AI Meets SaaS with Allison Pickens, Board Director, dbt Labs and Commvault
Explore AI investments and creating Human-First Products in this expert discussion covering how AI's role in customer success.
>> I love it.
>> We're going to go on mute and head out and then you guys can take it from
here whenever you're ready, Nick.
>> Great. Three, two, one, go.
Hi, everyone. I'm Nick Meda, CEO of GainSight,
and I welcome you to the next episode of AI Meet SaaS,
where we talk about the implications from generative AI and AI more broadly on
the world of B2B SaaS,
because we know it's affecting all of our personalized,
but we think it can transform business as well.
I think I'm more excited about this episode than anyone I've ever done,
although don't tell the other presenters.
I want to welcome Alison Pickens back to the GainSight stage.
Alison, how you doing?
>> I'm doing great. I felt the same way when I interviewed you on my sub-stack.
It was so much fun doing that episode.
Also, I think people could tell we knew each other really well and we also got
great feedback.
I'm excited to join yours now.
>> Love it. Alison helped create GainSight.
The early early days of GainSight came in, did so many different roles.
Eventually, ending up as our chief operating officer and actually being prior
to that chief customer officer,
so helped build everything that we have today,
and I'm immensely grateful to Alison forever,
and then has done amazing work since being a very active investor herself,
and lucky to be an investor in her fund,
as well as being on the board of DBT Labs and Conval.
She's done so much together.
We actually wrote a book together years and years ago.
Over many flights to India, if I don't not mistake it, that was great.
Today, though, we're going to talk about gender and AI more broadly.
You've also written a lot about this and spoken a lot about this.
I think there's a lot we can get on this.
Before we dive into that, though, we always have a nice breaker as you know
very well.
I'm going to go back to an oldie but goodie because I think I have a guess of
what you're going to say.
What's your go-to karaoke song?
Oh my gosh, I can't say I've done karaoke in a while being the mother to two
very young children.
My favorite all-time karaoke song is "Timber,"
which you might remember me performing at an early gig site event,
and whenever I think of karaoke, I think of that song.
Me too. I better think of you. I think of "Timber" too.
So "Pipple Timber."
You might be doing a little more like "Kids Bop Music Now" in the next few
years or frozen.
Maybe "Bumble Bee."
Yeah, exactly.
That's recurring for us.
You're a karaoke that now, if you want.
Okay, so we searched to AI.
The first one is a more general question about how you think about AI.
How do you use it in your own life personally, professionally?
I've even heard perhaps you use it on weekends sometimes.
The one I was using on a weekend was, again, being the mother to two young kids
My husband and I have not gotten out all that much lately, I would say,
although we're trying to do better at it.
We did spend a Saturday night, date night, at home,
playing around with the main journey earlier this year.
And it was so much fun poking around and being in the Discord forum
and seeing how other people use it.
And it gave me early exposure to what prompting means and why it matters,
how you phrase it.
I also have used "Pilot AI," which I thought was really interesting
as sort of a conversational partner.
And I experimented in a few ways with it.
One, let's look at probably a topical cover later.
Like, if you're a customer success manager,
you can use this AI as a conversational partner,
potentially to brainstorm about client situations.
Like, what to do?
You could give it the total scenario.
I'm trying to get a hold of the stakeholder.
They won't respond and hear all the things that are going on.
And so I poked around with it in that way.
And also started asking it for feedback on my sub stack.
And it actually had some interesting ideas,
which I initially rejected maybe out of hand because I'm like,
this isn't AI.
Like, what does it know about, you know, what I'm doing?
But actually, it's inspired lots of trains of thought later.
So I think that's been sort of a useful endeavor.
And then actually, the way that I use it,
probably on a more ongoing basis professionally,
is with my investment memos.
I actually have a contractor who used to be a gain site intern of mine,
by the way, who's phenomenal.
And I gave her the broad mission to go figure out
how to streamline the creation of investment memos
based on notes that I've taken about the company,
other observations, decks and things like that.
And figure out how to create investment memo very quickly.
And notably, I had heard do it because there has to be human in the loop.
It's not enough just to, like, for me to like,
be like chat GPT, like create something there.
There are certain like actions that still need to be done by human
and certain forms of like, you know, judgment calls and error checking
and things like that that she needs to do.
But that's been, you know, become pretty core to my investment memo creation
process.
That is so cool.
And actually, I read your investment memos are quite good.
So I didn't realize that chat GPT is working behind the scenes.
But I know your judgment goes to too.
Of course, yeah. And of course, like there's a layer of me on top of it.
Exactly.
But it's nice actually eliminating a lot of the busy work.
Well, it's a good, it's a good example to where you're like using
chat GPT and AI to augment kind of human work, right?
Versus totally replace it.
So that's going to be a prime theme that comes up.
So, okay, so let's talk about it because you're in,
you've done SAS for so long and you're also thinking a lot about AI.
So what do you think are some of the biggest opportunities right now
to really have AI change the world of SAS?
So, you know, people talk about there being different layers of the generative
AI stack,
not just the application layer, which is probably what people are most
interested in
when they're thinking about what the opportunities are.
But, you know, just to kind of go through the layers,
of course, we have the foundation models and they're ones, you know, produced
by OpenAI
and Thropic that are the best known.
But actually, a lot of people think now that there are going to be tens of
thousands of models,
many of them very specialized with a small number of parameters
that each one becomes very good at specific tasks.
And whenever you have a task that needs to get done,
it can be routed to the best specialist AI.
So, I think there's actually a ton of opportunity for folks to create,
you know, very smart individual models that are very good at a particular task
and can be easily observed and monitored because the number of parameters is
small.
Then you've got, you know, vector databases where, you know, companies like
Pinecone are taking off.
I'm not, I can't say I'm an expert on the space.
I don't know if there's like opportunity for more vector databases,
but, you know, Pinecone has emerged as a leader in this space,
helping you figure out how to, you know, search and store, you know,
cross, you know, vector data, which is really important for the creation of AI
models.
And then you've started up to just so the audience can track it.
Do you want to describe like how, if you, if you have anything more
than that, how a vector database fits with the foundational model, like how
that helps a use case?
Yeah, again, can't say I'm like an expert on the space.
But I think the general idea is that if you're a model,
you basically got to ingest, you know, thousands of different attributes about
what you're scanning.
Like if it's an image or something composable and a vector is essentially like
a series of numbers,
each of which might characterize like a particular attribute of that piece of
content that you are
analyzing. And so, and we do that actually. We use it today because we're
bringing in all the
note data from Gainsite, a lot of the timeline posts, like basically, notes CS
Ms are taking.
And we put them into a vector database and then use the LLM to kind of generate
basically like a
understanding like it's actually called cheat sheet, like a prep note for an
executive about
everything that's happened with this client, similar to your investment memo
idea, but actually for
like a customer prep meeting. And so that vector database was quite important
because there's just
so much data. We can't just pass it all into the LLM as a prompt. So yeah,
quite similar.
Oh, interesting. Okay, great. Then you've got the middleware layer where, you
know, you've tools
like laying chain and fix the AI, which make it easier to build applications
using LLM. And then
you've got the application layer, which again, I think is what most people
think about SAS.
They're like, this is where things get interesting. And they're, you know, I'm
seeing some notable areas
of opportunity. I think one is selling into automating services, which are
inherently, you know,
composed of a lot of human effort. So examples of this might be, you know,
company called EvenUp,
which is one of my portfolio companies, sells to personal injury attorney
attorneys and their
firms, right? And the idea here is that you want to automate the process for
ingesting data about
cases that are similar to the injury case that you're working on, ingest data
about, you know,
that particular cases, you know, the medical situation, the harm to the client
done, the fault, you know,
all that stuff. And then, you know, you can auto create a demand letter, which
sent to, you know,
the accused, I guess, saying like, this is the amount that will settle for. And
so it basically
automates like huge percentage of the work that apparel legal might otherwise
be doing painstakingly
and probably in a lot of cases, not enjoyably. And it actually gives up time
for the apparel
legal to go do more like client relationship work, which for customer success
people, of course,
is like probably more exciting, especially. So that's an example of like, you
know, automating
services. And then there are also companies that are trying to automate
services done by,
you know, boutique firms, like consulting firms, not necessarily boutique, but
you know,
the likes of Deloitte and others that are helping to configure software. You
know,
with for example, like Salesforce.com. So, you know, companies like Superframe
and SwanTide are
trying to, you know, go after that challenge. And then I think there's another
opportunity
application layer that has to do with unlocking new markets that haven't
previously bought software,
personal injury attorneys, I think probably a good example of that. But micro
SMBs are
probably the most notable example where these are, you know, small companies
that haven't even
bought, you know, gusto or other SMB oriented software that we're familiar with
. They don't
have a need for it, but they might have a need for generative AI, which is very
easy to do.
Super streamlined onboarding and can take a lot of costs out of their efforts.
For example,
if you're a hairdresser and you're spending all day, you know, cutting people's
hair,
you may not have the ability to answer the phone when someone calls to make a
hair appointment.
And people like actually often call to make their hair appointment instead of
like booking it online
in some way. So there are now, you know, a couple of companies I've seen that
are cropping up to
create AI voice assistance that allow a customer to interact with AI and like
easily book a meeting,
which is, you know, cost effective for the hairdresser and generates a big
revenue lift for them.
I love that so many good examples. So it sounds like there's certain types of
use cases you can
enable. Like the personal injury example, there's certain customer segments you
can target.
And I thought that middle one is pretty interesting for a lot of folks
listening,
because we're going through this too of making software easier to manage and
configure, right?
I think that that was a point around sales force and actually against that, we
're working a whole
bunch of stuff to let you configure gain site through prompts based creative
report through a
prompt set up a health score, things like that. So it could change the way of
how people use
software in general, where it's like, I don't know what you think, but like
reduce the friction
of using software more broadly. Totally. Yeah. And I think it's you. I mean,
the fact that
gainset is working on this brings up an interesting point about, you know,
should every company be
developing its own way of auto configuring their software? Or is there a more
horizontal play here?
And these early stage companies, I think, would like to think that there is a
horizontal play,
but you know, we'll see how how it pans out. Yeah, I'm sure both will happen to
some extent.
So that's great. And then if you've looked at, we talked to us a little bit,
but people really
innovating in AI, one thing I'd be really interested is the challenges they're
running into. So like,
what are the, like it could be like you said, a small company doesn't have the
data or doesn't
have the workflow. What are some of the challenges, both small companies, as
well as more mature
companies are dealing with in like actually doing this, like making it happen?
You know, I would say there are a couple challenges that I've seen. You know,
one is that I think
in enterprise companies have a lot of budget available to try out AI products.
If you're in a
boardroom of a big company, chances are AI is on the agenda every time it's
over. And you're seeing
a list of all the vendors that they're trying out. Notably, they are trying
things out. They're
playing playing around with things is like another question that I've heard.
And so, you know,
startup might sell into an enterprise, get a six figure contract, but then the
key is can they
actually deliver on a value proposition that's enduring? And at the end of that
one year contract,
you know, can they renew that contract? I think for a lot of vendors, there
will be a lot of churn,
you know, after a year because of this. And so it's, I think it's really
important that AI
startups invest in customer success. However, you define that like just making
your customer
successful. The founder has to dedicate a lot of attention. I've also seen
challenges from
startups that were built before the release of chat GPT. So, we've entered an
AI way that,
you know, envisioned a different kind of ecosystem that they're selling into
another,
trying to figure out how to adapt. And a lot of them, you know, started
incorporating LLMs into
their product, but in ways that felt kind of ancillary to the search for a
product market fit and
maybe a distraction, you know, they might be building a chat bot.
Maybe, maybe you could argue that that's just going to be table stakes for all
software applications
growing forward and you like have to have an AI chat bot. But you know, is that
really worth
your time when you have 12 to 18 months of runway and you're trying to deliver
on like a poor value
prop and show that you're an aspirin and not a vitamin, you know, I don't know.
So I do worry
about like some companies getting distracted. That's really super interesting.
One of the things
we wrestle with and tell me if you have seen this, probably my guess is it
happens at bigger teams,
is, is AI like a team inside engineering or is AI something everyone's working
on? And that's
something I think as you get into hundreds of engineers or whatever, it's like,
okay,
we create a separate team for AI or do you create like get everyone to work on
it? But most people
don't know that much about it. So that's something we're wrestling. I don't
know if you've seen that
at some of the companies are on the board of or things like that, but I'm
seeing them more and
more. Yeah, I can't say direct exposure to that. But it reminds me of a
conversation you and I have had
earlier about how in several different technology waves, there was initially
one department that's
possible for it, whether it was like, you know, your mobile strategy, or maybe
before that,
your internet strategy, more recently, your staff strategy. And then eventually
that theme
just becomes the whole company and everyone needs to be up to speed. So maybe,
maybe going back to
your question, it's just a matter of time before everyone becomes an AI
engineer. Yeah, it's
interesting because that's what when I we're spending a lot of time on it
because we have a whole
bunch of stuff coming out, but also the possibilities feel endless. Like, like,
I don't know if you
feel that when you start ideating on this stuff or talking to entrepreneurs, it
like even in our
business, it's like, Oh my gosh, every single thing can not 90% of things get a
lot better
with with the general AI. And yeah, just came off another customer event where
that we're brainstorming
about gender, to AI stuff to do around survey feedback and NPS and things like
that. So that's
really, really cool. So we talked about innovation. And you know, you get
pitched a lot of startups,
which is must be super fun in terms of just seeing the ideas out there. I can
imagine also,
there's an question of filtering. So how do you everyone's going to say they're
an AI company now,
I'm guessing like 95% or something. How do you figure out like the ones that
are serious about it,
that it's foundational to their company versus like a little bit of icing on
the cake? Like,
how do you filter? Yeah, you know, I am very suspicious, less of AI companies
that make it
sound like they're AI and not doing it and more companies that are trying to
build in a very
crowded space. Many really crowded spaces, like examples include note taking
applications,
like ingest, you know, transcript of a call and generate summary. That could be
just a feature
of gain site as I thought we're adding we're shipping it next month actually. I
think I remember
learning about that. So, you know, that's an example of a very crowded space.
You know, other
spaces like, you know, scan publicly available data sources about a company,
say from 10Ks or
posted job descriptions and surface talking points to sales reps, more, you
know, potentially
like customer success folks so that they can more easily sell or renew or
otherwise like
align with the customer. Again, like probably a feature of another company,
maybe gain sites
product. So, you know, and tons of startups are going after that problem. So, I
think that's
where I tend to be suspicious. I think, you know, what's particularly
interesting is founders that
have a lot of direct exposure to a very specific pain point. They have a lot of
domain knowledge,
great founder market fit. There's a lot of white space and, you know, they're
in like potential
to take out a ton of tons of cost. I love that. Yeah. I mean, your personal
injury example seems
like a really good one where that's so specialized that it's not just, oh, use
chat, you know,
chat GPT APIs and run some data through it. I'm sure there's a lot of workflow
and things as well.
Yeah. I think there's a there's just to give it a couple other examples. There
's a company called
Fulcrum that I invested a bit in recently. And, you know, that team has a lot
of experience
at consulting firms, you know, McKinsey and others helping to, you know, reduce
like basically ship
products that cut out work for back offices. And so now they're delivering a
product that's
essentially UI path, but in an AI context. So like AI for RPA, robotic
automation.
Yeah. Imagine, you know, dramatically taking out costs from BPOs or like shared
service teams
that are based in, you know, India or like other places that, you know, are
sort of known to be
like lower costs. So, you know, very interesting opportunity there. And then I
think also, you know,
kind of to my point earlier, I'm really interested in companies that are going
after enterprise
budgets because there's a lot of budget there. And the key question is like,
are the founders
enterprise ready? Do they have prior relationships, the companies? Do they
exhibit like
tremendous go to market instincts, which of course, a lot of technical founders
and AI, you know,
may not. But if you can have that combination of like great technical skills
and really strong
enterprise go to market instincts, you know, I think it can be unbeatable.
I love it. Well, let me close out on the human side. So, you know,
Allison actually helped create our purpose statement, which again,
insight, which is to be living proof, you can win in business while being human
first. And that's
been very durable since 2016 when we all came up with that in an offsite. And I
also remember
vividly you did this keynote at Pulse, which is our annual conference. And you
talked about like
human first products and like your experience of technology. And sometimes all
of us getting
overwhelmed with technology and having, I think I remember something about you
not using tech
right in the morning, right? And so that was, you know, five, six, seven years
ago, right,
quite a while ago. And you were thinking about back then, and now obviously,
generative AI is
right here. And we're thinking about the human aspect versus technology. And
you had this quote
recently, when you're chatting with generative AI, you're chatting with someone
who's supremely
creative and more attuned to divergent thinking. So what's your latest
reconciliation of human
first and like AI and everything we're talking about? It's a great question.
And I can't say that I
have like a new framework for describing how AI should be human first. If I did
, I would be out,
you know, promoting it guys. But a few things that I would call out, I think
one is, if you are
attuned to your market, chances are you are finding a way to make humans more
productive using AI.
So you have you almost by definition have a human first mindset. If you were
looking at people and
thinking, what needs can I solve for people? Because at the end of the day,
people who have
money to like spend on AI, if you are more messianic about AI, if you have sort
of a religious
attachment to it, which I do think some people do, right, I think you will end
up building things
that are not human first. And I think and would hope would not actually be
bought by people.
I'll give you an example actually of a story I heard recently that I found
troubling and hopefully,
you know, it will be defeating in some way in the end. Like I heard that,
again, not to be like too provocative here, but I heard that at an open AI, you
know,
meeting, there was sort of a discussion about how GPT was like officially autom
ating what
writers like professional writers were doing. And maybe it was a response to
the writer strike.
And there was like a celebration in the room about like, yay, like we've
automated the job of
writer. And there's an open AI engineer who I know quit in response to this
meeting.
Oh my gosh.
That's an example where I think like, I don't think we should be celebrating
automating
professions in itself. Like that's not an interesting celebration to me. It
actually
speaks to like a religious attachment to all of me. I think people need to do
work for money,
but also for purpose. Like I think we need stuff to do when we get up in the
morning. And if AI
can help us do that stuff or do better stuff or stuff that we want to do, like
that is success
for AI. And I think also the market will reward products like that.
I love that. That's such a good way to kind of close out, which is just
thinking about how
AI can help the human first, which obviously will help the business too. So let
me close
out with word association every time somebody has say three things. Tell me the
first thing that
comes to mind. So generative AI. Future. Future customer success.
We start the open over. Yeah. We're preparing answer to the
okay. We start the whole thing over. Yeah. So we're gonna do no worries. We're
gonna do word
association to close this out. So I'm gonna say three things. You just say the
first thing that
comes to mind first term or phrase. generative AI. Future. Customer success.
Happy. Happy. Gain site. Pulse. Pulse. There you go. Awesome. Well, we'll
hopefully see you in
another pulse sometime soon. Allison, thanks so much. This has been incredible
kind of
walk down memory lane, but also some great insights for the future. So really
appreciate it.
Thanks for having me Nick. Awesome. I was really
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