Preparing Public Sector IT Teams for the Next Phase of Infrastructure Modernization

It’s no secret that the recent AI wave is accelerating digital transformation across state and local government. Agencies have been actively modernizing infrastructure to support growing service demand, increasing data volumes, and more performance-intensive workloads. According to a recent survey, AI, cybersecurity, modernization and digital government consistently rank among the top strategic priorities for state CIOs.

But technology itself is only part of the story. The next phase of infrastructure modernization isn’t about getting rid of legacy systems or deploying something new. It’s about preparing IT teams to operate hybrid environments, support AI-enabled workloads, and continuously adapt. Without the capacity, skills, and operational support to manage increasingly complex environments, even the most advanced infrastructure investments can fall short.

The good news: Workforce readiness doesn’t require agencies to start from scratch. Instead, it begins with thoughtful planning and continues throughout every stage of the modernization journey. With that in mind, here are four strategies agencies can use to prepare their IT teams for the next phase of infrastructure modernization.

1. Start with a Holistic Assessment

Before investing in new technologies, agencies should first understand the environments that they’re modernizing. That means assessing their current infrastructure, identifying operational dependencies and understanding how systems, people, and processes interact. Gartner estimates that approximately 40% of infrastructure systems have technical debt concerns, affecting performance, scalability and resilience. A holistic assessment helps agencies identify those risks early so they can prioritize modernization efforts based on mission and business impact, rather than simply the age of the technology.

Too often, modernization efforts focus on individual technologies rather than the broader ecosystem they support. A holistic assessment also helps agencies identify capability gaps, anticipate downstream impacts and align stakeholders around shared objectives before implementation begins.

This planning phase is also an opportunity to establish governance, define success metrics, evaluate cybersecurity posture and ensure workforce needs are considered alongside technology requirements. Taking the time to understand the current state creates a stronger foundation for every modernization decision that follows and helps ensure technology investments align with organizational goals.

2. Build Workforce Readiness into Every Modernization Milestone

Workforce readiness shouldn’t be treated as the final step before go-live. It should be embedded throughout the modernization process. That starts with helping employees understand the “why” behind change. As AI becomes part of daily operations, many employees naturally have questions about how it will affect their roles. Organizations that address those concerns early in the process and provide structured learning opportunities are much more likely to gain workforce buy-in. 

Training doesn’t always require formal certifications or assessments. Self-paced learning, guided education, peer collaboration and hands-on experience are all effective ways to build workforce capability. Leadership support is equally essential. When workforce development is prioritized at the organizational level and (even better) modeled by leadership, employees are more likely to invest in learning new skills. 

Preparing employees also means teaching them how to work effectively with AI. That includes learning how to ask better questions, evaluate AI-generated responses critically, recognize potential security and privacy risks, and use AI as a decision-support tool rather than treating every output as automatically correct. According to the World Economic Forum, 39% of workers’ core skills are expected to change by 2030, making continuous learning a strategic necessity rather than a one-time initiative.

3. Use Infrastructure Telemetry to Understand Workforce Needs

Infrastructure telemetry is often viewed solely as a tool for monitoring system performance. But it can also provide valuable insight into workforce readiness. As agencies modernize, operational data can help leaders understand not only how systems are performing, but also how effectively IT teams are supporting them.

Performance dashboards, utilization metrics and observability tools can reveal operational bottlenecks, highlight where institutional knowledge is concentrated among only a few employees and expose critical dependencies on specific teams or individuals. For example, if telemetry consistently shows that a system is underutilized or frequently misconfigured, it may signal a skills or training gap within the team responsible for managing it. These insights enable agencies to proactively identify where additional training, cross-skilling, or staffing adjustments are needed, reducing the risk that knowledge gaps evolve into operational vulnerabilities.

Telemetry also creates an important feedback loop after new technologies are deployed. Rather than assuming a modernization effort is successful once systems are live, agencies can continuously monitor infrastructure performance to validate that new environments are operating as intended and identify opportunities to improve technology and workforce processes over time.

As AI-enabled infrastructure becomes more common, these insights become even more valuable. Demand for data engineers, data analysts and other specialized roles continues to grow, while IT organizations are increasingly being asked to support more sophisticated environments with limited resources. By using operational data to identify capability gaps early, agencies can make more informed decisions about training, workforce development and staff redeployment, ensuring the right expertise is available where it’s needed most.

4. Create Shadow Operations Before Major Cutovers

One of the most effective ways to reduce modernization risk is to avoid treating implementation as a single event. Instead, agencies can operate legacy and modernized environments in parallel for a defined transition period. This approach mirrors blue/green deployment strategies commonly used in cloud environments, where parallel environments allow organizations to validate new systems, minimize downtime, and quickly roll back if issues arise. Shadow operations allow IT teams to gain hands-on experience while validating system performance, refining operational processes and identifying issues before the legacy environment is retired.

This approach is becoming increasingly common with AI deployments, where organizations validate solutions in sandbox environments before expanding into production. That measured approach allows agencies to evaluate return on investment while ensuring governance, security, and operational objectives have all been addressed.

Infrastructure modernization will only become more complex as AI adoption accelerates. Agencies that succeed won’t simply be the ones that deploy new technologies first, but the ones that prepare their workforces with the same level of intention.

Burnie Legette is a Solution Architect and Specialist for AI and Data Operations at Intel.