[SPEAKER_01]: If you want to grow the reach, revenue, and impact of your learning business, you're in the right place.
[SPEAKER_01]: I'm Celisa Steele.
[SPEAKER_00]: I'm Jeff Cobb, and this is the leading learning podcast.
[SPEAKER_00]: If you're grappling with how to start or how to continue responsible AI use in your learning business, this episode can help.
[SPEAKER_00]: If you're unsure about guardrails, worried about data risk or
[SPEAKER_01]: This episode, number 465 features my conversation with Tori Miller-Loo, President and CEO of the Association for Intelligent Information Management, and she's very passionate about collaboration with people, collaboration with technology, and how people can collaborate effectively with technology.
[SPEAKER_01]: Aim helps organizations turn messy, unstructured information into fuel for performance, and Tori brings a pragmatic and personal lens to AI adoption.
[SPEAKER_00]: Tory talks about why data access and data quality are the real-gating factors.
[SPEAKER_00]: How to pick one to three AI concrete use cases that augment people rather than replace them, and why a lightweight AI usage policy you can revisit beats a 12-month strategy document.
[SPEAKER_01]: Toy and I also talk about mission versus margin tradeoffs, product profitability, and how aim support folio supports its learners.
[SPEAKER_00]: In short, there's lots in this episode to help you take action.
[SPEAKER_00]: So let's roll the interview with Tori Miller-Loo.
[SPEAKER_01]: For listeners who may not be familiar with the Association for Intelligent Information Management, give us a short version of what aim does and the problem it exists to solve or the resources it exists to be.
[SPEAKER_02]: So we were founded in 1944 recently celebrated our 80th anniversary and we are a nonprofit organization that serves information leaders in over 67 countries around the world and our vision is really to create a world where every organization can benefit.
[SPEAKER_02]: from information management.
[SPEAKER_02]: And for those who are not as familiar with that terminology, information management is the management typically of unstructured and semi-structured data.
[SPEAKER_02]: So that's what people might call dirty data that's typically outside of your tidy databases.
[SPEAKER_02]: So think about emails, invoices, contracts,
[SPEAKER_02]: engineering, schematics, text messages, social media posts, even videos and audio.
[SPEAKER_02]: That's all unstructured data.
[SPEAKER_02]: These days, it is absolutely the fuel for generative AI.
[SPEAKER_02]: So it's got in quite a bit more focus in the last three years than it had before that.
[SPEAKER_02]: But folks have been doing unstructured data management since before even the 1940s since the establishment of our organization.
[SPEAKER_02]: And then, you know, it started with records management and our
[SPEAKER_01]: So tell us a little bit about how you came to lead aim and sort of the path that got you there.
[SPEAKER_02]: Yeah, absolutely.
[SPEAKER_02]: So I started with aim in November 2022.
[SPEAKER_02]: And I should mention that aim is aim looks at the practice of information management through the lens of technology.
[SPEAKER_02]: So we're very, very focused on artificial intelligence and automation.
[SPEAKER_02]: So aim when they were looking for a new CEO, really specifically wanted someone with a technology background.
[SPEAKER_02]: And my background happens to be
[SPEAKER_02]: in IT.
[SPEAKER_02]: I was formerly a chief information officer for an association.
[SPEAKER_02]: So that was a really natural fit and I've been really pleased and happy to help aim grow and we help people understand this landscape of information management through certification, resources, research and training and it's all that kind of stuff that I love doing as a CIO that I now get to help our members with.
[SPEAKER_01]: Well, wonderful.
[SPEAKER_01]: And you mentioned in talking about that certification training, so maybe go ahead and give us a little bit of a fuller picture of what aim offers in terms of education and learning resources.
[SPEAKER_02]: Yeah, absolutely.
[SPEAKER_02]: So we have a certified information professional certification and that's a certification that's for experienced technology focused practitioners in the unstructured data management space.
[SPEAKER_02]: For our events and our educational programs, we have our homework program, which is our annual AI, I am global summit.
[SPEAKER_02]: That's a recently redesigned and rebranded event that serves a 400 person.
[SPEAKER_02]: group of individuals who really want to explore the intersection between AI and information management.
[SPEAKER_02]: And we do that through interactive sessions, hands-on workshops, and also cohort-based learning.
[SPEAKER_02]: So we have about 40 to 45 cohorts that are meeting throughout that annual event.
[SPEAKER_02]: And they also meet beforehand and afterwards.
[SPEAKER_02]: So it's a very interesting educational program.
[SPEAKER_02]: Beyond that, we have regional events.
[SPEAKER_02]: We have forums that are day-long problem-solving events that feature round tables and solution mapping.
[SPEAKER_02]: And then we have regional exchanges that are two-hour round table events that are focused on critical trends.
[SPEAKER_02]: We also have a wealth of virtual programs so we do live virtual training workshops around M365 and also preparing for our certification and the exam.
[SPEAKER_02]: We also do webinars and insight series that explore critical trends in the space.
[SPEAKER_02]: And we have an online training library and several different certificates, including an AI certificate and about 45 plus and growing on demand courses as part of that library.
[SPEAKER_01]: So it sounds like a pretty robust set of offerings that you have there.
[SPEAKER_01]: Tell me a little bit about the size of the team that is behind making all of this possible.
[SPEAKER_02]: small.
[SPEAKER_02]: We were a staff of five and I did as part of my rule of CEO, I did have to leave the organization through some transformation.
[SPEAKER_02]: So we've reorganized, reduced workforce over the last three years.
[SPEAKER_02]: So now we have a very efficient and just stellar group of staff that are doing a ton of work and keeping kind of a big shit moving with a skeleton crew.
[SPEAKER_01]: Yeah, that's I think pretty impressive to hear all that you were able to outline in terms of what aim offers and then to hear that that's being done with with five staff behind it.
[SPEAKER_01]: So impressive.
[SPEAKER_01]: The other thing that I heard when you're talking through those offerings is that emphasis on collaboration, I may sound like you have a lot of these cohorts, you have these round tables.
[SPEAKER_01]: Is that something that
[SPEAKER_01]: I guess prior to you joining aim is that's sort of been something about kind of in the DNA of these folks who work in information management or has that been more of a intentional shift towards more peer-based learning in more recent years.
[SPEAKER_02]: I would say it's really an intentional shift and probably a reaction, that's just, it's kind of ironic, well, while we talk a lot about AI and automation and we're, you could say, we're pro AI and pro automation of course, we also really understand that the way that you learn about emerging technologies and feel comfortable with them is often through human to human peer to peer learning.
[SPEAKER_02]: and understanding and actually being able to talk through case studies and talk to peers and crowdsource your problems.
[SPEAKER_02]: So it's been a very, very deliberate shift over the last three years to really make sure that that's kind of at the heart of all of our educational programming.
[SPEAKER_02]: And we also do community conversations on an informal basis, but on a formal basis we've found a lot of success with the virtual live training workshops and all the round tables and co-work based learning.
[SPEAKER_01]: So I know that for many learning businesses that are part of an association, there can be a bit of attention between mission and margin, especially when it comes to those education offerings.
[SPEAKER_01]: And so, where does aim sit in terms of how you think about the educational learning that you provide in terms of supporting mission versus generating revenue, which one is more important?
[SPEAKER_02]: Our organization has been in a period of financial transformation and so we're finally beyond that hurdle and in a position where we can stop worrying about keeping the lights on and focus on thriving.
[SPEAKER_02]: So that said, I mean, we didn't really have the luxury of not thinking about product profitability.
[SPEAKER_02]: And I think a lot of small associations and even the large ones have been in a similar situation.
[SPEAKER_02]: I think if you're so focused on mission that you're not focused on whether or not your business is actually healthy and functioning, you're delusional, I mean, I think the business model of constantly eating into reserves.
[SPEAKER_02]: is not sustainable.
[SPEAKER_02]: And part of our responsibility is association executives.
[SPEAKER_02]: It's not just fulfill the mission, but also to protect the legacy of this organization.
[SPEAKER_02]: And it's sustainability.
[SPEAKER_02]: So I'm a little torn.
[SPEAKER_02]: I mean, we're a mission first organization.
[SPEAKER_02]: We always ask when we're introducing new programs is this advancing our mission of fulfilling our mission.
[SPEAKER_02]: But if there's no profitability, we are not doing it.
[SPEAKER_02]: We're not moving forward.
[SPEAKER_01]: And so that sounds like then it's part of the initial discussion around maybe standing up a product, you know, is it supporting mission and is there a business model that can support it talk a little bit about one subproducts out there in the market if it turns out to not perhaps be bringing in the revenue is aim intentionally looking at things like sun setting or periodically reviewing the portfolio to say maybe we need to call some of these things.
[SPEAKER_02]: We do, yeah, and that our board and our finance committee have been really instrumental in helping me do that.
[SPEAKER_02]: So we do have a product profitability matrix where we're looking at both the direct and indirect costs of the products that we produce and it's the pains and constantly update, but it is really helpful and at least starting that dialogue, you know, we do have one or two products in our portfolio that.
[SPEAKER_02]: aren't necessarily meeting our profit margin that we would want, but the board has at least intentionally had a discussion of, know this is court or our mission.
[SPEAKER_02]: We do need to see if we can improve the profitability, but we are unwilling to sunset this.
[SPEAKER_02]: And in other cases, we have stopped programs because they just, they were not worth the squeeze.
[SPEAKER_02]: The juice is over at the squeeze.
[SPEAKER_02]: This is one of my whole bosses used to say that
[SPEAKER_01]: I know that you've mentioned AI and, you know, how that has kind of changed a bit of the focus in your area of information management how that's become so much more important in the AI era.
[SPEAKER_01]: I know you've just
[SPEAKER_01]: written and spoken a lot about AI and about how there's kind of AI aspiration, but then there's this gap between that aspiration and kind of the reality.
[SPEAKER_01]: So what do you see as the biggest barriers to being able to use AI in the way that you think organizations should be?
[SPEAKER_01]: Is it governance or skills or fear or something else?
[SPEAKER_02]: I appreciate all the thought leadership that's occurred around shifting culture and changing minds, that's in change management around AI.
[SPEAKER_02]: And while I appreciate it, and I agree with that, like people are fundamentally critical to successful technology implementations.
[SPEAKER_02]: As AIMS Presidency, I'm really focused on
[SPEAKER_02]: I think of a paramount obstacle which is data quality and data access.
[SPEAKER_02]: So when we lack access to content for AI, that's huge.
[SPEAKER_02]: If you can't actually put data in a large language model to train it, then you're already shooting yourself in the foot.
[SPEAKER_02]: The other side of that is when you're permitting access to content that should not be permitted, you are jeopardizing your association.
[SPEAKER_02]: And I know organizations won, they've been rolling out tools like Microsoft Co-Pilot, which is a great tool.
[SPEAKER_02]: That's one of the biggest hurdles that you have to overcome, that it's shining a spotlight on security issues.
[SPEAKER_02]: And then the third thing is, is your data even good quality?
[SPEAKER_02]: Are you missing metadata?
[SPEAKER_02]: Is it an old version?
[SPEAKER_02]: The funny thing about all this is none of this is really a new problem.
[SPEAKER_02]: I mean, we're doing data analysis for over a decade now and as organizations are rolling out tools like Tableau and Power BI, that was shining a spotlight on content access and content qualities.
[SPEAKER_02]: So I think really what AI's done is pointed in even bigger spotlight on those existing issues.
[SPEAKER_02]: And so there does really need to be quite a bit of attention paid to data.
[SPEAKER_01]: And so there needs to be attention paid to data.
[SPEAKER_01]: How does an organization go about doing that?
[SPEAKER_01]: So they want to be able to embrace what AI can offer.
[SPEAKER_01]: They come to the realization that maybe their data isn't in the best shape.
[SPEAKER_01]: What are some of the practical steps or ways that an organization can start addressing that?
[SPEAKER_02]: Well, first of all, don't allow yourself to get paralyzed by the magnitude of the task.
[SPEAKER_02]: I think it's really unreasonable for some leader to come in and say, oh, well, data is the problem.
[SPEAKER_02]: So fix all of the data.
[SPEAKER_02]: That is not attainable unless you have a ton of resources to back up that challenge.
[SPEAKER_02]: So I think it's really about managing scope.
[SPEAKER_02]: So
[SPEAKER_02]: With an AI project, you need, you want to identify a use case first, look at the data that you need to be successful with that use case and manage the scope.
[SPEAKER_02]: And then it becomes a much smaller dilemma to deal with and you can learn from that experience, you can learn from saying, all right, for this particular AI project, maybe it's.
[SPEAKER_02]: say matchmaking AI system where I'm going to give it all of my membership data and I am going to give it all of the sessions for our conference, for instance, where all of the interest areas for our members and now I have three sets of data that I want to look at and I want to help match people who might have similar interests.
[SPEAKER_02]: That's a much more manageable project than saying, oh, I need to look at all my finance data.
[SPEAKER_02]: I need to look at all my publications, data, events, data, et cetera, keep it small, keep it focused, and then really focus on how good is good enough.
[SPEAKER_02]: You don't need the data to be perfect for AI, but it does need some fundamental things, especially around metadata.
[SPEAKER_02]: It needs to know, is this data the most current version.
[SPEAKER_02]: There might be other metadata fields that you need populated depending on the circumstance in the use case, but there are consultants that can help you with that if all of that seems still overwhelming, and there's also certainly quite a number of certifications and data certifications that you could look at to help build those skill sets and understanding, not just from a hand, but groups like Dima, there's lots of groups that offer really good training that can help you feel a little more confident.
[SPEAKER_01]: So you also mentioned security and risk when you were talking about some of those barriers to AI implementation or in really getting to where organizations might aspire to be, so recognizing that there are those risks and potentially sharing data that maybe shouldn't be shared with the AI, all of those things, but then also seeing the potential for what AI can do.
[SPEAKER_01]: I mean, where do you put yourself on that spectrum between AI enthusiasts, AI skeptic,
[SPEAKER_01]: in, you know, with other terms if you prefer, but sort of how do you describe how you like to think about AI and your opinion of it?
[SPEAKER_02]: I think, I don't know if this is an option on this spectrum, but I'm probably an enthusiastic skeptic.
[SPEAKER_02]: I'm very pro-AI.
[SPEAKER_02]: I think fundamentally it can help us make better decisions and help us process.
[SPEAKER_02]: And,
[SPEAKER_02]: Make use of really large sets of data that just were not humanly possible for us to make any use of before.
[SPEAKER_02]: So I think, you know, when it comes to diagnostic care in health care or when it comes to helping us make decisions about.
[SPEAKER_02]: cash in association, like anything from, you know, what is of all these proposals, which proposal helps us most meet this critical learning objective.
[SPEAKER_02]: Those are all like cases where it's a ton of data and it's great for us to have AI as a tool.
[SPEAKER_02]: And very skeptical when it's AI replacing human creativity.
[SPEAKER_02]: I think humans and in a responsible AI world, and I hope we're investing in implementing responsible AI, I think humans are responsible for hiding oversight and context.
[SPEAKER_02]: And I also think that my biases are probably against AI replacing human creativity.
[SPEAKER_02]: I don't love, I have a background in the arts, so I don't love when AI is replacing.
[SPEAKER_02]: writing or film or audio or images, I think humans are best still providing that level of creativity and ingenuity.
[SPEAKER_02]: But I am skeptic also for implementations and that's the role of information managers and data managers.
[SPEAKER_02]: I love the project management institute talks about every AI project is also a data project.
[SPEAKER_02]: So every AI project needs someone who is curating and stewarding the data and they are best as skeptics.
[SPEAKER_02]: You want them to be skeptical about the quality of the data, otherwise they're not really fulfilling the role on that project.
[SPEAKER_02]: So I think having that healthy dose of skepticism around
[SPEAKER_02]: content quality content accessibility system interoperability is really healthy when you're trying to implement successful AI.
[SPEAKER_01]: So there's a lot of potential for AI to help organizations with what they're trying to achieve.
[SPEAKER_01]: You've been throwing out some examples which I think are great to just think about the potential ways that an organization might make use of AI.
[SPEAKER_01]: So if you think about an organization that might kind of still be sort of on the sidelines, you know, maybe daddling a little bit in AI, but not really in any sort of systematic or full-fledged way kind of in trying to explore what it might do, I mean, what do you see as the the cost that comes with sitting on the sidelines, you know, and not really trying to think about AI adoption at this point?
[SPEAKER_02]: There's a part of me that just feels like it's so naive
[SPEAKER_02]: If it's a choice of when and how, right?
[SPEAKER_02]: You're not delaying the decision or deciding we're just not going to use AI.
[SPEAKER_02]: You're just now running the risk of inviting shadow AI into your organization.
[SPEAKER_02]: Your workforce is going to adopt the use of AI, whether you like it or not.
[SPEAKER_02]: Your members are going to adopt the use of AI.
[SPEAKER_02]: I even had to remember a couple of weeks ago that this wasn't a bad thing.
[SPEAKER_02]: I'm not judging them for doing this, but they took one of our research papers and created an AI application to help them better understand the research.
[SPEAKER_02]: It was a really interesting dilemma, but that's an example of like shadow AI.
[SPEAKER_02]: And I don't want to take us off course talking about how I manage that, but it's going to happen whether or not you like it, right?
[SPEAKER_02]: Couple that with your vendors are already rolling out AI features.
[SPEAKER_02]: And in some cases, they're not giving IT leaders a choice in whether or not those AI features are exposed to users or not.
[SPEAKER_02]: I think the big point is you saying, well, we're not ready for AI is just you delaying kind of the inevitable I think when and then the problem is when you're lacking direction and intentionality there's a cost for that and I think the cost is going to come first and foremost through an impact on employees satisfaction and members satisfaction, which is eventually going to trickle to market share.
[SPEAKER_02]: It's essentially the equivalent of like, let's time travel back to them in 90s.
[SPEAKER_02]: If your association was saying, well, we're not going to create a website.
[SPEAKER_02]: Like, eventually we all did, right?
[SPEAKER_02]: And you just lost some market share while you were dragging your feet on creating a website.
[SPEAKER_02]: That's all.
[SPEAKER_02]: So I just think that's going to repeat itself.
[SPEAKER_02]: We've been here before with websites with social media.
[SPEAKER_02]: I don't think fundamentally it's all that different from historical scenarios.
[SPEAKER_01]: When you think about the next 12 to 24 months and you're thinking about aim and aims future, what are the opportunities and the challenges which may or may not be tied to AI and all that we've been talking about.
[SPEAKER_01]: But I'm curious to know what are those challenges and opportunities that you're thinking about and then maybe also adding sort of that lens of education and learning and how that might impact what you're offering to the market in terms of your learning portfolio.
[SPEAKER_02]: think our biggest challenge right now is our research is showing that the practitioner themselves are changing.
[SPEAKER_02]: So we're moving away from a lot of the traditional titles of records manager and document manager.
[SPEAKER_02]: So the titles are all over the place.
[SPEAKER_02]: I have members who are like engineers and developers now and the organizations also all over the place.
[SPEAKER_02]: So a lot of our members are no longer
[SPEAKER_02]: in legal or inside an insular little records management team, they're in the IT team or in their the marketing or HR teams.
[SPEAKER_02]: So that's all to say the state of the practice is really changing.
[SPEAKER_02]: They're being pulled into AI projects.
[SPEAKER_02]: So we're looking at what's happening to the people that we serve
[SPEAKER_02]: and really recognizing that their transforming from information managers to information leaders in AI era.
[SPEAKER_02]: So it's a much different educational challenge to train someone on here best practices and information governance and records management to
[SPEAKER_02]: here is how we prepare you to sit at the table of an AI project and be that data's logistician, that data's steward.
[SPEAKER_02]: So a lot of our certification and education improvements and enhancements are wrapped around that.
[SPEAKER_02]: It started with us rolling out an AI certificate in the past 12 months, but
[SPEAKER_02]: It's going to continue with how we update our certification exam, what new courses we introduce.
[SPEAKER_02]: It's even impacted the education strategy that my staff has developed for 2026 to make sure that the education that we do is really focused on how do you prepare that person for that seat at the table of an AI project.
[SPEAKER_01]: that's great.
[SPEAKER_01]: I mean, it seems like you have a lot of clarity, despite the fact that there's so much shifting and changing in the roles, but that this idea of okay imagine this person at that table helping make decisions about those AI projects that then allows you to step back from that and understand then, okay, what education, what learning what resources they need to support you and that and then you're making those available.
[SPEAKER_01]: It sounds really smart and on target.
[SPEAKER_02]: I want that sound by, it sounds like you have a lot of clarity.
[SPEAKER_02]: So I can play it on repeat in the morning and it's like morning affirmation.
[SPEAKER_02]: Because when you're doing it, it feels like you're like swimming in chaos.
[SPEAKER_02]: So I need to release this saying, how about you have a lot of clarity?
[SPEAKER_02]: I'll just play that like 30 times to make myself feel better.
[SPEAKER_01]: Great glad I could give you that.
[SPEAKER_02]: Thank you.
[SPEAKER_01]: So again, if we have organizations wherever they might be in their AI journey at this point, but if there's something they could do kind of in the next really short time, you know, next week in the next seven days or so, you know, what is it that you would recommend that an organization do to just help them get a little bit more AI ready in that
[SPEAKER_02]: I'll keep it really simple for folks because it's so easy to get overwhelmed with all this nonsense.
[SPEAKER_02]: I think challenge yourselves if you haven't already to come up with one to three, really good use cases focusing on complementing human potential and not replacing it.
[SPEAKER_02]: So that would be step one.
[SPEAKER_02]: And you can
[SPEAKER_02]: did that with your leadership team, with your board, who member?
[SPEAKER_02]: I think the other big thing is if you don't already have an AI usage policy, do some research into that.
[SPEAKER_02]: Again, you can keep this really simple.
[SPEAKER_02]: I am firmly against really comprehensive strategies and comprehensive policies around AI because I think it's going to change and it's going to adapt.
[SPEAKER_02]: This technology
[SPEAKER_02]: 12 to 18 months in developing a strategy or developing a usage policy, I just think it's kind of wasted efforts.
[SPEAKER_02]: So keep a really simple challenge yourselves to put down the very basics of what you need to feel like you're responsibly implementing AI and have that conversation with folks.
[SPEAKER_02]: I did over the summer work on a facilitation guide with Alex Mao, with Amazon and Fadlery,
[SPEAKER_02]: And it's literally a conversation guide.
[SPEAKER_02]: Like this is how you have conversations with stakeholders, with your board and your staff about AI.
[SPEAKER_02]: So you can download that.
[SPEAKER_02]: I'm happy to share it with folks.
[SPEAKER_02]: But there's also other resources out there.
[SPEAKER_02]: It's really just about having the conversations.
[SPEAKER_02]: And then beyond that, once you have use cases identified, once you have,
[SPEAKER_02]: the guard rails around how you want to approach AI.
[SPEAKER_02]: You can start thinking about AI, maturity and readiness and that comes with looking at the available data, looking at its quality, looking at its accessibility and really focusing on your staff and do they have the data literacy and information literacy to advance.
[SPEAKER_02]: your use cases to make use of AI or automation to to actually accomplish the goals.
[SPEAKER_02]: So I still feel like that.
[SPEAKER_02]: I always wear that still feels really complicated, but it's not, you can talk to folks like me.
[SPEAKER_02]: It's not really as bad as you think it is.
[SPEAKER_01]: By doing that conversation guide is an excellent resource and we can make sure to make that available to listeners.
[SPEAKER_01]: And I might be misremembering the number of points, but you also shared aims AI usage policy at some point and it's like five points or something.
[SPEAKER_02]: It's a very short, no simple.
[SPEAKER_02]: And we just had to update it again.
[SPEAKER_02]: I just updated it with my staff four months ago because it even though it was only like a year old.
[SPEAKER_02]: It was already out of date.
[SPEAKER_02]: The stuff is changing so rapidly just out of curiosity.
[SPEAKER_01]: Do you remember what what you tweaked or what changed in the policy.
[SPEAKER_02]: Yeah, before it was it was really binary around we.
[SPEAKER_02]: When we first came up with it, it was really early days of chat GPT, right?
[SPEAKER_02]: So this was like early 2023 when we were thinking about this.
[SPEAKER_02]: And we originally said, we're going to disclose any time we use AI.
[SPEAKER_02]: That is not sustainable task or requirement of my staff.
[SPEAKER_02]: Because now it just bleeds into the work.
[SPEAKER_02]: Like, while we're not, we don't copy and paste AI.
[SPEAKER_02]: It's really hard to distinguish.
[SPEAKER_02]: Well, how much of this was AI editing versus.
[SPEAKER_02]: human authorship.
[SPEAKER_02]: It's impossible.
[SPEAKER_02]: So that we removed unless it's, um, we can kind of confirm like, this is like 85% AI, then we'll probably disclose it.
[SPEAKER_01]: Okay, great, very interesting.
[SPEAKER_01]: So this is the leading learning podcast.
[SPEAKER_01]: And so when we have a guest on, it's always fun, at least for me, to get to ask them how they tend to approach their own lifelong learning.
[SPEAKER_01]: So, toward you have practices or habits or resources that you like to use to help you continue to learn and grow.
[SPEAKER_02]: I like treating association management, almost like a scientific or academic discipline.
[SPEAKER_02]: So for me, that really means that I'm seeking out, even if we don't have scientific journals for a association management, I'm seeking out new research and thought leadership almost as a daily practice and that comes from ASA Collaborate, Association's now CEO update, Association Shared, LinkedIn, and anywhere where I can get it and I find it interesting,
[SPEAKER_02]: It's for me.
[SPEAKER_02]: I'm like two chaotic to like say, oh, I'm going to spend an hour every day doing this.
[SPEAKER_02]: It's almost like every time I pick my phone up, I'm like, oh, let's see what people are talking about now, but I do do it on a daily basis.
[SPEAKER_02]: And then beyond that, I have challenged myself for the last three years to sort of set.
[SPEAKER_02]: one big educational goal and sometimes that ends up in me getting a certificate in something.
[SPEAKER_02]: This year I was really focused on specifically executive leadership and uncertain times, which kind of directed where I was spending my own professional development dollars.
[SPEAKER_02]: But in prior years, I've got an certificate in objectives and key results.
[SPEAKER_02]: Last year, I got a certificate in facilitation.
[SPEAKER_02]: So those are the kind of like the big goals that I like to set
[SPEAKER_01]: So we've covered a fair amount of ground in our time.
[SPEAKER_01]: So I'm just thinking if you were to point listeners to one specific thing or maybe two or three things that we've touched on, like, what is it that you would hope they would walk away from listening to this conversation?
[SPEAKER_02]: Okay, three things.
[SPEAKER_02]: One, and probably the most important is for associations to continue to thrive, we have to focus on what makes us unique in the AI era.
[SPEAKER_02]: And that means human connections, human creativity, and a human generated content.
[SPEAKER_02]: So first and foremost, focus on that.
[SPEAKER_02]: And then I think that can feed into like what's the appropriate use case for AI and all that good stuff.
[SPEAKER_02]: Second one is keep it simple with AI.
[SPEAKER_02]: So like I said, don't if you're doing the usual governance stance where you're spending 12 months developing strategies and plans, that's not going to work.
[SPEAKER_02]: Keep it agile but keep it responsible.
[SPEAKER_02]: And then remember that responsible AI starts with data and information literacy.
[SPEAKER_02]: You need to understand data to be able to be successful with AI but also to be
[SPEAKER_00]: We're not done quite yet.
[SPEAKER_00]: Stick around for a recap of this conversation with Tory Miller Lou.
[SPEAKER_01]: You'll find show notes in a transcript at leadinglearning.com slash episode 465, along with links to the AIM website, the AI conversation guide that Tory mentioned and a link to her profile on LinkedIn.
[SPEAKER_00]: If you got value from this episode, please share it with a colleague or leave a rating and a review.
[SPEAKER_00]: Those help others find the show and support the work we're doing with the leading learning podcast.
[SPEAKER_01]: I think Toy did a very nice job of summarizing three key takeaways, so I won't reiterate those.
[SPEAKER_01]: Instead, I'll say that I really appreciated her reminder that AI efforts are really data efforts and having access, governance, and a mindset of good enough quality for specific AI use cases are what are going to enable real results.
[SPEAKER_00]: And I'll echo Tori's skepticism around AI replacing human creativity.
[SPEAKER_00]: Like Tori, even I have backgrounds in the arts and I prefer for songs and poems to remain human.
[SPEAKER_00]: So I appreciated her suggestion of picking one to three practical use cases where AI augments and doesn't replace people, doing something with them and learning from them.
[SPEAKER_01]: Thanks again for listening and see you next time on the leading learning podcast.