The hidden debt slowing America’s AI future

Federal agencies face a paradox: they are spending nearly 80% of IT budgets maintaining legacy systems while simultaneously being mandated to adopt transformational technologies like artificial intelligence. This is not simply a resource allocation challenge — it is a strategic readiness deficit that compounds daily.

Every day modernization is deferred, agencies accrue digital readiness debt, which widens the gap between current infrastructure and the foundational capabilities required to deploy AI responsibly and at scale. This debt is measured not in code, but in mission throughput, cyber risk exposure and the integrity of the data underpinning critical decisions. As AI becomes mission-essential, this debt now represents a mounting threat to national security, public health and citizen trust.

AI without mission discipline

The rapid push to “do something with AI” has triggered a wave of pilots across the government. Yet many efforts are not anchored to defined mission outcomes, performance measures or scaling pathways. When experimentation occurs without governance, modernization risks becoming a disconnected portfolio of proofs-of-concept.

The warnings are visible. The VA’s Electronic Health Record modernization reset and the Army’s cancellation of Future Combat Systems illustrate what happens when ambition moves faster than operational readiness.

Moreover, empirical evidence shows that AI’s productivity impact remains context-specific. In a large-scale field experiment, Erik Brynjolfsson and colleagues at MIT found roughly 14% average productivity gains — but concentrated among less-experienced workers — and that experts often benefit less from current AI tools. A 2024 Upwork Research Institute survey further observed that nearly half of employees using AI were unsure how to achieve expected productivity gains and over three-quarters reported increased workload.

Technology alone does not deliver transformation

Federal modernization efforts have demonstrated their potential. The Technology Modernization Fund has shown that milestone-based investment can accelerate secure digital transformation, with more than $1 billion invested across 70-plus high-impact projects. Meanwhile, the Zero Trust mandate — arising from Executive Order 14028 and OMB policy — has reshaped cybersecurity into a prerequisite for trustworthy AI, enabling granular access control and validated data lineage throughout the model lifecycle.

Similarly, cloud migration continues to expand the government’s ability to scale analytics and secure sensitive mission data. Initiatives such as Login.gov now provide secure identity services to well over 120 million users across agencies, offering proof that aligned modernization delivers measurable citizen and mission value. 

Yet despite such progress, readiness debt continues to accumulate. Too many efforts remain siloed, unevenly executed or insufficiently tied to mission outcomes.

A framework to retire digital readiness debt

Modernization must now be guided by a unifying objective: retiring the readiness debt. Progress requires investment in four interlocking pillars.

First, agencies must prioritize the mission data layer. Authoritative, interoperable, well-governed data is the single strongest determinant of whether AI becomes mission-enhancing or simply a costly demonstration.

Second, procurement must shift from paying for deliverables to paying for performance. Contract structures such as modular and outcomes-based acquisitions allow agencies to mitigate risk while capturing real mission benefits.

Third, privacy and cybersecurity must be designed as foundational inputs. Zero Trust principles, combined with FedRAMP-aligned cloud environments, ensure AI systems start from a secure baseline rather than inheriting legacy vulnerabilities.

Fourth, modernization must include the workforce. Closing the AI skills gap — especially in data engineering, security policy and AI governance — is essential to scale emerging capabilities beyond isolated early adopters.

A human–machine workforce, built on trust

The objective of AI in government is not workforce substitution — it is workforce elevation. AI should automate repetitive tasks while preserving human judgment where accountability and values matter most. As Forrester notes in its 2025 Human-Centered AI Adoption Insights Report, leaders who view AI solely as a technology exercise risk missing the human-experience factors that ultimately determine adoption and success.

To build trust, agencies must integrate human oversight with transparent AI governance practices: maintaining model documentation, tracking decisions and data sources and empowering employees through continuous enablement. Clear operational playbooks — generated and reinforced by AI — can convert complex tasks into repeatable, confidence-building workflows.

When humans and AI advance together, readiness debt declines.

Execution is now the differentiator

Adversaries are advancing AI-enhanced tactics abroad, while citizens demand faster, more secure digital services at home. With OMB’s latest memoranda transitioning AI from policy abstraction to execution-driven expectation, agencies that act decisively over the next budget cycle will define the government’s AI trajectory for the rest of the decade.

However, AI will not repair disjointed data architectures or obsolete workflows. It will amplify them — exponentially.

Federal leaders must now assess their digital readiness debt honestly, resource its reduction strategically and measure progress relentlessly. Zero Trust must become the baseline. Outcome-based modernization must be the standard. Mission data quality and workforce enablement must take priority over technology novelty.

Retiring this readiness debt is no longer an IT aspiration. It is a mission assurance requirement — and a national resilience imperative.

Nick Dunn has served as the CEO of PCI-Government Services since August 2019 and is a subject matter expert when it comes to federal procurement-related issues.