AI-Enhanced Member Services: Turning Strategy Into Action | [Sidecar Sync Episode 75]

Summary:

In this milestone 75th episode of Sidecar Sync, Amith and Mallory return for part two of their deep dive into AI-enhanced member services. This second installment of AI-Enhanced Member Services shifts from ideas to action. Amith and Mallory focus on the infrastructure, mindset, and change management strategies needed to implement AI solutions within your association. You’ll hear clear advice on system integration, data platforms, and the human side of transformation—including how to reframe staff roles and build a culture of innovation. It’s a must-listen for association leaders ready to take the next step.

Timestamps:

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More about Your Hosts:

Amith Nagarajan is the Chairman of Blue Cypress 🔗 https://BlueCypress.io, a family of purpose-driven companies and proud practitioners of Conscious Capitalism. The Blue Cypress companies focus on helping associations, non-profits, and other purpose-driven organizations achieve long-term success. Amith is also an active early-stage investor in B2B SaaS companies. He’s had the good fortune of nearly three decades of success as an entrepreneur and enjoys helping others in their journey.

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Mallory Mejias is passionate about creating opportunities for association professionals to learn, grow, and better serve their members using artificial intelligence. She enjoys blending creativity and innovation to produce fresh, meaningful content for the association space.

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Read the Transcript

Amith: 0:00

And there will be two categories of people in the future. There will be people who are AI literate, ai proficient and even AI expert, and there will be the unemployed. Welcome to Sidecar Sync, your weekly dose of innovation. If you're looking for the latest news, insights and developments in the association world, especially those driven by artificial intelligence, you're in the right place. We cut through the noise to bring you the most relevant updates, with a keen focus on how AI and other emerging technologies are shaping the future. No fluff, just facts and informed discussions. I'm Amit Nagarajan, chairman of Blue Cypress, and I'm your host. Greetings everybody and welcome to the Sidecar Sync, your home for content every single week at the intersection of artificial intelligence and all things about associations. My name is Amith Nagarajan.

Mallory: 0:53

And my name is Mallory Mejias.

Amith: 0:56

And we're your hosts, and in this special episode we are doing part two of two of AI enhanced member services. In part one, if you haven't caught it yet we covered a number of specific tasks or use cases where AI could have a meaningful impact on the world of member or customer or event services all aspects of services. So if you haven't checked out that episode yet, I'd encourage you to go back in time to that episode and check it out before you listen to this one, because that will make this episode make so much more sense. Before we dive into part two of AI Enhanced Member Services, though, let's just take a moment to hear a quick word from our sponsor.

Mallory: 1:38

If you're listening to this podcast right now, you're already thinking differently about AI than many of your peers, don't you wish there was a way to showcase your commitment to innovation and learning? The Association AI Professional, or AAIP, certification is exactly that. The AAIP certification is awarded to those who have achieved outstanding theoretical and practical AI knowledge. As it pertains to associations, earning your AAIP certification proves that you're at the forefront of AI in your organization and in the greater association space, giving you a competitive edge in an increasingly AI-driven job market. Join the growing group of professionals who've earned their AAIP certification and secure your professional future by heading to learnsidecarai. Amit, we were just talking about how we're nearly to episode 75 of the Sidecar Sync podcast. How does that make you feel?

Amith: 2:34

It's exciting. I love doing this every week and it's a lot of fun. It's a great moment in time each week to kind of step back and reflect on what's going on, and 75 episodes is quite a bit. It's a pretty decent body of work. I know the best thing about it is I hear from listeners pretty regularly saying that they've gotten something of value from this effort, and that's what it's all about. We're trying to help people in their AI journey, so I find it really rewarding and really exciting as well. How about you?

Mallory: 3:03

I agree with you. It's a very special moment. It's kind of to take a second process, everything that's going on, have this conversation and then walk away from it feeling inspired and having new insights. And then, like you said, hearing from listeners is the best thing in the world, like it brings me so much joy to hear you know random little tidbits from my life that I've shared that someone will say hey.

Mallory: 3:25

Mallory, how's Atlanta? How's the wedding, all these things. A good reminder, certainly, of how much I share on the podcast too. But it's always incredible to hear when someone says that they've learned something new or they tried out a tool that we discussed, or suggest new topics for us. So it feels crazy to think that we will have done this for basically 75 weeks Kind of insane?

Amith: 3:47

Yeah, it is. It is pretty cool, you know, and actually it's interesting. We use the term learning journey a lot, which is you know. My belief personally about just about everything in life is that you're on this continual journey and learning is this process, and so we want this to be a valuable resource for folks, particularly with their AI learning journey but, more broadly speaking, just their journey as a professional, through their career journey and being part of that. And many of our listeners have been with us pretty much since the beginning. I know some folks who have been, who've listened to every episode of the Sidecar Sank, so we appreciate that engagement and that follow followers, listeners very, very much. So thank you.

Mallory: 4:27

Yes, a million, a million thank yous to every single one of you and, if you're new, welcome. If you've been here from the beginning, we totally appreciate you, and feel free to connect with Amit and me on LinkedIn If you ever have any topic recommendations or anything like that that you want us to cover. We are always all ears and we love to tackle topics that we hadn't considered prior, so feel free to connect with us on social. All right, diving into today's part two of two episode of AI Enhanced Member Services. So, as Amit said in part one, we went through lots of tasks, lots of use cases and explored ways that AI can enhance your member services department and function within your association. This part two episode is more about moving into action, so talking about the foundation that you need, kind of technically and otherwise, to be able to do this kind of work in your association, and then, as I mentioned in part one, we will be kind of talking about that human element, that change management element, which, yes, feels uncomfortable but is also critical. So now we're focusing on those building blocks that you need to implement these AI enhanced solutions effectively.

Mallory: 5:41

Most AI powered member service solutions, we would say, require kind of three core elements. First and foremost we touched on this in part, one for sure, but system integration, particularly with your AMS or CRM. This enables AI to access member data, interaction history and organizational information. You need to evaluate whether your current systems have accessible APIs and what integration capabilities their vendors offer. Second, of course, data quality and structure. Ai is only as good as the data it works with Member profiles, engagement history, communication logs, transaction records. These are going to be essential and the key here is this data must be accessible.

Mallory: 6:25

Third, knowledge management. For AI to answer member questions accurately, it needs to access your association's knowledge base policies, procedures, member benefits, event information and more or even actual content that you're creating. This often requires documenting institutional knowledge that may currently exist only in staff members' heads. We hate to say it, but it might be true. With some of these foundations in place, we hope you can begin implementing AI for member services with confidence and starting where you'll see the most immediate impact. So, amit, my first question here is we went through a lot of tasks in the part one episode. It can seem overwhelming daunting. Where would you recommend that an association start their AI enhanced member services journey?

Amith: 7:14

Well, I do think your points about data are really critical, mallory, and this idea of system integration is super important. I would first actually say there that don't let great be the enemy of good enough. So what I mean by that is, yes, building a continual update process, where you have an AI data platform in the center of your ecosystem, where you're doing all this stuff from and you have real-time APIs updating that AI data platform all the time from the source systems in this beautiful way. Yes, that's an ideal state, but a lot of times data doesn't change that frequently. So let's just say, hypothetically, you have a legacy AMS and that AMS doesn't have any APIs, but maybe it's one of the really old-school AMSs where you have the database located either on-premise or in an environment you control in the cloud. You can just take a copy of that database and then unpack that entire database and just update it once a month or something like that, and it's probably good enough. So again, that's a little bit more of a creative solution than coming up with a more elegant, perfect solution. But don't let perfect or great get in the way of good enough, because right now you probably have nothing in this area. So that's one quick side note it is actually probably my answer to your question, in the sense of where you should start is if your data house is in order. If you don't have data in an accessible area where you can run AI tools against it, you can't really do a whole lot. I do think, going back to part one of this two-part episode, we talked about ways that people can do things kind of with consumer-grade tools right, using Cloud, using ChatGPT, using Google's Gemini, and I would encourage folks to run those experiments, to do things by hand, but with AI as their assistant. One transaction at a time, right. So if you're going to write an email, see if the AI can do it with the right prompting. If you're going to try to recommend products, try to do that with an AI, but do it one at a time because you don't have the integration yet. And then, of course, the goal is to automate these things and to put a lot of integration in place, but I do think that's one of the things people get choked up on really fast.

Amith: 9:19

A related comment this is a little bit of me getting on a soapbox about it, but I simply say this Don't replace your AMS right now unless it is literally the Titanic and you're going down in the cold Atlantic in the middle of the night. Do not replace your AMS right now. It is going to take you a year, two, three years. It's going to take you lots of six-figure checks, maybe a seven-figure check or two seven-figure check or two. It doesn't make sense because your AMS isn't going to solve any of this. Your AMS is going to give you, sure, much better infrastructure, but you're going to spend a lot of time replacing something where the net result is maybe a 10% to 30% improvement in functionality and in quality and, most of the time, you're not going to get any of these capabilities. Instead of that, what you could do right now is implement your own approach to AI by extracting the data from the AMS, doing all these things that we talk about on this podcast, and then determining what you actually want your future AMS to be, because that's the other point is that, a the AMS isn't going to move you forward in this way, but B you don't actually have any idea whatsoever what you want your AMS to do two years from now, when you finally go live, because the world is changing so incredibly rapidly, so kind of sucking it up and dealing with it in terms of having a legacy system.

Amith: 10:34

Again, unless you literally are bleeding to death on the side of the road, I would suggest to you that the AMS is not your priority for 2025 or even 2026. Priority for 2025 or even 2026. And that's because. Why do I bring that up, mallory? It's because it's a resource availability thing, it's a prioritization thing.

Amith: 10:51

I talked to tons of CEOs of associations who are like I really want to do more with AI. I'm super frustrated with me. How can I do this? How can I do more? And I'm like well, what's taking up so much of your time right now? They're like well, we're in the middle of this AMS implementation, or we're about to select a new AMS, or maybe it's an LMS, right, but it's some large, really, really important but not necessarily urgent infrastructure project, right?

Amith: 11:15

So, again, you don't know yet what you will need in two years, which is the timeframe for a typical go live in these projects, and you certainly could use those resources, both time and dollars, in other ways. And that actually solves a lot of the problems here, where you get to actually do some deeper work in the category of system integration or data quality and so forth. So to me it's a lot of that. It's figuring out your priorities and then stopping projects. Even if, like let's say, you just bought a new AMS and you're starting to implement it, well, that doesn't mean you have to keep going. You can hit pause on that and come back to it in six months and do a bunch of AI experimentation for the rest of this year and then come back to it later in the year, and by that point in time maybe your priorities have changed further. But just because you made the decision to go down a path that may no longer make sense doesn't mean you have to blindly follow it.

Mallory: 12:02

You can choose to hit pause sense doesn't mean you have to blindly follow it. You can choose to hit pause. That actually was going to be my follow-up question is what about for the people who are in the midst of it? So you're suggesting to pause, if you can. Amit, I saw a post on LinkedIn and I wish I had saved it, but I didn't know. We were going to be talking AMSs actually on this episode, but someone was suggesting, if you're in the process of switching to a new AMS, maybe you actually don't need to bring over all this historical data from your old AMS to your new one, especially if you ask yourself the question what am I going to do with it? And you don't know the answer, then you probably don't need to take all that historical data with you. But when we're talking about AI, enhanced member services, it does seem like historical data could be useful. So kind of what would you say in that instance? I know I don't have all the details, but what are your thoughts?

Amith: 12:47

Yeah. So in my 20 plus years of experience in doing AMS work, I would say there's three primary things that kill projects. Number one it's trying to reinvent the past, meaning like you're trying to recreate the AMS you're leaving. So a lot of people will say, oh, I hate my AMS, it's the worst thing ever. And then they buy a new AMS from some vendor and they say, oh, you got to recreate the way my old AMS did. It's like, didn't you just get done, telling us in the sales process that you hate that the way that it's done.

Amith: 13:14

But reality is people get used to what they're used to. Right, that's the simplest way to think about it is you get used to what you're used to or you basically become a victim of the environment that you've just been in for a long time, to put it another way. So that's one issue. The other issue is data conversion, trying to bring in all the data. Coming back to your question, so some people are taking a hybrid approach, saying, hey, I'm only going to bring in the last year of data and then I'm going to keep a copy of the old system around so I can look at it if I need to go back beyond a year, and that does simplify data conversion because you don't have to reconcile as much of the data. A lot of times the data coming from the old system is really really bad and so it's hard to reconcile to begin with. So there's kind of like that belief that you can kind of have a cutoff period and that can help.

Amith: 14:01

And then the final thing people really get wrong with AMS or any large system implementation is under-investing in training. So what ends up happening with these systems is people go live, they've, you know, customized it in a way that they shouldn't have. They've figured out a way to finally, like you know, slam their data in, and then they don't train their users and they say, hey, good luck, here's two days of training, god help you, you know, and that's kind of what happens in these implementations, and that is bad. That's a really bad idea, because your users are going to hate the new system if they haven't been trained not only adequately but continuously after they go live. That's why I always say, like it takes two to three years to gain value from that investment. Even if you go live in 12 to 18 months, which is kind of the typical timeframe, it usually takes you a full year after that to actually start having net benefit, because there's so much incredible amount of pain that people go through in these conversions.

Amith: 14:54

So, for all those reasons because AI is doubling in a six-month clip and all this other crazy stuff we always talk about on this pod you would probably better serve to say hey, this is going to take us.

Amith: 15:03

Even if you just pull the trigger and you just bought a new AMS and you're holding the keys to that brand new car, so to speak, you might want to say that's cool, but we're going to hit pause for six months to do AI experiments, to work on our process, because that's actually going to inform what you actually want that AMS to do. So I always think there's a way. You just have to be willing to stop and take a breath, and sometimes that also means going back to your board and saying, hey, you know that seven figure check you just wrote for this AMS. It's not a throwaway, but we're just going to hit pause for six months so that we can explore what's happening in the world. Not all boards are going to be favorable to a statement like that, but many will be if you go to them with that proactive mindset.

Mallory: 16:15

For associations with limited resources, would you say a common data platform or an AI data platform is kind of that minimum viable foundation necessary to kind of implement these kind of AI enhanced member services at scale, building on quicksand, meaning like you're building all these AI systems, rules, agents, whatever.

Amith: 16:40

You want to build on top of a moving foundation, because your other systems are going to constantly be changing. You're going to get a new marketing system, you're going to get a new CRM, you're going to get a new AMS. Eventually, you're going to get a new financial management system. These are not things that are set in stone. You don't want them to be either. You want those line of business applications to be capable of being fluid so you can pick the right apps for your business, and you probably have a lot of them and a growing number of them, and so if you don't have something that brings your data together, then your AI systems are sitting on top of this constant moving foundation. It's not an advisable approach, whereas if you have an AI data platform, what you're doing is you're pulling data from all of those different source systems into one unified data ecosystem and then you're building your agent strategy, your analytics strategy, everything on top of this foundational layer that you can rely on to be there over the long run.

Amith: 17:34

Obviously, given what we do, with Member Junction being the data platform we put into the world as an open source thing. We obviously believe in that. We've put a tremendous investment into that offering and it's open source. It's totally free. Anyone can download it and use it. That's the whole point of it being open source. But whatever you use, you could just use like a straight up database. You could go buy a CDP or an AI data platform from a vendor if you wanted to. Ultimately, it doesn't matter that much. If you have something good, you just need to have that layer in place, because if you build AI agents and AI analytics on top of your source systems directly, you're asking for a problem.

Mallory: 18:10

Amit you and Blue Cypress have worked directly with many kind of early adopter associations that are implementing personalization AI data platforms Across the board. Have you been able to identify any common obstacles that associations face in these processes that you could share with our listeners?

Amith: 18:31

To me, the biggest thing is the mindset. That's like an accounting mindset around data. What I mean by that is you know, with accounting you always want all of your books to tie up, you want your financial statements to reconcile, you want your ledgers to add up correctly all the terms right that are really important from an accounting and finance perspective. You wouldn't want to run a business without all those reconciliations and rules down to the penny perfection essentially all those reconciliations and rules down to the penny perfection, essentially. But the real world of data outside of a given system like that is extremely messy, and so when people try to unify their data into any database system whether it's an AI data platform or something else if you're going for that level of perfection, you will almost certainly fail. So to me, that's the biggest obstacle is the mindset that you need to bring your data in as it is, because if you try to perfect your data before you get into your data platform, it's kind of like saying, hey, you know, before we go to the modern world, we're going to hang out in the Stone Age and we're going to use hand tools. You know we're going to like literally make our tools by hand and then we're going to use those hand tools to try to fix this data problem over there. Past the horizon, there's the modern world, and if we just take all of our junky data with us, we'll have these really powerful tools over there to fix it, which is what we're saying.

Amith: 19:48

In an ai data platform, you have this thing called ai that can help you make your data a lot better right, whereas in a environment where you don't yet have ai, you're basically back over there hanging out with these really crude handheld tools and no power tools and certainly no AI. So get yourself over an AI data platform. First, bring your data as it is. As nasty as your data may be to you, just bring it as it is, dump it in there, and then let's put AI to work to actually help you cleanse it to some extent, but even if it still remains and always is forever dirty.

Amith: 20:23

From your perspective, the beauty of AI is hey, I don't really care that much. Not really as much as you do. You're disgusted by how many duplicates you have. You get really thrown off by how your data is inconsistent in a lot of ways. The beauty of this technology is it's finally able to really make sense of the data, even with all of its imperfections. So that is this kind of issue that people are having is they're taking this approach from traditional system conversions, which is this kind of accounting reconciliation mindset, to implementing an AI data platform, which, of course, is the prerequisite to doing personalization. It is largely a prerequisite to doing something along the lines of the core topic here, which is how to drive AI enhanced member services. If you don't have your data house in order, you can't do a whole lot of these things. You can do some of the basics right, but without that in place, you really are limited.

Mallory: 21:14

And then, once you have that data house in order, you've brought over all your Stone Age data into the modern world. What task or use case that we talked about in part one would you immediately go after, as this is the one we're trying first?

Amith: 21:29

I mean, to me, the number one thing I'd go after is handling the rote inquiries that come in.

Amith: 21:35

So people are emailing you or sending you messages all the time that take a lot of effort from you to respond to.

Amith: 21:43

You can solve that actually with very little effort. You don't even need most of your data in place, right. You just need to have an AI agent that knows how to respond to email. And you need a knowledge agent that understands your content and understands your FAQs and all the content the same things that your people would read in order to answer the questions themselves. So that does require something in place infrastructure-wise and it requires a couple of agents one that's good at like the asynchronous communication stuff and all the pieces and parts that are important there, like security and logging and so forth, and you need a knowledge agent in the mix. So to me, that's the number one thing because it's going to solve even if it only solves 20 or 30% of the inquiries that come in. It's going to make those inquiries so much faster and better. It's going to give you some time back to then think about what's the next 20 or 30% I can lob off.

Mallory: 22:35

Now it's time for the juicy part of the conversation, which is talking about change management and the human side of all this. So I feel like there's always an elephant in the room with these types of conversations, because you think about a world where you revamp your member services function within your organization. You have AI agents responding to member inquiries, managing your database, proactively reaching out to members in a personalized way and consistently upselling and cross-selling. All that sounds amazing, but the lingering question there is well, what happens to the humans who are doing that job? We believe the human element is just as critical to successful AI implementation and member services as the technical building blocks and member services as the technical building blocks. And, to be totally transparent and honest, we don't have all the answers here, but we must face these uncomfortable conversations head on. So our thoughts are one start by addressing concerns openly.

Mallory: 23:32

Staff may worry that AI will replace their jobs, and that's fair, but it can also transform roles in positive ways, handling routine tasks, so your team can focus on meaningful work requiring human empathy and judgment. Also, of course, invest in your team's AI literacy and skills development. To AI-enable member services, you will absolutely need humans involved in the rollout, the maintenance and quality assurance. We believe there's space for staff to contribute to this transformation, but they must have some level of AI literacy first and then also create new career pathways for evolving member service roles AI trainers who improve system responses, member success specialists who handle complex issues, and maybe experienced designers who optimize the service journey across human and AI touch points.

Mallory: 24:19

The bottom line here, I think, is that AI will change member service staffing needs. Some routine positions may be eliminated or consolidated as AI takes on repetitive tasks, but new roles will emerge that focus on AI oversight, complex problem solving and strategic member engagement. We believe AI has the ability to totally transform the way members interact with your organization, but this transformation does require honest conversations about how roles will change, what new skills are needed and how you will support staff during this transition. So, amit, how do you approach AI-enabled member services through the lens of staffing? What do you have to say there?

Amith: 25:01

Well, I mean, the first thing we have to really reinforce is what you've already covered, which is it is so critical to train your people on AI, and if you don't do that, you are not leading them. That's it. If you are not getting your people trained on AI and not pitching sidecars AI learning at the moment, just to be clear, like whatever any AI resource, there's tons of free stuff If you're not pushing that and aggressively getting your team up to speed on AI, you are not serving them as a leader, and there will be two categories of people in the future. There will be people who are AI literate, ai proficient and even AI expert, and there and so, within your own organization, it is your job as a leader whether you're an individual contributor or if you're the CEO or on the board you have to make sure your people are trained. So that's the most critical thing Now. If people have been trained and are continuing to train continuously on AI, then they can contribute to this, because they're going to have the knowledge and the practical experience to contribute to making the solution great and they're going to level up. But without that background, people are going to be lost and they will 100% be running for the hills. They will be fearful, they will be worried about what their future looks like, because you haven't taken the time and invested the resources time and dollars in some cases to help them figure out their role. So I know I'm making kind of the same point repetitively, but I can't emphasize it enough.

Amith: 26:29

You know, just as a little side note, last week we published a post I put it on my LinkedIn profile about the Sidecar MVP program and in that post we announced our goal, which is really for us a deeply held belief and mission that we have to go hit, which is that by the end of this decade by 2030, we at Sidecar want to educate a million people in this market, and that's association staff primarily, and also close-in volunteers, meaning your volunteers that are deeply involved in the association board, other volunteers that work with you closely, and that's obviously a very big number. We're not a huge company, but we have big ambitions, and the reason that particular goal is so important to us is we think we're putting a little bit of a dent in that problem right if we're educating that many people. We got a quarter million views on that post on LinkedIn, which is quite a bit more than what I usually get when I post something, and so clearly that resonated. Of course, the 15 MVPs that are part of the Sidecar first annual MVP program, which is a program we built to engage key leaders in the market, to help drive that mission to reach more and more people we're instrumental in that as well.

Amith: 27:43

But the bottom line is really that simple. It's that if we can bring everyone along, then there's no reason for fear. This could be the greatest moment for humanity looking ahead. If we harness this technology, we improve everyone's lives, and so that's exciting. But if we don't take that responsibility seriously, then we really are letting everyone down as leaders, and that's the most important thing is how we grow our people. So to me, that's the critical thing our people. So to me, that's the critical thing.

Amith: 28:10

And then, ultimately, the last thing I'd say about all of that is ultimately, the role that someone is in is going to change. Not may change, it's going to completely change, and some of the roles that currently exist will be gone. Now, most associations aren't really focused on reducing force, reducing their workforce, too much, and in fact, even when they have the opportunity, so usually they find other job for people. So I think there's a really positive aspect of this to look for is what are the roles that will be both perhaps more interesting but also just more valuable for the organization's mission itself. So I get excited about this. I know this is I'm in a position that I'm not worried about my job being replaced, et cetera. So I obviously am insulated from the kind of the raw emotion side of it, but I think we just have to hit this thing hard directly on that.

Amith: 28:56

It's the responsibility of leadership to go help their team figure this out.

Mallory: 29:02

I think of a scenario. You know we're very fortunate across the Blue Cypress family of companies to have this culture, that kind of prizes, innovation and AI. We talk about AI all the time. So, amit, if you came to me and said, mallory, in your role, we're going to automate this thing that you do, we're going to automate this thing that you do, this thing that you're working on, forget about it, because we're going to have an agent assist you with that process, I wouldn't be panicked, right, because I I wouldn't be panicked right, because I know that that's kind of the culture of the Blue Cypress family of companies. But I can imagine if an association leader goes to its culture.

Amith: 30:43

Yet how they can kind of get there. Yeah, I think you have to recognize at first what you just said is important, that the culture you have doesn't mean that's the culture you have to have, right? So you can start by making incremental change in your culture, really by leading by example. So if you're the CEO of the organization or somewhere in senior leadership, you can start by being vulnerable and explaining where you're at in your learning journey and sharing that with people and illustrating for people your own experimentation in your role and share what's worked well. And the vulnerable part is to share what didn't work well.

Amith: 31:14

Right, a lot of people don't like that. A lot of cultures kind of reject failure. In this, you know, kind of automated way, almost right, the culture just like kills the failure and buries it deep, deep down. Failures exist everywhere, obviously, but people don't really inspect it, they don't look at it and they don't necessarily celebrate it, but they kind of evaluate it, they learn from it. They say, hey, like we had a failure here, this is what we learned from it and let's figure it out. The other thing that's a very powerful culture change tool is to start asking two questions regularly. One is why. The other one is why not? So the why question is really helpful when you're talking about current processes and current products and current kind of ways of doing things in your culture. Why do we do it that way? And what do you hear often when you ask that question? Mallory, what would you guess that you often hear from people when you ask them why do you do it this way?

Mallory: 32:07

Because we've always done it that way.

Amith: 32:09

Exactly, and that is not a good answer. Right, that is an answer. It's not necessarily a valid reason to do it that way, but that is how most things get done right. Of course, there's lots of good things that come from repetition and refinement and saying hey, like you know, the Toyota production process right, One of the most effective and efficient processes in the world came from a lot of refinement. They haven't always done it that way, but they've had that process Right, and some of it actually can't be replicated because it's inculcated into the culture so much so, more so than a guidebook.

Amith: 32:43

So I'm not saying that all current processes are bad, but what I am saying is ask the question. Ask why? Because by asking that question, you're opening the door to discussion, as opposed to people making the assumption which is the next part of you know the thing I asked you, Mallory, about like we've always done it that way. And then if you ask people, well, could we change it? They'd say no, we're not going to change it. That's how we've always done it. Right, Because ever since they've been there, it's always been done that way. Is management, is leadership, willing to change it? And the answer would be no, they're not going to change it. But if you, as the leader, start asking those questions, the why question, regularly, people are going to go, huh, maybe they are open to changing and maybe I could suggest something different. So that's one thing.

Amith: 33:26

The other question is why not? So when you start talking about doing things differently, when you start asking about could we build a new service, a new product, a new offering, Could we do a new process, why not? Like, why not, in general, Like, is it possible to do right? Do the laws of physics prevent said activity from occurring? Generally, not right. So there's no first principles rationale to why something is or isn't happening. So the why not? Question is good.

Amith: 33:51

And then a follow-up to why not, by the way, is why not us? So we asked, when it comes to AI, education for association, why can't a million people learn AI in the next five years, next four and a half years? There's no real good answer. It's not that many people and education can be delivered digitally and it's possible to reach those people because of social media and advertising dollars and whatever. So why not? There's really actually no good reason. The why is really. It's really really important, right, We've said that. But the why not really not a good answer. And then we asked well, why not us? Well, no one else is doing it, and so let's go. Let's go do this thing. It's exciting, it's important, it's a deep mission. We can make a great sustainable business out of it. So let's go do this thing. And so that mindset, I think, is really important. I think it's kind of part of the entrepreneur's credo in a way. But I think associations could do well to adopt those basic questions in their culture.

Mallory: 34:47

What else I think is interesting is you and I have discussed on the Pot of Meath how typically in most businesses there's kind of a laundry list of activities, things, goals you would like to do, items on your strategic plan that you just don't have time to get around to, and if you have staff within your member services department, you know, allocated to different activities because perhaps you have AI, augmenting member inquiries, database management, so on and so forth maybe you can have those staffers contribute to some of the other activities you have on your strategic plan, or goals that you have or things you've never gotten around to because they've just seem impossible in terms of time and resources. So that's also interesting is thinking bigger, not thinking just in terms of staff reduction but thinking in terms of staff opportunity as well.

Amith: 35:35

Totally, and that translates to organizational opportunity. You know, think about, like what most people are just treading water, right, they're not moving forward, they're not moving backward, they're just kind of like happy to not be sinking Right and because they have so much volume of activity, they're trying to tread water. And so if you say, ok, well, now we've like given you a platform to stand on so you're not like, you're not scurrying about and treading water, you're literally know that you're stable in that position. Let's move forward. You can start asking some really interesting questions like hey, mallory, you're a member services person. You've been doing this for 10 years.

Amith: 36:08

You've talked to thousands of our members over the course of 10 years. What are some of their pain points? That we could build products or change our experience in some ways. And you know maybe you know a whole hell of a lot about our members, which a lot of the member services folks know way, way better than like anyone else in the organization, right, because they spent all their time talking to them. They can help build new products. They can help build new experiences all the stuff you just mentioned. It's exciting. That's an opportunity to look ahead rather than just treading water.

Mallory: 36:39

And getting staff engaged. Getting your team engaged is a great way to just increase investment in the project as a whole and make everyone feel like they have ownership as well, so I think that's essential.

Amith: 36:47

I will say one more thing on this thread that is an important topic and I think this is also uncommon to be addressed directly in the association market and that is culture fit. And so if you are committed to building a culture that's more adaptable, more flexible, more innovative and looking ahead, one of the things you're going to have, you're going to run into in most organizations, is people who don't want that, and they may be lower level, they may be higher level, but if you as an organization are committed to moving ahead and you run into that, by all means try to bring the people along, try to help them understand what the vision of the future is, what the organization's committed to, and that you're giving them a hand to try to help them re-skill, re-tool, learn new things, et cetera. But sometimes you're gonna have some people in an organization that are unwilling not incapable, but unwilling to adapt. They themselves are unwilling to adapt and unfortunately, those are folks you're going to have to say goodbye to. If you keep them around, you will undermine your transformation.

Amith: 37:50

It's that simple.

Amith: 37:51

You cannot allow naysayers to stay in an organization if they are fundamentally opposed to the idea of the change, if they are unwilling to change themselves, and that is something that's a pill a lot of associations aren't willing to swallow, unfortunately, but it's a critically important part of culture change.

Amith: 38:08

And this might be a person who's been a fantastic employee for years, but you've never asked them to adapt.

Amith: 38:14

And now that you have to adapt, you know the forces of the world. The rate of external change right is so much greater than your rate of internal change, which means you're out of date and you have to change in order to make the organization not only viable but thriving in the future and to serve your mission. You're going to have to make some tough calls, and that's going to exist in every organization on the planet. It's certainly going to happen in a lot of associations because there's been, you know, so much consistency to say it nicely over a long period of time, and if you want to drive some change, you're going to have to look at it very, very carefully Again. You know, do everything you can as a leader to bring these folks along, but at some point you have to make a choice like that, and you might have multiple such scenarios and if you don't make those choices, you're going to be anchoring your organization in the past and preventing yourself from being able to move forward.

Mallory: 39:04

Everyone, thank you for tuning in to part two of this two-part episode around AI-enhanced member services. We hope you're feeling inspired, we hope you have some practical next steps for how you can get started, and we want to remind you that at the end of the day even though member services is kind of a phrase we've thrown around a lot on these two episodes that you really owe this to your members. If you were the anointed, trusted source of content in your space, you owe it to your members to reduce friction, to get access to that content and to keep coming back to you. So with that, we will see you all next week.

Amith: 39:41

Thanks for tuning into Sidecar Sync this week. Looking to dive deeper? Download your free copy of our new book Ascend Unlocking the Power of AI for Associations at ascendbookorg. It's packed with insights to power your association's journey with AI. And remember, sidecar is here with more resources, from webinars to bootcamps, to help you stay ahead in the association world. We'll catch you in the next episode. Until then, keep learning, keep growing and keep disrupting.