Trust: The Key To Unlocking Federal AI's Full Potential

Executive Summary 

Federal agencies are under pressure to scale AI responsibly. New directives such as OMB’s M-25-21 emphasize innovation, governance, and public trust. Yet trust in AI is declining among citizens and federal employees, even as use cases multiply. Without trust, agencies face oversight pushback, workforce resistance, and slower adoption. 

This blog explores why trust is harder than it looks, what risks agencies face if they ignore it, and how a practical framework can turn trust into momentum. We highlight lessons from other sectors, share a federal case example, and introduce RightSeat’s Trust Lab, our structured approach to building AI programs employees and citizens believe in. 

The takeaway: trust is not a compliance checkbox. It is the multiplier that determines whether federal AI adoption accelerates or stalls. 

Programs Stall Without Trust 

In April 2025, the Office of Management and Budget issued memo M-25-21 directing agencies to accelerate responsible AI adoption. The memo emphasized innovation, governance, and public trust. Federal leaders can no longer treat trust as optional. 

Agencies are under pressure to show results quickly. Yet many pilots focus on technical milestones such as model accuracy, speed of deployment, or proof-of-concept demos. What often gets left behind is the human side of adoption. If employees doubt the tools or citizens question their fairness, progress slows. 

Public skepticism is rising. The Edelman Trust Barometer shows U.S. trust in AI has dropped from 50% in 2019 to just 35% in 2025. Meanwhile, GAO found that federal AI use cases nearly doubled in a year, with generative AI adoption increasing ninefold. The same report warned that credibility could erode if these systems are not explainable or governed. Federal employees echo this concern: surveys show limited confidence in their agencies’ ability to use AI responsibly. 

The risk is clear. Without workforce and citizen trust, AI tools will not achieve their intended impact. 

Context: Why Trust Is Harder Than It Looks 

Trust is not new in government. Agencies have long been required to safeguard data, operate transparently, and build accountability into programs. AI complicates these responsibilities. Models can behave in opaque ways, generate unpredictable outputs, and rely on datasets that may contain bias. 

Several factors make building trust in AI uniquely challenging: 

• Speed vs. scrutiny. AI evolves quickly, while government processes are designed for caution. 

• Transparency trade-offs. Oversimplify and explanations feel evasive. Overcomplicate and they confuse. 

• Cultural resistance. Employees may fear deskilling. Citizens may worry about fairness or surveillance. 

• Fragmented governance. What one agency approves may raise red flags elsewhere. 

Other industries show the same pattern. In healthcare, AI diagnostics can outperform humans, but adoption lags because doctors and patients hesitate to trust machine judgment. In banking, algorithmic credit scoring improves efficiency but raises fairness concerns. In both, trust rather than technology decides whether tools scale. 

The federal space is no different. 

Solution: Build Trust Into Designs From Day One 

Research and practice both show that trust must be designed in, not added later. 

• Mandates require it. M-25-21 directs agencies to embed governance and manage AI risk up front. 

• Scale multiplies impact. GAO reported that federal AI use cases jumped from 571 to over 1,100 in a single year. Without trust, small problems scale quickly. 

• Trust gaps reduce adoption. Studies show that a perceived lack of transparency reduces citizen confidence even when efficiency improves. 

A practical framework includes: 

• Governance by design – Define accountability early. Who owns the model? Who monitors outcomes? How are risks escalated? 

• Transparency at the right level – Executives need clarity on trade-offs. Oversight bodies need detailed documentation. Employees and citizens need plain-English explanations. 

• Human-in-the-loop safeguards – Keep people involved in decisions with material impact. 

• Workforce engagement – Involve employees early. Address concerns, highlight opportunities, and provide training. 

• Citizen-centric design – Start small with public-facing pilots, ensure fairness, and create feedback loops. 

These steps move trust from an abstract principle to a design choice. 

Case Example: Turning Resistance Into Buy-In 

A civilian agency piloted generative AI to draft regulatory summaries. The technical pilot worked, but employees resisted. They worried the tool would deskill their roles and miss nuance. 

The agency reframed the tool as an assistant, not a replacement. Employees were trained to edit and approve drafts, with clear accountability lines. Adoption rose. Employees reported higher satisfaction, since the tool cut repetitive work but left judgment in their hands. 

The lesson is clear. When employees trust the role AI plays, adoption accelerates. 

Risks of Ignoring Trust 

Leaders under pressure to move fast may see trust frameworks as slowing them down. The opposite is true. Programs that skip trust invite: 

• Oversight pushback from GAO, Congress, or Inspectors General. 

• Workforce resistance if employees feel sidelined. 

• Public skepticism when citizens encounter errors or lack recourse. 

History shows how fragile credibility can be. Healthcare.gov’s troubled 2013 launch stumbled not because of weak policy but because trust was lost after technical failures. Recovery required costly, visible interventions. AI programs risk the same outcome if adoption outpaces trust. 

RightSeat Point of View: The Trust Multiplier 

At RightSeat AI, we believe trust is the prerequisite for successful AI adoption in government. That belief led us to create the RightSeat AI Trust Lab. 

The Trust Lab is not a compliance checklist. It is a structured approach that helps agencies: 

• Earn workforce buy-in 

• Align with governance standards 

• Strengthen public trust 

We co-pilot with leaders to translate mandates like M-25-21 into practical steps that fit their context. Not every system needs the same level of explainability. Not every pilot requires the same oversight. The key is clarity about where risks lie and how safeguards address them. 

We call this the Trust Multiplier: 

• Without trust → adoption slows, oversight grows, credibility erodes. 

• With trust → adoption accelerates, oversight aligns, credibility strengthens. 

Moving Forward: Building Based On Trust 

Federal leaders cannot afford to treat trust as secondary. Programs that launch without trust frameworks risk delay, resistance, and reputational damage. Programs that lead with trust unlock faster, more sustainable scale. 

The path forward is not about chasing the newest AI tool. It is about building systems employees and citizens believe in. 

Agencies that treat trust as central will scale responsibly, deliver faster, and avoid costly setbacks. 

If your agency is ready to turn trust into momentum, our team is ready to co-pilot the journey. The RightSeat AI Trust Lab is built to help you get there practically, responsibly, and with confidence. 

Sources 

• Government Accountability Office, Artificial Intelligence: Federal Efforts to Ensure Responsible Use (2024). 

• Partnership for Public Service, State of the Federal Workforce and AI Adoption (2024). 

• Office of Management and Budget, M-25-21: Accelerating Federal Use of AI through Innovation, Governance, and Public Trust (2025). 

• ArXiv, Responsible AI in Government: Transparency and Citizen Confidence (2025).