Trust, But Verify: A Principle for Leading People—and Managing AI

Why This Principle Still Matters

“Trust, but verify” may sound like Cold War advice, but it’s one of the most practical leadership tools we still have—especially in a world where we’re leading both people and machines.

It boils down to three simple but powerful practices:

• Trust: Extend confidence so others (or AI systems) can move fast and take ownership.
• Verify: Check in, follow up, and validate so you’re not flying blind.
• Balance: Avoid the extremes—too much trust gets you burned, too much checking slows everything down.

Trust Builds Teams, but Accountability Sustains Them

In leadership, trust is the fuel for performance. Teams that feel trusted are more likely to speak up, act boldly, and deliver. But even strong performers benefit from structure and visibility.

If someone says they’ve got it handled, trust them—but also ask how it’s going. A short check-in or shared progress note keeps the momentum real. It’s not micromanaging; it’s reinforcing a culture of ownership with clarity.

AI Is Fast—But Not Always Right

The same logic applies to generative AI.

These tools can generate content, organize thinking, and move work forward faster than ever. But they don’t have judgment. They don’t know when something feels off, sounds wrong, or lacks integrity.

So yes, trust them for speed—but verify everything critical. Check facts. Skim outputs. Adjust tone. Make sure the AI isn’t overpromising or underdelivering on your behalf.

Practical Tips for Managing AI Reliably

If you’re using AI in your work, especially in ways that affect decisions or deliverables, here are a few ways to apply the “trust, but verify” mindset:

1. Cross-check when it matters. Running the same prompt through multiple AIs can reduce hallucinations and expose gaps. Just use this technique sparingly—it comes with real economic and environmental costs.
2. Know your tools. Understand the model you’re working with—its training range, sophistication, and limitations. You should know your AI like you know your team: strengths, blind spots, and when not to rely on it.
3. Spot-check important work. Always do your own quick review before using AI output on anything high-stakes. For instance, when I used AI to recommend a product based on its terms of service, a manual review showed that I couldn’t actually use it the way the AI claimed I could.

The point isn’t to distrust the tool—it’s to use it responsibly.

Trust but Verify Leadership: Finding the Space Between Confidence and Oversight

The real discipline is in the balance. “Trust, but verify” means not defaulting to doubt or control—but also not checking out. It’s a posture of engagement. A way of staying close to what matters, without getting in the way.

That mindset keeps teams aligned and leaders informed—and helps us use AI with the confidence of a partner, not a blindfold.