Learning and development (L&D) professionals are facing an unprecedented challenge. They are being asked to drive artificial intelligence (AI) adoption across incredibly diverse functions, from engineering to human resources (HR), from sales to operations, each with unique workflows, specialized tools and distinct success metrics. No one expects organizations to master every nuance of how a data scientist uses AI differently from a marketing manager. But here’s what I’ve learned from working with dozens of organizations: The most effective L&D teams don’t try to be AI experts for every role. Instead, they become expert AI learners who can model the discovery process itself.

The Reality of Role-Specific AI Adoption

Let’s acknowledge something up front: AI deployment is inherently contextual. The way a financial analyst uses AI to build models has almost nothing in common with how a customer service rep uses it to draft responses. The prompts that work brilliantly for generating marketing copy will likely fail when it comes to technical documentation. This isn’t a training problem but rather the nature of AI itself.

An L&D team we worked with recently shared the challenge of creating meaningful AI training across a 10,000-person organization. “We can’t possibly understand every workflow,” they said. They were right; they couldn’t and shouldn’t try to become experts in every functional area. But what they could do was become experts at the learning journey itself.

Leading by Learning

The most successful L&D teams take a different approach. Rather than trying to teach specific AI applications for every role, they focus on developing their own authentic AI practice. The focus isn’t on becoming technical experts but on experiencing the real journey of AI adoption, complete with its frustrations, breakthroughs and gradual confidence building.

When you’ve personally experienced the moment when AI finally “clicks” for your own work, you bring something invaluable to your training programs: genuine empathy and practical wisdom. You understand why people resist at first. You know which fears are justified and which are overblown. Most importantly, you can share real stories of transformation. One chief learning officer shared her team’s approach with me: “We decided to use AI for everything we could in L&D for 90 days. Not to become experts, but to become experienced beginners. The struggles we faced creating our first AI-assisted curriculum taught us more about change management than any framework ever could.”

Starting With Your Own Workflows

The beauty of starting with L&D workflows is that they’re surprisingly diverse. Your team already does many of the same types of tasks that exist across the organization. When employees use AI for these tasks, they experience the same journey your learners will face. You’re discovering which types of requests work well, how to iterate on prompts and when AI adds value — and when it doesn’t.

Here’s when L&D’s role becomes incredibly powerful: Once your team has developed its own AI fluency, you can shift from being the sole source of AI knowledge to being expert facilitators of peer learning. You become the connectors who identify and elevate the AI champions already emerging in each department. So, how does this work?

The L&D team identifies power users in different functions and creates forums for them to share their discoveries. A procurement specialist teaches others how she uses AI to analyze vendor contracts. A project manager shows how he creates risk assessments. The L&D team doesn’t need to be experts in procurement or project management; they need to be experts at facilitating knowledge transfer and creating safe spaces for experimentation.

Measuring What Really Matters in AI Adoption

One of the most important things L&D teams learn from their own AI journey is how vulnerable the learning process feels. You’re essentially admitting that a machine can do parts of your job possibly better than you can. That’s scary. One challenge L&D teams face is measuring AI adoption without being overly intrusive. At Larridin, we’ve learned that the best approach isn’t to track every click, but to measure meaningful application. When L&D teams use these measurements on themselves first, they understand what realistic adoption looks like. For instance, one team discovered that their own AI usage naturally ebbed and flowed based on project cycles. During content creation phases, usage spiked. During facilitation phases, it dropped. This insight helped them set more realistic adoption expectations for other departments and identify when low usage indicated a problem versus a natural workflow pattern.

Creating Your L&D AI Practice

Here’s a practical framework for building your L&D team’s AI fluency:

• Month 1: Explorer Phase. Give everyone on your team license to experiment. Set aside time each week for AI exploration. Share discoveries without judgment. Document both successes and failures. The goal here is to build familiarity and comfort with AI, not to achieve perfection.
• Month 2: Application Phase. Choose a key L&D project — such as redesigning onboarding, launching a leadership training program or analyzing training effectiveness — and commit to using AI throughout. This way, your team experiences the full lifecycle of AI-assisted work.
• Month 3: Reflection and Sharing. Synthesize what you’ve learned. What surprised you? What frustrated you? What became indispensable? Turn these insights into design principles for your organization-wide AI adoption programs.

When L&D teams genuinely embrace AI in their own work, it changes the entire conversation around AI adoption. Instead of theoretical discussions about “the future of work,” organizations can have practical conversations about today’s opportunities. As a result, your role evolves from teaching specific tools to helping teams discover their own AI applications. And L&D leaders become coaches who ask better questions: What tasks drain your energy? Where do you need a faster turnaround? These questions, informed by your own experience, help others identify their highest-value AI opportunities.

Moving Forward Together

For L&D teams, the path to organizational AI adoption is less about mastering every detail and more about gaining the fluency needed to support others on their journey. When L&D teams experiment with AI firsthand, they learn where employees are likely to get stuck — whether that’s overcoming fear of the technology, figuring out prompts or integrating AI into daily workflows. That experience equips them to anticipate challenges, share strategies and celebrate progress alongside their learners.

Today’s employees need L&D teams who can honestly say, “We’re learning too, and here’s what we’ve discovered so far.” The organizations advancing with AI build cultures of experimentation, where L&D leads the way and models continuous learning.