Everyone is breathlessly watching AI models evolve. Almost no one is watching the system holding them up, the platform it runs on, and the discipline to keep that platform current. Models are visible, benchmarkable, and easy to sell as capability upgrades. The system around them is none of those things. So, when the platform cannot flex, and the culture never developed the discipline to keep it current, the models fail to deliver operational impact.

Decision advantage requires both model and system maturity to be successful. A brilliant model on a rigid system is still a rigid system.

The Department Has Already Named Both Halves

DoW CIO Kirsten Davies described this exact distinction in a July interview outlining her four-pillar digital transformation strategy. Her first pillar, an enduring digital foundation, is the platform: network transport, data centers, and the infrastructure AI depends on. Her second, agile digital capabilities, is the practice: real software development discipline, what she pointedly distinguishes from a “fast waterfall.”

She is describing the two halves that mission success actually requires. The gap is not that the Department has failed to name them. The task now is translating both from high-level strategy into funded, staffed, everyday practice.

The Real Pattern: Bolt-Ons, Not Builds

This mirrors a pattern seen elsewhere. Most AI pilots that never reach the field fail for the same reason. The model is rarely the problem. The data, the workflow, the sponsorship, and trust often stumble first.

Right now, each new model gets integrated as its own one-time, bolt-on project. The platform underneath, meaning the data pipelines, the workflows, and the accreditation boundary, never gets built to actually flex. The result is a growing stack of one-off integrations sitting on infrastructure that was never designed to absorb continuous change.

The Platform: The Architecture Exists on Paper

The mechanism to build a flexible platform already exists in policy.

The Software Acquisition Pathway (DoDI 5000.87) exists specifically to enable continuous integration and delivery, and the Secretary directed all components to adopt it as the preferred approach in March 2025. Continuous Authorization to Operate already lets an accredited platform assess and deploy continuously within its own boundary rather than repeating authorization for every change. The FY25 NDAA mandates presumptive reciprocity across the Department once one authorizing official approves a platform.

The Artificial Intelligence Strategy for the Department of War, issued January 9, 2026, names decision superiority as an objective and builds its entire acceleration plan around removing bureaucratic and infrastructure barriers, not around acquiring better models. Its success metric is telling: the strategy directs the CDAO to track and report AI deployment and cycle-time metrics monthly. The Department is already measuring the thing this argument is about.

The Practice: Discipline Has Not Caught Up

A platform without discipline stalls the same way a mandate without enforcement stalls.

Continuous authorization has been Department policy since 2022, yet a new cybersecurity risk management construct arrived in September 2025 specifically because adoption never became the norm. The system kept relying on the same static, point-in-time assessments cATO was never meant to replace. The Army, for example, offers the clearest scoreboard: as of early 2026 it had four approved continuous ATO platforms, one at a command and three within the program offices. Davie’s own agile digital capabilities pillar names this same gap. The mechanism exists. The discipline to run it consistently does not, but it can.

Budgeting reinforces the problem. Traditional O&M and acquisition processes still reward one-time delivery. The Department’s own FY2025-26 Software Modernization Implementation Plan acknowledges that platform funding comes largely from the individual programs that use it, with no consistent enterprise funding model. That leaves platform capacity dependent on whichever programs happen to be paying at a given moment, rather than funding as a standing capability the way readiness or sustainment is funded.

Running any of this well requires people, and right now, the talent pipeline is struggling to keep pace. Davies’s fourth pillar explicitly names workforce readiness, acknowledging that modern platforms demand modern talent. Yet, the Department continues to lose thousands of technical employees each year. The strategy accurately identifies the goal, but the workforce required to actually execute it is still shrinking.

Bridging the Gap: Five Steps to Operationalize AI

None of this requires new policy. It requires execution.

1. Fund the platform as a standing capability. Stop treating platforms as program-dependent. The Department’s own implementation plan already flags this gap and has tasked itself with proposing an enterprise funding model.
2. Close the distance between preferred and required. The SWP mandate directs adoption but leaves interpretive room as a preferred rather than sole pathway. Removing that ambiguity is follow-through, not new policy.
3. Apply the reciprocity already required by law. The FY25 NDAA mandates presumptive reciprocity. Extending that standard to new models entering an already accredited pipeline uses existing authority rather than inventing new policy.
4. Measure discipline by throughput, not milestones. Evaluate platforms on how fast they absorb the next model or dataset, not on a single go-live date. This aligns directly with the AI Strategy’s own velocity and cycle-time metrics.
5. Build the workforce pipeline alongside the technical one. Fund the people who operate these platforms with the same seriousness as the infrastructure itself, so capacity does not become the next bottleneck once the platform is in place.

The Side With the Better System Wins

In warfare, the side with the better system wins. Not the side with the newest model, and not the side with the most pilots launched. The platform that can absorb the next model faster than the adversary can adapt, run with the discipline to keep doing it, is the actual source of decision advantage.

Stop counting how many models have been integrated. Start asking whether the platform underneath them got any easier to change as a result, and whether the discipline to keep it that way is actually being funded and staffed. The Department’s own CIO has already named the foundation and the practice as the two pillars that matter. What remains is closing the distance between naming them and actually funding, staffing, and running both.

About the Author
Collin Lee is the Chief Innovation Officer at OMNI, where he leads technology strategy for defense and intelligence customers. He brings 25 years of experience in disruptive technology adoption across government and industry, including prior roles as an Intelligence Officer, a director at the White House, and a staffer on the House Appropriations Committee. Connect with Collin on LinkedIn.