States’ responses to the rapid rise of AI have varied widely, ranging from governor-mandated task forces to informal departmental guidelines. But by now, most leaders in state government would agree that AI is more than just a tool for back-office efficiency. Increasingly, they recognize it as a foundational capability that can transform how public agencies fulfil their mission.
But what should that transformation look like? What kind of AI solutions should state agencies invest in? Which use cases pose the least risk and deliver the most value? How do you ensure transparency for AI-led processes that involve citizen data and public services?
Answering those questions requires an airtight understanding of a few key principles. Consider them “rules of the road” for state governments’ AI journey.
1. Acquiring the technology is not the first step—or even the second.
State agencies that leap into AI adoption without first aligning people, processes, and policies risk over-indexing certain capabilities and making poor procurement decisions that result in low return on investment (ROI). Agencies shouldn’t invest in AI technology—or even start shopping around for it—before conducting a readiness assessment. Such assessments should answer five critical questions:
- Is there a clear strategy for how AI will support the agency’s mission?
- Are there governance and oversight structures in place to assure responsible use?
- Is the agency’s data accurate, accessible, and secure?
- Does the infrastructure technology stack support scalable AI solutions?
- Are employees equipped with the skills and mindset to adopt AI?
A readiness assessment should provide a detailed picture of the organization’s current state in every one of those dimensions, and it should articulate the organization’s objectives—its desired state—with equal clarity. That allows leadership teams to identify gaps and achieve alignment on how to close them before making critical procurement and deployment decisions.
2. When choosing AI solutions, think platform.
It’s tempting to seek out multiple AI tools that each address a single high-priority use case. But investing in targeted AI solutions with limited interoperability can lead to a costly, fragmented integration and is rarely a sustainable strategy. Cloud-based AI platforms and off-the-shelf generative AI tools can deliver functionality faster and evolve better with the organization over time. They’re also:
- Upgradeable and scalable
- User-friendly and accessible to a wider range of workers
- Less likely to require specialized personnel
3. Understand the total cost of ownership.
AI platforms aren’t appliances that you plug in, switch on, and run in the background. They’re more like living organisms that require care and feeding. Ongoing costs like storage, processing, and maintenance must be factored into budgeting and procurement decisions, taking into account the AI solution’s full life cycle. That cycle starts well before deployment: just cleaning and de-siloing your organization’s data can be a costly undertaking. Other cost sources to consider include:
- Workforce training and upskilling initiatives
- Customizing the AI solution and integrating it with existing systems, workflows, and legacy technology
- Licensing and subscriptions
- Hiring and paying dedicated specialists to maintain the platform
- Recurring usage, data transfer, and compute costs
Understanding these costs at the outset and factoring them into procurement decisions can ultimately lead to smoother integration.
4. Embed risk management and governance from the start.
With new policies at the federal level putting an emphasis on speed and deregulation, risk management, governance, and compliance might seem like second-tier priorities. On the contrary, they should form the armature around which a state-government AI strategy is built. Waiting to act until risks become threats isn’t an option. Start by addressing these imperatives:
- Establish explainability and auditability standards for AI use, including bias reviews
- Create escalation protocols for AI outputs
- Ensure traceability and defensibility of decisions related to AI
- Install human oversight at key decision thresholds
- Develop a robust AI cybersecurity strategy supported by simulation-driven incident response, adversarial testing, real-time monitoring, and AI-enabled threat detection and response
Governance, risk mitigation, ethics, and compliance need to be built into procurement decisions from the beginning, and, in the current deregulation-focused environment, they need to be proactive, not reactive. A structured governance framework is a precondition for an AI strategy that’s aligned with the agency’s mission.
5. Define what ROI looks like.
Unlike the private sector, where return on investment is often measured in dollars and cents, public agencies must consider a broader set of outcomes. Cost savings matter, but so do improvements in service delivery, constituent satisfaction, and operational resilience. Value should be framed in terms of both back-end efficiency and front-end impact—how well government operates and how constituents experience its services. Leadership teams at state agencies need to start by reassessing ROI through three lenses:
- Operational (time and cost savings, reduction of errors, speed of task completion, etc.)
- Citizen-focused (inquiry response times, number of people accessing services, etc.)
- Strategic (impacts on policy development, resource allocation, workforce upskilling, etc.)
Leaders also need to develop better KPIs: metrics that are built on reliable baseline data about the outcomes that matter most to the agency and its constituents—be it error rates in manual processing or citizen satisfaction survey results.
6. Be aware of common pitfalls—and know how to avoid them.
Even with a strong governance framework in place, the pressure—both internal and external—to move fast is enormous. That can make it harder to spot hazards on the road to adoption. Knowing what to look out for is key:
- Misaligned expectations: AI is not a silver bullet. Define success metrics early, especially during pilot phases.
- Inadequate end-user training: Tools often go unused if those doing the work aren’t shown how to integrate them into their day-to-day tasks.
- Shadow AI: Unsupervised use of unapproved tools can introduce security and ethical risks. Clearly articulate guidelines around accessing AI on personal devices and experimenting with new applications and platforms.
- Erosion of trust: AI must include human oversight, especially in critical decisions affecting health, rights, or benefits. That oversight needs to be visible and transparent.
- Leadership-workforce capability gaps: If managers aren’t versed in the same tools workers are being asked to use, adoption suffers.
7. Focus on use cases that can deliver concrete outcomes right away.
In our work with state agencies and governor’s offices around the country, a number of AI use cases have emerged as particularly effective drivers of high-impact benefits for government workers and citizens:
- AI-powered virtual assistants and chatbots: One of the most widely adopted AI solutions in state governments today, chatbots can provide 24/7 multilingual support, reduce call center volumes, and improve access to information. These tools are especially powerful when integrated into a “single front door” portal, allowing constituents to access multiple services through one interface.
- Fraud detection and anomaly monitoring: AI excels at identifying patterns in large datasets, making it ideal for detecting fraud in benefits programs, tax enforcement, and grant disbursements.
- Agent workflow automation: Replacing traditional robotic process automation, agent automation uses AI agents to streamline workflows. A prime example is the permitting process—often a pain point for constituents. Automating this can reduce friction, improve turnaround times, and free up staff for higher-value tasks.
- AI in transportation and infrastructure: Some departments of transportation (DOTs) are leveraging AI to improve traffic management, predict roadway maintenance needs, and enhance public safety.
- Emergency response and disaster recovery: AI can analyze weather data, infrastructure conditions, and social media activity to support real-time decision-making and improve responders’ situational awareness during disasters. It also helps model long-term recovery strategies, helping to ensure smarter and more resilient rebuilding.
Assessing the risks associated with new use cases is important, but moving with caution and moving with speed don’t have to be mutually exclusive priorities. Government agencies are increasingly introducing “fast lane” governance models that accelerate procurement and implementation for low-risk use cases that don’t involve sensitive data or the automation of high-stakes decisions.
Looking ahead—and outward
As state agencies move beyond pilot initiatives toward organization-wide integration, they’ll need to not only deepen their internal investments—particularly in data infrastructure and upskilling—but also evolve their procurement models to enable ecosystem alliances outside the public sphere, specifically with commercial-sector AI innovators.
Just as important, as agentic applications mature, state leaders will need to prepare for a profound workforce transformation, one in which government employees are freed to perform higher-order work that allows them to engage with citizens in more meaningful ways.
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