AI Procurement in Government 

Artificial intelligence is a growing priority across the federal government. Executive orders, agency strategies, and budget allocations all signal an AI-driven future. Yet despite this momentum, many agencies face a common challenge: AI procurement isn’t keeping up.

Ambitious pilots stall. Contracted solutions underdeliver. Agencies often receive tools labeled as “AI” that don’t serve the mission—or can’t be deployed at all. The problem isn’t a lack of interest or talent. The problem is that traditional procurement methods don’t align with how AI works. AI isn’t a product you buy once. It’s a system that evolves, depends on quality data, and requires oversight. Unless agencies adapt, outcomes will remain limited.

This article breaks down why AI procurement often fails, and how to do it differently.

Buying AI Like Traditional Software Doesn’t Work

AI is not a static product. It’s not a document management system or a CRM. AI systems are shaped by data, use cases, and feedback loops. But many RFPs treat AI like off-the-shelf software, expecting a plug-and-play solution.

This leads to flawed expectations:

• Fixed deliverables that don’t allow for model refinement

• No planning for model monitoring or improvement

• No requirement for human oversight or governance

Vague Requirements Lead to Vague Results

RFPs frequently include statements like:

• “Provide an AI solution to improve efficiency”

• “Use AI to analyze customer feedback”

• “Leverage machine learning to optimize outcomes”

These are goals, not requirements. Without clear expectations or context, vendors are left to define what “AI” means.

A better approach:

• Define the business process to improve (e.g., classifying public comments)

• Provide sample data or describe data conditions

• Ask vendors how they will address bias, transparency, and integration

AI must be trained, tuned, and tested within its environment. That starts with clear, specific requirements—not buzzwords.

Agencies Often Skip the Data Readiness Check

You can’t build AI on broken data. Yet procurement often moves faster than data preparation. Agencies issue RFPs for predictive analytics or language models before knowing whether their data is clean, current, or even accessible.

This leads to:

• Post-award delays as vendors struggle to work with poor data

• Misplaced blame on the technology

• Quiet project failures due to unprepared foundations

Many agencies now include data readiness assessments before contract award or early in phased contracts. This aligns with the Federal Data Strategy, which emphasizes governance and usability as prerequisites for modernization.

📚 Explore the Federal Data Strategy 

Vendors Overpromise. Agencies Underspecify.

When requirements are vague, vendors oversell. This is especially risky with AI, where phrases like “machine learning” and “natural language processing” can hide serious limitations.

Common issues in awarded solutions:

• Use of generic models with no tuning for agency context

• Lack of explainability or traceability

• Noncompliance with federal standards (e.g., FISMA, Section 508, NIST AI RMF)

To avoid this, agencies should require:

• Model documentation and data lineage

• Performance monitoring and drift mitigation plans

• Demonstrated compliance with the NIST AI Risk Management Framework

📚 NIST AI Risk Management Framework 

When these aren’t explicitly required, vendors won’t build them in—and the agency is left with a black-box tool that can’t be trusted or scaled. 

There’s No Plan for Post-Award Success

Even strong RFPs fall short without a post-award strategy. AI doesn’t end at delivery—it needs monitoring, human input, and continuous evaluation.

Common post-award gaps:

• No workflow for human-in-the-loop review

• No success metrics or performance thresholds

• No plan for retraining or version control

Agencies can address this by:

• Building performance reviews into the contract (e.g., quarterly)

• Requiring documentation of model updates

• Including users in post-deployment feedback loops

This strengthens outcomes and supports long-term trust.

Smarter AI Procurement Means Better Public Sector AI 

AI is not mysterious. But buying it well requires a shift in mindset. Agencies must treat AI as a living capability, not a finished product, and design procurement strategies accordingly.

At Mathtech, we help federal clients:

• Design AI RFPs aligned with real operational needs

• Ensure data and procurement strategies support mission success

• Vet solutions for compliance, transparency, and scalability

If your agency is planning to procure AI, or has already tried and seen limited success, we can help make the next step smarter.

📰 We’re Featured on OrangeSlices 

Mathtech is proud to be featured on OrangeSlice.ai, where we’re contributing to the broader conversation on AI readiness and government modernization. 

In our featured posts, we explore how agencies can move beyond AI buzzwords and take practical steps toward transformation; starting with clean, connected data and grounded strategy: 

📎 Data Modernization Is Your Onramp to AI
📎 AI in the Federal Government: Readiness Before Rollout 

 

Let’s Talk About What’s Next  

Whether you’re ready to modernize legacy systems, improve data readiness, or explore AI-driven solutions, Mathtech Federal is here to help. 

🔗 Engage with Us – Connect with our team to discuss your agency’s modernization goals. 

🤝 Explore Partnership Opportunities – We collaborate with government contractors, AI innovators, and cloud providers to deliver integrated solutions.