Practical AI Use Cases for Associations:

The most useful thing AI can give association staff may be time.

On a recent episode of The Member Engagement Show, Joe DeLisle, Jr., Director of Council Relations for the American Medical Group Association (AMGA), shared how his team has been using AI to improve the value they provide to members. They use AI to summarize community discussions, prepare meetings, research member organizations, analyze reports, and handle routine administrative work.

None of those examples removes people from the process. Joe and his team still decide what members need, review the output, correct mistakes, and make the final call.

The payoff is capacity. They spend less time reformatting agendas, reviewing discussion threads, filling out forms, and searching for information. That leaves more time for member outreach, community support, and better programming.

Start With Work Your Team Repeats

Joe’s team supports 12 councils, several regional meetings, a leadership development program, and a full calendar of virtual and in-person member experiences.

Each council serves a different audience. AMGA has councils for CEOs, chief medical officers, CFOs, women in leadership, and other roles or groups. The subject matter changes, but the work behind each council is often similar.

Every meeting needs an agenda. That agenda may also need to become a webpage, a script, an evaluation, an attendee resource, and a run of show for vendors.

Before AI, staff had to build each of those pieces separately. Now, once the agenda is ready, Joe can use AI to create a first draft of the related materials. Staff review and refine them, but they don’t have to start from a blank page every time.

That’s a useful place to begin if your organization is still looking for its first practical AI use case. Map the tasks your team repeats every week or month. Pay attention to work that involves summarizing, reformatting, comparing, categorizing, or turning one source into several deliverables.

You’re not looking for a process to hand over completely. You’re looking for the part AI can prepare so your staff can focus on the decisions.

Make Community Knowledge Easier to Find

AMGA’s councils use online discussions to connect members working through similar healthcare challenges. Those messages arrive in real time because that’s what their members prefer. But real-time delivery has an obvious downside: important conversations can disappear into a crowded inbox.

Joe now uses AI to create a monthly recap for each council.

He sorts the community emails into folders. Each month, the AI tool reviews the messages in a folder, identifies the topics members discussed, summarizes the responses, and flags questions that didn’t receive an answer.

Then Joe reads it and checks the summary against the original conversations, adjusts anything that isn’t accurate enough, and posts the recap back to the council.

The response has been positive. One member told AMGA that the recap helped them find a conversation they’d missed, even though it was directly relevant to something their organization was dealing with.

The unanswered-question piece matters, too. Joe’s team already knew that reposting a quiet discussion could help it get a response. They didn’t always have time to find those discussions consistently. Now the recap surfaces them.

Pair that with the built in AI Search Assistant in Higher Logic Thrive Community, and it’s easier than ever for members to get questions answered and find what they’re looking for.

This is where associations have a real advantage. A general AI tool can produce an answer, but it can’t recreate the knowledge shared among members who understand the same roles, pressures, and decisions. AMGA is using AI to make that human knowledge easier to find.

Use AI to Build Better Meeting Experiences

One of Joe’s most interesting examples started with an event evaluation.

A member suggested a reverse “Shark Tank” session where attendees could help solve a real problem. Joe liked the idea but was struggling to figure out a workable format.

If the entire room focused on one person’s challenge, other attendees might not find it relevant. If each table worked on a different attendee’s problem, the person who submitted it could end up repeating the same explanation all hour. And some members might not want to discuss a sensitive organizational problem openly.

Joe worked through the constraints with AI to figure out a process:

• His team sends a survey to attendees of an upcoming event to determine a common issue. For one meeting, that issue was patient access.
• Members then answer a set of questions about what they were seeing in their organizations.
• Joe then provides the anonymized responses to the AI tool he uses and asks it to create composite scenarios based on the themes. Each scenario needs to feel realistic, include the important details members raise, and incorporate discussion questions.
• Staff  then reviews the scenarios before using them.

The result is a set of credible, blinded situations that attendees can discuss without exposing one person or organization. Members can also easily move between tables, bring their own experience to the conversation, and compare different approaches.

This “shark tank” session format worked well enough to become a strong closing session for AMGA’s events, which meeting planners know isn’t always easy. Rather than leaving early, people are more willing to stay so they can participate in this interactive session.

AI didn’t invent the session. A member had the original idea, Joe understood the risks, and staff decided what would work for the audience. AI helped them get through the design work faster.

Make Event Decisions With Better Data

AMGA also uses AI to help choose locations for regional meetings.

Joe used to review a Google map of member locations, look for clusters, zoom in, count the organizations, zoom back out, and compare that area with another potential city. “It’s comical hearing it right now,” he said in the episode. But it was the best process available at the time.

The old process left room for human error. A city might appear to have a large member concentration when several locations actually belong to the same health system. Or several institutions might be close together on a map and it might not be clear how many member organizations are in one area.

Now, Joe can provide (anonymized) member location data and ask AI to identify major and secondary cities with the highest concentration of members within a three-hour radius. Not only does this save him time, it’s helped identify locations they were missing before. For example, AMGA identified Sacramento as a regional meeting location, with the hope they might have 40 or 50 people might attend and they ended up with 80 people registered – a huge success in an area they were previously missing.

That result doesn’t guarantee that every suggested city will outperform expectations. But it does show what can happen when staff combine better analysis with what they already know about their members.

Turn Industry Changes Into Timely Outreach

Trade association staff need to know when leaders change roles, retire, or move to a different organization. The same is true when member organizations merge or make an acquisition.

Joe has built a weekly workflow to help AMGA stay current. The AI tool reviews a set of industry sources, looks for personnel and organizational changes, and compares what it finds with AMGA’s member information. Each Monday, Joe automatically gets a list of changes involving members and prospects.

Some items only require a database update. Others point to a clear next step.

A newly appointed executive might be a good fit for one of AMGA’s councils. A former member champion who moves to a prospective organization may be a great candidate for an outreach conversation. A merger could affect several member contacts and create a timely topic for future programming.

The AI workflow Joe set up surfaces the signal, allowing Joe and his colleagues to decide what to do with it. That’s especially useful for small teams. Staff can’t personally scan every industry site and announcement each week. They can review a focused list and spend their time on the outreach.

Check Your Existing Technology First

You may not need another platform to get started with AI.

Organizations sometimes overlook functionality they already have when trying to improve a process or solve a problem. Before buying a new tool or creating a complicated external workflow, look at the systems your team already uses. Community, marketing, analytics, content, and workplace platforms may already include automation or intelligent features that reduce manual work.

During the podcast conversation, we talked about Higher Logic Thrive Community’s bulk upload and tagging functionality as a great example. When staff need to add a large group of resources to a community, those built-in capabilities can reduce the work involved in uploading, categorizing, and organizing each item.

Joe shared a similar example involving reporting. A vendor asked whether AMGA struggled with reports from an existing software product. Joe explained that his team could take the report they already had, provide it to their approved AI tool, and ask for additional analysis.

They didn’t need to replace the original system or immediately buy a separate reporting product. They found another way to use its output.

Before adding new technology, ask a few practical questions:

• Where does the current workflow slow down?
• What features are already available in the systems you have that you might not be using yet?
• Can an approved AI tool handle the remaining manual step?
• Does a new product solve a real gap, or are you rebuilding something you already have?

This is usually easier on staff, too. People don’t have to learn a separate system for every use case, and the organization can build on permissions, data practices, and workflows that are already in place.

Find the Annoying Work

There’s a lot of work that’s necessary but also tedious.

Maybe that’s creating attendee rosters, updating webpage code, drafting session descriptions, summarizing virtual meetings, anonymizing discussion recaps, or something else. These are a great place to try applying AI.

Most association teams don’t need to begin with a major AI initiative major AI initiative. Start with something your staff already does often and doesn’t enjoy. A small task that takes 20 minutes every week adds up. So does the mental drag of knowing it’s waiting for you.

Joe’s advice is to expect AI to get you about 80% of the way there. Staff provide the other 20%: context, accuracy, judgment, and an understanding of the audience.

“Don’t think, ‘How is this going to DO my job?’” he said. “Think, ‘How is this going to HELP me do my job?’ It’s augment and amplify, not replace.”

You don’t need a perfect prompt, either. Tell the tool what you do, which parts take too long, and where you get stuck. Ask it what information it needs before answering. Tell it to point out what you may be missing.

Set Guardrails Before You Scale Access

AMGA didn’t buy AI licenses for the entire staff and tell everyone to start experimenting.

Similar to Higher Logic’s own process, the organization’s CEO first asked the CIO to evaluate several AI tools. The review covered privacy, data-training practices, staff interest, and feedback from people who had already used the products.

AMGA chose an enterprise tool that met its data-protection requirements. The organization then invited employees to join a pilot group. About 20 staff members participated. They met monthly for four months to discuss what worked, what didn’t, and where they had concerns.

After the pilot, AMGA offered licenses to employees who wanted one. They don’t require everyone to use AI.

The organization also formalized an AI committee that continues to meet monthly. The conversations range from larger workflow questions to basic platform use. Staff have discussed how to adjust settings, work with new features, manage saved conversations, and create visual content.

That regular space gives people permission to learn in public. No one has to pretend to be an expert.

AMGA’s policies cover the other side of adoption. Staff use approved tools, avoid entering prohibited or sensitive information, and review AI-generated work before it goes to members. When a resource has been created primarily with AI assistance, AMGA notes that AI compiled it and staff reviewed and edited it.

Policies tell people what’s allowed. The monthly conversations help them figure out what actually works.

Put the Time Back Into Members

The biggest win here is that time saved means that the association has the capacity to improve their value proposition and the member experience.

“It’s extra time that’s now being invested back in the members themselves,” he said.

That may mean reaching out to a new executive, following up with a council member, reviving a discussion that didn’t get an answer, or designing a meeting session that gives members a better way to learn from one another.

Smaller teams can now analyze more information and produce work that once required more people or more time. They may not need to build a new technology stack to do it. Sometimes the first step is learning what their current systems can already handle.

Associations (like AMGA) aren’t looking to automate the relationship, but AI can help reduce everything that gets in its way.

Frequently Asked Questions About AI for Associations

How are associations using AI?

Associations are using AI to summarize community discussions, prepare events, repurpose content, analyze reports, support research, complete administrative work, and identify opportunities for member outreach.

Where should an association start with AI?

Start with a process your team repeats often. The best early use cases usually have a clear input, a predictable output, and a person who can review the work before it’s used.

You should also check the automation and intelligent features already available in your current systems before buying another product.

Will AI replace association staff?

AI can prepare drafts, organize information, and surface patterns. Staff still need to understand the members, evaluate the output, protect sensitive data, and make the final decision. Joe describes the goal as getting 80% of the way there faster, then applying the human judgment that the work still needs.

Many associations have a lot of jobs to do and not enough time to do them; AI shouldn’t replace staff but instead open up their capacity.

Do associations need a separate AI platform?

Not usually. Built-in features within community, marketing, analytics, and productivity systems may already address an associations most pressing needs.

An approved general-purpose AI tool may help with tasks that fall outside of existing systems. A new platform makes sense when there is a clear gap the existing technology can’t fill.

What AI guardrails should associations establish?

At minimum, organizations should approve the tools staff may use, define what data can be entered, protect member information, require human review, and explain when meaningful AI assistance should be disclosed.

Staff also need ongoing education. A written policy won’t teach someone how to use a tool well.

How can associations use AI without weakening member relationships?

Use it to reduce the work around the relationship: summarizing, organizing, researching, formatting, and spotting useful signals.

Then let staff spend the time they save talking with members, facilitating peer exchange, and responding to what the community needs.

Hear More From Joe DeLisle

Listen to the full episode of The Member Engagement Show to hear Joe DeLisle explain how AMGA uses AI for community recaps, meeting planning, member intelligence, staff workflows, data protection, and ongoing employee education.