Every few months the association sector adopts a new frame for talking about technology. Right now that frame is “AI-first” — the idea that organizations should rebuild their workflows, their staffing, and their strategy around AI capability as the starting point.
I think the instinct behind it is understandable. I also think the frame itself is worth questioning before your organization builds something expensive around it.
What “AI-First” Gets Right
The underlying urgency isn’t wrong. AI genuinely changes what’s possible for small teams. A two-person marketing department can now produce research, draft content, analyze data, and build systems at a scale that would have required five people three years ago. Ignoring that capability is a real disadvantage, and the organizations treating AI as something to evaluate in two years are already behind the ones using it today.
AI-first advocates are right to push against complacency. That part of the argument holds.
Where the Frame Breaks Down
The problem is that AI-first inverts the order of operations. Strategy should come first. Member needs should come first. Organizational purpose should come first. AI is the tool that serves those things — not the frame that defines them. At least, that’s how I’ve seen it work well.
When AI becomes the primary frame, a predictable failure mode tends to follow: organizations adopt tools and then look for applications. They optimize for capability rather than outcome. They produce more content faster without asking whether the content is serving the member. They automate processes without asking whether the process was right to begin with.
The association that builds an AI-first content operation and produces 40% more content that members still don’t read hasn’t improved anything. It’s more efficiently producing the wrong thing. I don’t think that’s what anyone is going for, but the pattern shows up more than it should.
A Better Frame: Member-First, AI-Enabled
The question isn’t “how do we use AI in our marketing?” It’s “what does our member need, and how does AI help us deliver it better than we could without it?” Those questions produce different answers and different investments.
In practice, this means starting with an honest look at where member communication is actually failing. Is the newsletter unread because it’s produced manually? Unlikely. It’s probably unread because it’s not specific enough, not relevant enough, or not consistent enough. AI can help address all three of those problems — but only if the diagnosis happened first.
The organizations using AI most effectively aren’t necessarily the ones with the most AI in their stack. In my experience, they tend to be the ones who understood their member communication problems clearly enough to know which problems AI could actually solve — and were honest about which ones it couldn’t.
A Starting Point Worth Sitting With
Before adopting any AI tool for marketing, three questions are worth sitting with: What member outcome are we trying to improve? What’s currently in the way of that outcome? Is the blocker a time and capacity problem, or a strategic and judgment problem?
AI solves time and capacity problems well. It doesn’t solve strategic and judgment problems — and applying it to those problems can make them worse by producing more output at higher speed in the wrong direction. That’s not a reason to avoid AI. It’s a reason to be clear about what you’re asking it to do.
Member-first identifies the problem. AI-enabled helps solve it. That order matters, even when the path between diagnosis and solution isn’t perfectly clean.
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