December 17, 2025 • 45 mins
As AI reshapes the workplace, employees and leaders face questions about meaningful work, automation, and human impact. In this episode, Jason Beutler, CEO of RoboSource, shares how companies can rethink workflows, integrate AI in accessible ways, and empower employees without fear. The discussion covers leveraging AI to handle routine tasks (SOPs or "plays") and reimagining work for smarter, more human-centered outcomes.
Featuring:
- Jason Beutler – LinkedIn
- Chris Benson – Website, LinkedIn, Bluesky, GitHub, X
- Daniel Whitenack – Website, GitHub, X
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- Framer – Design and publish without limits with Framer, the free all-in-one design platform. Unlimited projects, no tool switching, and professional sites—no Figma imports or HTML hassles required. Start creating for free at framer.com/design with code `PRACTICALAI` for a free month of Framer Pro.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Jerod (00:04):
Welcome to the Practical AI Podcast, where we break down
the real world applications ofartificial intelligence and how
it's shaping the way we live,work, and create. Our goal is to
help make AI technologypractical, productive, and
accessible to everyone. Whetheryou're a developer, business
leader, or just curious aboutthe tech behind the buzz, you're
(00:24):
in the right place. Be sure toconnect with us on LinkedIn, X,
or Blue Sky to stay up to datewith episode drops, behind the
scenes content, and AI insights.You can learn more at
practicalai.fm.
Now, onto the show.
Daniel (00:48):
Welcome to another episode of the Practical AI
Podcast. This is DanielWightnack. I am CEO at
Prediction Guard. I'm joined asalways by my cohost, Chris
Benson, who is a principal AIresearch engineer at Lockheed
Martin. How are you doing,Chris?
Chris (01:03):
Hey. I'm doing very well today. How's it going?
Daniel (01:05):
It's going great. Wrapping up the year, reflecting
over the past year. It's alwaysa kinda time of the year to
reflect, and it's been a greatyear for for the podcast and for
for business and for otherthings. So, yeah, just feeling I
think feeling particularlyblessed as we kinda head into
the head into the end of theyear. And I think, also blessed
(01:29):
to have made a bunch of goodconnections this year to people
that are that I'm learning from.
One of those is our guest today,Jason Butler, who's CEO at
RoboSource. Welcome, Jason.
Jason (01:42):
Hey, thanks so much.
Daniel (01:43):
Yeah. Yeah. It's great to have met this year, I think
from a variety of directions, abunch of different connections,
but you did also joined us atthe Midwest AI Summit, which
was, fun. Chris was there. Iknow that you have a particular
passion for kind of thinkingabout the way that people work
(02:07):
and helping them do good workand meaningful work, I guess.
Yeah. Do you wanna expand onthat idea a little bit? Because
I do think it's interesting tothink about that topic when
people are grappling with, is AItaking over my job? What is my
purpose at my job? What jobs aregonna stick around?
(02:31):
What jobs aren't gonna stickaround? From your perspective,
what does that mean? Likehelping people do good work, or
from your perspective, what doesthat mean kind of in in light of
the current ecosystem in whichwe live?
Jason (02:43):
Yeah. Well, I just think people wake up in the morning
and they want their jobs tomatter. Wanna feel like they're
making a difference. And so theythey want they wanna build
relationships. They wanna buildcommunity.
They wanna they wanna know thatwhat they're doing is having a
strategic impact on thecommunity they're with, the
people they're with, the thepeople they're spending their
day to day with. And I thinkwhen that happens, you work
(03:04):
different. I I I think mygrandfather read this story, but
he he would tell it to me oftenwhen talking about, you know, he
knew someone that worked at afactory and spent all their time
working basically just doing thesame thing over and over again,
was happy as he's ever been. Andthe new guy had come in and was
doing the same thing and justfelt miserable the whole time
(03:25):
and felt like he was it wasmeaningless and that what he was
doing didn't really matter atall. And couldn't understand why
this older guy was so thrilled.
And he's like, all we're doingis putting the same screw into
the same hole and all thesedifferent different vehicles or
whatnot. And the older guy goes,no. I'm protecting my kids. I'm
protecting my children. I'mprotecting the the millions of
families that are gonna buy thiscar because I put this thing on
(03:47):
right.
So his version of meaningfulwork made him joyful about the
day to day stuff that he wasdoing. So I got really
passionate around that. I waslike, how can we how can we help
people know that what they'redoing matters and help give them
some context around that? And sopart of that is finding the
things that feel meaningless andremoving that. And that's really
(04:08):
where our our organizationstarted, almost fifteen years
ago now, was how can we findthings that that people feel in
their day to day isn'timportant, isn't adding value,
isn't adding meaning, and howcan we move that aside so that
they can focus on the stuff thatis unique to them, that allows
them to bring that value?
And that's different foreverybody. It's not like again,
(04:30):
the grandpa putting on the thescrew was happy as could be
because he was making adifference and the 25 year old
was feeling meaningless. Like,they were doing the same job. So
it's not the job you're doing,it's the impact that you can
have when you are able to getthe things out of your way that
mentally keep you from beingeffective.
Daniel (04:47):
Do you think that in our world of AI influencing every
job at almost every level of anyorganization, in your
interactions day to day with thefolks that you serve and work
with, do you see that shiftingin terms of what people think is
(05:08):
meaningful or is there maybejust more fear that what they
feel is meaningful might begoing away or or or something
like that?
Jason (05:17):
You know, most of the people that I interact with are
afraid that that theirleadership is gonna see their
work as not meaningful. Andthat's where I think the fear is
coming in is this uncertaintyaround how leadership's gonna
view them. And so what they'retrying to do in my conversations
with them is they're trying tofigure out how can I make sure
(05:37):
that the leadership knows thatI'm adding value and that I'm
doing meaningful things? Andthis world of AI is throwing
some, you know, ambiguity intothat because where how they used
to add value is starting toshift. And as that starts to as
that becomes more and more,exacerbated and we're now AI is
(05:57):
doing more and more of the kindof monotonous or repetitive
tasks that you used to bringvalue around or even some of the
analytic tasks where it'sstarting to say, hey, I'm seeing
insights that you might not evenbe able to see.
We start to question our ownvalue and how we interact with
that with with the organization.
Daniel (06:15):
But I guess that's where I
Jason (06:16):
keep coming back to this as a psychological problem
because business is done withpeople. Like, business is a
people enterprise. Like, it itit always has been, and I think
it always will be. Yes. We cando tasks, and and AI can help
take some of that off.
But at the end of the day, westill there's there's still
people involved on how businessgets done. There's still
(06:37):
relationships that drive that.You know, my my daughter just
graduated from college and it'slike, how do you get a job?
Well, you go meet people. Like,it's people that help you get
jobs.
Like so it's not the automatedsystems. It's it's when you meet
someone face to face, that'swhere you find find work. So so
I think in the day to day, like,yes, some of our tasks are
(06:59):
shifting and we're afraid of thevalue that we bring, but there's
still relational components.There's still a human to human
piece that exists, and we've gotto embrace that. And I think the
more we embrace it, the morewe're gonna see our impact and
the and the meaning that we canbring to that organization is
just going to accelerate.
Chris (07:17):
I love I love kind of that. It's a it's an optimistic
take in a, you know, that isdoable and, and, you know, that
we've all kind of lived by foryears. And it's very easy in the
current environment to kind offorget that. And I think, I
think, you know, the youngpeople entering the workforce
today, you know, maybe have nothad the benefit of those
(07:39):
experiences that give someone alittle bit older, that
perspective. I am curious,acknowledging all these
industries that AI is impactingare changing rapidly now, and
that management at lots ofdifferent companies across
industries are trying tonavigate that dichotomy between
(08:02):
human interaction and the factthat we have all this amazing
automation available and they'retrying to find applications so
they they want the benefit ofthe automation to make their
businesses more efficient.
But at the same time, as youpointed out, it's a you know,
the workforce is human. And it'snot only that, but it's not
homogenous. Every situation is abit different. Every employee is
(08:25):
a bit different, the jobs aredifferent. There's a lot of
diversity in terms of bothpeople and process there.
So as you're kind of coachingpeople into this brave new world
that we're all navigating now,how do you approach different
management teams aboutnavigating those challenges and,
you know, not only finding theefficiencies that they're
(08:46):
looking for to be profitable,but also reassuring their
workforce, and all thatuncertainty that there is a
place for you the employee inthe future. Because I know a lot
of a lot of people out there arelooking for that right now. I'd
love any insight you have interms of how how you tackle
that.
Jason (09:06):
Yeah. That's a fun question. That's a lot there's a
lot to unpack there. Here here'swhat I believe. At least where
AI stands today, it does nothave access to all of the
context in order to make thedecisions that a business
actually wants made.
And therefore, the real valuethat happens is in the
conversation with people tosolve problems that, frankly,
(09:29):
the technology can't evencomprehend because it doesn't
even know exist. So from amanagement standpoint, my my
first approach is get the peoplein the room on a Zoom call, get
them in a place, havingconversations about the real
challenges you're dealing with.Now, can we be more efficient
around that? Yeah. Let's recordthe let's record the
(09:50):
conversation.
Let's pull a transcript. Let'shave the AI extract some
actionable things from that.Even go so far if you're in
software, let the AI code thething you all just talked about.
But at the end of the day, thereal value is the conversation
that happens because we're allaware of the context of what
we're trying to solve. So youbring in, you know, Chris, your
perspective and Daniel, yourperspective and my perspective,
(10:12):
put us all into a room.
We're coming up with a verydifferent solution than if I'm
just sitting in the room bymyself with AI. And so I I just
I embrace the yes. Let's be AIfocused. Let's make sure that
we're leveraging these tools.That's just good business.
Like, it's a tool. We should beusing it. You know, I I I don't
buy a a a drill or a powerscrewdriver and then sit it in
(10:35):
my garage and not use it. I buyit to use it. So let's use it.
It's good business. But let'suse it in the right ways. And to
use it in the right way, youhave to start with people. And
so I just think from amanagement standpoint, it's the
same formula. It's not reallychanged, though it feels like it
has.
Daniel (10:53):
And do you think part of that kind of responsibility
that's growing for management inthose cases is, part of what
I've seen is maybe there's afrom the top statement that goes
out, you know, we will transformour business with AI, right? And
(11:13):
actually the management isn'talso using AI or leading kind of
by example in that. Andcertainly there's, management
I'm sure that are, but how doyou think about, I'm wondering
your perspective on kind of likethose of us that are supervisors
or those of us that aremanagers, how we can actually
(11:37):
lead by example in showing howkind of, I guess, human and AI
can team together to, like yousay, create efficiencies, but
also bring in that valuablehuman element like you're
talking about?
Jason (11:52):
Yeah. I I think an interesting thing I've observed
with my team, because I can talkspecifically about how they're
working. We set out and we'relike, we're gonna be AI driven.
And so we looked at our currentworkflows. We looked at all the
steps of our current workflows,and we're like, which of these
workflow pieces of the workflowcan we, you know, either replace
with AI or augment with AI insome way, shape, or form?
(12:13):
And so we went through all ofour so we're soft we do a lot of
software development. Right? Sowe have an SDLC. So we're going
through all the steps of ourSDLC, and we're like, oh, we can
automate this step. And so webreak it all down.
Here's what ended up happening.It was like we were more
inefficient. We we startedhaving all these weird, like,
oh, we gotta move stuff intodifferent places for AI to help.
And it was like, this isn't thisisn't right. We're we're
(12:36):
inefficient in our approach.
When we took a step back andwe're like, what if we assumed
that AI could do things thatright now we've assumed it can't
do? Like, so what if we'd, like,completely rethink our entire
software development life cyclefrom a concept of maybe AI could
do all the things that we weretrying to do? So, like, for
instance, right now, we createa, like, a product requirements
(12:57):
document. It's like so when wewere looking at doing AI, we
said, hey. Let's have AI createthe products requirements
document.
Okay. It did it did okay, andthen we had to work with it.
What if we don't need a productrequirements document? Like,
what if we can all sit like,what if we rethink the entire
workflow? And what if we recordthe conversation we're having
(13:17):
around what the feature shouldlook like and then take that
straight to a AI project plan onhow to execute that in the code.
Skip the seven steps in betweenaround how that's working. Maybe
that's viable. So I say that tosay, I think we have to start
from, like, from scratch. Ithink we have to take a step
back and kind of throw out theway we do work currently and
(13:39):
rethink from a whole newperspective because the tools
are they just work different.And and they're they're tools
that we we don't even know howto think about yet.
And so you've got to challengeyour kind of base assumptions
for you to actually start to toput real tools and real power
into place. So that's where Ilike to start is like, let's,
(13:59):
you know, let's start fromscratch. Like, literally, let's
throw out what we had. Andthat's hard. Change management
is hard because that wassomebody's five years worth of
effort to get that SDLC right.
And we're saying, hey. What ifwe don't do it? Like so that
there there is a a relationalthing, a relational management
(14:19):
piece that needs to happen thereto make that accessible. But
when you do, all of a sudden,what used to take us weeks is
starting to take us days becausethe tools are that powerful.
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Chris (15:45):
So Jason, I wanted to actually even go a little bit
farther into that what you weredescribing, because that I love
where you're taking that interms of kind of reimagining
that. I think I think a lot ofpeople stumble at that point in
terms of what it takes toreimagine, and trying to figure
out like, as they're looking attheir own process, and they're
(16:08):
they're looking at trying tofind places to fit AI
capabilities in to solve theirproblems more efficiently than
they had before. You know, itcomes down to you know, once
upon a time, it was kind of justlike the mundane tasks. But then
we you know, generative AI camealong and like the the
imagination, if you will, of AIis is remarkable. And so I think
(16:31):
that's led to a lot of ambiguityor confusion in terms of where
the all the the plethora of AItools can can bring those
benefits on on that.
Can you talk a little bit aboutlike, how do you get someone to
start down that path and levelset the initial assumptions that
they're gonna make in their ownprocess, recognizing it's a
(16:53):
little bit different acrossorganizations and teams? How do
you get there? Because I mean,AI is so capable now that trying
to find that that point ofapplication that just getting
out of the the the startingblock, I think, is a challenge.
Jason (17:06):
So take a slightly different approach to that and
the way that I answer that. Myson's a a musician. He's an
artist. He writes he writes alot of music. When he sits down
to write a song, we often end upin writer's block.
We end up just stuck. We'relike, I don't know what to do.
(17:28):
So he starts playing with it,he's like, I know the chords. I
know how to put them together. Ican play licks all day long.
Nothing's connecting. What Ifind though is when I say, hey.
Let's pick up a cover song.Let's grab the Beatles. Let's
start rocking on the Beatles alittle bit.
And we just start goofingaround. And we're just fiddling
around playing the Beatles,singing it together, and and,
(17:49):
you know, all of a sudden, he'lllook at me and he'll go, oh, I
have an idea. And then he startsriffing on a new song. So
sometimes it's just the practiceof playing and letting your mind
go that lets you create newideas. And and I think the same
thing applies in AI.
If we're not just being creativeand playing with it, I'm not
(18:13):
sure we're gonna find creativenew ways on how to plug it into
our process. So we've gotta justembrace that it's gonna be
messy, but we gotta play. Like,my son and I, we don't play the
Beatles perfectly. We've beenworking on the Eagles recently.
Right?
Like, you know, little sistergolden hair. And it's like, we
can play on this sounds awesome.But though by just playing those
(18:35):
chords and seeing and seeing howother people are doing it, it
starts to get your mind thinkingdifferently. Same thing with AI.
You might not have a great usecase for it right off the top of
your head.
Part of that is because we don'teven know how to think about it
yet until we start playing withit enough for our minds to start
to make those connections thatwe never would have made without
(18:56):
it. So I encourage people justto get in and start throwing
stuff at it. Not because they'renecessarily gonna get a lot of
great value out of what they'rethrowing at it, because most of
the people I talk to are we'restill treating it like Google. I
mean, like like, we haven'treally pushed it to the new
limits. But by getting in andasking it those questions and
seeing what it's doing andlearning just little bits and
(19:17):
pieces here and there, all of asudden, they start to go, oh,
what if?
And because they have theexperience and the connection to
their processes and the way theydo work, it naturally goes to,
oh, man. If I didn't have to dothese five things, can you
imagine what I would do with mytime instead? But they don't
know to think about it becausethey've not played with it
enough to even know. So I likejust to play. Like, it's a it's
(19:40):
unconventional, but I think itdoes it is what spurs the
creativity.
Daniel (19:45):
And maybe part of that play is what leads you to have
some of these insights aroundlike, oh, maybe the automation
or maybe the AI driven processshould actually look different
than its human equivalent,right? Otherwise you sort of
don't know where it should lookdifferent or where it shouldn't.
(20:06):
I always, I love, read some ofRichard Hamming's work and some
of what he talks about is likeseeing so many different
generations of technology thathave mechanized certain
processes or automated certainprocesses. Basically his
observation is the ones that endup transforming things are those
(20:29):
that do a process but in a waydifferent than the equivalent
human process. And I thinkthere's kind of the common way
of saying this is like, if youautomate some well established
process, you just sort of get avery efficient bad process, not
a new transformative process.
(20:50):
I find this though very, verydifficult in actual
conversations with people tohave them grasp this idea
because the tendency is for usto say, well, I have this
process that X person does in mycompany. I would like to
automate that now. And I knowyou think about automation all
(21:11):
the time. How do you get peopleto get Maybe part of it is
building that intuition withgetting hands on with the tools,
but how do you get to that pointof from here is our process that
we execute manually to the hereis how we might do it with an AI
(21:34):
augmented approach, which isnecessarily, I I mean, if it's
going to be transformative, likewe're kind of making this
assumption should look differentthan a human process.
Jason (21:44):
Yeah. And you're so right. Almost every client I
could start with is, here's thething I want you to automate it.
Yes. Here's a task I want you toautomate it.
And I'm like, you could havedone that ten years ago. Right.
A lot of what you're asking todo is not new. So, there is this
this kind of a mental gap or ahurdle between, you know, pure
automation and what AI iscapable of doing. To answer your
(22:06):
question on how we actually getto the the transformative, I I
do think depending upon who'shaving the conversation, there's
different ways you can go aboutit from a conversational
standpoint.
I tend to be pretty high energy,and I tend to be very
inquisitive. So that plays to mystrengths. So when I'm in there
having a conversation, I'm like,oh, we're excited. Look at all
the cool stuff we can do. Hey.
(22:27):
Check this out. Like, I'm highenergy with them all. And then
they say, well, here's whatwe're doing. And I'm like, oh,
why are doing that? Really, I'musing the five why strategy of,
like, I'm digging into what isthe root cause of why they're
actually making the decisions inthe process they're making so
that I can then start to offeralternatives around, if this is
the outcome you really want andyou did these six steps to get
(22:50):
to that outcome, maybe there's adifferent way we can get that
same outcome.
And maybe the the six stepsaren't necessary anymore. But
for me, I do that through justhigh energy and creative,
inquisitiveness. So
Chris (23:04):
As you dive into that, I'm curious. And you kinda
talked about the, you know, thatinitial engagement with the CEO
and stuff like that. But to toto kinda circle back and bring
the employees in, as you'regoing into that process, how do
you wrap those employees into itin a in a positive and
productive way, so that theyalso see that same value that
(23:24):
you you know, you automaticallydo because you're doing this,
but that the CEO is as well asstarting to see so that they're
engaging, you know, with thatsame energy and creativity
instead of instead of worry orconcern, that kind of thing. How
do you how do you navigate thathuman element as you're going
through the beginning of thisprocess, flow?
Jason (23:46):
Yeah. So being candid, I'm still figuring a lot of that
out.
Chris (23:50):
As are we all, I think. Yeah.
Jason (23:51):
So so that that is hard. Yeah. That being said, we do
know that change management is ascience. It has been studied. I
am reading on it constantlyright now.
So I I think the same principleswill apply. I do think there is
an idea a concept of leadershiphas to buy in. If leadership's
(24:13):
not bought in, the cost ofchange is gonna be too great. So
we've gotta get leadershipbought in. Once we have
leadership bought in, then I dothink there is some idea of I
hate the word committee, butbringing in people who are on
the front lines and are actuallydealing with the reality of work
to be a part of theconversation.
I also think it's important forthe the leadership to be in the
(24:35):
room and reaffirming intent. Iknow there are scenarios, and
I'm not ignorant enough to saythat there aren't scenarios
where AI is gonna replace jobs.I know there are. Most actually,
all of the businesses that we'veworked with understand that
we're not in a we're not lackingwe're not we're not wanting
people to leave. We're wantingpeople to work on other things.
(24:59):
So they're not coming in going,we're trying to eliminate place
positions. They're coming insaying, we have so much
opportunity that we can't takeadvantage of. We wanna get you
moving in the in these otherplaces. Leadership's gotta be
real vocal about that and, hasto be very, very clear and
transparent on that front. So II think we gotta get the front
line in place.
I think we gotta get leadershipcommunicating expectation, and
(25:21):
then we've gotta create and thisis what I'm trying to figure out
right now, is how to createenvironment where we can play in
a safe place to start to spursome of that creativity. Yeah.
I've not figured out how to dothe the the play part yet where
it's fun. It still feels likethey're learning engineering
stuff.
Daniel (25:38):
Yeah. And some of this too, I guess some of the points
that you made around likedifferent people being motivated
by different things. Certainlyin the If we just take AI out of
the picture, if a business isoperating and someone really
just loves to write handwrittenletters, right? And they're
(26:00):
like, I'm not gonna send anyemails. I'm just going to write
handwritten letters to everyoneand we're gonna communicate that
way.
Obviously there's ainconsistency with how actual
business operates now. Maybepart of the disconcerting thing
right now is that we sort ofdon't quite know how business
(26:21):
will operate as things kind ofas this adoption happens, right?
So we don't know if doing thething that is the equivalent of
writing handwritten lettersinstead of sending emails,
right? And I guess that's stilljust, I mean, it seems like
sometimes, so I'm also curiouson your perspective on this,
(26:46):
Jason, because it seems likesometimes that if we look out at
the world, every business isusing AI pervasively. And
sometimes like I got on a callthe other day and I asked like,
Hey, are you using AI any way inyour business?
And the response was, Oh,everybody is. What kind of
question is that? In myexperience, actually, that is
(27:09):
very much not the case. I'mwondering about your experience.
Maybe just to give peoplecomfort that are listening to
this, most of the calls I get onfolks are are not what I would
consider having adopted AI andare using it pervasively across
their business.
Would you what what is yourexperience? I'm hoping it's the
(27:31):
same.
Jason (27:31):
It is it is exactly the same. Everyone says they're
embedding it into every part oftheir processes, and nobody is
actually doing it in any way,shape, or form effectively. And
so, you know, one of theconversations I have with
clients most often is they'relike, well, I'd love to use it
more, but every time I wanna goask it a question, I have to
(27:52):
spend fifteen minutes telling itall the background information
so it'll answer itappropriately. I was like, yeah,
that seems reasonable. Like likethat that was what I would
expect.
So those are most people are notusing it. And if they are, CEOs
like to say they're using itbecause they're using it to
generate social media contentand marketing and some other,
like, you know, they roll rightemails for them, things along
(28:14):
those lines. But actually usingit in a productive, way that's
creating efficiency, I I runinto very few companies that are
doing that.
Chris (28:23):
You know, I've been, I've been kind of pondering one of
your previous answers as we'vecontinued talking in the back of
my mind. And, you know, youtalked about like the navigating
the human side is, you know,there's the psychology around
it. And, and it occurs to mejust to just to throw it out
that it's, it's, we have a habitof framing, you know, bringing
(28:44):
AI in as a new problem. But in asense, it's really not. Because
if you go back before, you know,this moment, where where we have
generative AI and other AImodels that we're bringing in,
And it's creating that sense ofuncertainty.
If you go back in time, and I'mgonna I'm gonna make a reference
that maybe will make you smileto like the movie office space.
(29:04):
And at that point, they had kindof a two comic characters, which
maybe some folks in the audienceremember called the two bobs,
which were talking aboutprocess. And the employees in
the company were very concernedabout their jobs, and they were
going through that. And as itoccurs to me, it's much the same
concern, it may not be whetherthat process, know, which had
(29:25):
nothing to do with AI or reallyeven technology in the movie,
you know, but but that thatnotion of process automation
being a frightening thing foremployees to be thinking about.
Am I safe?
Am I gonna be okay? And it'sstill really what we're talking
about now. We're just talkingabout kind of AI as an actor in
that in that sequence. So and Iknow one of the points of the
(29:46):
movie is you're still trying tofigure it out. Humans are
complicated or emotional.
There's not a quick answer thatpeople just get happy about. And
as you were saying veryhonestly, which I really
appreciated, that you were like,you know, that you were still
trying to learn your way intothat, which I think is a
fantastic answer because it's soit's so honest that, you know,
it's not just reframing. I'mjust wondering is is I throw
(30:09):
that that kind of office spaceanalogy. Does that resonate with
you in terms of do you do youthink there is a a truth there
that is sore somewhat timeless?And is there anything, you know,
what is new potentially in theAI being thrown into the
equation on top of that officespace timeless aspect?
Jason (30:28):
Yeah, that resonates deeply. Actually, I'm gonna have
to go pull out that clip and useit in my next talk. It's the
same problem y'all. Yeah. We'reliterally like like, we don't
like change.
Like, no one does. You know,we're we're very happy to know
what's like, how things arebeing done and not have you
know, don't move my cheese typeof idea. Right? So, you know,
(30:49):
these these problems have havebeen around for a while. So this
is a new flavor of it.
And it's one that feels like themagnitude of the of the wave is
larger. It feels like it's gonnamaybe rock more boats than other
changes in the past have, butbut it's the same problem. And
so I I feel like we we should beable to, leaders, be able to use
(31:14):
the the same tools that havebeen used in times past to help
people navigate this currently.And I think that should bring
comfort to us, because it's weare solving somewhat of a
similar issue. Now, again, I Idon't wanna downplay the impact
of a of artificial intelligence.
Its impact is significant. Likelike, there there is a lot that
(31:36):
this is going to impact. But I Ido think we're, people wise,
kinda dealing with the sameissues.
Daniel (31:42):
And this kind of leads into something I I would love to
talk about because I I get thesense just having seen what what
you all are doing with ProcessCoach, which is one of the
things that that you're offeringas a product, which we were
talking about this a little bitbefore we started recording. I'm
all for anything that's not justanother chat bot. I love it when
(32:07):
people are thinking aboutdifferent ways of interacting
with AI and integrating it intotheir kind of business processes
or the way that they work,which, you know, as folks have
already, have heard from you,you are thinking all the time
about, you know, the way peopleare working and what they find
meaningful. But I'm wondering ifyou could just give us a little
(32:29):
bit of backstory on ProcessCoach, because I do think, you
know, the way that you'reapproaching this integration of
AI and the way that you'rethinking about automation and
what you're doing maybe isdifferent than of the ways that
other people are approaching it.
Jason (32:45):
Yeah. Well, we're pretty excited about this this tool,
mostly because, again, we're wethought if you're a business,
particularly a small and midsized business that's trying to
figure out how to navigate this,and you're trying to get people,
your team in line and and and,like, hey, this is actually
gonna be valuable to you.There's a lot of fear. There's a
(33:07):
there's a lot of a lot of thingsthat kinda stay in your way. And
and a lot of that comes down tohow do we get the day to day
operations so that it actuallyis leveraging AI in some way,
shape, or form.
So when we took a step back andwe're talking to our clients and
looking at at everything, we'relike, you know, MBAs all say, to
scale your business, you have tohave business process. And I
(33:28):
think we all know that to acertain degree, that, you know,
if you don't have somethingstandardized, it's impossible to
really to improve it. So weunderstand that, but very few
people, I think, have actualizedit. And we thought, man, if we
could create an environmentwhere we could simplify figuring
it out, get it get it defined,and then actually make it
(33:48):
useful. So a lot of theautomation around process ends
up getting too fragile.
I don't know if if you'veexperienced that or not, but,
like, the high level, sure.Those are those deterministic
steps, like, gather all theinformation, put it all in the
CRM. Like, okay, those you canpretty much easily go down the
line. But when you get into,like, what does gathering all
the information mean? Andthere's, like, a million little
(34:10):
issues in the decision tree.
Well, and I can help solve a lotof that. So if you combine the
two together and we create astandard operating procedure, we
actually call them plays becausewe think more in that that,
like, playbook mindset. But youyou create a play that's like,
here's how we're gonna solvethis problem. And we let AI
manage the context of all theinformation that's happening
(34:30):
from step to step and fromperson to person. All of a
sudden now my job becomes prettyeasy.
And what we end up seeing is,you can interact with the with
the AI agent and say thingslike, I don't know what to do
here. What would you do? And itwill go, well, I have the
context of what's happening. Ihave access to your various
tools. Let me go do someresearch and answer it for you.
(34:53):
And the answers are pretty good.And so we're freeing people up
from the monotony of hitting alltheir websites, doing all their
things, all these tools, they'reable just to run a standard
operating procedure and it andthe AI is kind of managing how
it all gets done. This opens upsome options for business. And
that that's what we're we'repretty excited about. I'm still
(35:15):
learning how to talk about it.
Alright? The product isrelatively new. But, as we're
we're getting it in people'shands, this rethinking of how
your processes look, Now put ontop of, I have access to your
tools. I have access to yourknowledge base, and I'm managing
who gets assigned what so thatwe're all working on a process
(35:36):
together. All of a sudden now,we just have a much, much wider,
much richer, deeper context forthe conversation that's actually
happening in between us aspeople and between the computer
so that it can do the work foryou.
And that's pretty cool. Andwe're getting some really neat
outcomes from that.
Chris (35:51):
Yeah. It strikes me that that you're kind of you're kind
of fulfilling that thing thatyou're talking about before, in
that, you know, as we havetalked over the course of the
show about the the psychologyand the expectations, the humans
involved, that you're bringingyour tool, and you're putting it
not only in front of themanagement, but as you talked
(36:12):
about kind of a committee of thepeople that are doing the work,
where all that knowledge iscurrently residing and helping
them without replacing them.You're helping them do better
things with that knowledge. Andso you're you're you're sort of
giving those humans a bit ofsuperpower for free without it
being a threatening thing. AndI've just I'm I'm wondering if
(36:34):
maybe by using the tool itselfin that context, it's actually
kind of starting to assuage someof the concerns and fears that
those employees might otherwisehave.
It seems like a fantasticstrategy to give them a good
experience upfront that thatstarts off by saying, I'm kind
(36:54):
of implicitly acknowledging yourfears, but here's an experience
to to guide you going forward.
Jason (37:00):
Yeah. You're dead on. And that's what that's what I get
excited about it. And I'll eventalk with the leadership. It's
like, hey, let's not go in andjust throw a whole agent in
there and say, hey, we now haveagents is off and running.
Nobody trusts them. Like, I II'm cool with agents, but let's
let's ease into it a little bit.Let's let the let's let the team
become comfortable with it. Sowe create this hybrid where we
(37:23):
literally start off and we'relike, let's create the process,
you know, a through z, and eachstep is getting handed to a
person. And we interact with,like, teams, Slack, email, text
message, so that they're nothaving to log in to another
system.
Right? So it kinda feels likeyou're talking to just an
external employ external teammember. And so you're just
having a conversation. It'sasking you questions. Hey.
(37:44):
I just had a great meeting withDaniel. Help me onboard him.
Hey. In order to do that, I needto know the company. I need to
know and it's asking youquestions.
You're just kinda going back andforth with it. Then as you get
tools and the AI tools and MCPservers, everything open up,
then it starts going, you justwant me to add that for you?
Yes, please. It now starts tofeel like it's a helpful
(38:05):
assistant until eventually youget to the point and AI gets
smart enough because every newmodel that comes out is getting
better. You now get to a pointwhere it's like, just do it for
me.
Like like, I've gotten to apoint where I trust you now, and
so just do it for me. That's ourstrategy for adoption is, you
don't have to train anyone howto use email. They know how. So
(38:28):
change management barrier goesdown. It's just asking them
questions.
Get it so that they feel morecomfortable over time and that
it's doing work for them. Andthen eventually flip it into
agent mode and it just goes offand does it. But you know what
it's doing because you were inthe loop while it was getting,
structured in the way that itshould be executing in the first
place. It's just a veryaccessible way for small and mid
(38:49):
sized businesses to get theirteam comfortable with what AI is
is going to be able to do forthem.
Daniel (38:54):
Yeah. I I love that fact of the way that you're tying
into things that people arealready using. And also I've
seen, of course, I actuallyreally I do really like these
systems where for me personally,that like, oh, I can drag and
drop and create this like DAGpipeline to automate and like
create these things and makecustom API calls and all this
(39:15):
stuff. And I love that. It's agreat user experience for me.
But putting that in front of adifferent audience, it's so
overwhelming and terrifying. AndI think one of the things that
you showed me at one point isthis kind of, just like describe
your process in words, right?Yeah. Upload that in words, not
(39:38):
as a prompt. So you don't haveto learn how to prompt.
You don't have to learn how tolike build this DAG pipeline.
You essentially describe likeyou would to maybe an intern or
a new hire or something likethat. So I'm wondering, was that
initially kind of part of thingsor was that another one of these
(40:00):
like, hey, we tried to tellpeople how to use this DSL or
something and they like had noidea how to do it. How did that
perspective come out, I guess?
Jason (40:11):
So as with any SaaS product, we're on our third
version. Yeah. The secondversion was all drag and drop,
like what you talked about. Andit was like a and we would show
it to business leaders, youknow, CEOs, presidents,
operations officers. Andimmediately, every time they're
like, oh, go talk to IT.
Like, we're not even gonna lookat it. It's like, well, I I
(40:34):
appreciate IT. I actually loveIT. We do IT. Like like, I love
all of that.
But if you're gonna get AI inyour business, the leadership's
gotta be doing it. So theconversation was, what does it
need to look like for you toactually engage what your AI
agents are gonna be doing? Andit basically came back as I need
to be able to read it kinda likeit's a standard operating
(40:54):
procedure because I understandthat. That's my world. So if I
can read it and it's like, oh,this is the order it's gonna get
done.
Here's what's gonna get donealong the way. I can comprehend
and be like, no. I don't wantthat to happen. Okay. Well, how
would you change that?
Tell me in plain English, andthey can rewrite it. And
suddenly now it's accessible. Sowhen you start to give tools and
(41:17):
we don't have this tool yet.It's on our road map, but I'm
really excited about it coming.Because the processes are
managed by AI, we can do ABtesting of processes.
Like, that's pretty unique. Andif you can, as a small or mid
sized business, say, here's myprocess now, I'm gonna make some
changes and I'm going tobasically say, take version a,
(41:37):
send 50% of my runs there, takeversion b, send 50% there, and
now compare the efficiency andwhat's actually happening, the
amount of time we're spending onit, like, that allows you to be
scientific in how you start toscale your process. So I I get
pretty excited about that kindof stuff, but people aren't
gonna do that if it feels tootechnical. It's gotta feel
extremely accessible, which iswhy we have to go with with,
(41:59):
like, plain English, to get themto engage it.
Chris (42:03):
Works. So Jason is as we start winding up, and you're
kind of looking out at you asyou're kind of pioneering, you
know, process change and, andhow to make this work with these
new tools and how people canreimagine it in a in a way
that's different from whatthey've done before. As you're
looking forward at this fieldand kind of the the
(42:24):
possibilities, you know, so notnecessarily what you're doing
now or what's on the immediateroadmap, but kind of out, Like,
what how do you see thisevolving over time? How do you
see the future looking in termsof how companies as they move
forward and and adoption becomeswidespread, but you're pushing
the limits a little bit onwhat's possible at each point in
(42:46):
time. How do you see thisunfolding for companies?
And what are some of the thingsthat people might look forward
to in in the, you know, over thenext few years?
Jason (42:56):
Yeah. The the thing that I'm starting to imagine is
happening, I kind of I'm notsure that business leaders are
really gonna be interacting withcomputers. I think it's gonna
all come down to their phone,and there's gonna be some kind
of just voice conversationthat's happening. And behind the
scenes, if you're able to uploadyour processes into a, again, a
(43:18):
external team member, and itknows how to execute them, and
you can kinda manage from there,It's a whole lot easier for me
just to pick up my phone andhave a conversation with an
external team member that can dothat work for me than it is for
me to log on and try and figureout what to do. And I see that
coming pretty like, in the nextyear, I kinda feel like that's
gonna be have to be a majorthing.
We're not imagining it how to doit right now. I'm just now kind
(43:41):
of envisioning that this isgonna be, like, probably the
main use case. Because I justthink the way that with with the
power of AI and the way that itunderstands that the intent of
conversation and it's able tologically break down what it is
we're actually trying to say,the computer itself is gonna
(44:01):
become less and less of a focalpoint of business and is gonna
become more of just an expectedconversation that's happening
behind the scenes. And I I feellike we're gonna have to embrace
that really quickly. And I don'tknow what that looks like.
I that's really hard to dreamabout. Like, I mean, my whole
life, I've had a computer.Right? Like like, I can't
imagine what it looks like tobasically say, I am going to not
(44:22):
have one. I tried to do, like,an iPad for, like, six months,
and I couldn't handle it.
Right? Like, so what does thatworld look like? I that's hard
for me to to to imagine how wecould get to that kind of a
place. I mean, it's kind of theStar Trek button. Right?
Like, hey, computer. Go do this.Like, that kind of idea. But I
think that's real. I think it'scoming.
(44:43):
If it's I mean, it's probablyalready here. I'm just not
caught up yet.
Daniel (44:46):
So Well, hopefully, I I get my pen sometime in 2026. I'm
I would I would look forward totrying it for sure. Really
appreciate your insights today,Jason. It's been a real
pleasure. I appreciate the waythat that you and the RoboSource
team are innovating.
Thanks for taking time to talkto us. Really appreciate it.
Jason (45:05):
Well, I had a lot of fun. Thank you both, and it was just
it was a fun conversation.
Jerod (45:16):
Alright, that's our show for this week. If you haven't
checked out our website, head topracticalai.fm and be sure to
connect with us on LinkedIn, X,or Blue Sky. You'll see us
posting insights related to thelatest AI developments, and we
would love for you to join theconversation. Thanks to our
partner Prediction Guard forproviding operational support
for the show. Check them out atpredictionguard.com.
(45:38):
Also, thanks to BreakmasterCylinder for the Beats and to
you for listening. That's allfor now, but you'll hear from us
again next week.
Welcome to the Practical AI Podcast, where we break down
the real world applications ofartificial intelligence and how
it's shaping the way we live,work, and create. Our goal is to
help make AI technologypractical, productive, and
accessible to everyone. Whetheryou're a developer, business
leader, or just curious aboutthe tech behind the buzz, you're
(00:24):
in the right place. Be sure toconnect with us on LinkedIn, X,
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practicalai.fm.
Now, onto the show.
Daniel (00:48):
Welcome to another episode of the Practical AI
Podcast. This is DanielWightnack. I am CEO at
Prediction Guard. I'm joined asalways by my cohost, Chris
Benson, who is a principal AIresearch engineer at Lockheed
Martin. How are you doing,Chris?
Chris (01:03):
Hey. I'm doing very well today. How's it going?
Daniel (01:05):
It's going great. Wrapping up the year, reflecting
over the past year. It's alwaysa kinda time of the year to
reflect, and it's been a greatyear for for the podcast and for
for business and for otherthings. So, yeah, just feeling I
think feeling particularlyblessed as we kinda head into
the head into the end of theyear. And I think, also blessed
(01:29):
to have made a bunch of goodconnections this year to people
that are that I'm learning from.
One of those is our guest today,Jason Butler, who's CEO at
RoboSource. Welcome, Jason.
Jason (01:42):
Hey, thanks so much.
Daniel (01:43):
Yeah. Yeah. It's great to have met this year, I think
from a variety of directions, abunch of different connections,
but you did also joined us atthe Midwest AI Summit, which
was, fun. Chris was there. Iknow that you have a particular
passion for kind of thinkingabout the way that people work
(02:07):
and helping them do good workand meaningful work, I guess.
Yeah. Do you wanna expand onthat idea a little bit? Because
I do think it's interesting tothink about that topic when
people are grappling with, is AItaking over my job? What is my
purpose at my job? What jobs aregonna stick around?
(02:31):
What jobs aren't gonna stickaround? From your perspective,
what does that mean? Likehelping people do good work, or
from your perspective, what doesthat mean kind of in in light of
the current ecosystem in whichwe live?
Jason (02:43):
Yeah. Well, I just think people wake up in the morning
and they want their jobs tomatter. Wanna feel like they're
making a difference. And so theythey want they wanna build
relationships. They wanna buildcommunity.
They wanna they wanna know thatwhat they're doing is having a
strategic impact on thecommunity they're with, the
people they're with, the thepeople they're spending their
day to day with. And I thinkwhen that happens, you work
(03:04):
different. I I I think mygrandfather read this story, but
he he would tell it to me oftenwhen talking about, you know, he
knew someone that worked at afactory and spent all their time
working basically just doing thesame thing over and over again,
was happy as he's ever been. Andthe new guy had come in and was
doing the same thing and justfelt miserable the whole time
(03:25):
and felt like he was it wasmeaningless and that what he was
doing didn't really matter atall. And couldn't understand why
this older guy was so thrilled.
And he's like, all we're doingis putting the same screw into
the same hole and all thesedifferent different vehicles or
whatnot. And the older guy goes,no. I'm protecting my kids. I'm
protecting my children. I'mprotecting the the millions of
families that are gonna buy thiscar because I put this thing on
(03:47):
right.
So his version of meaningfulwork made him joyful about the
day to day stuff that he wasdoing. So I got really
passionate around that. I waslike, how can we how can we help
people know that what they'redoing matters and help give them
some context around that? And sopart of that is finding the
things that feel meaningless andremoving that. And that's really
(04:08):
where our our organizationstarted, almost fifteen years
ago now, was how can we findthings that that people feel in
their day to day isn'timportant, isn't adding value,
isn't adding meaning, and howcan we move that aside so that
they can focus on the stuff thatis unique to them, that allows
them to bring that value?
And that's different foreverybody. It's not like again,
(04:30):
the grandpa putting on the thescrew was happy as could be
because he was making adifference and the 25 year old
was feeling meaningless. Like,they were doing the same job. So
it's not the job you're doing,it's the impact that you can
have when you are able to getthe things out of your way that
mentally keep you from beingeffective.
Daniel (04:47):
Do you think that in our world of AI influencing every
job at almost every level of anyorganization, in your
interactions day to day with thefolks that you serve and work
with, do you see that shiftingin terms of what people think is
(05:08):
meaningful or is there maybejust more fear that what they
feel is meaningful might begoing away or or or something
like that?
Jason (05:17):
You know, most of the people that I interact with are
afraid that that theirleadership is gonna see their
work as not meaningful. Andthat's where I think the fear is
coming in is this uncertaintyaround how leadership's gonna
view them. And so what they'retrying to do in my conversations
with them is they're trying tofigure out how can I make sure
(05:37):
that the leadership knows thatI'm adding value and that I'm
doing meaningful things? Andthis world of AI is throwing
some, you know, ambiguity intothat because where how they used
to add value is starting toshift. And as that starts to as
that becomes more and more,exacerbated and we're now AI is
(05:57):
doing more and more of the kindof monotonous or repetitive
tasks that you used to bringvalue around or even some of the
analytic tasks where it'sstarting to say, hey, I'm seeing
insights that you might not evenbe able to see.
We start to question our ownvalue and how we interact with
that with with the organization.
Daniel (06:15):
But I guess that's where I
Jason (06:16):
keep coming back to this as a psychological problem
because business is done withpeople. Like, business is a
people enterprise. Like, it itit always has been, and I think
it always will be. Yes. We cando tasks, and and AI can help
take some of that off.
But at the end of the day, westill there's there's still
people involved on how businessgets done. There's still
(06:37):
relationships that drive that.You know, my my daughter just
graduated from college and it'slike, how do you get a job?
Well, you go meet people. Like,it's people that help you get
jobs.
Like so it's not the automatedsystems. It's it's when you meet
someone face to face, that'swhere you find find work. So so
I think in the day to day, like,yes, some of our tasks are
(06:59):
shifting and we're afraid of thevalue that we bring, but there's
still relational components.There's still a human to human
piece that exists, and we've gotto embrace that. And I think the
more we embrace it, the morewe're gonna see our impact and
the and the meaning that we canbring to that organization is
just going to accelerate.
Chris (07:17):
I love I love kind of that. It's a it's an optimistic
take in a, you know, that isdoable and, and, you know, that
we've all kind of lived by foryears. And it's very easy in the
current environment to kind offorget that. And I think, I
think, you know, the youngpeople entering the workforce
today, you know, maybe have nothad the benefit of those
(07:39):
experiences that give someone alittle bit older, that
perspective. I am curious,acknowledging all these
industries that AI is impactingare changing rapidly now, and
that management at lots ofdifferent companies across
industries are trying tonavigate that dichotomy between
(08:02):
human interaction and the factthat we have all this amazing
automation available and they'retrying to find applications so
they they want the benefit ofthe automation to make their
businesses more efficient.
But at the same time, as youpointed out, it's a you know,
the workforce is human. And it'snot only that, but it's not
homogenous. Every situation is abit different. Every employee is
(08:25):
a bit different, the jobs aredifferent. There's a lot of
diversity in terms of bothpeople and process there.
So as you're kind of coachingpeople into this brave new world
that we're all navigating now,how do you approach different
management teams aboutnavigating those challenges and,
you know, not only finding theefficiencies that they're
(08:46):
looking for to be profitable,but also reassuring their
workforce, and all thatuncertainty that there is a
place for you the employee inthe future. Because I know a lot
of a lot of people out there arelooking for that right now. I'd
love any insight you have interms of how how you tackle
that.
Jason (09:06):
Yeah. That's a fun question. That's a lot there's a
lot to unpack there. Here here'swhat I believe. At least where
AI stands today, it does nothave access to all of the
context in order to make thedecisions that a business
actually wants made.
And therefore, the real valuethat happens is in the
conversation with people tosolve problems that, frankly,
(09:29):
the technology can't evencomprehend because it doesn't
even know exist. So from amanagement standpoint, my my
first approach is get the peoplein the room on a Zoom call, get
them in a place, havingconversations about the real
challenges you're dealing with.Now, can we be more efficient
around that? Yeah. Let's recordthe let's record the
(09:50):
conversation.
Let's pull a transcript. Let'shave the AI extract some
actionable things from that.Even go so far if you're in
software, let the AI code thething you all just talked about.
But at the end of the day, thereal value is the conversation
that happens because we're allaware of the context of what
we're trying to solve. So youbring in, you know, Chris, your
perspective and Daniel, yourperspective and my perspective,
(10:12):
put us all into a room.
We're coming up with a verydifferent solution than if I'm
just sitting in the room bymyself with AI. And so I I just
I embrace the yes. Let's be AIfocused. Let's make sure that
we're leveraging these tools.That's just good business.
Like, it's a tool. We should beusing it. You know, I I I don't
buy a a a drill or a powerscrewdriver and then sit it in
(10:35):
my garage and not use it. I buyit to use it. So let's use it.
It's good business. But let'suse it in the right ways. And to
use it in the right way, youhave to start with people. And
so I just think from amanagement standpoint, it's the
same formula. It's not reallychanged, though it feels like it
has.
Daniel (10:53):
And do you think part of that kind of responsibility
that's growing for management inthose cases is, part of what
I've seen is maybe there's afrom the top statement that goes
out, you know, we will transformour business with AI, right? And
(11:13):
actually the management isn'talso using AI or leading kind of
by example in that. Andcertainly there's, management
I'm sure that are, but how doyou think about, I'm wondering
your perspective on kind of likethose of us that are supervisors
or those of us that aremanagers, how we can actually
(11:37):
lead by example in showing howkind of, I guess, human and AI
can team together to, like yousay, create efficiencies, but
also bring in that valuablehuman element like you're
talking about?
Jason (11:52):
Yeah. I I think an interesting thing I've observed
with my team, because I can talkspecifically about how they're
working. We set out and we'relike, we're gonna be AI driven.
And so we looked at our currentworkflows. We looked at all the
steps of our current workflows,and we're like, which of these
workflow pieces of the workflowcan we, you know, either replace
with AI or augment with AI insome way, shape, or form?
(12:13):
And so we went through all ofour so we're soft we do a lot of
software development. Right? Sowe have an SDLC. So we're going
through all the steps of ourSDLC, and we're like, oh, we can
automate this step. And so webreak it all down.
Here's what ended up happening.It was like we were more
inefficient. We we startedhaving all these weird, like,
oh, we gotta move stuff intodifferent places for AI to help.
And it was like, this isn't thisisn't right. We're we're
(12:36):
inefficient in our approach.
When we took a step back andwe're like, what if we assumed
that AI could do things thatright now we've assumed it can't
do? Like, so what if we'd, like,completely rethink our entire
software development life cyclefrom a concept of maybe AI could
do all the things that we weretrying to do? So, like, for
instance, right now, we createa, like, a product requirements
(12:57):
document. It's like so when wewere looking at doing AI, we
said, hey. Let's have AI createthe products requirements
document.
Okay. It did it did okay, andthen we had to work with it.
What if we don't need a productrequirements document? Like,
what if we can all sit like,what if we rethink the entire
workflow? And what if we recordthe conversation we're having
(13:17):
around what the feature shouldlook like and then take that
straight to a AI project plan onhow to execute that in the code.
Skip the seven steps in betweenaround how that's working. Maybe
that's viable. So I say that tosay, I think we have to start
from, like, from scratch. Ithink we have to take a step
back and kind of throw out theway we do work currently and
(13:39):
rethink from a whole newperspective because the tools
are they just work different.And and they're they're tools
that we we don't even know howto think about yet.
And so you've got to challengeyour kind of base assumptions
for you to actually start to toput real tools and real power
into place. So that's where Ilike to start is like, let's,
(13:59):
you know, let's start fromscratch. Like, literally, let's
throw out what we had. Andthat's hard. Change management
is hard because that wassomebody's five years worth of
effort to get that SDLC right.
And we're saying, hey. What ifwe don't do it? Like so that
there there is a a relationalthing, a relational management
(14:19):
piece that needs to happen thereto make that accessible. But
when you do, all of a sudden,what used to take us weeks is
starting to take us days becausethe tools are that powerful.
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Chris (15:45):
So Jason, I wanted to actually even go a little bit
farther into that what you weredescribing, because that I love
where you're taking that interms of kind of reimagining
that. I think I think a lot ofpeople stumble at that point in
terms of what it takes toreimagine, and trying to figure
out like, as they're looking attheir own process, and they're
(16:08):
they're looking at trying tofind places to fit AI
capabilities in to solve theirproblems more efficiently than
they had before. You know, itcomes down to you know, once
upon a time, it was kind of justlike the mundane tasks. But then
we you know, generative AI camealong and like the the
imagination, if you will, of AIis is remarkable. And so I think
(16:31):
that's led to a lot of ambiguityor confusion in terms of where
the all the the plethora of AItools can can bring those
benefits on on that.
Can you talk a little bit aboutlike, how do you get someone to
start down that path and levelset the initial assumptions that
they're gonna make in their ownprocess, recognizing it's a
(16:53):
little bit different acrossorganizations and teams? How do
you get there? Because I mean,AI is so capable now that trying
to find that that point ofapplication that just getting
out of the the the startingblock, I think, is a challenge.
Jason (17:06):
So take a slightly different approach to that and
the way that I answer that. Myson's a a musician. He's an
artist. He writes he writes alot of music. When he sits down
to write a song, we often end upin writer's block.
We end up just stuck. We'relike, I don't know what to do.
(17:28):
So he starts playing with it,he's like, I know the chords. I
know how to put them together. Ican play licks all day long.
Nothing's connecting. What Ifind though is when I say, hey.
Let's pick up a cover song.Let's grab the Beatles. Let's
start rocking on the Beatles alittle bit.
And we just start goofingaround. And we're just fiddling
around playing the Beatles,singing it together, and and,
(17:49):
you know, all of a sudden, he'lllook at me and he'll go, oh, I
have an idea. And then he startsriffing on a new song. So
sometimes it's just the practiceof playing and letting your mind
go that lets you create newideas. And and I think the same
thing applies in AI.
If we're not just being creativeand playing with it, I'm not
(18:13):
sure we're gonna find creativenew ways on how to plug it into
our process. So we've gotta justembrace that it's gonna be
messy, but we gotta play. Like,my son and I, we don't play the
Beatles perfectly. We've beenworking on the Eagles recently.
Right?
Like, you know, little sistergolden hair. And it's like, we
can play on this sounds awesome.But though by just playing those
(18:35):
chords and seeing and seeing howother people are doing it, it
starts to get your mind thinkingdifferently. Same thing with AI.
You might not have a great usecase for it right off the top of
your head.
Part of that is because we don'teven know how to think about it
yet until we start playing withit enough for our minds to start
to make those connections thatwe never would have made without
(18:56):
it. So I encourage people justto get in and start throwing
stuff at it. Not because they'renecessarily gonna get a lot of
great value out of what they'rethrowing at it, because most of
the people I talk to are we'restill treating it like Google. I
mean, like like, we haven'treally pushed it to the new
limits. But by getting in andasking it those questions and
seeing what it's doing andlearning just little bits and
(19:17):
pieces here and there, all of asudden, they start to go, oh,
what if?
And because they have theexperience and the connection to
their processes and the way theydo work, it naturally goes to,
oh, man. If I didn't have to dothese five things, can you
imagine what I would do with mytime instead? But they don't
know to think about it becausethey've not played with it
enough to even know. So I likejust to play. Like, it's a it's
(19:40):
unconventional, but I think itdoes it is what spurs the
creativity.
Daniel (19:45):
And maybe part of that play is what leads you to have
some of these insights aroundlike, oh, maybe the automation
or maybe the AI driven processshould actually look different
than its human equivalent,right? Otherwise you sort of
don't know where it should lookdifferent or where it shouldn't.
(20:06):
I always, I love, read some ofRichard Hamming's work and some
of what he talks about is likeseeing so many different
generations of technology thathave mechanized certain
processes or automated certainprocesses. Basically his
observation is the ones that endup transforming things are those
(20:29):
that do a process but in a waydifferent than the equivalent
human process. And I thinkthere's kind of the common way
of saying this is like, if youautomate some well established
process, you just sort of get avery efficient bad process, not
a new transformative process.
(20:50):
I find this though very, verydifficult in actual
conversations with people tohave them grasp this idea
because the tendency is for usto say, well, I have this
process that X person does in mycompany. I would like to
automate that now. And I knowyou think about automation all
(21:11):
the time. How do you get peopleto get Maybe part of it is
building that intuition withgetting hands on with the tools,
but how do you get to that pointof from here is our process that
we execute manually to the hereis how we might do it with an AI
(21:34):
augmented approach, which isnecessarily, I I mean, if it's
going to be transformative, likewe're kind of making this
assumption should look differentthan a human process.
Jason (21:44):
Yeah. And you're so right. Almost every client I
could start with is, here's thething I want you to automate it.
Yes. Here's a task I want you toautomate it.
And I'm like, you could havedone that ten years ago. Right.
A lot of what you're asking todo is not new. So, there is this
this kind of a mental gap or ahurdle between, you know, pure
automation and what AI iscapable of doing. To answer your
(22:06):
question on how we actually getto the the transformative, I I
do think depending upon who'shaving the conversation, there's
different ways you can go aboutit from a conversational
standpoint.
I tend to be pretty high energy,and I tend to be very
inquisitive. So that plays to mystrengths. So when I'm in there
having a conversation, I'm like,oh, we're excited. Look at all
the cool stuff we can do. Hey.
(22:27):
Check this out. Like, I'm highenergy with them all. And then
they say, well, here's whatwe're doing. And I'm like, oh,
why are doing that? Really, I'musing the five why strategy of,
like, I'm digging into what isthe root cause of why they're
actually making the decisions inthe process they're making so
that I can then start to offeralternatives around, if this is
the outcome you really want andyou did these six steps to get
(22:50):
to that outcome, maybe there's adifferent way we can get that
same outcome.
And maybe the the six stepsaren't necessary anymore. But
for me, I do that through justhigh energy and creative,
inquisitiveness. So
Chris (23:04):
As you dive into that, I'm curious. And you kinda
talked about the, you know, thatinitial engagement with the CEO
and stuff like that. But to toto kinda circle back and bring
the employees in, as you'regoing into that process, how do
you wrap those employees into itin a in a positive and
productive way, so that theyalso see that same value that
(23:24):
you you know, you automaticallydo because you're doing this,
but that the CEO is as well asstarting to see so that they're
engaging, you know, with thatsame energy and creativity
instead of instead of worry orconcern, that kind of thing. How
do you how do you navigate thathuman element as you're going
through the beginning of thisprocess, flow?
Jason (23:46):
Yeah. So being candid, I'm still figuring a lot of that
out.
Chris (23:50):
As are we all, I think. Yeah.
Jason (23:51):
So so that that is hard. Yeah. That being said, we do
know that change management is ascience. It has been studied. I
am reading on it constantlyright now.
So I I think the same principleswill apply. I do think there is
an idea a concept of leadershiphas to buy in. If leadership's
(24:13):
not bought in, the cost ofchange is gonna be too great. So
we've gotta get leadershipbought in. Once we have
leadership bought in, then I dothink there is some idea of I
hate the word committee, butbringing in people who are on
the front lines and are actuallydealing with the reality of work
to be a part of theconversation.
I also think it's important forthe the leadership to be in the
(24:35):
room and reaffirming intent. Iknow there are scenarios, and
I'm not ignorant enough to saythat there aren't scenarios
where AI is gonna replace jobs.I know there are. Most actually,
all of the businesses that we'veworked with understand that
we're not in a we're not lackingwe're not we're not wanting
people to leave. We're wantingpeople to work on other things.
(24:59):
So they're not coming in going,we're trying to eliminate place
positions. They're coming insaying, we have so much
opportunity that we can't takeadvantage of. We wanna get you
moving in the in these otherplaces. Leadership's gotta be
real vocal about that and, hasto be very, very clear and
transparent on that front. So II think we gotta get the front
line in place.
I think we gotta get leadershipcommunicating expectation, and
(25:21):
then we've gotta create and thisis what I'm trying to figure out
right now, is how to createenvironment where we can play in
a safe place to start to spursome of that creativity. Yeah.
I've not figured out how to dothe the the play part yet where
it's fun. It still feels likethey're learning engineering
stuff.
Daniel (25:38):
Yeah. And some of this too, I guess some of the points
that you made around likedifferent people being motivated
by different things. Certainlyin the If we just take AI out of
the picture, if a business isoperating and someone really
just loves to write handwrittenletters, right? And they're
(26:00):
like, I'm not gonna send anyemails. I'm just going to write
handwritten letters to everyoneand we're gonna communicate that
way.
Obviously there's ainconsistency with how actual
business operates now. Maybepart of the disconcerting thing
right now is that we sort ofdon't quite know how business
(26:21):
will operate as things kind ofas this adoption happens, right?
So we don't know if doing thething that is the equivalent of
writing handwritten lettersinstead of sending emails,
right? And I guess that's stilljust, I mean, it seems like
sometimes, so I'm also curiouson your perspective on this,
(26:46):
Jason, because it seems likesometimes that if we look out at
the world, every business isusing AI pervasively. And
sometimes like I got on a callthe other day and I asked like,
Hey, are you using AI any way inyour business?
And the response was, Oh,everybody is. What kind of
question is that? In myexperience, actually, that is
(27:09):
very much not the case. I'mwondering about your experience.
Maybe just to give peoplecomfort that are listening to
this, most of the calls I get onfolks are are not what I would
consider having adopted AI andare using it pervasively across
their business.
Would you what what is yourexperience? I'm hoping it's the
(27:31):
same.
Jason (27:31):
It is it is exactly the same. Everyone says they're
embedding it into every part oftheir processes, and nobody is
actually doing it in any way,shape, or form effectively. And
so, you know, one of theconversations I have with
clients most often is they'relike, well, I'd love to use it
more, but every time I wanna goask it a question, I have to
(27:52):
spend fifteen minutes telling itall the background information
so it'll answer itappropriately. I was like, yeah,
that seems reasonable. Like likethat that was what I would
expect.
So those are most people are notusing it. And if they are, CEOs
like to say they're using itbecause they're using it to
generate social media contentand marketing and some other,
like, you know, they roll rightemails for them, things along
(28:14):
those lines. But actually usingit in a productive, way that's
creating efficiency, I I runinto very few companies that are
doing that.
Chris (28:23):
You know, I've been, I've been kind of pondering one of
your previous answers as we'vecontinued talking in the back of
my mind. And, you know, youtalked about like the navigating
the human side is, you know,there's the psychology around
it. And, and it occurs to mejust to just to throw it out
that it's, it's, we have a habitof framing, you know, bringing
(28:44):
AI in as a new problem. But in asense, it's really not. Because
if you go back before, you know,this moment, where where we have
generative AI and other AImodels that we're bringing in,
And it's creating that sense ofuncertainty.
If you go back in time, and I'mgonna I'm gonna make a reference
that maybe will make you smileto like the movie office space.
(29:04):
And at that point, they had kindof a two comic characters, which
maybe some folks in the audienceremember called the two bobs,
which were talking aboutprocess. And the employees in
the company were very concernedabout their jobs, and they were
going through that. And as itoccurs to me, it's much the same
concern, it may not be whetherthat process, know, which had
(29:25):
nothing to do with AI or reallyeven technology in the movie,
you know, but but that thatnotion of process automation
being a frightening thing foremployees to be thinking about.
Am I safe?
Am I gonna be okay? And it'sstill really what we're talking
about now. We're just talkingabout kind of AI as an actor in
that in that sequence. So and Iknow one of the points of the
(29:46):
movie is you're still trying tofigure it out. Humans are
complicated or emotional.
There's not a quick answer thatpeople just get happy about. And
as you were saying veryhonestly, which I really
appreciated, that you were like,you know, that you were still
trying to learn your way intothat, which I think is a
fantastic answer because it's soit's so honest that, you know,
it's not just reframing. I'mjust wondering is is I throw
(30:09):
that that kind of office spaceanalogy. Does that resonate with
you in terms of do you do youthink there is a a truth there
that is sore somewhat timeless?And is there anything, you know,
what is new potentially in theAI being thrown into the
equation on top of that officespace timeless aspect?
Jason (30:28):
Yeah, that resonates deeply. Actually, I'm gonna have
to go pull out that clip and useit in my next talk. It's the
same problem y'all. Yeah. We'reliterally like like, we don't
like change.
Like, no one does. You know,we're we're very happy to know
what's like, how things arebeing done and not have you
know, don't move my cheese typeof idea. Right? So, you know,
(30:49):
these these problems have havebeen around for a while. So this
is a new flavor of it.
And it's one that feels like themagnitude of the of the wave is
larger. It feels like it's gonnamaybe rock more boats than other
changes in the past have, butbut it's the same problem. And
so I I feel like we we should beable to, leaders, be able to use
(31:14):
the the same tools that havebeen used in times past to help
people navigate this currently.And I think that should bring
comfort to us, because it's weare solving somewhat of a
similar issue. Now, again, I Idon't wanna downplay the impact
of a of artificial intelligence.
Its impact is significant. Likelike, there there is a lot that
(31:36):
this is going to impact. But I Ido think we're, people wise,
kinda dealing with the sameissues.
Daniel (31:42):
And this kind of leads into something I I would love to
talk about because I I get thesense just having seen what what
you all are doing with ProcessCoach, which is one of the
things that that you're offeringas a product, which we were
talking about this a little bitbefore we started recording. I'm
all for anything that's not justanother chat bot. I love it when
(32:07):
people are thinking aboutdifferent ways of interacting
with AI and integrating it intotheir kind of business processes
or the way that they work,which, you know, as folks have
already, have heard from you,you are thinking all the time
about, you know, the way peopleare working and what they find
meaningful. But I'm wondering ifyou could just give us a little
(32:29):
bit of backstory on ProcessCoach, because I do think, you
know, the way that you'reapproaching this integration of
AI and the way that you'rethinking about automation and
what you're doing maybe isdifferent than of the ways that
other people are approaching it.
Jason (32:45):
Yeah. Well, we're pretty excited about this this tool,
mostly because, again, we're wethought if you're a business,
particularly a small and midsized business that's trying to
figure out how to navigate this,and you're trying to get people,
your team in line and and and,like, hey, this is actually
gonna be valuable to you.There's a lot of fear. There's a
(33:07):
there's a lot of a lot of thingsthat kinda stay in your way. And
and a lot of that comes down tohow do we get the day to day
operations so that it actuallyis leveraging AI in some way,
shape, or form.
So when we took a step back andwe're talking to our clients and
looking at at everything, we'relike, you know, MBAs all say, to
scale your business, you have tohave business process. And I
(33:28):
think we all know that to acertain degree, that, you know,
if you don't have somethingstandardized, it's impossible to
really to improve it. So weunderstand that, but very few
people, I think, have actualizedit. And we thought, man, if we
could create an environmentwhere we could simplify figuring
it out, get it get it defined,and then actually make it
(33:48):
useful. So a lot of theautomation around process ends
up getting too fragile.
I don't know if if you'veexperienced that or not, but,
like, the high level, sure.Those are those deterministic
steps, like, gather all theinformation, put it all in the
CRM. Like, okay, those you canpretty much easily go down the
line. But when you get into,like, what does gathering all
the information mean? Andthere's, like, a million little
(34:10):
issues in the decision tree.
Well, and I can help solve a lotof that. So if you combine the
two together and we create astandard operating procedure, we
actually call them plays becausewe think more in that that,
like, playbook mindset. But youyou create a play that's like,
here's how we're gonna solvethis problem. And we let AI
manage the context of all theinformation that's happening
(34:30):
from step to step and fromperson to person. All of a
sudden now my job becomes prettyeasy.
And what we end up seeing is,you can interact with the with
the AI agent and say thingslike, I don't know what to do
here. What would you do? And itwill go, well, I have the
context of what's happening. Ihave access to your various
tools. Let me go do someresearch and answer it for you.
(34:53):
And the answers are pretty good.And so we're freeing people up
from the monotony of hitting alltheir websites, doing all their
things, all these tools, they'reable just to run a standard
operating procedure and it andthe AI is kind of managing how
it all gets done. This opens upsome options for business. And
that that's what we're we'repretty excited about. I'm still
(35:15):
learning how to talk about it.
Alright? The product isrelatively new. But, as we're
we're getting it in people'shands, this rethinking of how
your processes look, Now put ontop of, I have access to your
tools. I have access to yourknowledge base, and I'm managing
who gets assigned what so thatwe're all working on a process
(35:36):
together. All of a sudden now,we just have a much, much wider,
much richer, deeper context forthe conversation that's actually
happening in between us aspeople and between the computer
so that it can do the work foryou.
And that's pretty cool. Andwe're getting some really neat
outcomes from that.
Chris (35:51):
Yeah. It strikes me that that you're kind of you're kind
of fulfilling that thing thatyou're talking about before, in
that, you know, as we havetalked over the course of the
show about the the psychologyand the expectations, the humans
involved, that you're bringingyour tool, and you're putting it
not only in front of themanagement, but as you talked
(36:12):
about kind of a committee of thepeople that are doing the work,
where all that knowledge iscurrently residing and helping
them without replacing them.You're helping them do better
things with that knowledge. Andso you're you're you're sort of
giving those humans a bit ofsuperpower for free without it
being a threatening thing. AndI've just I'm I'm wondering if
(36:34):
maybe by using the tool itselfin that context, it's actually
kind of starting to assuage someof the concerns and fears that
those employees might otherwisehave.
It seems like a fantasticstrategy to give them a good
experience upfront that thatstarts off by saying, I'm kind
(36:54):
of implicitly acknowledging yourfears, but here's an experience
to to guide you going forward.
Jason (37:00):
Yeah. You're dead on. And that's what that's what I get
excited about it. And I'll eventalk with the leadership. It's
like, hey, let's not go in andjust throw a whole agent in
there and say, hey, we now haveagents is off and running.
Nobody trusts them. Like, I II'm cool with agents, but let's
let's ease into it a little bit.Let's let the let's let the team
become comfortable with it. Sowe create this hybrid where we
(37:23):
literally start off and we'relike, let's create the process,
you know, a through z, and eachstep is getting handed to a
person. And we interact with,like, teams, Slack, email, text
message, so that they're nothaving to log in to another
system.
Right? So it kinda feels likeyou're talking to just an
external employ external teammember. And so you're just
having a conversation. It'sasking you questions. Hey.
(37:44):
I just had a great meeting withDaniel. Help me onboard him.
Hey. In order to do that, I needto know the company. I need to
know and it's asking youquestions.
You're just kinda going back andforth with it. Then as you get
tools and the AI tools and MCPservers, everything open up,
then it starts going, you justwant me to add that for you?
Yes, please. It now starts tofeel like it's a helpful
(38:05):
assistant until eventually youget to the point and AI gets
smart enough because every newmodel that comes out is getting
better. You now get to a pointwhere it's like, just do it for
me.
Like like, I've gotten to apoint where I trust you now, and
so just do it for me. That's ourstrategy for adoption is, you
don't have to train anyone howto use email. They know how. So
(38:28):
change management barrier goesdown. It's just asking them
questions.
Get it so that they feel morecomfortable over time and that
it's doing work for them. Andthen eventually flip it into
agent mode and it just goes offand does it. But you know what
it's doing because you were inthe loop while it was getting,
structured in the way that itshould be executing in the first
place. It's just a veryaccessible way for small and mid
(38:49):
sized businesses to get theirteam comfortable with what AI is
is going to be able to do forthem.
Daniel (38:54):
Yeah. I I love that fact of the way that you're tying
into things that people arealready using. And also I've
seen, of course, I actuallyreally I do really like these
systems where for me personally,that like, oh, I can drag and
drop and create this like DAGpipeline to automate and like
create these things and makecustom API calls and all this
(39:15):
stuff. And I love that. It's agreat user experience for me.
But putting that in front of adifferent audience, it's so
overwhelming and terrifying. AndI think one of the things that
you showed me at one point isthis kind of, just like describe
your process in words, right?Yeah. Upload that in words, not
(39:38):
as a prompt. So you don't haveto learn how to prompt.
You don't have to learn how tolike build this DAG pipeline.
You essentially describe likeyou would to maybe an intern or
a new hire or something likethat. So I'm wondering, was that
initially kind of part of thingsor was that another one of these
(40:00):
like, hey, we tried to tellpeople how to use this DSL or
something and they like had noidea how to do it. How did that
perspective come out, I guess?
Jason (40:11):
So as with any SaaS product, we're on our third
version. Yeah. The secondversion was all drag and drop,
like what you talked about. Andit was like a and we would show
it to business leaders, youknow, CEOs, presidents,
operations officers. Andimmediately, every time they're
like, oh, go talk to IT.
Like, we're not even gonna lookat it. It's like, well, I I
(40:34):
appreciate IT. I actually loveIT. We do IT. Like like, I love
all of that.
But if you're gonna get AI inyour business, the leadership's
gotta be doing it. So theconversation was, what does it
need to look like for you toactually engage what your AI
agents are gonna be doing? Andit basically came back as I need
to be able to read it kinda likeit's a standard operating
(40:54):
procedure because I understandthat. That's my world. So if I
can read it and it's like, oh,this is the order it's gonna get
done.
Here's what's gonna get donealong the way. I can comprehend
and be like, no. I don't wantthat to happen. Okay. Well, how
would you change that?
Tell me in plain English, andthey can rewrite it. And
suddenly now it's accessible. Sowhen you start to give tools and
(41:17):
we don't have this tool yet.It's on our road map, but I'm
really excited about it coming.Because the processes are
managed by AI, we can do ABtesting of processes.
Like, that's pretty unique. Andif you can, as a small or mid
sized business, say, here's myprocess now, I'm gonna make some
changes and I'm going tobasically say, take version a,
(41:37):
send 50% of my runs there, takeversion b, send 50% there, and
now compare the efficiency andwhat's actually happening, the
amount of time we're spending onit, like, that allows you to be
scientific in how you start toscale your process. So I I get
pretty excited about that kindof stuff, but people aren't
gonna do that if it feels tootechnical. It's gotta feel
extremely accessible, which iswhy we have to go with with,
(41:59):
like, plain English, to get themto engage it.
Chris (42:03):
Works. So Jason is as we start winding up, and you're
kind of looking out at you asyou're kind of pioneering, you
know, process change and, andhow to make this work with these
new tools and how people canreimagine it in a in a way
that's different from whatthey've done before. As you're
looking forward at this fieldand kind of the the
(42:24):
possibilities, you know, so notnecessarily what you're doing
now or what's on the immediateroadmap, but kind of out, Like,
what how do you see thisevolving over time? How do you
see the future looking in termsof how companies as they move
forward and and adoption becomeswidespread, but you're pushing
the limits a little bit onwhat's possible at each point in
(42:46):
time. How do you see thisunfolding for companies?
And what are some of the thingsthat people might look forward
to in in the, you know, over thenext few years?
Jason (42:56):
Yeah. The the thing that I'm starting to imagine is
happening, I kind of I'm notsure that business leaders are
really gonna be interacting withcomputers. I think it's gonna
all come down to their phone,and there's gonna be some kind
of just voice conversationthat's happening. And behind the
scenes, if you're able to uploadyour processes into a, again, a
(43:18):
external team member, and itknows how to execute them, and
you can kinda manage from there,It's a whole lot easier for me
just to pick up my phone andhave a conversation with an
external team member that can dothat work for me than it is for
me to log on and try and figureout what to do. And I see that
coming pretty like, in the nextyear, I kinda feel like that's
gonna be have to be a majorthing.
We're not imagining it how to doit right now. I'm just now kind
(43:41):
of envisioning that this isgonna be, like, probably the
main use case. Because I justthink the way that with with the
power of AI and the way that itunderstands that the intent of
conversation and it's able tologically break down what it is
we're actually trying to say,the computer itself is gonna
(44:01):
become less and less of a focalpoint of business and is gonna
become more of just an expectedconversation that's happening
behind the scenes. And I I feellike we're gonna have to embrace
that really quickly. And I don'tknow what that looks like.
I that's really hard to dreamabout. Like, I mean, my whole
life, I've had a computer.Right? Like like, I can't
imagine what it looks like tobasically say, I am going to not
(44:22):
have one. I tried to do, like,an iPad for, like, six months,
and I couldn't handle it.
Right? Like, so what does thatworld look like? I that's hard
for me to to to imagine how wecould get to that kind of a
place. I mean, it's kind of theStar Trek button. Right?
Like, hey, computer. Go do this.Like, that kind of idea. But I
think that's real. I think it'scoming.
(44:43):
If it's I mean, it's probablyalready here. I'm just not
caught up yet.
Daniel (44:46):
So Well, hopefully, I I get my pen sometime in 2026. I'm
I would I would look forward totrying it for sure. Really
appreciate your insights today,Jason. It's been a real
pleasure. I appreciate the waythat that you and the RoboSource
team are innovating.
Thanks for taking time to talkto us. Really appreciate it.
Jason (45:05):
Well, I had a lot of fun. Thank you both, and it was just
it was a fun conversation.
Jerod (45:16):
Alright, that's our show for this week. If you haven't
checked out our website, head topracticalai.fm and be sure to
connect with us on LinkedIn, X,or Blue Sky. You'll see us
posting insights related to thelatest AI developments, and we
would love for you to join theconversation. Thanks to our
partner Prediction Guard forproviding operational support
for the show. Check them out atpredictionguard.com.
(45:38):
Also, thanks to BreakmasterCylinder for the Beats and to
you for listening. That's allfor now, but you'll hear from us
again next week.
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