AI for Defense Production: Build Munitions at Speed

A decade ago, U.S. forces hit a hard limit that had nothing to do with flight hours, tactics, or training: precision weapons inventories weren’t deep enough for sustained high-tempo operations. Gen. (ret.) CQ Brown, Jr. describes watching weapons expenditure spike during Operation Inherent Resolve in late 2015—while already knowing the uncomfortable truth behind the scenes: the coalition’s shelf stock of precision munitions wasn’t built for long campaigns.

Fast-forward to December 2025 and the pressure is worse. Ukraine and the Middle East continue to drain inventories. Indo-Pacific timelines loom. And Congress just lived through more fiscal churn than anyone in uniform can operationally afford. The problem isn’t that America forgot how to build. It’s that our industrial and budgeting machinery still runs on a tempo designed for a different era.

Here’s the stance I’ll take: portfolio budgeting and multi-year procurement won’t deliver the speed we need unless the defense industrial enterprise uses AI to see, decide, and execute faster than its bureaucracy. AI isn’t a buzzword here. It’s how you turn “we need to double production” from a slogan into a managed system.

The real bottleneck isn’t factories—it’s decision latency

The biggest constraint on defense production isn’t always machining capacity; it’s the time it takes to align money, contracts, suppliers, and oversight. Brown’s article names the core tension: war moves in days, the Pentagon and Congress often move in quarters or years.

The U.S. defense enterprise is optimized for control: thousands of line items, rigid appropriation categories (“colors of money”), and compliance-heavy acquisition rules. That architecture reduces certain risks. It also creates a new one: strategic irrelevance through slowness.

Decision latency shows up in predictable places:

• Budget volatility whiplashes demand signals (munitions up, then down, then up again).
• Continuing resolutions freeze starts, production increases, and long-lead buys.
• Regulatory overload (thousands of pages across acquisition and financial rules) drives a culture where avoiding process mistakes beats delivering capability.

AI in defense doesn’t fix any of this by itself. But it can measurably reduce the time between signal → decision → contract action → supplier execution.

What “speed” actually means in a defense industrial system

Speed is not “award faster” in isolation. Speed is the end-to-end throughput of the enterprise, including:

• requirements changes that don’t break production
• funds that can be reallocated without a congressional trust collapse
• contracts that can absorb vendor shifts
• suppliers that can forecast labor, materials, and surge
• oversight that catches waste without turning into gridlock

That’s exactly where AI earns its keep: sensemaking, forecasting, anomaly detection, and workflow automation across the industrial network.

Portfolio management needs AI, or it becomes portfolio paperwork

Portfolio management is the most practical acquisition reform idea on the table—if it’s treated like an operating model, not a re-org. Brown points to the shift from program-by-program micromanagement toward portfolios led by empowered executives who can move resources to what’s working.

That model can shrink time-to-field because it rewards outcomes: deliver capability, scale what works, stop funding what doesn’t.

But there’s an obvious failure mode: portfolios become a bigger bucket with the same uncertainty, the same reporting burdens, and the same fights over “why did you move money?”

AI is how you keep portfolios honest.

AI as the “truth layer” for portfolio budgeting

If Congress is going to accept more flexibility, it will demand more transparency. That’s not a political preference—it’s a control mechanism.

A credible AI-enabled portfolio approach looks like this:

• Near-real-time dashboards that show obligations, burn rates, unit costs, lead times, and supplier health by subcomponent
• Automated variance explanations (what changed, why it changed, and what decision is needed)
• Scenario planning for reallocation (if we add $200M to rocket motors, what breaks downstream?)
• Anomaly detection to flag cost spikes, delivery slips, or quality escapes early

Put bluntly: AI can convert oversight from after-the-fact auditing into live risk management. That’s how flexibility becomes palatable.

Where portfolios work best (and why)

Not everything should be managed this way. Portfolios fit when technology evolves quickly and the system benefits from rapid iteration.

The best candidates align with Brown’s list:

• munitions components and subassemblies
• autonomous systems (drones and counter-UAS)
• electronic warfare and cyber tooling
• space services and launch support
• logistics software and information technology capabilities

In these areas, AI can connect budget flexibility to operational outcomes, because data is available and change is constant.

The demand-signal problem: AI can stabilize production without pretending budgets are stable

Industry can’t scale on vibes. Brown highlights the issue defense suppliers complain about most: inconsistent demand signals.

When procurement swings sharply year to year, suppliers react rationally:

• they avoid hiring and capital investments
• they keep fragile sub-vendors on life support (or lose them)
• they price in risk

Multi-year procurement helps—when requirements are stable and demand is predictable. But in modern conflict, requirements change. So the question becomes: how do you create enough predictability to scale, without locking into the wrong thing?

AI-enabled “adaptive multi-year” planning

Here’s a workable middle ground I’ve seen succeed in other complex supply chains: commit to capacity and options, not just quantities.

AI can support that approach by forecasting and optimizing across uncertainty:

• Probabilistic demand models based on training needs, contingency plans, and operational consumption rates
• Capacity reservation optimization (how much surge capacity to pay for vs. how much inventory to buy)
• Long-lead material planning for shared bottlenecks like solid rocket motors, propulsion components, and specialty metals
• Supplier risk scoring for single points of failure (machine tools, energetics, microelectronics)

This is how you build an “arsenal of agility” without pretending it’s 1943.

A practical example: munitions bottlenecks are often upstream

When people say “double missile production,” they picture a final assembly line. In reality, the constraint is commonly upstream:

• rocket motor cases
• energetics and propellants
• guidance components
• castings and forgings
• test equipment availability

AI-based planning tools can map these constraints and answer operationally relevant questions:

• If we surge interceptor production by 30%, which sub-vendor breaks first?
• What inventory buffers reduce schedule risk the most per dollar?
• Which quality checks create the most rework, and why?

That’s not abstract. It’s the difference between a 12-month “surge” plan and a 30-month disappointment.

Simplifying acquisition: use AI to remove work, not just rewrite rules

Brown makes a point many leaders avoid saying out loud: we already have authorities to go faster, but they’re buried under guidance and risk aversion. Rewriting regulations in plain language helps. But the day after the rewrite, contracting officers still face overflowing inboxes, documentation checklists, and approval chains.

AI should be applied where it removes labor and reduces errors.

Where AI helps contracting and acquisition teams immediately

The fastest wins are narrow, controlled, and measurable:

1. Requirements hygiene and traceability

   • Detect contradictions across requirement documents
   • Track requirement changes and downstream impact automatically

2. Contract writing assistants (bounded and auditable)

   • Generate first-draft clauses, SOW language, and evaluation criteria
   • Enforce consistency with policy and program constraints

3. Market intelligence at scale

   • Map non-traditional vendors by capability, certifications, and past performance
   • Identify dual-use suppliers already serving adjacent industries

4. Invoice and performance anomaly detection

   • Flag unusual labor mix changes, unit cost jumps, and delivery pattern shifts

5. Workflow automation for approvals and documentation

   • Pre-fill forms from authoritative data sources
   • Route packages with “what changed” summaries

The goal isn’t fewer people. It’s more decisions per week, with fewer preventable mistakes.

The cultural shift: transparency beats risk aversion

Risk aversion thrives when leaders can’t see what’s happening until it’s too late. AI-enabled transparency changes the incentive structure:

• When schedules slip, the system shows where and why.
• When cost rises, the system shows which component moved.
• When funds shift, the system shows the operational trade.

That kind of visibility makes it easier to reward teams that ship capability, not teams that produce immaculate paperwork.

A “proof point” mission area: integrated air and missile defense

If the U.S. wants to prove it can operate as a true defense industrial enterprise, it should pick one mission area and run it end-to-end as a demonstration. Brown suggests integrated air and missile defense, and he’s right: it’s complex, supplier-heavy, and operationally urgent.

A serious proof point would combine:

• portfolio budgeting for interceptors, sensors, and critical subcomponents
• multi-year contracting for long lead-time items
• AI-driven supply chain visibility from primes to sub-vendors
• AI-supported oversight dashboards shared across DoD and Congress

What success metrics should look like (in months, not years)

If the proof point can’t be measured, it will be debated forever.

Use metrics that tie to readiness and throughput:

• lead time reduction for top 10 constrained components (target: 20–30%)
• schedule adherence improvement on priority lots (target: +15% on-time delivery)
• unit cost stability across annual budget cycles (target: variance band reduced)
• supplier base resilience (target: reduce single-point-of-failure parts)

Pick a baseline. Publish it internally. Then improve it every quarter.

What leaders should do next (Pentagon, Congress, industry)

Execution as a “one team” enterprise won’t happen by goodwill. It happens when each stakeholder makes commitments that reduce uncertainty for the others.

Here’s a pragmatic next-step list that fits December 2025 reality.

For DoD acquisition and readiness leaders

• Stand up 2–3 AI-enabled portfolios (munitions components, counter-UAS, EW/cyber are strong candidates).
• Fund a shared industrial data layer: supplier health, lead times, quality, capacity, and inventory buffers.
• Require every portfolio to publish a quarterly throughput scorecard tied to fielded capability.

For Congress and oversight staff

• Trade flexibility for transparency: demand live portfolio dashboards instead of extra static reports.
• Expand multi-year authorities where demand is stable, and allow capacity reservation models where it’s not.
• Measure continuing resolution damage in operational terms (lost lots, delayed training) and treat it as readiness impact.

For primes and non-traditional vendors

• Treat AI not as an internal tool but as a shared interface with suppliers and government partners.
• Invest in digital thread basics: configuration management, quality data, and traceability.
• Offer “surge-ready” options with clearly priced capacity tiers.

A defense industrial enterprise that can’t share trusted data can’t move fast, because it can’t trust fast decisions.

Where this fits in the AI in Defense & National Security series

This post sits at the less glamorous end of AI in defense: not ISR automation, not autonomous vehicles, not cyber offense. It’s industrial readiness—the ability to produce and sustain what strategy demands.

AI is increasingly the connective tissue between operational plans and industrial output. If the United States wants deterrence to be credible in 2027 timelines and beyond, it can’t treat production capacity as an afterthought.

The open question I keep coming back to is simple: when the next surge hits, will we be arguing about authorities and line items—or watching dashboards that show exactly what to build next and how fast it’ll arrive?

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If you’re evaluating AI for defense acquisition, industrial base modernization, or munitions production planning, the fastest path is usually a focused pilot: one portfolio, one data layer, one set of throughput metrics, and a 90-day window to prove impact.