Start with the end in mind: A guide for federal CAIOs implementing the AI action plan
As CIAOs look to speed up their procurement and implementation of AI systems, particularly agentic tools, they have a responsibility to get it right.
Reuben Richards
October 17, 2025 2:49 pm
4 min read
The Trump administration’s recently released AI Action Plan aims to put the U.S. at the front of the global AI race, including by accelerating the adoption of AI across the federal government while taking the necessary precautions to protect citizen data and bolster public trust. As chief AI officers (CAIOs) across agencies look to speed up their procurement and implementation of AI systems, particularly agentic tools, they have a responsibility to get it right.
Integrating AI into government comes with inherent risks. Agencies handle highly sensitive, confidential data, ranging from social security numbers to critical national security secrets. So the path forward needs to take into account much more than just efficiency — CAIOs must start with the end in mind. This means thinking not just about what a system can do, but how it will be used, by whom and under what conditions.
A few core principles are important for CAIOs navigating this challenge:
1. Design for human oversight
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Especially in the case of agentic AI systems, CAIOs must define up front where human oversight is required and how it will be implemented. Take an example from the private sector: credit card fraud detection. Thousands of transactions are autonomously scored in real-time by predictive models. Yet when anomalies surface, a human is brought back into the loop — often through an automated text or email alert directly to the client — to make the final call about whether a transaction is legitimate or not. This is a design choice that maintains both efficiency and accountability.
The same mindset must govern federal AI deployments. It’s important to identify where a person would need to intervene and what would trigger that intervention.
Designing for human oversight also means having a single pane of glass to monitor how models are performing, on what data they were trained, bias evaluations and more.
2. Governance is your acceleration engine
Strong data governance is not a constraint — it’s an accelerator. With rigorous governance in place, agencies can reduce time spent wrangling data. A good measure is the 80/20 rule: 80% of analytics work is data preparation, and 20% is the actual value coming from the analytics. Effective governance can shorten the time it takes to get data preparation done, improving the output of the analytics.
A clear example of this in action is in law enforcement. When officers pull someone over on the road and run a license plate in their system, they want all the information that could be useful to them, not just the name of the person to whom the car is registered. That means they need a single, holistic interface that brings together information from a variety of different sources, making it easy to quickly get all the data they might need. This level of integration is only possible with strong governance and interoperability.
For CAIOs, establishing this kind of robust governance must be the first step. The AI Action Plan calls for exactly this type of organization. Governance is not just a compliance exercise; it’s the foundation of trustworthy AI.
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3. Use agentic AI to enhance decision-making
Agentic AI can save agencies an incredible amount of time on administrative tasks, helping direct human attention and resources to more complex matters.
For example, AI-augmented optical character recognition (OCR) can be integrated into health care claims processing. Rather than having highly trained and highly paid medical professionals pouring through thousands of pages of documents representing medical histories of specific claims, OCR models can analyze hundreds of pages of unstructured documents — some handwritten or with duplicate entries — and extract key clinical information and summarize findings. Importantly, those outputs should be delivered with clear traceability. The findings should be presented to the medical professional with bookmarks back to the source where it came from, who can then analyze and override if needed.
This approach to agentic AI cultivates trust not just with the federal employees using the tool, but with the public. We need to build systems with traceability that work with, and for, humans.
4. Build for scale
Scaling AI is not the same as scaling traditional applications. There must be a framework developed in the early stages to ensure consistent model behavior from the pilot stage to enterprise data application. Agencies must develop robust data validation to prevent data pipeline brittleness. And security and governance cannot be retrofitted — they must be built into the foundation of AI applications as they are scaled, while remaining flexible to accommodate new compliance requirements over time.
That’s why CAIOs must think holistically and build for scale from the start. Governance, data validation, compliance and human oversight must be built in from the beginning.
People must remain at the center of AI integration. From the initial governance design to the oversight of AI tool outputs, the role of the human is critical to success. As CAIOs implement AI across their organizations, they must remember this. So, they must start with the end in mind—and never lose sight of the people they serve.
Reuben Richards is the technical director for the SAS U.S. government business vertical.
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