Why AI Raises the Stakes for Government Data
Government agencies have been managing imperfect data for decades. Siloed systems, inconsistent records and unclear ownership are familiar challenges. Historically, those issues slowed reporting or complicated audits.
With generative AI, the consequences are far greater.
AI systems don’t “understand” context the way people do. They infer patterns from the data they’re given — including its gaps, inconsistencies and biases. When agencies begin trusting AI-generated outputs without fully understanding the underlying data, errors can scale quickly. In government, that can mean incorrect benefits determinations, flawed public safety insights or citizens receiving the wrong information at the wrong time.
That’s why AI readiness is fundamentally a data problem, not just a tooling problem.
Think in Terms of Minimum Viable Data Governance
One of the biggest misconceptions I see is the belief that becoming AI-ready requires ripping and replacing decades of technology investments. That simply isn’t realistic for most public sector organizations.
Instead, agencies should focus on minimum viable data governance — doing enough, in the right places, to ensure AI can be deployed safely and effectively.
“Minimum viable” doesn’t mean minimal effort. It means targeted, outcome-driven work that aligns to specific use cases. If you’re deploying AI to improve permitting, customer service or internal productivity, ask a simple question: What data does this system depend on, and how confident are we in it?
From there, you can prioritize the foundational steps that matter most.
READ MORE: AI is a top management priorities for government CIOs.
What Makes a Data Foundation AI-Ready?
Across state and local government, I see five core capabilities that consistently separate AI-ready organizations from those struggling to scale.
1. Modernized data platforms
AI relies on both structured and unstructured data: databases, documents, forms, emails and more. Agencies don’t need a single monolithic platform, but they do need architectures that allow data to be accessed, integrated and governed consistently across systems.
Hybrid and cloud-friendly designs are especially important, enabling agencies to scale AI workloads without sacrificing security or control.
2. Clear data governance and accountability
AI amplifies whatever data issues already exist. Governance frameworks help agencies define data ownership, establish quality standards, document lineage and clarify retention and privacy rules.
This isn’t about creating bureaucracy; it’s about ensuring someone knows where critical data came from, how it’s being used and whether it’s fit for purpose.
3. Secure data sharing across agencies
Many of the most valuable AI use cases — in public safety, health and human services — depend on data from multiple entities. Secure, well-governed data-sharing models allow agencies to collaborate without increasing risk.
Without this foundation, AI initiatives remain trapped inside organizational silos.
4. Automated data quality checks
AI systems don’t flag messy data, they consume it. Automated profiling, validation and monitoring help surface issues early, before they show up as incorrect or misleading AI outputs.
Even small inconsistencies, such as mixed data types or incomplete fields, can have outsized impacts when models start making inferences at scale.
5. Humans in the loop
No matter how advanced AI becomes, human judgment remains essential. Agencies need trained staff who understand both the mission context and the data underneath AI systems.
This is especially critical in government, where mistakes can affect access to services, benefits or public safety. AI should accelerate decision-making, not replace accountability.
DIVE DEEPER: Experts say CIOs should build AI readiness around data.
Start With Assessment, Not Assumptions
One of the biggest risks CIOs face is assuming their data is “good enough” because systems appear to work today. AI has a way of exposing hidden issues — quickly and publicly.
That’s why many agencies begin their journey with an AI Readiness Data Quality Assessment. Rather than relying on anecdotes, this approach provides a quantitative view of data quality maturity, identifies gaps that could undermine AI initiatives and delivers a prioritized action plan tied to real outcomes.
The goal isn’t perfection; it’s clarity — knowing where you stand, what matters most and what to tackle first.
In the end, AI success in state and local government isn’t measured by model sophistication. It’s measured by trust — trust from employees, trust from leadership and trust from the public.
That trust is built on data foundations that are secure, governed and fit for purpose. By focusing on minimum viable data governance, aligning investments to real use cases and putting assessment before automation, agencies can move forward with confidence.
AI is coming — in many cases, it’s already here. The agencies that succeed in 2026 will be the ones that prepared their data to meet the moment.
