From pilots to performance: Rethinking agencies’ approach to embedding AI

Federal agencies have embarked on thousands of AI proof-of-concept projects. “But the reality is, less than 1% of those are ever going to stick — not because of the technology, but because we are placing things in the wrong place,” warned a leading technology provider at an AI government technology conference in Washington, DC.

“It’s not about the model,” said Jamison Braun, a former defense official and now SVP and managing director for SAP’s U.S. Public Services business. “It’s what you get out of the model. Fundamentally, we actually fail at the operational layer,” he said, speaking to several hundred government and industry IT leaders at AITalks.

Braun, who spent three decades in the Defense Department, explained that the public sector is currently trapped in an “insight gap” in which technology can identify problems — such as broken supply chains — but lacks the operational integration to fix them.

To bridge this divide, he urged the audience of federal leaders to adopt a philosophy he calls “Mission Forward,” which shifts the focus from AI that merely generates answers to systems that deliver mission outcomes. The technology to do that is increasingly available through new agreements with the General Services Administration, which offer federal agencies access to its services at significantly discounted rates.

The operational layer: Where AI succeeds or fails

Braun’s core thesis is that the application of AI is a “process problem” that resides at the operational layer. He urged the audience to stop obsessing over specific large language models and instead focus on building intelligent execution platforms.

According to Braun, these platforms require three critical pillars to be effective:

• A contextual backbone: AI must be internal to core systems and the flow of work rather than sitting above systems trying to grab instrumental data.
• Trusted data: This involves moving beyond massive data lakes toward data that fundamentally supports how work is performed.
• Authority to execute: The most vital component is ensuring the system has the mandated authority to trigger autonomous actions.

Real-World Execution: From Potholes to Procurement

To illustrate the power of embedded execution, Braun shared several anecdotes where AI transitioned from a novelty to a mission-critical tool. He highlighted a federal entity that used an operational AI model to solve citizen inquiries, achieving an 80% auto-repatriation rate and slashing backlogs from over 760,000 to nearly zero in a single year.

In another instance, he cited how the City of Tacoma, Washington, used sensors to trigger autonomous actions in public works, freeing up 50% of its workforce.

Redefining AI governance

Braun also addressed a common pain point in government: how governance can often become a “handbrake” on progress. He challenged this view, suggesting that in a “Mission Forward” world, governance is a “handshake” that moves organizations forward.

“It becomes the transmission in your vehicle that translates the raw power that exists in the back-end systems directly into controlled movement,” he said.

He pointed to commercial giants like Blue Diamond, which saved over a million dollars in logistics and distribution costs by capitalizing on SAP AI-enabled systems through a governance-backed model.

As agencies look toward 2026, Braun emphasized that AI must be integrated into standard workflows to handle crises in real-time. He cited a recent example of Maersk, the world’s second-largest container shipping company, rerouting a significant number of ships in real time during the Iranian conflict.

“Think of what that does for an organization like the Defense Logistics Agency,” he posed. In another example, he noted that Anheuser-Busch can now autonomously pivot from beer production to bottled water for FEMA during national emergencies.

Braun concluded that AI should not be a separate login or become a confusing choice of models. It should be the invisible engine that reduces friction and creates “white space” for employees to focus on higher-level tasks.

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