Aligning people and mission to unlock federal IT modernization
Modernizing legacy federal technology platforms presents agency leaders with a rare opportunity to rethink how work gets done. When AI is deliberately integrated into core workflows, agencies can improve mission delivery, reduce operational costs, and create a more effective experience for both staff and the public they serve.
But executing large-scale digital modernizations in the public sector comes with distinct challenges. Agencies operate in an ecosystem of aging infrastructure, complex governance, siloed stakeholders, and nuanced missions. And, like all large organizations, federal agencies have workforces that may be hesitant and apprehensive about change.
ROI from modernization and artificial intelligence depends on more than technology. Agencies achieve the greatest returns when they put people at the center of transformation. Doing so requires a strategic approach to change management that reflects the realities of the artificial intelligence age. While every agency and mission has unique constraints and priorities, these change management best practices can help leaders move beyond adoption to sustained, confident use of artificial intelligence and modern technology.
1. Communicate a shared vision
Change often provokes fear, which can surface as resistance. To ease staff anxiety, agency leaders should communicate frequently and consistently throughout the modernization initiative, keeping employees informed about what is happening and why. These communications should be grounded in a clear vision that directly ties technology modernization to the agency’s mission goals and cultural values. Leaders should also engage internal and external stakeholders in shaping this vision, ensuring it clearly articulates what changes are coming, why they are necessary, how they will affect employees’ roles, and why now is the right time to act.
2. Stay focused on outcomes
Leaders must emphasize that achieving the vision for technology modernization will not always follow a linear path. Rather than adhering to a rigid set of requirements, the agency should remain focused on the outcomes defined in the vision and stay adaptable in how it reaches them.
3. Use experimentation to fuel adoption
About half of federal agency employees are being trained in AI, cloud, and open-source technologies according to the 2025 Federal Software Reimagined report. But this training should move beyond traditional, top-down delivery models. When learning is embedded in day-to-day work and encourages experimentation, agencies are more likely to earn staff buy-in than through directive, one-size-fits-all mandates.
4. Build a safe-to-fail culture
Agency leaders can support AI experimentation by providing secure technical environments and fostering a culture of trust—one where learning from failure does not negatively affect missions or morale. As employees test AI solutions within their specific use cases, leaders should emphasize that even unsuccessful efforts can yield valuable lessons—clarifying how new tools can (and cannot) advance mission outcomes and pointing toward better paths forward. Leaders can model this safe-to-fail culture by openly sharing their own missteps and demonstrating how those setbacks contribute to progress. When leaders acknowledge their mistakes, it can significantly strengthen trust across the workforce.
5. Encourage input
Feedback is essential to helping agencies stay nimble through change. Leaders should encourage staff and external stakeholders to lean into their curiosity. They should feel comfortable sharing ideas and asking questions about processes and decisions. This incentivizes continuous learning and allows the change management plan to evolve as technology advances and workforce needs change.
6. Be transparent about decision-making
When many experiments are underway across an agency, it’s expected that not all will progress to production. By using clear prioritization metrics—such as cost savings, delivery time, and efficiency—leaders can make transparent decisions about which AI applications will be scaled and standardized.
7. Empower staff to become change advocates
Enthusiastic early adopters can become powerful change champions among their peers. Leaders should intentionally identify and empower these employees—particularly managers, who are often trusted voices on their teams—by offering early access to new features. As advocates, these managers can encourage broader participation in AI experimentation and strengthen feedback loops that surface both challenges and successes across the agency.
Incentivizing adoption while accelerating innovation
Beyond improving adoption, this outcomes-oriented, experiment-based approach gives agencies a speed advantage. Parallel experimentation across the workforce accelerates learning, surfaces high-value AI use cases sooner, and enables leaders to scale proven solutions with confidence.
ICF helps federal agencies implement this approach to change management, applying our deep expertise in technology development, human-centered design, and federal policy. By focusing on both technology and the people who use it, we help agencies achieve AI deployments that stick and deliver a greater return on their tech investments.
