Most AI efforts in government don’t fail. They simply never make it to race day. AI pilots across government are generating real promise. The challenge is no longer proving AI can work – it is operationalizing it at scale.
As we close this phase of the series, one pattern continues to emerge: agencies increasingly have access to capable AI tools, but many are still evolving the operational structures needed to integrate those capabilities into mission delivery.
In racing terms, many organizations now have the car. Far fewer are truly prepared to race it.
Across federal environments, the pattern is familiar:
- Proofs of concept that demonstrate clear value
- Pilots that improve specific workflows
- Demonstrations that generate strong stakeholder interest
And then.. progress slows. These efforts do not fail. They simply do not transition into scaled, sustained capability. It is the equivalent of strong practice laps.. without every lining up for the race.
Experimentation is Necessary. But It Is Not the Goal
It is important to be clear: Pilots and experimentation are not the problem.
They allow agencies to:
- Understand AI capabilities in their mission context
- Build confidence across stakeholders
- Explore responsible use within regulatory constraints
In high-performance environments, this is practice. It is where teams learn, test limits, and refine approach.
But practice is not performance.
What this means: Agencies should define how successful pilots transition into ownership, funding, governance, and operational adoption before experimentation begins.
Where Progress Begins to Break Down
The issue is not that pilots are unsuccessful. It is that successful pilots often remain isolated.
They demonstrate value in controlled conditions, but do not translate into:
- End-to-end workflows
- Operational decision-making
- Cross-program consistency
- Sustained outcomes tied to mission performance
This creates the appearance of progress without corresponding transformation. The car proves it can perform. But it never enters the race.
What this means: Success criteria should extend beyond technical performance to include adoption, workflow integration, and measurable mission outcomes.
The Transition Is the Hardest Part
The most critical, and most challenging moment, is the transition from:
Experiment à Operational Capability
This is where many agencies stall. Not because of the model Not because of the use case. But because the transition itself is not primarily technical. It is operational.
Moving beyond pilots requires agencies to intentionally operationalize AI capabilities rather than treating them as isolated innovation efforts. That includes establishing:
- Clear ownership of AI-enabled capabilities
- Integration into real workflows, not parallel processes
- Governance and guardrails that enable trust and compliance
- Measurement frameworks tied to mission outcomes
- Feedback loops that inform decisions and continuous improvement
What this means: Agencies should treat operational integration as part of AI delivery – not as a follow-on activity after implementation.
In racing, performance is not just about the car. It is the driver, the pit crew, the strategy, and the coordination working together. Scaling AI requires that same kind of system.
Industry partners play a role as well. Demonstrating technical capability alone is not enough. Supporting operational integration, governance, workforce adoption, and long-term sustainment is increasingly becoming part of successful AI delivery in government environments.
When the System Isn’t There, Progress Stalls
In many agencies, that system is still emerging.
As a result:
- Pilots remain disconnected
- Capabilities are not institutionalized
- Adoption varies across programs
- Momentum slows despite early success
The work is not abandoned. It is simply not operationalized. Fast cars. No race. This is why it can feel like progress is happening – while core mission delivery remains largely unchanged.
What this means: Building repeatable governance, decision rights, and feedback mechanisms may accelerate value more than launching additional pilots.
Performance Is Defined in Operations, Not Pilots
In racing, performance is not defined by what happens on an empty track. It is defined on race day, under real conditions, with real constraints.
For agencies, that means:
- Operating within regulatory and oversight frameworks
- Coordinating across multiple programs and stakeholders
- Maintaining accountability, transparency, and trust
AI must function within that reality. The agencies seeing the most meaningful progress are increasingly focusing not just on experimentation, but on how AI integrates into operational delivery, governance, and decision-making at scale. Without alignment to operational conditions, even strong capabilities cannot deliver sustained value.
What this means: Agencies should evaluate AI initiatives under real operational conditions – not solely in controlled demonstrations.
This Is Not a Failure Problem
It is important to emphasize: This is not a failure of agencies.
Progress is real:
- Learning is happening
- Capabilities are improving
- Use cases are expanding
The challenge is not discovery. It is in translation.
What this means: The next phase of maturity is less about discovering new use cases and more about scaling what already works.
Moving Beyond the POC Plateau
To move from experimentation to sustained capability, agencies should focus on five priorities:
- Design pilots with operational adoption in mind
- Measure success beyond technical performance
- Establish ownership and governance early
- Integrate AI into existing workflows and decisions
- Scale proven capabilities deliberately and responsibly
Industry partners also play an important role by helping agencies operationalize – not simply demonstrate – AI capabilities.
Organizations that make this transition successfully will likely not be the ones experimenting the fastest. They will be the ones that build the systems needed to translate promising pilots into mission outcomes.
Because ultimately, performance is not defined in the pilot.
It is defined on race day.
About Dan Foster
Dan Foster has more than 25 years of experience in information technology and services, specializing in business agility transformation, Lean-Agile frameworks, and AI-enabled operating models. As a Transformation Leader at Snowbird Agility, Inc., he partners with executives, portfolios, and delivery teams to implement SAFe®, align strategy to execution, and improve flow, predictability, and measurable outcomes.
He may be reached at [email protected]
View the article as published in Orange Slices here
Tom Munro
Mike Kleiman