When associations look at where AI can be useful, the practical applications are usually the easiest place to start.
AI can help staff find information faster, draft routine communications, clean up records, or produce reports without somebody spending three hours in Excel. These are useful applications that can save staff time today.
Most associations will eventually have access to these capabilities whether they actively go looking for them or not. Many are already being built into the software associations use every day.
The more interesting question is what associations are learning about their members.
Associations have an enormous amount of member information. They know what people register for, what they open, what they buy, what committees they join, what education they complete, when they renew and, in many cases, when they begin to disengage.
Much of that information has historically been scattered across different systems or simply hasn’t been captured in a useful way.
We’re reaching a point where having that history could become extremely valuable.
Think about member retention. Most associations would like to know six months in advance that a particular group of members is becoming less likely to renew.
There probably isn’t one piece of information that tells you that. It may be a combination of things: fewer event registrations, declining email engagement, no recent purchases, a change in website activity or a dozen other small signals.
Given enough good historical information, AI can become very useful at finding those patterns.
The same applies to member recommendations. Getting better at suggesting the right education, events or resources requires an understanding of what members have actually done over time.
If an association decides in 2028 that it wants to analyze three years of member behavior, it needs to have started capturing the right information in 2025. There isn’t a software product it can buy in 2028 that will recreate activity that was never recorded.
That doesn’t mean associations should rush to put AI-generated recommendations in front of every member.
These systems need time, good information and a fair amount of testing before the results should be trusted.
One recent conference offered a good example of the problem. Its chatbot confidently answered attendee questions using information from the previous year’s event. An incorrect answer delivered quickly and confidently isn’t much of an improvement.
There is a reason to separate the work of collecting and learning from the decision to put AI-generated results in front of members. The first can begin well before an organization is comfortable with the second.
Before You Get Too Far, Look at Your Data
Duplicate records, missing information, inconsistent fields and disconnected systems have been problems for associations long before anyone started talking about AI. Organizations starting with those problems will have a difficult time getting much value from the more sophisticated uses of AI.
This is also an area where some of the simpler AI tools can be useful. Data cleanup, record matching and identifying inconsistencies can reduce a considerable amount of manual work.
For association leadership teams, the useful conversation isn’t simply about which AI tools the organization should adopt.
Look at the information being collected today. Look at what isn’t being collected. Then consider what the organization may want to know about its members three or five years from now.
Some of that history takes years to build. Starting earlier gives associations more to work with when the technology is ready for it.
Associations Rewired is rethinking tech strategy and selection with AI-driven analysis and expert human insights.
