From Tools to Trust: Building AI Fluency at Work

From Tools to Trust: Our Journey Toward AI Fluency

Lately, I’ve been reflecting on how organizations—not just tech giants—are learning to live with AI in the everyday flow of work. We often think the challenge is purely technical: install the right tool, flip the switch, and productivity will skyrocket. But what GitHub’s own internal journey taught me (and what I want to share here with you) is that building an AI-powered workforce is less about tools and more about people.

GitHub didn’t just roll out AI assistants like Copilot and call it a day. They created what they call an “AI enablement model”—a system that blends leadership vision, peer learning, clear policies, and dedicated champions. What struck me most is how they reframed adoption as a change management problem. That means the real work isn’t in buying licenses, but in rewiring how people collaborate, trust, and grow together.

And maybe that’s the lesson we all need: AI won’t succeed in our workflows unless we succeed first in preparing our people to use it, question it, and even reshape their own roles around it.

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Here’s how GitHub approached it, and what we can take away:

• Executive Support as a Starting Point
  Leaders at GitHub didn’t just hand out tools; they explained why AI mattered. To engineers, it was framed as freedom from repetitive tasks so they could focus on creative problem-solving. To the broader company, it was about shipping faster and serving customers better. That clarity helps employees connect abstract strategies to daily routines.

• Policies That Empower, Not Restrict
  One fear with AI is using it “wrong.” GitHub solved this by creating a tiered policy system. Tools in Tier 1 are fully vetted and safe for sensitive work. Tier 2, like consumer AI apps, are only for public data. No lengthy “don’ts,” just a clear framework. I find this refreshing—it makes AI less intimidating and more accessible.

• The Role of AI Advocates
  Instead of relying only on formal trainers, GitHub tapped into volunteer advocates: everyday employees who genuinely love experimenting with AI. These advocates serve as mentors, translators, and cheerleaders. What I love about this is that it shifts expertise from “the top” to “among us.”

💡 Tip: If your company wants to adopt AI, don’t underestimate grassroots efforts. Sometimes the best teachers are our peers who figured something out yesterday and are excited to show us today.

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Building Community, Not Just Competence

One of the most overlooked yet powerful strategies was creating Communities of Practice. GitHub didn’t let AI conversations scatter in random Slack threads; they built structured spaces. Some communities are general (#how-do-I-ai), others specific (#copilot-users, #ai-for-sales). Each has leaders, charters, and momentum.

What does this mean for us? Well, it tells us that learning AI isn’t a solo adventure—it’s a communal one. Think about how quickly we learn when we share shortcuts, mistakes, or real use cases. AI communities are not just support groups; they are accelerators of culture.

Another point worth noting: GitHub linked AI learning directly to onboarding. New employees encountered AI from day one, signaling that “this is who we are.” That makes me pause and ask—what message do we send to our own new hires? Do we show that innovation and adaptability are part of the DNA, or do we treat AI as an optional add-on?

💡 Tip: If we’re serious about AI fluency, we need to embed it into the employee lifecycle—not just as a training module but as a mindset introduced from the start.

The Human Side of AI Change

What I appreciate about GitHub’s model is how it respects the anxieties people feel. They didn’t dismiss fears with “your jobs are safe.” Instead, they said, “our jobs will change, and here’s how we’ll help you adapt.” That level of honesty builds trust, and trust is the currency of transformation.

For managers, this means leading not only by adopting AI themselves but by reimagining workflows and redefining value. For senior individual contributors, it means stepping up as role models who don’t just use AI but shape how AI is used. Both roles matter because adoption spreads faster when people see their peers—not just executives—benefiting.

And let’s not forget measurement. GitHub tracks adoption through metrics like Monthly Active Users (MAU), depth of engagement, and eventually, business impact like cycle time and developer happiness. Why does this matter? Because without measurement, it’s too easy to let hype replace results.

💡 Tip: If we experiment with AI, we should also track progress—not just “who’s using it” but “how is it changing our work?” Surveys, usage stats, even informal feedback loops help us see if it’s more than just a shiny tool.

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Bringing It Back to Us

Reading GitHub’s “AI for Everyone” playbook made me realize something: their story isn’t just theirs. It’s a roadmap we can learn from.

Here’s what stands out for me:

1. AI isn’t about software. It’s about people. The real shift is cultural, not technical.

2. Peer influence is powerful. Advocates and communities make AI feel approachable.

3. Transparency builds trust. Acknowledging job changes and offering upskilling pathways makes adoption sustainable.

4. Measurement matters. Success isn’t just about rolling tools out—it’s about proving they make a difference.

If I had to leave you with one thought, it’s this: AI fluency isn’t a finish line we’ll cross one day—it’s a practice, something we nurture daily through conversations, experiments, and shared learning. We don’t need to be perfect or even experts; we just need to be curious enough to keep trying together.

So, as we watch organizations like GitHub pave the way, maybe our task is simpler than it seems: take what works for them, adapt it to our own context, and keep asking—how can AI make us not just faster, but also better at being human together?

What’s your next spark? A new platform engineering skill? A bold pitch? A team ready to rise? Share your ideas or challenges at Tiny Big Spark. Let’s build your pyramid—together.

That’s it!

Keep innovating and stay inspired!

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