AI adoption in associations isn’t just about tech enablement. It’s about culture transformation. As someone leading AI strategy at our association, I knew this wasn’t going to be a traditional software rollout. It was going to challenge our assumptions about creativity, efficiency, and even professional identity.
Here are the five most impactful lessons I’ve learned—not just about AI itself, but about what it takes to lead a future-ready association.
1. AI is a culture strategy, not a technology project
You don’t just plug in AI and walk away. Leading this effort revealed that the real work lies in change management. It’s about building a culture of curiosity, openness, and experimentation.
The tools are secondary. The transformation begins when staff across departments start to see AI not as a threat to their value but as a pathway to deepen their impact. That shift doesn't happen by accident. It requires visible leadership, shared learning, and consistent reinforcement that AI is aligned with—not at odds with—our values and mission.
2. Give people permission to use AI and talk about it
One of the most revealing moments in this journey was realizing how many people were quietly using AI but didn’t feel comfortable saying so.
There was a sense that using AI was “cheating” or that it diminished the authenticity of their work. This emotional discomfort, especially in purpose-driven environments, was real. But it was also addressable.
We gave our team permission. Not just to use AI, but to talk about it, share their wins, and even their missteps. That psychological safety unlocked innovation and empowered our staff to move from secret experimentation to confident application. That’s when real adoption began.
3. AI education isn’t one-and-done, even for the experts
One of our early hurdles? Even our most tech-savvy staff quickly realized that AI isn’t plug-and-play. It requires new workflows, new questions, and a new level of critical thinking. Learning AI wasn’t just about tools. It was about unlearning habits and embracing experimentation.
Sidecar’s AI education program helped me get up to speed quickly and gave me the confidence to lead this work organization-wide. For staff, we adopted a layered, accessible approach. We held all-staff working sessions that allowed employees to explore AI in real time using familiar scenarios such as:
· Planning travel for our annual meeting
· Drafting member emails
· Summarizing articles and research
· Creating first drafts for board reports
· Exploring speaker trends in our specialty
We also supplemented these sessions with curated expert content to ensure every learning style was supported. The result? Staff didn’t just learn AI. They began to integrate it confidently into their work.
4. Start with pain points, not promises—and make it a shared goal
We didn’t impose a one-size-fits-all AI directive. Instead, we made AI implementation an organization-wide goal and invited each department to shape what that looked like for them. Every department was asked to identify one real pain point or opportunity where AI could make a measurable impact. Then, each team built a pilot around that.
This structure gave us two things: shared momentum across the organization and personal relevance at the team level. Every department could move at its own pace, but we were moving forward together.
For Marketing, our specific goals were to optimize campaign performance and streamline content workflows. But across the association, success looked different in every corner. And that was the point. AI wasn’t something we were told to adopt. It became something we co-created.
5. Guardrails signal trust, not control
We took the time early on to co-develop an internal AI use policy. Not to limit exploration, but to guide it. The policy emphasized transparency, data integrity, intellectual property, and ethical use. And importantly, it gave staff the confidence to move forward.
When people know the boundaries, they’re more willing to take initiative within them. That clarity fuels momentum.
Final thought: the future isn’t about AI. It’s about leadership.
At its best, AI gives us time, clarity, and reach. But the true differentiator isn’t the tech itself. It’s how we lead through it.
Leading AI in an association setting is about more than adoption. It’s about modeling a mindset: grounded in mission, inspired by possibility, and unapologetically human.