A Phased Approach to Government LLM Deployment

A roadmap for phased, accountable LLM deployment in the public sector.

Government agencies are eager to harness large language models (LLMs) to improve operations and decision-making, but rushing into full autonomy carries significant risks. A prudent strategy is to deploy LLMs in three phases, gradually expanding their role while maintaining strong human oversight. This phased approach allows controlled expansion with minimal disruption, ensuring each stage builds on lessons learned from the last. In fact, many organizations start with internal or “shadow mode” AI uses to prove reliability before scaling up. Government adoption is already underway – by early 2024 over 90,000 U.S. public sector employees had used ChatGPT for tasks like translating documents and drafting policy memos. The framework below illustrates how agencies can move from cautious pilot uses to confident, accountable integration of LLMs into their workflows.

Phase 1: Advisory Role (LLM as Assistant)

In Phase 1, LLMs serve strictly as advisory assistants to human staff. They can generate content and insights, but have no operational authority. Every AI output is verified and approved by a person before any use. This ensures that humans remain the decision-makers and catch any errors or inappropriate suggestions from the model. The LLM’s role is supportive: helping civil servants work faster and smarter, without ever acting on its own. Key characteristics of Phase 1 include:

• Supportive Tasks Only: The LLM helps with research, drafting, classification, and recommendations. It might summarize lengthy documents, suggest wording for a report, or classify incoming requests, but always as a first draft or suggestion. For example, an AI could draft a policy memo or RFI response which a human official then edits and approves. The model can rapidly summarize laws or pull together information across agencies to aid coordination, saving staff time while improving the breadth of information considered.

• Strict Human Review: All LLM-generated material undergoes strict human review and editing before it influences any decision or leaves the agency. At this stage the AI is essentially a research assistant or junior analyst. It cannot approve or implement anything itself. Human oversight is the final safeguard to ensure no erroneous or biased AI content slips through. This “human-in-the-loop” approach is critical because LLMs are known to sometimes produce inaccurate or fabricated information (hallucinations). Officials treat the AI’s output as informative input, not as fact, double-checking all critical details.

• Example Use Cases: Phase 1 deployments are already proving useful for routine knowledge work. Agencies have employed generative AI to draft internal reports, policy memos, and meeting minutes, which human staff then refine. LLMs excel at summarizing laws and regulations into plain language, helping officials digest complex statutes. They can also quickly consolidate information from different departments. For instance, generating a synopsis of all agency inputs on a cross-agency initiative. All these applications lighten the burden on employees without compromising accountability, since humans still perform the final analysis and approvals.

By confining LLMs to an advisory role, agencies avoid immediate operational risks. Any mistake the model makes (e.g. a misleading summary or an off-base recommendation) is caught in review before it can do harm. This phase builds comfort and trust in using AI tools: officials learn the LLM’s capabilities and quirks, and the organization develops policies for responsible use (such as guidelines on handling privacy or bias in AI-generated content). Many early concerns, like the model misinterpreting context or lacking ethical judgment, are mitigated by the requirement that a human evaluates everything. In short, Phase 1 lets the agency capture quick wins (speed and efficiency gains) safely, establishing a foundation for deeper AI integration.

Phase 2: Shadow Work (Parallel AI-Human Execution)

In Phase 2, the LLM graduates from purely hypothetical assistance to performing work in parallel with human employees – a “shadow” mode where it mirrors tasks without yet owning them. The idea is to test the AI’s consistency, accuracy, and biases in a realistic setting while keeping decision authority with humans. By comparing the LLM’s outputs side-by-side with human outputs on the same tasks, the agency can benchmark the model’s performance and identify any issues, all without risking real-world consequences if the AI is wrong. Key aspects of Phase 2 include:

• Parallel Task Replication: The LLM is set up to replicate certain decision or analysis processes simultaneously with staff. For example, if human analysts are triaging public inquiries or reviewing permit applications, the AI system does the same in the background. It might generate its own classification or recommendation for each case, without acting on it. The human continues to do the official work, but now the agency can observe where the AI agrees or diverges. This “shadow mode” deployment means the AI’s suggestions are recorded for evaluation but not applied without human approval. It’s akin to an internship or simulation and the AI is learning the ropes under supervision.

• Performance Benchmarking and Bias Checking: Running AI alongside humans allows agencies to measure the model’s accuracy and identify biases or error patterns. Are there cases where the LLM’s recommendation consistently differs from the experienced staff’s decision? If so, was the AI missing context or using flawed reasoning? These comparisons help validate the AI’s consistency and reliability. Any systematic issues (like the model over- or under-estimating certain risk factors) can be detected and addressed at this stage. Essentially, Phase 2 provides real-world QA/testing for the LLM: the organization gathers data on how the AI performs under actual agency workflows and where adjustments or additional training might be needed.

• No Impact on Operations: Crucially, even though the AI is working on live cases or data, there is zero decision risk in Phase 2 because the LLM’s outputs remain advisory and unimplemented. Humans continue to make all decisions and take all actions. If the AI’s parallel output is incorrect, it has no effect other than to inform improvements. This controlled environment limits the “blast radius” of any AI mistakes. It’s a safe sandbox: the agency gains insight into AI capabilities under real conditions (including edge cases and stress scenarios) without endangering mission outcomes or public trust.

• Building Confidence to Proceed: As Phase 2 progresses, the agency can quantitatively assess whether the LLM is ready for a larger role. For instance, if over several months the AI’s recommendations match human decisions, say, 95% of the time (and the 5% of differences are explainable or minor), that builds confidence. This cautious approach ensures that by the time the AI is trusted with more autonomy, it has effectively “earned” that trust through demonstrated performance. The agency can also use Phase 2 to develop standard operating procedures for AI oversight, such as dashboards to monitor AI outputs and workflows for data scientists to review any problematic cases the AI flagged differently than humans.

Phase 2 is about validation and iteration. The LLM might be tuned further during this phase as developers learn from its mistakes. Any necessary model fine-tuning or policy adjustments (e.g. adding guardrails to prevent undesirable outputs) can be implemented before real stakes are involved. The outcome of Phase 2 is a well-characterized AI system with known accuracy levels and mitigated weaknesses. If the AI does not meet expectations, the agency can decide to keep it in an assistive role or refine it further, rather than prematurely relying on it. But assuming Phase 2 is successful, the stage is set to carefully grant the AI a larger proactive role.

Phase 3: Autonomous Proposals with Human Approval

In Phase 3, the LLM becomes an active proposer of actions or decisions, essentially taking on a role akin to a high-level aide or analyst that can initiate recommendations. However, human oversight remains in force through a “final approval” requirement for anything the AI proposes. This phase is about leveraging the AI’s ability to synthesize information and generate well-reasoned options at a speed and scale beyond human capability, while still preserving human judgment for the final step. In other words, the AI can recommend, but it cannot execute or decide alone on consequential matters. Key features of Phase 3 include:

• AI-Driven Proposals: The LLM is now entrusted to analyze data and suggest courses of action or draft decisions in areas it has proven competent. For example, the AI might draft a complete policy proposal, recommend allocating resources to a particular emergency response based on data, or propose answers to an inter-agency strategy question. Unlike Phase 1, where the AI only responded to direct prompts, here it might proactively identify issues and formulate options (within its domain scope). This is possible because by Phase 3 the model is likely deeply customized to the agency’s needs. It is fine-tuned on relevant internal data and rules, so its outputs align with the agency’s context and objectives. Each agency can also embed its specific governance constraints: for instance, an AI assistant at the Department of Health and Human Services (HHS) will be tuned to health data privacy laws, while one at DHS will incorporate homeland security protocols. Such domain-specific tuning ensures the AI’s proposals are not one-size-fits-all but respect the nuances of the field (e.g. “autonomy” means something different in a military context vs. a healthcare context, which fine-tuning accounts for ).

• Human Approval as Gatekeeper: No matter how sophisticated the AI’s recommendation, a human official must review and formally approve any action before it is implemented. This maintains legal and ethical accountability. The AI is not a decision-maker, but a proposal generator. Agencies may implement multi-level review workflows, where an AI-generated plan is routed to the appropriate authority who evaluates it and either signs off or sends it back for revision. The principle of “human on the loop” applies: humans monitor the AI’s autonomous suggestions and intervene as needed. This structure addresses the paramount concern that critical decisions should not be made by a machine alone. Even in this advanced phase, the human remains ultimately responsible, which aligns with emerging regulations and ethical guidelines that demand human accountability for AI-driven outcomes.

• AI Ownership of the Prep Work: Within these guardrails, the LLM can own the preparatory and analytical workload leading up to a decision. It might gather and analyze thousands of pages of data, run simulations, and compile a recommended course of action. By Phase 3, agencies often trust the AI to handle this pipeline end-to-end: for example, an LLM could routinely generate draft budget allocations or policy updates which officials then review. This dramatically speeds up policy development and operational responsiveness. Humans are freed to focus on judgment calls and stakeholder inputs, rather than crunching numbers or wading through paperwork. The LLM essentially becomes a tireless strategic analyst, surfacing insights and fully fleshed-out options for leaders to consider.

• Continuous Oversight and Improvement: The relationship between the AI and human decision-makers in Phase 3 should be one of continuous feedback. Each time a human approves or modifies an AI proposal, that feedback can be used to further tune the model or update its knowledge base. Agencies will likely have established AI governance boards or oversight committees by this phase to monitor AI recommendations, audit their quality, and ensure compliance with laws and ethics. Any proposal the AI generates is logged and traceable, creating an audit trail. This is crucial for transparency: if an AI’s suggestion influences a policy, the record of that input should be preserved. (Notably, even AI prompts and outputs may be subject to public records laws – for instance, a UK minister’s ChatGPT drafts were released under FOI requests, underscoring that AI-assisted policymaking must still be transparent.) By actively governing the AI’s contributions, agencies ensure that increased autonomy does not mean unchecked power. The AI remains a tool, albeit a highly advanced one, serving the agency’s mission under human guidance.

• In Phase 3, the agency truly capitalizes on AI at scale: LLMs can propose innovative solutions and streamline decision pipelines across the organization. Yet the phased approach’s core safeguard stays intact, where humans are in the loop to verify and approve. This final phase works best when agencies also adapt their culture and training: employees must understand how to interpret AI outputs and not become overly reliant on them. When executed properly, Phase 3 yields a powerful human-AI synergy: the AI provides breadth, speed, and unbiased pattern recognition; the humans provide context, values, and final judgment. Many private enterprises have found that keeping humans in charge of final decisions, even as AI agents handle routine steps, leads to better outcomes and acceptance. Government agencies, with their heightened accountability, will likewise benefit from this “AI co-pilot” model over a fully autonomous AI model. The phased journey can stop at Phase 3, as full automation without any human approval is generally not advisable in public sector contexts given the need for accountability and public trust. Phase 3 strikes the balance: maximal AI assistance, minimal risk.

Model Customization and Tuning for Government Needs

A critical success factor for deploying LLMs in government is customizing the models for the specific domain and agency context. Out-of-the-box, general-purpose LLMs (like a vanilla ChatGPT) will not understand government terminology, applicable laws, or the nuances of a specific agency’s work. They also might hallucinate facts or have errors in specialized queries. To address this, agencies should invest in fine-tuning and domain adaptation of LLMs as part of Phase 2 and Phase 3:

• Fine-Tuning on Domain Data: Fine-tuning involves training the base LLM on agency-specific data – such as past policy documents, regulations, technical manuals, or historical case files. This gives the model relevant domain knowledge and vocabulary, dramatically reducing errors. For example, national security agencies report that fine-tuning LLMs on internal data and industry-specific publications cuts down on contextual mistakes and hallucinations, while increasing the relevance of outputs. By learning agency jargon (“BP” meaning blood pressure in health context, not British Petroleum, etc.), the LLM can provide far more accurate and trustworthy assistance. Each agency or domain (health, finance, law enforcement, etc.) can develop its own tuned model or “LLM module” optimized for its needs.

• Incorporating Policy Constraints: Customization isn’t only about data; it’s also about aligning the AI with legal and ethical constraints of government work. During fine-tuning or through prompt engineering, agencies can instill rules (for example, an AI assistant for benefits determination must follow eligibility criteria and cannot use protected characteristics in recommendations). These guardrails help ensure the model’s behavior stays within authorized boundaries. Agencies may also integrate retrieval-augmentation (RAG) techniques, where the LLM is provided with authoritative reference documents from agency knowledge bases for each query. This grounds the model in verified facts and up-to-date policy, mitigating the risk of unsanctioned or outdated information influencing its output.

• Privacy and Security in Custom Models: Fine-tuning has the added benefit of enabling secure, on-premise deployments. Many off-the-shelf LLM services require sending data to a third-party cloud, raising concerns for sensitive government data. By fine-tuning a model that can be hosted within a government’s own servers or vetted cloud environment, agencies can retain full control over data and model usage. This addresses confidentiality and compliance requirements. Moreover, a well-tuned smaller model can often achieve the needed performance on specific tasks at a fraction of the computational cost of a giant general model. Government teams have found that adapting a pre-trained LLM to their narrow domain not only improves accuracy but lowers ongoing costs, since the model doesn’t waste effort on irrelevant general knowledge. For instance, a fine-tuned model might require fewer tokens to process a query because it “knows” the context, thus saving on cloud inference charges.

• Continuous Tuning and Learning: Model customization is not a one-off process. In Phases 2 and 3, as the LLM is used in practice, agencies should continuously gather feedback (where did the model perform well? where did it stumble?). This data can be used to further refine the model. Some agencies might establish an internal AI training team or partner with vendors to periodically update the model with new data (e.g. new regulations, evolving threats) so it remains current. Over time, each agency can build up a library of fine-tuned models. For example, a legal-analysis LLM, a cybersecurity threat triage LLM, etc. – each optimized for a slice of government operations. Sharing these within the government community (subject to security clearance) could accelerate AI adoption across agencies facing similar challenges.

In summary, customization tailors LLMs into effective civil service assistants. It bridges the gap between a general AI and a mission-specific expert system. Agencies that skip this step often find generic AI answers unsatisfactory or risky for high-stakes uses. By contrast, those who invest in tuning unlock the full potential of LLMs and achieve both higher accuracy and better compliance with government standards. This effort pays off especially as the AI moves into Phase 3 roles, where domain knowledge and context sensitivity are essential for the AI’s proposals to be sound.

Legal Accountability and Oversight

Deploying LLMs in government must go hand-in-hand with frameworks for legal accountability, transparency, and ethical oversight. Public sector decisions can have a significant impact on citizens, so even if an AI is involved in shaping those decisions, the mechanisms of accountability cannot be relaxed. Several measures ensure that the phased approach remains grounded in democratic principles and the rule of law:

• Preserve Human Responsibility: As a baseline, agencies should formalize the policy that no AI system, no matter how advanced, has the authority to make binding decisions – that authority lies with designated human officials. AI-generated recommendations or drafts are advisory inputs to an inherently human decision-making process. This principle may be codified in internal guidelines or even legislation. For instance, a 2025 White House memorandum on AI in federal agencies explicitly requires human oversight, the ability for human intervention, and clear human accountability for high-impact AI use cases. In practice, this means if an AI helps draft an enforcement action or a benefits decision, a human officer must review and sign off, remaining answerable for the outcome just as if they’d written it themselves. The AI is a tool, not a scapegoat. Accountability cannot be outsourced to an algorithm

• Documentation and Transparency: To maintain public trust, agencies need to document AI involvement in decision processes, so that there is a transparent record of how a conclusion was reached. This might include logging AI prompts and outputs, version-tracking AI-generated content, and indicating in official records when a document was AI-assisted. Such records are important not only for internal audit and improvement, but also potentially for public disclosure. Notably, communications by officials via AI can be subject to freedom-of-information (FOI) laws. Governments should anticipate this by proactively maintaining logs and being prepared to explain how an AI’s input factored into any decision. This level of transparency will help answer questions from oversight bodies, courts, or the public in the event an AI-informed decision is controversial or challenged.

• Bias and Fairness Auditing: Legal accountability also means ensuring that AI does not introduce impermissible bias or inequality into government services. Agencies should conduct regular audits of LLM outputs for compliance with anti-discrimination laws and equity principles. This might involve testing the model with a diverse range of scenarios to see if it produces unfair outcomes for certain groups. If an AI is used to propose resource allocations or enforcement targets, rigorous checks are needed to confirm it isn’t inadvertently perpetuating bias (for example, basing decisions on historically biased data). Many jurisdictions are developing or have passed laws around algorithmic accountability that require assessments and documentation of an AI system’s impact on fairness and rights. Governments must be ready to demonstrate that their LLM deployments were vetted for such concerns and include mitigation measures.

• Robust Oversight Mechanisms: Just as any important government activity has oversight (inspector generals, audits, judicial review), AI usage should be subject to oversight. Agencies could establish AI oversight boards comprising ethicists, legal experts, and community representatives to periodically review how LLMs are being used and advise on policy adjustments. Internally, performance reviews of AI should be institutionalized – e.g. requiring a quarterly report on AI-assisted decisions, error rates, and any incidents. In case of an AI error that leads to a problem (such as an AI drafted a public communication with an embarrassing mistake that wasn’t caught, or recommended an action that was legally questionable), agencies should have an incident response plan, just as they do for data breaches or other operational issues. This might involve notifying affected parties, correcting the decision, and retraining the model to prevent repeats. The courts also have a role: any decision that is appealed or litigated must be explainable. If an AI was involved, the agency should be able to articulate the rationale in human-understandable terms, rather than saying “the computer decided.” Maintaining that chain of reasoning (through techniques like storing the reference data the LLM used, or using explainable AI tools for complex models) will be important to uphold due process and administrative law standards.

• Legal Procurement Clauses: As touched on earlier, procurement contracts should include compliance with relevant laws (privacy, security, intellectual property) and indemnities for misuse. Vendors might need to agree to certain auditing rights or to fix issues if the model violates legal norms. Additionally, agencies must navigate intellectual property questions – e.g. who owns an AI-generated work product and can it be released or cited? These are evolving areas of law, so a cautious approach (treat AI outputs similarly to staff drafts) is prudent until clearer guidance emerges.

Above all, legal accountability in government LLM deployment is about keeping the chain of responsibility intact. The phased approach actually helps in this regard: by the time an AI system reaches Phase 3, it has been rigorously tested and integrated into governance processes, with plenty of human oversight baked in. Each phase allows the agency’s lawyers, policymakers, and stakeholders to adjust and ensure that the AI usage aligns with existing legal frameworks. This evolutionary integration makes it far less likely to have a catastrophic legal or ethical failure. It also signals to the public and oversight entities that the agency is being responsible. Such diligence is not only good governance but also critical for sustaining public trust as AI becomes a more common tool in the public sector.

Building Institutional Readiness and Risk Mitigation

Adopting LLMs is as much an organizational change management challenge as it is a technical one. A phased deployment gives the institution time to build AI literacy, update workflows, and cultivate an AI-ready culture. Each phase serves as a training ground for both the AI and the people. By the end of Phase 3, the goal is an agency that fully reaps AI’s benefits while confidently controlling its risks. Key elements in building this readiness include:

• Workforce Training and AI Literacy: Agencies should invest in upskilling employees to work effectively with AI. In Phase 1, this might mean training analysts on how to craft good prompts for the LLM and how to critically evaluate AI outputs. By Phase 3, training extends to managers and decision-makers on interpreting AI recommendations, understanding the basics of how the model works, and knowing its limits. Some agencies have created AI Centers of Excellence or AI Task Forces to share best practices and provide expert support to teams using LLMs. Over time, AI literacy becomes part of the organizational DNA. This helps reduce fear and resistance, as employees see AI as a tool to empower them rather than a threat to their jobs. It’s important to communicate from leadership that the objective is a human-AI partnership, where mundane tasks are automated and employees can focus on higher-value work. Such messaging, combined with proper training, fosters acceptance and even enthusiasm for the new tools.

• Gradual Change Management: The phased approach inherently supports change management by introducing AI in manageable steps. Early wins in Phase 1 (e.g. an AI-generated report that saved the team a week of work) can be celebrated and publicized internally, building momentum. In Phase 2, involving staff in the evaluation of the AI’s performance can actually engage them in the improvement process. Front-line workers might provide feedback on where the AI got things wrong or right, giving them a voice in shaping the tool. This inclusive approach helps avoid the feeling of a top-down AI imposition. By Phase 3, many processes will have been adapted based on iterative lessons, and staff will have had time to adjust. Phasing it in allows the agency to learn and adapt incrementally, which is a classic best practice for implementing any transformative technology.

• Risk Mitigation and “Fail-Safe” Mechanisms: Each phase functions as a risk filter. Phase 1 confines risks to trivial levels (nothing leaves human oversight). Phase 2 exposes the AI to real data but with a safety net (no autonomous actions). By Phase 3, when the stakes are higher, the AI has a strong track record and numerous safeguards. Additionally, agencies should maintain manual overrides and contingency plans even in Phase 3. If the AI system outputs start to look unreliable (say due to a change in data patterns), there should be an easy way to pause AI-generated proposals and revert to purely human-driven work until issues are resolved. Monitoring systems can be put in place to detect anomalies in AI behavior or performance dips, triggering an alert or rollback to earlier phases if needed. This means institutional readiness is not just about preparing to use AI, but also about being prepared not to use it if it’s not performing as expected.

• Leadership and Governance Commitment: Successful AI deployment requires champions at the leadership level who understand the technology’s potential and pitfalls. Designating a Chief AI Officer or similar role can anchor accountability and oversight for the initiative. This leader can coordinate across IT, legal, procurement, and mission units to ensure everything from data quality to ethical use is addressed holistically. Leadership should also frequently communicate the vision – for example, emphasizing that AI is being adopted to improve public service delivery, and that it will be done responsibly. This top-level support and clear vision help align the institution. Governance structures (like an AI steering committee) should regularly review progress at each phase and decide readiness to move to the next. They will consider metrics from Phase 2, the legal reviews, stakeholder feedback, etc., before green-lighting Phase 3.

Through these efforts, an agency builds not just a successful AI project, but enduring institutional capacity for innovation. The phased approach can serve as a template for future technology introductions as well, demonstrating that the organization can absorb change in a disciplined way. By avoiding reckless deployment and instead earning trust step by step, the agency also sets an example to the public that it values caution and competence over hype. This is crucial in government, where a single high-profile AI failure could set back public acceptance for years. Instead, phased success stories can build public confidence. With proper procurement, customization, and oversight woven in, this approach turns LLMs into powerful allies for public servants, not ungoverned agents. The result is an institution that can innovate rapidly with AI, all while keeping its fundamental obligations to the public trust intact.