Community First: Rethinking Volunteer Onboarding in AI Spaces

Onboarding in tech spaces is rarely simple. The tools are often unfamiliar, the projects can be complex, and the people showing up to contribute are coming from different parts of the world. Each brings a unique perspective, level of experience, and reason for getting involved.

Some arrive with technical skills. Others come in curious, eager to learn, or hoping to gain experience. And many are somewhere in between. What they all share is a desire to contribute to something meaningful.

That’s what makes early-stage onboarding such a critical and often overlooked part of building a healthy AI community. It’s not just about delivering information. It’s about creating a process that helps people settle in, find their footing, and begin to engage without being overwhelmed.

An Emerging Identity Within the Community

Recently, I’ve been working with a nonprofit that’s growing an online Slack community around their mission. They’ve been expanding quickly, and like many early communities, they reached a point where things began to shift. New contributors were joining regularly. Questions were increasing. New behaviors and patterns were starting to influence how the space functioned.

What was really happening was this: the community was forming its identity.

In digital spaces, a community’s identity doesn’t emerge through formal branding or mission statements alone. It begins to take shape through the tone of conversations, the speed of responses, the tools people rely on, and the types of questions that get asked. Over time, small behaviors compound. People notice what gets celebrated and what gets ignored. They start to model each other. Roles emerge. Norms solidify.

And this is important: even if the mission remains clear, the culture of the community is shaped by participation. Which means the way people are brought in can influence how they show up—and how they expect others to show up too.

At that stage, we paused to ask a few key questions. Where are people coming from? What are they seeing first? And what do those first steps actually feel like?

Because in many communities—especially in AI spaces—people are often asked to absorb too much, too fast. They are introduced to dense systems, complex workflows, and long documents before they have a chance to breathe. The result is that the energy and curiosity that brought them in starts to fade under the weight of confusion or cognitive overload. This highlights the need for simplicity.

The Role of Simplicity in Complex Work

Most people joining AI communities today are not looking for a perfect fit. They are looking for a way in. Whether it’s an open-source initiative, a volunteer-led project, or a hybrid space with both contributors and core staff, people show up because they want to be part of something. They may not know what an agent is, or how prompt engineering works, or how your internal project structure is set up. But they want to learn. What they need at that moment is not more information. They need orientation.

In early-stage communities, especially those centered on AI, onboarding often gets reduced to logistics. Just get people in the door and everything else will follow. And automation for many organizations is the way to do it.

In many of the communities I’ve worked with, automation is often seen as the default solution for bringing people in—especially in early stages when teams are small and growth is accelerating. And while it can be useful in parts of the process, it’s not always the right tool for building connection or sustaining engagement.

Sometimes what’s needed most is a human presence. Not across every step, but in the moments that shape first impressions. A short message. A direct welcome. A moment of acknowledgment. These small, personal gestures go a long way in helping people move from interest to involvement, especially when the work ahead is unfamiliar or complex.

In the communities I’ve supported and observed, being more intentional about human-centered onboarding—especially during those early, uncertain stages—has consistently led to meaningful shifts:

• Contributors entered with clearer expectations

• The team responded with more focus and care

• People felt welcomed, not processed

Rethinking Growth: From Volume to Value

Even with those early signs of progress, a familiar challenge often emerges as communities grow: sustainability. Momentum may build at first, but it doesn't always carry forward on its own. Without a strong foundation, community activity can start to depend too heavily on one or two people to keep things moving.

That’s when I started reaching out to others who had built project-centered communities from scratch. Their advice reshaped my strategy: stop focusing on how many people are coming in, and start investing in the few who are already in the community and actively engaging.

At one point, that meant turning my attention to just three to five people. That small, committed group became the foundation. Over time, it grew to 27. And the community started to sustain itself—not because we had more people, but because we had the right people.

The hardest part was learning to be selective. When resources are limited, saying yes to everyone can actually slow things down. So we raised the bar. We got clear on what kinds of skill sets and interests we needed. Yes, the number of new volunteer submissions went down, but the value of their contributions went up. That shift saved time and increased project completion across the board.

As that core group strengthened, I began to recognize distinct rhythms in how people contributed. That clarity made it easier to support everyone based on where they were in their journey. Over time, I’ve noticed that growing communities often end up with three general types of contributors:

1. People who work on active projects

2. People contributing at a slower pace (observing, listening in and giving occasional input)

3. People exploring ideas or contributing to incubated projects in the background

That decision to prioritize quality over volume taught me that a community doesn’t need to be large to be effective. It needs to be centered, intentional, and built around people who are truly ready to engage.

In AI spaces, where the work often evolves faster than people can catch up, that kind of clarity and commitment at the core is not just helpful. It is essential to growth and long-term sustainability.

Thinking about implementing AI or building a community? I’d love to help or answer any questions you have. I also offer workshops and strategy support—learn more on my website!