March 30, 2026
AI Infrastructure: Building a Foundation for Scalable AI
To accelerate their artificial intelligence initiatives, enterprises must implement scalable, secure infrastructure capable of supporting applications with extraordinary performance requirements.
As enterprises attempt to move from artificial intelligence experimentation to production, many are encountering limitations related to scalability, governance and operational complexity. Often, these problems have a common cause: infrastructure. To support AI programs that create real value, organizations must implement scalable, high-performance infrastructure designed for modern AI workloads. Usually, this involves a mix of on-premises infrastructure and cloud resources, including predesigned “cloud landing zones” built specifically for AI. Governance and security are critical concerns during any infrastructure build, and AI presents a number of new challenges, including the risk of data exposure through automatically generated outputs, AI-specific threats including prompt injection and the uncertainty that surrounds emerging technologies such as agentic AI. By aligning infrastructure strategy with business objectives, organizations can enable faster innovation, improve workload efficiency and reduce risk. Driven in part by the need to move quickly, many organizations turn to a trusted partner for external expertise on infrastructure design and ongoing optimization.
As enterprises attempt to move from artificial intelligence experimentation to production, many are encountering limitations related to scalability, governance and operational complexity. Often, these problems have a common cause: infrastructure. To support AI programs that create real value, organizations must implement scalable, high-performance infrastructure designed for modern AI workloads. Usually, this involves a mix of on-premises infrastructure and cloud resources, including predesigned “cloud landing zones” built specifically for AI. Governance and security are critical concerns during any infrastructure build, and AI presents a number of new challenges, including the risk of data exposure through automatically generated outputs, AI-specific threats including prompt injection and the uncertainty that surrounds emerging technologies such as agentic AI. By aligning infrastructure strategy with business objectives, organizations can enable faster innovation, improve workload efficiency and reduce risk. Driven in part by the need to move quickly, many organizations turn to a trusted partner for external expertise on infrastructure design and ongoing optimization.
Across industries, organizations are embracing artificial intelligence at breakneck speed.
According to a 2025 study from Google Cloud, 98% of organizations are actively experimenting with, developing or using generative AI in production. What’s more, 79% of technology leaders consider AI to be either “very important” or “extremely important” to their organization’s current and future business operations. “AI is no longer a futuristic concept,” the report’s authors write. “It’s a core business driver and a fundamental shift in how organizations work. IT leaders have moved past talking about acknowledged potential and have turned to building an infrastructure that can support the growing demands of AI workloads. The infrastructure decisions you make today will determine your organization’s ability to compete in an AI-driven future.”
However, many leaders are unsure how to even begin making these decisions, and there remains a significant gap within most organizations between AI ambitions and infrastructure readiness. Pressure is growing for organizations to not only embrace AI applications but to begin pushing these solutions from pilot to production and start using them to create measurable business value. This shift from small, disconnected experiments to integrated, enterprise-scale systems requires dedicated, AI-ready infrastructure that both meets the demands of the moment and offers seamless scalability for future growth.
Infrastructure is foundational to AI success for several reasons. First, the performance demands of AI applications are enormous, especially for intensive processes such as model training. Organizations must adopt not only high-performance computing infrastructure but also advanced storage solutions and low-latency networking tools that can keep up with the speed of AI applications. Additionally, this infrastructure must be highly flexible and scalable to accommodate a technology that continues to evolve rapidly. Some organizations that purchased data center infrastructure only two years ago have found that their investments are already incapable of supporting their envisioned future AI use cases. This creates a vexing challenge: Organizations might spend 18 to 36 months building out or retrofitting a data center to accommodate the latest AI-ready infrastructure, only to find that their facility is nearly outdated by the time it is ready to open.
For most organizations, the question is not whether to adopt AI but how to build a foundation that facilitates real results on an accelerated timeline. Organizations that invest now in scalable, AI-ready infrastructure will be best positioned to move quickly, validate new use cases and give themselves a competitive advantage.
33%
The percentage of organizations that cite a lack of visibility and monitoring for AI workloads as a major infrastructure challenge, highlighting the difficulty of establishing strong governance and oversight practices
Source: A10 Networks, “The State of AI Infrastructure Report 2025,” March 2026
Despite years of digital investment, the healthcare continuum remains deeply fragmented for patients and clinicians. Leaders often imagine a linear path from acute to post-acute and home settings, but the reality can be episodic and confusing, especially during transitions. Patients frequently leave one environment with little visibility into what comes next, while clinicians lack timely access to information once care moves beyond their system.
Lack of integration creates clinical risks and higher costs. Breakdowns in communication, data access and coordination lead to duplicated tests, delayed care, increased clinician workload and patient frustration. Inadequate information sharing can also contribute to adverse drug events and lack of follow-up on treatment plans, raising the risk of readmission.
Even when organizations share electronic health record (EHR) vendors, care handoffs often rely on manual processes that undermine safety and efficiency. The continuum works best when leaders view it not as a sequence of locations but as a connected experience that feels coherent to the patient. This shift in perspective sets the foundation for smart care: an approach that prioritizes continuity, visibility and orchestration across environments, rather than siloed technology deployments.
Within the hospital, smart care enables patients to move efficiently through admission, testing, treatment and discharge. A seamless experience builds trust, which matters when 85% of patients choose providers based on perceived safety and 45% recommend providers based on communication quality. At home, smart care includes home health services and clear follow-up so patients and families understand their next steps. For clinicians, smart care reduces friction points that contribute to burnout.
Ultimately, smart care is an ongoing journey, built on the right infrastructure and strategic, iterative progress. Organizations that embrace connected, outcome-driven design across the continuum are better positioned to deliver care that is safer, more efficient and more human-centered, now and in the future.
AI Infrastructure: By the Numbers
74%
The percentage of organizations that primarily use a hybrid cloud approach to support generative AI workloads
Source: Google Cloud, “State of AI Infrastructure,” June 2025
33%
The percentage of organizations that cite compute limitations (including insufficient CPU and GPU processing power) as a major bottleneck in their AI environments
Source: A10 Networks, “The State of AI Infrastructure Report 2025,” March 2026
65%
The percentage of organizations that report that legacy systems create challenges for their AI infrastructure environments, such as an inability to scale for business demands
Source: DDN, “State of AI Infrastructure Report,” January 2026
AI Infrastructure: By the Numbers
74%
The percentage of organizations that primarily use a hybrid cloud approach to support generative AI workloads
Source: Google Cloud, “State of AI Infrastructure,” June 2025
33%
The percentage of organizations that cite compute limitations (including insufficient CPU and GPU processing power) as a major bottleneck in their AI environments
Source: A10 Networks, “The State of AI Infrastructure Report 2025,” March 2026
65%
The percentage of organizations that report that legacy systems create challenges for their AI infrastructure environments, such as an inability to scale for business demands
Source: DDN, “State of AI Infrastructure Report,” January 2026
- BUILDING AI-READY INFRASTRUCTURE
- ENABLING GOVERNANCE AT SCALE
- ACCELERATING TIME TO VALUE
The demands of AI workloads are enormous, and supporting an AI strategy requires purpose-built infrastructure spanning compute, storage, networking and cloud environments. While traditional IT architectures may support smaller AI experiments, infrastructure often becomes a bottleneck when organizations attempt to move these pilots into full production. Ideally, AI infrastructure should be an integrated stack, rather than a collection of individual technologies, as different components such as data pipelines and compute environments must work together to support the performance, governance, and visibility needs of model training and inference tasks.
SCALABLE COMPUTE: AI workloads require significantly more compute power than most other enterprise applications, especially during model training. To support this demand, organizations are increasingly relying on GPU-accelerated clusters and high-performance computing environments that can handle the massive parallel processing demands of modern machine learning models. However, this hardware is expensive and sometimes difficult to source, and many organizations also turn to the public cloud to meet their infrastructure needs. In addition to sheer compute capacity, leaders should consider scalability as they build out their compute infrastructure, adopting modular solutions that can scale without rearchitecting.
HIGH-PERFORMANCE STORAGE: Data is the foundation that supports all AI applications. To perform effectively, AI systems must be able to instantly access the massive data sets that are used to train AI models. High-performance storage solutions allow organizations to move large volumes of data efficiently between storage, compute and training environments. Often, data pipelines span multiple environments, including on-premises systems, cloud platforms and edge locations. Storage architectures must be designed with these data pipelines in mind, or teams may end up spending more time moving and preparing data than actually developing models.
LOW-LATENCY NETWORKING: As AI environments scale, networking performance can become as important as compute and storage. With so much data moving between compute nodes, storage systems and cloud services, any unexpected latency can quickly throttle performance, leading to delays in model training and overall AI development. Modern AI architectures emphasize high-speed interconnects, software-defined networking, and low-latency connectivity across data centers and cloud environments, helping to maintain performance standards even as data volumes continue to grow.
CLOUD LANDING ZONES: An AI cloud landing zone is a predesigned, governed cloud environment that provides a secure foundation for building, deploying and operating AI workloads at scale. Typically, these landing zones include the following critical elements: identity and access management tools such as role-based access controls and single sign-on, network topology, security and compliance features such as encryption and logging, and cost management measures to prevent overspending. Enterprises often turn to these environments to accelerate their AI programs without losing control of security and governance, as well as to create consistency across the organization. With cloud landing zones, every AI team gets access to the same well-architected environment.
ECOSYSTEM ALIGNMENT: Successful AI initiatives typically involve an ecosystem of technology partners spanning hardware, cloud platforms, software frameworks and specialized AI tools. Many organizations also rely on external expertise to support early architectural decisions and help guide AI programs as they mature. Structured engagements such as infrastructure assessments and readiness workshops can help identify gaps in compute capacity, data architecture and operational processes before large-scale deployments begin, allowing internal teams to leverage both their specific business knowledge and external, AI-specific technical expertise.
Click Below To Continue Reading
Money is a major consideration in AI infrastructure decisions, with 83% of tech leaders citing it as a key factor when evaluating solutions. Leaders make these investments with the hope that they will pay off in the form of measurable benefits such as increased productivity, revenue growth and reductions in recurring costs.
Where do you expect the largest ROI from generative AI?
Increase employee productivity
22%
Improve customer satisfaction and engagement
21%
Streamline workflows and processes
20%
Improve competitiveness and gain market share
18%
Accelerate revenue growth
14%
Increase sales and revenue
13%
Reduce operational costs
13%
Source: Google Cloud, “State of AI Infrastructure,” June 2025
As organizations move from AI experimentation to production deployment, governance, security and compliance grow in importance. AI workloads depend on critical data, complex infrastructure environments and rapidly evolving models, and organizations without strong governance frameworks risk putting sensitive information at risk and losing control over their growing AI environments. Organizations must implement robust data management, security and visibility practices to prevent data exposure, unauthorized access and unpredictable model behavior. They must also ensure that their operational environment can support AI at scale, moving beyond isolated pilots to well-managed ecosystems supported by the appropriate infrastructure and talent. As AI systems become more autonomous, organizations will need governance models that define how AI agents interact with data, systems and employees. Over time, as AI becomes more deeply embedded in business processes, strong governance, monitoring and operational practices will be essential to maintaining security, reliability and trust.
DATA MANAGEMENT: When AI initiatives fail, data is often the reason. Even when organizations have clear AI strategies and robust infrastructure in place, gaps in data preparation can make AI outputs unusable due to problems with clarity and accuracy. According to a 2025 report from Google Cloud, 20% of tech leaders say that data readiness is one of the greatest challenges holding back their organization’s AI adoption. Organizations need not only large volumes of valuable data but also a comprehensive data management strategy that includes governance policies, data preparation processes, and clear controls over how data is accessed and used.
SECURITY BY DESIGN: AI workloads introduce new security considerations because they often interact with large volumes of enterprise data across multiple systems. If data sets are not properly tagged, secured and governed, AI tools can inadvertently surface sensitive information to users who should not have access to it. Even internal productivity tools can pose these risks when underlying data environments lack proper security controls. For example, if a poorly governed AI tool is trained on a company’s HR records, users might be able to access other employees’ sensitive information. Organizations must also protect their systems and data against AI-specific external threats, such as prompt injection attacks and data poisoning attempts.
WORKLOAD VISIBILITY: Enterprises that make a large number of tech investments in a short period of time often experience “sprawl,” a problem that occurs when business and IT leaders lose track of all of the different systems operating in their environments. This can lead to redundancy and waste, and it can also make it difficult to detect when employees use unauthorized technologies for work (“shadow AI”). Businesses have seen this sort of sprawl in the past with cloud investments, and it is becoming a problem for AI environments as well. Effective observability platforms provide dashboards and telemetry that allow teams to track performance, diagnose issues and measure the quality of AI interactions, helping to provide consistent user experiences while managing overall spending.
OPERATIONAL READINESS: AI is no longer an experiment. Rather, it has become a critical operational capability. While early deployments may have involved small teams experimenting with models or applications, production systems require coordinated processes across infrastructure teams, developers and business stakeholders. This shift requires new levels of collaboration between technical teams and business leaders to ensure that infrastructure investments, performance expectations and AI outcomes remain aligned. Organizations must also establish processes for ongoing optimization of their AI environments, with consideration given to performance, reliability and cost-efficiency.
AGENTIC AI: Increasingly, organizations are exploring autonomous agents capable of interacting directly with enterprise data. These systems can analyze information, make recommendations and potentially take actions on behalf of users. While this creates powerful new opportunities for process automation, agents also introduce new governance challenges. As they look to adopt agentic AI, leaders must establish guardrails that determine whether and how autonomous agents can read, modify or delete information and ensure that those actions remain auditable and controlled. Without these safeguards and visibility measures in place, AI agents may take actions that violate agreements with users and customers, or even interfere with the delivery of products and services.
Organizations are racing to stand up the necessary infrastructure to support AI, and those that create mature AI environments first will create a significant competitive advantage. Many organizations turn to a trusted partner such as CDW to accelerate this process with expert-led infrastructure design and optimization.
READINESS ASSESSMENTS: AI is still an emerging technology, and many leaders don’t know what they don’t know. Organizations typically lack in-house expertise on AI infrastructure, and even seasoned IT leaders might be unsure of how existing environments and data stores might support AI applications. Through collaborative workshops and readiness assessments, CDW helps organizations uncover high-impact use cases, align data and governance strategies, and design infrastructure that scales securely and sustainably. Without strategic planning to align AI initiatives with their broader IT ecosystems and business priorities, organizations risk fragmented deployments and delayed ROI.
EXPERT DESIGN SERVICES: Although AI applications demand extraordinarily high performance from supporting infrastructure, organizations cannot simply purchase top-end compute and networking hardware and expect their new systems to work seamlessly. Instead, they must architect and integrate high-performance computing resources, modern data platforms and automation tools to align with AI workloads. Most enterprises operate hybrid environments that combine cloud platforms, on-premises infrastructure and edge systems, and AI workloads may run in different locations depending on factors such as security requirements and performance needs. CDW works with organizations to design AI-ready infrastructure that can support demanding workloads such as model training and large-scale inference environments.
PARTNER ECOSYSTEMS: AI initiatives require expertise across infrastructure architecture, cloud platforms, security, data management and operational monitoring. Few organizations have deep internal expertise across each of these domains, and so many organizations turn to a partner such as CDW to bridge this gap. CDW connects organizations with a broad ecosystem of technologies and services while providing vendor-agnostic guidance on deployment strategies. Industry-leading vendor partnerships ensure that organizations will find the best possible fit for their specific IT environment, business goals and budget. Partners such as CDW can accelerate implementation timelines by providing proven processes and preconfigured infrastructure designs for staging and deploying complex systems.
MEASURING SUCCESS: Robust monitoring and analytics capabilities can help organizations evaluate how their AI systems are performing and identify opportunities for improvement. By defining success metrics early and measuring results consistently, leaders can quickly understand what works and build on those efforts, while refining or discontinuing projects that are not advancing the organization’s most important business goals. CDW works with customers to integrate telemetry, dashboards and performance metrics that allow IT teams to correlate infrastructure performance with intended outcomes. With the right visibility and operational processes in place, organizations can expand successful AI initiatives across additional departments and use cases, translating early experimentation into sustained success.
ITERATIVE OPTIMIZATION: One thing is certain about the future of AI: It is going to involve rapid, dramatic and frequent change. As more users interact with AI systems and workloads grow in complexity, organizations must monitor performance, manage costs and refine operational processes. Production environments require observability tools that allow IT teams to track system performance, identify bottlenecks and ensure consistent service levels. CDW supports these efforts through lifecycle services that include performance monitoring, infrastructure tuning and periodic health checks. By treating AI environments as dynamic systems rather than one-off deployment projects, organizations can improve efficiency, reduce operational risks, and ensure that AI initiatives deliver value as technologies and business needs evolve.
- BUILDING AI-READY INFRASTRUCTURE
- ENABLING GOVERNANCE AT SCALE
- ACCELERATING TIME TO VALUE
The demands of AI workloads are enormous, and supporting an AI strategy requires purpose-built infrastructure spanning compute, storage, networking and cloud environments. While traditional IT architectures may support smaller AI experiments, infrastructure often becomes a bottleneck when organizations attempt to move these pilots into full production. Ideally, AI infrastructure should be an integrated stack, rather than a collection of individual technologies, as different components like data pipelines and compute environments must work together to support the performance, governance, and visibility needs of model training and inference tasks.
SCALABLE COMPUTE: AI workloads require significantly more compute power than most other enterprise applications, especially during model training. To support this demand, organizations are increasingly relying on GPU-accelerated clusters and high-performance computing environments that can handle the massive parallel processing demands of modern machine learning models. However, this hardware is expensive and sometimes difficult to source, and many organizations also turn to the public cloud to meet their infrastructure needs. In addition to sheer compute capacity, leaders should consider scalability as they build out their compute infrastructure, adopting modular solutions that can scale without rearchitecting.
HIGH-PERFORMANCE STORAGE: Data is the foundation that supports all AI applications. To perform effectively, AI systems must be able to instantly access the massive data sets that are used to train AI models. High-performance storage solutions allow organizations to move large volumes of data efficiently between storage, compute and training environments. Often, data pipelines span multiple environments, including on-premises systems, cloud platforms and edge locations. Storage architectures must be designed with these data pipelines in mind, or teams may end up spending more time moving and preparing data than actually developing models.
LOW-LATENCY NETWORKING: As AI environments scale, networking performance can become as important as compute and storage. With so much data moving between compute nodes, storage systems and cloud services, any unexpected latency can quickly throttle performance, leading to delays in model training and overall AI development. Modern AI architectures emphasize high-speed interconnects, software-defined networking, and low-latency connectivity across data centers and cloud environments, helping to maintain performance standards even as data volumes continue to grow.
CLOUD LANDING ZONES: An AI cloud landing zone is a predesigned, governed cloud environment that provides a secure foundation for building, deploying and operating AI workloads at scale. Typically, these landing zones include the following critical elements: identity and access management tools such as role-based access controls and single sign-on, network topology, security and compliance features such as encryption and logging, and cost management measures to prevent overspending. Enterprises often turn to these environments to accelerate their AI programs without losing control of security and governance, as well as to create consistency across the organization. With cloud landing zones, every AI team gets access to the same well-architected environment.
ECOSYSTEM ALIGNMENT: Successful AI initiatives typically involve an ecosystem of technology partners spanning hardware, cloud platforms, software frameworks and specialized AI tools. Many organizations also rely on external expertise to support early architectural decisions and help guide AI programs as they mature. Structured engagements such as infrastructure assessments and readiness workshops can help identify gaps in compute capacity, data architecture and operational processes before large-scale deployments begin, allowing internal teams to leverage both their specific business knowledge and external, AI-specific technical expertise.
Click Below To Continue Reading
Money is a major consideration in AI infrastructure decisions, with 83% of tech leaders citing it as a key factor when evaluating solutions. Leaders make these investments with the hope that they will pay off in the form of measurable benefits like increased productivity, revenue growth and reductions in recurring costs.
Where do you expect the largest ROI from generative artificial intelligence?
Increase employee productivity
22%
Improve customer satisfaction and engagement
21%
Streamline workflows and processes
20%
Improve competitiveness and gain market share
18%
Accelerate revenue growth
14%
Increase sales and revenue
13%
Reduce operational costs
13%
Source: Google Cloud, “State of AI Infrastructure,” June 2025
As organizations move from AI experimentation to production deployment, governance, security and compliance grow in importance. AI workloads depend on critical data, complex infrastructure environments and rapidly evolving models, and organizations without strong governance frameworks risk putting sensitive information at risk and losing control over their growing AI environments. Organizations must implement robust data management, security and visibility practices to prevent data exposure, unauthorized access and unpredictable model behavior. They must also ensure that their operational environment can support AI at scale, moving beyond isolated pilots to well-managed ecosystems supported by the appropriate infrastructure and talent. As AI systems become more autonomous, organizations will need governance models that define how AI agents interact with data, systems and employees. Over time, as AI becomes more deeply embedded in business processes, strong governance, monitoring and operational practices will be essential to maintaining security, reliability and trust.
DATA MANAGEMENT: When AI initiatives fail, data is often the reason. Even when organizations have clear AI strategies and robust infrastructure in place, gaps in data preparation can make AI outputs unusable due to problems with clarity and accuracy. According to a 2025 report from Google Cloud, 20% of tech leaders say that data readiness is one of the greatest challenges holding back their organization’s AI adoption. Organizations need not only large volumes of valuable data but also a comprehensive data management strategy that includes governance policies, data preparation processes, and clear controls over how data is accessed and used.
SECURITY BY DESIGN: AI workloads introduce new security considerations because they often interact with large volumes of enterprise data across multiple systems. If data sets are not properly tagged, secured and governed, AI tools can inadvertently surface sensitive information to users who should not have access to it. Even internal productivity tools can pose these risks when underlying data environments lack proper security controls. For example, if a poorly governed AI tool is trained on a company’s HR records, users might be able to access other employees’ sensitive information. Organizations must also protect their systems and data against AI-specific external threats, such as prompt injection attacks and data poisoning attempts.
WORKLOAD VISIBILITY: Enterprises that make a large number of tech investments in a short period of time often experience “sprawl,” a problem that occurs when business and IT leaders lose track of all of the different systems operating in their environments. This can lead to redundancy and waste, and it can also make it difficult to detect when employees use unauthorized technologies for work (“shadow AI”). Businesses have seen this sort of sprawl in the past with cloud investments, and it is becoming a problem for AI environments as well. Effective observability platforms provide dashboards and telemetry that allow teams to track performance, diagnose issues and measure the quality of AI interactions, helping to provide consistent user experiences while managing overall spending.
OPERATIONAL READINESS: AI is no longer an experiment. Rather, it has become a critical operational capability. While early deployments may have involved small teams experimenting with models or applications, production systems require coordinated processes across infrastructure teams, developers and business stakeholders. This shift requires new levels of collaboration between technical teams and business leaders to ensure that infrastructure investments, performance expectations and AI outcomes remain aligned. Organizations must also establish processes for ongoing optimization of their AI environments, with consideration given to performance, reliability and cost-efficiency.
AGENTIC AI: Increasingly, organizations are exploring autonomous agents capable of interacting directly with enterprise data. These systems can analyze information, make recommendations and potentially take actions on behalf of users. While this creates powerful new opportunities for process automation, agents also introduce new governance challenges. As they look to adopt agentic AI, leaders must establish guardrails that determine whether and how autonomous agents can read, modify or delete information and ensure that those actions remain auditable and controlled. Without these safeguards and visibility measures in place, AI agents may take actions that violate agreements with users and customers, or even interfere with the delivery of products and services.
Organizations are racing to stand up the necessary infrastructure to support AI, and those that create mature AI environments first will create a significant competitive advantage. Many organizations turn to a trusted partner such as CDW to accelerate this process with expert-led infrastructure design and optimization.
READINESS ASSESSMENTS: AI is still an emerging technology, and many leaders don’t know what they don’t know. Organizations typically lack in-house expertise on AI infrastructure, and even seasoned IT leaders might be unsure of how existing environments and data stores might support AI applications. Through collaborative workshops and readiness assessments, CDW helps organizations uncover high-impact use cases, align data and governance strategies, and design infrastructure that scales securely and sustainably. Without strategic planning to align AI initiatives with their broader IT ecosystems and business priorities, organizations risk fragmented deployments and delayed ROI.
EXPERT DESIGN SERVICES: Although AI applications demand extraordinarily high performance from supporting infrastructure, organizations cannot simply purchase top-end compute and networking hardware and expect their new systems to work seamlessly. Instead, they must architect and integrate high-performance computing resources, modern data platforms and automation tools to align with AI workloads. Most enterprises operate hybrid environments that combine cloud platforms, on-premises infrastructure and edge systems, and AI workloads may run in different locations depending on factors such as security requirements and performance needs. CDW works with organizations to design AI-ready infrastructure that can support demanding workloads such as model training and large-scale inference environments.
PARTNER ECOSYSTEMS: AI initiatives require expertise across infrastructure architecture, cloud platforms, security, data management and operational monitoring. Few organizations have deep internal expertise across each of these domains, and so many organizations turn to a partner such as CDW to bridge this gap. CDW connects organizations with a broad ecosystem of technologies and services while providing vendor-agnostic guidance on deployment strategies. Industry-leading vendor partnerships ensure that organizations will find the best possible fit for their specific IT environment, business goals and budget. Partners such as CDW can accelerate implementation timelines by providing proven processes and preconfigured infrastructure designs for staging and deploying complex systems.
MEASURING SUCCESS: Robust monitoring and analytics capabilities can help organizations evaluate how their AI systems are performing and identify opportunities for improvement. By defining success metrics early and measuring results consistently, leaders can quickly understand what works and build on those efforts, while refining or discontinuing projects that are not advancing the organization’s most important business goals. CDW works with customers to integrate telemetry, dashboards and performance metrics that allow IT teams to correlate infrastructure performance with intended outcomes. With the right visibility and operational processes in place, organizations can expand successful AI initiatives across additional departments and use cases, translating early experimentation into sustained success.
ITERATIVE OPTIMIZATION: One thing is certain about the future of AI: It is going to involve rapid, dramatic and frequent change. As more users interact with AI systems and workloads grow in complexity, organizations must monitor performance, manage costs and refine operational processes. Production environments require observability tools that allow IT teams to track system performance, identify bottlenecks and ensure consistent service levels. CDW supports these efforts through lifecycle services that include performance monitoring, infrastructure tuning and periodic health checks. By treating AI environments as dynamic systems rather than one-off deployment projects, organizations can improve efficiency, reduce operational risks, and ensure that AI initiatives deliver value as technologies and business needs evolve.
Eryn Brodsky
Solution Practice Lead for Server and Storage
