{"id":98077,"date":"2026-07-07T19:12:12","date_gmt":"2026-07-07T19:12:12","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/98077\/"},"modified":"2026-07-07T19:12:12","modified_gmt":"2026-07-07T19:12:12","slug":"government-ai-cant-scale-and-its-not-the-models","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/98077\/","title":{"rendered":"Government AI can\u2019t scale \u2014 and it\u2019s not the models"},"content":{"rendered":"

This is the 15th article in our IT lifecycle management series,\u00a0Delivering the tech that delivers for government<\/a>.<\/p>\n

Federal agencies aren\u2019t struggling to experiment with artificial intelligence. They\u2019re struggling to scale it.<\/p>\n

That disconnect is becoming the defining challenge for federal systems integrators, points out Scott Stapp, chief technology officer and chief revenue officer at DEFCON AI<\/a>. And Stapp has inside knowledge. A retired Air Force brigadier general, he\u2019s spent the better part of three decades helping the government solve data management and decision-making challenges both inside the government but also leading industry technology teams.<\/p>\n

In a conversation for our series Delivering the tech that delivers for government<\/a>, he made a direct point about what\u2019s really holding government back.<\/p>\n

Agencies do not have the \u201cdata fabric and ontology that allows things to cross. If you\u2019re going to develop an AI tool for anything \u2014 whether it\u2019s the Department of Homeland Security, Energy or War \u2014 you need to share that information,\u201d Stapp said. \u201cThe whole idea of using an AI tool is the more data you have, the better. The more people who can participate in it, the better. But right now, that ontology is not connected.\u201d<\/p>\n

That reality is keeping agencies stuck in pilot mode, Stapp said.<\/p>\n

The integration gap is the real bottleneck<\/p>\n

In the commercial world, scale comes from connectivity, he said. Data flows. Systems talk to one another. Models build on shared inputs.<\/p>\n

Government doesn\u2019t work that way. \u201cIt is not well connected,\u201d Stapp said. \u201cIt doesn\u2019t talk. The [military] services don\u2019t necessarily connect and talk to each other.\u201d<\/p>\n

The result? AI solutions deliver value in isolated pockets, but they can\u2019t easily expand. \u201cWhat you find out is that fabric doesn\u2019t exist to actually do that,\u201d he said.<\/p>\n

For FSIs, this is the mission. The answer does not lie in creating more or better algorithms. It\u2019s also about building the underlying architecture that lets the algorithm operate across systems, services and environments, Stapp said.<\/p>\n

Cloud strategy is shaping everything<\/p>\n

Agencies are moving deliberately when it comes to cloud and AI, not because they\u2019re slow but because they\u2019re cautious about long-term constraints.<\/p>\n

The government \u201cis really trying to be careful to not get vendor locked into [any specific] cloud environment,\u201d Stapp said.<\/p>\n

That concern is reshaping how AI systems are designed and procured. Portability matters. Interoperability matters more.<\/p>\n

\u201cOnce you start to look at those ontologies, where data and information can be passed seamlessly across multiple cloud environments, you\u2019re going to see the use of AI tools grow drastically,\u201d he said.<\/p>\n

For integrators, that raises the bar, Stapp said. Their solutions will all need to be able to move seamlessly too.<\/p>\n

The return of the prime integrator<\/p>\n

One of the clearest signals is the re-emergence of integration as a central function, he said, and that weaves together software, data and AI ecosystems.<\/p>\n

Historically, the federal government leaned on prime contractors to integrate complex physical systems. That model is now reappearing in the AI era.<\/p>\n

Right now, there are limited prime contractors able to do this work, Stapp said, but increasingly agencies will be saying, \u201cHey, we do need people and companies who can come in and tell us how we start fitting these things together.\u201d<\/p>\n

He pointed to his own company\u2019s recent deal with the Marine Corps. The corps in June awarded DEFCON AI a five-year, $115 million prototype agreement focused on logistics modernization and AI integration. It involves deploying tools plus ensuring they work together within a common data framework.<\/p>\n

\u201cThey want to bring all these new AI tools in, and they have us looking at all those tools to ensure that they fit within a data framework and open architected capability,\u201d Stapp said. The corps aims to be an AI-first force<\/a>.<\/p>\n

For FSIs, that signals that the market is shifting toward orchestration by aligning vendors, tools, data and environments into something that works, Stapp said.<\/p>\n

Edge constraints force smarter design<\/p>\n

Scaling AI also creates a physics problem. At the edge, compute and power can be limited. Latency can disrupt mission effectiveness. That changes how systems are built.<\/p>\n

\u201cYou want minimal amounts of data needed in that edge for the specific decisions,\u201d Stapp said.<\/p>\n

That\u2019s a departure from cloud-native thinking, where more data and more compute typically lead to better outcomes. But that\u2019s neither efficient nor effective in real-time work and decision-making that might take place far from an agency\u2019s headquarters.<\/p>\n

For integrators, it means designing systems that are:<\/p>\n

Distributed
\nEfficient
\nPurpose-built for specific missions<\/p>\n

The days of simply extending centralized architectures are over, he said.<\/p>\n

Government data is fundamentally different<\/p>\n

Another reality is that AI in government is more complex than in commercial environments, and not just because of scale but because of other conditions too.<\/p>\n

\u201cYou have an adversary whose goal in life is to show you information that is not accurate and is deceptive,\u201d Stapp said.<\/p>\n

That introduces challenges many commercial systems don\u2019t face:<\/p>\n

Deceptive or manipulated data
\nIncomplete datasets
\nClassified data silos<\/p>\n

In the defense realm, Stapp noted, \u201cthe only people who have all the data are the people at the highest level, and those aren\u2019t the people who are on the battlefield, right? The people on the battlefield are typically at a collateral level.\u201d<\/p>\n

That leads to data becoming increasingly less useful for driving decision-making at the edge. The dynamic forces a different approach to AI design, Stapp said. Models must operate with less information but still produce reliable outcomes.<\/p>\n

Modularity isn\u2019t optional<\/p>\n

If integration is hard, then modularity becomes essential. Each agency or service, system and mission environment operates differently.<\/p>\n

Even when the problem looks similar, the implementation rarely is, Stapp said.<\/p>\n

\u201cYou must make it modular because really it doesn\u2019t work with anything else,\u201d he said. That means the same application often needs to be adapted across environments.<\/p>\n

\u201cYou have to do tweaks and changes to make the same application work with a different service,\u201d he said.<\/p>\n

Therefore, modular design isn\u2019t just good architecture, it\u2019s the only viable approach, Stapp said.<\/p>\n

Start small to scale at all<\/p>\n

Despite the push for enterprise AI, large-scale transformation isn\u2019t the starting point in government, he said. \u201cYou start with smaller problem sets. And then try to grow it out.\u201d<\/p>\n

An incremental approach reflects the realities of data access, classification and infrastructure maturity. It also aligns with where AI is gaining traction now, Stapp said.<\/p>\n

The path to AI at scale in government runs through integration by aligning data, architectures, clouds and mission needs in a fragmented environment, he reiterated.<\/p>\n

AI won\u2019t scale until integration does, Stapp said. The onus is on the government\u2019s industry partners to help agencies address the challenge.<\/p>\n

\u201cThe number one thing with any customer is you\u2019re there to solve their problem.\u201d<\/p>\n

What it takes to scale AI in government
\nDEFCON AI\u2019s Scott Stapp believes helping the government scale artificial intelligence is about better integration, not better models. He suggests focusing on a few critical capabilities:
\nBuild the data fabric first: AI doesn\u2019t scale without shared data. Interoperability and ontology alignment are foundational.
\nDesign for multicloud from day one: Agencies want flexibility. Vendor lock-in is a nonstarter.
\nLead as the integrator: The opportunity is in connecting tools, vendors and data, not delivering point solutions.
\nGo modular by default: Every environment is different. Solutions must be adaptable across services and systems.
\nOptimize for the edge: Limited compute and power demand smaller, mission-specific AI models.
\nStart small, then expand: Narrow use cases are the fastest path to adoption and scale.<\/p>\n

Discover more smart tips and tactic for FSIs shared by leading technologists for our\u00a0Delivering the Tech That Delivers for Government series<\/a>.<\/p>\n

Copyright
\n \u00a9\u00a02026 Federal News Network. All rights reserved. This website is not intended for users located within the European Economic Area.\n <\/p>\n","protected":false},"excerpt":{"rendered":"This is the 15th article in our IT lifecycle management series,\u00a0Delivering the tech that delivers for government. Federal…\n","protected":false},"author":2,"featured_media":98078,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,25,51182,30492,2676,30493,51183],"class_list":["post-98077","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai","tag-ai","tag-artificial-intelligence","tag-defcon-ai","tag-delivering-the-tech-that-delivers-for-government","tag-future-tech","tag-future-tech-enterprise","tag-scott-stapp"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/98077","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=98077"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/98077\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/98078"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=98077"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=98077"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=98077"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}