Bar Raiser
Summary: The verbatim LinkedIn post behind the wiki concept of the same name — reproduced word for word as it was published.
Sources: LinkedIn Posts.md (“Bar Raiser,” posted 2025-10-21), verbatim
Last updated: 2026-07-11
Originally posted October 21, 2025.
As AI accelerates, the most important voice in the room might not be the one developing the model—but the one asking if the outcome is right for the world beyond it.
Early in my career, I heard a phrase repeated so often it became a cliché: “People are our most important asset.”
But it was true—and I spent a large part of my leadership journey proving it out. Hiring, developing, and retaining great people—those with diverse backgrounds and a willingness to challenge assumptions—became one of the most important parts of my work.
On a visit to AWS years ago, I was introduced to their concept of the Bar Raiser. If you’ve never looked into it, it’s worth reading about. At its core, the idea is simple but powerful: every hiring decision includes someone outside the immediate organization empowered to ask one question—
Will hiring this person make us better?
Will they raise the bar for all of us?
We adopted a version of that approach in my organization with great results. It changed not just who we brought in, but how we thought about our own culture.
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As AI gained momentum, I began to see a similar principle taking shape—only this time, around governance. The constant balancing act between speed and safety became a daily conversation. Safety through design, not supervision was our mantra. But both Karmela Malone and Lori Walters introduced an evolution I’ll always be grateful for: what if AI governance had its own Bar Raiser role?
Someone empowered—yet removed enough—to ask a different kind of question:
Yes, we can do this. But should we?
In a world accelerating with generative AI, responsible automation, and predictive systems, that question matters more than ever. Governance is not a brake pedal—it’s the navigation system. It ensures that what we deploy is not just powerful or efficient, but appropriate, safe, and enduring. It forces us to think ahead to how models will behave outside the lab—how they’ll learn, interact, and evolve in the complexity of the real world.
Building responsible AI isn’t about rules after the fact; it’s about embedding curiosity, challenge, and accountability into the process—from ideation to deployment to ongoing trust maintenance.
Every organization should have its own Bar Raiser for AI—a person or process that ensures innovation doesn’t outpace intention. Because in the end, what sustains trust isn’t how fast we move, but how wisely we choose our direction.
How does your organization ‘raise the bar’ for responsible AI?