The AI Readiness Gap: Why Public Agencies Must Get Their Data in Order
Artificial Intelligence (AI) is transforming the public sector, streamlining operations, and improving service delivery. However, a critical gap exists in AI readiness: poor data management. As AI adoption accelerates, public agencies must ensure they are data-ready. "Generative AI systems rely on high-quality, well-documented, and machine-readable data to function effectively" (U.S. Department of Commerce, 2025). Without structured, accessible, and clean data, AI applications cannot deliver meaningful insights or automation.
TECHNOLOGYARTIFICIAL INTELLIGENCE
Dr. Shawn Granger
2/23/20252 min read
The Misconception of Data Management in Public Agencies
Many agencies have implemented document management systems like Laserfiche, but few have organized their data correctly. Instead of systematically preparing data, employees often scan documents and store them in digital folders without adding necessary metadata. This fragmented approach limits AI’s ability to analyze, retrieve, and process information efficiently. "Metadata offers context on data, helping you understand what it means and how to use it" (Atlan, n.d.). AI systems struggle to derive accurate insights without proper metadata, leading to inefficiencies and unreliable outputs.
Organizing Data for AI: Best Practices
To ensure AI readiness, public agencies should implement the following data management strategies:
Comprehensive Metadata Management: Standardized metadata frameworks should describe data attributes, lineage, and usage policies, improving discoverability and usability.
Consistent Data Formatting: Data must be stored in machine-readable formats such as JSON or XML to ensure seamless integration with AI models.
Robust Data Documentation: Agencies should provide detailed documentation outlining data sources, structures, and transformation processes to maintain transparency.
Data Quality Assurance: Regular assessments should be conducted to maintain data accuracy, completeness, and consistency.
Secure Data Storage and Access Controls: Implementing encryption and access restrictions protects sensitive information and ensures compliance with privacy regulations.
Interoperable Data Standards: Using interoperable standards allows seamless data sharing and integration across different platforms.
Ethical Data Governance: Clear policies on data usage, bias mitigation, and legal compliance foster responsible AI deployment.
Why AI Needs Good Data
The phrase "good AI depends on good data" is not just a cliché; it’s a foundational truth. AI might seem like magic, but it cannot fix bad data. "An agency should consider six critical components of AI readiness: Strategy, People, Processes, Data, Technology & Platforms, and Ethics" (TechSur Solutions, 2024). Neglecting proper data preparation hampers AI’s potential, leading to flawed insights and poor decision-making.
Conclusion: The Path Forward
Public agencies must shift from viewing AI as a plug-and-play solution to recognizing the importance of structured, high-quality data. Agencies can unlock AI's true potential by adopting best practices in data organization and improving operational efficiency and service delivery. Investing in data readiness today ensures that AI-driven public services are effective, ethical, and impactful.
References
Atlan. (n.d.). Data Readiness for AI: 4 Fundamental Factors to Consider. Retrieved from https://atlan.com/know/ai-readiness/ai-ready-data/
TechSur Solutions. (2024). AI Readiness in Public Agencies: A Strategic Framework. Retrieved from https://techsur.solutions/ai-readiness-government-agencies/
U.S. Department of Commerce. (2025). AI Governance and Data Integrity Report. Retrieved from https://www.commerce.gov/news/blog/2025/01/generative-artificial-intelligence-and-open-data-guidelines-and-best-practices