Pentagon Acquisition Reform: Faster Paths for Defense AI

A procurement system that takes years to buy software is incompatible with a security environment where models, sensors, and tactics can change in weeks. That’s why the Pentagon’s newly announced acquisition overhaul has people like Steve Blank calling it “mind-blowing”—not because the ideas are brand new, but because they’re packaged as an operational reset: fewer silos, more speed, and a stronger bias toward commercial technology.

For anyone building or buying AI in defense and national security, this matters more than another org chart. AI capabilities—computer vision for ISR, cyber anomaly detection, sensor fusion, targeting support, autonomous logistics—don’t behave like traditional weapons programs. They’re living systems: they need iterative releases, continuous testing, data pipelines, model updates, and user feedback loops.

Here’s my take: the Pentagon’s acquisition overhaul is fundamentally an AI adoption story. If it works, it won’t just “accelerate buying.” It will change which AI capabilities get fielded, how quickly they improve, and who wins the next decade of defense innovation.

The reform’s real point: break the handoffs that kill speed

The core problem is simple: handoffs and silos create delay. Requirements get written by one group, prototyping happens somewhere else, acquisition sits in another lane, contracts in another, sustainment in another. Each handoff adds waiting, re-interpretation, and risk avoidance. That’s survivable when you’re buying tanks. It’s fatal when you’re fielding AI-enabled systems that need frequent iteration.

The proposed shift—consolidating activity under portfolio acquisition executives (PAEs), rather than maintaining a maze of program executive offices organized around legacy categories—signals a move toward outcome-oriented portfolios. Instead of “buy a widget,” the portfolio framing becomes closer to: support a warfighting concept, improve a mission thread, or deliver a capability set.

Why portfolio acquisition maps to AI realities

AI development behaves more like a product line than a single program milestone. Portfolio thinking enables three things AI teams need:

• Shared infrastructure decisions (data pipelines, labeling, MLOps, evaluation harnesses) across multiple apps
• Reuse of models and components (vision models, language models, fusion layers) across mission areas
• Continuous improvement based on real-world performance, not paper requirements

If the Pentagon treats AI as a portfolio, it can stop funding “one-off demos” and start funding systems that mature through measured releases.

“Lean iteration” in the Pentagon: useful, but only if they measure the right things

Blank’s argument—borrow startup methods like lean iteration, pivots, incremental releases, and “good enough” delivery—will resonate with engineers and frustrate anyone who’s lived through compliance-heavy programs. Both reactions are valid.

The practical question is this: what replaces the old gating mechanisms? In a safety- and mission-critical environment, you can’t simply ship and apologize.

The answer is not “more process.” It’s better measurement.

What “good enough” should mean in defense AI

For AI-enabled defense systems, “good enough” can’t be vibes. It should be expressed as:

1. Mission-based performance thresholds (detection rate, false alarm rate, time-to-alert, analyst workload reduction)
2. Operational test design that reflects contested conditions (jamming, spoofing, degraded comms, camouflage, adversarial behaviors)
3. Model governance: traceability for data sources, model versions, evaluation datasets, and failure modes
4. Update discipline: defined cadence and controls for pushing model improvements

In other words: ship faster, but ship with evidence.

One sentence I wish every acquisition leader would adopt is: “Speed is a feature only if you can prove performance.”

The coming “year of chaos” is predictable—and manageable

Blank predicts six months to a year of confusion as training, authorities, and incentives shift. That’s not pessimism; it’s pattern recognition. Large reorganizations create:

• Authority ambiguity (“Who approves what now?”)
• Process drift (“What’s the new standard?”)
• Passive resistance (people protecting their turf)
• Vendor confusion (who to talk to, how to sell)

The overlooked point in the interview is arguably the most important: the system has to retrain itself. Historically, the Defense Acquisition University taught people how to operate inside the FAR/DFARS gravity well. The reform implies a different skill set: portfolio management, rapid experimentation, commercial contracting pathways, and software-centric sustainment.

How to reduce the chaos for AI programs

If you’re a defense innovation leader—or a vendor trying to be a good partner—focus on three stabilizers:

• Clear “mission threads”: pick 3–5 workflows where AI demonstrably helps (e.g., maritime domain awareness triage, SIGINT tip-and-cue, cyber incident prioritization) and measure them end-to-end.
• Standard evaluation playbooks: define how models are tested, red-teamed, and monitored in operation.
• Data access agreements: solve the boring part early—permissions, labeling, retention, and security boundaries.

AI adoption fails more often from data and workflow friction than from model quality.

Commercial-first buying could finally match how AI is built

One of the most consequential ideas in the interview is the implied buying preference:

1. Buy commercial off-the-shelf
2. Buy commercial, then modify
3. Only then build bespoke

For defense AI, this is directionally correct. The modern AI stack is largely commercial: cloud primitives, GPUs, data tooling, model frameworks, and increasingly, foundation models and agentic workflows.

But here’s the catch: commercial-first works only if “integration-first” is real. Buying point solutions that don’t connect to data, identity, and mission systems just produces expensive shelfware.

A better commercial-first rule for defense AI

I’ve found a more useful filter is:

• Commercial-first for platforms and primitives (compute, storage, orchestration, observability, MLOps)
• Mission-tailored for last-mile workflows (the UI, the decision logic, the integration to existing C2 systems)

That balance keeps you from reinventing commodity tech while still delivering mission advantage.

The primes won’t disappear—so design AI acquisition to prevent “innovation capture”

Blank is blunt about primes: they’re not useless, and they’re not going away. Nobody expects a startup to build an aircraft carrier next quarter. The risk is different.

The risk is innovation capture: large incumbents buying or boxing out small AI providers, then slowing iteration to match legacy incentives.

That’s not a moral accusation; it’s what happens when revenue depends on long timelines and complex customization.

What prevents innovation capture in AI programs

Acquisition reform should hard-code three protections that keep AI improving after award:

1. Versioned deliverables: contracts should require model/version updates with measurable performance improvements.
2. Government-owned evaluation: the government should run (or control) the test harness, datasets, and scoring—not just accept vendor claims.
3. Modular architecture: require APIs and portability so components can be swapped without rebuilding the whole system.

If you don’t do this, “commercial-first” becomes “vendor-locked.”

Where AI fits immediately: ISR, cyber, and autonomous operations

The campaign question is straightforward: How does this overhaul accelerate AI adoption in national security?

Answer: by making it easier to field AI where it’s already proven, and by letting operators influence the next release.

ISR and intelligence analysis

AI’s near-term value is often not “fully autonomous targeting.” It’s workload reduction and time advantage:

• Automated triage of full-motion video and satellite imagery
• Entity extraction and link analysis across multilingual reporting
• Sensor fusion that prioritizes what analysts see next

Portfolio acquisition matters here because ISR is a system-of-systems problem. If the portfolio owner can fund data, integration, and workflow changes—not just a model—AI starts to stick.

Cybersecurity and mission assurance

Cyber is where iterative delivery is already culturally normal. AI can accelerate:

• Anomaly detection and alert clustering
• Phishing and malware classification
• Predictive risk scoring across assets and identities

Faster procurement helps because threat tactics mutate quickly. Buying cycles that take a year are functionally buying yesterday’s attack patterns.

Autonomous and semi-autonomous operations

Autonomy is where governance and test rigor have to be strongest. The reform’s promise isn’t “deploy autonomy faster at any cost.” The promise is: tight test-feedback loops where autonomy can be validated in constrained mission sets, then expanded.

The fastest safe path I’ve seen is staged autonomy:

1. Decision support (recommendations)
2. Supervised autonomy (human approves)
3. Conditional autonomy (human monitors)
4. Full autonomy only in bounded environments

Acquisition that supports incremental releases makes this progression realistic.

What buyers and builders should do in Q1 2026

This reorganization won’t reward teams who wait for perfect clarity. It will reward teams who can operate in partial ambiguity without breaking compliance or trust.

If you’re in government

• Pick one portfolio-level mission thread and build a measurable baseline in 30–60 days.
• Stand up an AI evaluation cell (small team) that owns datasets, scoring, and red-teaming.
• Write contracts that fund sustainment as improvement, not just “operations.” AI that doesn’t improve degrades.

If you’re a vendor or integrator

• Sell an outcome, not a model. Show reduced analyst minutes, faster triage, higher detection under realistic conditions.
• Bring an evaluation plan. Your credibility rises fast when you propose how you’ll be tested.
• Make integration boring. Clear APIs, identity integration, audit logs, and deployment options win deals.

If you want a single north star: prove you can ship, measure, and update without drama.

The bigger story in this AI in Defense & National Security series

Across this series, the theme is consistent: AI advantage is a systems problem. Models matter, but the decisive edge comes from data access, operator workflows, testing discipline, and the ability to improve faster than the adversary.

Pentagon acquisition reform is a bet that the U.S. can regain that improvement velocity—without sacrificing safety, accountability, or mission assurance. I think that bet is worth making. But it will only pay off if leaders treat AI as a continuously managed capability, not a one-time procurement.

If you’re responsible for fielding AI-enabled defense systems—or you’re building them—this is the moment to get specific: Which portfolio do you serve, what mission thread do you improve, and what evidence will you bring to prove it?