1.) Understand where you are on the AI maturity curve
The first step toward successful AI adoption is assessing organizational readiness. This includes understanding AI’s potential to support short- and long-term goals, identifying whether policies governing AI use are in place and evaluating the broader appetite for innovation.
Some agencies have formalized AI use policies, while others rely on other agency guidance or are in the process of developing their own frameworks. Leadership buy-in, staff engagement and access to change management resources all influence how readily an organization can adopt new technologies.
Establishing a clear view of the starting point allows agencies to shape an AI strategy that aligns with both immediate capabilities and long-term goals.
2.) Identify current assets and opportunities
An inventory of existing data assets, technology tools and business processes often reveal opportunities where AI can enhance operations or decision-making. In many cases, agencies discover they already have foundational elements—such as workflow management systems, data repositories or customer service platforms—that can support successful AI applications.
Evaluating these assets with project partners can help agencies identify realistic, high-value use cases, such as AI-assisted traffic incident detection or intelligent document review. This process also provides an opportunity to avoid common pitfalls, such as pursuing solutions that exceed available data quality or are not compatible with current technology.
3.) Build buy-in and readiness
Internal alignment is essential for sustainable AI adoption. The successful adoption and implementation of AI relies on engaged leadership within an agency that can help clearly define the vision for how AI will support existing goals and improve operations. It also requires addressing common concerns around data security, governance and risk.
Developing a culture of innovation that supports responsible experimentation allows agencies to test AI applications in controlled, manageable ways. This approach not only builds trust but also generates early insights that can inform broader adoption.
Peer learning can complement these efforts. Agencies that have developed AI policies or piloted early applications can provide valuable perspective as others advance their own readiness.
4.) Align use cases with organizational capacity and build momentum
For agencies taking their first steps with AI, early applications often focus on improving operational efficiency. Some small-scale efforts can deliver time savings while allowing staff to become familiar with AI tools and build momentum for broader adoption.
Workflow automation simplifies repetitive processes, increasing efficiency without requiring major operational changes. Several agencies have deployed chatbots to help navigate complex documentation or policies, reducing manual inquiries and providing time savings.
As agencies gain experience, AI can be applied in more complex and impactful ways. Next-wave opportunities include predictive maintenance modeling, real-time incident detection using machine learning and AI-driven decision support tools for long-range planning and asset management. While these solutions require more robust data systems and organizational maturity, they represent the future state many agencies are moving toward — and highlight the value of building a strong foundation now.
5.) Plan for adoption and scaling
As agencies build experience and confidence, they can increase the scope and impact of AI tools by broadening the data inputs, applying models to higher-value use cases or extending deployment across multiple departments or geographies. For example, a chatbot initially developed to support internal users could evolve into a public-facing tool with additional training, content integration and safeguards around privacy and accessibility.
Technical scalability often hinges on an agency’s ability to move from manually supported pilots to integrated solutions — those embedded in enterprise systems, automated workflows and repeatable processes. This may require investment in systems integration, improved data pipelines or coordination with others to ensure compatibility with core platforms.
Scaling is most successful when paired with clear communication, updated training and intentional change management that fosters trust and alignment across teams. Agencies that scale well typically treat AI not as a standalone tool, but as a capability that evolves alongside internal systems and strategic priorities.
Craig Bettmann
Jesse Newberry