Preparing agency data for AI at scale

Federal agencies have no shortage of data. But as artificial intelligence moves from experimentation to operational use, the challenge shifts from data access to readiness. Agency data must support AI safely, responsibly, and at scale, across routine applications such as assisted work tasks and more advanced use cases like automated analysis and decision support. For those reasons, agency leaders must elevate data readiness from a technical necessity to a strategic imperative that shapes trust, oversight, and mission outcomes.

What does it mean for data to be “AI-ready”?

In an AI-ready data ecosystem, datasets are structured, documented, and evaluated in ways that make them directly usable for AI and machine learning. Beyond data quality alone, true AI readiness reflects how well information can be transformed into analytical insight.

Strategies for developing an AI-ready data ecosystem

Informed by our work across generative and established AI, including participation in the National Institutes of Health data challenges, the following considerations can help agencies assess whether their data is ready to unlock AI’s full potential.

Start with the mission challenge, not the technology

Most AI use cases fall into three broad categories: improving user experience, increasing workflow efficiency, or strengthening core operations. Each places different demands on data volume, structure, and quality. By defining their mission challenge upfront, agencies can focus their data preparation efforts in the right category and avoid investing time and resources that won’t support the intended outcome.

Some challenges are data-intensive, where scale and completeness are critical. For example, the U.S. Food and Drug Administration is applying machine learning to analyze large volumes of drug label data, making the review process much more efficient and consistent. Other processes, such as clinical decision support, depend more on well-structured, high-quality data than on large historical datasets.

Apply data quality standards built for AI use

In federal environments, data quality directly affects confidence in AI-supported decisions. FAIR principles—findable, accessible, interoperable, and reusable—remain foundational for accessibility and reuse. But on their own, they are not sufficient for AI at scale.

Agencies should complement FAIR principles with additional AI-ready quality dimensions: accuracy, completeness, consistency, and relevance. Accuracy reduces the risk of errors propagating through AI models. Completeness ensures critical information is not missing. Consistency supports reliable interpretation across systems. Relevance ensures the data aligns with the specific mission objective.

Applying these standards together helps agencies build AI systems on data that is defensible, trustworthy, and fit for purpose—especially in high-stakes or regulated environments.

Make data machine-readable from the start

AI systems depend on standardized formats, consistent definitions, and well-defined metadata. Unstructured or inconsistently labeled datasets—such as scanned documents or free text fields—often require additional human intervention before they can be used effectively by AI systems.

Preparing data to be machine-readable early in the process reduces downstream risk, improves model accuracy, and accelerates time to insight. Clear structure and metadata also make it easier to reuse data across programs and applications as AI adoption expands.

Design data for explainability and trust

Agencies must understand the datasets an AI system uses as well as how those data are processed, and outputs are generated. Transparency supports auditability, compliance with oversight requirements, and sustained trust in AI-enabled decisions.

Designing data with explainability in mind also creates a foundation for iterative improvement as agencies move from routine AI applications to more transformational use cases. It ensures that AI’s insights can be validated, decisions defended, and models refined over time.

Setting the stage for AI discovery

ICF teams have used this framework to develop modular frameworks that assess and optimize AI readiness across diverse datasets, particularly within federal health agencies. This work is grounded in real-world challenges and validated through national initiatives.

Advancing Data Quality for Diabetes Research

As part of the National Institute of Diabetes and Digestive Kidney Diseases (NIDDK) Data Centric Challenge, we won the 2023 competition by transforming 48 raw datasets into a unified, AI-ready format. The solution normalized key data elements and addressed missing values. This work enabled multiple use cases, including predictive modeling and analysis of diabetes risk factors and outcomes.

Improving Readiness of Cancer Data

In the National Cancer Institute’s (NCI) 2024 Artificial Intelligence Data Readiness Challenge, our team developed workflows that enhanced the AI readiness of biomedical datasets for downstream machine learning applications. These efforts advanced the NCI’s data harmonization and readiness practices across research datasets, helping to strengthen interoperability and reproducibility for future AI-driven discovery.

Accelerating Discovery in Pediatric Oncology

As part of the NCI’s 2025 Data Jamboree, we developed a scalable framework designed to evaluate and refine the AI-readiness of pediatric cancer cohorts from several cancer centers. This framework, which includes interactive dashboards and decision support tools, empowers researchers to make informed tradeoffs between sample size and data quality to better meet their study’s goals.

Building a durable foundation for mission ready AI

As agencies move from isolated AI pilots to broader operational use, data readiness becomes the determining factor in whether AI delivers real mission value—or introduces new risk. Investments in quality, structure, and transparency shape not only model performance, but also trust, oversight, and long-term sustainability.

In practice, progress often starts small: identifying a limited set of high-impact use cases, assessing whether existing data can support them responsibly, and strengthening data foundations where gaps exist. Agencies that take this deliberate, mission-aligned approach are better positioned to scale AI in ways that are defensible, explainable, and durable over time.