Let's be honest about something most association leaders already feel but haven't said out loud: you are being pitched AI from every direction. Your vendors have added it to every demo. Your board wants to know what the plan is. Your staff are experimenting on their own, some excited and some quietly worried about what it means for their jobs. And when AI comes up in your leadership meeting, the question on the table usually sounds like "Where can we use AI?" or "Should we build an AI agent?"
Those are fair questions. They are also backwards.
When the conversation starts with the technology, organizations end up with tools in search of a problem. It's like walking into a hardware store before you've decided what you're building. You'll come home with something. It just may not be what the project needed.
The better starting point is the same one behind every good technology decision: the business need.
Start With the Business Need
Before anyone schedules another vendor demo, I encourage leadership teams to step back and ask:
What are we trying to improve?
Where are we spending too much time?
Where are our members struggling?
What decisions would we make differently if we had better information?
What is keeping us from reaching a strategic priority?
None of these questions mention AI, and that's the point. The question isn't "Where can we use AI?" It's "What problem is worth solving?"
A strong use case connects directly to an outcome your organization already cares about: improving member retention, reducing staff time spent on repetitive work, increasing engagement, making institutional knowledge easier to find, improving decision-making, or delivering a more personal member experience.
Just as important is who defines it. The best use cases aren't handed down from the executive suite. They come from the staff who will be most affected, the people who know exactly where the friction lives because they work around it every day. Bring them in early. You'll get better ideas, and you'll build the trust that turns a new tool from something staff are told to use into something they want to use.
From Need to Use Case
Here is a simple framework for moving from a priority to something you can act on:
Business Priority → Problem or Opportunity → Desired Outcome → AI Use Case → Requirements
Here's how it works for a priority nearly every association shares:
Priority: Improve member retention
Problem: We don't identify disengaged members early enough
Desired outcome: Identify at-risk members before renewal
AI use case: Analyze engagement patterns and flag members who may be at risk
Requirements: Reliable engagement data, defined indicators, integration with the AMS or CRM, appropriate governance, and a staff workflow for acting on the information
That last line is where most of the real work lives. A flag that says a member is at risk only matters if your team trusts it and knows exactly what to do next.
Not Every Problem Needs AI
This is one of the most important points I can make, and one we return to often at Strategico.
Once you've clearly defined the problem, the answer may turn out to be a process change, better reporting, a workflow automation, a system configuration, or simply better data. Plenty of what gets labeled an "AI opportunity" is really a process and data problem wearing a technology costume.
That isn't a failure of your AI strategy. That is your strategy working.
So don't measure AI maturity by the number of AI tools you've deployed. Measure it by whether your technology is helping you advance the outcomes that matter to your members and your mission.
Are You Ready to Execute It?
Once you've identified a valuable use case, there's one more question to ask before going further: Are we actually ready to do this?
AI depends on the information and processes underneath it. If your data is incomplete, inconsistent, hard to access, or poorly governed, a use case can look great on a whiteboard and fall apart in practice. The at-risk member flag above is only as good as the engagement data feeding it. If that data is scattered across systems or was never captured at all, AI won't fill in the gaps. It will give you confident answers built on an incomplete picture.
Chris Maynard and I spent an hour on exactly this question in our recent UST Education webinar, The Executive Playbook for AI-Ready Data Strategy. If you missed it, or want to share it with your leadership team, the recording is available on demand.
Sort by Value and Readiness
Most organizations can generate dozens of AI ideas in a single brainstorm. They shouldn't pursue all of them.
Put each idea through a quick gut check on two dimensions:
Business value: How much impact could this have on a priority that matters?
Organizational readiness: Do we have the data, technology, processes, governance, and people to execute it?
Then sort:
High value, ready now: Start here. These are your early wins, and they build confidence for what comes next.
High value, not ready yet: Invest in the foundation. The data and governance work you do now is what makes these possible later.
Lower value: Park them. A good idea that doesn't move a priority forward is still a distraction.
Define Success Before You Shop
Here is where many AI initiatives quietly go off track: the organization skips straight from the idea to the product.
"We want an AI chatbot" is not a requirement. It's a shopping list.
Compare that with: "We want members to receive accurate answers to common questions without staff intervention, while maintaining appropriate controls around sensitive information."
Now you have something you can evaluate solutions against. You know what accurate means for your members, which questions are in scope, and where the guardrails belong. And when a vendor walks you through a demo, you'll know whether you're looking at a real fit or a well-rehearsed presentation.
Final Thought
The next time AI comes up in your leadership meeting, change the question. Instead of asking "How should we be using AI?", ask:
"What are the three most important business problems or opportunities we need to address over the next 12 to 18 months, and could AI help us address any of them?"
That one shift moves AI out of experimentation for its own sake and into strategic planning, where it belongs.
Start with your people. Start with the need. Then let the technology earn its place.
If your leadership team is working through that question now, I'd welcome the conversation.