Latest / The DEX Show: A Show for IT Change Makers / Reality Bytes #23 – Introducing Nexthink Assist w/ Paul Gentile

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Reality Bytes #23 – Introducing Nexthink Assist w/ Paul Gentile · The DEX Show: A Show for IT Change Makers

0:01
Welcome it changemakers to the Deck Show with Tim Flower and Tom McGraw. Let's
0:07
get into it. Hello everyone, I'm Tom McGraw. Welcome to the Deck
0:11
Shows show within a show within a show, which is to say, it's
0:15
one of the special editions of Reality Bites. We have a Reality Bites team
0:20
in this case, today that's Tim Dena, Sean and myself get to interrogate
0:25
one of our fascinating Next Think colleagues, and today that's Paul Genteel, VP
0:30
of Product Marketing, and we're gonna be talking to Paul about Next Think's latest
0:36
foray into the fascinating world of AI. Approximately. So, Paul, welcome
0:41
to the Deck Show for the first time. I'm lovely to have you on.
0:43
How are you doing today? Excellent? Excellent, and I'm thrilled to
0:46
be here. Truly, I'm not just saying that because I'm with my colleagues.
0:49
I really do mean that this is exciting and really looking forward to chitchatting
0:53
with all of you guys about these great developments and what else we might have
0:57
in the harbor. We are excited to have you on, and I just
1:00
want to say I just want to say, Paul has spontaneously mentioned Deck Show
1:03
episode stuff to me in the office. You know, just like you know
1:07
you actually listen, Paul. Is the impression you've given me before, which
1:11
I was very grateful and touched about. There are no lies in those statements.
1:15
I do religiously listen to the Deck Show. I have it plugged into
1:21
my Spotify account as a favorite, and it's great windshield time for me on
1:26
my long, long commute on the day, So I love it. It's
1:30
great to have you on, mate. And look, since it is your
1:33
your is your first time and all, I guess a good place to start
1:36
it be to invite you to just introduce yourself a little more broadly to our
1:38
audience and tell them about what it is you do at Next Thing. Excellent.
1:44
Um, yes, well, as you said earlier, M, I
1:46
oversee product marketing here at Next Thing, and I always talk about product marketing
1:49
covering three particular three particular pillars. One go to market strategies. So everything
1:56
we've got going on from a packaging standpoint, what we need to do from
2:00
an attack standpoint, and how we're going to be successful with our overarching go
2:06
to market approach to messaging and positioning. So everything related to what we're looking
2:10
to tell from a story standpoint with our products, with our portfolio, and
2:15
not also influencing things that I worked hand in hand with you on right related
2:19
to thought leadership and then three voice of customer. So much of the core
2:24
of what product marketing is about is figuring out how to take different inputs from
2:29
all different pockets of the organization, but certainly from external champions and from customers
2:35
to understand what we need to do to develop products, next to improve our
2:38
go to market strategies, but representing that inside the organization and then back out
2:44
to folks externally as well. Excellent, And you know, congratulations on assist
2:50
because there's been a ton of buzz around it and we're gon we're gonna dig
2:53
into it momentarily before we do, before we get into the wider topic of
2:58
AI. And next thing a large I'm interested to no ball in your own
3:02
personal life, in your life outside of work. Have you had like the
3:06
direct encounter with AI that's made you go, oh, this is a really
3:12
big deal or it impressed itself upon you And what was that encounter? Um,
3:17
what a great, great question, and there's so many different ways.
3:22
Um. Well, first I'll say it's my you know, artificial girlfriend.
3:27
Um, you know, sometimes my marriage is on the rock and I'm just
3:30
kidding. I'm just kidding. I'm just kidding. I was a second Yeah
3:38
exactly. No, no, no, no, no, I'll joking aside.
3:43
I mean, the reality bites is, uh, is that there's so
3:46
many different examples of AI in my personal life, um, that it's it
3:51
almost becomes you know, you don't even realize that, you don't even understand,
3:54
you don't even appreciate it as much as we actually should. But a
3:59
couple quicker examples for me, and because my life is so busy and we
4:02
rely on all different types of sort of funding and financial aspects is banking,
4:08
right, banking apps. I use my Bank of America app literally every single
4:13
day for different pieces that we're doing for you know, our kids don't really
4:16
carry cash anymore, so we have a what we call a green light car
4:19
shifting money over to them. But that's not necessarily AI. But banking for
4:26
how it protects what we've got going on and how we get alerts notifications when
4:30
there is some sort of suspicious activity. That's a great example that is really
4:33
influenced and help protect my family. That does oftentimes go unappreciated, but it
4:40
is a great example of how we're leveraging and utilizing this in a really really
4:44
meaningful way to protect what we've got going on within our family, what we've
4:47
got going on with in our society. The other one that I'll I'll bring
4:53
up is you know, some of the smart home devices that we have.
4:57
We've got Nest from a we have going on for me m a HVAC standpoint,
5:02
making sure it's reading how we want things to go and how it's adjusting
5:05
things on the fly based on what we what my kids, what my wife
5:11
are telling it to do ahead of time. Um. And then it also
5:14
influences other digital voice assistance, which is not I don't want to be too
5:17
much of a precursor into what we're going to talk about with next think assist,
5:21
but we use it every So we use those every single day, from
5:24
Ciri to Alexa, different pieces as I just mentioned from a a Google Home
5:28
standpoint, those are all elements that I and my family leverage every single day
5:32
away from work that are truly influencing and making our lives that much easier,
5:36
that much better. Nice. So, Paul, great to have you on
5:40
the show. Paul and I work together almost on a daily basis. It's
5:43
really cool to have you here. And as efficient as I like to be
5:47
in, Paul's going to be taken on my sales enablement team and that role.
5:50
So thankful for you to take that on, Paul, And you're gonna
5:54
You're gonna kill it and free me up to do more of the things like
5:57
this. But I love being efficient. We have a meeting the calendar later.
6:00
Can we bang out some of those agenda items here? Do you mind?
6:04
You've probably got a bad idea. No, I'll joke on his side.
6:10
You know, we've on AI. We've been talking about AI since the
6:15
sixties. Right, You're gonna go back to start Jack and see them talking
6:18
to a computer and it's telling telling people what they need to know. But
6:21
it really seems like it's exploded onto the stage just in the last two to
6:27
three months. It's gotten so much visibility. But let's let's start by defining
6:31
what next think ASSIST is and isn't. What's it all about and what makes
6:36
it special? Great question? Great question. Um, it's true, and
6:43
you're right, Um. Much of the explosion that has happened, or at
6:46
least the you know, the reverb that has made it given AI such attention
6:50
is kind of anchored behind chat GPT, And while I don't want to sort
6:56
of give you know, overblow over, make that two over blown on what
7:00
that is actually doing, it is sort of making it much more apparent to
7:03
everybody on how it is going to change things for everyone across the board.
7:10
And that's really, on some level, what Next Think Assistant assist is doing.
7:15
And it's literally our first truly aipowered virtual assistant, not only for our
7:19
organization, but for this particular space at large, and at a very very
7:24
core level, it's simply allowing it teams to address all different types of common
7:30
and custom troubleshooting scenarios and instantly, instantly delivering value to them through natural language.
7:38
And that's the key piece. By asking it a question, not only
7:42
are we leveraging one particular AI engine, but multiple AI engines to produce and
7:48
retrieve all different types of results that make the use of next Think that much
7:54
more simple, that much more easy, because it's producing those results which normally
7:58
might have required some one to write a particular type of query to do some
8:01
additional investigations. Next Think Assist is doing that for them based on one particular
8:09
chat type AI engine, and then also AI engines that are built into our
8:13
back end that allow it to retrieve that information through a just mountains and piles
8:18
of information and data that normally somebody would have to go searching for, we're
8:24
doing all of that for them inside of next Think Assist. Some of the
8:28
some of the demos that I've seen, the early demos, it's really cool
8:33
to watch somebody asks something like, hey, are people having a problem with
8:37
a blue screen today, and it come back immediately say yes, there are
8:41
three twelve people. Is it related to one specific area, Yes, they're
8:45
all in London. So it's it's very cool to see that kind of query,
8:50
natural query language just produce immediate results. It's really slick. Yeah.
8:56
Yeah, completely agreed to him, And that's why it's so excited, right.
9:01
It's it's literally leveraging large language models and putting them in the hands of
9:05
our customers, which normally would require, like we just mentioned a moment ago,
9:09
somebody to search or do that type of queering on their own. But
9:13
we're leveraging all of those large language models and putting that in the hands of
9:18
them to make their lives that much simpler when utilizing Next Thinking and the platform
9:22
and portfolio. And it's really neat to see how mainstream companies are integrating AI
9:28
technologies like this. I saw a video on what photoshop. Adobe is doing
9:33
with photoshop and starting with just a kids sitting on a rock, you can
9:39
say, add a pond at a pond or a lake to this picture and
9:43
it adds an actual photo of a lake and it looks like it was taken
9:46
there. Put a fishing rod in the kid's hand. It's amazing what AI
9:50
can do with applications that we've had around for such a long time. Where
9:58
what are you you believe is the connection? So Next Think has Uh,
10:03
we're a little more rooted to this technology, right, we've been we've been
10:07
doing this for a while. What do you think the connection is. We
10:11
don't quite go back to the sixties, but how do you connect our past
10:15
through the kind of our timeline of history up to this point in terms of
10:18
this this kind of capability. No, it's it's it's such a it's such
10:22
a great sort of like you said, sort of uh, connective tissue based
10:26
on the roots of what Next Think is about. And truly one of the
10:28
reasons that I ended up joining Next Thing over a year ago is because when
10:33
I saw a demo of the product well before Next Think assist, right,
10:37
but I just saw the core product from a demo standpoint. When I was
10:41
going through the interview process, I had this light bulb moment. It seemed
10:45
like it was magic on what was happening because of what it's what it's opening
10:50
up from a visibility standpoint to two IT teams that might normally be working in
10:54
a particular silo or not know how to have those connective tissue pieces relating to
11:01
different pieces that would make digital employee experience or making their employees that much more
11:05
productive, that much more successful. When I saw that, it was that
11:09
light bulb moment that I mentioned Togo, and I said, Wow, this
11:11
is such a cutting edge type of technology. How can I get in here?
11:15
How can I make sure that I'm leveraging it from my own career but
11:18
also for my own learning that because it is it is that that type of
11:24
technology that is is just carving a new path on how IT teams should be
11:31
holding their organizations and themselves to a different standard of excellence, and now with
11:37
with what we're able to do, not only previously with the visibility that next
11:41
Think allows for those I teams, but to make their path that much simpler,
11:46
make their truly the user experience combined with elements of user interface because of
11:54
what the natural language models ultimately unlock. That is such a great path forward,
11:58
and that painting a picture for how we can help make those IT teams
12:03
that much more productive, that much more successful, and move that much faster,
12:09
so that ultimately our vision. But what really Next Think is about for
12:13
me is to provide a beautiful digital experience for every employee, no matter where
12:18
they are on the globe. We're able to achieve that because of the technology
12:22
and what we're putting in the hands of those IT teams, And if we
12:24
can make them put that in the hands of them, to make it that
12:28
much more fast, right, make it that much more available, make it
12:31
easier for them to use than ever before. That's an amazing journey. That's
12:35
a tremendous story that we can tell. That sort of is rooted in the
12:39
original vision of what next Think is about, but going to the sort of
12:43
absolute next next level. Yeah, I had that same light bulb moment,
12:46
but mine was as a customer sitting at the Hartford when we saw this,
12:48
it was, oh my god. We've been waiting for this for thirty years
12:52
literally, and we called our project project Edison and we use the lightbulb on
12:56
every piece of communication we had. I didn't know that, Yeah, yeah,
13:01
it was. The light bulb is an enduring analogy for me and this
13:05
technology and is kind of thinking about where it goes next. It's taken that
13:11
light bulb and turning it into a laser, laser like precision and really sharpening,
13:16
sharpening that point of light to exactly what you need to see. So
13:20
let's look at the future, right, rather than our history and our past
13:22
leading to now. You hinted it at the top of the show. What's
13:26
in the hopper? Where do you see AI advancing or what additional capabilities do
13:30
you see us being able to leverage down the road? Yeah, a couple
13:35
a couple of different pieces of where we where the vision is for next think
13:39
assist one, right, some of the proficiency that's required for engineers and those
13:46
that are actual practitioners of the next thing is to get onboarded as quickly as
13:50
help as possible, and to leverage different help tutorials that we have, well
13:54
what if and this is where we're headed right to make product onboarding of next
13:58
Thing that much more simple, to give them built in self help components that
14:03
would help them understand all the different nuances to achieve additional value, not just
14:09
the core value, but the additional or sort of exemplary value that might come
14:13
from some of the advanced features inside of our product and platform. That's where
14:18
next nink assists could come into play too, by simply asking the question and
14:22
then pointing out to them here's how you would do this, and here's where
14:24
you would need to go in order to get more experience with this particular product
14:28
in this particular pocket of the product and platform. To certainly generating all sorts
14:35
of employee engagement surveys right now, that's required for somebody to manually input those
14:41
in. But imagine a world where, based on the data that's being pulled
14:45
from those large language models, for next nink assists to be able to suggest
14:48
to somebody, here's something you might want to dig in on, and I'm
14:52
going to automatically and magically generate that employee engagement survey for you and push it
14:58
out so that you you don't even have to think about what you need to
15:01
do next. See what I did there, next think, not have to
15:05
think about something next. Right there. There's also some of the there's some
15:09
rooted in some of our history. I've been told that before. I've been
15:16
told that before. Tim. I appreciate that, UM No, but truly
15:18
being able to generate those types of those types of surveys automatically UM on their
15:24
own is something that would be just incredible to again make the lives of those
15:28
I practitioners or those next Think practitioners UM that much more simple, that much
15:31
more quick UM. Then then there's also everything that comes with the actual power
15:37
of our our core value prop UM that's available to people today, but helping
15:43
them really sort of use advanced level features UM or actually not forcing them to
15:50
have to grind on some of the advanced level features that they have to do
15:54
today, such as building custom dashboards UM, creating system alerts, Next Think
15:58
assists vision, And what we're going to be moving forward with is being able
16:02
to do that all all behind the scenes, based on the technology and the
16:07
aim models that we have inside there, so that those IT teams have it
16:12
that much more available to them, at that much more prevalent readily available.
16:17
And then of course you know decks right, digital employee experience analyzing the results
16:22
right, next thik assists, we want to make sure that we've got opportunities
16:26
for additional analysis to happen. And next thik assists will have that type of
16:32
that type of experience, but that type of knowledge to be able to help
16:36
it teams analyzing results in a more meaningful way. Hey, Paul, No,
16:41
it's it's very beautiful how you're explaining all the different kinds of examples.
16:45
But can you give us one specific or a couple of specific examples on what
16:51
assist users can do with the tool now and walk us through like an engagement
16:57
the core example right now. And Tim hit kind of referenced it earlier when
17:00
some of the demos that he had seen. But imagine this. You're inside
17:06
the next Thing tool. You're inside with the next Think infinity right, and
17:10
you, as a practitioner, Dinas say to next Think Assist, show me
17:14
all the execution crashes associated with outlook that are part of our executive teams devices.
17:23
Next Think Assist, immediately spit something back and says, here's a list
17:27
of all of those Outlook crashes on the executive team's devices, and here's where
17:33
it's crashing most, and here's what the types of devices that it has over
17:37
that is crashing most over the last fourteen days. So you immediately get information
17:42
that would normally require you to query around with specific parameters. But next Think
17:48
assist is doing that all for that particular user. Then you can actually go
17:52
a step further right and the IT team or Dina, you could ask next
17:56
Think assist, Okay, thank you for that information, but what is the
18:02
type of agent or some type of a component that is influencing or creating those
18:10
crashes of Outlook? Because it's not always a direct one to one. Sometimes
18:15
the correlation component that light bulb moment that we talked about before is something that
18:18
the actual practitioner would have to start to stitch together. But you're able to
18:23
ask next Think assist what would help them go further with that and what might
18:29
be installed that would be causing those crashes. Next Think Assist then responds and
18:33
says, here's a list of these dot exs that are causing or that are
18:38
installed on each one of those Executive Committee team members devices that is likely causing
18:45
that particular issue. It's version six point seven and it's installed on four of
18:52
those particular machines, and that is the root cause of what's happening to those
18:56
devices over the last fourteen days. You can see why taking the sort of
19:03
even some of the thought process and the investigative aspects that would be required by
19:07
the actual IT teams, making them that much more proficient, that much more
19:12
quick because we're doing a lot of that work for them based on those large
19:17
language model processing components that we talked about earlier that are automatically built into the
19:22
technology. Does that help? Yeah, that is amazing. I'm just visualizing
19:26
what you were trying to describe a LERN. If we're able to also say,
19:32
mixing assist, can you please create a dashboard or a couple of alerts
19:36
based off of that information, it will be great because it will save a
19:40
lot of time for us to know and I know in your job, that
19:45
would make your life a lot easier too, what's yes, Yeah, a
19:48
lot easier. Well, let me also say one of the things, those
19:52
those other custom dashboards and those alerts pieces, that's part of the vision,
19:56
right, there's still a tremendous amount of engineering work and you know, research
20:02
and development work that would be required in order for us to get there.
20:03
But that is absolutely the direction that we are committed to heading that we are
20:08
committed committed to going towards. But as with anything specifically with this type of
20:15
AI technology, right, it's going to take several different iterations in order for
20:18
us to get there. But could not be more proud and more excited about
20:22
what we've put out there today, not only to be first to market,
20:25
but to sort of put a stake in the ground and say, this is
20:29
where this industry needs to go, This is where the technology, how the
20:33
technology should start to shape everything we've got going on within the digital employee experienced
20:37
realm, but also influencing surrounding pockets, surrounding categories as well, because it's
20:42
the first step towards really advancing things and get to the question you just mentioned.
20:47
So the follow up question is how do you expect this kind of innovation
20:51
to change it over the next couple of years. It will influence things in
20:56
two particular ways in my not so humble opinion, One, it will make
21:03
the I practitioners' lives that much more simple, and I've referenced it a couple
21:07
times throughout the conversation, right, But part of what we're committed to is
21:12
making sure that IT teams have the ability to be as productive and to be
21:19
as proficient as they possibly can, not only by leveraging the core technology and
21:25
our core value prop that we talked about earlier what Next Think was sort of
21:27
spawned out of, right, but making sure that inside of using our technology,
21:33
that the user experience for those practitioners, for those IT teams is that
21:37
much more simple. That's the single reason why I'm most excited about what Next
21:41
Think Assists does today, because the idea of making the tool easier for practitioners
21:49
takes a huge leap with what we've already launched, It's going to take another
21:53
leap by making it that much more simple for practitioners. And the idea of
21:59
IT teams going away is not relevant here. I want to point that out.
22:03
We're trying to make their lives easier, make the utilization of the tool
22:07
easier and more accessible for them in different ways that they haven't even been able
22:11
to dream of earlier. But making the user experience that much more simple because
22:18
of what Next Thing does and what digital employee experience tools as a whole unlock.
22:23
It requires a level of detail, a level of nuance, a level
22:27
of training that is required to to be successful. And this user experience component,
22:36
the next thing assists is able to capitalize on and make that much more
22:38
simple. Is going to change things moving forward over the next couple of years,
22:42
no doubt. Two. I think it will absolutely make digital employee experienced
22:49
tools as a whole that much more prevalent. Digital employee experience is already on
22:56
the rise. So many different statistics that we hear from Gartner, from Forrester,
23:00
from our own research shows that these tools will be at a fifty percent
23:07
clip of adoption from a strategy standpoint over the next two years for most enterprise
23:12
organizations. But really making sure that it can be utilized and adopted at a
23:17
faster pace because of the ease of use component and because of the chat capabilities
23:22
and the large language model components that we talked about earlier, that will make
23:26
the adoption of these types of tools spike even further and faster than before.
23:33
And so that's really how I see it in influencing things over the next couple
23:36
of years, faster adoption and easier utilization of the tools as a whole very
23:42
good. So, Paul, I know it's still kind of early days with
23:45
next Think Assist, but what's the interest level been like so far? Are
23:51
people kind of overwhelmed with it? Or is there a real appetite to get
23:53
to know the tool? From an editing standpoint, the response has been really
24:03
fantastic. There are a ton of existing customers that have said thank you for
24:08
jumping on board with what M and riding the wave alongside of us, because
24:14
we have responsibilities as it teams to continue to stay on the bleeding edge of
24:18
what's going on, and because we're so committed to and wedded to what next
24:23
Thing does you all? Putting this in our at our fingertips is a really
24:29
exciting and opportunistic way for us to explore what we um, what you have
24:33
going on, and where we would want to partner with you moving forward.
24:37
There's been some early hand raisers because this is sort of our announcement to the
24:41
public and we've shown it through our early private technical preview partners on what they
24:47
would want to um, what they would want to see, and how we're
24:49
where we're headed and where we're looking to go from a development standpoint, and
24:55
as soon as we open this up, I believe it's going to be in
24:56
June or July to some of those public we already have a long waiting list
25:03
of people that are saying, yes, please put me on that I want
25:06
to explore and utilize this as quickly as possible as soon as you make it
25:08
available. And no doubt, as we have with so many of our customers,
25:14
they're chomping at the bit to provide some feedback on the flip side as
25:17
you, as you asked, are people overwhelmed by it? You know,
25:21
not necessarily our particular solution, because people that are next think users tend to
25:26
be just wowed every single day and say I can't live without this particular tool.
25:33
It really has changed the way that I look at my particular role and
25:37
responsibility and what I'm providing back to the organization. But for AI as a
25:41
as a whole, yeah, there's a little bit of skepticism. There's a
25:45
little bit of you know, cautious optimism related to what it can do.
25:51
And it's also hard even for me, even for all of us, to
25:55
stay on track with how how quickly things are moving and what is the right
26:00
sort of balancing act for leveraging AI but not going too far, but also
26:07
not letting it consume so much of your day to day where it can because
26:11
there's so much information out there where you end up losing losing some of your
26:17
core productivity because you can spend your entire day, nine ten hours a day
26:22
researching on different ways to advance things from an AI standpoint, and that I
26:26
totally understand. I don't think we want to be spending our days having conversations
26:32
with the chat pot and waiting spot. But I guess I'm curious if we
26:37
could just wrap up here. You know, who's kind of the ideal assist
26:41
user if there is one, and then how can these folks get in touch
26:45
with you? Excellent question. Ideal assist user is anyone that is a next
26:53
Thing practitioner, and what I mean by that is a tends to be a
26:57
end user, computing engineer, somebody who's utilizing Next Thinks products and platform on
27:03
a daily basis, that is leveraging things like our nqel, our quel,
27:11
existing Q query queries and trying to figure out how to make this how to
27:18
determine some of the the connection points behind things that they're seeing inside of next
27:23
Think and wanting to go deeper. Those are the individuals that we're really looking
27:29
to get signed up to leverage next Think assist again going back to that user
27:33
experience and the simplification of those pieces. Those are the folks that we really
27:37
want to get find out or identify from a hand raiser standpoint, and as
27:41
soon as we do open this up to make sure that they have opportunities to
27:45
utilize the tool, give us feedback, but then to literally sort of uncover
27:52
different types of patterns and just ultimately reduce the type of complexity that they faced
27:57
day to day inside of their particular role and anything that would be associated with
28:03
some of the more nuanced components or a deep power user components of next Think
28:08
as a whole. How would they get in touch with us? Very very
28:11
simple, easily reach out to us through their solution conson to their through their
28:19
through their sales rep and then we will have a we will be opening up
28:25
a form for anybody that would want to sort of be part of the early
28:27
pilot run that we can make available through all of you after the fact.
28:33
But that'll be the easiest way to get in touch with us. Fantastic poland
28:36
we'll be putting links in the show notes for anybody who wants to find out
28:40
more about Assist or indeed make contact with us here at Next Thing. But
28:45
listen, Man, wonderful to have you on the show. Could talk to
28:47
you about this one all day. I think you've once again already demonstrated just
28:51
the depth of ingenuity and excitement and passion we really have here at Next Thing,
28:56
particularly at the moment with all these amazing new products you and your team
28:59
are helping to roll out. So thanks so much for coming on, and
29:02
I hope to have you on again sometime. I hope so too. It
29:06
was a pleasure. This is really exciting, and I appreciate the opportunity rock
29:11
and roll and as I oftentimes say, onward and upward, let's go.
29:15
Thanks pul Thanks man. To make sure that you never miss an episode,
29:19
subscribe to the show in Apple Podcasts, Spotify for your favorite podcast player,
29:25
and if you're listening on Apple Podcasts, make sure to leave a rating of
29:29
the show. Just have the number of stars you think the podcast deserves.
29:33
If you'd like to learn more about how Next Think can help you improve your
29:36
digital employee experience, head over to nextthink dot com. Thank you so much
29:40
for listening. Until next time,