The Association Intelligence Podcast by Betty:AI

0:00 20:50

[Thomas Altman] (0:05 - 0:10)

Today's big question, how did we make the Association Intelligence Podcast and why?

[Rob Barnes] (0:10 - 1:06)

We are Thomas Altman and Rob Barnes and this is episode zero of the Association Intelligence Podcast. Thomas, I'm so glad that you came up with the idea of doing an episode zero because it reminds me of the conversations we were having before we started writing the Association Knowledge Cycle book, which is now nearly three years ago. But also because I think that, and I've said this to everyone that's bought the book or taken a copy of the book, I said, please just go to the epilogue.

My favorite part of the book is the epilogue that you put together, which is about how we wrote the book. Because it was such an interesting experiment, seeing that we wanted to experiment with the interplay between our human endeavor, the thoughts, the ideas that we wanted to share and how we used AI tools to help us ultimately write the book.

[Rob Barnes] (1:06 - 1:26)

And over the 12 months it took us to write the book, how dramatically the AI that we were using to do this changed in capability and intelligence and insights and pushback, like that whole experiment. And I feel like this is exactly the same experience again, but in a completely unique way.

[Thomas Altman] (1:26 - 2:50)

Yeah, I think it's really, really cool. So the idea here is we've now recorded all the episodes for this podcast. So kind of set the table a little bit because hopefully this is the first one you're listening to.

What this season of the Association Intelligence Podcast has is a series of 12 questions, right? 12 questions that we either are asked a lot and deserve really, good questions, or that we think aren't asked enough, but are really, really important. And kind of, we went through this whole process of creating this really cool, awesome podcast for you guys, and then took a step back and my thought went to the same place to the epilogue of our book, the Association Knowledge Cycle.

And wanted to give you guys a little peek behind the scenes, like how did this all come together? Why is this episode just humans, but the rest are going to be humans and AI kind of back and forth? And how can, I think more importantly, how can you use sort of this experience to inform your own ideas around AI and how you can employ it at your association, right?

I think that that's really, really important. I think all of it is grounded, for those of you who have not kind of, are not familiar with the Association Knowledge Cycle concept that we've been playing with for a few years now. The idea here is that, one, associations are the organizations best situated to benefit from generative AI.

We fully believe that, we've believed that since before ChatGPT launched.

[Thomas Altman] (2:51 - 3:26)

And what we, the way we see that happening is kind of through this interplay around intellectual property, which associations are the owners of, the creators of, the guardians of for your industry, right, or your profession. And the interaction between members and the public with that intellectual property, with that knowledge. And what generative AI gets us when done correctly and deployed correctly, is the ability to see those interactions at scale, across all your members, across time, and then learn from those interactions in a way that allows you to create the next wave of knowledge.

[Thomas Altman] (3:27 - 4:10)

And as we've been building Betty and deploying Betty across kind of almost 200 different associations now, we've been good about that first part, right? We've gotten to the point where we've got, we've scaled the conversations and people can see them, but we haven't quite executed on that last, that last mile of taking those and creating something useful. So this podcast is kind of us putting our own words into action, looking at how people interact with our version of Betty, and turning that into something that we believe is kind of more broadly useful across the entire association ecosystem to help understand sort of good and bad and sort of beyond use cases for AI.

So that's kind of where this, this all came together.

[Rob Barnes] (4:10 - 7:00)

I remember when we first started scoping out, like, how did we want this to kind of take shape? And I was reflecting on how the nature of the conversations I'm having about Betty and AI, the application, the insights, those conversations have markedly changed over the last 12 months because of where the maturity around conversations, but also where things like AI fatigue are really starting to, to hit association executives. And so we wanted to go back a little bit and listen.

And so the first part of the first step was, well, let's build a repository of knowledge around what the conversations are in the association sector about AI. And that repository of knowledge would then become, you know, part of the source, you know, the major part of the source of how we would produce, you know, the episodes. And that's how we arrived at the, you know, the big questions of the day, even that experiment alone, and how long and how purposeful we stepped through what the listening tool that we built, how it was following that process, right?

You know, we wanted, like, there's amazing consultants in the association industry. There's amazing other podcasts, some of the, you know, 14, 15 best podcasts already around the association industry. There is books, there's newsletters, there is, you know, ASAE's Association Now, there's associations for associations.

You know, we had the AI tools listening to all of this over a period of time. It wasn't just a one and done. We didn't just accept the first outputs that came.

We were like, okay, that's super interesting. That doesn't sound right. Like, tell us where you got that from and where's that?

Like, we really kind of interrogated that knowledge base pretty critically. And even that in itself is an experiment that, you know, any association can think about what is being said about our industry or our profession out there broadly on the internet from reputable sources, and what do we do with that? And so, yeah, this view of that broad kind of listening, but really narrowing in on some key questions that are still being asked, that are being debated.

Then, okay, we're going to turn this into something that is usable, was always where this was going to go. We did it with the book. We had a thought.

We had a methodology. We had research. We produced a book.

Now, what are we going to produce that's also showing that interplay, that interoperability between us as humans and AI can produce something that's really meaningful? So, I think the hosts are synthetic, but the questions aren't. That's a really great way to frame where we ended up, right?

[Thomas Atlman] (7:01 - 10:49)

So, yeah, I think that's the point. So, what we want to do in this episode zero is kind of pull back the curtain a little bit, tell you how we came to this, how this was made. We want to inject that transparency, that human element back into it, keep this pretty unpolished for that purpose.

So, you know, it is more human. And the way we did this, the way we came across this is we started kind of with the conversations, right? We did this listening.

And then we had this repository of kind of the conversations that are being had, our own external research. And we said, what do we, we didn't even know we wanted to do a podcast at this point. We kind of went to the AI and said, what are some ideas here, right?

And one of the ones that popped out was, hey, what about a podcast, a serial style podcast where we ask and answer the most important questions per episode and the light bulb and all our heads went off. Like, that's a great idea. I don't, I don't know that any of us would have thought about that on our own.

And the way we made this, I think is important. As I think if you're not careful, if you guys are thinking about trying to do something like this yourself, if you're not intentional about how you employ these tools, you will end up with AI slop, right? We do not want, this is not slop.

Hopefully it's not slop. That is not our goal here. And the reason we're confident it's not is because we didn't just throw stuff at AI and copy and paste it and send it on.

We were very active in the way that we interacted with the AI on the front end, right? We were very intentional. Our own ideas were what initiated this process when we started kind of creating the podcast themselves.

We listened to the podcast, right? When we used a notebook LM to create these, it's got a very distinct style. We listened to the podcast.

We read through the transcripts. We were very critical, right? Most of these episodes went through six, seven, eight iterations where a couple interesting things happened.

On the first end, sometimes it was just not what we wanted. Most of the time, the first episode was just not it. Maybe it was mostly it, but there was stuff about it that was like, oh, no, I don't like that.

So we'd go back and kind of rewrite kind of our instructions back to the AI and have this dialogue back and forth. But the other thing, and I think maybe the more important thing that happened, so one, we were governed here. We advocate governed AI.

We absolutely governed what we did here. The other thing that happened, though, that I thought maybe was more interesting was that dialogue itself, like when I was getting frustrated that it wasn't doing what I wanted it to do, it forced me to do some self-reflection, right? It forced me to think about what actually I wanted, right?

It actually forced me, because I wasn't just accepting what AI did and pasting it, as being critical of the output. I was being critical of my own ideas, and it made me crystallize kind of my own thoughts about this in a way that I don't think I ever would have if I wasn't using AI to challenge myself. I frame it like that on purpose.

It's not AI was challenging me. AI was a tool that I used to challenge myself to think more deeply about the concept. And what we wanted to do here was kind of, one, open up that, you know, pull back that curtain and show you guys, you know, how we did this.

But then, two, as we did it, you'll find the structure of all our episodes starts with myself and Rob having a little bit of a back and forth, throwing it over to the Notebook LM produced podcast, and then an outro where we talk about it, right? We want to cap in both of those with the human experience and the way we interact with it. And what we're hoping is you can kind of see how that interaction happens, and then you yourself can contribute that too.

Hopefully, this forces you to be a little bit more critical, more thoughtful, disagree with, you know, both the AI and with us, right? But do so in a way that has kind of concrete, crystallized actions behind it. And that's kind of our goal here, and that's why we did this and how we did it.

[Rob Barnes] (10:50 - 14:54)

Yeah, the point that you made there, like most people who know me know that I'm relatively self-aware, but self-reflection hasn't necessarily been the strong suit. And you're quite right. At this point in the Betty journey, you know, we're two and a half years into this company, and we've done almost 200, you know, AI projects with different associations around the world.

And I didn't want to use that as a, I didn't want to make any assumptions about what would be meaningful or purposeful in this exercise, just because of that experience. And so you're right, on a far more rudimentary level than you, but my frustrations with what the AI was outputting was making me stop and think about, is it just assuming a bunch of things because the only thing that I've given it is my learned experience, I'm not asking it to explore outside of that. And so you're quite right, the process of, you know, interrogating it or sharing my frustrations with it or asking it to come back and challenge me to answer questions that I wouldn't have thought about, in and of itself created, again, some knowledge assets that became super useful in working out, well, what am I learning from these episodes each time?

You know, so actually going and reading a transcript and listening to an AI-produced podcast about stuff that we've researched is, I mean, it's a lot of fun, if nothing else, and particularly in the Notebook LM format. But it's actually insightful to look back and go, I would never have gleaned that insight from the same research just by myself, I just wouldn't have. And so being able to go and say, yeah, our format that we wanted is intentional, the human experience up front, us talking about what we, a preview about the episode, what we liked about it, where we had some challenges with it perhaps, then the outro, you know, the end of it, like where we're recapping it, but also saying, you know what, this was one of those episodes where we didn't necessarily agree with exactly what came out, nor do we have to agree, it just became the point of conversation and the point of learning, which every listener is going to interpret for themselves.

And that in itself is another important point, that you can go through this exercise to produce an asset, if you like, and in this case for us, it's 12 podcast episodes on the big questions that are being talked about in associations right now. But the learning is in the learning, you know, it's like it's learning how to learn from these things, the process by which we produced it, how we then fell into this really nice routine of previewing it and critiquing it afterwards, the number of times we went through that, but in really quick time, again, because the AI allowed us to do the fun stuff, which was to dig into the things that we learned and liked or didn't like.

And then also, you know, just making sure there's teachable takeaways, you know, from each of this, right, we want to make sure that association executives who do invest the time to listen to us and listen to what we've produced, understand the very clear distinction between what we're talking about, what the AI has produced, but also the companion asset, which again was a great idea. Let's have something that they can just take away and use, even if they don't ever listen to the podcast ever again, there is a document here that they can download and go, I can actually use this today in my job, then kind of our work is done, you know, and the amount of time we've invested to pull this together for everybody is well worth it if just one person says, well, I used this checklist or I got this, you know, I asked these questions in my staff meeting, it's like, okay, great, you know, that's exactly what we wanted to do it for. It's still the most fun thing to talk about how we did it and why we chose some of the tools as it is actually listening to the quality of the content that was produced from the research that we did.

[Thomas Atlman] (14:55 - 16:07)

I want to lean in that the companion asset, which I think is a really important part of this. So each episode has a companion asset, a workbook that allows you to kind of engage in that dialogue, you know, take it offline and think deeply about what you may have learned in each episode. And I want to call that out, one, it is a tangible benefit to you as you go through this that you can kind of hold on and reflect on and, you know, share around as you need to.

And I think you'll get a lot of value from that. I want to call that out too, because at Betty, we are all about this idea of governed AI, right? This is where we see the value for associations in that the AI is governed, it's traceable, it's auditable, it's coachable, right?

And we talk about that throughout the 12 questions. So the podcast itself is about that, but I want to call this out too, is that it's also an example of it. So the way that those companion assets were produced was through a process of governed AI, right?

It wasn't just like this idea of AI swap. We didn't just create stuff. We know where the sources came from.

We traceable to it. We understand how it was made. It's auditable.

When it was wrong, we coached it so that the next time it was right.

[Thomas Altman] (16:07 - 16:16)

And we've got a legacy, a repository of that coaching that then will inform kind of future versions of this experiment, whatever that might be, right?

[Thomas Altman] (16:16 - 16:36)

We're can kind of now expand out our knowledge asset creation. So yes, we are all about governed AI, but those companion assets are examples of how that is put into action. So kind of leading by example in this situation or just showing kind of eating our own, no, drinking our own champagne, almost said eating our own dog food, but we drink our own champagne at Betty.

[Rob Barnes] (16:36 - 18:39)

That's right. Yeah. And I think there's a great, you know, for us, it was a great experiment with some of the different tools that are available to us right now and, you know, how we produce those assets and, you know, even how we produce the name of the podcast in the first place and kind of iterate it on all of these sorts of things.

It can be a lot of fun with that kind of disciplined approach to the application of the applications as well. So the episodes as well are interchangeable. Like you don't have to follow it from one to 12.

You grab a topic, grab a question that's of interest to you right now, dig into that particular one. So in any order, I think it's going to be, you know, useful for people. And I think while voice has become very topical, I don't necessarily think it's that important, but it's very topical at the moment, like giving AI a voice.

The podcast was just a natural extension of that exploration because, you know, NotebookLM has had this audio capability for a good 12 months now and it's pretty cool. And so like, okay, what does our research sound like if we gave it to that? And I think that's the part that's like, you know, the sources are the important point.

It wasn't the hosts. It wasn't what the hosts sounded like. It had much nicer voices for podcasting than we do probably anyway, Thomas.

But because we governed the sources and we governed the way we wanted it to be produced, the hosts followed that. And by the time we got to the publishable versions of the episodes, we are stand behind. We are very confident that this is not AI slop, that this is rooted in genuine research and genuine questions and then capped off with our own experiences, you know, at the start and the end.

[Thomas Altman] (18:41 - 19:15)

So what I think we want you to do is, yeah, jump in wherever you need to. This is not, you know, you don't have to go from one through 12. Find the topic that's interest to you and then engage in the dialogue, whatever that means to you, whether that's through the companion asset, whether that's just sort of chatting with your colleagues at work or wherever you may be.

Engage with the dialogue yourself and kind of try to crystallize your own thoughts about AI. And if you disagree with us, that's great. That's what we, that's what you want.

You can disagree with us and the AI and have your own idea, but we ask that you share it. So find a way to share that back with us and we will love to learn from you guys.

[Rob Barnes] (19:15 - 20:42)

Yeah, I feel like that's actually a little, that is a little permission slip for everybody that's listening to the podcast at any point, is if you want to take the companion asset and load it into your AI tool of choice, if you're a co-work, if you're on a chat GPT, if you like perplexity, load it in there and have it do some critical analysis for you and see what it comes back with. And if you start getting some advice as to, like, this is a crackpot idea, this one, whatever, we would love to, like, book time with us at any point. If you want to have a conversation with us about the things that we learn from these big questions, just book time.

We can talk, you know, we can talk about this stuff under wet cement with a mouth full of marbles, I think, as my mother used to say. So we're open to those conversations, truly, because every time we have one of these conversations, we learn so much more about how these issues are impacting associations. And that's our wheelhouse.

That's where we, that's where we work. That's the work that we love every day. And, you know, producing the podcast is just another contribution, I think, to the body of knowledge for the associations industry that we know and love, that we hope people will have a bit of fun with and hopefully do some of their own experimenting after this.

Thanks, Thomas. Thank you, everyone, for listening to the Association Intelligence Podcast. This was episode zero. Join us on the first episode where we discuss this question: "Are we ready for AI?"

What this helps your team answer

How to use AI to research a topic without ending up with "AI slop" - what a rigorous, governed process for turning AI research into a real deliverable looks like.

An example model for pairing human judgment with AI output.

How to make AI-assisted work feel human and trustworthy instead of generic or robotic.

0:00 31:28

[Thomas Altman] (0:10 - 0:13)

Today's big question, are we ready for AI?

[Rob Barnes] (0:14 - 1:04)

We are Thomas Altman and Thomas Altman, and this is Episode 1 of the Association Intelligence Podcast. Before we get into it, this episode's companion asset is the AI Content Governance Readiness Checklist. Yes, that's a lot of words, but it's a very important asset.

It's linked in the show notes, so grab it now if you want to follow along, or download it after and put it to work. Today, we are digging into the first big question that we discovered online, and it's really one of the things that we hear a lot in conversation with associations, this idea of are we ready for AI and what that even means. Thomas, when you were doing the research for pulling together these big questions for associations, how did this resonate with you as one of the big things that we needed to start with?

[Thomas Altman] (1:05 - 1:51)

Yeah, so this was, to me, the reason why we're starting with this one is it's the biggest one, right? I think it's the one where there's the most hesitation. And what we've been finding is that this hesitation a lot of times is unfounded or turned on its head, but then, too, it's the most value-destroying hesitation you can have when approaching this question.

So one of the things we wanted to do through this episode was to kind of flip the script a little bit, kind of show you how you can get started with AI in a way that's going to be super valuable to you. And it all starts with this idea, the two dreadful words I know I hear a lot, Rob, I think you hear it every day, is digital transformation, and digital transformation specifically being a stumbling block. Rob, do you ever hear that?

Do you ever hear people say, oh, we're in the middle of a digital transformation?

[Rob Barnes] (1:52 - 3:17)

And it's funny how quickly in the last year or two that the meaning of that has changed dramatically. And yet it's still being treated the same. You know, like depending on what level of association executive we're talking to, yeah, digital transformation is either a really cool strategic excuse not to deal with a software vendor, a particular vendor, or literally that's the funded mandate from the board, is to undertake this digital transformation, and it needs to cut across user experience online, the tech stack itself is going to be completely either consolidated or expanded depending on which administration is in at the time. And I also hear that we need to get our house in order first.

You know, this concept that association's knowledge is so disparately housed that until you get it all consolidated or collaborated into one kind of place, you can't move forward with AI. And that's really interesting to me, this idea that it's fragmented, but it's the right content. You know, it might be incomplete, but only incomplete from what we know about our own body of knowledge.

You're right. It's such a challenging environment, and it is holding a lot of associations back from moving forward.

[Thomas Altman] (3:18 - 5:56)

So I think flipping the script a little bit, it's not get our house in order, a digital transformation so that we can start AI. What we've seen, and we've done nearly 200 successful implementations of AI projects for associations now, it's the other way around. Get AI in order so that you can get your house in order.

The process of getting AI set up is quicker time to value, and it informs a better strategy, a more future-looking strategy around kind of how you can do that digital transformation. And the way we want you to think about this, the thing that I think leads to people getting the most of value out of AI first, and then a digital transformation later, is when you think about what's the value that one, an association serves as related to its intellectual property, right? How can AI enable kind of those gaps, right, in getting that value from the intellectual property?

And then how can you inform that through whatever your strategy is going forward? So what we recommend is instead of thinking about, like, how do I do these crazy taxonomies, or how do I kind of reorganize my website, or I've got to get another new AMS, it's been five years since our last AMS, so we've got to get a new one, right? All that other stuff, I'd say, not so important.

More focused in, what's that end value? If what you're doing as an association is creating valuable intellectual property that will be consumed by your members, its general public, or whoever your constituencies are, and the problem is people are either not able to find it, not able to use it, not able to incorporate it in their day-to-day workflow so that they get value from this content. You can put AI first, but you need to do, the way you get your house in order is not digitally.

It's not through technology. It's through understanding your members, understanding what value they get, understanding where that content already lives. You don't have to reorganize or put it all in a single sort of data warehouse.

It can live in disparate places, in different kind of platforms, but as long as you know where it is and you can point the AI to that with the goal of helping people consume it, getting that process up and running, one, it shortens your time to value. You get more value in a shorter time frame by going that route first. Two, it informs the digital strategy going forward.

You're going to learn so much by doing that process first that once you finally get to kind of reorganize your website, you'll know what you want to do there. That's a lesson we've learned over and over again, and it's the biggest hurdle we've seen organizations like yours have trouble in overcoming before they get started. There's this fear of movement, and by taking that barrier down, we see people have a lot of success, both with AI and beyond.

[Rob Barnes] (5:57 - 6:36)

What I liked about this episode and the challenge that lives there is this idea that the biggest hidden ROI isn't really about member satisfaction. It's about an organization agreeing what the official answers are, what are the responses that the organization should be giving to a request to an AI, and is that really the silver lining? I think that's where this episode goes, and I really enjoy it is what is the silver lining that comes from all of this work, and then the interface with the members.

So I'm really looking forward to digging into it.

[Thomas Altman] (6:37 - 7:03)

Before we hand this over to our AI podcast host, a quick reminder that the following is generated using Notebook LM, but with heavy involvement from the Betty team to ensure that the content generated is valuable. For a deeper dive on our approach, we recorded a full episode where we discuss how and why we made this. If you're interested in understanding how this all came together, we recommend you listen to episode zero.

And with that, over to our AI hosts.

[AI Host 1] (7:07 - 7:14)

Right now, thousands of organizations are deliberately delaying artificial intelligence.

[AI Host 2] (7:14 - 7:15)

Yeah, they really are.

[AI Host 1] (7:15 - 7:30)

And they're doing it for what sounds like the most responsible, mature reason in the world. If you spend any time in leadership meetings, you've probably heard someone sigh and say, we can't deploy AI yet because we're in the middle of a massive digital transformation.

[AI Host 2] (7:31 - 7:32)

Right, the classic stall.

[AI Host 1] (7:33 - 7:47)

Exactly. It's this looming cloud. The assumption is that you simply must wait for the new website to be built or the new content management system to launch or that endless taxonomy overhaul to be perfectly finalized before you can even think about AI.

[AI Host 2] (7:47 - 7:54)

I mean, it sounds incredibly logical on the surface, right? You don't put new furniture into a house while you're still pouring the foundation.

[AI Host 1] (7:54 - 7:54)

Makes sense.

[AI Host 2] (7:54 - 8:05)

But based on the sources we're diving into today, that assumption is actually strategically backward. Waiting until your digital transformation is quote-unquote done is the wrong play.

[AI Host 1] (8:05 - 8:08)

Because the AI changes the transformation itself.

[AI Host 2] (8:08 - 8:26)

Exactly. Governed AI fundamentally changes the requirements of the very transformation you're trying to finish. See, that completely flips the standard boardroom logic.

Because the ultimate question everyone asks is, are we ready for AI? But what you're saying is that the premise of that question is just flawed from the start.

[AI Host 1] (8:26 - 8:49)

It is flawed. If governed AI is going to become a primary interface to your organization's knowledge, bolting it on at the very end of a website rebuild makes no sense. It alters how you need to think about natural language access, how you connect different sources, and how you establish access rules.

It really needs to come along for the ride.

[AI Host 2] (8:49 - 8:50)

Not just an afterthought.

[AI Host 1] (8:50 - 8:59)

Exactly. And we should ground this right away for you listening, this isn't just theory. These insights are drawn from patterns across nearly 200 association AI implementations.

[AI Host 2] (8:59 - 9:00)

Wow, 200.

[AI Host 1] (9:00 - 9:08)

Yeah. We're looking at the recurring hurdles and the actual successful pivots leaders make when dealing with vast amounts of member or staff knowledge.

[AI Host 2] (9:09 - 9:23)

That context is vital. And as you think about your own organization's digital ecosystem, which I'm guessing has a few messy corners, we actually have a highly practical tool linked in the show notes for you. Yeah.

That's the AI content and governance readiness checklist.

[AI Host 1] (9:23 - 9:38)

Exactly. Keep that handy because we're going to cover exactly how to apply it during this deep dive. But let's start with the paralysis itself.

Why do leaders feel they have to wait for the perfect website or system before bringing in AI?

[AI Host 2] (9:39 - 9:48)

Well, it stems from what we can call multi-platform fear. Most leaders know their institutional knowledge isn't sitting in one pristine database. It's totally scattered.

[AI Host 1] (9:48 - 9:49)

Oh, yeah. Everywhere.

[AI Host 2] (9:49 - 10:08)

You have data in your membership database, your AMS. You have articles in the website's CMS, courses in the learning platform, the LMS. Plus, like, decades of trapped knowledge in old PDFs and recorded committee meetings. Right. So the assumption becomes we have to clean all this up and move it into one centralized master system before an AI can read it.

[AI Host 1] (10:08 - 10:10)

Which sounds like a five-year project, honestly.

[AI Host 2] (10:10 - 10:22)

Minimum. And it creates a false binary. Leaders think the only options are either to pause everything and spend years moving files into one repository or to just recklessly connect an AI to everything and see what happens.

[AI Host 1] (10:22 - 10:24)

Upload everything and pray.

[AI Host 2] (10:24 - 10:34)

Yeah. But neither is true. Governed AI doesn't need you to build a single new filing cabinet.

Technologically, it functions more like a highly secure smart librarian.

[AI Host 1] (10:35 - 10:36)

OK, a smart librarian. I like that.

[AI Host 2] (10:36 - 10:46)

Yeah. So through API connections and secure indexing, it maps out where the information lives across your existing platforms without physically moving the files.

[AI Host 1] (10:46 - 10:48)

Oh, so the files stay where they are.

[AI Host 2] (10:48 - 10:55)

Exactly. It just needs to be told where the approved sources are and what the access rules are for those specific sources.

[AI Host 1] (10:55 - 11:07)

I want to make sure I understand the mechanism here, though. If the files are in completely different formats, say an old Word document on a shared drive, a modern Web page and, I don't know, a transcribed video file.

[AI Host 2] (11:07 - 11:07)

Yeah.

[AI Host 1] (11:07 - 11:11)

The AI doesn't need us to standardize all of those into one file type first.

[AI Host 2] (11:11 - 11:19)

Not at all. Modern AI parsing and indexing systems can ingest text from wildly different formats and convert them into mathematical representations. They're called vectors.

[AI Host 1] (11:20 - 11:20)

Vectors.

[AI Host 2] (11:20 - 11:34)

Yeah. So the AI isn't reading a PDF and a Web page differently. It's reading the underlying concepts mapped out in those vectors.

As long as it has a secure key to the room where that PDF lives, it can retrieve the answer.

[AI Host 1] (11:34 - 11:42)

So waiting to use AI because your files are scattered is basically like refusing to hire a brilliant researcher just because your books are in three different rooms.

[AI Host 2] (11:43 - 11:44)

That's a great way to put it.

[AI Host 1] (11:44 - 11:58)

The researcher doesn't care as long as they have the keys to the rooms. But wait, if the AI is just indexing concepts across all these different systems, what happens to the, like, hundreds of hours organizations spend building website navigation?

[AI Host 2] (11:58 - 11:59)

The taxonomy projects?

[AI Host 1] (11:59 - 12:03)

Yeah. The endless debates over taxonomy and drop-down menus.

[AI Host 2] (12:03 - 12:23)

Well, this is where Govern AI changes the priority of your digital transformation. It drastically reduces your dependence on perfect site navigation. Really?

Yeah. Think about the mental load we currently put on a user to find, let's say, a 2022 policy update. They have to reverse engineer the mental model of the person who designed the website.

[AI Host 1] (12:23 - 12:27)

They have to guess if it's under resources or advocacy or maybe about us.

[AI Host 2] (12:28 - 12:29)

Right. And if they guess wrong.

[AI Host 1] (12:30 - 12:31)

They assume the document doesn't exist.

[AI Host 2] (12:31 - 12:37)

Precisely. But AI doesn't navigate by clicking folders. It operates on natural language intent.

[AI Host 1] (12:37 - 12:39)

Meaning they just ask for what they want.

[AI Host 2] (12:39 - 12:55)

Yes. A member can just type, what changed in the safety policy a few years ago regarding site inspections? The AI understands the semantic intent of that question, the relationship between safety, inspections, and recent history, and fetches the relevant approved source directly.

[AI Host 1] (12:55 - 12:56)

That's huge.

[AI Host 2] (12:56 - 13:02)

It is. And even better, deploying Govern AI early actually accelerates your taxonomy project.

[AI Host 1] (13:02 - 13:06)

Wait, how so? By telling you what people are actually looking for.

[AI Host 2] (13:06 - 13:19)

Exactly. It produces real-time signal. Instead of guessing how members navigate, you see exactly what natural language shorthand they use, how they group topics in their minds, and where your current structure is failing to deliver.

[AI Host 1] (13:19 - 13:36)

So you use the AI's data to build a better website map. Let me push back on that, though. Because if the AI can perfectly read intent and deliver the right document, a web developer might hear this and think, great, we don't need a structured website anymore.

Just put a search bar on a blank page.

[AI Host 2] (13:36 - 13:46)

No, no, no. Structure still matters immensely. Members still want to browse, they still want curated experiences, and you definitely still need an organized digital presence.

[AI Host 1] (13:46 - 13:47)

Okay, good to clarify.

[AI Host 2] (13:47 - 14:02)

What AI does is remove the urgency of that structure being absolutely perfect before you can deliver value. You don't have to freeze your AI rollout while committees debate a sitemap for six months. The AI bridges the gap.

[AI Host 1] (14:02 - 14:16)

That shifts the pressure in a really healthy way. So if we accept that we don't need perfect navigation or a single master database to begin, we have to look at the content itself. Right.

Because the fear isn't just that the content is scattered, it's that the content is messy.

[AI Host 2] (14:16 - 14:32)

And let's be honest, everyone's content is messy. The key is understanding what kind of messy you are dealing with. The implementation patterns give us a very practical framework for this.

Instead of trying to clean everything at once, you triage your existing content into three distinct buckets.

[AI Host 1] (14:33 - 14:44)

Okay, let's unpack this. It's like deciding what to pack when you're moving houses, right? So if the goal is to figure out what's ready for AI without a massive cleanup, I imagine the first bucket has to be the content we actually trust.

[AI Host 2] (14:44 - 14:48)

That's it. The first bucket is content that is fragmented but accurate.

[AI Host 1] (14:49 - 14:50)

Fragmented but accurate.

[AI Host 2] (14:50 - 15:01)

Yeah. The knowledge might be spread out across recent journal articles, formal policy PDFs, and official conference transcripts. The storage is fragmented, but the source truth is highly trusted by your organization.

[AI Host 1] (15:02 - 15:03)

So what's the action for that bucket?

[AI Host 2] (15:04 - 15:11)

For this bucket, the action is simple. It is ready for launch or a strong pilot. You don't need to reorganize it.

You just let the governed AI index it.

[AI Host 1] (15:11 - 15:28)

That makes sense. It's like your good silverware scattered in different drawers, just pack it. But what happens when we have a topic where the documentation isn't entirely comprehensive, where maybe the official handbook has the basics, but the real nuance lives in the heads of your senior subject matter experts?

[AI Host 2] (15:28 - 15:45)

Ah, that brings us to bucket two. Incomplete or uneven. You have valuable knowledge, but the coverage is spotty.

If you put an AI purely on this content, it might give surface-level answers because it lacks the deeper context your staff usually provides in person.

[AI Host 1] (15:45 - 15:49)

So do we hold off on AI for that content until the staff writes everything down?

[AI Host 2] (15:49 - 15:55)

No. You run a narrow controlled pilot. You use the AI system as a diagnostic tool.

[AI Host 1] (15:55 - 15:56)

Oh, to see what it can't answer.

[AI Host 2] (15:56 - 16:09)

Exactly. When the AI struggles to answer a complex question from a test group, the system's data highlights the exact gap. You expect to coach the system here, and you use the AI to figure out exactly which documents your experts actually need to write.

[AI Host 1] (16:09 - 16:23)

Rather than having them just guess what's missing. So it's like a puzzle missing a few pieces. Pack it, but note it.

Which brings us to the third bucket. If bucket one is ready to go, and bucket two needs a pilot, bucket three has to be the liabilities.

[AI Host 2] (16:23 - 16:43)

Yes. Bucket three is contradictory, obsolete, permission confused, or high risk. Oh, bad stuff.

The bad stuff. We are talking about stale technical standards that were superseded five years ago, but never deleted. Or like conflicting compliance policies that could create legal risk if interpreted incorrectly.

[AI Host 1] (16:43 - 16:44)

So what do we do with this?

[AI Host 2] (16:44 - 16:56)

The action here is a hard stop. Do not scale this broadly to your members. You must clean this content, establish authoritative ownership, and set strict guardrails before AI is ever allowed to surface it.

[AI Host 1] (16:56 - 17:01)

So it's not about feeding everything into the machine. Bucket three is actual garbage don't pack the garbage.

[AI Host 2] (17:01 - 17:03)

Exactly. Don't pack the garbage.

[AI Host 1] (17:03 - 17:13)

And that garbage, the high risk, obsolete content, really naturally leads to the most critical part of governed AI, right? Making sure the system understands what it's not allowed to share.

[AI Host 2] (17:13 - 17:15)

Yes, the governance guardrails.

[AI Host 1] (17:15 - 17:29)

Let's talk about permissions, because a lot of organizations have deep archives on shared network drives or, you know, internal SharePoint sites. If an organization just plugs a generic AI search tool into their internal servers to index everything, what actually happens?

[AI Host 2] (17:30 - 17:34)

It creates a massive exposure problem. And it usually involves stale permissions.

[AI Host 1] (17:34 - 17:36)

Stale permissions, like what?

[AI Host 2] (17:36 - 17:48)

Well, think about how organizations typically work. Six years ago, an HR team drafts a sensitive internal policy. They put it in a shared folder.

Over time, people move on, folder permissions get inherited or changed.

[AI Host 1] (17:48 - 17:48)

Right.

[AI Host 2] (17:48 - 17:56)

And suddenly that draft is technically accessible to a wider group of staff, but no one realizes it because it's buried 30 folders deep.

[AI Host 1] (17:57 - 18:01)

So it's protected by obscurity. It was so hard to find that the broken permissions didn't matter.

[AI Host 2] (18:01 - 18:06)

Exactly. But an AI doesn't experience obscurity. It just reads the permissions.

[AI Host 1] (18:06 - 18:06)

Oh, wow.

[AI Host 2] (18:06 - 18:21)

If you ask a generic AI a question about that HR policy, it will instantly retrieve that half-baked sensitive draft because it perfectly matches the semantic intent of your prompt. And the system technically has access to it. The AI is just doing its job.

[AI Host 1] (18:21 - 18:22)

That is terrifying.

[AI Host 2] (18:22 - 18:33)

It is. Which is why the safest first move is to draw a hard boundary. When you start, you point the governed AI only at approved, external, or clearly member-facing content.

[AI Host 1] (18:33 - 18:35)

You just leave the internal stuff out.

[AI Host 2] (18:35 - 18:43)

You leave the internal drafts, and the messy staff network drives completely out of scope until you have audited who actually owns what.

[AI Host 1] (18:43 - 18:52)

Okay, that covers the internal permissions. But here's where it gets really interesting for me. What about public-facing content that just changes over time?

[AI Host 2] (18:53 - 18:53)

Versioning.

[AI Host 1] (18:53 - 19:05)

Yeah, versioning. Let's say we have an official safety standard from 2015 that is fully approved and public. But then we published a totally updated safety standard in 2025.

Both are public. Both are technically approved.

[AI Host 2] (19:06 - 19:06)

Yeah.

[AI Host 1] (19:06 - 19:11)

How does the AI know how to handle a question about safety standards? Doesn't it just look at the date?

[AI Host 2] (19:11 - 19:32)

You would hope so, but a generic generative model doesn't inherently understand version control unless it's explicitly governed. Yeah. Generative AI is designed to synthesize information.

Left to its own devices, it might pull a foundational paragraph from the 2015 standard and combine it with a new bullet point from the 2025 standard because both documents conceptually answer the prompt.

[AI Host 1] (19:32 - 19:36)

So it synthesizes them into a Franken-standard that doesn't actually exist in the real world.

[AI Host 2] (19:36 - 19:58)

Precisely. And in technical or compliance-driven fields, a synthesized Franken-standard is incredibly dangerous. We can imagine.

To prevent that, the system requires explicit current source rules. You have to apply source labels. Now, the 2015 standard is still valuable, right?

Members often need to compare the new regulation against the old one so you don't delete the 2015 document.

[AI Host 1] (19:58 - 19:59)

Use governance.

[AI Host 2] (19:59 - 20:13)

Exactly. The governance layer explicitly instructs the AI, treat the 2025 document as the authoritative truth for all current queries. Only reference the 2015 document if the user explicitly asks for historical context.

[AI Host 1] (20:14 - 20:17)

The AI cannot be left to infer that nuance on its own.

[AI Host 2] (20:17 - 20:18)

No, it can't.

[AI Host 1] (20:18 - 20:32)

That completely demystifies the idea that AI is just a plug-and-play magic wand. It requires human guidance to set those rules, which implies that rolling out AI isn't just an IT project. It's an ongoing operational practice for the staff.

[AI Host 2] (20:33 - 20:46)

It requires an active process called coaching. An AI model, out of the box, does not know the unique culture or the specific dialect of your organization. Your staff and your subject matter experts have to teach it over time.

[AI Host 1] (20:46 - 20:49)

What does coaching actually look like in practice, though?

[AI Host 2] (20:49 - 20:58)

It's about teaching the AI your organizational shorthand, abbreviations, retrieval cues. For instance, your members might constantly ask about the big show.

[AI Host 1] (20:58 - 21:00)

Oh, like a nickname for an event.

[AI Host 2] (21:00 - 21:15)

Right. The AI doesn't know what that means. A staff member coaches the system by mapping that phrase to the official annual global convention.

You are taking the tacit knowledge that usually only lives in the heads of your senior staff and embedding it into the AI's understanding.

[AI Host 1] (21:15 - 21:24)

That makes the system increasingly valuable over time because it's learning the specific language of the community. But, you know, coaching requires knowing what the members are actually asking. How do we see that?

[AI Host 2] (21:25 - 21:32)

Through usage data, which is often referred to as insights. This is where Governed AI becomes a strategic engine for the whole organization.

[AI Host 1] (21:33 - 21:33)

How so?

[AI Host 2] (21:33 - 21:49)

Well, every time a member asks a natural language question, the system attempts to answer it using the approved sources. When you review the insights data, you aren't just looking at search volume. You are looking for the queries where the AI struggled to generate a high confidence answer.

[AI Host 1] (21:50 - 21:56)

Because if the AI couldn't answer it based on our approved documents, it means our approved documents are missing the information.

[AI Host 2] (21:56 - 22:09)

Exactly. It turns real member questions into a literal roadmap for your content team. Unanswered questions show you exactly what standards you need to update, what blog posts you need to write, or what training modules you need to develop next.

[AI Host 1] (22:09 - 22:11)

That's incredibly powerful.

[AI Host 2] (22:11 - 22:21)

It is. We see this dynamic play out quietly with tools like Betty, which is a really practical example of this kind of governed, source-bound, multi-tier AI.

[AI Host 1] (22:21 - 22:21)

Right.

[AI Host 2] (22:21 - 22:29)

A system like Betty is coachable, and it's designed to activate the knowledge you have approved and then use insights to reveal the gaps you didn't know existed.

[AI Host 1] (22:30 - 22:33)

But importantly, a system like that doesn't write the new standard for you.

[AI Host 2] (22:33 - 22:41)

No, it doesn't. It doesn't magically fix your bad guidance, it doesn't repair your broken SharePoint permissions, and it absolutely does not replace human governance.

[AI Host 1] (22:41 - 22:43)

It's just activating what you have.

[AI Host 2] (22:43 - 22:51)

Yes. It relies entirely on the parameters you set. Which brings us to what we should call the honest beat of this entire readiness conversation.

[AI Host 1] (22:51 - 22:53)

The honest beat. I like that. The reality check.

[AI Host 2] (22:53 - 23:14)

Yes. Here's the candid truth. If an organization has a high-stakes, highly sensitive topic, but no one on staff can confidently identify what the actual authoritative source document is, or who owns the access rights to it, that topic is simply not ready for broad, member-facing AI.

[AI Host 1] (23:14 - 23:25)

Wow. So what does this all mean for you, the listener? It means AI isn't a magic wand that cleans your messy room.

It is simply a very bright flashlight that shows you exactly where the mess is.

[AI Host 2] (23:25 - 23:26)

That's exactly it.

[AI Host 1] (23:26 - 23:31)

And if the mess is a liability, you don't shine a spotlight on it for your members. You fix the source first.

[AI Host 2] (23:32 - 23:40)

It illuminates the gaps. That is the core of readiness. It's not about waiting for a pristine digital ecosystem.

It's about knowing your sources.

[AI Host 1] (23:40 - 23:58)

We've covered a lot of ground here in this deep dive. We started with the stall, that fear of the incomplete digital transformation. We walked through how AI actually acts like a smart librarian, indexing across platforms without moving data, and how it navigates by intent rather than rigid website structures.

[AI Host 2] (23:59 - 23:59)

Right.

[AI Host 1] (23:59 - 24:11)

We looked at sorting content into those three actionable buckets, the dangers of stale permissions, Frankenstandards, and the necessity of coaching. So how does a listener actually start this tomorrow without feeling overwhelmed?

[AI Host 2] (24:11 - 24:13)

You do a readiness pass, and you start small.

[AI Host 1] (24:13 - 24:14)

Okay.

[AI Host 2] (24:14 - 24:23)

Pick one single use case for your organization. Don't try to solve the whole website, just one use case. First, list the authoritative sources for that specific topic.

[AI Host 1] (24:23 - 24:24)

Just the trusted ones. Right.

[AI Host 2] (24:24 - 24:28)

Second, name the actual human content owners who are responsible for those sources.

[AI Host 1] (24:28 - 24:28)

Right.

[AI Host 2] (24:28 - 24:35)

Third, set the access tier. Clearly define exactly who gets to see this information. Is it public?

Is it for members only?

[AI Host 1] (24:35 - 24:39)

And then flag any of the versioning or permission risks we discussed.

[AI Host 2] (24:39 - 24:56)

Yes. And once you have that use case mapped out, look at the content and assign it to a bucket. Is it bucket one, fragmented but accurate and ready to launch?

Is it bucket two, incomplete and ready for a pilot? Or is it bucket three, and you need to clean it before moving forward?

[AI Host 1] (24:56 - 25:04)

It takes something that feels like an impossible multi-year IT overhaul and turns it into a targeted content strategy.

[AI Host 2] (25:04 - 25:05)

It makes it manageable.

[AI Host 1] (25:05 - 25:18)

And to make that strategy tangible, you can go to the show notes right now and grab the AI content and governance readiness checklist. It walks you through that exact sorting and planning process so you can take the safest next step for your association.

[AI Host 2] (25:18 - 25:48)

It gives you the map. And, you know, I want to leave you with one final thought to mull over. We spend so much time talking about the ROI of artificial intelligence in terms of member satisfaction or search efficiency.

But what if the greatest immediate ROI of AI isn't actually about giving your members perfect answers on day one? What if the greatest value is that implementing governed AI finally forces your organization to sit down, look at your scattered files, and agree on what your official answers actually are?

[AI Host 1] (25:48 - 26:00)

That is something to chew on. Forcing organizational alignment as the hidden ROI of AI. Love that.

We will leave it there for today. Grab the checklist in the show notes, start sorting those buckets, and thanks for joining us on this deep dive. We'll see you next time.

[Rob Barnes] (26:07 - 26:41)

So the big question today was, are we ready for AI? And I thought it was really interesting, Thomas, that the research pulled out this kind of risk use case around a document that was buried deep in, you know, and I think it's called it buried by obscurity. But all of a sudden it was resurfaced again, just because of the capability of generative AI and some of the challenges, you know, getting ready.

Do we even have to worry about being more cautious because of things like that potentially happening?

[Thomas Altman] (26:42 - 29:06)

Yeah, I think to me, that was super interesting, too. It wasn't something I was expecting, but it made me think. And one of the things I liked about it was this idea of understanding sort of the buckets of content that you can get started with first, right?

So this idea of if it's in SharePoint, a lot of times if you've got a legacy sort of Microsoft system that was set up in the 90s, and you've gone through like 10 different IT directors in the interim, who knows who has permissions or access to what? And really what's preventing people from accessing stuff they shouldn't access is the fact that they don't even know that they can. But AI would be able to, right?

So that's a genuine, real concern. And it's something that I really caution people around when they're thinking about adopting a solution like Copilot, right? Not that Copilot's bad, but like, it's something you need to think about.

And to me, that is definitely like a risk. It's something you don't want to think too much about. So then flipping that around, where is there content that is de-risked?

So I liked that the AI duo kind of dialed in on, it was around like the seven, seven and a half minute mark, where they started talking about like these different buckets, right? And the idea that you've got some content that I think you already are pretty confident in, right? So that might be your, you know, if you've got journal articles that you publish that went through peer review, if you've got stuff that's published on your website, you know, around upcoming events or things that just need to be correct and have gone through some process where you trust it.

Or if you've got standards or a handbook or a textbook, right? These are kind of sources of truth that have been vetted, right? So understanding what those vetted pieces of content are, they're more trustworthy, and you probably know who the audience is, you know, who's going to use it, you know how they want to use it, right?

So we're starting to build these sort of defendable use cases here, you know, the content, it's trustworthy, you know, who should have access to it and how they are intended to use it. And then you internally probably already have a set of rules that govern not just access, but you know what it can be used for. And once you have those three things, an audience, the sources, and the governing rules, that is a kind of a clean bucket that you then can use AI for.

So kind of carving those out and starting with those while you clean up your SharePoint, right? Those kind of permissions.

[Rob Barnes] (29:06 - 31:28)

And we hear this quite often, that sense of, what I love about associations is even through the natural onboarding of new teams over time, people who work there now know what the trusted sources of information is almost immediately. They know that the policy documentation that exists is the current truth, the journal articles, you know, the white papers, these sorts of things. They might not know the full extent of the library of all of that, but they know that that's the stuff that everybody can trust and that the members, the profession trusts in them sort of straight away.

So, you know, when they started talking about what AI isn't going to do, you know, in terms of fixing bad guidance or repairing, you know, the broken permissions of who's got access to what to do what, that human oversight, and I think, you know, we've always talked about the human in the loop, uh, is still an incredibly important part and a much more focused function.

And that’s what’s useful about today’s, um, companion asset that’s available for everyone to download, it’s really look at what is the purpose for implementing AI. You know, instead of just taking the board’s recommendation, which is “We need to be doing AI,” whatever that really means, look for the purpose first. And don’t be so concerned about the full, kind of, audit. You know, it’s not about creating a single clearing house necessarily. AI is going to help you get to that sort of path, but form follows function has always been a fun sort of thing for me. If you decide on the function that you need to solve, the form that the solution takes, in some cases, becomes kind of self-evident. I think the readiness checklist that we’ve prepared for everyone today is something that everyone is going to get a benefit from following this.

So, I would say, one more time before you go, everybody, download the companion asset today. It’s the fastest way to actually put today’s episode to work for you.

Thanks for listening to the Association Intelligence Podcast. Join us on the next episode to answer this question: “What do we tell the board?”

What this helps your team answer

Why you should get AI in order first, and let it inform your digital transformation strategy.
How to triage content into readiness buckets before launch: fragmented but accurate, incomplete/uneven, and contradictory/obsolete, or permission-confused.
Two big governance risks to guard against: stale permissions and outdated versioning. Both require explicit source labeling, access rules, and ongoing human coaching.

Companion Asset

AI Content Governance Readiness Checklist

0:00 30:41

[Thomas Altman] (0:05 – 0:09)

Today's big question, what do we tell the board?

[Rob Barnes] (0:09 – 1:27)

We are Thomas Altman and Rob Barnes, and this is episode two of the Association Intelligence Podcast. Before we get into today's episode, this episode's companion asset is the board brief, Governed AI for Association.

It's linked in the show notes, grab it now if you want to follow along, or download it afterwards and put it to work. Thomas, this is an excellent question, and I'm glad it came up in the research that we did for Association Intelligence Podcast, because oftentimes, and I think even what do we tell the board? I'm hearing a lot is like what the board has told us, you know, from association executives.

It's like, as we talked about in the last episode, it's like, we have to do AI, but without any kind of real framework for any of that, and, you know, bringing a board a solution without any of the, I guess the preambles, like, I think about it from an alignment perspective, again, purpose, the purpose of the organization is not to become a software company and start developing its own AI. You know, do we see organizations tie themselves in knots now at a board level because of this question around, we have to be doing AI?

[Thomas Altman] (1:27 - 3:31)

Who doesn't love, who doesn't love the board meetings, right? Who doesn't love getting up and having to, you know, stand in front of a group or do your presentation, right? And to me, anytime I'm in front of a board, and we've done several of these, right?

It's the ultimate herding cats experience, right? You've got a bunch of different people from a bunch of different backgrounds, all who do care about the well-being of the association, but they bring their own biases or their own experience, right? So, you've got sort of the person who's obsessed with AI, and they want to be the AI guy, and they kind of get too into the weeds, and they lose sight, or you have kind of the, the person's a bit maybe more fearful of AI, and right, they're like, well, we don't want to do this, or we don't want to do that.

And how do you get all of these people on the same page, so that you're delivering value back to your members at the end of the day, right, that you're executing on the mission of your association, the best way you can with the technology that we have. And that's kind of the goal of this episode. And then sort of where people ask, like, I got to prep for my board meeting, what I need to do.

The thing that I thought was interesting is calling out that it's not really the AI that you want to focus on. It's the mission of the association, right? You don't want to bring a demo, because demos, or you don't want to bring just a shiny demo, and then that becomes a tax service, like people just start picking apart, like, oh, should this button be blue or red, right?

You don't want to get to that level. Or you don't want to start going in the weeds around sort of technical jargon, right, because, or like your favorite model, this model or that model, like, that doesn't matter. At the end of the day, what I think the thread that we were able to pull on with this, and kind of it's been at least my experience, I think Rob, yours as well, you want to be able to deliver a kind of a finely crafted, this is advancing our mission in this way.

It's protecting us from these particular concerns that are fairly common throughout any board meeting and they're valid, right? And this is the way we're going to do it. So focus on what you're delivering, how you're protecting your downside, and the value you're creating at the end of the day.

And Rob, I know you've been in many board meetings, I don't know what your take is.

[Rob Barnes] (3:31 - 5:53)

Yeah, the board meeting that has the self-anointed AI expert on the board, because they happen to do it in their job or for their company or wherever, it's that I've seen boards be derailed by that level of enthusiasm, because that individual has forgotten their role as a steward of the organization. And got into the exciting kind of new tool. And that also has an impact on the staff that are trying to navigate alignment.

And this is what this episode, I think, really brings to the fore quickly is this concept of alignment. When you are thinking about AI that's to solve a problem and create value, value needs to be aligned to strategic purpose. And this is where we want to keep our board is on the strategy.

They've agreed to three to five years strategic plan. They're still very common. So when we're looking at value, align it to the things that the board has already agreed to and remind them at this opportunity to say, hey, this is the stewardship that you've already agreed to.

This is the strategy going forward. This is an opportunity for us to, and you're right, it's less, hopefully at that point, it's kind of less about the tool itself. The boards that we talk to, they've got, they understand their fiduciary risks.

They're looking to protect the organization from any potential threats that seem to be well publicized around AI these days. But if we can bring that back to, again, the blueprint, this is the opportunity. This is the value.

This is the alignment to strategy. Then it does get us a little bit closer to that. And there are some great guardrails.

You know, I think in the episode it talks about some really interesting guardrails that everybody can kind of learn from. And, you know, that's part of the companion asset as well. You know, we see some of this stuff go sideways pretty quickly with boards and with an association, but we're also seeing ones that have got a little bit of courage mixed in with a real understanding of their stewardship, start to take great strides very quickly and get great value and realize the impact.

And ultimately that's what we want, right? Is we want the association to have impact, right?

[Thomas Altman] (5:52 - 6:18)

And with that, over to our AI hosts.

[AI Host 2] (6:24 - 6:25)

What do we tell the board?

[AI Host 1] (6:25 - 6:32)

Oh, man, that is, I think that's the single most critical question hanging over executive leadership right now.

[AI Host 2] (6:32 - 6:40)

Yeah, absolutely. I mean, if you're listening to this deep dive, chances are you are an association CEO or, you know, a COO, maybe a senior leader.

[AI Host 1] (6:41 - 6:42)

Staring down an upcoming board meeting.

[AI Host 2] (6:42 - 6:50)

Exactly. You know, artificial intelligence is a priority. Your members are asking about it.

Your staff is like already experimenting with it on their phones.

[AI Host 1] (6:50 - 6:52)

Yeah, on their personal devices, for sure.

[AI Host 2] (6:52 - 7:09)

And you are expected to present this coherent path forward to your governing body. But there's this massive gap between knowing you need a strategy and actually standing in front of a fiduciary board without the whole meeting just, you know, turning into a complete derailment.

[AI Host 1] (7:09 - 7:14)

Which happens a lot. And the approach we're outlining today really relies on what we called the demo versus blueprint dynamic.

[AI Host 2] (7:15 - 7:16)

Right. Break that down for us.

[AI Host 1] (7:16 - 7:30)

Well, the absolute worst maneuver an executive can make is walking into that boardroom, pulling up a screen and just asking the board to, like, approve AI based on some flashy generic technology demonstration.

[AI Host 2] (7:30 - 7:32)

Because that just screams risk to them.

[AI Host 1] (7:32 - 7:41)

Exactly. So we're going to map out the alternative. We're going to detail why you need to bring your board a one page blueprint instead of a pitch.

[AI Host 2] (7:41 - 7:55)

A blueprint that details what concrete value, specific risk controls, internal ownership and really strict rollout boundaries. Got it. Now, before we get into the actual mechanics of that, I do want to give you all a tool right out of the gate.

[AI Host 1] (7:56 - 7:57)

Oh, yeah. The companion asset.

[AI Host 2] (7:57 - 8:08)

Right. We have a companion asset for this deep dive called the board brief. Consider it like your show notes tool, specifically designed to help you structure this exact internal board conversation.

[AI Host 1] (8:08 - 8:09)

It's super helpful.

[AI Host 2] (8:09 - 8:19)

It really is. We'll circle back to it later. But just know that you don't need to furiously take notes or memorize the framework we're about to build.

That board brief is your practical next step.

[AI Host 1] (8:19 - 8:27)

Perfect. So before we can discuss how to present the right blueprint, we really have to look at what the board is actually apprehensive about.

[AI Host 2] (8:27 - 8:28)

You know, what's keeping them up at night.

[AI Host 1] (8:28 - 8:39)

Right. Looking at patterns from nearly 200 association AI implementations, the mapping shows us that board anxiety generally clusters around a few very specific areas.

[AI Host 2] (8:39 - 8:40)

Like what?

[AI Host 1] (8:40 - 8:45)

Content scope, intellectual property leakage, multi-tier access, traceability.

[AI Host 2] (8:46 - 8:47)

Traceability is a big one.

[AI Host 1] (8:47 - 8:55)

Huge. Plus answer quality, member trust, reputation risk. And honestly, who actually owns the review process?

[AI Host 2] (8:55 - 9:04)

OK, let me play devil's advocate here for a second, because if I'm an executive who's really pushing for modernization, I might find those fears a bit overblown.

[AI Host 1] (9:04 - 9:05)

Really? How so?

[AI Host 2] (9:05 - 9:18)

Well, I mean, if an association's PDFs and articles are already sitting on their public facing website, hasn't a system like ChatGPT already scraped and read them anyway? Like, why is the board acting like this is some sudden new threat to our intellectual property?

[AI Host 1] (9:18 - 9:31)

OK, I hear that a lot, but there is a profound mechanical difference between a public search engine scraping your public marketing copy and an association taking its proprietary multi-million dollar member research.

[AI Host 2] (9:31 - 9:33)

Oh, you mean the stuff hidden behind the paywall.

[AI Host 1] (9:33 - 9:38)

Exactly. Taking those paid assets and just plugging them into an unmanaged third party AI model.

[AI Host 2] (9:38 - 9:39)

Right. That makes sense.

[AI Host 1] (9:39 - 9:53)

Board members have a fiduciary duty to protect the organization's assets. So when they ask if a new AI tool is going to absorb their peer review journals and just feed them to the open Internet, I mean, they're asking a fundamental governance question.

[AI Host 2] (9:54 - 9:55)

They want to know if the pipe flows both ways.

[AI Host 1] (9:55 - 10:07)

Exactly. They need to know the pipe is sealed. But the common mistake we see leaders make is misinterpreting this apprehension as like a lack of technical understanding.

[AI Host 2] (10:08 - 10:09)

So they try to overcompensate?

[AI Host 1] (10:09 - 10:19)

Yes. The leader walks into the boardroom and tries to overcompensate by explaining the plumbing. They start throwing around terms like large language models, our RAG, vector databases.

[AI Host 2] (10:19 - 10:20)

Just trying to sound cutting edge.

[AI Host 1] (10:20 - 10:22)

Right. And it completely backfires.

[AI Host 2] (10:23 - 10:32)

Wait, before we move past that, for the executive listening who keeps hearing AAG from their IT department, what actually is that? Let's just define it quickly so we know what concept we're supposed to be avoiding in the boardroom.

[AI Host 1] (10:32 - 10:50)

Sure. So RAG stands for retrieval augmented generation. Instead of asking an AI to answer a question from its general memory, which is, by the way, how you get hallucinations and totally made up facts, RAG forces the AI to run a search through your specific documents first.

[AI Host 2] (10:50 - 10:52)

Oh, OK. So it has to look it up.

[AI Host 1] (10:52 - 11:02)

Yeah. It retrieves your document, reads it, and then generates an answer based strictly on what it just read. Think of it as changing the AI's task from a closed book exam to an open book exam.

[AI Host 2] (11:02 - 11:22)

Where the only book allowed is your official archives. That makes total sense. But to your point, pitching the mechanics of retrieval augmented generation or showing some flashy demo to a board without a governance model.

I mean, it's like trying to sell someone a sports car by only showing them the top speed right when they're asking to see the safety crash ratings.

[AI Host 1] (11:22 - 11:31)

That is a perfect analogy. And treating the phrase we have an AI policy as if it is the same thing as having a governed system. That's another major pitfall.

[AI Host 2] (11:31 - 11:34)

Yeah. A policy on a piece of paper isn't a system.

[AI Host 1] (11:34 - 11:45)

Right. Leading with hype or competitor fear, like saying we have to do this because rival associations are instead of leading with architecture. Well, that guarantees the board will pump the brakes.

[AI Host 2] (11:45 - 11:50)

But safety ratings alone don't sell the car either. Nobody buys a Volvo just to leave it parked in the driveway.

[AI Host 1] (11:50 - 11:51)

Fair point.

[AI Host 2] (11:51 - 11:59)

We need governance. Sure. But governance is simply the condition that makes it safe to unlock the actual value sitting in the association's archives.

[AI Host 1] (11:59 - 12:10)

Right. The board conversation cannot be a referendum on AI enthusiasm. If the boardroom devolves into a philosophical debate about whether AI is good or bad for humanity, the initiative is dead.

[AI Host 2] (12:11 - 12:11)

Oh, totally dead.

[AI Host 1] (12:11 - 12:22)

The conversation must be entirely about value activation. Because associations are sitting on a staggering amount of intellectual property that they have already paid to create.

[AI Host 2] (12:22 - 12:37)

Yeah. We are talking about decades of conference recordings, webinars, certification guides, industry standards, peer reviewed research, FAQs, not to mention the subject matter expertise that's just trapped in the heads and like the email outboxes of senior staff.

[AI Host 1] (12:37 - 12:49)

Exactly. And much of that value is completely inert right now. It's trapped in a PDF on some clunky legacy portal or buried in an MP4 video file from an annual conference three years ago.

[AI Host 2] (12:49 - 12:59)

Right. If a member needs to know the association's stance on a highly specific regulatory change, they are definitely not going to sit down and watch a two hour conference video from 2022.

[AI Host 1] (12:59 - 13:04)

No way. Not just to find the three minute segment where a panelist happened to discuss it.

[AI Host 2] (13:04 - 13:15)

Right. A recorded annual conference session shouldn't be a one time asset. A member shouldn't have had to be sitting in a specific hotel ballroom on a specific Tuesday two years ago to get the value of that insight.

[AI Host 1] (13:15 - 13:23)

Beautifully said. And what a governed source bound knowledge assistant does mechanically is multiply that existing value by making it findable and evergreen.

[AI Host 2] (13:23 - 13:25)

It brings it back to life.

[AI Host 1] (13:25 - 13:39)

Yes. It transforms an association's static dormant archives into an active on demand mentor. The AI itself is just a translation layer.

The actual product is your historical knowledge delivered at the exact moment a member experiences a problem.

[AI Host 2] (13:39 - 13:50)

OK. So if the goal is to safely unlock that trapped value without, you know, triggering the board's risk aversion, we really need to talk about how to construct that one page blueprint.

[AI Host 1] (13:50 - 13:51)

The blueprint is everything.

[AI Host 2] (13:52 - 14:02)

You mentioned five specific boundaries that define the exact shape of the box we are asking the board to approve. Let's look at the mechanisms behind those. The first one is the source boundary.

Right.

[AI Host 1] (14:02 - 14:18)

The source boundary dictates exactly what content the system is mechanically permitted to read. You present a hard line to the board. This system will only pull from our approved 2023 and 2024 conference transcripts, our peer reviewed journals and our official policy handbook.

[AI Host 2] (14:18 - 14:21)

And it is completely disconnected from the open Internet.

[AI Host 1] (14:21 - 14:28)

Exactly. That immediately lowers the temperature regarding intellectual property leakage. You are defining the universe of knowledge.

[AI Host 2] (14:28 - 14:47)

But what about the access boundary? Because how does a system actually differentiate between what a non-member is allowed to see versus a premium paying member? Like if I'm on the board, I am terrified that an AI is going to accidentally summarize our $500 certification study guide for a free website visitor.

[AI Host 1] (14:47 - 14:55)

Oh, absolutely. And that's why the access boundary requires the AI to integrate with your existing association management system or AMS. OK.

[AI Host 2] (14:56 - 14:56)

How does that work?

[AI Host 1] (14:56 - 15:10)

Well, when the user logs in, the AMS passes a token to the AI saying, hey, this person is a tier one premium member. The AI then searches the vector database, but it only retrieves documents tagged for tier one.

[AI Host 2] (15:10 - 15:11)

Oh, wow.

[AI Host 1] (15:11 - 15:18)

So if a non-member asks the exact same question, the system physically cannot see the premium study guide. It respects the entitlement structures you have already built.

[AI Host 2] (15:18 - 15:24)

That is super smart. OK, let's push on the third boundary, because I think this is where implementations actually die.

[AI Host 1] (15:24 - 15:25)

The answer and traceability boundary.

[AI Host 2] (15:25 - 15:33)

Right. Because even if you restrict the sources and lock down the access tiers, large language models are just notorious for trying to be overly helpful.

[AI Host 1] (15:33 - 15:34)

They want to please you.

[AI Host 2] (15:34 - 15:51)

Right. They combine two correct facts into one massive, embarrassing hallucination. If I am a board member, my nightmare is our official AI giving a member legally disastrous advice.

How do you prevent them from shutting the project down the very first time it makes a mistake?

[AI Host 1] (15:51 - 16:02)

You architect the system so it is fundamentally incapable of returning an answer without providing a literal clickable footnote back the specific paragraph it pulled from.

[AI Host 2] (16:02 - 16:11)

So instead of thinking of the AI as a college graduate who read all your files and is trying to summarize them from memory, it's more like an ultra fast intern with a highlighter.

[AI Host 1] (16:11 - 16:22)

I love that. Yes, an intern with a highlighter. It isn't generating original thoughts.

It is only allowed to fetch and highlight the exact paragraph in your archives. It has to show its work.

[AI Host 2] (16:22 - 16:24)

So mechanically, how does it do that?

[AI Host 1] (16:24 - 16:33)

The system maps the user's query to an exact digital coordinate in your database, a vector point. It finds the paragraph sitting at that exact coordinate and retrieves it.

[AI Host 2] (16:33 - 16:34)

And if it can't find a match?

[AI Host 1] (16:34 - 16:49)

If it cannot find a match within the approved source boundary, it is programmed to say, I don't know, but here is a human you can contact. Traceability gives the board the accountability they require. Proving the system is only fetching your approved truth.

[AI Host 2] (16:49 - 17:02)

OK, but what happens when the intern highlights the wrong paragraph? Or, you know, what if the source document it fetches is technically correct based on a 2019 policy, but functionally wrong because the industry moved on?

[AI Host 1] (17:03 - 17:08)

And that brings us to the human coaching boundary. AI is not a set it and forget it technology.

[AI Host 2] (17:08 - 17:09)

No, definitely not.

[AI Host 1] (17:10 - 17:17)

The board needs to know whose desk the buck stops at, which specific staff members or subject matter experts own the coaching and review process.

[AI Host 2] (17:17 - 17:19)

So there has to be a way to course correct.

[AI Host 1] (17:19 - 17:31)

Exactly. If an answer needs to be adjusted, there must be a dashboard. Where human staff member can look at the query, see the document the AI fetched and manually correct the pathway for the future.

You are showing the board who holds the steering wheel.

[AI Host 2] (17:31 - 17:43)

Which naturally leads to the final piece of the blueprint, the rollout and success boundary. I imagine you never walk into a board meeting and announce a plan to launch an AI to 50,000 members globally on a Tuesday.

[AI Host 1] (17:44 - 17:53)

Oh, please do not do that. A phased pilot is universally more credible than a sweeping, untethered transformation plan. You contain the blast radius.

[AI Host 2] (17:53 - 17:54)

Start small.

[AI Host 1] (17:54 - 18:06)

Right. You propose rolling the system out exclusively to the internal membership team for the first 30 days to help them answer phone queries faster. Or you run a beta test strictly with your top tier committee members.

[AI Host 2] (18:06 - 18:25)

You tell the board exactly who gets it first and what metrics will determine if it expands. Exactly. So we have built this highly governed, deeply accountable box.

But how do we measure if it is actually worth the investment without promising the board guaranteed financial outcomes? Because if we over promise ROI in year one, we are setting ourselves up for failure.

[AI Host 1] (18:26 - 18:42)

We definitely frame the rollout as a series of measurable hypotheses. You are bringing the board pads to test. And looking at the implementation patterns across those 200 associations, we see three primary hypotheses that organizations test during a pilot phase.

[AI Host 2] (18:42 - 18:43)

Let's hear them.

[AI Host 1] (18:43 - 18:45)

First is a member return behavior hypothesis.

[AI Host 2] (18:45 - 18:53)

OK, so how does the AI mechanically shift a member from visiting the website once a month to, say, once a week?

[AI Host 1] (18:53 - 18:57)

Think about it. Traditional association search bars are often incredibly frustrating.

[AI Host 2] (18:57 - 18:58)

Oh, the worst.

[AI Host 1] (18:58 - 19:07)

A member types in a query about a complex compliance issue and the search bar returns 40 unhelpful links to massive 100 page PDF documents.

[AI Host 2] (19:07 - 19:12)

And then the member has to download them and use CTRL plus F just to find their answer. Right.

[AI Host 1] (19:12 - 19:25)

But if the new system provides an instant plain language summary with a direct citation to the exact page they need, all that friction vanishes. So the hypothesis is, if we remove the friction of finding knowledge, does the frequency of member engagement increase?

[AI Host 2] (19:26 - 19:27)

I'd bet it does. What's the second hypothesis?

[AI Host 1] (19:28 - 19:36)

The second is the non-member preview or the join path hypothesis. This seems like it could fundamentally alter how marketing departments operate.

[AI Host 2] (19:37 - 19:38)

Oh, interesting. How so?

[AI Host 1] (19:38 - 20:02)

Consider the psychological trigger of the access boundary we discussed earlier. A non-member lands on your site and asks a highly technical industry question. The system provides a high level two sentence summary.

But the deep dive citations, the actual mechanics of the solution. Are blurred out. Yes, they're blurred out or locked behind a prompt that says log in or join to read the full standard.

[AI Host 2] (20:02 - 20:16)

Oh, that is the ultimate show don't tell for membership value. You aren't just sending them some generic marketing email claiming you have the best resources. You are proving you have the exact answer to the problem that you're trying to solve right now and offering them the key to unlock it.

[AI Host 1] (20:16 - 20:25)

Exactly. It's incredibly powerful. And the third hypothesis might actually be the most operationally transformative.

The intelligence and content gap hypothesis.

[AI Host 2] (20:26 - 20:29)

Moving from search bar zero to actual query intelligence.

[AI Host 1] (20:30 - 20:45)

Exactly. When a member uses a traditional search bar and finds nothing, they just leave. The association never knows what they were even looking for.

But with a conversational system, you capture the exact natural language questions your industry is asking.

[AI Host 2] (20:45 - 21:04)

So if hundreds of members suddenly query the system about a new international supply chain regulation and your system returns empty handed because your association hasn't written a policy on it yet, you now have real time intelligence. You are literally crowdsourcing your editorial calendar based on immediate member needs rather than just guessing what topics to cover at the next annual conference.

[AI Host 1] (21:05 - 21:19)

Bingo. A practical example of this structural approach in the real world is a system like Betty. It is designed around these exact patterns, a governed, source bound, multi-tier coached and traceable association AI.

[AI Host 2] (21:19 - 21:20)

Right.

[AI Host 1] (21:20 - 21:30)

And when we look at implementations like the one at ISA, the International Society of Automation, what is really notable is how they built their deployment team. They didn't just treat it as an isolated IT project.

[AI Host 2] (21:30 - 21:30)

Which is so common.

[AI Host 1] (21:30 - 21:37)

Right. Instead, they assembled a cross-functional group involving governance, membership, sponsorship and IT.

[AI Host 2] (21:38 - 21:53)

And having a board liaison involved directly in the pilot group is just an incredibly strategic move because then the board is no longer sitting in judgment of some black box technology at the end of the quarter. They are active participants in tuning the governance model.

[AI Host 1] (21:54 - 22:03)

It demonstrates profound organizational maturity. However, any executive presenting this blueprint must deliver an honest caveat to the board.

[AI Host 2] (22:03 - 22:04)

There's always a catch.

[AI Host 1] (22:04 - 22:15)

Always. Even with the perfect blueprint, the most robust governance model and a brilliant cross-functional team, we have to set realistic expectations about what source bound AI absolutely cannot do.

[AI Host 2] (22:15 - 22:26)

Right. Because if the association's source material is an unorganized mess full of contradictory policies from different decades, the AI is not going to magically organize it into a cohesive strategy.

[AI Host 1] (22:26 - 22:52)

Exactly. Source bound AI is a mirror. It does not fix bad source content.

If your PDS from 2018 directly contradicts your web copy from 2024, the AI will simply fetch both and expose that conflict with incredible speed. Yeah. It will not resolve internal policy disputes.

It does not remove bias that is already present in your historical source material. And it absolutely does not solve existing organizational governance problems.

[AI Host 2] (22:53 - 22:54)

Garbage in, garbage fetched.

[AI Host 1] (22:55 - 22:55)

Precisely.

[AI Host 2] (22:56 - 23:13)

But honestly, I would position that as another point in favor of the blueprint approach. An executive can stand in front of the board and say, look, this system is going to shine a harsh light on where our archives are messy and where our content is weak. And we welcome that.

We want to know where the gaps are so we can clean them up.

[AI Host 1] (23:14 - 23:29)

That is a great spin. It turns a potential operational negative into a strategic positive. It manages expectations beautifully.

The board won't expect a flawless omniscient oracle. They will expect a tool that retrieves what the association has built exactly as it was built.

[AI Host 2] (23:29 - 23:44)

We have covered a tremendous amount of ground today regarding the mechanics of governance, boundaries and hypotheses. The core takeaway for the listener preparing for that upcoming meeting is clear. Do not walk into the boardroom and ask your governing body to approve AI.

[AI Host 1] (23:44 - 23:44)

We don't do.

[AI Host 2] (23:45 - 23:54)

If you do, you are asking them to underwrite an unlimited, unbounded concept and their fiduciary risk aversion will shut the initiative down immediately.

[AI Host 1] (23:55 - 23:57)

Bring them a one page architecture and value brief.

[AI Host 2] (23:57 - 23:57)

Yes.

[AI Host 1] (23:57 - 24:06)

Show them the bounded box. Show them exactly what historical knowledge assets will be activated, what the system can access and what it physically cannot access.

[AI Host 2] (24:06 - 24:12)

Show them the traceability mechanism and define where the initial rollout is intentionally restricted.

[AI Host 1] (24:12 - 24:12)

Yes.

[AI Host 2] (24:13 - 24:25)

And to help you translate the strategy into a physical document, you can actually slide across the boardroom table. I want to remind you to grab the board brief companion asset we mentioned at the start of this deep dive.

[AI Host 1] (24:25 - 24:26)

It's basically a cheat sheet.

[AI Host 2] (24:27 - 24:33)

It really is. It's structured specifically to help you build out this exact one page conversation. It is your immediate practical next step.

[AI Host 1] (24:34 - 24:56)

And as you prepare that brief and define the value of unlocking your archives, consider the long term mechanical shift this creates. Once a member can instantly query your association's entire history of standards, webinars and peer reviewed research at the exact moment they face a crisis at their desk. How does that fundamentally shift the core value proposition of an association?

[AI Host 2] (24:56 - 24:57)

That's a huge question.

[AI Host 1] (24:57 - 25:05)

It is moving away from just being about networking and annual events to actually be an indispensable daily operational partner. That's the real transformation.

[Rob Barnes] (25:11 - 25:19)

Super interesting. The big question today, what do we tell the board? Thomas, what did you take away from that?

[Thomas Atlman] (25:20 - 27:23)

So I like this one and I like the way it kind of it drove to what I think was a pretty conclusive ending, right? That the idea that you want to approach the board with sort of this value brief, this this way to unlock kind of your archives, consider the long term mechanical shift. I think that was the the idea here.

How does your value proposition as an association change and have a brief ready for the board in that matter? So the idea to me is thinking along those terms of. Kind of what are what are we actually asking the board to approve?

Like you don't you're not just blindly saying we're asking the board to let us use AI. We're asking the board to let us use AI so that we can fill in the blank. Right.

What is that so that we can and then thinking in those terms like who are who are the people you're using it for? Like frame it more as value towards your member, advancing your association's ambition, your three to five year plan concretely using this tool. Right.

And it's not you want to shift the conversation away from the tool and more about the value and thinking in those terms, thinking the value breeding thing, how it contributes to the larger strategic mission that the board is there to oversee and then thinking about the guardrails, sort of how you want to protect your your downsides. Right. So if questions are.

Does this protect our IP or are we giving away our IP to be trained on some other model and we actually lose control? Right. Understanding what that is, that's a very common concern that I think gets addressed.

The other one is who has access to it by letting people use AI? Are we kind of undercutting our value proposition? Right.

That's another concern that I think you need to have ready to address there. Like do non-members get access to paid materials and how do we govern that? So once you identify those guardrails, you can create a governing strategy around that and including that as part of your brief, I think that makes it much more powerful and it makes it something the board is excited to approve instead of worried about.

[Rob Barnes] (27:23 - 29:10)

It's super interesting to me that the research that picked out International Society for Automation part of this, because I felt like, you know, when we look at the five boundaries that are shown in the companion asset for today's episode, I reflect back on the conversations I was having with the team at ISA and they had a very good sense of these boundaries like right from the get-go and which probably speaks to the success of MIMO, award-winning MIMO, I might add, now because they really did have a good sense of what was the, you know, what's the source, what's the approved content that we wanted to make accessible in ways that wasn't possible before, you know, who can get access to it, how they had learned that, you know, that automation engineers, professionals around the world were wanting, you know, the responses that they needed from this content source, you know, these assets themselves, and always this really strong sense of the human in the loop, you know, they have this cross-functional team that own MIMO and the way MIMO performs, it's not just an IT thing, it's not just a membership or governance or marketing thing, it's very much some cross-ownership, and so that human in the loop element was there right from the get-go, and I think that the, well, I know that the way they have sort of reported upstream and downstream, you know, the way the board have taken ownership of MIMO's value and alignment to strategy has really helped keep them not just, that's thriving, like it's growing in its capability and its use because of that strategic alignment, the board owns that now.

[Thomas Atlman] (29:11 – 30:09)

And I think, like it ended on this note, it really stuck with me, this podcast, where it says you want to move away from being sort of about networking and annual events and more into an indispensable daily operational partner, right? So the idea here is that because you've invested this time in identifying the value and helping people unlock kind of value that's already there, like get access to stuff, you aren't this punctuated, like at our annual conference, people are engaged, but then we don't see them again until the next annual conference, or people stay alongside with you as an operational partner, the association then gets embedded in the workflows and the daily lives of its members. And kind of reframing the value around that, which I think is something ISA did really really really well. And kind of presenting that as the end goal back to your board, I think makes it much more of an exciting prospect and let’s you get to yes much more easily than kind of getting dragged into the weeds around what model do you want to use.

[Rob Barnes] (30:10 – 30:41)

So one more time, before you go, thanks everyone for listening today. Download the Board Brief: Governed AI for Associations from the show notes. It’s the fastest way to actually put today’s episode to work. Thank you for listening to the Association Intelligence Podcast. Join us next week to answer this question: “Why not just use ChatGPT or Copilot?”

What this helps your team answer

How to frame AI adoption without triggering board risk aversion.
Which outcomes matter to governance-minded leaders, and what boundaries (source, access, traceability, human ownership, rollout scope) need to be defined to earn board confidence and approval.
How to set realistic, measurable expectations for a pilot instead of overpromising ROI, and what you should track to prove its working.

Companion Asset

Board AI Briefing One-Pager

The Association Intelligence Podcast

The AI Questions Every Association Needs to Answer

Start with the question your team is stuck on.

How we built it

Are we ready for AI?

What do we tell the board?

Want to pressure-test your association's AI strategy?