By upgrading to servers with a higher performance per core, Intel IT consolidated commercial database cores by 82%—significantly reducing licensing costs—with a six?month return on investment.

Intel’s business is driven by data and informed by data-driven decisions. Therefore, commercial databases are a critical component of Intel’s day-to-day operations, forming the backbone for hundreds of online transaction processing (OLTP) and online analytical processing (OLAP) workloads, such as enterprise data warehouses, data analytics, and smart factory applications. It is crucial that Intel IT minimize database costs while providing business units with the database performance they require. That’s a constant balancing act.

Challenge:
Recent changes in commercial database and virtualization solution pricing structures have resulted in escalating software licensing costs. Projections from one database vendor indicated that these costs might triple in the coming months. These looming expenses motivated Intel IT to investigate how to reduce the number of cores used to run the commercial databases in Intel’s database-as-a-service (DBaaS) environment, without negatively affecting database query performance. Two options that we considered were rewriting applications to use non-commercial databases or transitioning to a software-as-a-service (SaaS) model. Neither of these options seemed feasible because rewriting applications is time-consuming, expensive, complex, and risky; likewise, SaaS solutions don’t include licensing fees but instead come with their own high costs and often require employee retraining. We sought a solution that would create a cost-effective core density, deliver equal or better performance, and support quick and seamless migration to the new infrastructure—all while reducing total cost of ownership (TCO).

This year’s Intel IT Annual Performance Report chronicles Intel IT’s recent efforts and achievements within four categories: enabling the business to thrive, driving operational excellence, delivering an amazing employee experience, and shaping a resilient workforce.

The report includes an opening letter from our CIO, Motti Finklestein, and numerous success stories. Here is just a sampling:

• We have developed an enterprise-grade, scalable GenAI platform and a dashboard that enables us to track the lifecycle and business value of all GenAI projects across Intel. 30,000 monthly users have already created more than 7,000 personal assistants, and our 400+ GenAI use cases have generated an estimated USD 376 million in business value.
• We are nearing completion of modernizing our enterprise resource planning (ERP) platform. So far, we have over 56 million master and transactional data records loaded, and during our first and second releases, we achieved 100% and 90% data load success, respectively. We are now poised to start scaling our data strategy across the enterprise.
• We are transforming our data centers by using sustainable technology like advanced cooling techniques and fuel cell technology; deploying software-defined networking; and evolving our enterprise architecture (EA) processes and practices. The EA effort alone has eliminated more than 300 in-house applications, removed 1,400 commercial software products from the environment, and generated USD 13 million in cost avoidance.
• Factory modernization initiatives include implementing a 5G private network and extending our industrial Internet of Things (IIoT). We have also deployed over a dozen robots equipped with multiple integrated IoT sensors; these robots can autonomously roam Intel facilities to perform facility inspections and read values from instrumentation.
• We are transforming the employee experience through cloud-based client device management, improving the Wi-Fi experience using client analytics, and benchmarking the PC experience to pinpoint applications that negatively affect battery life. We have cut the number of “How do I…” support tickets by 30% by deploying a GenAI-based chatbot.
• We are creating a strong workforce that can keep innovating despite disruption and rapid change. Our new People Council is staffed by leaders who focus on developing and retaining top talent, talent trends, and strategic workforce planning. Our employee training programs center on five top skills for the future: AI, SAP, cloud, business relationship management, and data.

Read the entire APR for the details of these stories and more.

In 2024, Intel IT launched our OneIT Resiliency Program, geared to instill resiliency across technologies, operations, and culture and organizational entities. In addition to defining these three pillars, we codified resiliency through a unique combination of framework, principles, standards, maturity model, and roadmaps. We focused first on applications, closing 100% of all resiliency gaps in Tier 1 applications by the end of the year. We also successfully weathered a major incident caused by one of our security software vendors: Despite widespread PC failure across the company, we restored functionality in just a few hours for Tier 1 and Tier 2 applications and under 1.5 days for all applications at Intel.

During 2025, we will extend our efforts to infrastructure and data centers. We hope that our resiliency framework, standards, blueprints, recipes, and maturity model will inspire other IT departments to pursue a similar path to IT—and enterprise—resilience.

In 2013, we deployed an “ultra” data warehouse that replaced our older fragmented and siloed databases. It has stood the test of time, easily scaling as we added yet more data and data domains. As Intel transitions to a foundry model, its factories will become more complex and will generate even more data than before. Plus, security is becoming even more critical—Intel must not only protect its own manufacturing data from unauthorized access; it must also ensure that Intel Foundry customers have confidence that their data is in safe hands and guaranteed to be separate from Intel’s data. We have implemented process-specific security (PSS), which helps prevent unauthorized data access while still delivering efficient query execution.

Our highly secure and high-performance data warehouse, running on modern Intel architecture, helps unlock important insights hidden in massive manufacturing data volumes. This solution has shown that it can reliably scale to handle not only today’s data challenges but also grow with Intel as it expands its factories to support its foundry model and IDM 2.0.

Intel’s smart manufacturing environment relies on AI to improve yield, perform root-cause analysis, and accelerate anomaly detection. As Intel transforms to an internal foundry model, it is crucial that the Manufacturing Automation team helps improve factory efficiency by enabling faster, more cost-efficient deep-learning (DL) and machine-learning (ML) workloads.

We recently benchmarked a manufacturing object detection DL model training workload. We compared the performance of our legacy GPU-based system, a vendor upgrade to the GPUs, and Intel® Gaudi® accelerators. Our AI-based solution using Intel Gaudi accelerators produced the following benefits:

• A 20% improvement in DL model training time
• Reduced manual processes for employees and faster defect detection
• Streamlined workflows and enhanced manufacturing efficiency
• Improved cost efficiency and better supply chain availability for reduced operational expenses

Intel’s AI hardware and software ecosystem is a competitive and efficient solution for high-volume manufacturing (HVM) use cases. The manufacturing efficiency offered by Intel Gaudi accelerators will help us improve production yields and increase Intel factory revenue.

After evaluating the components and architectures of SDN, we opted for an open, standards-based architecture rather than a supplier-centric solution. As our SDN architecture has evolved, we have established a standardized and scalable data center network architecture that extensively uses automation. The open interface provides the flexibility to integrate additional business-driven automation, enabling us to meet our growth objective and timeline requirements.

Our network architecture strategy relies on five pillars:

• Scalability through standardization. We maintain compliancy with our standards for hardware, OS, device roles, topology, configurations, and solutions to enable automation and rapid scalability at large data centers.
• Programmability. This allows our workforce to adapt to significant growth in network scale with improved speed. It also facilitates full lifecycle provisioning of network infrastructure from Day 0 to end of life.
• Security. We have the capability to segment the network using a common infrastructure to support various use cases and enhance data center security.
• Resiliency. Built-in network resiliency helps ensure the continuous operation of network functionality, facilitates rapid recovery, and maintains performance even in compromised conditions.
• Supportability. We strive to be sure the network maintains its designed level of performance and availability of the network. Adhering to standards facilitates troubleshooting.

Over the past five years, we have migrated the majority of our data centers to a new SDN architecture that employs a leaf-spine underlay combined with overlay networks. By utilizing Industry-standard components and protocols, we have significantly improved network delivery times while reducing the need for human resources, thereby enhancing overall efficiency. Additionally, we have increased the stability and reliability of the network and consolidated multiple dedicated customer networks onto a common infrastructure that’s integrated with enhanced security controls.

A decade of validation has underscored that emphasizing business value in cloud hosting decisions is advantageous. However, we continue to adapt our multicloud strategy as both business needs and the IT ecosystem evolve.

Some of those adaptations include the following:

• Developing a unified hosting portal for our cloud management platform
• Embracing open-source offerings for our database as a service (DBaaS) solutions.
• Honing our application hosting decisions so our use of software as a service (SaaS), public cloud, and private cloud is optimized.
• Rapidly scaling our containers as a service (CaaS) environment.
• Supporting the growth of AI and automation.

But the cloud journey is never finished. New trends in the enterprise—such as the increasing use of AI, as well as innovations in the IT landscape—mean that we must continue to evaluate and evolve our multicloud strategy to consistently support the business in the most cost-efficient manner possible.

We know employees want to be as productive as possible, and battery life is crucial to Intel’s highly mobile workforce. On the other hand, our IT build includes several components that help keep Intel’s data and intellectual property safe. Necessarily, these additional software components consume their fair share of power. To fully understand what users are experiencing with their laptops—as well as how the Intel IT build introduces power consumption overhead—we rigorously benchmark the corporate PC experience.

In collaboration with Intel’s Client Computing group, we recently overhauled our PC experience benchmarking methodology and toolset. We adopted new benchmarking tools that more accurately represent the applications that Intel employees use. We also now use the Intel® Battery Life Diagnostic Tool (Intel® BLDT) to identify specific background processes and applications that are responsible for excessive power consumption.

We use these tools to reduce power consumption rates on users’ PCs and help the Client Computing group drive improvements to client devices and meet customers’ expectations. By sharing our benchmarking approach and results, we hope to encourage other IT departments to take a similar approach.

CBP is a planning methodology that drives business-focused outcomes by providing a systematic, objective, and holistic view of a group’s capabilities. In our case, these capabilities center around InfoSec, such as digital identity and access management and privacy management. Using a four-phase cyclical process of alignment, assessment, planning, and management, we can:
• Determine the maturity of every capability using a precise, mathematical formula.
• Identify and prioritize gaps between the current capability maturity and the desired level of maturity.
• Create a roadmap to resolve those gaps.

This comprehensive method of capability mapping and scoring delivers a unified view of capabilities and objectives so that teams can collaborate and optimize cross-functional efficiency.

Adopting CBP has provided several benefits to the InfoSec group:
• Strengthened InfoSec strategic alignment
• Optimized resource allocation for critical security capabilities
• Proactive risk management
• Enhanced security performance tracking
• Improved collaboration for shared security
• Responsive adaptation to third-party security frameworks

As other Intel IT groups—and even business units—see the positive outcomes of CBP, we hope they also begin to adopt it, which would multiply the benefits across all of Intel. Furthermore, CBP has already fostered collaboration across departments and teams.

Our comprehensive method of capability mapping and a formulaic scorecard captures the strategic importance, business goal importance, capability maturity score, and quantified risk. It highlights what is required to execute initiatives successfully. Our solution addresses our longstanding InfoSec planning challenges but can also be applied to other IT groups and even other industries. Adopting CBP is an excellent way to align every decision with its strategic vision and capabilities and drive sustained enterprise success.

Minimizing factory downtime and improving operational efficiency have always been the driving forces behind Intel IT’s push to digitize equipment in both factory and subfab environments. Now, we’re expanding these efforts by working with Intel’s Corporate Services (CS) team to connect critical factory-support infrastructure, including surveillance systems in guardhouses, electrical supply equipment, water systems, and more.

Connecting equipment in remote outdoor areas to the corporate network has always been a challenge. Wi-Fi coverage is limited; hard wiring is costly and time-intensive; and relying on public networks introduces concerns with cost, performance, and security. After reviewing the options, we chose to roll out 5G private networks at Intel factories to extend wireless connectivity for key use cases in Internet of Things (IoT), robotics, operations, and security. This deployment, combined with 5G-enabled IoT Gateways and an array of sensors, lets us reach further and deliver more robust connectivity where it’s needed.

With our 5G private network in place, CS technicians can tap into sensor data to gain deeper visibility into equipment health, enabling predictive and preventative maintenance. This enhanced connectivity also allows technicians to connect field devices, like tablets, directly to the corporate network to increase efficiency. Currently, our 5G private program supports only 13 use cases—a fraction of the potential applications. Our 5G deployment spans five factories and is projected to generate a net present value of USD 35M over the next five years.

We hope that by sharing our 5G private network journey, we can inspire other IT departments to explore using this technology to continue the digital transformation of their previously unconnected environments.”

Based on its existing market presence with PC Wi-Fi products, Intel’s Client Computing product group developed Intel Connectivity Analytics technology. The product group collaborated with Cisco—a well-known enterprise Wi-Fi network infrastructure supplier—and Intel IT to develop compelling use cases and solutions that utilize Intel Connectivity Analytics. Intel IT provided product feedback and practical applications of the technology in our enterprise environment and helped optimize the use cases over Intel’s network infrastructure. Our input helped to better define the data and analytics needed to achieve the desired business outcomes, such as faster troubleshooting, mean time to repair (MTTR), reduced network total cost of ownership, and a better UX.

Intel IT now uses Intel Connectivity Analytics—delivered through the Cisco Catalyst Center (previously Cisco DNA Center) and cloud dashboard—to improve the management of Intel’s Wi-Fi network. We are also working with the overall ecosystem to expand the use cases for Intel Connectivity Analytics and further enhance AI for IT Operations (AIOps). We have reduced some client-side troubleshooting from upwards of 15 minutes to 10–15 seconds. Similarly, finding the root cause for ubiquitous network issues—which could take days—now often takes seconds as well.

We encourage other IT departments to consider deploying IT tools powered by Intel Connectivity Analytics for Wi-Fi, which is built into Intel Wi-Fi adapters and requires no software installation or maintenance. It can be utilized by Cisco and other Intel Connectivity Analytics Program members.

In addition, we are now working with the Intel Client Computing product group to develop an additional Intel Connectivity Analytics offering that shares data through a PC agent to the cloud and expands the available connectivity analytics beyond Wi-Fi to include Thunderbolt technology, Bluetooth, Ethernet, and more.

Silicon chip Design engineers at Intel face ongoing challenges: integrating more features into ever-shrinking silicon chips, bringing products to market faster, and keeping Design engineering and manufacturing costs low. Design engineers run more than 273 million compute-intensive batch jobs every week. Each job takes from a few seconds to several days to complete.

As design complexity increases, so do the requirements for compute capacity, so refreshing servers and workstations with higher-performing systems is cost-effective and offers a competitive advantage by enabling faster chip design. Refreshing older servers also enables us to realize data center cost savings. By taking advantage of the performance and power-efficiency improvements in new server generations, we can increase computing capacity within the same data center footprint, helping to avoid expensive data center construction and reduce operational costs due to reduced power consumption.

To meet Design engineers’ computing capacity requirements, Intel IT conducts ongoing throughput performance tests using real-world Intel silicon Design workloads. These tests measure EDA workload throughput and help us analyze the performance improvements—and in turn, business benefits offered by newer generations of Intel® processors.

We recently tested two-socket servers based on the Intel® Xeon® Platinum 8400 and 8500 processor Series, along with the Gold 6400 processor Series. The test included operating single- and multi-threaded EDA applications running Intel silicon Design workloads for more than four days. Select results include the following:

• Higher frequency for per-core performance. For critical-path EDA workloads, selecting a high-frequency CPU like the Intel Xeon Gold 6444Y processor (32 cores per server) can deliver up to 1.14x higher per-core performance compared to lower-frequency, higher-core-count CPUs in the same generation of processors.

• Higher core counts for throughput. For volume validation runs, selecting a higher-core-count CPU at optimal frequency like the Intel Xeon Platinum 8462Y+ processor (64 cores per server) can deliver up to 1.75x higher Register Transfer Level (RTL) Simulation throughput per server when compared to a lower-core-count CPU (32 cores per server) in the same generation of processors. The Intel Xeon Platinum 8462Y+ processor (64 cores per server) completed workloads up to 2.17x faster than a previous-generation Intel Xeon Gold 6346 processor-based server, which has only 32 cores. Compared to a 2nd Gen Intel Xeon Gold 6246R processor (32 cores per server), the server with the newer processor outperformed the older processor by up to 2.40x in throughput.

• Additional benefits from 5th generation processors. At the same Thermal Design Power (TDP) of 350W, the Intel Xeon Platinum 8580 processor completed workloads up to 1.27x faster than a previous-generation Intel Xeon Platinum 8468 processor-based server.

Based on our performance assessment and our refresh cycle, we are deploying servers based on the 4th and 5th Gen Intel Xeon Scalable processor family in our data centers. By doing so, we have significantly increased EDA throughput performance to improve the overall EDA design cycles and optimize time to market of Intel chips.

Intel Manufacturing Automation has developed an innovative methodology for performing FMEA in manufacturing by using artificial intelligence (AI) techniques to analyze users’ emotional tone—positive, negative, or neutral—about the manufacturing equipment.

Failure mode and effects analysis (FMEA) is a critical process in manufacturing that is used to identify potential failures, assess their impact, and prioritize preventive actions. The FMEA process involves breaking down the system into its individual components; analyzing each component for potential failure modes; determining the effects of these failures; and assigning a risk priority number based on severity, occurrence, and detectability.

Traditionally, FMEA involves time-consuming manual extraction and analysis of large text data from tool logs and other sources, leading to weeks of engineering effort per analysis. This approach must be repeated across large fleets of tools by the engineers who support these tools.

Our new approach uses natural language processing (NLP) and sentiment analysis (SA) to reduce the labor required for FMEA from weeks to seconds. When we compared the traditional FMEA approach to our SA-based system, the software discovered everything the engineers found with their manual system approach—plus even more that they missed. Our system performed FMEA on six months’ worth of data in under one minute, saving weeks of engineering time.

Our SA analysis extracts keywords from comments, notes, charts, and quality systems data to find words with negative connotations about a tool, such as “abort” or “fail.” We’ve customized the analysis for domain-specific language, such as words and phrases that are specific to the semiconductor fab environment (e.g., “defect” and “excursion”). We also implemented custom replacement keywords (“ABORT” vs. “ABT” vs. “ABRT”) to accommodate various abbreviations and spelling errors commonly found in tool logs and other data sources.

The system analyzes sentence structure and filters out brackets for HTML and other special characters used in programming languages, allowing for a wide variety of inputs. While Intel’s manufacturing logs are predominantly in English, the libraries can be extended to handle multilingual input.

Integration with our in-house factory data analytics platform, Data on the Spot (DOTS), further democratizes FMEA results by enabling users to easily access this data and use it to identify root causes from the original sources. The system is now deployed across all Intel semiconductor factories, with a focus on front-end manufacturing.

The Intel® architecture-based components of our new FMEA system enable us to deploy new features rapidly. The FMEA system is powered by Intel® Xeon® processors, which provide the computational power and performance needed for real-time analysis of large datasets. The system uses high-speed SSDs and advanced memory configurations to ensure rapid data access and processing. Fast Intel® Optane™ persistent memory is employed to enhance caching capabilities and accelerate data retrieval.

This transformative approach promises to dramatically streamline the FMEA process, freeing up engineers’ time to focus on developing innovative solutions to further enhance Intel’s manufacturing tools and processes.

Creating efficient client management processes is crucial to maintaining IT efficiency as well as bolstering a positive user experience (UX). That is why we continue our journey toward modern, cloud-native client management with cloud-first capabilities.

Over the last few years, we have migrated both existing devices and new devices to a modern client stack composed of cloud-based components such as Microsoft Intune, Entra ID, Autopilot, and Windows Update for Business with Delivery Optimization. Together, these components support seamless client provisioning that improves the UX for end users as well as substantially reduces IT effort associated with client management.

Benefits accruing from the move to the cloud are plentiful. A few examples include:
• The ability to set up, patch, and manage a device without needing to connect on-premises.
• A reduced attack surface created by eliminating several on-premises management tools and consistently requiring multifactor authentication.
• A substantial reduction in technical debt.

We still have additional plans for continuing our modern client journey. However, we—and our users—are already reaping huge rewards, such as the following:
• 38% reduction in issues related to PC setup
• 40% reduction in onsite PC build effort
• 10,000 fewer person-days per year in end-user downtime
• 100% reduction in security patch preparation (10,208 user-facing patching days per year saved)
• 40-60% savings in bandwidth due to Delivery Optimization

This white paper shares our client management strategy, our roadmap, and our key learnings. We hope this story encourages other IT departments to pursue the benefits of cloud-based client management and makes their journey a little easier.”

Building on previous investments and techniques, our data center strategy has generated savings exceeding USD 9.6 billion from 2010 to 2023. We are continually improvising our data center strategy to further our data center transformation. We will accomplish this by using disruptive server, storage, network, infrastructure software, and data center facility technologies. These can lead to unprecedented quality-of-service levels and a reduction in total cost of ownership (TCO) for business applications. And they will enable us to continue to improve IT operational efficiency and be environmentally responsible.

Transformational elements of our data center strategy include the following:
• A focus on three primary KPIs. These metrics enable us to measure the success of data center transformation: Meet growing customer demand (SLAs and QoS) within constrained spending targets (remaining cost-competitive) while optimally increasing infrastructure asset utilization (asset efficiency).
• Stimulating bolder innovation by changing our investment model. Comparing our current capabilities to a “best achievable model” encourages us to strive for innovation that will transform our infrastructure at a faster rate than if we sought only incremental change.
• New unit-costing financial model. This model enables us to better assess our data center TCO based on the business capabilities our infrastructure is supporting. The model measures the cost of a unit of service output and enables us to compare investments and make informed trade-o? decisions across business functions. This enables us to maximize ROI and business value.

Some of our most notable achievements include:
* USD 9.6 billion in savings
* 44% savings with disaggregated server design, compared to a full-acquisition refresh
* 400% increase in data transfer rates between sites through international WAN links
* Over 1.57 million KW hours saved, compared to industry-standard data centers
* One-day deployment with our process transformation for new physical server deployment
* 577x capacity increase in our 7th generation high-performance computing (HPC) environment and a 164x quality improvement

Our data center transformation strategy is critical for Intel IT to stay competitive. We are committed to providing a foundation for continuous innovation that will improve the quality, velocity, and efficiency of Intel IT’s business services.

How efficiently is your factory operating? Are you able to provide accurate delivery dates to customers and consistently meet those dates? Are you maximizing the utilization of your equipment and raw materials to keep your factory running optimally and cost effectively?

If not, Intel Factory Pathfinder was developed just for you. Factory Pathfinder can help you quickly identify and eliminate bottlenecks and constraints in your factory and equipment before they happen.