Overcoming Challenges in Scaling AI Adoption in Government

Why Government AI Stalls Before It Scales

AI could change how public services run by automating benefits processing, improving resident services, and making better use of limited resources. Yet many pilots never reach production. When GAO reviewed federal AI inventories for fiscal year 2022, agencies reported about 1,200 current and planned use cases, but only about 200 were actually in use ^[1].

Vision isn't the missing ingredient. Agencies run into technical, organizational, and institutional barriers at the same time:

• Poor data quality undermines models.
• Regulatory uncertainty slows decisions.
• Budget limits force trade-offs between innovation and compliance.
• Procurement processes built for traditional software fit poorly with iterative AI work.

For government CIOs and agency heads, scaling AI has become one of the defining modernization challenges of this decade.

The Pressures Agencies Face

Government operates at enormous scale. Federal spending alone runs into the trillions of dollars each year ^[2], and even modest efficiency gains in licensing, benefits, and permitting could save significant money while improving service.

The policy environment has also shifted. Executive Order 14110 (2023) prompted agency-level AI governance work, but it was revoked in January 2025 ^[3]. Federal agencies now operate under OMB Memorandum M-25-21 ^[4]. It requires:

• Chief AI Officers
• governance boards
• AI strategies
• minimum risk management practices for high-impact AI

The NIST AI Risk Management Framework remains a widely used voluntary reference.

The constraints are just as real:

• IT budgets are flat or shrinking in real terms at many agencies.
• Legacy infrastructure makes integration complex.
• Government systems remain frequent targets for cyberattacks.
• Compliance requirements (FISMA, HIPAA, FedRAMP, and procurement rules) can stretch implementations from months into years.

Five Barriers to Scaling AI

1. Data Quality and Integration

AI is only as good as its data. Most agencies run on fragmented legacy systems with separate databases for each program, inconsistent standards, and incomplete records. A benefits agency building a fraud-detection or application-screening tool may first need to reconcile data from many systems, which can consume a large share of a project's timeline. McKinsey also flags data-sharing agreements as a particular hurdle for states and cities ^[5].

What helps:

• Establish data governance before implementing AI, not after
• Consolidate siloed data with appropriate de-identification and access controls
• Audit data quality and fix gaps before training models
• Use synthetic data carefully to supplement limited datasets while protecting privacy

Agencies that invest in data infrastructure first are better positioned for shorter timelines and more accurate models.

2. Regulatory Uncertainty and Compliance Complexity

Agencies operate under overlapping rules. Federal agencies answer to FISMA, health agencies add HIPAA, and defense contractors add CMMC. Each regime treats AI governance, data handling, and vendor vetting differently, so compliance teams often see AI as a risk multiplier. For federal agencies, M-25-21's minimum practices for high-impact AI now provide a common baseline ^[4].

What helps:

• AI governance policies developed with compliance officers, not handed to them by IT
• Risk assessments tailored to the use case (predictive analytics carries different risks than a chatbot)
• Vendor evaluation criteria that weigh compliance architecture, not just features
• Compliance built into procurement and implementation from the start

Clear, compliance-informed governance also gives procurement teams consistent benchmarks for evaluating vendors.

3. Organizational Resistance

Government technology projects fail more often because of people than systems. Staff trained on legacy processes for decades don't switch easily to AI-assisted work. Some managers see automation as a threat. Procurement officers may be wary of unfamiliar vendors.

What helps:

• Executive sponsorship that frames AI as support for workers, not replacement
• Gradual rollouts that prove value with early-adopter teams first
• Training designed for non-technical staff, not just IT
• Feedback loops that let frontline workers shape how tools work

Tracking adoption metrics alongside technical performance helps leaders see whether change management is working.

4. Budget Constraints and ROI Uncertainty

Agencies work within fixed annual budgets, and AI projects carry uncertainty. They can run long, need ongoing retraining, and require vendor support that traditional licensing doesn't cover.

What helps:

• Separating infrastructure costs (such as cloud compute) from capability costs (such as model development)
• Phasing investments across fiscal years
• Defining outcome measures early (applications processed per hour, resident satisfaction, compliance issues prevented)
• Using outcomes-based contracts instead of seat-based licenses where possible

5. Vendor Fragmentation

The market for government AI is crowded and uneven. Many vendors offer AI tools, but fewer understand government compliance. Some lack FedRAMP authorization, and some haven't been tested in real government operations. Evaluating them is hard when AI itself is still unfamiliar to many buyers. Federal buyers should also follow OMB Memorandum M-25-22, which governs AI acquisition ^[6].

Practices From Successful Deployments

A Phased Implementation Model

Successful programs tend to follow a clear progression:

Phase 1: Foundation (months 1 to 6)

• Audit existing data and systems
• Prioritize use cases tied to policy goals
• Establish a compliance baseline
• Identify pilot teams and success metrics

Phase 2: Pilot (months 6 to 12)

• Deploy AI in a limited scope with real workflows
• Measure against predefined metrics
• Iterate based on user feedback
• Document lessons learned

Phase 3: Scale (months 12 to 24)

• Expand to more teams and locations
• Refine based on pilot results
• Build organizational capability through training and change management
• Plan for ongoing model maintenance

This sequence lets agencies prove value at small scale before committing to agency-wide rollouts.

Cross-Functional Teams

Agencies that scale successfully put compliance, procurement, IT, and program staff on the same team from the start. That prevents a common failure: IT builds a system that compliance rejects or procurement can't buy. McKinsey recommends a cross-functional governance group that includes agency leaders, data scientists, and engineering, legal, and cybersecurity experts ^[5].

The Federal Reserve Board's published compliance plan for M-25-21 offers one concrete model ^[7]. An AI Program team reviews every current or planned use case against set criteria for high-impact characteristics. Any high-impact use requires explicit approval from the Chief AI Officer.

Vendor Partnership Models

Leading agencies treat AI vendors as implementation partners rather than feature suppliers. That means:

• Longer-term contracts that give both sides stability
• Joint problem-solving on data, compliance, and integration
• Success metrics tied to policy outcomes
• Transparency requirements about model behavior and limitations

The Path Forward

Scaling AI in government is achievable. It takes:

1. Structured governance that builds compliance into decisions from the start
2. Phased implementation that proves value before agency-wide rollout
3. Cross-functional teams that align IT, procurement, compliance, and program operations
4. Vetted partners who understand government constraints
5. Documented frameworks that future projects can reuse

The GovWire Exchange can help in the early discovery phase by surfacing technology providers focused on the public sector. It's a starting point for identifying potential partners, not a substitute for your agency's own procurement and compliance evaluation.

Start Exploring

Your agency's scaling challenges aren't unique, and many technology providers are building solutions for the public sector. Visit GovWire.ai to explore curated government-focused AI solutions, implementation perspectives, and public sector technology insights.

The real work is building the internal framework and partner relationships to scale AI well. Your procurement, legal, and IT security teams should be central to that process from day one.