How contractors can keep AI missions on track despite supply chain volatility

Adoption of artificial intelligence by federal agencies is accelerating rapidly.

Figures from the Office of Management and Budget show more than 3,600 AI use cases underway across the government, a 70% increase over the previous year.

Adoption, however, isn’t the hard part anymore. Achieving the mission objectives is.

Federal contractors and systems integrators play a crucial role in helping agencies turn AI aspirations into real capability: sharper intelligence analysis, faster threat detection, better-informed and executed operations.

But many contractors I have talked to have shared their growing concern that the data infrastructure that supports these AI programs can’t keep pace. Every one of those capabilities depend on fast and reliable access to trusted mission data.

The infrastructure that data depends on is under more strain than it has been in years, as geopolitical tension and manufacturing constraints make it harder to secure, plan, procure, and scale.

When that infrastructure cannot keep pace, contractors cannot move and process mission data at the speed AI models require. Rising data latency and degrading model performance can cause critical program delays. For industry, weak data infrastructure is a direct risk to contract performance, making it harder to deliver the mission outcomes agencies expect.

While industry cannot control global and supply chain instability, they can reduce its impact on AI infrastructure by planning for changing demands, strengthening data resilience, and using existing capacity more efficiently.

Plan for unpredictability

A recent report predicts that lead times for certain infrastructure purchases could extend by six to 12 months. In addition, major U.S. technology companies are expected to invest roughly $600 billion in AI initiatives by the end of 2026, adding further pressure to already constrained global supply chains.

Research estimates that 80% of AI projects fail, with poor data quality and insufficient infrastructure among the contributing factors. Constrained supply makes those existing weaknesses even harder to overcome.

The takeaway is straightforward: supply volatility has to be built into program planning early. Pricing, capacity, and delivery timelines should account for the possibility that compute and storage infrastructure could cost more or arrive later than expected.

That gives contractors and systems integrators more room to adjust before constrained supply turns into a schedule slip, a missed milestone, or weaker contract performance.

Build for volatility

Where possible, contracts should move away from relying on rigid infrastructure refresh timelines. By building in operational buffers, contractors and systems integrators can maintain continuity when resources are delayed or over budget, avoiding the need for rushed, reactive decisions.

Procurement adjustments may provide temporary relief, but contractors also need to help agencies reduce their exposure to constrained supply by improving the efficiency of infrastructure already in place.

Better utilization, data compression, and longer asset lifecycles can recover capacity and give contractors more control over modernization timing. Improving effective utilization from 60% to 80%, for example, could recover capacity comparable to a significant infrastructure expansion without requiring an immediate purchase in a constrained market.

Traditional three-to-five-year refresh cycles were built around more predictable market conditions. This environment, however, is characterized by price volatility, refresh delays, and overprovisioning. Contractors can use this period of supply constraint to build more resilient, efficient, and flexible systems.

More agile approaches, such as hardware-as-a-service or pooled infrastructure, can better align spending with actual demand, reduce the impact of component shortages, and help maintain continuity when market conditions shift.

This isn't standard IT modernization running quietly under an O&M line item anymore – data management is mission critical, and it needs to be planned like it. That means phased modernization: smaller decisions instead of a rigid refresh cycle built on assumptions the market no longer supports.

Contractors who plan this way keep data access and program delivery intact even when market conditions blow up the original plan.

Bolster data resilience and efficiency efforts

None of that works if the data itself isn't available and resilient. Too many federal data environments are still fragmented – duplicated information, workloads locked to specific applications and systems, and no clear view of what's where. Data compression and deduplication, tiering, and lifecycle management reclaim capacity. Better visibility simplifies governance and shows contractors and systems integrators where data and workloads can move as mission needs shift.

Contractors should design data environments that support portability across approved environments. That flexibility can help agencies adjust workload placement when capacity becomes constrained or operational needs shift, without forcing a costly migration or weakening control over sensitive information.

In a volatile market, the ability to adapt to new constraints while keeping mission-critical data accessible, secure, and usable is a core element of resilience.

Contractor readiness matters

Contractors who identify supply chain risk early, tighten up the data environments they already manage, and build in real resilience will keep mission data available when the market won't cooperate.

Ultimately, the contractors who embrace this proactive approach will ensure that vital AI missions remain on track, regardless of the volatility in the underlying supply chain.

Katherine Hennessey is the head of U.S. government strategy for Everpure.