Every state IT leader I talk with right now is stuck in the same bind: Legislators and agency heads want AI results now, while the data underneath most agencies still looks like it did a decade ago — scattered across a dozen systems, inconsistently defined and locked behind access rules nobody remembers writing. I spent the better part of thirty years inside that bind, first as Washington’s CIO, then North Carolina’s, and I can tell you exactly where the AI conversation always breaks down. It’s never the model. It’s the data underneath it.
That’s a less exciting answer than a chatbot demo, but it’s the honest one. AI readiness doesn’t start with a pilot project or a new application. It starts with your data.
This isn’t a hunch. NASCIO’s 2026 State CIO Top 10 Priorities survey just marked the first time in the report’s 20-year history that AI has displaced cybersecurity from the top spot — and buried inside that AI priority is data quality and governance, because my former peers know you can’t separate the two. Meanwhile, Code for America’s 2026 Government AI Landscape Assessment found that nearly every state has now piloted some form of generative AI, but only a handful have moved past pilots into enterprise-scale use, and fewer still have any way to measure whether those deployments are delivering public value. That gap isn’t a tooling problem. It’s a foundation problem.
I saw this firsthand in North Carolina, where one of our first moves on AI wasn’t procuring a model — it was launching a framework that was inclusive of data discovery to understand what data we actually had, where it lived, how it was classified and how clean it was. That unglamorous work is what makes everything downstream possible. Fragmented systems limit visibility. Weak governance introduces risk. And in government, where every decision is subject to public scrutiny in a way the private sector rarely faces, inaccurate inputs don’t just produce bad outputs — they erode the public’s trust in the institution itself.
That’s why I’d encourage agency leaders to stop treating AI readiness as a new initiative competing for budget and instead treat it as the next chapter of the data modernization work most of you are already funding. The platform decisions you make now determine what’s possible later. A data environment built for integration across agencies, timely access and strong governance doesn’t just support today’s reporting needs but also lets you eventually tackle fraud detection, intelligent case management, public health trend analysis, emergency response coordination and personalized constituent services. The path to AI value runs through the data platform, not around it.
When agencies build modern data environments, they are not just solving for current storage or performance needs. They are creating the conditions for future analytics, automation and AI-driven outcomes. A platform that supports data integration across systems, enables timely access to information and maintains strong governance can help agencies move from reactive reporting to predictive insights. It can support use cases such as fraud detection, intelligent case management, public health trend analysis, infrastructure planning, emergency response coordination and personalized constituent services.
This is an important shift in perspective for public sector leaders. Too often, AI is framed as something separate from the core work of IT modernization — a new layer to be added later, once budgets or demand justify it. But that mindset can create delays, duplication and disconnected investments. AI readiness cannot be a side project. It is the result of disciplined foundational work that agencies should already be doing: consolidating data silos, improving data quality, strengthening governance, modernizing infrastructure and making information more accessible to the people and systems that need it.
For state and local governments, this approach is both practical and strategic. It lets leaders prepare for AI without chasing hype or overcommitting to tools before the underlying environment is ready — and I’ve watched enough agencies do the opposite to know how that story ends. It also ensures that investments made today deliver value whether an agency is focused on reporting, analytics, digital services or future AI applications. A strong data foundation supports all of it.
There’s also a trust dimension public agencies can’t afford to overlook. AI systems will increasingly influence decisions, recommendations and service delivery. If residents are going to trust those outcomes, agencies have to be able to trust the data behind them first. That makes governance a public confidence issue, not just an operational one. Any CIO who’s sat in a legislative hearing knows the question isn’t “does the model work” — it’s “can you tell me where this data came from and who touched it.” Agencies need clear policies around data ownership, quality, lineage, access, retention and security, and they need answers ready before someone asks. AI makes those questions more urgent, not less.
The good news is that most of the steps required for AI readiness are ones I watched agencies already grinding through — modernizing legacy systems, strengthening cybersecurity, improving cross-agency collaboration. AI readiness doesn’t require a separate mission. It requires pointing that same work at a clear goal: Better data management today enables better intelligence tomorrow.
That’s the opportunity in front of state and local governments right now. You don’t need a fully defined AI roadmap to start. Start with the fundamentals: trusted data, accessible data, governed data and platforms built for flexibility and scale. Those are the strategic capabilities that will determine how effectively your government can use analytics and AI in the years ahead.
In the end, becoming AI-ready is not about adopting the most advanced algorithm first but about building the kind of data foundation that makes innovation possible, responsible, defensible, and sustainable. For state and local governments, that foundational work is not separate from the future of AI. It is the future of AI.
For state, local, and education leaders thinking through what that looks like in practice, see additional resources on AI readiness, cyber resilience, and building a more predictable data foundation.
Jim Weaver is a former state CIO and nationally recognized public-sector technology leader with deep experience guiding government IT strategy, modernization, and cybersecurity initiatives. He served as Secretary and Chief Information Officer for the North Carolina Department of Information Technology, where he oversaw statewide IT strategy, procurement, cybersecurity, and broadband expansion. Prior to that, he served as CIO for the state of Washington, helping strengthen the state’s IT infrastructure and advance technology adoption across government. Jim also served as president of the National Association of State Chief Information Officers (NASCIO), contributing to IT policy and collaboration nationwide.
Leave a Reply
You must be logged in to post a comment.