IT vendors' overall Smart City market strategies.