Many association leaders treat artificial intelligence like an optional professional development elective. They might offer a stipend for an online course, share a few interesting articles in a Slack channel, or encourage staff to experiment with ChatGPT when they have a spare moment. While well-intentioned, this passive approach often creates a digital divide within the organization. A few early adopters sprint ahead while the rest of the team remains paralyzed by uncertainty or the belief that AI does not apply to their specific roles.
Torey Carter-Conneen, CEO of the American Society of Landscape Architects (ASLA), took a more deliberate path. Rather than leaving adoption to chance, he built AI training directly into how ASLA operates — moving the organization from scattered experimentation to tiered internal coursework and AI-powered workflows that now generate member reports and respond to inquiries in minutes. His experience is a useful proof point for what's ahead: training should not be a suggestion; it should be a core component of the job. Making AI education mandatory is not about imposing a burden on staff, but rather about fulfilling a leadership obligation to prepare the workforce for a future that has already arrived.
When a leader decides to make AI training mandatory, they are often met with concerns about staff capacity or the potential for technology to replace human roles. However, the greater risk lies in inaction. Failing to push AI as a critical professional development priority can be viewed as a form of leadership malpractice. A leader’s primary responsibility is to their people, which includes ensuring they have the skills necessary to remain successful and employable in an evolving market. For most employees, their current association role will not be their last. By requiring staff to master AI tools, leaders are providing them with a competitive advantage that will serve them throughout their entire careers. This approach reframes AI from a looming threat to a valuable form of job training that enhances professional value.
At organizations where innovation thrives, this commitment to growth is often baked into the culture. It starts with fostering an environment of curiosity and candor. Leaders should encourage staff to open up their drawers of old ideas—the ones that were previously dismissed as too complex or time-consuming—and see if AI can finally bring them to life. This cultural shift requires leaders to be transparent about the goals of adoption. It is not a campaign for job replacement; it is a campaign for effectiveness. When staff understand that the goal is to free them from repetitive, manual tasks so they can focus on high-value member needs, the resistance to mandatory training begins to dissolve. The focus remains on the professional, ensuring they are equipped with the best possible tools to perform their work at the highest level.
This leadership stance also requires a shift in how risk is perceived. Association executives are often comfortable navigating governance, financial, or advocacy risks, but they may freeze when faced with technological change. They might feel they need to wait for a perfect board-approved policy or a comprehensive set of bylaws before starting. In reality, leaders can and should move forward by applying the same measured risk assessment they use in other areas of the business. By starting with internal training and small-scale applications, leaders can build the organizational reps necessary to handle larger implementations later. The goal is to move beyond the pilot phase until AI is no longer a special project but simply the way work gets done.
One of the most significant hurdles to AI adoption is the intimidation factor. For staff who haven't worked with these tools before, new terminology and unfamiliar frameworks can make AI feel like a distant, technical concept rather than something they can use in their daily work. This is especially true for longtime staff who feel their hard-won expertise is suddenly being tested. A strong training program from an outside expert lays the foundation, but that foundation holds best when someone inside the building reinforces it. Peer-led follow-up is one of the most effective ways to do that, because it builds on existing trust and shared language. When a colleague from the finance or membership department leads a session, the technology feels close to home and immediately relevant.
Internal, peer-led sessions create a safe space where staff feel comfortable asking questions they might hesitate to raise in a room full of outside experts. In a small group led by a peer, there's no fear of sounding uninformed. This approach also keeps training grounded in the day-to-day realities of the association: the clunky database, the overwhelming volume of member inquiries, the manual process of generating chapter reports. A peer instructor already knows these pain points and can show AI solutions that speak directly to them. If a member services team watches a colleague use an AI agent to triage emails or reset passwords in seconds, the value becomes obvious fast.
To put this into practice, associations can identify early adopters within their own ranks to serve as internal instructors. These are the staff members already experimenting with tools and finding ways to save time. Empowering them to lead sends a clear signal that AI expertise is valued at every level of the organization, not just at the top. This peer-to-peer model also helps break down the silos that often slow innovation. When the CFO or a marketing director leads a session for the whole staff, it reinforces that AI is a cross-functional tool that belongs to everyone. It shifts the conversation from abstract capability to practical, collaborative problem-solving.
Mandatory training does not mean a one-size-fits-all approach. Staff members enter the AI conversation at different levels of comfort and experience. To account for this, associations can adopt a tiered internal curriculum, often structured into 100, 200, and 300-level courses. This allows individuals to progress at their own pace while ensuring that the entire organization eventually reaches a baseline level of competence. A 100-level course might focus on the basics of generative AI, prompt engineering, and data privacy. A 200-level course could delve into specific departmental applications, while 300-level sessions might involve building custom AI agents or integrating tools into existing workflows. This structured progression creates a sense of achievement and helps staff see a clear path toward mastery.
This tiered approach also helps the organization build a shared language around technology. When everyone from the executive suite to the administrative staff understands the same core concepts, collaboration becomes much easier. It eliminates the confusion that arises when different departments use different terminology or have wildly different expectations of what AI can do. Graduation from one level to the next can be celebrated internally, further reinforcing the cultural value placed on continuous learning. This is particularly important for departments that might initially feel that AI does not apply to them, such as editorial or creative teams. By bringing them through the same foundational levels as the rest of the staff, they can begin to see how AI might assist with research, transcription, or brainstorming without compromising their creative integrity.
Furthermore, a tiered model allows the association to leverage its internal experts more effectively. Different leaders can take ownership of different levels based on their own strengths. For instance, a CFO who is particularly skilled at data analysis might lead the advanced courses on using AI for financial reporting or membership data. Meanwhile, a people and culture lead might focus on the foundational levels, ensuring that the human element and ethical considerations are prioritized from the start. This distribution of teaching responsibilities prevents any single person from becoming a bottleneck for training and ensures that the expertise is spread across the leadership team. It turns the training program into a collective effort that reflects the organization’s commitment to its people.
Once the initial training is underway, the challenge shifts to sustaining momentum and ensuring that staff continue to apply what they have learned. One of the most effective ways to do this is by integrating AI case studies into regular staff meetings. By having a rotating volunteer present a brief demonstration of how they used AI in their work that week, the organization creates a constant stream of social proof. It is one thing for a CEO to speak about the theoretical benefits of AI; it is another thing entirely for a colleague to show how they turned a five-hour manual task into a thirty-minute automated process. These real-world examples from within the organization are the most powerful drivers of adoption.
These presentations should focus on concrete ROI and practical application. For example, a staff member might demonstrate how they used an AI tool to personalize renewal communications based on a member’s specific involvement history, rather than just using a standard template. Another might show how they created an automated system for chapter leaders to request custom membership reports via email, receiving a response in minutes rather than waiting days for staff to pull the data. When other team members see these successes, it sparks their own curiosity. They begin to ask, "If they could do that for chapter reports, could I do something similar for our event registrations?" This creates a virtuous cycle of innovation where ideas are shared and refined across departments.
This practice also helps to normalize the use of AI as a standard tool in the professional toolkit. It moves the technology out of the realm of the extraordinary and into the realm of the everyday. Over time, these case studies help to identify new use cases that may not have been apparent during the initial training. They also provide a platform for staff to discuss the challenges they faced and how they overcame them, which is essential for collective learning. By making these presentations a recurring part of the organizational rhythm, associations ensure that AI remains a priority and that the staff continues to push the boundaries of what is possible. The end result is an organization that is not just using AI, but is truly AI-powered, with a staff that is confident, capable, and prepared for whatever comes next.
Transitioning to a mandatory AI training model is a bold move that signals an association’s commitment to both its members and its employees. It moves the organization away from the hesitant, wait-and-see approach that characterizes many nonprofits and positions it as a leader in the digital era. By making training a requirement, leaders remove the friction of choice and ensure that no one is left behind. They create an environment where curiosity is rewarded, and where technology is seen as a partner in achieving the association’s mission. This is not just about efficiency; it is about building a more resilient and agile organization that can respond to member needs with unprecedented speed and personalization.
Ultimately, the success of an association depends on the quality and capability of its people. In an era where AI is rapidly reshaping the professional landscape, providing staff with the tools and training they need to thrive is the most important investment a leader can make. By adopting a peer-led, tiered, and case-study-driven model, associations can build a culture of innovation that lasts. The goal is to reach a state where AI is no longer a pilot program or a topic of debate, but simply a fundamental part of how the work gets done. For leaders who are ready to take that step, the time to start is now. The future of the association, and the careers of the people who power it, depend on this transition.