The 3 Decisions to Make Before You Shop for an Association AI Assistant

Author’s Attestation: This blog was written by me, Robert Barnes, CAE. The original framework, ideation, research, and initial drafts were created with the help of Claude.ai & Betty:AI. The published Blog was written by the author directly. 

The pressure is real. Your board wants to know your AI strategy. Your peers are launching assistants with names like "Ivy" and "Mimo." Vendors are filling your inbox with demo invitations. And somewhere between the hype and the hesitation, you're trying to figure out what actually makes sense for your association. 

Before you schedule another demo or fire off another RFP, take a breath. 

The most important work isn't comparing features or negotiating pricing. It's answering three foundational questions that will determine whether your AI investment creates lasting value—or becomes another pilot that quietly disappears into the "we tried that once" graveyard. 

The data is sobering.  

MIT's 2025 State of AI in Business report found that 95% of organizations are getting zero return on their generative AI investments—despite tens of billions in enterprise spending.  

Let that sink in. Ninety-five percent getting nothing. Associations don’t have that kind of financial freedom when they are the stewards of their members’ funds. 

The gap between AI success and AI disappointment isn't budget, technology, or timing. It's preparation. The associations getting real results made three critical decisions before they ever talked to a vendor. Those decisions shaped everything that followed—from the questions they asked in demos to how they measured success twelve months later. 

Before you shop, you need to decide. 

3 Decisions at a Glance 

What Outcome Are You Actually Trying to Achieve? 

It's tempting to start with the technology. "We need an AI chatbot" feels like progress. But it skips the question that actually matters: what do you want to be different twelve months from now? 

Rick Bawcum, co-founder of Cimatri, put it well in a recent interview: "Do we know the problem we're trying to solve with our AI strategy so we're not falling in love with the solution?" 

It's a deceptively simple question. And in my experience, most organizations can't answer it clearly. They've got a vague sense that AI is important and a genuine fear of being left behind—but when you ask "what's the outcome you're chasing?"... crickets. 

When associations skip this step, they end up with AI that's interesting but not useful—a shiny demo that never finds its footing in the real work of serving members. The alternative is outcome-first thinking: starting with the business result you need and working backward to determine whether AI is even the right path to get there. 

For associations, meaningful outcomes tend to cluster in a few areas: 

Reducing friction. Members know you have the answer—they just can't find it. If your team spends hours every week answering questions that are already addressed somewhere in your content library, that's a solvable problem with a measurable outcome: fewer support tickets, faster resolution, happier members. 

Increasing engagement. Twenty years of conference recordings, a decade of journal articles, countless webinars—all sitting in a digital archive that nobody browses. The outcome here isn't "launch an AI assistant." It's "make our content library a place members actually visit and use." 

Surfacing insight. What are members actually asking about? Where are the gaps in your content? AI doesn't just deliver answers—it can reveal patterns in what your community needs. That's an outcome that shapes programming, content strategy, and even advocacy. 

Creating new value. Some associations are using AI-powered knowledge access as a member benefit—or even a standalone revenue stream. If monetization is on the table, put it on the table. Name it. 

The point isn't to pick the "right" outcome from a list. It's to be specific about what success looks like for your organization before you start evaluating tools. A vendor can show you what their platform does. Only you can decide what it's for. 

💬 Pause and ask your team: "If our AI investment is wildly successful, what's different in 12 months? Be specific." Drop that in Slack right now. The answers—or the silence—will tell you a lot. 

What Is Your Organization's Philosophy About AI's Role? 

Right, this one sounds a bit lofty. Stick with me. 

This isn't about drafting an ethics policy or ticking a governance box. It's about something more fundamental: what do you believe AI should do within your organization, and what should it never do? 

MIT Sloan Management Review's most-read article of 2025, "Philosophy Eats AI," argues that philosophy—not technology—determines whether AI investments create real value. Authors Michael Schrage and David Kiron put it directly: "Without thoughtful and rigorous cultivation of philosophical insight, organizations will fail to reap superior returns and competitive advantage from their generative and predictive AI investments." 

That sounds academic. It isn't. It's actually the most practical question you'll face. And here's the thing—if you don't answer it deliberately, you'll answer it accidentally. The defaults will decide for you. And the defaults might not be what you want. 

Consider three dimensions every association should think through: 

Purpose. What should AI achieve for your members? Is it a research assistant that helps them dig deeper? A concierge that points them to the right resource? A study buddy that supports certification prep? The answer shapes everything from how the tool is designed to how you introduce it to members. "We have AI now" is not a purpose. That's a press release, not a strategy. 

Knowledge. What counts as a trusted source? This is where associations have a critical choice to make. General-purpose AI tools draw from the open internet—which means your members might get answers sourced from Reddit threads, outdated blog posts, or your competitors. A knowledge assistant trained on your content draws only from what you've vetted. For organizations built on being the authoritative voice in their field, this isn't a technical detail. It's an existential one. 

Representation. How should AI represent your expertise and your voice? If a member asks about best practices in your industry, should the AI answer with confidence or with caveats? Should it speak like a peer or like a reference librarian? These choices shape member trust—and they're choices you should make intentionally, not discover after launch when someone screenshots a weird response and posts it on LinkedIn. 

Most vendors won't ask you these questions. They'll show you features. But the associations that get lasting value from AI have already done this thinking. They know what they want AI to be—and just as importantly, what they don't. 

💬 Pause and ask your team: "When a member interacts with our AI, what should it feel like? What should it never do?" Send that to your senior team. Their answers will reveal whether you've got alignment—or homework. 

Is Your Content—and Your Culture—Actually Ready? 

You can have perfect clarity on outcomes and a well-defined philosophy. But if your content is a mess, AI will just serve up that mess faster. Garbage in, garbage out—but now with more confidence and a friendly interface. 

AI doesn't create knowledge. It surfaces what you already have. That's the opportunity—and the risk. If your best thinking is scattered across a legacy website, a dozen PDF libraries, and three different platforms your team has used over the years, AI will inherit that fragmentation. Members will ask good questions and get incomplete answers. Or worse, they'll get confident answers drawn from something you published in 2016 and forgot to update. 

The readiness question has two parts. 

Content readiness. Is your knowledge organized, current, and accessible? This doesn't mean everything needs to be perfect before you start—perfection is the enemy of progress, and you'll be waiting forever. But you need an honest inventory. Where does your best content live? What's outdated? What's locked in formats that are hard to work with? 

Some associations discover that AI adoption becomes the forcing function for content governance they've been putting off for years. That's not a bad thing. Sometimes you need a reason to finally clean out the garage. But it's better to know what you're walking into than to discover it three months after launch. 

Culture readiness. This is the one that catches people off guard. Jamie Notter, a culture consultant who works extensively with associations, put it bluntly after a recent gathering of association leaders: "Your culture remains the biggest obstacle to unlocking the value and potential of AI in your organization." 

His point isn't about training or policies—it's about the unspoken rules that shape behavior. Does your culture punish failure? Do people feel they need to always have the right answer? Is experimentation something that happens in secret because no one wants to be seen trying something that doesn't work? 

AI demands experimentation. The tools are evolving, there are no established best practices, and the only way to find what works for your members is to try things, learn quickly, and iterate. If your culture quietly discourages that—if people would rather play it safe than risk a visible misstep—you'll struggle to get value from any AI investment, no matter how good the technology. 

You don't need everything figured out before you move forward. But you do need to know what you're working with—and whether your organization is ready to learn in public. 

💬 Pause and ask your team: "What would need to be true for us to feel ready to support an AI tool? What's holding us back?" That question might surface concerns nobody's said out loud yet. Better to hear them now than after you've signed the contract. 

Before You Shop, Decide 

Demos are easy. Decisions are hard. 

The associations getting real value from AI didn't start by comparing chatbot features or chasing the vendor with the slickest presentation. They started by getting clear on what they were trying to achieve, how AI should operate within their organization, and whether their content and culture could support the change. 

These three decisions won't make the vendor selection process instant. But they'll make it intentional. You'll know what questions to ask. You'll recognize when a product fits your philosophy—and when it doesn't. You'll be able to articulate what success looks like, which means you'll actually be able to measure it. 

And perhaps most importantly, you'll avoid the quiet failure that plagues most AI initiatives: launching something that technically works but never finds its footing because no one was clear on why it existed in the first place. 

The pressure to move on AI is real. Your board is asking questions. Your peers are making moves. The technology is ready. 

But readiness isn't just about the tools—it's about you. 

Decide first. Then shop. 

Ready to pressure-test your thinking? Book a conversation with our team—not a sales pitch, but a real discussion about whether your association is ready for AI and what it would take to get there. 

Tags: AI Technology, Associations, AI-first, Strategy, Membership Value, AI for Associations