The Next Era of State and Local Government Data Center Infrastructure
Modern solutions and tailored services help agencies shift legacy data centers from cost centers to strategic foundations for artificial intelligence initiatives and enhanced citizen services.
IN THIS ARTICLE
State and local governments are in transition, driven by escalating demands on the data center, the high cost of memory and compute, and increasing pressure to adopt artificial intelligence. Agencies must boost efficiency and reduce costs while also delivering higher levels of digital citizen services and navigating complex compliance and cybersecurity landscapes. In addition, many agencies face headwinds stemming from legacy data center infrastructure that is burdensome to manage, difficult to secure and inhospitable to modern IT solutions.
A strategic approach to data center modernization helps SLGs address these priorities through a combination of increased infrastructure efficiency, reduced technical debt and stronger security. Modern data center approaches, including Data Center as a Service, give agencies the flexibility to expand capacity and incorporate agentic AI as future needs emerge. With the right partner, agencies can build a modern foundation that is secure, scalable and adaptable.
Data center optimization helps state and local governments scale strategically and leverage AI to increase efficiency and improve services.
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- Compliance and Security
- Protecting the Public Trust
- Modernizing IT Infrastructure
SLGs operate within a challenging security environment and a complex compliance landscape, the latter encompassing data sovereignty, data privacy and environmental regulations. Because compliance requirements are continually evolving, with AI being a prime example, agencies need infrastructure that improves visibility and auditing capabilities and lets them adapt as needs change.
EVOLVING COMPLIANCE RISKS: An OpenGov survey found that while 58% of local government leaders say compliance drives AI adoption, 66% are worried about AI-related compliance accountability. Moreover, many SLG IT decision-makers are concerned about the lack of regulations and standards for AI development. In this murky environment, decision-makers must understand how data center decisions could help or hinder compliance and how to weigh these considerations against other factors. For instance, standardizing architectures, policies and processes across agencies reduces compliance risk (along with technical debt). At the same time, supply chain volatility and pricing spikes have made many agencies receptive to a diversification strategy that lets them prioritize flexibility.
DCAAS AND COMPLIANCE: Data Center as a Service models enable agencies to maintain the governance and oversight that compliance requires while offloading operational burden. This model works well for agencies that have limited purchasing power, lack the IT resources for hands-on management or want to stand up an environment for AI. Because of the unique rules governing SLG funding and procurement, regulatory fluency — as well as technical capability — should be a core vendor selection criterion for DCaaS.
AI COST AND REPORTING: AI tokenomics relates to compliance in terms of data governance, fiscal responsibility and procurement. AI adoption often outpaces governance and visibility, and most organizations are still learning how to measure and optimize AI token use. Together, these factors create uncertainty about AI’s true costs and ROI, which prevents officials from accurately reporting to stakeholders and ensuring that AI use is transparent, value-driven and appropriately managed. Unlike legacy environments, modern data centers can support AI visibility and control solutions, enabling governance and improving fiscal responsibility.
MODERNIZING SECURITY: Security concerns in aging data centers have new urgency because of the need to defend against AI-enabled attacks, a capability that outdated systems don’t have. The Information Technology and Innovation Foundation has documented increased cybersecurity risks across SLGs, owing to legacy systems, growth in cyberattack volume and sophistication, and rapid digital transformation that, while beneficial, “has often outpaced cybersecurity investments, leaving many systems exposed.”
Often, the steps that IT leaders take to reduce AI risks — such as training staff, controlling access to AI data and employing data encryption — aren’t enough to make up for legacy systems’ vulnerabilities. Older systems can’t support new security protocols and capabilities, and they are typically fragmented in ways that impede visibility. Modernizing the data center significantly reduces security gaps, strengthens proactive controls and positions agencies for continued maturation against security threats.
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The right modernization partner can help SLGs achieve their data center optimization goals quickly and effectively. CDW is deeply experienced in the public sector, with a nuanced understanding of agencies’ technological, political and budgetary challenges. CDW’s comprehensive vendor ecosystem allows us to tailor recommendations for every customer, helping them to invest strategically and avoid common pitfalls as they modernize today and plan for tomorrow.
Agentic AI Infrastructure Assessment: This custom engagement evaluates agencies’ on-premises readiness for AI operations. CDW will identify gaps, constraints and opportunities across compute, storage, networking, observability, operations and AI platform capabilities.
GPU Cluster Assessment: This diagnostic and advisory engagement helps agencies optimize NVIDIA GPU investments with a data-driven analysis of utilization, allocation efficiency, workload distribution, memory consumption and capacity headroom.
Hybrid Infrastructure Innovation Team: CDW’s HIIT, a specialized group of experts and architects, helps organizations optimize hybrid environments with a Next-Generation Server Storage Assessment, Virtualization Advisory Assessment and other services.
Data Center Power Assessment: Over time, data centers often become under- or overpowered because these resources are never revisited after initial setup. Configuration services can reduce waste and improve performance.
Data center optimization for SLGs poses a distinct set of considerations relevant to the public trust. These include responsible stewardship of taxpayer dollars, efficient and environmentally sustainable water and energy consumption, transparency about AI use and the ability to implement proactive safeguards against AI bias.
ENERGY CONSUMPTION: Energy and water consumption and other environmental impacts are a priority, and data center optimization is a powerful efficiency play. However, efficient infrastructure alone doesn’t necessarily reduce resource strains over time. Although data center modernization brings efficiencies and frees up capacity, agencies frequently see power or cooling savings absorbed by new, CPU-intensive workloads. Strategic planning helps agencies protect the efficiency gains they achieve through modernization.
FISCAL RESPONSIBILITY: Data center optimization ensures that agencies pay only for the resources they need, and it reduces the amount of components that must be installed, monitored and managed. As agentic AI adoption accelerates, agencies must ensure that efficiency measures are scalable so they can continue to manage consumption and control costs. Modern data center solutions enable proactive visibility into resource consumption and deeper insights into workload behavior — information that improves manageability, accountability and security.
CITIZEN SERVICES: Modern data centers enable continued progress on the digital transformation initiatives that have defined public sector IT investment in recent years. AI continues to raise citizens’ expectations for consumer-level conveniences, and agencies will face increasing pressure to leverage AI in citizen-facing services and back-office operations. For example, a growing number of IT vendors are incorporating agentic AI management platforms into their data center solutions, but agencies must modernize their environments to take advantage of these tools. IT teams that leverage AI can spend less time maintaining and troubleshooting outdated systems and more time enhancing services.
AI TRANSPARENCY: Citizens have more trust and confidence in AI initiatives when governments communicate proactively about use cases, energy consumption and governance. NASCIO found that while a majority of state CIO organizations use generative AI, only 53% have implemented data governance strategies that would address concerns such as bias and privacy, and only 41% have taken steps to ensure transparency and accountability related to generative AI usage.
FUTURE PLANNING: Every infrastructure decision that agencies make today must have future AI workloads in mind. Generally, that means supporting an agentic workforce that requires heavy AI inference as opposed to AI model training. By planning ahead, agencies can update data centers in ways that are flexible, thereby preventing future barriers to entry. Planning also means being strategic about future token spending. Agencies must weigh the costs of using AI hyperscalers versus investing in hardware that would enable them to generate their own tokens. Across industries, organizations are increasingly establishing AI “factories” that are separate from other IT systems and provide a dedicated resource for AI workloads.
A modern, standardized infrastructure increases operational efficiency; improves cybersecurity through increased visibility and the ability to support more advanced tooling; and provides a foundation for AI initiatives, advanced analytics and automation. Depending on the environment, solutions may include servers, storage, software-defined networking and 5G connectivity, cloud-based platforms and high-performance computing.
DCAAS CONSIDERATIONS: DCaaS is a cost-effective strategy for modernizing capabilities without the expense of building on-premises. DCaaS combines the flexibility of as-a-service models with contractual, reliable pricing. Clarity about what it will cost to scale is increasingly attractive for agencies that are worried about memory market volatility but eager to plan ahead for incremental expansion. DCaaS relationships vary widely, so it’s crucial to understand the pros and cons of various platforms and service-level agreement details. For instance, smaller agencies may opt for a DCaaS solution that lets them offload much of the management, audit and compliance oversight to a vendor partner. Agencies with more IT resources can save money with a less robust DCaaS offering and take on more of the work themselves. Others pair DCaaS with select managed services, fashioning a strategy that aligns precisely with their needs.
HCI AND DHCI: Hyperconverged infrastructure, which combines compute, storage and networking resources in a software-defined platform, is a modernization approach offering scalability and the ability to support agentic management platforms. While HCI remains a viable model, more agencies are choosing disaggregated HCI, in which compute and storage can expand independently. Both approaches, whether implemented on-premises or obtained through DCaaS, allow agencies to scale resources and AI workloads as needed.
AGENTIC AI STRATEGY: Organizations with agentic-ready infrastructure can potentially use AI to improve service desk, network and hosting operations; IT service management; and cost and contract management. Building an internal AI orchestration layer doesn’t make sense for every agency, however. Agencies must weigh long-term savings against upfront costs and the complexity of operating, maintaining and securing AI models over time. From an optimization standpoint, agencies should start by identifying their projected AI workloads. For instance, many agencies have invested in AI infrastructure to support computer vision, a use case they often choose to retain control over price, data and performance. A partner can help agencies evaluate their AI opportunities and determine which data center strategy makes the most sense.
CDW’S GOVERNMENT EXPERTS: The right partner for data center optimization can connect agencies with a broad ecosystem of vendors, allowing for a tailored implementation rather than a one-size-fits-all approach. CDW offers vendor-neutral, full-stack expertise and can help decision-makers understand the nuanced strengths and weaknesses of every vendor’s platform and how well they align with a specific environment. CDW’s dedicated public sector team has extensive experience with governments of all sizes and understands the regulatory, procurement and political considerations that make the public sector unique.
Wherever agencies are in their data center modernization journey, CDW’s end-to-end solutions and tailored services can help them consolidate infrastructure to increase efficiency and reduce costs; transition to advanced models that can support future AI workloads; and establish secure, scalable architectures that can evolve as needs change.
Read More
- Compliance and Security
- Protecting the Public Trust
- Modernizing IT Infrastructure
SLGs operate within a challenging security environment and a complex compliance landscape, the latter encompassing data sovereignty, data privacy and environmental regulations. Because compliance requirements are continually evolving, with AI being a prime example, agencies need infrastructure that improves visibility and auditing capabilities and lets them adapt as needs change.
EVOLVING COMPLIANCE RISKS: An OpenGov survey found that while 58% of local government leaders say compliance drives AI adoption, 66% are worried about AI-related compliance accountability. Moreover, many SLG IT decision-makers are concerned about the lack of regulations and standards for AI development. In this murky environment, decision-makers must understand how data center decisions could help or hinder compliance and how to weigh these considerations against other factors. For instance, standardizing architectures, policies and processes across agencies reduces compliance risk (along with technical debt). At the same time, supply chain volatility and pricing spikes have made many agencies receptive to a diversification strategy that lets them prioritize flexibility.
DCAAS AND COMPLIANCE: Data Center as a Service models enable agencies to maintain the governance and oversight that compliance requires while offloading operational burden. This model works well for agencies that have limited purchasing power, lack the IT resources for hands-on management or want to stand up an environment for AI. Because of the unique rules governing SLG funding and procurement, regulatory fluency — as well as technical capability — should be a core vendor selection criterion for DCaaS.
AI COST AND REPORTING: AI tokenomics relates to compliance in terms of data governance, fiscal responsibility and procurement. AI adoption often outpaces governance and visibility, and most organizations are still learning how to measure and optimize AI token use. Together, these factors create uncertainty about AI’s true costs and ROI, which prevents officials from accurately reporting to stakeholders and ensuring that AI use is transparent, value-driven and appropriately managed. Unlike legacy environments, modern data centers can support AI visibility and control solutions, enabling governance and improving fiscal responsibility.
MODERNIZING SECURITY: Security concerns in aging data centers have new urgency because of the need to defend against AI-enabled attacks, a capability that outdated systems don’t have. The Information Technology and Innovation Foundation has documented increased cybersecurity risks across SLGs, owing to legacy systems, growth in cyberattack volume and sophistication, and rapid digital transformation that, while beneficial, “has often outpaced cybersecurity investments, leaving many systems exposed.”
Often, the steps that IT leaders take to reduce AI risks — such as training staff, controlling access to AI data and employing data encryption — aren’t enough to make up for legacy systems’ vulnerabilities. Older systems can’t support new security protocols and capabilities, and they are typically fragmented in ways that impede visibility. Modernizing the data center significantly reduces security gaps, strengthens proactive controls and positions agencies for continued maturation against security threats.
Click Below To Continue Reading
The right modernization partner can help SLGs achieve their data center optimization goals quickly and effectively. CDW is deeply experienced in the public sector, with a nuanced understanding of agencies’ technological, political and budgetary challenges. CDW’s comprehensive vendor ecosystem allows us to tailor recommendations for every customer, helping them to invest strategically and avoid common pitfalls as they modernize today and plan for tomorrow.
Agentic AI Infrastructure Assessment: This custom engagement evaluates agencies’ on-premises readiness for AI operations. CDW will identify gaps, constraints and opportunities across compute, storage, networking, observability, operations and AI platform capabilities.
GPU Cluster Assessment: This diagnostic and advisory engagement helps agencies optimize NVIDIA GPU investments with a data-driven analysis of utilization, allocation efficiency, workload distribution, memory consumption and capacity headroom.
Hybrid Infrastructure Innovation Team: CDW’s HIIT, a specialized group of experts and architects, helps organizations optimize hybrid environments with a Next-Generation Server Storage Assessment, Virtualization Advisory Assessment and other services.
Data Center Power Assessment: Over time, data centers often become under- or overpowered because these resources are never revisited after initial setup. Configuration services can reduce waste and improve performance.
Data center optimization for SLGs poses a distinct set of considerations relevant to the public trust. These include responsible stewardship of taxpayer dollars, efficient and environmentally sustainable water and energy consumption, transparency about AI use and the ability to implement proactive safeguards against AI bias.
ENERGY CONSUMPTION: Energy and water consumption and other environmental impacts are a priority, and data center optimization is a powerful efficiency play. However, efficient infrastructure alone doesn’t necessarily reduce resource strains over time. Although data center modernization brings efficiencies and frees up capacity, agencies frequently see power or cooling savings absorbed by new, CPU-intensive workloads. Strategic planning helps agencies protect the efficiency gains they achieve through modernization.
FISCAL RESPONSIBILITY: Data center optimization ensures that agencies pay only for the resources they need, and it reduces the amount of components that must be installed, monitored and managed. As agentic AI adoption accelerates, agencies must ensure that efficiency measures are scalable so they can continue to manage consumption and control costs. Modern data center solutions enable proactive visibility into resource consumption and deeper insights into workload behavior — information that improves manageability, accountability and security.
CITIZEN SERVICES: Modern data centers enable continued progress on the digital transformation initiatives that have defined public sector IT investment in recent years. AI continues to raise citizens’ expectations for consumer-level conveniences, and agencies will face increasing pressure to leverage AI in citizen-facing services and back-office operations. For example, a growing number of IT vendors are incorporating agentic AI management platforms into their data center solutions, but agencies must modernize their environments to take advantage of these tools. IT teams that leverage AI can spend less time maintaining and troubleshooting outdated systems and more time enhancing services.
AI TRANSPARENCY: Citizens have more trust and confidence in AI initiatives when governments communicate proactively about use cases, energy consumption and governance. NASCIO found that while a majority of state CIO organizations use generative AI, only 53% have implemented data governance strategies that would address concerns such as bias and privacy, and only 41% have taken steps to ensure transparency and accountability related to generative AI usage.
FUTURE PLANNING: Every infrastructure decision that agencies make today must have future AI workloads in mind. Generally, that means supporting an agentic workforce that requires heavy AI inference as opposed to AI model training. By planning ahead, agencies can update data centers in ways that are flexible, thereby preventing future barriers to entry. Planning also means being strategic about future token spending. Agencies must weigh the costs of using AI hyperscalers versus investing in hardware that would enable them to generate their own tokens. Across industries, organizations are increasingly establishing AI “factories” that are separate from other IT systems and provide a dedicated resource for AI workloads.
A modern, standardized infrastructure increases operational efficiency; improves cybersecurity through increased visibility and the ability to support more advanced tooling; and provides a foundation for AI initiatives, advanced analytics and automation. Depending on the environment, solutions may include servers, storage, software-defined networking and 5G connectivity, cloud-based platforms and high-performance computing.
DCAAS CONSIDERATIONS: DCaaS is a cost-effective strategy for modernizing capabilities without the expense of building on-premises. DCaaS combines the flexibility of as-a-service models with contractual, reliable pricing. Clarity about what it will cost to scale is increasingly attractive for agencies that are worried about memory market volatility but eager to plan ahead for incremental expansion. DCaaS relationships vary widely, so it’s crucial to understand the pros and cons of various platforms and service-level agreement details. For instance, smaller agencies may opt for a DCaaS solution that lets them offload much of the management, audit and compliance oversight to a vendor partner. Agencies with more IT resources can save money with a less robust DCaaS offering and take on more of the work themselves. Others pair DCaaS with select managed services, fashioning a strategy that aligns precisely with their needs.
HCI AND DHCI: Hyperconverged infrastructure, which combines compute, storage and networking resources in a software-defined platform, is a modernization approach offering scalability and the ability to support agentic management platforms. While HCI remains a viable model, more agencies are choosing disaggregated HCI, in which compute and storage can expand independently. Both approaches, whether implemented on-premises or obtained through DCaaS, allow agencies to scale resources and AI workloads as needed.
AGENTIC AI STRATEGY: Organizations with agentic-ready infrastructure can potentially use AI to improve service desk, network and hosting operations; IT service management; and cost and contract management. Building an internal AI orchestration layer doesn’t make sense for every agency, however. Agencies must weigh long-term savings against upfront costs and the complexity of operating, maintaining and securing AI models over time. From an optimization standpoint, agencies should start by identifying their projected AI workloads. For instance, many agencies have invested in AI infrastructure to support computer vision, a use case they often choose to retain control over price, data and performance. A partner can help agencies evaluate their AI opportunities and determine which data center strategy makes the most sense.
CDW’S GOVERNMENT EXPERTS: The right partner for data center optimization can connect agencies with a broad ecosystem of vendors, allowing for a tailored implementation rather than a one-size-fits-all approach. CDW offers vendor-neutral, full-stack expertise and can help decision-makers understand the nuanced strengths and weaknesses of every vendor’s platform and how well they align with a specific environment. CDW’s dedicated public sector team has extensive experience with governments of all sizes and understands the regulatory, procurement and political considerations that make the public sector unique.
Wherever agencies are in their data center modernization journey, CDW’s end-to-end solutions and tailored services can help them consolidate infrastructure to increase efficiency and reduce costs; transition to advanced models that can support future AI workloads; and establish secure, scalable architectures that can evolve as needs change.
Data centers are evolving rapidly. CDW can help SLGs keep pace.
Jay Carlile
AI solutions Executive
