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This season has been kindly sponsored by the Philadelphia Convention and Visitors Bureau. Visit discoverPHL.com to start planning your next life sciences meeting.
In this episode of the Convene Podcast, we explore with Paula Rowntree, Head of Events at the Australian Psychological Society, the evolving role of AI in associations—how it’s reshaping member engagement, streamlining operations, and unlocking new revenue streams.
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Convene Podcast Transcript
Convene Series: AI for Associations - Paula Rowntree on How to Harness Data, Streamline Events, and Enhance Member Value
*Note: the transcript is AI generated, excuse typos and inaccuracies
[00:04] Paula Rowntree: The one thing I would say to associations is stop using free versions. Stop it. Stop. Stop, stop, stop, stop. Invest in your AI.
[00:17] Magdalina Atanassova: Welcome to Season 6 of the Convene Podcast, brought to you by Philadelphia Convention and Visitors Bureau. In this episode, we explore the evolving role of AI in associations—how it’s reshaping member engagement, streamlining operations, and unlocking new revenue streams. Joining us is Paula Rowntree, a leader in the association and events industry. As Head of Events for the Australian Psychological Society and founder of Business Events Network, Paula has spent her career helping associations, venues, and corporates create meaningful experiences backed by the science of event design. With her deep expertise in digital transformation and strategy, Paula is passionate about ensuring AI isn’t just a buzzword but a tool that associations use to drive real impact. In this conversation, Paula shares practical ways associations can use AI, from automating administrative tasks to analyzing data for deeper member insights. We also discuss the pitfalls of free AI tools, how to approach AI ethically and securely, and why investing in AI is no longer optional but essential for long-term success.
We start now.
[01:46] Magdalina Atanassova: Hi Paula and welcome to the Convene Podcast.
[01:49] Paula Rowntree: Hi Maggie, thanks for having me. I'm a little bit excited. I haven't done a lot of podcasts, so I'm feeling like a very much a podcast version at the moment. So I'm looking forward to today.
[02:00] Magdalina Atanassova: Well, I'm happy that we're starting this together at the beginning of the year.
[02:06] Paula Rowntree: Me too. I think you'll like my second podcast. So I'm like, ooh, here's hoping I don't embarrass myself.
[02:11] Magdalina Atanassova: No, it will be great, I'm sure. So let's start directly in the deep with the first question. How do you see AI transforming the way associations operate and what strategic opportunity does it offer for delivering greater member value?
[02:28] Paula Rowntree: Associations are such unique beasts in that a lot of times people think that how one association operates is how all associations operate. But we're quite varied, region to region and size to size. So there are some associations who financially are doing wonderfully and you know, they'll have in house teams for everything. So they've got their marketing team, their communications team, media teams, international teams, membership teams, everything. And then you've got other associations where you've got, you know, this little sole person who's everything from HR to events to everything. And I think that's where AI will have some of the biggest impacts, those associations that don't have the finances to resource, because I can take over a lot of that. So AI can be your Marketing manager. It can be your media person, it can be your strategist, it can help with your finances. If you learn to harness and use the right AI tools, it can really help backfill a lot of those administrative resources. The minute that they're freed up, you've then got the individual who's actually able to concentrate more on what it is that the members want. So they're not spending their time doing the repetitive administrative tasks like, you know, sending out renewal notices or, you know, trying to analyze member feedback and member sentiment. Instead, they can take what comes from that member sentiment and go, wow, we didn't realize that our members would actually get a lot more value if we did this for them. That's more likely to increase our renewal and retention rates. They can actually start strategizing a bit more. And I think that in itself creates member value. You know, I think it's going to help them understand their members a little bit more. If they use, again, the right tools to create member Personas and again, really look at their data analytics from member feedback, they're going to understand their members a lot more and use AI to help them generate new ideas, new products, new services that they can offer members and have a bigger impact on, I guess, a positive, in a positive way about how members do what they j.
[04:32] Magdalina Atanassova: I like what you were saying about, you know, having AI kind of impersonate these different roles. Do you have an example of that? Because I feel that for event planners, it's a bit hard to understand how to actually do that. So do you have an example that you can share?
[04:50] Paula Rowntree: Yeah. So we were talking about this yesterday. So we had the Spark AI lab and we were talking specifically about events because that's obviously what Spark AI is. It's AI by event professionals for event professionals. And I've spent the last seven years in medical associations, and for any of the listeners out there who are in medicine, they'll probably have a little sneaky here. But medical associations love a good concurrent session. 32 concurrents, sure, let's run 32 concurrents at the same time. Like, they love it, trying to put that program together. When you've got a program grid, you've done a call for abstracts or a call for submissions. So, you know, you've got 3, 400. Some of them have thousands of call for submissions. Then you've got to try to Tetris a program where you go, here's a submission, here's all the session blocks I've got. How do I fit this together? So AI can Help with little things like that. Which is instead of you spending hours going through and saying, here are all the call for submissions, here are the scores. You can put that spreadsheet into AI and go, right, I want you to find every session that, you know, aligns with this theme runs, you know, for this amount of time. Here's my program grid, and I want you to fit them within streams. You know, track A and track three, for example, and have AI do some of that work for you. Get the initial grunt work done. And that way you're just finessing and you're tweaking. So I think some of the basics like that that you can do with AI, it was really interesting. Marketing is another big one, I think, from an events perspective. I asked AI, I asked Spark, so I uploaded a whole pile of marketing that we'd done over the last three years, and I said, give me analysis. What's been overdone, what's been used. And it turns out I use the word ‘We're excited. We're thrilled.’ More than I should.
[06:38] Magdalina Atanassova: We're all guilty.
[06:40] Paula Rowntree: Sometimes we just don't have the mental capacity to be able to come up with new ways to phrase things. So it's little things like that that we would sit there in front of a computer, you know, your fingers are poised on the keyboard and you're like, what am I? What am I going to say? How else do I say we're excited? And you end up just going, we're excited. So I think there's things like that that AI will really help to reduce the amount of administrative time that we spend on tasks, particularly with events.
[07:06] Magdalina Atanassova: I just love that you're giving example with the excited. Because my feeling is that AI is always giving these very flamboyant words. It's always very big and every time, like, can you calm down and, like, remove all these little additions?
[07:23] Paula Rowntree: I love the way that Spark has it. I talk about this all the time. It's got the tones. So all these preset tones. Because sometimes I'll go, hey, show me what that would look like in a sarcastic and witty. Just because that's not a tone that I would use. And it comes up with something and I'm like, I could use that.
[07:40] Magdalina Atanassova: I want to see sarcastic and witty in a medical association program. I'm probably a bit worrying.
[07:49] Paula Rowntree: Meanwhile, there's probably a lot of medical association professionals going, just talk to them.
[07:55] Magdalina Atanassova: That's true. They come from that world. I can relate.
[07:58] Paula Rowntree: And I love that most, like, medical professionals have such dry senses of Humor that they are some of the most wittiest people, but you've got to just be on their same humor length to get it.
[08:09] Magdalina Atanassova: Yeah, I totally know what you mean and we kind of touched on that or you touched on that with AI and analytics. So let's talk a little bit about that. How would you go about it? What, what is your advice towards association professionals and how can they do that?
[08:29] Paula Rowntree: I love associations because I think when it comes to data collection, I think associations, hands down, when it comes to collecting the data, we'll collect data. We do member surveys, we do engagement, we do post event surveys. You know, we release a white paper, we do a survey, we have all of this data coming in. But again, I think most associations don't have a data analytics specialist. You know, we don't, we just don't have that position. And now we've got AI to be able to actually upload all of that data and get it to tell us our trends. You know, we've been uploading like post event surveys, but also our member, what we do. So as someone goes into the event process and registers for an event, we actually ask them two key questions. So the first question is, what do you value most from your membership? And the second question is, if we were to be able to provide one product or service that could ease your life as a psychologist and make your, I guess, you more satisfied, what would that be? So we've actually been, we've been collecting that data for the last three years and we've never done anything with it because we're like, well, how do we analyze this? So we've been able to upload three years worth of Excel spreadsheets into AI and ask it to just look at those two questions alone. And it's been able to give us the top five things that members value the most. So all of a sudden we've got our retention and renewal campaigns, we have our campaigns for, you know, generating new members. But we've also then been able to break that down via demographic because we ask those demographic questions for events as well. So we've been able to break that down to also determine what students want. So for new membership we can go, well, students really are looking for this, this and this. And the biggest challenges that they're facing are this, this and this. So we've also been able to use that to determine what are there new products and services that we can bring in to look at either increasing member satisfaction. So it's a new product or service that we just include in the current member fee or could it be a new product or service which actually helps us generate new revenue and becomes a diversified revenue stream. Before AI, that would have been, you know, someone looking at a spreadsheet and going, I'll put this into category A because it's education. This goes into category B because it relates to this. You know, someone would have to do all that manually and we would get that wrong. So AI has just reduced that all down for us. And now, you know, we have key phrases for renewal campaigns, for events. The reason we do it for events is because everybody who comes to an event has to register. So you get, yeah, you get better intel because you know, you might have anywhere from 150 for a small event to thousands for a larger event. You don't get those responses in a post event survey or in a member survey. So we try to capture key data there.
[11:19] Magdalina Atanassova: And do you also fill in the holes with people that were not there? We kind of survey the same people because those that come, they tend to come again. So what about those that you exclude?
[11:32] Paula Rowntree: I think it's hard with associations because, and most of us, I think we tend to use the same phrase where we go, the squeaky wheel gets the most attention in that we do just have subsets of association members who are just the loudest. They're not necessarily representing the majority, they're just the loudest. And it's hard because we talk a lot within associations about engagement. How do we get members more engaged? How do we help members be more engaged rather than going, actually there's a large percent of members who just don't want to be engaged. They're happy to just get what they get. They don't need the survey. The fact that they're renewing every year tells you that they're satisfied. When they're dissatisfied, they disconnect. And I think sometimes the only way to get information from those people is with picking up a phone and having a conversation and then being able to record that and then using that, then supplement the data that you've already uploaded into AI. But we forget sometimes that a survey is not the best way. It has to be a phone call for someone to say, hey, we really want to, we appreciate that you've, you know, we're not trying to resell you. We don't necessarily want you to re sign up, but we just want to better understand how we could have taken better care of you. How might we have done this differently? Could you share some of that intel and sometimes just Having that conversation will give you more data than any survey that you send out.
[12:53] Magdalina Atanassova: Yeah. So I don't hear any AI used in that.
[12:57] Paula Rowntree: No. Well, it is interesting. I saw a clip of an AI that has actually been developed to combat scamming phone calls where the AI just keeps having a conversation with the scammer until they hang up. So I think eventually we'll get to the stage where AI will be able to have those conversations, same way, you know, exit interviews that HR does. I think eventually AI there will be, you know, whether it's a chatbot or an avatar or something like that. And we see AI already in some areas where people are more comfortable having a conversation with, you know, an AI health. And I'm saying I'm using, for people who can't see me, I'm using the inverted commas with my fingers here, where people will have a conversation with like a health bot about, here's my symptoms, what could it possibly be? So I think it's coming. Yeah, yeah, I know. Imagine that you've just like, you think you've just escaped from a membership that you've got. I'm not really getting value, I'm just gonna not renew. And next minute, you know, a chat bot's calling you and hi, you're like, damn it, they caught me.
[14:05] Magdalina Atanassova: Why are you not renewing? Renewing.
[14:09] Paula Rowntree: But if you think about that, I don't know about you, Maggie, but I would be more comfortable being honest and transparent with a chat bot than I would with a human being. Yeah, because with the human, we think we might offend, we might upset.
[14:21] Magdalina Atanassova: Exactly.
[14:22] Paula Rowntree: But with an AI, you can't upset them because there's no emotion there. They are simply just asking the question. So I think you can be more transparent and honest in your feedback.
[14:32] Magdalina Atanassova: Yeah, how can we not, you know, associations in general, how can they skip that step? So leveraging AI before the person stops being a member. So can they. I don't know, maybe you can think of something that, you know, just AI seeing that process kind of happening or being built on data and saying, you know, this member might not renew because I don't see these specific actions occurring.
[15:04] Paula Rowntree: I think maybe AI would be more geared right now, again, from an analytics perspective, if you were to say, upload your last 5 to 10 years of membership resignation data, it might be able to pull out some trends. So you might find. So I think it might be more trend focused. So you might find that you've got a trend where mid career professionals, for example, who you know, are of A certain age bracket in a certain financial demographic might be dropping their membership, you know, and you might be able to then align that with say, economic data. So is it that membership drops when we see the cost of living increase because people are more cautious about their membership. So I think you'd be able to upload different data sets and ask it to try to analyze and find trends for you and then get it to try to use those trends to then future predict whether it's there or not. I think an AI, probably someone who's really into the data analytics side of AI, would probably be able to know if there's a tool that could do that. But I think they're the types of things that you can get it to do for you.
[16:06] Magdalina Atanassova: A word from our sponsor.
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[16:38] Magdalina Atanassova: Now back to the program.
[16:42] Magdalina Atanassova: Yeah, and before we started we were speaking about, you know, prompting and how we're getting prompting not necessarily right, but also not necessarily wrong. So this makes me think of that. Exactly. Creative use of, you know, data and prompting.
[17:00] Paula Rowntree: Do you know what I find fascinating with AI is we don't use AI to ask AI how to use AI, if that makes sense. So as an example, you could actually put that question into AI to say, hey, I want to better understand my member trends and see if I can predict what our, you know, retention and resignation rates are going to be in the next five years. What data can I give you to help me, you know, find out this information so you can ask AI to help you. What else have I missed? What else can I tell you so you can actually ask what are the right prompts that I should be putting in? Here's the data that I want out. What data do you need from me to be able to do this analysis? And I think we forget that we can actually ask it to tell us how to prompt better. And I know I do now, like, I'm like, here's what I want to achieve. Here's what I want to use, you know, the outcome for? I want to achieve this. What do you need from me to achieve that in the best possible way? And go, well, do you have this data? This data, this data and ask this question, this question, this question. I'm like, oh, thank you AI. I feel very smart.
[18:07] Magdalina Atanassova: Yeah, I think that's exactly the two ways you can prompt either to ask AI to think on your behalf or to ask it to help you think better.
[18:18] Paula Rowntree: Yeah, I think it's really key when you go into AI. It's understanding what do you want the end result to be, if you can articulate very clearly the end result. So I want to understand, you know, why people are resigning from our association and how I might create, you know, better value. How can I increase the value of membership? You know, is it that I need to lower the price or I'm looking for ideas, you know, do I need to add value in, you know, increasing products and services? So if you give it the prompts and say, here's what I really want to get out of it, can you help me do this better? It'll help because it. I think AI is. It's a tool in your training. It. The more questions you ask it, the more information you give it, the more you're training it to do better for you. I said, I use this analysis the other day with someone where I said, I think people go into AI the same way they go into, like a psychic reading, where they go, well, you're the psychic. I don't have to tell you anything. You tell me. I'm just gonna sit here and stare at you until you tell me what my future is and just go, it's not gonna work that way because that's mind reading and nobody can mind read. AI is the same. It can't read your mind. If you put in a crap prompt and that might be beeped out, you're going to get rubbish out, rubbish in, rubbish out. Yeah, but the more you're curious and the more questions you ask it, you go, okay, I understand that. That's not quite the answer that I wanted. I was looking for something more aligned with this. Could you try the analysis again or could you try the prompt again? Or could you try to rewrite that to give me this. Okay, I really like that part of it, but this part of it doesn't make sense. Could you try that again and just keep asking, have fun with it, because it's. It's not going to get upset. The more questions that you ask it, it's going to love it even more.
[20:06] Magdalina Atanassova: Yeah, I think that's a very important point. And I believe, at least from what I've heard, that's where people lose interest and they stop trying is because they expect they've heard the story. AI gives you an output very quickly, but the fact that you have to ask it to fix things. It takes time. So I feel that people are like, oh, it takes too much time. But yet again, does it really take too much time?
[20:36] Paula Rowntree: No, it doesn't. I think it's. It's. We expect miracles. And you go, that's just not. It's not how anything in life works. I think I'm trying to think if I can give a really good example. A great example would be, so the APS has developed neurodiverse inclusive guidelines for the events industry. So how can the events industry be more inclusive for people who are neurodiverse? So it's great. We've got the psychologist, we can help develop that. But I could definitely put in prompts that say, hey, how could an event be more neurodiverse inclusive? And it might come up with something. But then I can say, actually, what I'd like to do is have a section in there that relates directly to adhd. Or I'd like the guidelines to focus on each type of neurodiversity. So we haven't gone into that detail, but they're the types of prompts that you can put into AI. And then I can say, great, here's our first draft. Could you do an analysis and see if there are other guidelines, you know, in the industry that relate not necessarily to events, but someone may have done neurodiverse guidelines for the workplace. Could you overlay this with that and see if there's any learnings that I can adapt to add into our guidelines that might help event professionals? And it's just continuing to ask the questions and have. I think once you start seeing it as something that you can have fun with, I find it now I have more fun the more prompting that I ask, because I'm like, oh, wow, I never would have thought that way, or I never would have thought to cover that. So I come away from my interactions with AI feeling as though I've learned something. I don't feel as though it's done my job. I don't feel as though it's made me feel stupid. I go, actually, you've really helped me learn to rephrase and to rethink and to try to challenge myself a little bit more. And it's seeing it more that way rather than seeing it as just, it's a tool. I just want to put something in and get something out. You've got to be willing to invest the time.
[22:32] Magdalina Atanassova: Yeah. And speaking of tools, what AI tools are you using?
[22:38] Paula Rowntree: We've got a couple.
[22:39] Magdalina Atanassova: So you mentioned Spark.
[22:41] Paula Rowntree: So Spark AI I use a lot. I use a lot. And I know the Give Me team and PCMA will probably hear this and I'll be like, paula, stop it. Because I push, I use Spark so they'll have content repurposing, you know, is something that you can use Spark for. And I'm like, yeah, I also use it for this because it also does like. So I love that it's such a complex tool. What I love about Spark is that a lot of the areas within it I just have to answer questions and it does the prompting for me. The other thing that I do love about Spark is, and I only learned this recently, is that different large language models or LLMs. So at any given point in time the LLM. So a ChatGPT is an LLM, Copilot is an LLM, Claude is an LLM. So we'll say use those three. It could be that at one point in time Claude is excelling all the others in data analytics. It just happens to be what. However many people are using it for that task, it's learned data analytics a lot more quickly. The thing that I didn't realize is that Spark pulls across multiple LLMs, it's not just one. So that's why I tend to use it for a lot. I do sometimes use ChatGPT, but I also tend to use the AI tools that are within other products. So Canva, because I'm not a designer, but there are times where I need design inspiration or so I'll use the AI tool there to get it to help me. I tend to try to see what's out there. I think we don't realize how much AI is integrated automatically.
[24:20] Magdalina Atanassova: Oh yeah.
[24:22] Paula Rowntree: So I think we forget that it is built into so much that we do already. But I use Spark, I tend to use ChatGPT. APS as a whole uses Copilot because we are on the 365 platform. The one thing I would say to associations is stop using free versions. Stop it. Stop, stop, stop, stop, stop. Invest in your AI. It's not expensive, but you have to start investing because what you get with paid versions is so much more. But the biggest thing that you get is data security and IP security. And I think a lot of associations forget that as they upload onto these all these multitude of platforms. Everything that you upload, if you're not on a paid version or an enterprise version or something that guarantees the security of what you're uploading, that is out for everybody.
[25:15] Magdalina Atanassova: Oh yeah. And how would you combat the question about Budget. Because often we hear that, right, it's budgetary constraints are a thing.
[25:26] Paula Rowntree: Yeah. I would, I would put it like this. If you are spending money on like a, a membership database, so a CRM, and you would invest in that. Because you know that if you don't invest in a CRM, anybody could hack into your system and steal your spreadsheet with all of your member information. If you would invest in that data security, why would you not invest in the AI data security? Because if you're uploading, you know, post event surveys or member survey data and trying to get, you know, AI to analyze that for you, if you're not doing that on a paid version, you've just released all of that member data into the, you know, into the effort, you know, it's, it's out, it's circulating. It's like anything where I say to associations, they have to choose what they're spending their money on. AI can it's return on investment. AI can vastly reduce administrative time. It can give them, you know, would you rather pay for three different roles? Do you have the budget to pay for three different roles or would you rather pay, you know, $20 a month in a subscription for something like Spark, which is an individual one or an enterprise where it's actually giving you six different roles roles or seven different roles? Well, it performs 150 different tasks.
[26:38] Magdalina Atanassova: Yeah.
[26:39] Paula Rowntree: So it's backfilling, you know, the job resources of three people. So it's actually saving you money, not costing you money. And they've got to start looking at it that way. And that AI is an additional resource. Multiple additional resources.
[26:52] Magdalina Atanassova: Yeah, I love that, I love how you put it. Any quick examples about ways to integrate AI for associations?
[27:03] Paula Rowntree: I think a lot of associations are looking for revenue diversification and the way that I see that directly relating to events is we collect so much at events and so we, you know, record sessions or we have all of those presentations. I would really say look at how AI can help you repurpose a lot of your content to generate additional revenue. Really look again at data analytics and using AI to analyze the data that you've got. Because most associations have got years worth of data that we've never done anything with. So I think they're my two key things is a lot of associations rely or their biggest source of revenue is membership fees. And the minute we do that, we're all at risk. We can't have too many eggs in that one basket. So how might you use the information that you gather through your conferences and events and all of that to create white papers, eBooks. How might you repurpose that content into, you know, quick take home summaries for people who didn't attend the conference, who don't want to pay, you know, or can't afford to pay large fees in the travel. So how do you make it more affordable giving more members access to the education, therefore you know, you're selling to more members at a lower price, still generating revenue. How might you use that data to create more opportunities but also how might you use it to look at different opportunities for new members that you might not have thought of? So you know, you've always targeted this particular group. Oh well, here's a whole new segment that might be interested in just purchasing this from you. So really use AI to help you think differently about generating revenue within your association because it's the revenue that allows us to do the key advocacy work that generates no revenue.
[28:50] Magdalina Atanassova: I love that. And there's a way how AI pays for itself.
[28:54] Paula Rowntree: Exactly, exactly. And the other thing I would say is it's not a human being. It's not going to get upset with you. Have some fun with it, like challenge it. Put your big girl pants on and go, you know what? Challenge accepted. AI pow, pow, pow, pow, pow. I'm going to push you and keep pushing you until. Until you surprise me and dazzle me and wow me. The other thing that I would say to associations is AI will never replace the understanding they have of their industry. It's not a sole source of truth. Don't just copy and paste what comes out of it. Use that to start different ways of thinking and to get the ball rolling and then bring in that depth of human connection that you have with your members to finesse everything that you get out of AI to make it make more sense to your members. Because that's why people want to be part of a member organization. And part of an association is the human to human connection and the sense of belonging. AI can't generate that. It can just help do the administrative work of what we do.
[29:58] Magdalina Atanassova: Can I just sneak in one last question?
[30:00] Paula Rowntree: You can sneak in as many masters as you want.
[30:03] Magdalina Atanassova: Because you just mentioned it with all these use of AI, let's say they're association like yours, you use AI. How do you do it transparently to actually keep the trust of members, first of all that the data is secured, but also that you know, they are still speaking with human beings.
[30:23] Paula Rowntree: Yeah, I think if we were to ever go to. And we don't do it at the moment. So we don't have like an online chatbot that answers members online queries. We don't have that. So they will always be able to ring up and speak to a human being. I think if ever we were to go down that alleyway, it would be about being transparent. To say, if you want to speak to a human, here's how you do that. If you're happy to, you know, chat with AI, here's how you can do that. And it's really about being honest with members because it's also understanding that as individuals, different individuals feel comfortable doing different things. Not everybody's comfortable speaking to a human being, but some people feel more comfortable just being able to ask basic questions of, you know, an AI chatbot, for example. So it's about being very transparent so that people understand who it is that they're talking to. So I think that's the first thing, I think also about data security, it is about ensuring that you are paying, that if you are uploading any files, that's the big one, that you are using paid versions. And that way, if a member comes to you and says, you know, has there been a data breach? Or how is my data being used? You can say, well, we use this particular AI software. It's an enterprise version, It's a closed system in terms of what we upload stays within the closed system. But when we ask it to analyze, then it does pull. It can still pull from external sources, but it won't release any of our IP out into the Internet.
[31:48] Magdalina Atanassova: And do you see it currently somewhere on the website or if members ask how you're using AI within the association?
[31:58] Paula Rowntree: The APS has a lot of different position statements. Right now. Our biggest focus, and this is another one for associations, our biggest focus with AI is how our how's it being used within the space of psychology. And we've seen some stuff that really is frightening where, you know, we've got people who come from, say, a tech background, so who have no psychological evidence, no neuroscience, who are coming up with mental health care, AI bots.
[32:27] Magdalina Atanassova: Scary.
[32:27] Paula Rowntree: And that's frightening. That really is frightening because AI doesn't have the human connection that a psychologist does to understand what might else be driving the way people are feeling or responding and reacting. So as an association, our biggest concern right now is trying to guide and be in front of AI as it relates to the mental health and wellbeing of individuals. And that's what I'd say to associations, if they're not advocating and watching what's happening within AI, within their space and really pushing for, with their governments to have more regulation around it. And that's where they need to be is in government getting regulation around this so that not everybody can just. Just come up, you know, with a GPT or a bot or something that, you know, responds to this. They've got to be driving it and controlling it for their industry and their sectors.
[33:16] Magdalina Atanassova: Yeah. Anything else that we should add before we wrap up?
[33:21] Paula Rowntree: Not for me. Not for me. But I do. I say, you know, AI, I think, is. Look, I'm always in two minds because I'm like, oh, well, look, if we go down the way of the Matrix, I'm the first to admit that I'm probably high on the list of, you know, that AI is going to get a lot of energy from my body. So I do go into AI. I have to say precautionary, where I'm like, please. Thank you. I'm very polite to it, just in case. But no, I think with people, it's really about what's repetitive, what's the repetitive stuff that you can use. Start there, reduce your administrative time.
[34:01] Magdalina Atanassova: That's good. You know, saying please and thank you. Somebody in one of the previous seasons we recorded said, you know, we're. It's like small children. We're teaching the manners. So it's very important to say please and thank you. So I feel good saying it to AI. I know they'll grow up and be polite.
[34:21] Paula Rowntree: I. I agree. I'm always thanking Siri. Oh, thanks, Siri.
[34:27] Magdalina Atanassova: Yeah, it just makes you feel good. Well, thank you so much. I have, like, a bajillion more questions. I think we'll have to do another episode and cover more. Thank you so much for the time and the energy. Thanks for having me and all the knowledge.
[34:43] Paula Rowntree: Really, my pleasure. I really appreciate being here and thanks for the opportunity. Thank you and good luck with everybody's AI journeys.
[34:49] Magdalina Atanassova: Oh, yeah.
[34:50] Paula Rowntree: Let us know how you go.
[34:53] Magdalina Atanassova: Thank you.
[34:57] Magdalina Atanassova: Remember to subscribe to the Convene Podcast on your favorite listening platform to stay updated with our latest episodes. We want to thank our sponsor, Philadelphia Convention and Visitors Bureau. Visit discoverPHL.com to start planning your next life sciences meeting. For further industry insights from the Convene team, head over to PCMA.org/convene. My name is Maggie. Stay inspired. Keep inspiring. And until next time.