June 25, 2026
Hybrid Cloud in Government Drives AI-Readiness
A hybrid cloud model provides state and local governments a scalable foundation for AI tools that improve citizen services and employee experiences.
Hybrid cloud has emerged as a preferred infrastructure model to help state and local governments balance performance, cost and security as they lay a foundation for innovation enabled by artificial intelligence. Many agencies currently have fragmented infrastructure, duplicative data stores and legacy systems that make it difficult to support AI workflows. In addition to helping governments overcome these infrastructure gaps, hybrid cloud models address challenges related to cybersecurity threats, workforce shortages and cross-agency data fragmentation.
Ultimately, an AI-ready hybrid cloud is not the end goal. Rather, it is a means to help agencies deliver better public services and more efficient operations. By taking a phased approach and working with trusted partners, state and local governments can build an infrastructure environment that is both powerful enough to support current innovation needs and flexible enough to accommodate future applications and workflows.
CDW can help your state or local agency build out an AI-ready hybrid infrastructure.
Hybrid cloud has emerged as a preferred infrastructure model to help state and local governments balance performance, cost and security as they lay a foundation for innovation enabled by artificial intelligence. Many agencies currently have fragmented infrastructure, duplicative data stores and legacy systems that make it difficult to support AI workflows. In addition to helping governments overcome these infrastructure gaps, hybrid cloud models address challenges related to cybersecurity threats, workforce shortages and cross-agency data fragmentation.
Ultimately, an AI-ready hybrid cloud is not the end goal. Rather, it is a means to help agencies deliver better public services and more efficient operations. By taking a phased approach and working with trusted partners, state and local governments can build an infrastructure environment that is both powerful enough to support current innovation needs and flexible enough to accommodate future applications and workflows.
CDW can help your state or local agency build out an AI-ready hybrid infrastructure.
State and local governments are entering a new phase of digital transformation. Infrastructure decisions are no longer focused on keeping the lights on in the data center or providing baseline capacity for routine services. Rather, these decisions are increasingly tied to long-term innovation goals, particularly those centered on artificial intelligence.
State and city governments aren’t just riding the wave of AI hype. Many are already using the technology to improve citizen services and make their own operations more efficient. Examples include translating complex documents into simple, citizen-friendly language; helping the public access information and services; speeding up processing for benefits applications; streamlining administrative work and hunting down cybersecurity vulnerabilities.
Artificial intelligence (including generative AI, agentic AI and machine learning) tops NASCIO’s list of state CIOs’ top priorities for 2026, beating out other critical areas such as cybersecurity, modernization and even cost control. However, some observers have noted many state and local governments are taking something of an ad hoc approach to the technology, using free or inexpensive tools without a clear strategic framework. And NASCIO reports that only one-quarter of states have dedicated funding for their generative AI initiatives.
Infrastructure considerations are key to AI success, and many organizations are landing on hybrid cloud as the ideal IT environment to support AI workflows. While on-premises infrastructure offers superior control over data and workloads, it is impractical for even well-funded organizations to procure enough graphics processing units (GPUs) to scale AI solutions and support peak demand. On the other hand, the public cloud provides speed and elasticity, but it can quickly become expensive for always-on workloads, and some organizations do not want to place their most sensitive data sets fully outside of their own environments. Hybrid cloud environments enable agencies to strategically place workloads based on sensitivity, performance and compliance requirements.
For state and local governments facing budget constraints, a hybrid cloud model offers other potential advantages: It can help government agencies assess how their existing technology infrastructure is meeting the needs of their AI initiatives. A hybrid model also allows agencies to use the public cloud to reduce new capital expenditures and invest in on-premises equipment only when necessary. Additionally, the hybrid cloud aligns with the growing trend of selective cloud repatriation, where agencies bring certain workloads back from public cloud environments to optimize cost and performance.
Governments are sometimes criticized for moving slowly when adopting new technology, but there are some benefits in this case. Many of the private sector organizations that raced to roll out AI projects as quickly as possible have struggled to scale their pilots. In the summer of 2025, a Massachusetts Institute of Technology report claimed that 95% of AI projects were essentially failing to produce any value at all.
By taking the time to get their infrastructure right, state and local governments can support cost-effective, high-performance AI solutions that deliver tangible benefits to citizens and public employees.
74%
The percentage of organizations that take a hybrid cloud approach to their AI infrastructure; by contrast, only 4% of AI environments are hosted entirely on-premises, and just 3% are hosted in a single public cloud
Source: Google Cloud, “State of AI Infrastructure,” February 2026
CDW can help state and local governments build out infrastructures that support AI now and in the future.
State and local governments are entering a new phase of digital transformation. Infrastructure decisions are no longer focused on keeping the lights on in the data center or providing baseline capacity for routine services. Rather, these decisions are increasingly tied to long-term innovation goals, particularly those centered on artificial intelligence.
State and city governments aren’t just riding the wave of AI hype. Many are already using the technology to improve citizen services and make their own operations more efficient. Examples include translating complex documents into simple, citizen-friendly language; helping the public access information and services; speeding up processing for benefits applications; streamlining administrative work and hunting down cybersecurity vulnerabilities.
Artificial intelligence (including generative AI, agentic AI and machine learning) tops NASCIO’s list of state CIOs’ top priorities for 2026, beating out other critical areas such as cybersecurity, modernization and even cost control. However, some observers have noted many state and local governments are taking something of an ad hoc approach to the technology, using free or inexpensive tools without a clear strategic framework. And NASCIO reports that only one-quarter of states have dedicated funding for their generative AI initiatives.
Infrastructure considerations are key to AI success, and many organizations are landing on hybrid cloud as the ideal IT environment to support AI workflows. While on-premises infrastructure offers superior control over data and workloads, it is impractical for even well-funded organizations to procure enough graphics processing units (GPUs) to scale AI solutions and support peak demand. On the other hand, the public cloud provides speed and elasticity, but it can quickly become expensive for always-on workloads, and some organizations do not want to place their most sensitive data sets fully outside of their own environments. Hybrid cloud environments enable agencies to strategically place workloads based on sensitivity, performance and compliance requirements.
For state and local governments facing budget constraints, a hybrid cloud model offers other potential advantages: It can help government agencies assess how their existing technology infrastructure is meeting the needs of their AI initiatives. A hybrid model also allows agencies to use the public cloud to reduce new capital expenditures and invest in on-premises equipment only when necessary. Additionally, the hybrid cloud aligns with the growing trend of selective cloud repatriation, where agencies bring certain workloads back from public cloud environments to optimize cost and performance.
Governments are sometimes criticized for moving slowly when adopting new technology, but there are some benefits in this case. Many of the private sector organizations that raced to roll out AI projects as quickly as possible have struggled to scale their pilots. In the summer of 2025, a Massachusetts Institute of Technology report claimed that 95% of AI projects were essentially failing to produce any value at all.
By taking the time to get their infrastructure right, state and local governments can support cost-effective, high-performance AI solutions that deliver tangible benefits to citizens and public employees.
CDW can help state and local governments build out infrastructures that support AI now and in the future.
AI Infrastructure: By the Numbers
73%
The percentage of public sector leaders who say their AI environments are already too complex for their teams to manage, higher than the cross-industry average
Source: DDN, 2026 State of AI Infrastructure Report, January 2026
90%
The percentage of states that are piloting generative AI initiatives; more than 70% are actively training state employees on the technology
Source: National Association of State Chief Information Officers, “The 2025 State CIO Survey: Leading Change Through Uncertain Times,” October 2025
47%
The percentage of organizations that cite high energy or cooling costs as an area of inefficiency for their AI workloads; 40% cite duplicated or fragmented infrastructure across teams
Source: DDN, 2026 State of AI Infrastructure Report, January 2026
AI Infrastructure: By the Numbers
73%
The percentage of public sector leaders who say their AI environments are already too complex for their teams to manage, higher than the cross-industry average
Source: DDN, 2026 State of AI Infrastructure Report, January 2026
90%
The percentage of states that are piloting generative AI initiatives; more than 70% are actively training state employees on the technology
Source: National Association of State Chief Information Officers, “The 2025 State CIO Survey: Leading Change Through Uncertain Times,” October 2025
47%
The percentage of organizations that cite high energy or cooling costs as an area of inefficiency for their AI workloads; 40% cite duplicated or fragmented infrastructure across teams
Source: DDN, 2026 State of AI Infrastructure Report, January 2026
- BUILDING AN AI-READY INFRASTRUCTURE
- HYBRID CLOUD SOLVES GOVERNMENT CHALLENGES
- A FOUNDATION FOR AI INNOVATION
AI-ready infrastructure requires more than raw compute power. Successful initiatives also depend on whether agencies can access governed data, protect their systems and scale promising use cases without rebuilding their architecture from scratch. The hybrid cloud combines the control of on-premises infrastructure with the flexibility and scalability of the public cloud, helping agencies support AI workloads while maintaining security and compliance standards.
BEGIN WITH THE DATA: Many state and local governments store more data than they need, for longer than they are required to store it. Also, governments frequently store duplicates of citizen records across different IT systems. This not only drives up storage costs, but it also creates complexity that makes AI implementation more challenging. By implementing cross-agency data lakes, governments can pool data resources, eliminate duplication and improve efficiency. When leaders start by cleaning up their data stores, they will have a better idea of which cloud and on-premises resources will be needed to support their AI initiatives.
CLOSE INFRASTRUCTURE GAPS: From a technical perspective, hybrid cloud supports AI through high-performance computing (HPC) and GPU-enabled environments; scalable storage and data pipelines; and edge computing capabilities for real-time processing. These capabilities are most effective when deployed across a hybrid architecture that can dynamically allocate resources where they are needed most. For many agencies, infrastructure gaps are not merely the result of insufficient hardware, but rather fragmented infrastructure that has grown over time to support specific departments, programs or legacy applications. By closing these gaps with a hybrid approach, agencies can move AI initiatives forward without taking on unnecessary cost, complexity or risk.
SECURE SENSITIVE DATA AND WORKLOADS: AI systems often process sensitive citizen and employee data, making zero-trust frameworks, encryption and strong governance essential to success. For example, agencies may use AI to streamline tasks such as benefits administration, licensing, permitting and public safety operations. The hybrid cloud allows agencies to keep regulated data in secure, on-premises environments while leveraging public cloud resources for compute-intensive workloads. Strong governance practices are as important as IT solutions. Agencies need to articulate which data can be used in AI systems, who can access it, how models are trained and what controls must follow data as it moves across environments.
START SMALL: Organizations that scale up their AI programs before defining practical use cases and validating their initial investments often end up burning through vast resources with little to show for their efforts. A hybrid cloud approach allows state and local governments to start small and then scale over time as their AI programs mature. Because of the modular and iterative nature of the hybrid cloud, the model allows agencies to scale without undertaking multiple major infrastructure overhauls. This both reduces risks and accelerates time to value.
ALIGN INFRASTRUCTURE WITH USE CASES: The goal of AI programs should not be to deploy the technology wherever possible, but rather to power use cases that bring value to employees and citizens. Only after defining their goals should leaders make decisions about the infrastructure needed to support relevant applications. In government, chatbots have become popular for providing fast, validated answers to natural-language queries. AI-powered document processing, which can reduce error rates, is another popular use case. By aligning infrastructure to applications such as these, agencies can turn their technical investments into sources of measurable value.
Click Below To Continue Reading
These four real-world use cases show the promise of AI in government.
REGULATORY REDUCTION: Virginia is piloting an agentic AI tool that analyzes hundreds of thousands of statutory provisions to flag mismatches between statutes and regulations, such as an agency charging higher fees than authorized by law.
TAX GUIDANCE: The Utah Tax Commission ran a pilot with four vendors to build an AI tool capable of answering common tax questions. One of the models answered 92% of questions as accurately as a knowledgeable agent.
WILDFIRE DETECTION: Washington’s Department of Natural Resources deployed 30 high-powered AI vision cameras on towers across the state to detect wildfires in real time, distinguishing genuine wildfire from smoke, dust storms and clouds.
SCHOOL SEARCH: Massachusetts developed a public-facing GenAI chatbot that helps parents search for preschool openings, including schools equipped to serve children with special needs. The idea emerged from a state contest that encouraged staff to develop AI use cases.
For state and local agencies, modernization efforts must fit within strict regulatory, financial and operational constraints. The hybrid cloud directly addresses these challenges by offering a flexible, yet controlled approach to infrastructure transformation.
BUDGET CONCERNS: Cost control is an ever-present concern for state and local governments, and budget ranked third among state CIOs in NASCIO’s 2026 top 10 priorities survey. The hybrid cloud offers two significant benefits related to budget efficiency. For one, the model facilitates cost optimization by allowing agencies to run workloads in the most cost-effective environment. Also, agencies with a hybrid cloud model can use the public cloud as a laboratory for their AI workloads, validating use cases before making capital investments.
WORKFORCE AND SKILLS GAPS: According to DDN’s 2026 State of AI Infrastructure Report, nearly two-thirds of organizations have abandoned AI projects due to a lack of skills. While many organizations plan to address these skills gaps through training, hiring and outsourcing, the report notes that the most common lever for addressing this challenge is actually the cloud. Nearly two-thirds (64%) of respondents say they plan to use cloud providers such as Google Cloud and Amazon Web Services to close their skills gaps and meet AI infrastructure goals, more than any other tactic. In a hybrid cloud model, agencies can leverage the skills, tooling and managed capabilities of public cloud providers for AI experimentation and scaling, while keeping sensitive workloads on-premises when needed.
CYBERTHREATS: Cybersecurity remains a top concern, not just for state and local governments, but for many organizations implementing AI. According to the Google Cloud 2025 State of AI Infrastructure Report, 62% of organizations rank security risks or data privacy as their greatest concern surrounding AI adoption. Hybrid cloud strengthens security by enabling layered defenses across environments, including on-premises controls, cloud-native security tools and centralized monitoring. Leaders should conduct due diligence to ensure every component of a hybrid cloud environment has the required compliance and security controls. Although some workloads will need to be kept on-premises for regulatory reasons, public cloud environments are not inherently less secure than government-operated data centers.
AGING INFRASTRUCTURE: Many state and local governments continue to run a great deal of legacy infrastructure, particularly within their data centers. According to the DDN report, 65% of organizations across all sectors cite at least one challenge tied to legacy systems. And the Google Cloud infrastructure report states that organizations are approaching an “inflection point” where traditional cloud computing and infrastructure will no longer be capable of keeping up with capacity and performance demands. A hybrid cloud approach allows agencies to modernize incrementally rather than through costly, large-scale replacement and initiatives such as infrastructure consolidation and hybrid cloud integration to improve performance and service delivery.
CROSS-AGENCY COLLABORATION: Finally, hybrid environments support cross-agency collaboration and data sharing, which are both essential for delivering integrated citizen services. By connecting systems across on-premises and public cloud environments, agencies can break down silos without sacrificing control. This is a critical priority for government IT leaders, with digital government and digital services (including cross-agency collaboration) ranking fifth on NASCIO’s 2026 list of state CIO priorities. The hybrid cloud is a practical solution to systemic challenges such as data silos, enabling agencies to modernize responsibly while maintaining mission continuity.
As state and local governments look toward the future, the convergence of hybrid cloud and AI represents a critical opportunity to transform public sector operations. Success requires a deliberate and strategic approach to implementation, which often involves assistance from trusted partners.
ASSESS READINESS: Before organizations begin deploying AI tools and investing in new infrastructure, leaders should both determine their goals and assess their existing environments. This includes identifying which data and applications must remain on-premises and which can benefit from cloud scalability. Agencies must also prioritize data strategy and governance early on, establishing policies for data classification, security and lifecycle management to maximize the value of their hybrid environments and keep AI workflows secure and compliant. CDW offers readiness and security assessments, as well as workshops to help leaders identify strengths and gaps in areas such as infrastructure and data to help organizations develop a roadmap for AI success.
MODERNIZE APPLICATIONS: AI success depends not only on data and infrastructure, but also on the applications and workflows that deliver services to citizens and employees. For example, chatbots must connect to reliable knowledge bases and back-end systems, and document processing tools need access to records management systems. CDW’s Strategic Application Modernization Assessment (SAMA) can help agencies prioritize these efforts. During a SAMA engagement, CDW’s experts analyze application portfolios in depth, looking at factors including complexity, security exposure, dependencies and cloud readiness. With this insight, agencies can identify which systems need to be rehosted, refactored, rebuilt or replaced to accommodate AI-enabled services.
LEVERAGE PARTNERSHIPS: Many state and local governments lack the budget and expertise needed to build AI-ready infrastructure on their own. Regional partnerships and shared services models can help agencies pool expertise, reduce duplication and spread out the expense of new investments. Also, technology partners such as CDW can help agencies close talent gaps. Hybrid cloud environments require expertise across infrastructure, security, compliance, application modernization and data management, all at a time when many internal IT teams are already stretched thin. CDW can support agencies with assessment, design, implementation and managed services, helping leaders to both architect new environments and manage hybrid cloud operations over time.
SCALE THROUGH PHASED MODERNIZATION: Small wins lay the foundation for sustained success. Rather than trying to solve every AI infrastructure challenge at once, state and local governments can take a phased approach that starts with focused, high-impact initiatives that have manageable risks and clear value. These early efforts might include projects aimed at improving citizen engagement, security analytics or document processing. Then, after repeatable processes have been established, agencies can apply validated governance processes, data practices and workload placement decisions across other departments and services. By deliberately sequencing their projects, agencies can prove value, build trust and expand AI-enabled services over time.
MEASURE PROGRESS BY MISSION OUTCOMES: Success with AI-ready infrastructure depends on how agencies align their technology investments with mission outcomes. The hybrid cloud is not an end in itself, but rather a means to deliver better services and improved efficiency for both employees and citizens. Clear success metrics can help ensure that investments are meeting goals around shorter wait times, fewer handoffs, lower total costs and reductions in manual work. By embracing hybrid cloud as a long-term strategy, state and local governments can create a secure AI-ready infrastructure with the scalability to support innovation today while adapting to the demands of tomorrow.
- BUILDING AN AI-READY INFRASTRUCTURE
- HYBRID CLOUD SOLVES GOVERNMENT CHALLENGES
- A FOUNDATION FOR AI INNOVATION
AI-ready infrastructure requires more than raw compute power. Successful initiatives also depend on whether agencies can access governed data, protect their systems and scale promising use cases without rebuilding their architecture from scratch. The hybrid cloud combines the control of on-premises infrastructure with the flexibility and scalability of the public cloud, helping agencies support AI workloads while maintaining security and compliance standards.
BEGIN WITH THE DATA: Many state and local governments store more data than they need, for longer than they are required to store it. Also, governments frequently store duplicates of citizen records across different IT systems. This not only drives up storage costs, but it also creates complexity that makes AI implementation more challenging. By implementing cross-agency data lakes, governments can pool data resources, eliminate duplication and improve efficiency. When leaders start by cleaning up their data stores, they will have a better idea of which cloud and on-premises resources will be needed to support their AI initiatives.
CLOSE INFRASTRUCTURE GAPS: From a technical perspective, hybrid cloud supports AI through high-performance computing (HPC) and GPU-enabled environments; scalable storage and data pipelines; and edge computing capabilities for real-time processing. These capabilities are most effective when deployed across a hybrid architecture that can dynamically allocate resources where they are needed most. For many agencies, infrastructure gaps are not merely the result of insufficient hardware, but rather fragmented infrastructure that has grown over time to support specific departments, programs or legacy applications. By closing these gaps with a hybrid approach, agencies can move AI initiatives forward without taking on unnecessary cost, complexity or risk.
SECURE SENSITIVE DATA AND WORKLOADS: AI systems often process sensitive citizen and employee data, making zero-trust frameworks, encryption and strong governance essential to success. For example, agencies may use AI to streamline tasks such as benefits administration, licensing, permitting and public safety operations. The hybrid cloud allows agencies to keep regulated data in secure, on-premises environments while leveraging public cloud resources for compute-intensive workloads. Strong governance practices are as important as IT solutions. Agencies need to articulate which data can be used in AI systems, who can access it, how models are trained and what controls must follow data as it moves across environments.
START SMALL: Organizations that scale up their AI programs before defining practical use cases and validating their initial investments often end up burning through vast resources with little to show for their efforts. A hybrid cloud approach allows state and local governments to start small and then scale over time as their AI programs mature. Because of the modular and iterative nature of the hybrid cloud, the model allows agencies to scale without undertaking multiple major infrastructure overhauls. This both reduces risks and accelerates time to value.
ALIGN INFRASTRUCTURE WITH USE CASES: The goal of AI programs should not be to deploy the technology wherever possible, but rather to power use cases that bring value to employees and citizens. Only after defining their goals should leaders make decisions about the infrastructure needed to support relevant applications. In government, chatbots have become popular for providing fast, validated answers to natural-language queries. AI-powered document processing, which can reduce error rates, is another popular use case. By aligning infrastructure to applications such as these, agencies can turn their technical investments into sources of measurable value.
Click Below To Continue Reading
These four real-world use cases show the promise of AI in government.
REGULATORY REDUCTION: Virginia is piloting an agentic AI tool that analyzes hundreds of thousands of statutory provisions to flag mismatches between statutes and regulations, such as an agency charging higher fees than authorized by law.
TAX GUIDANCE: The Utah Tax Commission ran a pilot with four vendors to build an AI tool capable of answering common tax questions. One of the models answered 92% of questions as accurately as a knowledgeable agent.
WILDFIRE DETECTION: Washington’s Department of Natural Resources deployed 30 high-powered AI vision cameras on towers across the state to detect wildfires in real time, distinguishing genuine wildfire from smoke, dust storms and clouds.
SCHOOL SEARCH: Massachusetts developed a public-facing GenAI chatbot that helps parents search for preschool openings, including schools equipped to serve children with special needs. The idea emerged from a state contest that encouraged staff to develop AI use cases.
For state and local agencies, modernization efforts must fit within strict regulatory, financial and operational constraints. The hybrid cloud directly addresses these challenges by offering a flexible, yet controlled approach to infrastructure transformation.
BUDGET CONCERNS: Cost control is an ever-present concern for state and local governments, and budget ranked third among state CIOs in NASCIO’s 2026 top 10 priorities survey. The hybrid cloud offers two significant benefits related to budget efficiency. For one, the model facilitates cost optimization by allowing agencies to run workloads in the most cost-effective environment. Also, agencies with a hybrid cloud model can use the public cloud as a laboratory for their AI workloads, validating use cases before making capital investments.
WORKFORCE AND SKILLS GAPS: According to DDN’s 2026 State of AI Infrastructure Report, nearly two-thirds of organizations have abandoned AI projects due to a lack of skills. While many organizations plan to address these skills gaps through training, hiring and outsourcing, the report notes that the most common lever for addressing this challenge is actually the cloud. Nearly two-thirds (64%) of respondents say they plan to use cloud providers such as Google Cloud and Amazon Web Services to close their skills gaps and meet AI infrastructure goals, more than any other tactic. In a hybrid cloud model, agencies can leverage the skills, tooling and managed capabilities of public cloud providers for AI experimentation and scaling, while keeping sensitive workloads on-premises when needed.
CYBERTHREATS: Cybersecurity remains a top concern, not just for state and local governments, but for many organizations implementing AI. According to the Google Cloud 2025 State of AI Infrastructure Report, 62% of organizations rank security risks or data privacy as their greatest concern surrounding AI adoption. Hybrid cloud strengthens security by enabling layered defenses across environments, including on-premises controls, cloud-native security tools and centralized monitoring. Leaders should conduct due diligence to ensure every component of a hybrid cloud environment has the required compliance and security controls. Although some workloads will need to be kept on-premises for regulatory reasons, public cloud environments are not inherently less secure than government-operated data centers.
AGING INFRASTRUCTURE: Many state and local governments continue to run a great deal of legacy infrastructure, particularly within their data centers. According to the DDN report, 65% of organizations across all sectors cite at least one challenge tied to legacy systems. And the Google Cloud infrastructure report states that organizations are approaching an “inflection point” where traditional cloud computing and infrastructure will no longer be capable of keeping up with capacity and performance demands. A hybrid cloud approach allows agencies to modernize incrementally rather than through costly, large-scale replacement and initiatives such as infrastructure consolidation and hybrid cloud integration to improve performance and service delivery.
CROSS-AGENCY COLLABORATION: Finally, hybrid environments support cross-agency collaboration and data sharing, which are both essential for delivering integrated citizen services. By connecting systems across on-premises and public cloud environments, agencies can break down silos without sacrificing control. This is a critical priority for government IT leaders, with digital government and digital services (including cross-agency collaboration) ranking fifth on NASCIO’s 2026 list of state CIO priorities. The hybrid cloud is a practical solution to systemic challenges such as data silos, enabling agencies to modernize responsibly while maintaining mission continuity.
As state and local governments look toward the future, the convergence of hybrid cloud and AI represents a critical opportunity to transform public sector operations. Success requires a deliberate and strategic approach to implementation, which often involves assistance from trusted partners.
ASSESS READINESS: Before organizations begin deploying AI tools and investing in new infrastructure, leaders should both determine their goals and assess their existing environments. This includes identifying which data and applications must remain on-premises and which can benefit from cloud scalability. Agencies must also prioritize data strategy and governance early on, establishing policies for data classification, security and lifecycle management to maximize the value of their hybrid environments and keep AI workflows secure and compliant. CDW offers readiness and security assessments, as well as workshops to help leaders identify strengths and gaps in areas such as infrastructure and data to help organizations develop a roadmap for AI success.
MODERNIZE APPLICATIONS: AI success depends not only on data and infrastructure, but also on the applications and workflows that deliver services to citizens and employees. For example, chatbots must connect to reliable knowledge bases and back-end systems, and document processing tools need access to records management systems. CDW’s Strategic Application Modernization Assessment (SAMA) can help agencies prioritize these efforts. During a SAMA engagement, CDW’s experts analyze application portfolios in depth, looking at factors including complexity, security exposure, dependencies and cloud readiness. With this insight, agencies can identify which systems need to be rehosted, refactored, rebuilt or replaced to accommodate AI-enabled services.
LEVERAGE PARTNERSHIPS: Many state and local governments lack the budget and expertise needed to build AI-ready infrastructure on their own. Regional partnerships and shared services models can help agencies pool expertise, reduce duplication and spread out the expense of new investments. Also, technology partners such as CDW can help agencies close talent gaps. Hybrid cloud environments require expertise across infrastructure, security, compliance, application modernization and data management, all at a time when many internal IT teams are already stretched thin. CDW can support agencies with assessment, design, implementation and managed services, helping leaders to both architect new environments and manage hybrid cloud operations over time.
SCALE THROUGH PHASED MODERNIZATION: Small wins lay the foundation for sustained success. Rather than trying to solve every AI infrastructure challenge at once, state and local governments can take a phased approach that starts with focused, high-impact initiatives that have manageable risks and clear value. These early efforts might include projects aimed at improving citizen engagement, security analytics or document processing. Then, after repeatable processes have been established, agencies can apply validated governance processes, data practices and workload placement decisions across other departments and services. By deliberately sequencing their projects, agencies can prove value, build trust and expand AI-enabled services over time.
MEASURE PROGRESS BY MISSION OUTCOMES: Success with AI-ready infrastructure depends on how agencies align their technology investments with mission outcomes. The hybrid cloud is not an end in itself, but rather a means to deliver better services and improved efficiency for both employees and citizens. Clear success metrics can help ensure that investments are meeting goals around shorter wait times, fewer handoffs, lower total costs and reductions in manual work. By embracing hybrid cloud as a long-term strategy, state and local governments can create a secure AI-ready infrastructure with the scalability to support innovation today while adapting to the demands of tomorrow.
CDW can help you plan and implement a strategy for your AI-ready hybrid infrastructure.
Asim Iqbal
CTO of Emerging Technology
