Why the 2026 NDAA Could Finally Speed Up AI Adoption

$900.6 billion is a headline number. The quieter headline is the Senate’s 77–20 vote to force the Pentagon to change how it buys technology—because that’s where AI programs usually stall.

The fiscal 2026 National Defense Authorization Act (NDAA) doesn’t just authorize money; it rewires parts of defense acquisition with the explicit goal of buying faster, buying smarter, and making room for commercial innovation. If you work in AI for defense, national security technology, or the broader dual-use ecosystem, this bill reads like a signal flare: Congress wants AI capabilities to move from demos to deployed systems.

This post is part of our AI in Defense & National Security series, where we track what actually changes adoption on the ground. The reality I’ve seen: AI progress in defense isn’t mostly blocked by model performance—it’s blocked by procurement mechanics, requirements churn, and slow transitions to production. The 2026 NDAA tries to take those blockers head-on.

The real AI bottleneck: acquisition speed, not algorithms

Defense AI adoption is constrained by procurement timelines and risk processes more than by technical feasibility. Modern AI systems improve quickly through iteration, data feedback, and frequent updates. Traditional weapons acquisition assumes the opposite: long requirement documents, slow milestones, and minimal change after “baseline.”

That mismatch creates predictable failure modes:

• Promising prototypes get stuck in pilot purgatory because there’s no clean path to production.
• Requirements are written as if the system will be static for 10–20 years, which is the wrong mental model for machine learning.
• Programs over-customize solutions for compliance instead of adopting commercial standards and iterating.

The 2026 NDAA’s acquisition reform push is, at its core, an attempt to bring cycle time down and make transition less painful—two conditions you need if you want AI to matter in real operations.

Just as importantly, the bill’s topline matters for scale: it authorizes $900.6B in defense funds, roughly $8B above the administration request, with recommended procurement levels including $26B shipbuilding, $38B aircraft, $4B ground vehicles, and $25B munitions, plus $400M for Ukraine and $175M for the Baltic Security Initiative. Authorization isn’t appropriation, but it sets intent—and intent shapes program priority.

Portfolio acquisition executives: why this structure matters for AI

The bill directs the Pentagon toward a “portfolio acquisition executive model,” shifting oversight from many siloed programs to fewer, broader portfolios. This is a structural bet: that the department can manage capabilities like a product family, not a one-off engineering project.

For AI-enabled defense systems, portfolios can be a big deal because AI rarely stands alone. It’s usually a layer across missions:

• Intelligence analysis and sensor fusion
• Cyber defense and anomaly detection
• Predictive maintenance and readiness analytics
• Targeting support, mission planning, and decision aids
• Autonomy and human-machine teaming

A portfolio approach can allow a senior executive to treat these as connected capability threads, which enables:

Faster reuse of data and infrastructure

AI programs fail when each one rebuilds the same plumbing. If portfolios centralize expectations for data pipelines, model evaluation, MLOps, and authority-to-operate patterns, you get less reinvention and fewer compliance surprises.

More rational tradeoffs across programs

A portfolio leader can decide, for example, that two programs should share a common perception stack or that a classified-only approach is overkill for a training pipeline. In the old model, each program defends its own budget and bespoke architecture.

Better alignment with how commercial AI ships

Commercial AI is managed like product lines with roadmaps, releases, and upgrades. Portfolios are closer to that operating model than traditional program silos.

My take: this is the most “AI-relevant” reform in the bill because it changes who has the authority to standardize and scale.

Commercial on-ramps: off-the-shelf preference and fewer small-firm burdens

The NDAA includes new requirements to consider off-the-shelf solutions and removes some compliance requirements on small commercial firms. That sounds procedural. It’s actually a direct response to a known problem: many AI-capable companies won’t sell to DoD because the cost of compliance, audits, and contract friction is too high relative to revenue.

For AI adoption in national security, off-the-shelf preference matters in three practical ways:

1. Speed: you can deploy a proven component quickly, then tailor at the edges.
2. Security upgrades: widely-used commercial tools get patched fast when vulnerabilities emerge.
3. Talent gravity: engineers prefer working on mainstream stacks; niche government-only stacks make hiring harder.

There’s a legitimate counterpoint: off-the-shelf isn’t automatically secure or mission-ready. But the better response is governance and testing—not forcing every vendor into a decade-long bespoke build.

What this changes for AI vendors

If you sell AI capabilities (models, data platforms, analytics, autonomy software), the near-term implication is clear: expect increased pressure to show readiness as a product, not a research project.

That means being able to answer questions like:

• How do you monitor model drift in deployment?
• What’s your approach to red-teaming and adversarial robustness?
• Can you operate with constrained connectivity (edge conditions)?
• How do you log decisions for after-action review and accountability?

In defense procurement, “commercial-like” only works if the product is truly operational.

BOOST: the transition gap gets a name (and a program)

The NDAA starts a new Defense Innovation Unit effort called the Bridging Operational Objectives & Support for Transition (BOOST) Program aimed at helping companies with operationally viable tech transition into production.

This is the part many teams will care about most because it targets the most common dead zone in defense innovation: the handoff from prototype to program of record.

A lot of AI in defense gets funded in small increments—enough to prove a concept, not enough to sustain deployment. The result is predictable:

• A unit tests it, likes it, then can’t buy it at scale.
• The vendor can’t justify continued support without production dollars.
• The capability fades, even if it worked.

BOOST is an acknowledgment that transition is not automatic. Transition needs funding, contracting pathways, and operational sponsorship. If BOOST is implemented with teeth—clear criteria, repeatable contracting patterns, and measurable transition outcomes—it could become one of the most practical accelerants for defense AI adoption.

If it becomes a branding exercise without budget authority or acquisition pull-through, it won’t.

The “right to repair” removal: a warning sign for AI-enabled systems

Right-to-repair provisions were stripped from the final bill. That’s not just a maintenance story; it’s an AI readiness story.

AI-enabled systems in the field need:

• Frequent software updates
• Hardware swaps and sensor recalibration
• Replacement parts that don’t require vendor gatekeeping

When operators can’t repair or replace components easily, uptime suffers. And when uptime suffers, the data feedback loop suffers too. That’s how an AI system quietly degrades: not because the model “stops working,” but because the operational ecosystem can’t sustain it.

This matters even more as the department pursues autonomous and semi-autonomous systems. Those platforms will only be as resilient as the logistics and repair rights behind them.

My opinion: if DoD wants AI at scale, it can’t treat sustainment and repair as secondary. Sustainment is where modern software either lives—or dies.

What the NDAA signals about AI priorities in 2026

Congressional action here signals strategic commitment to defense innovation, including AI, autonomy, and cyber. It also reflects a hard-earned lesson from the last few years: threats evolve on software timelines.

Several elements of the bill reinforce that direction:

• Emphasis on speed of delivery and production capacity
• Policies intended to widen participation from nontraditional defense firms
• Support for industrial base investments (including critical minerals and refining projects), which matters for AI hardware supply chains

The bill also includes policy provisions outside acquisition reform—like fencing 25% of the Defense Secretary’s travel budget pending delivery of specific unedited video to Congress, and restricting force reductions in Europe below 76,000 servicemembers until assessments are provided. Those details aren’t “AI provisions,” but they show how this NDAA mixes modernization with oversight and geopolitical posture.

Practical implications: what leaders should do now

The teams that benefit from acquisition reform are the ones prepared to meet the new operating expectations. If you’re in government, industry, or a dual-use startup, here are concrete moves worth making before FY26 decisions lock in.

For defense and national security program teams

1. Write requirements like a product roadmap, not a static spec. Define outcomes, interfaces, and evaluation metrics. Avoid locking in one model architecture.
2. Budget for data, MLOps, and sustainment from day one. If you can’t afford monitoring and retraining, you can’t afford deployment.
3. Standardize evaluation. Adopt repeatable test harnesses for accuracy, latency, drift, and adversarial behavior.

For AI vendors and integrators

1. Show your transition plan, not just your model. Procurement teams want to know how you’ll scale, secure, and sustain.
2. Package compliance as a feature. Be ready for security documentation, logging, and traceability without slowing delivery.
3. Design for edge realities. Assume limited connectivity, contested GPS, degraded sensors, and intermittent compute.

For primes and large defense contractors

1. Treat commercial AI firms as partners, not subcontractors to bury. Speed comes from clean interfaces and shared incentives.
2. Invest in modular architectures. Portfolios will favor platforms that can swap components without rewriting everything.

People also ask: what does this mean for AI procurement in defense?

Will acquisition reform automatically speed up defense AI adoption?

No. Reform changes the rules; implementation changes outcomes. Speed improves when programs adopt modular architectures, standard evaluation, and continuous delivery patterns.

Does “off-the-shelf” mean DoD will buy commercial AI as-is?

Not usually. It means starting from commercially proven components and tailoring where mission needs demand it, rather than reinventing the stack.

What’s the biggest risk to AI progress under the NDAA?

The biggest risk is the same old trap: prototype success without production authority and sustainment funding. BOOST is meant to address that, but it has to be resourced and enforced.

Where this leaves the AI in Defense & National Security story

The 2026 NDAA is a big number and a bigger message: Congress wants the Pentagon to buy at the pace of modern technology. For AI, that’s the difference between impressive pilots and operational advantage.

If you’re building or buying AI for national security, 2026 is shaping up to be a year where contracting pathways and organizational models matter as much as model performance. The teams who prepare for portfolio-driven buying, product-like delivery, and real sustainment will be the ones still standing when the money actually moves.

If acquisition reform really is the catalyst it claims to be, the next question is simple: which AI capabilities will be ready to transition when the Pentagon finally opens the faster lane?