by Paul D. Rempfer
Federal agencies are adopting AI at unprecedented speed. When it comes to artificial intelligence (AI), leaders are under pressure to modernize, demonstrate progress, and keep up with the commercial sector. I have spent more than 25 years working at the intersection of cyber, intelligence, and national security, and I have seen what happens when organizations move fast without looking far enough ahead. A myopic mindset always leads to higher long-term costs and higher operational risk. Agencies need to take a longer-term AI governance perspective.
AI is not a typical IT purchase. It changes how data flows, how systems interact, and how missions operate. When agencies buy AI tools to solve short-term problems without planning for governance, interoperability, or future scale, the result is fragile systems that will be outdated or unsafe within a few years.
The question is no longer whether agencies should adopt AI. The real question is, how can they do it in a way that will continue to hold up five years from now?
What a Myopic Mindset Looks Like in Government
A myopic mindset is a narrow focus on immediate gains instead of long-term readiness. It shows up when agencies treat AI the same way they treat a typical SaaS license or point solution: a few million dollars here, a pilot there, purchased on a three-year contract, and justified as a way to save time or reduce headcount. Only later do leaders realize the cost of integration, oversight, and security.
Agencies are buying AI without a complete understanding of how models will behave, what data they will touch, or how to govern them across departments. Since federal budgets are largely annual, with people rotating off programs every two or three years, there’s a structural incentive to buy the tool now and figure out the harder challenges of AI governance, standards, and sustainment later. That is exactly how you end up with stranded investments and escalating technical debt.
A Real Example: The Air Force GPT Breach That Few People Heard About
The consequences of a myopic mindset recently showed up inside the Department of Defense (DoD). In 2024, the Air Force Research Laboratory launched NIPR GPT on the DoD’s unclassified network. It gave Airmen, Guardians, civilian staff, and contractors a secure place to use generative AI for drafting, summarizing, and coding. According to GovCIO Media, demand surged far beyond developer expectations.
Then the problems began.
The U.S. Army quietly blocked the tool from all its networks. Officials cited “cybersecurity and data governance concerns.” Behind the scenes, data from multiple DoD components had passed outside the intended network boundary. Users in other services did not realize the tool was reaching beyond NIPRNet.
The incident was not caused by malicious behavior. It was caused by a lack of foundational readiness, AI governance, oversight, model understanding, and lifecycle planning. While the situation wasn’t widely publicized due to its seriousness, it’s a preview of what will continue to happen if agencies keep deploying AI tools without governing them as enterprise tools.
The Hidden Costs Agencies Will Face Later
Short-term AI buys create long-term challenges that only become visible after deployment.
- Foundational readiness. When agencies implement AI too quickly without first understanding their core processes, they risk automating inefficient workflows and creating new operational bottlenecks. Critical human judgment calls and legacy dependencies get overlooked, causing tools to drift away from mission needs. Without a plan for roles and skills, accountability erodes, compliance risk grows, and AI investments deliver less value than promised.
- Retooling costs. AI does not simply run beside existing systems. It reaches into databases and applications. Legacy systems must be re-engineered so AI can access them securely. This becomes a recurring cost every time models evolve.
- Fragmented ecosystems. Agencies such as Social Security, Treasury, HHS, and others rely on overlapping data. When each office buys its own AI tools, with different rule sets and privacy expectations, the result is inconsistent behavior and new points of exposure.
- Rapidly changing AI architectures. Commercial AI models are advancing at a pace that today’s government procurement cycle cannot match. In two or three years, many current tools will become less and less differentiated. The real value will come from private, behind-the-firewall models that use consistent rules and mission-specific data. Short-term, off-the-shelf solutions will not make that transition easily.
Where Government AI Governance Is Being Done Right
Across the federal government, experimentation is already underway. The Cybersecurity and Infrastructure Security Agency (CISA) created CISAChat to speed up analysis for cyber operators. The U.S. Army deployed CamoGPT to tens of thousands of users to test how soldiers apply generative AI in real workflows. U.S. Central Command (CENTCOM) developed CENTGPT, built from Air Force code, to support crisis planning and operational tempo. And, the National Institutes of Health (NIH) is using secure versions of Microsoft Copilot and ChatGPT Enterprise to accelerate research and administrative tasks.
Where a Qualified AI Consultancy Can Help Agencies Avoid the Myopic Trap
Agencies need AI they can defend, scale, and audit five years from now. This is where a strategic partner is able to support on both the strategy and engineering side.
- Business process analysis and workforce transition. An effective AI approach starts with mapping how work actually gets done with workflows, dependencies, data inputs, decision points, and bottlenecks. This way agencies can redesign future-state processes where AI can responsibly augment, streamline, or automate tasks in a manner that improves efficiency, accuracy, and throughput and aligns with federal standards for governance, privacy, security, and ethical use. In parallel, leaders need a workforce plan: identifying skill gaps, redefining roles, and training people to operate and oversee AI systems. Done together, this modernizes mission execution, improves decision quality, and ensures organizational resilience as AI evolves.
- Governance, strategy, and AI maturity. Working with CIOs, CISOs, counsel, and mission leaders, an AI partner can determine whether systems, policies, and data practices are ready for implementation. Key considerations include: Who owns the risk? What rules should govern how models see, use, and share data? The team defines ownership of risk, establishes rule sets for how models use data, and maps existing policies to federal expectations. They also build enterprise governance frameworks that help agencies identify gaps in their stewardship of shared data.
- Infrastructure and secure AI environments. Once the strategy is clear, the technical foundation can be developed. This includes designing AI-secure cloud environments, implementing zero-trust architectures, and creating the pipelines required to support private, behind-the-firewall AI models. AI partners manage model lifecycle processes, MLOps automation, and data engineering so that AI can connect to mission systems safely.
- Verification, auditing, and oversight. Independent validation of AI systems is valuable for monitoring model behavior, testing compliance with policy and law, and evaluating bias and data provenance. Partners also assess how different models interact within an agency environment, ensuring that AI systems behave the way leaders expect.
These are the controls that are often missing from short-term AI purchases, and they are the safeguards that prevent costly failure down the road.
Planning for Five Years Instead of Five Months
The next five years are the most important planning window of the AI era. Easy, short-sighted AI buys may create the appearance of modernization, but their shortcomings quickly become clear.
At the end of the day, the real risk is not buying too slowly. It is buying in a way that produces fragile systems, fragmented governance, and preventable exposure.
Agencies do not need to slow down. They need to plan smarter so that the tools they buy today will still serve their mission when the AI landscape changes.
PCI Federal helps agencies build that foundation. The cost of doing it right is lower than the cost of rebuilding later, and the signs are already showing that the government cannot afford another cycle of short-term thinking.
