March 27, 2025 • 40 mins
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.
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🛠 AI Tools and Resources Mentioned in This Episode:
ChatGPT ➡ https://openai.com/chatgpt
Google Gemini ➡ https://gemini.google
Chapters:
00:00 - Welcome to Episode 75 of Sidecar Sync
05:32 - The Three Building Blocks for AI Member Services
12:04 - In the Middle of an AMS Switch? Hit Pause
17:42 - Common Obstacles: The "Accounting" Mindset
20:44 - What to Automate First: Member Inquiries
22:05 - Change Management and the Leadership Imperative
28:32 - Culture Shifts: Asking “Why” and “Why Not?”
33:28 - Unlocking New Strategic Capacity with AI
37:45 - Culture Fit and Leading Through Transformation
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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 the Manager at Sidecar, and she's 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.
📣 Follow Mallory:
https://linkedin.com/mallorymejias
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Amith (00:00):
And there will be two categories of people in the
future.
There will be people who are AIliterate, ai proficient and
even AI expert, and there willbe the unemployed.
Welcome to Sidecar Sync, yourweekly dose of innovation.
If you're looking for thelatest news, insights and
developments in the associationworld, especially those driven
(00:20):
by artificial intelligence,you're in the right place.
We cut through the noise tobring you the most relevant
updates, with a keen focus onhow AI and other emerging
technologies are shaping thefuture.
No fluff, just facts andinformed discussions.
I'm Amit Nagarajan, chairman ofBlue Cypress, and I'm your host
.
Greetings everybody and welcometo the Sidecar Sync, your home
(00:43):
for content every single week atthe intersection of artificial
intelligence and all thingsabout associations.
My name is Amit Nagarajan.
Mallory (00:53):
And my name is Mallory Mejiaz.
Amith (00:56):
And we're your hosts, and in this special episode we are
doing part two of two of AIenhanced member services.
In part one, if you haven'tcaught it yet we covered a
number of specific tasks or usecases where AI could have a
meaningful impact on the worldof member or customer or event
services all aspects of services.
(01:17):
So if you haven't checked outthat episode yet, I'd encourage
you to go back in time to thatepisode and check it out before
you listen to this one, becausethat will make this episode make
so much more sense.
Before we dive into part two ofAI Enhanced Member Services,
though, let's just take a momentto hear a quick word from our
sponsor.
Mallory (01:38):
If you're listening to this podcast right now, you're
already thinking differentlyabout AI than many of your peers
, don't you wish there was a wayto showcase your commitment to
innovation and learning?
The Association AI Professional, or AAIP, certification is
exactly that.
The AAIP certification isawarded to those who have
achieved outstanding theoreticaland practical AI knowledge.
(02:00):
As it pertains to associations,earning your AAIP certification
proves that you're at theforefront of AI in your
organization and in the greaterassociation space, giving you a
competitive edge in anincreasingly AI-driven job
market.
Join the growing group ofprofessionals who've earned
their AAIP certification andsecure your professional future
(02:22):
by heading to learnsidecarai.
Amit, we were just talkingabout how we're nearly to
episode 75 of the Sidecar Syncpodcast.
How does that make you feel?
Amith (02:34):
It's exciting.
I love doing this every weekand it's a lot of fun.
It's a great moment in timeeach 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 ofwork.
I know the best thing about itis I hear from listeners pretty
regularly saying that they'vegotten something of value from
(02:55):
this effort, and that's whatit's all about.
We're trying to help people intheir AI journey, so I find it
really rewarding and reallyexciting as well.
How about you?
Mallory (03:03):
I agree with you.
It's a very special moment.
It's kind of to take a secondprocess, everything that's going
on, have this conversation andthen 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 itbrings me so much joy to hear
you know random little tidbitsfrom my life that I've shared
(03:24):
that someone will say hey.
Mallory, how's Atlanta?
How's the wedding, all thesethings.
A good reminder, certainly, ofhow much I share on the podcast
too.
But it's always incredible tohear when someone says that
they've learned something new orthey tried out a tool that we
discussed, or suggest new topicsfor us.
So it feels crazy to think thatwe will have done this for
(03:45):
basically 75 weeks Kind ofinsane?
Amith (03:47):
Yeah, it is.
It is pretty cool, you know,and actually it's interesting.
We use the term learningjourney a lot, which is you know
.
My belief personally about justabout everything in life is
that you're on this continualjourney and learning is this
process, and so we want this tobe a valuable resource for folks
, particularly with their AIlearning journey but, more
broadly speaking, just theirjourney as a professional,
(04:09):
through their career journey andbeing part of that.
And many of our listeners havebeen with us pretty much since
the beginning.
I know some folks who have been, who've listened to every
episode of the Sidecar Sank, sowe appreciate that engagement
and that follow followers,listeners very, very much.
So thank you.
Mallory (04:27):
Yes, a million, a million thank yous to every
single one of you and, if you'renew, welcome.
If you've been here from thebeginning, we totally appreciate
you, and feel free to connectwith Amit and me on LinkedIn If
you ever have any topicrecommendations or anything like
that that you want us to cover.
We are always all ears and welove to tackle topics that we
hadn't considered prior, so feelfree to connect with us on
(04:51):
social.
All right, diving into today'spart 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 exploredways that AI can enhance your
member services department andfunction within your association
.
This part two episode is moreabout moving into action, so
(05:14):
talking about the foundationthat you need, kind of
technically and otherwise, to beable to do this kind of work in
your association, and then, asI mentioned in part one, we will
be kind of talking about thathuman element, that change
management element, which, yes,feels uncomfortable but is also
critical.
So now we're focusing on thosebuilding blocks that you need to
(05:37):
implement these AI enhancedsolutions effectively.
Most AI powered member servicesolutions, we would say, require
kind of three core elements.
First and foremost we touchedon this in part, one for sure,
but system integration,particularly with your AMS or
CRM.
This enables AI to accessmember data, interaction history
(05:58):
and organizational information.
You need to evaluate whetheryour current systems have
accessible APIs and whatintegration capabilities their
vendors offer.
Second, of course, data qualityand structure.
Ai is only as good as the datait works with Member profiles,
engagement history,communication logs, transaction
(06:18):
records.
These are going to be essentialand the key here is this data
must be accessible.
Third, knowledge management.
For AI to answer memberquestions accurately, it needs
to access your association'sknowledge base policies,
procedures, member benefits,event information and more or
(06:39):
even actual content that you'recreating.
This often requires documentinginstitutional knowledge that
may currently exist only instaff members' heads.
We hate to say it, but it mightbe true.
With some of these foundationsin place, we hope you can begin
implementing AI for memberservices with confidence and
starting where you'll see themost immediate impact.
So, amit, my first questionhere is we went through a lot of
(07:02):
tasks in the part one episode.
It can seem overwhelmingdaunting.
Where would you recommend thatan association start their AI
enhanced member services journey?
Amith (07:14):
Well, I do think your points about data are really
critical, mallory, and this ideaof system integration is super
important.
I would first actually saythere 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 AIdata platform in the center of
(07:37):
your ecosystem, where you'redoing all this stuff from and
you have real-time APIs updatingthat AI data platform all the
time from the source systems inthis beautiful way.
Yes, that's an ideal state, buta lot of times data doesn't
change that frequently.
So let's just say,hypothetically, you have a
legacy AMS and that AMS doesn'thave any APIs, but maybe it's
one of the really old-schoolAMSs where you have the database
(08:00):
located either on-premise or inan environment you control in
the cloud.
You can just take a copy ofthat database and then unpack
that entire database and justupdate it once a month or
something like that, and it'sprobably good enough.
So again, that's a little bitmore of a creative solution than
coming up with a more elegant,perfect solution.
But don't let perfect or greatget in the way of good enough,
(08:21):
because right now you probablyhave nothing in this area.
So that's one quick side noteit is actually probably my
answer to your question, in thesense of where you should start
is if your data house is inorder.
If you don't have data in anaccessible area where you can
run AI tools against it, youcan't really do a whole lot.
I do think, going back to partone of this two-part episode, we
(08:42):
talked about ways that peoplecan do things kind of with
consumer-grade tools right,using Cloud, using ChatGPT,
using Google's Gemini, and Iwould encourage folks to run
those experiments, to do thingsby hand, but with AI as their
assistant.
One transaction at a time,right.
So if you're going to write anemail, see if the AI can do it
with the right prompting.
(09:02):
If you're going to try torecommend products, try to do
that with an AI, but do it oneat a time because you don't have
the integration yet.
And then, of course, the goalis to automate these things and
to put a lot of integration inplace, but I do think that's one
of the things people get chokedup on really fast.
A related comment this is alittle bit of me getting on a
soapbox about it, but I simplysay this Don't replace your AMS
(09:26):
right now unless it is literallythe Titanic and you're going
down in the cold Atlantic in themiddle of the night.
Do not replace your AMS rightnow.
It is going to take you a year,two, three years.
It's going to take you lots ofsix-figure checks, maybe a
seven-figure check or twoseven-figure check or two.
It doesn't make sense becauseyour AMS isn't going to solve
any of this.
Your AMS is going to give you,sure, much better infrastructure
(09:53):
, but you're going to spend alot of time replacing something
where the net result is maybe a10% to 30% improvement in
functionality and in quality and, most of the time, you're not
going to get any of thesecapabilities.
Instead of that, what you coulddo right now is implement your
own approach to AI by extractingthe data from the AMS, doing
all these things that we talkabout on this podcast, and then
(10:13):
determining what you actuallywant your future AMS to be,
because that's the other pointis 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 youwant your AMS to do two years
from now, when you finally golive, because the world is
changing so incredibly rapidly,so kind of sucking it up and
dealing with it in terms ofhaving a legacy system.
(10:34):
Again, unless you literally arebleeding to death on the side of
the road, I would suggest toyou 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 resourceavailability thing, it's a
prioritization thing.
I talked to tons of CEOs ofassociations who are like I
really want to do more with AI.
I'm super frustrated with me.
(10:55):
How can I do this?
How can I do more?
And I'm like well, what'staking up so much of your time
right now?
They're like well, we're in themiddle of this AMS
implementation, or we're aboutto select a new AMS, or maybe
it's an LMS, right, but it'ssome large, really, really
important but not necessarilyurgent infrastructure project,
right?
So, again, you don't know yetwhat you will need in two years,
(11:19):
which is the timeframe for atypical go live in these
projects, and you certainlycould use those resources, both
time and dollars, in other ways.
And that actually solves a lotof the problems here, where you
get to actually do some deeperwork in the category of system
integration or data quality andso forth.
So to me it's a lot of that.
It's figuring out yourpriorities and then stopping
projects.
Even if, like let's say, youjust bought a new AMS and you're
(11:42):
starting to implement it, well,that doesn't mean you have to
keep going.
You can hit pause on that andcome back to it in six months
and do a bunch of AIexperimentation for the rest of
this year and then come back toit later in the year, and by
that point in time maybe yourpriorities have changed further.
But just because you made thedecision to go down a path that
may no longer make sense doesn'tmean 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 bemy follow-up question is what
about for the people who are inthe midst of it?
So you're suggesting to pause,if you can.
Amit, I saw a post on LinkedInand I wish I had saved it, but I
didn't know.
We were going to be talkingAMSs actually on this episode,
but someone was suggesting, ifyou're in the process of
switching to a new AMS, maybeyou actually don't need to bring
(12:27):
over all this historical datafrom your old AMS to your new
one, especially if you askyourself 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 datawith you.
But when we're talking about AI, enhanced member services, it
does seem like historical datacould be useful.
So kind of what would you sayin that instance?
I know I don't have all thedetails, but what are your
thoughts?
Amith (12:47):
Yeah.
So in my 20 plus years ofexperience in doing AMS work, I
would say there's three primarythings that kill projects.
Number one it's trying toreinvent the past, meaning like
you're trying to recreate theAMS 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 AMSfrom some vendor and they say,
oh, you got to recreate the waymy old AMS did.
(13:08):
It's like, didn't you just getdone, telling us in the sales
process that you hate that theway that it's done.
But reality is people get usedto what they're used to.
Right, that's the simplest wayto think about it is you get
used to what you're used to oryou basically become a victim of
the environment that you'vejust been in for a long time, to
put it another way.
So that's one issue.
(13:30):
The other issue is dataconversion, 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'mgoing to keep a copy of the old
system around so I can look atit if I need to go back beyond a
year, and that does simplifydata conversion because you
don't have to reconcile as muchof the data.
A lot of times the data comingfrom the old system is really
(13:53):
really bad and so it's hard toreconcile to begin with.
So there's kind of like thatbelief that you can kind of have
a cutoff period and that canhelp.
And then the final thing peoplereally get wrong with AMS or any
large system implementation isunder-investing in training.
So what ends up happening withthese systems is people go live,
they've, you know, customizedit in a way that they shouldn't
have.
They've figured out a way tofinally, like you know, slam
(14:15):
their data in, and then theydon't train their users and they
say, hey, good luck, here's twodays of training, god help you,
you know, and that's kind ofwhat 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 theyhaven't been trained not only
adequately but continuouslyafter they go live.
That's why I always say, likeit takes two to three years to
(14:38):
gain value from that investment.
Even if you go live in 12 to 18months, which is kind of the
typical timeframe, it usuallytakes you a full year after that
to actually start having netbenefit, because there's so much
incredible amount of pain thatpeople go through in these
conversions.
So, for all those reasonsbecause AI is doubling in a
six-month clip and all thisother crazy stuff we always talk
(14:59):
about on this pod you wouldprobably better serve to say hey
, this is going to take us.
Even if you just pull thetrigger and you just bought a
new AMS and you're holding thekeys to that brand new car, so
to speak, you might want to saythat's cool, but we're going to
hit pause for six months to doAI experiments, to work on our
process, because that's actuallygoing to inform what you
actually want that AMS to do.
So I always think there's a way.
(15:21):
You just have to be willing tostop and take a breath, and
sometimes that also means goingback to your board and saying,
hey, you know that seven figurecheck you just wrote for this
AMS.
It's not a throwaway, but we'rejust going to hit pause for six
months so that we can explorewhat's happening in the world.
Not all boards are going to befavorable to a statement like
that, but many will be if you goto them with that proactive
(15:43):
mindset.
Mallory (15:45):
For associations with limited resources, would you say
a common data platform or an AIdata platform is kind of that
minimum viable foundationnecessary to kind of implement
these kind of AI enhanced memberservices at scale, building on
quicksand, meaning like you'rebuilding all these AI systems,
(16:09):
rules, agents, whatever.
Amith (16:10):
You want to build on top of a moving foundation, because
your other systems are going toconstantly be changing.
You're going to get a newmarketing system, you're going
to get a new CRM, you're goingto get a new AMS.
Eventually, you're going to geta new financial management
system.
These are not things that areset in stone.
You don't want them to beeither.
You want those line of businessapplications to be capable of
being fluid so you can pick theright apps for your business,
(16:32):
and you probably have a lot ofthem and a growing number of
them, and so if you don't havesomething that brings your data
together, then your AI systemsare 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 isyou're pulling data from all of
those different source systemsinto one unified data ecosystem
(16:54):
and then you're building youragent strategy, your analytics
strategy, everything on top ofthis foundational layer that you
can rely on to be there overthe long run.
Obviously, given what we do,with Member Junction being the
data platform we put into theworld as an open source thing.
We obviously believe in that.
We've put a tremendousinvestment into that offering
(17:14):
and it's open source.
It's totally free.
Anyone can download it and useit.
That's the whole point of itbeing open source.
But whatever you use, you couldjust use like a straight up
database.
You could go buy a CDP or an AIdata platform from a vendor if
you wanted to.
Ultimately, it doesn't matterthat much.
If you have something good, youjust need to have that layer in
place, because if you build AIagents and AI analytics on top
(17:37):
of your source systems directly,you're asking for a problem.
Mallory (17:41):
Amit you and Blue Cypress have worked directly
with many kind of early adopterassociations that are
implementing personalization AIdata platforms Across the board.
Have you been able to identifyany common obstacles that
associations face in theseprocesses that you could share
with our listeners?
Amith (18:01):
To me, the biggest thing is the mindset.
That's like an accountingmindset 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, youwant your ledgers to add up
correctly all the terms rightthat are really important from
an accounting and financeperspective.
(18:22):
You wouldn't want to run abusiness without all those
reconciliations and rules downto the penny perfection
essentially all thosereconciliations and rules down
to the penny perfection,essentially.
But the real world of dataoutside of a given system like
that is extremely messy, and sowhen people try to unify their
data into any database systemwhether it's an AI data platform
or something else if you'regoing for that level of
(18:44):
perfection, you will almostcertainly fail.
So to me, that's the biggestobstacle is the mindset that you
need to bring your data in asit is, because if you try to
perfect your data before you getinto your data platform, it's
kind of like saying, hey, youknow, before we go to the modern
world, we're going to hang outin the Stone Age and we're going
to use hand tools.
You know we're going to likeliterally make our tools by hand
(19:06):
and then we're going to usethose hand tools to try to fix
this data problem over there.
Past the horizon, there's themodern world, and if we just
take all of our junky data withus, we'll have these really
powerful tools over there to fixit, which is what we're saying.
In an ai data platform, you havethis thing called ai that can
help you make your data a lotbetter right, whereas in a
environment where you don't yethave ai, you're basically back
(19:28):
over there hanging out withthese really crude handheld
tools and no power tools andcertainly no AI.
So get yourself over an AI dataplatform.
First, bring your data as it is.
As nasty as your data may be toyou, just bring it as it is,
dump it in there, and then let'sput AI to work to actually help
(19:48):
you cleanse it to some extent,but even if it still remains and
always is forever dirty.
From your perspective, thebeauty of AI is hey, I don't
really care that much.
Not really as much as you do.
You're disgusted by how manyduplicates you have.
You get really thrown off byhow your data is inconsistent in
a lot of ways.
The beauty of this technologyis it's finally able to really
(20:09):
make sense of the data, evenwith all of its imperfections.
So that is this kind of issuethat people are having is
they're taking this approachfrom traditional system
conversions, which is this kindof accounting reconciliation
mindset, to implementing an AIdata platform, which, of course,
is the prerequisite to doingpersonalization.
It is largely a prerequisite todoing something along the lines
(20:31):
of the core topic here, whichis how to drive AI enhanced
member services.
If you don't have your datahouse in order, you can't do a
whole lot of these things.
You can do some of the basicsright, but without that in place
, you really are limited.
Mallory (20:45):
And then, once you have that data house in order,
you've brought over all yourStone Age data into the modern
world.
What task or use case that wetalked about in part one would
you immediately go after, asthis is the one we're trying
first?
Amith (21:00):
I mean, to me, the number one thing I'd go after is
handling the rote inquiries thatcome in.
So people are emailing you orsending you messages all the
time that take a lot of effortfrom you to respond to.
You can solve that actually withvery little effort.
You don't even need most ofyour data in place, right.
You just need to have an AIagent that knows how to respond
(21:21):
to email.
And you need a knowledge agentthat understands your content
and understands your FAQs andall the content the same things
that your people would read inorder to answer the questions
themselves.
So that does require somethingin place infrastructure-wise and
it requires a couple of agentsone that's good at like the
asynchronous communication stuffand all the pieces and parts
(21:41):
that are important there, likesecurity and logging and so
forth, and you need a knowledgeagent in the mix.
So to me, that's the number onething because it's going to
solve even if it only solves 20or 30% of the inquiries that
come in.
It's going to make thoseinquiries so much faster and
better.
It's going to give you sometime back to then think about
what's the next 20 or 30% I canlob off.
Mallory (22:05):
Now it's time for the juicy part of the conversation,
which is talking about changemanagement and the human side of
all this.
So I feel like there's alwaysan elephant in the room with
these types of conversations,because you think about a world
where you revamp your memberservices function within your
organization.
You have AI agents respondingto member inquiries, managing
(22:27):
your database, proactivelyreaching out to members in a
personalized way andconsistently upselling and
cross-selling.
All that sounds amazing, butthe lingering question there is
well, what happens to the humanswho are doing that job?
We believe the human element isjust as critical to successful
AI implementation and memberservices as the technical
building blocks and memberservices as the technical
(22:47):
building blocks.
And, to be totally transparentand honest, we don't have all
the answers here, but we mustface these uncomfortable
conversations head on.
So our thoughts are one startby addressing concerns openly.
Staff may worry that AI willreplace their jobs, and that's
fair, but it can also transformroles in positive ways, handling
routine tasks, so your team canfocus on meaningful work
(23:10):
requiring human empathy andjudgment.
Also, of course, invest in yourteam's AI literacy and skills
development.
To AI-enable member services,you will absolutely need humans
involved in the rollout, themaintenance and quality
assurance.
We believe there's space forstaff to contribute to this
transformation, but they musthave some level of AI literacy
(23:32):
first and then also create newcareer pathways for evolving
member service roles AI trainerswho improve system responses,
member success specialists whohandle complex issues, and maybe
experienced designers whooptimize the service journey
across human and AI touch points.
The bottom line here, I think,is that AI will change member
(23:53):
service staffing needs.
Some routine positions may beeliminated or consolidated as AI
takes on repetitive tasks, butnew roles will emerge that focus
on AI oversight, complexproblem solving and strategic
member engagement.
We believe AI has the abilityto totally transform the way
members interact with yourorganization, but this
transformation does requirehonest conversations about how
(24:16):
roles will change, what newskills are needed and how you
will support staff during thistransition.
So, amit, how do you approachAI-enabled member services
through the lens of staffing?
What do you have to say there?
Amith (24:31):
Well, I mean, the first thing we have to really
reinforce is what you've alreadycovered, which is it is so
critical to train your people onAI, and if you don't do that,
you are not leading them.
That's it.
If you are not getting yourpeople trained on AI and not
pitching sidecars AI learning atthe moment, just to be clear,
(24:52):
like whatever any AI resource,there's tons of free stuff If
you're not pushing that andaggressively getting your team
up to speed on AI, you are notserving them as a leader, and
there will be two categories ofpeople in the future.
There will be people who are AIliterate, ai proficient and
even AI expert, and there and so, within your own organization,
(25:16):
it is your job as a leaderwhether you're an individual
contributor or if you're the CEOor on the board you have to
make sure your people aretrained.
So that's the most criticalthing Now.
If people have been trained andare continuing to train
continuously on AI, then theycan contribute to this, because
they're going to have theknowledge and the practical
experience to contribute tomaking the solution great and
(25:37):
they're going to level up.
But without that background,people are going to be lost and
they will 100% be running forthe hills.
They will be fearful, they willbe worried about what their
future looks like, because youhaven't taken the time and
invested the resources time anddollars in some cases to help
them figure out their role.
So I know I'm making kind ofthe same point repetitively, but
(25:58):
I can't emphasize it enough.
You know, just as a little sidenote, last week we published a
post I put it on my LinkedInprofile about the Sidecar MVP
program and in that post weannounced our goal, which is
really for us a deeply heldbelief and mission that we have
to go hit, which is that by theend of this decade by 2030, we
(26:21):
at Sidecar want to educate amillion people in this market,
and that's association staffprimarily, and also close-in
volunteers, meaning yourvolunteers that are deeply
involved in the associationboard, other volunteers that
work with you closely, andthat's obviously a very big
number.
We're not a huge company, butwe have big ambitions, and the
reason that particular goal isso important to us is we think
(26:45):
we're putting a little bit of adent in that problem right if
we're educating that many people.
We got a quarter million viewson that post on LinkedIn, which
is quite a bit more than what Iusually get when I post
something, and so clearly thatresonated.
Of course, the 15 MVPs that arepart of the Sidecar first
annual MVP program, which is aprogram we built to engage key
(27:07):
leaders in the market, to helpdrive that mission to reach more
and more people we'reinstrumental in that as well.
But the bottom line is reallythat simple.
It's that if we can bringeveryone along, then there's no
reason for fear.
This could be the greatestmoment 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 thatresponsibility seriously, then
(27:29):
we really are letting everyonedown as leaders, and that's the
most important thing is how wegrow our people.
So to me, that's the criticalthing our people.
So to me, that's the criticalthing.
And then, ultimately, the lastthing I'd say about all of that
is ultimately, the role thatsomeone is in is going to change
.
Not may change, it's going tocompletely change, and some of
the roles that currently existwill be gone.
(27:49):
Now, most associations aren'treally focused on reducing force
, reducing their workforce, toomuch, and in fact, even when
they have the opportunity, sousually they find other job for
people.
So I think there's a reallypositive aspect of this to look
for is what are the roles thatwill be both perhaps more
interesting but also just morevaluable for the organization's
(28:10):
mission itself.
So I get excited about this.
I know this is I'm in aposition that I'm not worried
about my job being replaced, etcetera.
So I obviously am insulatedfrom the kind of the raw emotion
side of it, but I think we justhave to hit this thing hard
directly on that.
It's the responsibility ofleadership to go help their team
figure this out.
Mallory (28:32):
I think of a scenario.
You know we're very fortunateacross the Blue Cypress family
of companies to have thisculture, that kind of prizes,
innovation and AI.
We talk about AI all the time.
So, amit, if you came to me andsaid, mallory, in your role,
we're going to automate thisthing that you do, we're going
to automate this thing that youdo, this thing that you're
working on, forget about it,because we're going to have an
agent assist you with thatprocess, I wouldn't be panicked,
(28:54):
right, because I I wouldn't bepanicked right, because I know
(29:21):
that that's kind of the cultureof the Blue Cypress family of
companies.
But I can imagine if anassociation leader goes to its
culture.
Amith (29:24):
Yet how they can kind of get there.
Yeah, I think you have torecognize at first what you just
said is important, that theculture you have doesn't mean
that's the culture you have tohave, right?
So you can start by makingincremental change in your
culture, really by leading byexample.
So if you're the CEO of theorganization or somewhere in
senior leadership, you can startby being vulnerable and
(29:45):
explaining where you're at inyour learning journey and
sharing that with people andillustrating for people your own
experimentation in your roleand share what's worked well.
And the vulnerable part is toshare what didn't work well.
Right, a lot of people don'tlike that.
A lot of cultures kind ofreject failure.
In this, you know, kind ofautomated way, almost right, the
culture just like kills thefailure and buries it deep, deep
(30:06):
down.
Failures exist everywhere,obviously, but people don't
really inspect it, they don'tlook at it and they don't
necessarily celebrate it, butthey kind of evaluate it, they
learn from it.
They say, hey, like we had afailure here, this is what we
learned from it and let's figureit out.
The other thing that's a verypowerful culture change tool is
to start asking two questionsregularly.
(30:27):
One is why.
The other one is why not?
So the why question is reallyhelpful when you're talking
about current processes andcurrent products and current
kind of ways of doing things inyour culture.
Why do we do it that way?
And what do you hear often whenyou ask that question?
Mallory, what would you guessthat you often hear from people
when you ask them why do you doit this way?
Mallory (30:48):
Because we've always done it that way.
Amith (30:51):
Exactly, and that is not a good answer.
Right, that is an answer.
It's not necessarily a validreason to do it that way, but
that is how most things get doneright.
Of course, there's lots of goodthings that come from
repetition and refinement andsaying hey, like you know, the
Toyota production process right,One of the most effective and
efficient processes in the worldcame from a lot of refinement.
(31:13):
They haven't always done itthat way, but they've had that
process Right, and some of itactually can't be replicated
because it's inculcated into theculture so much so, more so
than a guidebook.
So I'm not saying that allcurrent processes are bad, but
what I am saying is ask thequestion.
Ask why?
Because by asking that question, you're opening the door to
discussion, as opposed to peoplemaking the assumption which is
(31:37):
the next part of you know thething I asked you, Mallory,
about like we've always done itthat way.
And then if you ask people,well, could we change it?
They'd say no, we're not goingto change it.
That's how we've always done it.
Right, Because ever sincethey'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,
(31:57):
the why question, regularly,people are going to go, huh,
maybe they are open to changingand maybe I could suggest
something different.
So that's one thing.
The other question is why not?
So when you start talking aboutdoing things differently, when
you start asking about could webuild a new service, a new
product, a new offering, Couldwe do a new process, why not?
(32:18):
Like, why not, in general, Like, is it possible to do right?
Do the laws of physics preventsaid activity from occurring?
Generally, not right.
So there's no first principlesrationale to why something is or
isn't happening.
So the why not?
Question is good.
And then a follow-up to why not,by the way, is why not us?
So we asked, when it comes toAI, education for association,
(32:42):
why can't a million people learnAI in the next five years, next
four and a half years?
There's no real good answer.
It's not that many people andeducation can be delivered
digitally and it's possible toreach those people because of
social media and advertisingdollars and whatever.
So why not?
There's really actually no goodreason.
The why is really.
It's really really important,right, We've said that.
(33:03):
But the why not really not agood answer.
And then we asked well, why notus?
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 sustainablebusiness 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 ofthe entrepreneur's credo in a
way.
But I think associations coulddo well to adopt those basic
(33:25):
questions in their culture.
Mallory (33:28):
What else I think is interesting is you and I have
discussed on the Pot of Meathhow typically in most businesses
there's kind of a laundry listof activities, things, goals you
would like to do, items on yourstrategic plan that you just
don't have time to get around to, and if you have staff within
(33:50):
your member services department,you know, allocated to
different activities becauseperhaps you have AI, augmenting
member inquiries, databasemanagement, so on and so forth
maybe you can have thosestaffers contribute to some of
the other activities you have onyour strategic plan, or goals
that you have or things you'venever gotten around to because
they've just seem impossible interms of time and resources.
So that's also interesting isthinking bigger, not thinking
(34:12):
just in terms of staff reductionbut thinking in terms of staff
opportunity as well.
Amith (34:17):
Totally, and that translates to organizational
opportunity.
You know, think about, likewhat most people are just
treading water, right, they'renot moving forward, they're not
moving backward, they're justkind of like happy to not be
sinking Right and because theyhave 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'renot like, you're not scurrying
(34:38):
about and treading water, you'reliterally know that you're
stable in that position.
Let's move forward.
You can start asking somereally interesting questions
like hey, mallory, you're amember services person.
You've been doing this for 10years.
You've talked to thousands ofour members over the course of
10 years.
What are some of their painpoints?
That we could build products orchange our experience in some
ways.
And you know maybe you know awhole hell of a lot about our
(35:00):
members, which a lot of themember services folks know way,
way better than like anyone elsein the organization, right,
because they spent all theirtime talking to them.
They can help build newproducts.
They can help build newexperiences all the stuff you
just mentioned.
It's exciting.
That's an opportunity to lookahead rather than just treading
water.
Mallory (35:21):
And getting staff engaged.
Getting your team engaged is agreat way to just increase
investment in the project as awhole and make everyone feel
like they have ownership as well, so I think that's essential.
Amith (35:29):
I will say one more thing on this thread that is an
important topic and I think thisis also uncommon to be
addressed directly in theassociation market and that is
culture fit.
And so if you are committed tobuilding a culture that's more
adaptable, more flexible, moreinnovative and looking ahead,
(35:49):
one of the things you're goingto have, you're going to run
into in most organizations, ispeople who don't want that, and
they may be lower level, theymay be higher level, but if you
as an organization are committedto moving ahead and you run
into that, by all means try tobring the people along, try to
help them understand what thevision of the future is, what
the organization's committed to,and that you're giving them a
hand to try to help themre-skill, re-tool, learn new
(36:11):
things, et cetera.
But sometimes you're gonna havesome people in an organization
that are unwilling not incapable, but unwilling to adapt.
They themselves are unwillingto adapt and unfortunately,
those are folks you're going tohave to say goodbye to.
If you keep them around, youwill undermine your
transformation.
(36:32):
It's that simple.
You cannot allow naysayers tostay in an organization if they
are fundamentally opposed to theidea of the change, if they are
unwilling to change themselves,and that is something that's a
pill a lot of associationsaren't willing to swallow,
unfortunately, but it's acritically important part of
culture change.
And this might be a person who'sbeen a fantastic employee for
(36:53):
years, but you've never askedthem to adapt.
And now that you have to adapt,you know the forces of the world
.
The rate of external changeright is so much greater than
your rate of internal change,which means you're out of date
and you have to change in orderto make the organization not
only viable but thriving in thefuture and to serve your mission
.
You're going to have to makesome tough calls, and that's
(37:13):
going to exist in everyorganization on the planet.
It's certainly going to happenin a lot of associations because
there's been, you know, so muchconsistency to say it nicely
over a long period of time, andif you want to drive some change
, you're going to have to lookat it very, very carefully Again
.
You know, do everything you canas a leader to bring these
folks along, but at some pointyou have to make a choice like
(37:34):
that, and you might havemultiple such scenarios and if
you don't make those choices,you're going to be anchoring
your organization in the pastand preventing yourself from
being able to move forward.
Mallory (37:46):
Everyone, thank you for tuning in to part two of this
two-part episode aroundAI-enhanced member services.
We hope you're feeling inspired, we hope you have some
practical next steps for how youcan get started, and we want to
remind you that at the end ofthe day even though member
services is kind of a phrasewe've thrown around a lot on
these two episodes that youreally owe this to your members.
(38:08):
If you were the anointed,trusted source of content in
your space, you owe it to yourmembers to reduce friction, to
get access to that content andto keep coming back to you.
So with that, we will see youall next week.
Amith (38:23):
Thanks for tuning into Sidecar Sync this week.
Looking to dive deeper?
Download your free copy of ournew book Ascend Unlocking the
Power of AI for Associations atascendbookorg.
It's packed with insights topower your association's journey
with AI.
And remember, sidecar is herewith more resources, from
webinars to bootcamps, to helpyou stay ahead in the
(38:45):
association world.
We'll catch you in the nextepisode.
Until then, keep learning, keepgrowing and keep disrupting.
And there will be two categories of people in the
future.
There will be people who are AIliterate, ai proficient and
even AI expert, and there willbe the unemployed.
Welcome to Sidecar Sync, yourweekly dose of innovation.
If you're looking for thelatest news, insights and
developments in the associationworld, especially those driven
(00:20):
by artificial intelligence,you're in the right place.
We cut through the noise tobring you the most relevant
updates, with a keen focus onhow AI and other emerging
technologies are shaping thefuture.
No fluff, just facts andinformed discussions.
I'm Amit Nagarajan, chairman ofBlue Cypress, and I'm your host
.
Greetings everybody and welcometo the Sidecar Sync, your home
(00:43):
for content every single week atthe intersection of artificial
intelligence and all thingsabout associations.
My name is Amit Nagarajan.
Mallory (00:53):
And my name is Mallory Mejiaz.
Amith (00:56):
And we're your hosts, and in this special episode we are
doing part two of two of AIenhanced member services.
In part one, if you haven'tcaught it yet we covered a
number of specific tasks or usecases where AI could have a
meaningful impact on the worldof member or customer or event
services all aspects of services.
(01:17):
So if you haven't checked outthat episode yet, I'd encourage
you to go back in time to thatepisode and check it out before
you listen to this one, becausethat will make this episode make
so much more sense.
Before we dive into part two ofAI Enhanced Member Services,
though, let's just take a momentto hear a quick word from our
sponsor.
Mallory (01:38):
If you're listening to this podcast right now, you're
already thinking differentlyabout AI than many of your peers
, don't you wish there was a wayto showcase your commitment to
innovation and learning?
The Association AI Professional, or AAIP, certification is
exactly that.
The AAIP certification isawarded to those who have
achieved outstanding theoreticaland practical AI knowledge.
(02:00):
As it pertains to associations,earning your AAIP certification
proves that you're at theforefront of AI in your
organization and in the greaterassociation space, giving you a
competitive edge in anincreasingly AI-driven job
market.
Join the growing group ofprofessionals who've earned
their AAIP certification andsecure your professional future
(02:22):
by heading to learnsidecarai.
Amit, we were just talkingabout how we're nearly to
episode 75 of the Sidecar Syncpodcast.
How does that make you feel?
Amith (02:34):
It's exciting.
I love doing this every weekand it's a lot of fun.
It's a great moment in timeeach 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 ofwork.
I know the best thing about itis I hear from listeners pretty
regularly saying that they'vegotten something of value from
(02:55):
this effort, and that's whatit's all about.
We're trying to help people intheir AI journey, so I find it
really rewarding and reallyexciting as well.
How about you?
Mallory (03:03):
I agree with you.
It's a very special moment.
It's kind of to take a secondprocess, everything that's going
on, have this conversation andthen 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 itbrings me so much joy to hear
you know random little tidbitsfrom my life that I've shared
(03:24):
that someone will say hey.
Mallory, how's Atlanta?
How's the wedding, all thesethings.
A good reminder, certainly, ofhow much I share on the podcast
too.
But it's always incredible tohear when someone says that
they've learned something new orthey tried out a tool that we
discussed, or suggest new topicsfor us.
So it feels crazy to think thatwe will have done this for
(03:45):
basically 75 weeks Kind ofinsane?
Amith (03:47):
Yeah, it is.
It is pretty cool, you know,and actually it's interesting.
We use the term learningjourney a lot, which is you know
.
My belief personally about justabout everything in life is
that you're on this continualjourney and learning is this
process, and so we want this tobe a valuable resource for folks
, particularly with their AIlearning journey but, more
broadly speaking, just theirjourney as a professional,
(04:09):
through their career journey andbeing part of that.
And many of our listeners havebeen with us pretty much since
the beginning.
I know some folks who have been, who've listened to every
episode of the Sidecar Sank, sowe appreciate that engagement
and that follow followers,listeners very, very much.
So thank you.
Mallory (04:27):
Yes, a million, a million thank yous to every
single one of you and, if you'renew, welcome.
If you've been here from thebeginning, we totally appreciate
you, and feel free to connectwith Amit and me on LinkedIn If
you ever have any topicrecommendations or anything like
that that you want us to cover.
We are always all ears and welove to tackle topics that we
hadn't considered prior, so feelfree to connect with us on
(04:51):
social.
All right, diving into today'spart 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 exploredways that AI can enhance your
member services department andfunction within your association
.
This part two episode is moreabout moving into action, so
(05:14):
talking about the foundationthat you need, kind of
technically and otherwise, to beable to do this kind of work in
your association, and then, asI mentioned in part one, we will
be kind of talking about thathuman element, that change
management element, which, yes,feels uncomfortable but is also
critical.
So now we're focusing on thosebuilding blocks that you need to
(05:37):
implement these AI enhancedsolutions effectively.
Most AI powered member servicesolutions, we would say, require
kind of three core elements.
First and foremost we touchedon this in part, one for sure,
but system integration,particularly with your AMS or
CRM.
This enables AI to accessmember data, interaction history
(05:58):
and organizational information.
You need to evaluate whetheryour current systems have
accessible APIs and whatintegration capabilities their
vendors offer.
Second, of course, data qualityand structure.
Ai is only as good as the datait works with Member profiles,
engagement history,communication logs, transaction
(06:18):
records.
These are going to be essentialand the key here is this data
must be accessible.
Third, knowledge management.
For AI to answer memberquestions accurately, it needs
to access your association'sknowledge base policies,
procedures, member benefits,event information and more or
(06:39):
even actual content that you'recreating.
This often requires documentinginstitutional knowledge that
may currently exist only instaff members' heads.
We hate to say it, but it mightbe true.
With some of these foundationsin place, we hope you can begin
implementing AI for memberservices with confidence and
starting where you'll see themost immediate impact.
So, amit, my first questionhere is we went through a lot of
(07:02):
tasks in the part one episode.
It can seem overwhelmingdaunting.
Where would you recommend thatan association start their AI
enhanced member services journey?
Amith (07:14):
Well, I do think your points about data are really
critical, mallory, and this ideaof system integration is super
important.
I would first actually saythere 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 AIdata platform in the center of
(07:37):
your ecosystem, where you'redoing all this stuff from and
you have real-time APIs updatingthat AI data platform all the
time from the source systems inthis beautiful way.
Yes, that's an ideal state, buta lot of times data doesn't
change that frequently.
So let's just say,hypothetically, you have a
legacy AMS and that AMS doesn'thave any APIs, but maybe it's
one of the really old-schoolAMSs where you have the database
(08:00):
located either on-premise or inan environment you control in
the cloud.
You can just take a copy ofthat database and then unpack
that entire database and justupdate it once a month or
something like that, and it'sprobably good enough.
So again, that's a little bitmore of a creative solution than
coming up with a more elegant,perfect solution.
But don't let perfect or greatget in the way of good enough,
(08:21):
because right now you probablyhave nothing in this area.
So that's one quick side noteit is actually probably my
answer to your question, in thesense of where you should start
is if your data house is inorder.
If you don't have data in anaccessible area where you can
run AI tools against it, youcan't really do a whole lot.
I do think, going back to partone of this two-part episode, we
(08:42):
talked about ways that peoplecan do things kind of with
consumer-grade tools right,using Cloud, using ChatGPT,
using Google's Gemini, and Iwould encourage folks to run
those experiments, to do thingsby hand, but with AI as their
assistant.
One transaction at a time,right.
So if you're going to write anemail, see if the AI can do it
with the right prompting.
(09:02):
If you're going to try torecommend products, try to do
that with an AI, but do it oneat a time because you don't have
the integration yet.
And then, of course, the goalis to automate these things and
to put a lot of integration inplace, but I do think that's one
of the things people get chokedup on really fast.
A related comment this is alittle bit of me getting on a
soapbox about it, but I simplysay this Don't replace your AMS
(09:26):
right now unless it is literallythe Titanic and you're going
down in the cold Atlantic in themiddle of the night.
Do not replace your AMS rightnow.
It is going to take you a year,two, three years.
It's going to take you lots ofsix-figure checks, maybe a
seven-figure check or twoseven-figure check or two.
It doesn't make sense becauseyour AMS isn't going to solve
any of this.
Your AMS is going to give you,sure, much better infrastructure
(09:53):
, but you're going to spend alot of time replacing something
where the net result is maybe a10% to 30% improvement in
functionality and in quality and, most of the time, you're not
going to get any of thesecapabilities.
Instead of that, what you coulddo right now is implement your
own approach to AI by extractingthe data from the AMS, doing
all these things that we talkabout on this podcast, and then
(10:13):
determining what you actuallywant your future AMS to be,
because that's the other pointis 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 youwant your AMS to do two years
from now, when you finally golive, because the world is
changing so incredibly rapidly,so kind of sucking it up and
dealing with it in terms ofhaving a legacy system.
(10:34):
Again, unless you literally arebleeding to death on the side of
the road, I would suggest toyou 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 resourceavailability thing, it's a
prioritization thing.
I talked to tons of CEOs ofassociations who are like I
really want to do more with AI.
I'm super frustrated with me.
(10:55):
How can I do this?
How can I do more?
And I'm like well, what'staking up so much of your time
right now?
They're like well, we're in themiddle of this AMS
implementation, or we're aboutto select a new AMS, or maybe
it's an LMS, right, but it'ssome large, really, really
important but not necessarilyurgent infrastructure project,
right?
So, again, you don't know yetwhat you will need in two years,
(11:19):
which is the timeframe for atypical go live in these
projects, and you certainlycould use those resources, both
time and dollars, in other ways.
And that actually solves a lotof the problems here, where you
get to actually do some deeperwork in the category of system
integration or data quality andso forth.
So to me it's a lot of that.
It's figuring out yourpriorities and then stopping
projects.
Even if, like let's say, youjust bought a new AMS and you're
(11:42):
starting to implement it, well,that doesn't mean you have to
keep going.
You can hit pause on that andcome back to it in six months
and do a bunch of AIexperimentation for the rest of
this year and then come back toit later in the year, and by
that point in time maybe yourpriorities have changed further.
But just because you made thedecision to go down a path that
may no longer make sense doesn'tmean 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 bemy follow-up question is what
about for the people who are inthe midst of it?
So you're suggesting to pause,if you can.
Amit, I saw a post on LinkedInand I wish I had saved it, but I
didn't know.
We were going to be talkingAMSs actually on this episode,
but someone was suggesting, ifyou're in the process of
switching to a new AMS, maybeyou actually don't need to bring
(12:27):
over all this historical datafrom your old AMS to your new
one, especially if you askyourself 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 datawith you.
But when we're talking about AI, enhanced member services, it
does seem like historical datacould be useful.
So kind of what would you sayin that instance?
I know I don't have all thedetails, but what are your
thoughts?
Amith (12:47):
Yeah.
So in my 20 plus years ofexperience in doing AMS work, I
would say there's three primarythings that kill projects.
Number one it's trying toreinvent the past, meaning like
you're trying to recreate theAMS 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 AMSfrom some vendor and they say,
oh, you got to recreate the waymy old AMS did.
(13:08):
It's like, didn't you just getdone, telling us in the sales
process that you hate that theway that it's done.
But reality is people get usedto what they're used to.
Right, that's the simplest wayto think about it is you get
used to what you're used to oryou basically become a victim of
the environment that you'vejust been in for a long time, to
put it another way.
So that's one issue.
(13:30):
The other issue is dataconversion, 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'mgoing to keep a copy of the old
system around so I can look atit if I need to go back beyond a
year, and that does simplifydata conversion because you
don't have to reconcile as muchof the data.
A lot of times the data comingfrom the old system is really
(13:53):
really bad and so it's hard toreconcile to begin with.
So there's kind of like thatbelief that you can kind of have
a cutoff period and that canhelp.
And then the final thing peoplereally get wrong with AMS or any
large system implementation isunder-investing in training.
So what ends up happening withthese systems is people go live,
they've, you know, customizedit in a way that they shouldn't
have.
They've figured out a way tofinally, like you know, slam
(14:15):
their data in, and then theydon't train their users and they
say, hey, good luck, here's twodays of training, god help you,
you know, and that's kind ofwhat 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 theyhaven't been trained not only
adequately but continuouslyafter they go live.
That's why I always say, likeit takes two to three years to
(14:38):
gain value from that investment.
Even if you go live in 12 to 18months, which is kind of the
typical timeframe, it usuallytakes you a full year after that
to actually start having netbenefit, because there's so much
incredible amount of pain thatpeople go through in these
conversions.
So, for all those reasonsbecause AI is doubling in a
six-month clip and all thisother crazy stuff we always talk
(14:59):
about on this pod you wouldprobably better serve to say hey
, this is going to take us.
Even if you just pull thetrigger and you just bought a
new AMS and you're holding thekeys to that brand new car, so
to speak, you might want to saythat's cool, but we're going to
hit pause for six months to doAI experiments, to work on our
process, because that's actuallygoing to inform what you
actually want that AMS to do.
So I always think there's a way.
(15:21):
You just have to be willing tostop and take a breath, and
sometimes that also means goingback to your board and saying,
hey, you know that seven figurecheck you just wrote for this
AMS.
It's not a throwaway, but we'rejust going to hit pause for six
months so that we can explorewhat's happening in the world.
Not all boards are going to befavorable to a statement like
that, but many will be if you goto them with that proactive
(15:43):
mindset.
Mallory (15:45):
For associations with limited resources, would you say
a common data platform or an AIdata platform is kind of that
minimum viable foundationnecessary to kind of implement
these kind of AI enhanced memberservices at scale, building on
quicksand, meaning like you'rebuilding all these AI systems,
(16:09):
rules, agents, whatever.
Amith (16:10):
You want to build on top of a moving foundation, because
your other systems are going toconstantly be changing.
You're going to get a newmarketing system, you're going
to get a new CRM, you're goingto get a new AMS.
Eventually, you're going to geta new financial management
system.
These are not things that areset in stone.
You don't want them to beeither.
You want those line of businessapplications to be capable of
being fluid so you can pick theright apps for your business,
(16:32):
and you probably have a lot ofthem and a growing number of
them, and so if you don't havesomething that brings your data
together, then your AI systemsare 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 isyou're pulling data from all of
those different source systemsinto one unified data ecosystem
(16:54):
and then you're building youragent strategy, your analytics
strategy, everything on top ofthis foundational layer that you
can rely on to be there overthe long run.
Obviously, given what we do,with Member Junction being the
data platform we put into theworld as an open source thing.
We obviously believe in that.
We've put a tremendousinvestment into that offering
(17:14):
and it's open source.
It's totally free.
Anyone can download it and useit.
That's the whole point of itbeing open source.
But whatever you use, you couldjust use like a straight up
database.
You could go buy a CDP or an AIdata platform from a vendor if
you wanted to.
Ultimately, it doesn't matterthat much.
If you have something good, youjust need to have that layer in
place, because if you build AIagents and AI analytics on top
(17:37):
of your source systems directly,you're asking for a problem.
Mallory (17:41):
Amit you and Blue Cypress have worked directly
with many kind of early adopterassociations that are
implementing personalization AIdata platforms Across the board.
Have you been able to identifyany common obstacles that
associations face in theseprocesses that you could share
with our listeners?
Amith (18:01):
To me, the biggest thing is the mindset.
That's like an accountingmindset 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, youwant your ledgers to add up
correctly all the terms rightthat are really important from
an accounting and financeperspective.
(18:22):
You wouldn't want to run abusiness without all those
reconciliations and rules downto the penny perfection
essentially all thosereconciliations and rules down
to the penny perfection,essentially.
But the real world of dataoutside of a given system like
that is extremely messy, and sowhen people try to unify their
data into any database systemwhether it's an AI data platform
or something else if you'regoing for that level of
(18:44):
perfection, you will almostcertainly fail.
So to me, that's the biggestobstacle is the mindset that you
need to bring your data in asit is, because if you try to
perfect your data before you getinto your data platform, it's
kind of like saying, hey, youknow, before we go to the modern
world, we're going to hang outin the Stone Age and we're going
to use hand tools.
You know we're going to likeliterally make our tools by hand
(19:06):
and then we're going to usethose hand tools to try to fix
this data problem over there.
Past the horizon, there's themodern world, and if we just
take all of our junky data withus, we'll have these really
powerful tools over there to fixit, which is what we're saying.
In an ai data platform, you havethis thing called ai that can
help you make your data a lotbetter right, whereas in a
environment where you don't yethave ai, you're basically back
(19:28):
over there hanging out withthese really crude handheld
tools and no power tools andcertainly no AI.
So get yourself over an AI dataplatform.
First, bring your data as it is.
As nasty as your data may be toyou, just bring it as it is,
dump it in there, and then let'sput AI to work to actually help
(19:48):
you cleanse it to some extent,but even if it still remains and
always is forever dirty.
From your perspective, thebeauty of AI is hey, I don't
really care that much.
Not really as much as you do.
You're disgusted by how manyduplicates you have.
You get really thrown off byhow your data is inconsistent in
a lot of ways.
The beauty of this technologyis it's finally able to really
(20:09):
make sense of the data, evenwith all of its imperfections.
So that is this kind of issuethat people are having is
they're taking this approachfrom traditional system
conversions, which is this kindof accounting reconciliation
mindset, to implementing an AIdata platform, which, of course,
is the prerequisite to doingpersonalization.
It is largely a prerequisite todoing something along the lines
(20:31):
of the core topic here, whichis how to drive AI enhanced
member services.
If you don't have your datahouse in order, you can't do a
whole lot of these things.
You can do some of the basicsright, but without that in place
, you really are limited.
Mallory (20:45):
And then, once you have that data house in order,
you've brought over all yourStone Age data into the modern
world.
What task or use case that wetalked about in part one would
you immediately go after, asthis is the one we're trying
first?
Amith (21:00):
I mean, to me, the number one thing I'd go after is
handling the rote inquiries thatcome in.
So people are emailing you orsending you messages all the
time that take a lot of effortfrom you to respond to.
You can solve that actually withvery little effort.
You don't even need most ofyour data in place, right.
You just need to have an AIagent that knows how to respond
(21:21):
to email.
And you need a knowledge agentthat understands your content
and understands your FAQs andall the content the same things
that your people would read inorder to answer the questions
themselves.
So that does require somethingin place infrastructure-wise and
it requires a couple of agentsone that's good at like the
asynchronous communication stuffand all the pieces and parts
(21:41):
that are important there, likesecurity and logging and so
forth, and you need a knowledgeagent in the mix.
So to me, that's the number onething because it's going to
solve even if it only solves 20or 30% of the inquiries that
come in.
It's going to make thoseinquiries so much faster and
better.
It's going to give you sometime back to then think about
what's the next 20 or 30% I canlob off.
Mallory (22:05):
Now it's time for the juicy part of the conversation,
which is talking about changemanagement and the human side of
all this.
So I feel like there's alwaysan elephant in the room with
these types of conversations,because you think about a world
where you revamp your memberservices function within your
organization.
You have AI agents respondingto member inquiries, managing
(22:27):
your database, proactivelyreaching out to members in a
personalized way andconsistently upselling and
cross-selling.
All that sounds amazing, butthe lingering question there is
well, what happens to the humanswho are doing that job?
We believe the human element isjust as critical to successful
AI implementation and memberservices as the technical
building blocks and memberservices as the technical
(22:47):
building blocks.
And, to be totally transparentand honest, we don't have all
the answers here, but we mustface these uncomfortable
conversations head on.
So our thoughts are one startby addressing concerns openly.
Staff may worry that AI willreplace their jobs, and that's
fair, but it can also transformroles in positive ways, handling
routine tasks, so your team canfocus on meaningful work
(23:10):
requiring human empathy andjudgment.
Also, of course, invest in yourteam's AI literacy and skills
development.
To AI-enable member services,you will absolutely need humans
involved in the rollout, themaintenance and quality
assurance.
We believe there's space forstaff to contribute to this
transformation, but they musthave some level of AI literacy
(23:32):
first and then also create newcareer pathways for evolving
member service roles AI trainerswho improve system responses,
member success specialists whohandle complex issues, and maybe
experienced designers whooptimize the service journey
across human and AI touch points.
The bottom line here, I think,is that AI will change member
(23:53):
service staffing needs.
Some routine positions may beeliminated or consolidated as AI
takes on repetitive tasks, butnew roles will emerge that focus
on AI oversight, complexproblem solving and strategic
member engagement.
We believe AI has the abilityto totally transform the way
members interact with yourorganization, but this
transformation does requirehonest conversations about how
(24:16):
roles will change, what newskills are needed and how you
will support staff during thistransition.
So, amit, how do you approachAI-enabled member services
through the lens of staffing?
What do you have to say there?
Amith (24:31):
Well, I mean, the first thing we have to really
reinforce is what you've alreadycovered, which is it is so
critical to train your people onAI, and if you don't do that,
you are not leading them.
That's it.
If you are not getting yourpeople trained on AI and not
pitching sidecars AI learning atthe moment, just to be clear,
(24:52):
like whatever any AI resource,there's tons of free stuff If
you're not pushing that andaggressively getting your team
up to speed on AI, you are notserving them as a leader, and
there will be two categories ofpeople in the future.
There will be people who are AIliterate, ai proficient and
even AI expert, and there and so, within your own organization,
(25:16):
it is your job as a leaderwhether you're an individual
contributor or if you're the CEOor on the board you have to
make sure your people aretrained.
So that's the most criticalthing Now.
If people have been trained andare continuing to train
continuously on AI, then theycan contribute to this, because
they're going to have theknowledge and the practical
experience to contribute tomaking the solution great and
(25:37):
they're going to level up.
But without that background,people are going to be lost and
they will 100% be running forthe hills.
They will be fearful, they willbe worried about what their
future looks like, because youhaven't taken the time and
invested the resources time anddollars in some cases to help
them figure out their role.
So I know I'm making kind ofthe same point repetitively, but
(25:58):
I can't emphasize it enough.
You know, just as a little sidenote, last week we published a
post I put it on my LinkedInprofile about the Sidecar MVP
program and in that post weannounced our goal, which is
really for us a deeply heldbelief and mission that we have
to go hit, which is that by theend of this decade by 2030, we
(26:21):
at Sidecar want to educate amillion people in this market,
and that's association staffprimarily, and also close-in
volunteers, meaning yourvolunteers that are deeply
involved in the associationboard, other volunteers that
work with you closely, andthat's obviously a very big
number.
We're not a huge company, butwe have big ambitions, and the
reason that particular goal isso important to us is we think
(26:45):
we're putting a little bit of adent in that problem right if
we're educating that many people.
We got a quarter million viewson that post on LinkedIn, which
is quite a bit more than what Iusually get when I post
something, and so clearly thatresonated.
Of course, the 15 MVPs that arepart of the Sidecar first
annual MVP program, which is aprogram we built to engage key
(27:07):
leaders in the market, to helpdrive that mission to reach more
and more people we'reinstrumental in that as well.
But the bottom line is reallythat simple.
It's that if we can bringeveryone along, then there's no
reason for fear.
This could be the greatestmoment 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 thatresponsibility seriously, then
(27:29):
we really are letting everyonedown as leaders, and that's the
most important thing is how wegrow our people.
So to me, that's the criticalthing our people.
So to me, that's the criticalthing.
And then, ultimately, the lastthing I'd say about all of that
is ultimately, the role thatsomeone is in is going to change
.
Not may change, it's going tocompletely change, and some of
the roles that currently existwill be gone.
(27:49):
Now, most associations aren'treally focused on reducing force
, reducing their workforce, toomuch, and in fact, even when
they have the opportunity, sousually they find other job for
people.
So I think there's a reallypositive aspect of this to look
for is what are the roles thatwill be both perhaps more
interesting but also just morevaluable for the organization's
(28:10):
mission itself.
So I get excited about this.
I know this is I'm in aposition that I'm not worried
about my job being replaced, etcetera.
So I obviously am insulatedfrom the kind of the raw emotion
side of it, but I think we justhave to hit this thing hard
directly on that.
It's the responsibility ofleadership to go help their team
figure this out.
Mallory (28:32):
I think of a scenario.
You know we're very fortunateacross the Blue Cypress family
of companies to have thisculture, that kind of prizes,
innovation and AI.
We talk about AI all the time.
So, amit, if you came to me andsaid, mallory, in your role,
we're going to automate thisthing that you do, we're going
to automate this thing that youdo, this thing that you're
working on, forget about it,because we're going to have an
agent assist you with thatprocess, I wouldn't be panicked,
(28:54):
right, because I I wouldn't bepanicked right, because I know
(29:21):
that that's kind of the cultureof the Blue Cypress family of
companies.
But I can imagine if anassociation leader goes to its
culture.
Amith (29:24):
Yet how they can kind of get there.
Yeah, I think you have torecognize at first what you just
said is important, that theculture you have doesn't mean
that's the culture you have tohave, right?
So you can start by makingincremental change in your
culture, really by leading byexample.
So if you're the CEO of theorganization or somewhere in
senior leadership, you can startby being vulnerable and
(29:45):
explaining where you're at inyour learning journey and
sharing that with people andillustrating for people your own
experimentation in your roleand share what's worked well.
And the vulnerable part is toshare what didn't work well.
Right, a lot of people don'tlike that.
A lot of cultures kind ofreject failure.
In this, you know, kind ofautomated way, almost right, the
culture just like kills thefailure and buries it deep, deep
(30:06):
down.
Failures exist everywhere,obviously, but people don't
really inspect it, they don'tlook at it and they don't
necessarily celebrate it, butthey kind of evaluate it, they
learn from it.
They say, hey, like we had afailure here, this is what we
learned from it and let's figureit out.
The other thing that's a verypowerful culture change tool is
to start asking two questionsregularly.
(30:27):
One is why.
The other one is why not?
So the why question is reallyhelpful when you're talking
about current processes andcurrent products and current
kind of ways of doing things inyour culture.
Why do we do it that way?
And what do you hear often whenyou ask that question?
Mallory, what would you guessthat you often hear from people
when you ask them why do you doit this way?
Mallory (30:48):
Because we've always done it that way.
Amith (30:51):
Exactly, and that is not a good answer.
Right, that is an answer.
It's not necessarily a validreason to do it that way, but
that is how most things get doneright.
Of course, there's lots of goodthings that come from
repetition and refinement andsaying hey, like you know, the
Toyota production process right,One of the most effective and
efficient processes in the worldcame from a lot of refinement.
(31:13):
They haven't always done itthat way, but they've had that
process Right, and some of itactually can't be replicated
because it's inculcated into theculture so much so, more so
than a guidebook.
So I'm not saying that allcurrent processes are bad, but
what I am saying is ask thequestion.
Ask why?
Because by asking that question, you're opening the door to
discussion, as opposed to peoplemaking the assumption which is
(31:37):
the next part of you know thething I asked you, Mallory,
about like we've always done itthat way.
And then if you ask people,well, could we change it?
They'd say no, we're not goingto change it.
That's how we've always done it.
Right, Because ever sincethey'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,
(31:57):
the why question, regularly,people are going to go, huh,
maybe they are open to changingand maybe I could suggest
something different.
So that's one thing.
The other question is why not?
So when you start talking aboutdoing things differently, when
you start asking about could webuild a new service, a new
product, a new offering, Couldwe do a new process, why not?
(32:18):
Like, why not, in general, Like, is it possible to do right?
Do the laws of physics preventsaid activity from occurring?
Generally, not right.
So there's no first principlesrationale to why something is or
isn't happening.
So the why not?
Question is good.
And then a follow-up to why not,by the way, is why not us?
So we asked, when it comes toAI, education for association,
(32:42):
why can't a million people learnAI in the next five years, next
four and a half years?
There's no real good answer.
It's not that many people andeducation can be delivered
digitally and it's possible toreach those people because of
social media and advertisingdollars and whatever.
So why not?
There's really actually no goodreason.
The why is really.
It's really really important,right, We've said that.
(33:03):
But the why not really not agood answer.
And then we asked well, why notus?
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 sustainablebusiness 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 ofthe entrepreneur's credo in a
way.
But I think associations coulddo well to adopt those basic
(33:25):
questions in their culture.
Mallory (33:28):
What else I think is interesting is you and I have
discussed on the Pot of Meathhow typically in most businesses
there's kind of a laundry listof activities, things, goals you
would like to do, items on yourstrategic plan that you just
don't have time to get around to, and if you have staff within
(33:50):
your member services department,you know, allocated to
different activities becauseperhaps you have AI, augmenting
member inquiries, databasemanagement, so on and so forth
maybe you can have thosestaffers contribute to some of
the other activities you have onyour strategic plan, or goals
that you have or things you'venever gotten around to because
they've just seem impossible interms of time and resources.
So that's also interesting isthinking bigger, not thinking
(34:12):
just in terms of staff reductionbut thinking in terms of staff
opportunity as well.
Amith (34:17):
Totally, and that translates to organizational
opportunity.
You know, think about, likewhat most people are just
treading water, right, they'renot moving forward, they're not
moving backward, they're justkind of like happy to not be
sinking Right and because theyhave 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'renot like, you're not scurrying
(34:38):
about and treading water, you'reliterally know that you're
stable in that position.
Let's move forward.
You can start asking somereally interesting questions
like hey, mallory, you're amember services person.
You've been doing this for 10years.
You've talked to thousands ofour members over the course of
10 years.
What are some of their painpoints?
That we could build products orchange our experience in some
ways.
And you know maybe you know awhole hell of a lot about our
(35:00):
members, which a lot of themember services folks know way,
way better than like anyone elsein the organization, right,
because they spent all theirtime talking to them.
They can help build newproducts.
They can help build newexperiences all the stuff you
just mentioned.
It's exciting.
That's an opportunity to lookahead rather than just treading
water.
Mallory (35:21):
And getting staff engaged.
Getting your team engaged is agreat way to just increase
investment in the project as awhole and make everyone feel
like they have ownership as well, so I think that's essential.
Amith (35:29):
I will say one more thing on this thread that is an
important topic and I think thisis also uncommon to be
addressed directly in theassociation market and that is
culture fit.
And so if you are committed tobuilding a culture that's more
adaptable, more flexible, moreinnovative and looking ahead,
(35:49):
one of the things you're goingto have, you're going to run
into in most organizations, ispeople who don't want that, and
they may be lower level, theymay be higher level, but if you
as an organization are committedto moving ahead and you run
into that, by all means try tobring the people along, try to
help them understand what thevision of the future is, what
the organization's committed to,and that you're giving them a
hand to try to help themre-skill, re-tool, learn new
(36:11):
things, et cetera.
But sometimes you're gonna havesome people in an organization
that are unwilling not incapable, but unwilling to adapt.
They themselves are unwillingto adapt and unfortunately,
those are folks you're going tohave to say goodbye to.
If you keep them around, youwill undermine your
transformation.
(36:32):
It's that simple.
You cannot allow naysayers tostay in an organization if they
are fundamentally opposed to theidea of the change, if they are
unwilling to change themselves,and that is something that's a
pill a lot of associationsaren't willing to swallow,
unfortunately, but it's acritically important part of
culture change.
And this might be a person who'sbeen a fantastic employee for
(36:53):
years, but you've never askedthem to adapt.
And now that you have to adapt,you know the forces of the world
.
The rate of external changeright is so much greater than
your rate of internal change,which means you're out of date
and you have to change in orderto make the organization not
only viable but thriving in thefuture and to serve your mission
.
You're going to have to makesome tough calls, and that's
(37:13):
going to exist in everyorganization on the planet.
It's certainly going to happenin a lot of associations because
there's been, you know, so muchconsistency to say it nicely
over a long period of time, andif you want to drive some change
, you're going to have to lookat it very, very carefully Again
.
You know, do everything you canas a leader to bring these
folks along, but at some pointyou have to make a choice like
(37:34):
that, and you might havemultiple such scenarios and if
you don't make those choices,you're going to be anchoring
your organization in the pastand preventing yourself from
being able to move forward.
Mallory (37:46):
Everyone, thank you for tuning in to part two of this
two-part episode aroundAI-enhanced member services.
We hope you're feeling inspired, we hope you have some
practical next steps for how youcan get started, and we want to
remind you that at the end ofthe day even though member
services is kind of a phrasewe've thrown around a lot on
these two episodes that youreally owe this to your members.
(38:08):
If you were the anointed,trusted source of content in
your space, you owe it to yourmembers to reduce friction, to
get access to that content andto keep coming back to you.
So with that, we will see youall next week.
Amith (38:23):
Thanks for tuning into Sidecar Sync this week.
Looking to dive deeper?
Download your free copy of ournew book Ascend Unlocking the
Power of AI for Associations atascendbookorg.
It's packed with insights topower your association's journey
with AI.
And remember, sidecar is herewith more resources, from
webinars to bootcamps, to helpyou stay ahead in the
(38:45):
association world.
We'll catch you in the nextepisode.
Until then, keep learning, keepgrowing and keep disrupting.
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