AI-Ready Defense Software: Fixing Air Force Bottlenecks

A single memo can slow down more than procurement. It can slow down learning.

That’s the real risk behind the Department of the Air Force’s recent attempt to tighten how software-as-a-service (SaaS) is purchased and managed. The original critique (and the subsequent signal that the memo would be rescinded and revised) isn’t just inside-baseball contracting drama. It’s a warning flare for anyone serious about AI in defense & national security: if you can’t ship, modify, and integrate software quickly, you can’t operationalize AI at scale.

I’ve seen teams do the hard part—stand up modern cloud environments, push continuous authorizations, build software factories, and partner with non-traditional vendors—then get tripped by policies that treat “control” as the same thing as “security.” AI doesn’t tolerate that confusion. AI systems require fast iteration, constant monitoring, and frequent updates as threats evolve.

The core problem: policy that optimizes for control, not speed

The fastest path to losing an AI-enabled advantage is forcing modern software into a process built for static systems.

The memo highlighted in the source article aims to reduce duplicated tools, prevent vendor lock-in, and improve oversight of SaaS spending. Those are valid goals. The execution is where it gets messy—especially when policies:

• Mandate narrow buying paths (single catalog, single “independent contracting action” approach)
• Centralize exceptions (approval concentrated at the top)
• Over-specify data rights without defining what “usable” actually means
• Ban customization and extensions, including via APIs

That combination doesn’t just slow down procurement. It slows down operational learning, which is the one thing modern conflict punishes you for lacking.

Why speed matters more in 2025 than it did in 2019

Speed is no longer a nice-to-have metric for software delivery in defense. It’s the difference between systems that keep up with an adaptive enemy and systems that become irrelevant.

From the Russo-Ukrainian war’s rapid tactics-to-software feedback loops to constant drone and EW adaptation seen across multiple theaters, the pattern is consistent: the side that iterates faster wins local advantage. AI amplifies that dynamic. Models drift, sensors change, data pipelines break, and adversaries spoof signals. If you can’t patch and ship quickly, you’re fielding yesterday’s logic.

Why SaaS rules can accidentally kneecap AI adoption

If you make SaaS harder to buy, harder to integrate, and harder to extend, you indirectly make AI harder to deploy.

AI in national security doesn’t live in a single “AI platform.” It lives in the seams:

• Data ingestion from multiple classifications and domains
• Workflow automation for analysts and operators
• Integration into mission planning tools
• Alerts and anomaly detection in cyber and logistics systems

Most of those capabilities arrive as software services—some government-built, many vendor-built, nearly all integration-heavy.

Mistake #1: Forcing “independent contracting actions” slows experiments

Requiring a separate contracting action for SaaS sounds like governance. In practice, it’s often a tax on experimentation.

AI programs rarely start with a perfect requirements document. They start with a hypothesis:

• “Can we reduce false positives in ISR triage by 30%?”
• “Can we cut maintenance delays by predicting part failures?”
• “Can we auto-generate air tasking order fragments faster, with human review?”

Those hypotheses need fast pilots. If every trial requires a brand-new contracting lane, the default becomes: don’t try.

A healthier pattern is to treat early pilots as controlled experiments with strong telemetry (usage, performance, security), then scale what works. Speed doesn’t mean chaos; it means short feedback cycles.

Mistake #2: A single enterprise catalog becomes a single point of delay

If the only approved buying channel is an enterprise service catalog, innovation depends on how quickly that catalog updates.

Catalogs can be useful, but they should behave like modern marketplaces:

• Clear entry criteria
• Transparent evaluation timelines
• “Provisional approval” tiers for pilots
• Automated security and compliance checks

If the catalog becomes a gate with opaque rules, it turns into an innovation choke point. In an era of shrinking acquisition workforce capacity, bottlenecks don’t self-resolve.

Mistake #3: “Usable data format” is a lawyer’s playground

Vendor lock-in is real. But the solution isn’t vague contract language that creates endless interpretation.

The practical issue is that raw exported data is often technically usable but operationally useless without:

• The data model context
• The schema and transformations
• The metadata and lineage
• The workflow logic that gave the data meaning

If the government wants portability, it should require portability artifacts, not just “data access.” Examples include:

• Documented schemas and versioning
• Export APIs with rate limits and test harnesses
• Data dictionaries and transformation logic
• Rehydration scripts for a reference environment

That’s measurable. “Usable format” isn’t.

Mistake #4: Banning customization and API extensions is anti-AI

Prohibiting custom code development and extension “beyond the platform’s original design” is the most damaging idea in the memo.

AI systems must integrate, adapt, and iterate. APIs are how you:

• Connect models to mission workflows
• Fuse data across systems
• Add guardrails, approvals, and human-in-the-loop checks
• Instrument monitoring for drift, bias, and performance

A blanket ban on extensions incentivizes shadow IT, workarounds, and brittle one-off processes. Worse, it freezes systems in time—exactly what you can’t afford in defense software delivery.

If a platform can’t be extended safely, it can’t be operationalized responsibly.

The better approach: AI-enabled governance instead of policy bottlenecks

The Air Force’s intent—visibility, de-duplication, and security—can be achieved without slowing delivery. The trick is using AI and automation for governance, rather than using governance to restrict change.

1) Automate de-duplication with an “inventory + similarity” layer

De-duplication is a data problem.

An AI-assisted software inventory can flag likely overlaps by comparing:

• Capability statements
• System interfaces and data types
• User groups and mission sets
• Cost structures and renewal cycles

Instead of banning contracting pathways, give program managers a tool that says:

• “You’re about to buy something similar to X and Y.”
• “This tool overlaps 70% with an existing contract.”
• “This product is new, but matches an approved category—pilot tier recommended.”

That enables speed and prevents waste.

2) Use tiered approvals: pilot fast, scale deliberately

Most companies get this wrong by treating every purchase like it’s a program of record.

A tiered model works better:

1. Pilot tier (30–120 days): lightweight contracting, strict telemetry, capped spend
2. Expansion tier (6–18 months): tighter security posture, integration requirements, portability artifacts
3. Enterprise tier: full lifecycle controls, negotiated data portability, performance SLAs

This aligns with how AI capability actually matures: test, learn, harden, scale.

3) Define portability as an engineering deliverable

Portability should be testable.

A strong portability clause focuses on deliverables like:

• Export API endpoints and documentation
• A reference dataset export + import demonstration
• Schema/version compatibility guarantees
• A “time to migrate” metric demonstrated in a tabletop exercise

If you can test it, you can enforce it. If you can’t test it, you’ll litigate it.

4) Replace “no customization” with “controlled extensibility”

Security doesn’t require banning change. It requires controlling change.

A controlled extensibility model includes:

• Approved API gateways and service accounts
• Code signing and artifact provenance
• Role-based access controls
• Continuous monitoring and audit logs
• Clear rules for what’s billable vs. included

For AI systems, add MLOps controls:

• Model registry and versioning
• Drift monitoring thresholds
• Red-teaming and adversarial testing gates
• Rollback procedures

That’s how you ship fast and avoid security theater.

What leaders should do next (and what to ask vendors)

If you’re building or buying AI-enabled defense software in 2026 budget cycles, here’s what I’d do differently—starting now.

For acquisition and program leaders

1. Demand short, measurable pilot pathways (weeks, not quarters).
2. Require telemetry by default: usage, outcomes, performance, and security events.
3. Build a “catalog intake SLA” so new tools don’t disappear into review limbo.
4. Standardize portability artifacts rather than vague “usable format” language.
5. Allow extensions with guardrails—and make the guardrails non-negotiable.

For SaaS and AI vendors selling into defense

Be ready to show, not tell. Bring receipts.

• A portability demo: export, rehydrate, validate
• Your security controls: logging, access, provenance
• Integration patterns: API limits, gateways, sandbox envs
• A cost model that works for pilots and scale
• How you handle updates in restricted environments

If your product can’t survive a portability drill or can’t integrate without heroics, it’s not ready for national security use.

Why this fits the bigger AI in Defense & National Security story

AI in defense isn’t blocked by model quality as often as people think. It’s blocked by software delivery constraints: procurement friction, policy ambiguity, and integration bans that treat adaptation as suspicious.

The Air Force has been one of the few places inside DoD that repeatedly proved a different model can work—software factories, shared cloud, faster ATO paths, and stronger partnerships with non-traditional vendors. Policies that narrow procurement options and restrict extensibility pull in the opposite direction.

If the revised memo keeps the goals but removes the bottlenecks—especially around catalog gating and bans on development—the Air Force can still be the place where AI-enabled capability moves from prototype to production.

The open question is straightforward: Will governance be built to manage fast change, or to prevent it?