By Anand Trivedi, AI Lead at REI Systems

Federal agencies are not short on AI pilots. They are short on AI capabilities that can survive contact with real mission environments.

The first phase of generative AI in government was about experimentation: chatbots, proofs of concept, and narrow use cases. The next phase is about outcomes. That transition is proving harder than expected.

ICF recently found that agencies take an average of 6.7 months to move from concept to prototype, and another 10 to 17 months to reach production. The bottleneck isn’t ambition,  it’s execution: procurement delays, fragmented data, and workforce readiness. Technology providers to the government note that trusted operational use requires deep grounding in the enterprise’s internal context and business rules, authorization-aware architecture, and runtime oversight. These are real production concerns that a working prototype doesn’t address on its own.

For many agencies, though, the root cause lies further upstream of deployment governance. It lies at the data layer.

Models Are Not the Only Bottleneck

As models become more powerful in tool-use and long-running tasks, they’re also becoming less of a differentiator. In a recent MeriTalk interview, Dell Technologies CTO and Chief AI Officer John Roese argued that agencies are moving from pilots and chatbots toward production AI systems that can improve mission delivery, speed, scale, and efficiency. He also warned that organizations without clear governance risk getting stuck in “POC purgatory.”

Governance decides which use cases deserve to move forward. What Roese also flagged is a second issue: data accessibility, whether an AI system can reach the specialized institutional knowledge it needs to actually be useful. Governance and data orchestration solve different problems. One decides what should happen. The other determines whether it can.

Why Pilots Stall

A proof of concept can get away with a narrow dataset and a controlled environment. Production can’t. It has to work across legacy systems, disconnected programs and financial databases, case management platforms, grants systems, and document repositories while respecting access rules, preserving context, and reconciling inconsistent schemas along the way.

Most pilots don’t stall because the model can’t generate an answer. They stall because the agency can’t reliably connect that model to the right data, from the right systems, under the right controls.

Agentic AI Raises the Stakes

Traditional AI tools mostly summarize, search, or generate content. Agentic systems go further, including reasoning across steps, triggering workflows, and operating closer to actual mission work. That deepens the dependency on trusted data access. An agent can’t support specialized government work if it can’t reach the institutional knowledge that defines that work.

For a grants program, that might mean applicant history, risk indicators, and compliance status. For case management, it might mean case records, policy criteria, and related entities. That information rarely lives in one place, and a chatbot wired to a single repository or a model pointed at a static extract will only ever produce a partial answer.

A Grants Management Example

Consider a program leader trying to figure out which awards are at risk, which recipients are falling behind, or where funding is producing measurable impact. Those answers rarely come from a single system: funding data sits in one platform, recipient history in another, performance reports in documents, risk indicators scattered across financial and compliance systems. Staff often reconcile the picture by hand.

In that environment, a pilot can look impressive in a demo while still being operationally incomplete: summarizing one dataset well while missing the cross-system context a real decision requires.

Where GovOrch™ AI Fits

This is the gap GovOrch™ AI is built to address. REI Systems designed it as an agentic data orchestrator. It is a way to make fragmented agency data accessible, governed, auditable, and usable for agent-native AI workflows through enterprise-grade interoperability. It connects to data where it already lives, autonomously understands its schema, turns natural language questions into auditable data transformation pipelines, and reconciles information across systems without requiring agencies to centralize everything.

The reasoning behind it tracks the broader argument here: agencies don’t need more AI that only works in clean demo environments. They need capabilities built around the realities of often stove-piped mission data from the outset, which means governed access to institutional knowledge, traceable workflows, and outputs leaders can validate before acting on them.

Escaping POC Purgatory

The agencies that get past POC purgatory won’t be the ones with the flashiest models or the longest pilot list. They’ll be the ones that identify the mission problems that matter, build governance around what moves forward, and invest in the data foundation that makes AI executable in real environments.

Production-ready AI doesn’t start with the model. It starts with whether the data underneath it is actually AI-ready.

Ready to see what governed data orchestration looks like for your mission? Contact REI Systems to schedule a GovOrch AI demo and find out what’s possible when your data — not just your models — is ready for production-grade AI applications.