Building AI That Works for Post-Sales: Uncovering Key Use Cases
In this session, we'll explore the unexpected and surprising use cases for AI in post-sales. Join us to expand your thinking on what AI can do and walk away with fresh ideas on how to leverage it in creative and unconventional ways in your own work.
>> Good afternoon, everyone.
Hi, welcome and thank you for coming.
We have the last session of the day.
So Jared and I will introduce ourselves.
We might do something a little bit different to get you guys involved too.
Yay.
But it's a nice one welcome.
My name is Rachel.
I am one of the directors of
customer success here in A-MARE.
So I see some friendly faces out there as well.
And I'm joined on stage with Jared.
>> Yeah, nice to meet everyone.
I am also a director of customer success.
I lead a few of our product lines and globally.
So for any community users out there,
I lead the team that manages that.
>> Fab. All right. I've got the power.
I've got the clicker.
>> We've got the power.
>> Okay. So today's session,
what we're going to be doing is about full pillars to our discussion.
We'll be talking about strategic use cases.
We'll also be looking at management use cases when it comes to AI
and customer success in the post sales world.
We'll also look at the lens of the CSM or the front line.
And then we'll talk a little bit about change management as well.
And we'll stop at each of those different points.
And I think it'd be nice for us to really spark ideas and maybe
collaborate around how you guys are using AI as well.
>> Perfect. Yeah. So we want to kick it off with
is how many of you feel like you've nailed AI for CS?
We can just get a raise of hands.
Like no questions at all.
You got it locked down.
If so, I think we'd all love to learn.
This is something that we're all everyone is learning in real time.
Since AI is evolving so much and we're going to get more into
some of the findings, some of the learnings that we've had as well.
And hopefully you can apply those.
But as Rachel mentioned, we want to make it as conversational as we can.
And I think we also want to learn from the audience.
And I'm sure that together we can come up with some really excellent ideas.
>> I've got another one.
>> Yeah. And then some of the other topics in just out of curiosity.
Maybe we'll get some hand raise this time, but we'll see.
How many of you have explored AI for CS?
>> Okay.
>> Yeah, excellent.
So the large majority of us are looking at AI as a way to drive efficiencies,
both for ourselves individually for our organization and for our teams.
So that checks out and that's about what we would expect that ratio.
>> All right, so after this session, you'll have nailed it.
>> Yeah, no questions.
>> All right, so just starting off, we actually ran a survey earlier this year.
And if you do want to dig a little bit deeper or you haven't read it before,
it is the state of AI in CS index report.
And what it can show here is probably what we would have guessed.
Where people are utilizing AI to help with activities such as analyzing data,
looking at reports and trying to translate that into insights.
No surprise as well, we're using AI to dive deeper into our customer knowledge.
So that we can then have better conversations, we can start planning better,
we can start to become more of that strategic advisor.
Interestingly, and I would recommend you do this in your own organization.
We ran a survey on gainsters.
And we asked them what are the top ways that you guys are leveraging AI?
And we got some surprising results from personal growth,
which I'm a huge advocate for, to wellness.
But of course, some not so surprising results in terms of problem thinking.
And so I know there's been loads of tracks around what are some of the key
skills sets for CSMs and what we're seeing within our own organization is that
we're utilizing AI to really try to sharpen our skill sets.
All right, I'm going to talk a little bit about the strategic use cases.
And the first use case here is how are we using AI to draw better insights?
And some of the examples that we've put up top here,
the one that I get quite excited about is actually looking at redefining,
I guess what is our ideal customer profile?
What does good look like?
When we're coming into the new year we're doing planning,
we're looking at resegmentation, we're looking at our engagement models.
One of the core use cases of AI that I've utilized is actually looking at the
data and
trying to be able to provide back to the business a case around resegmentation
and
also trying to better define IRICP.
Double clicking into that, we are of course using our own tools in order to
look at
things like team effort.
So basing it on customer ARR and the effort that team members are putting into
this account, you have to have those conversations to say,
is this really where we're putting our effort?
And what's the return of investment in the effort that we're putting?
So that's one of the key things that I've been utilizing over the last couple
of
months when I'm looking at planning for the next financial year.
Secondly, I think a lot of us traditionally have been using our gut instincts.
We've been using conversations with customers, conversations with our teams,
but that can take us so far.
But I think we've got really, really good instincts because we know what we're
doing and we are there in those customer calls.
What I found is so helpful is using AI to validate some of these statements
that I'm sending out back to the business around product development,
around what measures in our health score do we need to capture.
And so that's a real key use case is how do I have all of these gut instincts
that I know about my customer or I know what my team needs?
And how do I use AI to surface data from different areas to validate them and
bring them back to the business?
>> Yeah, now I'll add on the, can I prove my perceptions to product sales?
I think this is an area where CS has so much knowledge, but
it's how we communicate back to other functions and leaders and how they
receive
that information.
So think of like intent versus impact.
And we'll go into conversations as CSMs, as CS leaders thinking, okay,
I'm gonna rock this meeting, I'm gonna kill it, I'm gonna show sales,
I'm gonna show product exactly all of our challenges and our opportunities.
But it's how we present it.
It makes all the difference and AI is a great way to be able to train a GPT and
have it interact in the way that maybe a sales leader would interact or
product leader would interact.
And then you can stress test your communication style, your messaging.
And GPT will actually show you what the response might be or
what might be a representative response so you can tailor it accordingly.
That's been a big value.
So an example here is, so recently I came back, or a couple of months ago,
I came back from return to leave.
And I sent over a state of the business to my leadership team.
And I utilize staircase in order to help me validate some of those
churn risks or reasons.
And this was really gratifying, but
it actually helped me have more confidence in saying I'm responsible for
the voice of a mayor and I'm using our tool and our data to provide that
voice back to the business.
So it's really allowing me to have a stronger voice of the business, in my case
a mayor.
All right, I've got a poll.
But I mean, we could do a poll or I've got a microphone.
And so I've got a question of, well, how many of you are leveraging AI for
strategic use cases, but who's brave enough or who would like to join the
conversation?
See actually from a strategic standpoint, what use cases you've been using AI
for?
Nobody?
All right, that's okay.
>> It's very reminiscent of me in high school at the college.
>> [LAUGH]
>> That's okay, that's okay.
Maybe we'll do the poll and we'll see there.
>> Yeah.
[BLANK_AUDIO]
Does anyone have any guess on what percentage of the audience is saying,
yes, we're going to do, wherever you do this today?
Well, not much guesswork needed.
>> Jerry's behind you.
>> [LAUGH]
>> It's been a long day.
Okay, so we're starting to explore these use cases, we're getting there.
I'd be really keen to see in six months time if we actually did this survey,
where we're at and potentially some of the use cases that are coming out.
All right.
It's a little bit higher in terms of no, we don't use AI yet for this.
So that would actually be, I would be really keen to see some takeaways from
Pulse today, say actually how can I in the leadership position that I am in?
How can I utilize AI within your products or
gain site to help make your lives a little bit easier?
I read over to you my friend.
>> All right.
All right, I'm going to touch on management use cases for AI.
And again, Rachel, feel free to chime in on your perspective here as well.
So when we think about how as a manager to use AI to manage teams, my team's
time,
one of the key areas that we look at, we look at three, and it's streamlining
workflows.
So there's opportunities to drive efficiencies there to use AI to
understand what your existing workflows are.
And then get feedback on maybe ways that you can tweak it.
Sometimes as managers, we're so far into our challenges that it's difficult to
take a step back and look at it objectively.
And that's something that I personally use AI for,
to challenge my frame of thought, to get me thinking outside of my box,
and look at it from an outside in perspective.
We also use it to surface up risk signals within our CSM customer base.
So my CSM's now, they'll look at their customer base and
they look at the volume of customers, they have all the interactions.
And then I come over the top and I say, hey,
what customers are at risk within these CTA's?
Which ones do I really need to pay attention to?
And AI has helped us drive a ton of efficiencies around that.
Now, the other one is helping my team get better and faster in their day to day
So again, when we think about, from a management perspective,
this one hits home for me, because we ask so many, or so much of individuals,
generally as a society, but also as managers of a CS team in particular,
we're asking so much of those individuals.
And how I communicate this is I want AI to help them regain some of their
humanity,
both at work but also outside of work.
So if they can take a 45 hour work week, a 50 hour work week,
and drive efficiencies there to get five hours back,
even to get 45 minutes back, where they can take a walk outside, where they can
spend
time with their family, whatever that is for that individual,
then AI has done its job, in my opinion.
And that alone has a tremendous impact on the outcome that they bring to work
as well.
And then how I use it to manage my time, very handsy when I talk,
I'm going to hit my microphone quite a bit, I imagine.
But prioritizing engagement, so again, similar to the CSMs, and
how they use that to analyze their risks and identify which of their customers
really need a lot of attention for whatever reason.
I also use it to look across the entire book of business.
And between my CSMs, Gutcheck, and the AI analysis is being drawn, and my
analysis,
both of us are able to kind of come together and identify truly where do we
need to pay attention.
I think of it similar to a, so I have a background in sales somewhat.
And it's, again, say we, sales reps are going to have a forecast, but
then our pre-sales CMR solution consultants also share their perspective.
And it's a bit of a gut check, and we use it to normalize and say, okay,
this is what reality actually looks like.
And on risks, we do the same thing internally.
So I have my perception, CSMs have their perception, and there's always some
delta.
And then AI helps us normalize that and hone in on where we need to be spending
time,
what truly matters.
And then, again, they're able to drive efficiencies in their day to day.
Now, we also use it to support CSM onboarding.
So we'll take, gone calls, we'll take emails,
just interactions that we have internally, trainings that we've done.
And I'm able to take that and consolidate it, generalize everything, but
consolidate it into what is like an ideal CSM for where we are right now look
like.
Where do I need that individual focused on?
How can they have the biggest impact in their day to day for our company and
for
our customers?
And I use that to kind of frame out what a 30, 60, 90 day framework might look
like.
And then I have individuals, obviously on my team, but across Gainsite,
who can help enable that against that.
And then meeting summaries.
So I'm sure everyone is doing this, gone does it.
Just about every company we do it, we're in our CS instance,
where we'll take call recordings, we'll analyze, give a meeting summary,
we have AI cheat sheet, et cetera, take all that information.
And I can't tell you how much time I have saved by having those AI summaries in
place.
And frankly, they're pretty incredible.
They're accurate, you have to review them a little bit, but
they get 85, 90% of the way they are sometimes spot on.
And that has been a massive impact.
>> I'd say just adding QBRs.
It's a huge thing that we do or EBRs, we do with our customers,
but we do it internally as well.
So every quarter we're reflecting, we're looking at what went well,
it didn't go well, and it used to take me hours to, well,
a minute hours, but it took me a long time to create my own QBRs for my team,
identify trends, lessons learned.
Using AI has really helped me look good in front of my own team.
So thank you for that.
But it also then seriously is enabling me to help identify the trends,
but more importantly, our asks back to the business.
But then we also have created a custom EBR bot within Gainsite.
And so a lot of the enabling part of my job, which I do love,
I love enabling my team.
The bot almost does for me.
So we do ask here, how do we streamline EBRs so
that we are delivering some sort of consistent experience, but
we're able to tailor it to the customer.
We use our custom-made bot in order to generate a really strong agenda,
template, context, customer stories.
And that saved a lot of time in terms of maybe repetitive enablement that I've
had to do.
What I'll add on.
I will please that.
The efficiencies that Rachel gained, what that means for her team,
just keep doing it.
A teaser for me is when I think of AI, I think AI's greatest gift is going to,
it's going to reorient all of us back to our human nature.
It's going to drive the connections.
We're going to get pulled so far one way and not the other way that we're
going to miss those human interactions.
And I think we're going to start craving those again.
And I think the time savings that a manager gets allows us to then rebuild
those connections with our CSMs.
And we can start to get to know the individual, not the individual behind the
computer, but what do they like outside of work.
And that as a manager is what's going to allow you to change from managing your
team to leading your team.
And the leadership is what individuals are looking for.
And right now with everything being asked, again, managers often have,
as busy as CSMs are, managers are 0.5x that at least.
And we need to be focusing on that human element and that human connection.
And AI allows managers to have that time back.
And then we already touched on this a little bit.
But one way that we do that is through our co-pilot.
So again, we drink our own champagne, so to speak.
And we use this to kind of quickly assess where should my focus be?
What are when I'm going into a conversation with the customer?
What are the top priorities that they need to be focused on?
What are the gaps?
What do I need to know?
And co-pilot for us is a great tool to go ahead and do that with.
If you haven't checked it out in your CS customer, I highly encourage it,
because you're going to be able to drive tons of efficiencies there.
Without we're going to jump to our next poll, we're just going to go right to
Slido this time, I think.
So how many of us are leveraging AI for management or team oversight use cases?
[BLANK_AUDIO]
So yeah, no we don't.
Okay, yeah, that lines up.
I'm going to ask it, and this is the last time Rachel and I will ask.
But for the group that said yes, we do this today, I'm curious,
is there if somebody's willing, is there something that you're doing today that
you'd like to share with the audience that Rachel and I did not cover?
All right.
>> Yes.
>> I'm going to keep trying.
>> Okay.
>> Yeah, perfect.
[BLANK_AUDIO]
>> Thank you.
Now I'm really doing this, great.
[LAUGH]
No, it has nothing particularly to do with CS, but we do, or
I at least tried to create a larger prompt by helping,
by getting help from my proper AI guys in the company.
That helps me to conduct very comprehensive feedback meetings.
So it leads me through the whole sort of reverse interview process.
And afterwards I get a very comprehensive feedback table as we do this nine box
approach, and usually I would sit in front of an Excel and
have just a few topics and pillars, but no guiding questions and nothing like
that.
So it is a very important task, but it's also a very time consuming task.
But with this prompt, it guides you through the process step by step,
it's a GPT, and afterwards you have a really comprehensive feedback, and
you have the result already.
And I do this with voice to text, that's really powerful.
So I do this on the go, and I get already guiding questions for each of these
pillars, so I get a comprehensive feedback done in 15 minutes, and
it looks like I would have spent hours.
And the result is even better than it was before, so that's quite neat.
>> I love it.
Can you say your name and what company are?
>> Daniel from Drums in Germany.
>> Nice.
Anyone else want to spark a thought?
Yes.
>> I can share from the Slido team.
So our team of CSMs as well, we do quarterly reflections.
So some things super interesting that you mentioned is getting to know people,
their individual goals and their career as well.
So I use AI to build that quarterly reflection.
So I feed the data of our one on ones throughout that quarter, and
then I get really specific questions based on what I want my team member to
either improve what I should highlight and what are maybe the next steps or
opportunities that they could look into.
So it's quite handy.
>> Thank you.
>> Yeah, it's another excellent example.
We'll move on in a minute, but just thinking about this,
I also as a director of a global CS team,
I'm from the United States, there's so much like, it works in the US,
it's automatically going to work somewhere else.
And that's, I mean, you all can attest to this, that's obviously not the case.
So I use GPT to help stress test again.
As we go into other markets, what is the reality of those markets?
What are some of the nuances just staying on top of it?
And not making any assumptions going into a new area, but
really helping GPT or GPT helps me get a very comprehensive download of
what that market looks like, what's going to be relevant, what specific,
what's unique, so that we're taking a very thoughtful approach in our
interactions.
And we're not just blankly assuming one thing's going to work globally.
And GPT, historically that's taken a ton of research.
It's required a lot of cross functional effort and
GPT just is able to synthesize it and give me the quick highlights where we can
make quicker moves and still be an extremely thoughtful team.
And how we're interacting in the way that we're interacting.
Perfect.
Back to you, Rachel.
>> Yes.
>> Run, run, run.
>> Okay.
So I'll cover from the lens of a CSM.
And I'm sure you guys also have quite a few use cases from your team members
too.
Surprise, surprise using AI to, as we say here, eliminate the groundwork.
But a lot of what I've seen, especially from my team,
is that saving CSM meetings are called prep time.
That's huge, but also when we talk about meetings,
it's the preparation, it's the meeting itself, and then it's the follow up.
And the follow up is always a bit of a pain in the ass.
>> [LAUGH]
>> If I could say that.
But AI, what we're seeing is having that transcript translated straight into
our
product, so we have automated summary.
Then we have also generated suggestions around what are the tasks or the
actions.
I found, or my CSM team have found that's super useful.
Also to the extent of translating sentiment enthusiasm,
I speak quite enthusiastically, so it's even, I read transcripts for
meetings that I've been with my team where it's like Rachel,
enthusiastically said X, Y, and Z.
So it goes down to that level, but that really takes a burden to an extent off
a CSM.
I don't think it should eliminate it.
I don't want my CSM to become lazy, they should still know and
understand the transactions of a meeting and the outcomes.
But there's a lot that goes on, and so for us to have the technology for
them to have that mini CSM already on their shoulders,
being able to translate automatically what's been going on in engagement has
been a life changing item for them.
I also have been able to see really quick efficiencies in terms of answering
questions.
I really do hate answering, I don't know right now, but I'll get back to you.
And I think with AI, we are utilizing the ability to use things like
co-pilot in our own product to ask the questions on the fly and get those
answers.
So we can just move forward.
So it's really blocking out some of the space that occurs between an
ask from a customer and the answer.
>> I'll add one thing here is, and this goes back to what we were saying before
around CSM onboarding.
The onboarding and getting them used to your policies or
your processes internally is one thing.
It's a whole other thing to say, hey, here's your book of business,
I need you to get caught up so you can start owning that book of business and
driving those customers to be successful.
If for all the customers out there, or whoever, anyone who's dealt with the CSM
where you've had a CSM and then that CSM has changed,
there's a feeling on your end that you're the one who has to get that new CSM
caught up to speed and onboard them.
And that's an incredibly frustrating experience.
And AI is a great way, especially if we look at summarizing 39 timeline posts.
The fact that cheat sheet can go through and get the new CSM caught up to speed
on what's important not in the last two weeks, but in the last year plus.
Because that historical context is so important to the broader relationship.
And now that CSM can go into the call and add immediate value in that first
interaction and yeah, there's still learnings that have to be done.
You're still building a relationship.
AI is never going to just take away the relationship building side, but
just the fact that CSM can now go in and add immediate value and
not cause a six month delay as they're getting caught up is immensely important
>> And also just on that, a lot of what we do is stakeholder alignment.
And this comes into play from when there's opportunities, but also when there
are risks.
And of course it's not just on the CSM to identify, document,
manage and escalate risks.
And so there is literally now no excuse when a exact
a leader comes in and says, okay, Jared, film you on and everything.
A CSM can just point and say, I'm not going to have my glad things to do.
Go look at the cheat sheet and timeline, everything's there.
CSM feels so powerful when they do that.
I'm like, yes.
>> By the way, we've never had that ask from Nick.
Never.
>> [LAUGH]
>> This is actually a really cool example for one of your PX CSMs, I believe.
>> Yeah, yeah.
>> And so what she did, and I think this is a spotlight of success plan
creation.
And beforehand, we would go on a call, we would work with post-QBR,
EBR, we might have a kickoff call.
And we would then go away, it might take a couple of days for
us to create a success plan, send it off to validate to a customer, and
then we might then present it back to the exact team.
What this CSM did was she used a transcript from our GONG recording,
and she put it into a custom-made bot.
She then looked at her customers' website, looked at the values.
She also looked at LinkedIn on her stakeholder, and
sometimes they have what they're working on and what their achievements are.
She fed all of that data into this bot, asked it to create a success
plan based on the information.
And out popped a information around a success plan that she then put into
GANGSite.
So that took a mere couple of, well, of course she had to do the transcript,
etc, and the meeting, but that took them five minutes, potentially.
And it's our job to go to our customers with hypotheses.
We want to be able to say, based on what we know,
these are some of our recommendations of the goals that we want to work on
across the next six months to a year.
Let's validate that with you.
She's not going to just say, look, I've put everything into this bot,
now we're going to do this.
But this is her way of validating and
hypothesizing with her customer based on five minutes of work.
So I thought that was pretty cool from your team.
>> Yeah, no, let's slide on.
It's a huge impact.
>> Yes.
We've touched upon this, and I think you've also looked at this bit about the
handoff from CSMs.
But I generally when I ask my team how we're using AI to date,
they're saying that it enables them to have much better conversations with our
customers because they know a lot more, or they've got that wealth of
information there.
We also actually, and again, going back to stakeholder alignment,
AI is uncovering what CSMs are doing, but
also internally what other teams are doing across touch points,
across their customer journey.
And so we're utilizing things like a relationship map to better plan and
identify, okay, Liam from CS, Benjamin from product,
we have these relationships.
We're all invested in the success of this customer.
So it's helping my team strategically organize a plan and
utilizing existing relationships internally to then break into the customer.
So we've actually seen this work really well, because it's not only the CSM
that
will be carving, nurturing relationships, it's all of the people that touch the
customer.
I read.
So our last poll is, how many of you are leveraging AI for
CSM use cases?
And yes, we have the same answers here.
Okay, shot up to yes.
[BLANK_AUDIO]
>> A little closer.
>> Yeah, okay, so we've got maybe more of a spread on this one.
I kind of want to get you guys involved again.
>> I won't press yet, I won't press it.
>> I'm curious to know why for this poll we're using it today for
CSM use cases, but we're not using it for the other use cases.
Does anyone have any hypothesis?
>> Because we don't have the choice as you have the same.
>> Yeah.
>> But you mean?
>> I mean, it's a tool, I mean.
>> Yeah, yeah.
>> So we can, it's much easier to cover CSM use cases than to run
insights called a management because I will need to plug in the entire game
site
to get insights so I wouldn't need to compile it and do that.
We don't have it yet.
>> Okay.
>> Here are A/E team just running.
>> [LAUGH]
>> Maybe you have more CSM to the movement than Medicaid?
>> Might be true.
>> Might be the next question.
>> Perfect, so I think we'll talk about what's something very important when
we're rolling out anything, change management.
So hand it over to you.
>> Yeah, thanks Rachel.
So AI change management fundamentally is no different than any other change
management.
At the end of the day there's four key things that you need.
You need executive sponsorship to drive that change management and to have buy-
in.
You need to know what you're doing it for.
So what's going to be the ROI of this effort at the end of the day?
Again, that's no different fundamentally than any other technology.
You need the internal training, so you need to give your team, yourselves,
the time to be able to invest in it.
Just like anything else, if you're not willing to invest in the training,
you're not going to get the value out of it.
And then you need to be able to iterate.
So be dynamic, be willing to learn, and make quick changes.
So say, hey, this isn't working.
We're going to try it out for a week for a month.
It's not working, OK, we're going to make small tweaks.
Then we're going to pivot from there.
So these four things fundamentally, when we think about change management and
AI
specifically, these are your anchor points.
Now, lessons that we've learned-- and I'm just going to touch on a few of these
-- but lessons that we've learned with what works well.
So the executive alignment.
So strong leadership that is recommending that we adopt this.
So hopefully everyone attended the keynote.
And you heard Nick.
Nick is extremely passionate about it.
So our efforts internally started as a top down.
So it was Nick driving AI home within all of Gainsite.
Function didn't matter.
Interest didn't matter.
It was we have to learn how to adopt it.
So if you're not interested, then find a way to get interested.
And that doesn't necessarily mean Gainsite.
The beauty of Nick is he thinks so outside of the box
that if you can't think of using AI for your job, which
should be something that we're thinking about,
then he'll tell you to think about it in a personal use case.
But just get your hands on it.
Just start experimenting with it.
So that pressure came a lot from Nick
and we're extremely thankful as an organization that it did.
And then it's going to be--
I'm going to kind of touch on both of these two.
So training and skill development.
So we deployed a really thoughtful training program
internally for our employees.
Again, the function didn't matter.
But we heavily invested in having a couple of thought
leaders who expressed a high level of interest early on.
And then they developed a training program
so they could get everyone else in the organization up
to speed.
So now we're all at parity.
And that was really important and Nick and our leadership team
supported this effort.
So individuals who signed up for it, they asked.
They said, look, if you're going to do this,
don't make a joke of it.
Clear out your calendar and make sure you're attending.
And if you miss a meeting, then we're going to know.
And you might get kicked out of that program.
It was a very, very serious initiative internally
to make sure that we were fully invested in AI,
knowing that that's the direction,
and knowing the impact that it can have on individuals' lives,
our teams, and the business, and most importantly,
our customers.
And then space for experimentation and learning.
This is the one we so often, when change management occurs,
it's like, OK, we have one shot.
We're going to hit it, or we're not.
And that's not the way that we need to think about it.
You're going to start down a path.
You're going to experiment with it.
We're going to experiment with different use cases,
both within our product, as well as just
on a personal level, using a custom GPT.
Part of our training course was every individual
created their own GPT.
And it kind of leaned into that, something
that they were passionate about, something
that they were interested in.
And then the spirit of continuous learning.
So I'm a big believer in there's no losing and learning.
So we as an organization, especially my team,
it is if we learn anything, then there's
some positive takeaway from that.
And that as an organization is something that, again,
is going to filter down throughout from the top down.
But learning is so important.
And if we diminish that in any way,
then we're telling our individuals,
we're telling individuals who are trying to adopt AI,
that failure is a path to ending an interest.
It kills passion.
And that's not the case.
What doesn't work is introducing AI without explanation.
So I've chatted with organizations
who have said, hey, we've tried to adopt AI.
We've tried to incorporate it.
But it just-- it wasn't working.
And this is the first question that I ask,
is what were you trying to achieve?
And nine times out of 10, the answer is, well, AI is just
like a really hot topic.
It's trending.
Everyone's doing it.
We didn't know.
And we were hoping that AI was going to tell us
what we wanted to achieve.
It's spoiler alert.
It's never going to do that.
So you can ask questions.
It won't give a response.
You can have a conversation with it, et cetera.
But if you don't know what you're trying to solve for,
just like anything, you're not going to get the value out of it.
And then you're going to hit resistance across adoption
at all levels.
And then the other thing that I'll highlight
is overloading the team with too many tools.
So if you go at your team and you say, hey,
we're going to do these seven things.
I need you to adopt this new product.
I need you to do x, y, z.
Oh, and you have to learn this thing called AI,
that you're probably worried it's going to change,
like it's going to replace you.
You have no idea what to do.
It's so daunting.
Then, yeah, what do you-- like, people
are going to push back.
If they get pushed too far, it's human nature to say, wait, wait,
wait, wait.
I need to take a step back.
And I'm going to resist this change.
So that's something that I highly, highly stress.
Do not do.
Find out why you're investing in AI, what the focus is going
to be, and lead your team towards the adoption of it.
Help them understand what AI can do for them.
That it's not replacing them.
It's not replacing me as a manager.
It makes me a better individual.
It makes me a better leader to my team for CSMs.
It gives them time back in their day.
It gives them an ability to build those relationships
with their customers, instead of being
so focused on taking notes after a meeting,
on doing whatever else they're taking that time.
And they now have hours back in their week
to reach out to their customers, to build those interactions,
and to learn about who their customers are,
not the person behind the screen,
but the person on a weekend who likes to go fly fishing,
likes to go hiking, who cycles, whatever it might be.
That's what changes the game, is that level of knowledge.
With that, we're going to open it up for Q&A,
which I think is on--
which, oh, yeah, I get to moderate it as well.
All right, perfect.
So, Rachel, I'm going to pass this off to you.
We're going to start with-- we mentioned the usage
to help define the ICP.
What does that mean in more detail and how?
Mm.
So, when it talks about when we're looking at,
I guess, GainSight 2.0, which is what we've been,
as a company, looking at how do we win in business
in this economic situation.
And it's about how do we look at churn analysis
and also the ideal behavior across the customer journey.
And so, we have defined and looked at
across different segments, what is the customer journey
and what would we like to see the customer evolve
and outcomes achieve across the journey.
So, when we're looking at the use of an ICP,
it's actually saying, to help us get to where we need
to be with GainSight 2.0, how do we strongly identify
the behaviors of a churned customer and look out
in the new landscape, what is it that we want to go to.
And so, from a mere perspective, it's looking at,
for example, customer competitions,
where the product acquisition we want to go through.
And those are some of the things that we're looking at,
I would say.
Yeah, and I'll add in, like, when you think of your ICP,
that we often think of, okay, let's look at the customers
at churned and understand what that looks like.
The inverse is also true.
Let's look at the customers who are extremely successful
and let's find the delta between those two.
And let's start to learn why we're some customers
not successful, why we're some so successful
and what's happening in the middle in between there.
And that can help give clarity
into what your ideal customer might look like.
And then, again, just on the volume of information
that AI can synthesize and come back to you with thoughts,
with ideas, with the perspective of hypothesis on.
And we don't take that as truth.
So that's the other piece,
is that you use AI to get you to a point,
to drive a ton of efficiency,
and then you use your own intuition,
you use your data, you look into it,
you have conversations as a team,
you speak with some of your customers,
and it's either gonna validate what the AI showed,
or you can go back and now you can start to tweak it.
Have that interaction with the AI bot,
or your custom GPT and say,
"Hey, it's a great starting point, I learned XYZ,
"and now you're gonna get a refined approach."
And that's gonna help you understand what that ICP looks like.
And it's also important to,
sometimes what I'll do if I'm curious about a question,
is I'll just open it up.
I'll start with something so open-ended,
just to see what the response is gonna be,
and then I'll tweak it from there.
So I've asked, in this case,
I've asked a custom GPT that we've built in-house,
what is our ICP, without giving any input on it,
just to see what the response is,
and see how close it is to the hypothesis that I have.
And that allows me to really look at it again,
I go back to taking a step back out of my bubble,
and say I am so far ingrained in these challenges,
that I need to remove myself to be able to look at it
objectively, and AI's again,
I can't stress it, I have a great way to do that.
- It goes back to, so again,
Gainsite 2-PONOR means we have a set of objectives
as a company, and it's looking at those set of objectives
that streamline across all departments,
and that's a direction that we're going in one, two,
three, four, five years time,
and we have to use those as our guiding,
as a compass in order to develop the ICP.
So I put in our company objectives,
I put in our metrics and our targets,
and that's how we generated again
almost as hypotheses of our ICP,
and then I match that with what are the characteristics,
or I don't want to say problems, because that's not.
But what are the challenges that we have faced
with some of our customers that have churned,
and match those two together to try to define
this new ICP?
- Perfect, so the next question would be,
what are some concrete examples of how you use AI
to help your team get better and faster in their daily work?
I'll kick this one off.
- Okay.
- So for me, we touched on a few of these,
but the meeting summary is mind-blowing,
how accurate it can be.
The ability for CSMs to have an immediate follow-up
on an email, so instead of, it's funny,
email's one of those things that you look at,
you're like, oh, we've been writing emails forever,
it's not the hard-to-read email.
It's true, if it's one email.
If you have 13 calls in a day, or something like that,
that's 13 emails, each email takes 15, 20 minutes
if you really want to make it strong,
and make it tailored, and make it thoughtful.
That's two to three hours that a CSM saves,
and then if you extrapolate that
over the course of five days,
let's say you have 25 meetings in a week,
it just adds up so exponentially,
and that is wasted time for a CSM
to have to write all of that from scratch
when AI can get them 85% of the way there,
and they can make the thoughtful tweaks
that tailor and customize it,
and then just kind of like move on,
and focus again on those human interactions
and those human touch points.
I always look at what is my team accountable for,
and we're very much looking at the adoption stage,
and so a lot of the time in their daily lives,
they're looking at how do I surface some of the challenges
that my customers are telling me
around adopting some of our game-type features.
So part, it's daily work,
but actually it is through those meetings,
and customer requests, product feature requests,
surfacing them up all into one area,
and then being able to then translate it back into
the departments that need to hear it.
Like that ultimately is going to be able to help them
with their daily work,
is being able to look at tools like GainSight
to surface some of those trends,
and I don't think,
and I think that's something that we need to do better
and better of, is how do we surface trends
that we can make better actions?
Also looking at, a lot of my team is saying,
I am super busy on X, Y, and Z,
and actually validating that,
so going back to staircase and looking at,
okay, what customers are they spending time on,
and why are they spending time on said customer?
It could be a particular type of connector
that they're working on,
or it could be a particular type of blocker
in terms of the opportunities,
so we have multiple products,
and we want to make sure that we are looking
at the opportunities across those products.
I use staircase in order for me to validate
where there are blockers of my team,
and we do that on a daily basis.
It's not some high level thing.
At the end of every day,
I'm looking at staircase,
and I'm trying to validate some of the blockers
that my team are saying.
So that's talking about concrete examples,
but I hope that gives a few things
around how we're using it.
And we are at time.
- Oh.
- We can answer, folks want to stay in,
we can answer a few more.
Or do you want to go and get a drink?
- Go and borrow it.
- I suspect.
- The drink is it, but what were you gonna do?
- There's just one left which is about
what's your strategy on teaching CSM's level up,
their communication in human skills,
'cause it's kind of touched upon what you said earlier.
- Yeah.
- Do you want to take that one?
- Yeah, sure.
Yeah, so what, it allows me as an individual
when they're driving efficiencies,
being very transparent and vulnerable,
I don't have a set strategy.
What I do is I try to just show them
through my interactions with them.
So I'm very big into knowing the individual
on the other side of the call,
or the other side of the slack
or whatever it might be,
recognizing that that's not an AI
that I'm interacting with,
but it's actually an individual
who has stuff going on outside of work,
who has challenges,
who has excitement, maybe something amazing
just happened to them,
but getting to know that individual
and making your interaction with them at that point,
not generic.
So not going in and saying,
hey, so and so my name's Jared
and I'm gonna do X-YZ and so forth,
but kicking it off with something personal
that you talked about in the last meeting
and building that relationship
so you know that individual at a level
that they want to come to you
and have an honest conversation.
Now, I found in Europe,
you all can keep me honest,
that are a little more direct in Europe than in the US.
So this is probably,
you all might think,
well, Jared, what you're saying
might not be 100% relevant,
but I think there's still a human element
that sometimes you don't want to offend somebody.
But if you can build that relationship and that trust,
you're gonna have far more honest conversations
and honesty is the only way that you can move forward.
So if you're not having honest conversation,
if a CSM is not having an honest conversation
and hasn't built that level of trust
with their customer,
then everyone's gonna be blindsided by,
hey, I'm frustrated, I have negative sentiment,
I wish we would have covered X,
why haven't we done Z?
And so forth.
But if you build that trust
and AI gives us the ability to do that,
then you can be honest, you can be candid,
you can get everything out on the table
and figure out what that path forward is.
And you're no longer gonna be blindsided
because you don't want to miss out on an opportunity
'cause it's not that you're not being supportive
of company ABC,
it's that you're not being supportive of Jane.
And you make it personal at that level
and that's where everything changes, in my opinion.
So I try to help my team understand
and reorient themselves to that level.
And some of my team is pretty young.
So they were,
like the fact of going into an office
is like a mind-blowing moment for them.
And so it's a lot of just reminding ourselves
and reminding them around what these relationships can do.
For themselves, for their customers as well.
- Well, thank you everyone for spending
a half to 45 minutes with us.
(audience applauds)
And I hope to see you guys drink later on.
- Yeah, cocktail bar and the party afterwards.
Yep.
And if anyone has any other questions,
feel free to find Rachel and I.
