The government’s increasing enthusiasm for generative AI has led to a proliferation of pilot projects and proof-of-concepts, often accompanied by impressive metrics such as thousands of users and millions of logins. However, these figures frequently fail to reflect the actual utility of the tools in question, raising questions about their security, compliance, scalability, and genuine mission impact.
In essence, the focus has shifted to what can be termed “vanity metrics,” where participation numbers are celebrated over tangible outcomes. This focus mirrors the digital equivalent of boasting app download numbers without assessing whether users engage effectively with the application. Such superficial engagement metrics can obscure significant underlying issues, including security vulnerabilities, operational inefficiencies, and inadequate data training.
When government agencies track logins or active users, they measure engagement rather than actual outcomes. A system with thousands of users could still lack the necessary speed, accuracy, or automation to provide a mission advantage. Conversely, a small group employing the right AI model effectively may yield substantial savings and accelerate decision-making processes.
This misalignment of expectations is also illustrated by oversight reports, such as the Government Accountability Office’s (GAO) 2025 assessment of the Department of Defense’s major technology initiatives. The GAO found that despite appearances of transparency, many programs failed to provide reliable data on cost and schedule, resulting in a distorted view of progress and success.
To measure real impact, it is essential to focus on four key areas: workflow improvement, cost efficiency, security and compliance, and scalability. First, agencies should evaluate whether generative AI reduces decision-making time and automates manual tasks that advance the mission. Productivity gains often stem from cutting hours off repetitive workflows, allowing personnel to concentrate on strategic analysis.
Second, agencies must consider cost efficiency. If generative AI automates labor-intensive processes but necessitates ongoing contractor oversight, it merely shifts expenditure rather than reducing it. True efficiency manifests in leaner operations, fewer redundant tools, and measurable return on investment against program budgets.
Security and compliance are also crucial. AI tools must operate at the appropriate Impact Level (IL) and adhere to guidelines such as FedRAMP and Federal Acquisition Regulations Part 12. Many pilots overlook these critical requirements, leading to rework or abandonment when systems fail to pass accreditation. Security and compliance form the bedrock of mission-ready AI.
Lastly, the ability to scale and reuse AI solutions across different components or agencies without extensive redevelopment is vital. Achieving this allows agencies to standardize processes, lower costs, and foster a sustainable AI ecosystem. These metrics, while less headline-grabbing, provide a clearer picture of whether AI tools enhance decision-making, compliance, and taxpayer savings, rather than simply generating impressive usage numbers.
To bridge the gap between experimentation and substantial progress, government agencies must prioritize proven, secure AI solutions from the outset. This entails selecting tools that meet enterprise standards for security, interoperability, and governance, as opposed to funding pilots that lack scalability. While small prototypes may demonstrate some capability, they do not deliver sustained mission impact, which remains the ultimate metric of success.
True modernization requires a concerted effort to shift from mere experimentation to meaningful execution. Agencies should concentrate on shared architectures and reusable models that can integrate across various missions. The focus should not be on increasing the number of pilots but rather on achieving measurable, sustainable performance that enhances readiness, accountability, and returns on public investment.
Failure to prioritize outcomes over activity may lead to continued financial burdens on taxpayers for tools that appear effective but do not deliver. The private sector has long understood that dashboards and engagement charts do not translate to financial success; results do. If the government seeks to close the capability gap, it must apply the same level of scrutiny to AI performance as it does to cybersecurity and acquisition. This involves establishing clear metrics for impact, alignment with mission objectives, and security compliance, marking a transition from “how many” to “how much better.”
Nicolas Chaillan is founder and CEO of Ask Sage.
See also
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