The following article by Snowbird Transformation Leader Dan Foster appeared as a Feature in Orange Slices AI on March 24, 2026
Last November I attended the Formula 1 race in Las Vegas. Watching cars hit 200+ mph made me think about something unexpected: speed only works when safety is engineered into the system. That lesson applies directly to AI.
Speed Isn’t the Real Story
Watching the cars fly down the strip at more than 200 miles per hour is hard to describe if you’ve never experienced it in person. The speed is breathtaking. But what struck me most wasn’t just the speed. It was how much engineering exists to protect a single human life.
Modern Formula 1 cars are engineering marvels designed to protect drivers at extreme speeds. Over the decades the sport has introduced innovations like crash structures, telemetry monitoring, and the Halo cockpit protection system. Each new safety measure sparked debate at first. Some argued it would slow the sport down. But history has shown the opposite. The safer the sport became, the faster the cars were able to go.
And it got me thinking about something else moving incredibly fast right now: Artificial Intelligence.
Responsible AI Starts with People
AI adoption is accelerating across organizations. Teams are experimenting. Leaders are evaluating risk. Policies are still catching up.
The real question isn’t whether AI will be used. It’s how organizations ensure AI is used responsibly while still encouraging innovation.
Responsible AI is often framed as governance or compliance, but at its core it’s something broader. Responsible AI isn’t about protecting models or algorithms – it’s about protecting people. The goal is to design AI systems that improve human outcomes, preserve trust, reduce waste, and avoid unintended harm.
Why This Matters in Government
In government, the stakes are even higher. AI is not just supporting internal teams – it is shaping how services are delivered to citizens, how decisions are made, and how trust in institutions is maintained. Decisions influenced by AI can impact: Health outcomes; Access to services; Equity and fairness and Public trust. At the same time, there is increasing pressure to deliver services faster; improve efficiency; reduce burden on staff; and modernize legacy systems. This creates a natural tension: How do we move faster while maintaining trust, accountability, and compliance? And this isn’t just theoretical.
In many organizations today, teams are already experimenting with AI – summarizing notes, drafting communications, exploring decision support. At the same time, leadership is working to ensure those uses are safe, compliant, and aligned with policy. Without clear guidance, this often leads to hesitation. Teams slow down. Use cases stall. Or usage becomes inconsistent across the organization. Not because people don’t want to move forward – but because they’re unsure where the boundaries are. The instinct is to introduce more oversight. But oversight alone doesn’t solve the problem. Clarity does.
What’s needed is stewardship – responsibility embedded into how AI us used day-to-day.
For example: Defining clear guardrails so teams know where AI can be safely applied. Providing transparency into how AI contributes to decisions. Creating approved pathways that allow teams to move forward with confidence. This is where industry plays an important role as well – not just providing tools, but helping design systems that are transparent, governed, and aligned with mission needs.
When responsibility is built into the system this way, something shifts. Trust is maintained – and speed becomes sustainable.
The Responsible AI Stewardship Model
Responsible AI requires balancing responsibility across three domains.
- Human Responsibility
Protect people, dignity, fairness, and outcomes – especially for those relying on public services This shows up in how AI is used to support, not replace, human judgment.
For example: Ensuring staff can review and validate Ai-assisted summaries, recommendations, or drafted content before it is used in decisions or communications. Avoiding situations where AI-generated outputs are accepted without understanding the context or implications. Designing workflows where humans remain accountable for final decisions, even when AI is involved.
- Mission / Security Responsibility
Ensure trust, reliability, transparency, and alignment with laws, policies, and public expectations This is about maintaining confidence in both the system and the institution.
In practice, this includes: Making it clear when AI is used in drafting materials, informing decisions, or supporting casework. Ensuring outputs are traceable and can be explained during reviews, audits, or oversight processes. Aligning AI usage with existing policy, privacy, and records management requirements.
- Environmental Responsibility
Use taxpayer-funded resources responsibly and avoid unnecessary waste. AI introduces new forms of consumption – not just financial, but computational and operational.
This can look like: Applying AI to high-value use cases rather than broad, unfocused experimentation. Avoiding duplication of tools and efforts across programs or teams. Being intentional about cost, infrastructure, and long-term sustainment as AI adoption scales.
When these responsibilities are actively balanced, organizations don’t have to choose between speed and trust. They’re able to move forward with both – confidently and at scale.
Operational Principles
In practice, this stewardship shows up through several key principles:
- Human-Centered – AI should augment human capability, not replace human responsibility
- Safety – AI systems should behave predictably and minimize the risk of harm
- Quality – Outputs should meet defined standards and produce reliable results
- Consistency – AI systems should behave reliably across similar situations and use cases
- Transparency – People should understand when AI is being used and how it contributes to outcomes
- Ethics – AI should reflect the values and expectations of the organizations and communities they serve
- Sustainability – AI development and usage should consider energy use and responsible consumption of resources
These principles help ensure AI improves human outcomes, preserves trust, reduces waste, and avoids unintended harm.
Speed With Confidence
The lesson from Formula 1 is simple: Speed without safety eventually stops the race.
But when safety is engineered into the system, speed becomes sustainable. Responsible AI works the same way.
In government, moving fast without trust doesn’t just slow progress – it can stop it entirely. But when stewardship is built into how AI operates, something different happens.
Teams stop asking: “Are we allowed to use AI?” And start asking: “How can we use AI better?” And that’s when innovation really accelerates – safely, responsibly, and at scale.
Tom Munro
Mike Kleiman