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A podcast that explores how AI is transforming careers, businesses, and industries. Hosts Greg Boone and Erica Rooney deliver real-world use cases and actionable AI strategies to help professionals stay ahead of the curve.
Jason Johns: Adoption is key.
And I think that, so I come from the
Red Hat world, which is an open source
world, meaning it's not a, the executives
come up with a decision, push it down,
and everybody's supposed to adopt it.
It really becomes a, we have to bring
people along in the journey and the
more they feel like they're part of
the journey and decision making and
that their voice is being heard,
the more that they're going to then
really go on that journey with.
You.
Greg Boone: AI isn't the future.
It's now, and whether you're in hr,
sales, operations, or leadership, the
choices you make today will determine
whether you thrive or get left behind.
Erica Rooney: Today we have a very
special guest joining us and that is my
friend Jason Johns, who is the founder
and Cee O of Connections, an AI sourcing
platform, and B2B IT services marketplace.
Now he's got over a decade of
experiencing enhancing global partnerships
and streamlining IT solutions.
But he has taken a left turn into
the world of AI and he's really
become this leader in the community.
And that's why I'm so excited to
have him here because he's not just
focused on how it will change his
business and his life, but he is trying
to bring everyone along with him.
And that's really how we met.
And we actually just linked up in line
to board an airplane of all places.
So.
We're bringing him here to the podcast,
but if you are unsure about AI these
days, and you're wondering if it means
the end of the world is near, or maybe
you're just trying to get your company
on board, this conversation is for you.
We are gonna talk about Jason's tips
for business leaders, the latest AI
trends that he's excited about, and
how we can embrace AI as a. Competitive
advantage because on his LinkedIn he
said, y'all AI is no longer optional.
So get comfortable and get ready to be
inspired with some real world insights.
Jason, I am so excited to have you here.
How are you?
Jason Johns: I am great.
Thank you for having me.
Um, I am ready to just lock it in and,
and, uh, you know, put the seatbelt on.
Let's go.
Erica Rooney: Let's go.
And I'm gonna drop you right in
because you've been in tech for.
Forever, really.
Right.
Long time.
Since the beginning of time and now
you are leading an AI platform and
building community, can you take me
back to that moment in time where
you really realized that this is
what you wanted to do going forward?
I.
Jason Johns: Great question, so
thank you for having me here.
First of all, I think secondly,
you know, I've always been in
technology and emerging tech.
So when I think of, you know, back
in the days of Red Hat, when it
was really looking at Linux and
how do you scale and how do you.
Fight the giant of Microsoft.
You know, it was really rolling up the
sleeves, building a business there.
And uh, and ultimately then, uh, I've
been doing that with cloud computing and
other technologies for a long time and,
and really that emerging tech sector.
And when I looked at.
What we were building with our procurement
platform, it really came into play
that we realized that AI and AI agents,
uh, is going to revolutionize really
what the industry is going to be.
And so we looked into
what AI was going to mean.
Not only for our product, but also
how we're gonna run our business.
And that was really the
turning point of understanding,
learning and then implementing.
Erica Rooney: Okay.
What made you go all in on
building community around this?
And just for everybody listening, I
went to this incredible networking
event that he put together.
You brought leaders in ai, from
Google, from LabCorp, everyone
together to talk about it.
And in a very.
Ai.
Serious way.
Very technical way.
Right?
And this is why I love the
show, because you've got me,
I'm dipping my pinky toe in it.
I'm getting used to it.
You know, and so I, I got invited, yay.
And I went and I learned so much.
But what brought you to do that?
To lead the change in the community
and bring all these people together.
Jason Johns: It was really out
of necessity, the community.
So I've got a big network in the
Raleigh area, and, uh, ultimately
these CXOs CIOs, CFOs were asking me.
What I thought, and it
was, how do I do this?
How would this make a difference
in my, you know, business and
how I really develop my strategy?
Where do I go?
What technologies are right
for certain use cases?
How do I even make a
determination around these things?
So I decided to create
triangle technology innovators.
And we have then been, uh,
really bringing together leaders
within the area, um, to learn.
Uh, basically the motto of the
group is network, learn and grow.
Um, there's no selling in this group.
It is really about networking with
other key executives, understanding,
you know, who's doing what, uh, but also
then bringing in experts and leaders
so that they can provide guidance.
It's really hard right now because people,
there is really very few experts, but
there are people that have already done
it somewhat successfully or at least gone
through the initial phase successfully.
And they can share that information
with others and that's what we're doing.
Greg Boone: Yeah.
So there's effectively, there's folks
that have, uh, kind of led the charge,
but to your point, there's not a lot
of true experts in something that's.
Really?
Yes, AI has been around for a long time,
but when we talk about it, the crux of it
is around gen AI a lot of times, right?
And so that being a relatively new
technology, or at least one that is
exposed to a lot more folks, you know,
going back to November of 22 and open ai,
you know, kind of launches to the world
chat, GPT always tell folks that that
gave a bunch of executives air cover to
really start talking about ai, right?
So it's awesome that you're
bringing the community together.
I love this whole.
You know, network, learn and grow.
I think the, um, you know, I, I,
we took, we put together this AI
Voice of Victim podcast partly
outta necessity as well, right?
Kind of how you laid it out.
Now, this necessity was more about
making sure that those around us didn't
get left behind and become victims.
So we're trying to get the word out.
Like it is not optional.
AI is here, everyone's going to do it.
So you know, for me, when you're out
there, right, and you're having this
community of experts, or at least
more experienced folks in the field
with these business leaders, what
types of challenges are you hearing
these days around actually adopting?
Right?
Because everyone keeps
talking about use cases.
But very few people are actually
adopting the technology to be
able to make the use cases.
Jason Johns: Yeah, great question.
What I'm seeing is.
Uh, I'm gonna go back a level.
What I'm seeing is that that true
leaders, whether they are a C-level
or a board, they're looking at this
in just really a few different ways.
Number one, if you think of what is
the key differentiator that my company
is gonna provide to the market,
and how do I build that through
AI led and gen AI technologies.
Number two is how do I create
optimization or efficiency within my
company and doing that using technology.
So if these leaders are looking at
it as key differentiation and how
do I essentially be more efficient,
then that becomes such a key.
And core differentiator for
their company moving forward.
Um, if leaders are not doing that,
they are going to be the ones who
won't have that differentiation.
They're gonna still have their current
costs and they may be losing out.
That may be.
Six months down the road, it might be
10 years down the road, but if they're
not figuring that out now, then they
are going to be really on the bottom
and in a very vulnerable position.
So, so we look at that as, as
there's lots of things going on.
But if you're in leadership, when I talk
to leaders, they are talking a lot about
use cases, what they are struggling with.
What should my strategy be?
How do I develop my strategy?
Um, what is my infrastructure that
can support certain use cases?
How do I make sure that I don't
have information or data that's
going outside of my firewalls?
How do I make sure that I am
protecting my information?
Right?
Um, think that you start to look
at all of these different things.
When I talk to my customers,
am I comfortable using AI
and gen AI information?
If something goes wrong, is
that going to jeopardize my
relationship with that customer?
So what, what I'm seeing
right now are a couple things.
First generation strategy, which
will change and evolve over time.
It is not a set it and
forget it type of scenario.
It is.
I need to keep going back to it.
Number two, what does my infrastructure
look like and how can I. Have an
infrastructure that is ready for
use cases in in AI and Gen ai.
And three, ultimately, what are
those use cases that I can start with
internally that are gonna potentially
have an effect of my internal teams,
but I'm not going to put that out to my.
Anything that is customer facing
at this point until I can really
kick the tires and also measure it.
And then I think the last is, do
I have the right operations and
the right skills within my team?
They're now calling it AI
ops to able to even manage.
These different use cases.
And so we're seeing all of that across the
board and I'm having conversations with
CXOs and board, uh, board members almost
on a daily basis around these topics.
And it's very challenging at
the, at this present moment.
Erica Rooney: I mean, both of
us were like, we got a question.
My first question is like, around.
Readiness assessments, right?
Because that's when like
boom, everyone needs that.
They need to just know what that is.
But the problem is we don't
quite know what we need to be
fully ready for these things.
So how do we even think about
a readiness assessment or
what are you seeing out there?
I.
Jason Johns: So my company has
put together, um, a, a really
a, a group of experts that helps
with a readiness assessment.
It's an AI read, uh, readiness
assessment, and it comes down to looking
at what is their data and applications
and what is that flow of data.
It comes down to looking at governance.
What kind of governance do you have on
the data incoming, and how confident are
you in the data that you currently have?
And then looking at what are those?
Use cases that are going to fit what
you're looking for and what your
threshold is as a company around risk.
And you combine all of that and you then
start identifying what you could today.
What are those use cases?
How do you measure them,
and what does that roadmap.
And plan look like for six months,
12 months, 18 months down the road.
Erica Rooney: I got one comment,
then I'll let you talk about it.
Oh yeah.
I don't know that, here's my thought
as I'm hearing this, and I've
worked in a lot of companies where
all of the data is rough, right?
And that's just immediately I could feel
my anxiety start to come up in my throat.
And I imagine that's where a lot
of people live because they're just
like, I don't even know how to get
that data as clean as possible.
And I love how you said like, what is
that one thing that we can do today?
What is the one step we
can take really just.
Acknowledging that it is just that it's
a first step because I know the answer to
my own question, but I share it just to
acknowledge all, all of my other anxiety
ridden friends out there and their dirty
data is the time will pass anyways.
Right.
My mother has always said that to me.
When you have a big goal or like
if you're going after a PhD or a
four year degree, like don't tell
me how it's gonna take four years.
I mean, the time's gonna pass anyways.
Right?
And, and we know two things to be
true here is that AI is here and
it's only going to be coming even
more and the time will pass anyways.
So get after it.
Jason Johns: But go ahead Greg, what were
Erica Rooney: you
Jason Johns: gonna say?
And, and one comment though
to that it is not a sprint.
It's a marathon.
And what do you do when
it's a marathon train?
Accordingly, you are
gonna train accordingly.
So you are gonna set up a plan.
You are then going to, you know,
get after it and start to build up.
So you start with maybe a walk,
and then you do a longer walk, and
then you start to do a light jog.
So you almost have to take the exact same
approach in a metaphoric sense around ai.
A hundred percent.
You start to your point,
slow and you build into that.
And when you, four years later, you're
then going to be the superstar athlete
who has that endurance, who then has
that regiment and you can make it
the 26.2 miles and, and that you are
ready for it, but you don't try and
take it all, all in one big chunk.
You really break it up
into small milestones.
And that's part of really how do
you build not only a strategy,
but then tactics that help you get
there really one day at a time.
Erica Rooney: Hmm.
I love a good fitness analogy.
Greg Boone: Yeah.
We had a guest recently, uh, Dana
Pees that used this, the very
similar, she was both trying to
train for a 5K or something, but
also she was using the same parallel.
She's a consultant.
Right.
And then we had someone on it after that
that also, you know, took, uh, kind of
this kind of bite-sized, uh, approach.
One of the things, I was gonna come
back to one, one, she said that
she's worked at other companies.
She's worked for me twice.
Right.
And so when she said that I worked at
other companies with bad data, I was
just sitting there wondering like,
well, we've worked together twice
now, so I'm assuming one of those was
at, at least with me, but I digress.
The, uh, a couple points that I
just wanted to just follow up just
to reiterate to our audience is.
One of the things you described
was basically what, um, the, I, I
recently got a certification from
MIT on AI leadership transformation,
and they talked about the concept of
their forgiveness continuum, right.
From internal to external.
And they said, you're gonna
try these things first.
Try them internally.
Don't just go directly to your
customer because the forgiveness
there is not as high as internally.
Right.
And so, I guess a little bit of
my, not necessarily pushback,
but how I would flip the equation
based on those four kind of, uh.
Uh, levers, if you will,
that you talked about.
And I think the last one you talked
about something related to the
people, which is my firm belief.
And part of the reason, again, why
we have this podcast is that business
leaders need to focus on training every
employee in their organization on the
benefits and the productivity gains of
ai, not just on the business application.
'cause what people are missing is
that first of all, over 80% of of,
of all employees don't use AI today.
Right.
At least in a professional standpoint.
Right?
And so the adoption is still low, right?
But the, the point I was trying to make
here is just that this is the first
technology in my generation, right, that
affects every single employee, right?
And so typically when I'm doing
digital transformation, I'm focusing
on IT or marketing or some group.
I only gotta worry about one
or two potential saboteurs that
don't want to go with this.
Right now you have 90% of the organization
that say, Hey, I don't even know
what you're talking about right now.
Right?
And so what ends up happening is that the
business, myself included, I've fallen
into the trap in times, at times, right?
And say, Hey, we've got
these great use cases.
We can do these great things.
But 80, 90% of the org,
what they're hearing is.
AI is coming to replace me.
They're going to get rid of me.
And so what I implore the, you
know, senior, the, the CXOs out
there, it's like, look, spend some
time focusing on the productivity
because like yourself, right?
Always say it's a binary choice.
We're either gonna help you grow revenue.
Or we're gonna create operational
excellence, which generally means
you're gonna be able to cut costs.
And this is one of the first technologies
you can actually do both at the same time.
You can actually cut costs while
growing your business, creating
those key differentiators.
So I loved how you laid that out.
So my only real comment here is
that I, I really wish and hope.
That CXOs start spending a lot of time
training everyone, not just the technical
folks or the folks that are gonna be
in charge of the, uh, the TIGER team
for the new use case down the road.
Jason Johns: That's a great point.
I think once you have your strategy and
you start to implement the technologies.
It is really a requirement to bring in
those teams that are non-technical, um,
and train them up and to provide really
a kind of easy to use, easy button for
how you use this in your daily life.
And then to reinforce that, um, in your
key metrics, in your one-on-ones, and
you have to implement that technology
on a day in and day out basis, or it's
going to fail when you think about data.
Data has been a mess everywhere.
In fact, um, when I was at Red Hat, um,
I was my CTO currently for my company
and I were tasked with taking all of
our core data out of our ERP and Oracle
and move that over and cleanse and a
pen that data, because we were actually
working on the very first renewals model.
Really probably anywhere.
And then we had to load that into
Salesforce so that the sales reps had
visibility into those renewals so that
they can then engage those customers.
So I've been working in data, dirty
data, um, for a long period of time.
And the reality is, is that
you really need to make an
assessment around, you know.
First, how good is your data?
And are you gonna append that or
are you just gonna use it as is?
Or are you going to then look at the
governance if you don't have clean data?
So very few have clean data.
How do you actually create governance
that then modifies the input of that data?
So then you have a.
Better set of data and you might
have to actually create a new data
store or database that is with the
cleaner data that you are then gonna
start fresh and essentially build
your AI solutions and LLM models on,
Greg Boone: we used always talking,
uh, data migration that you, we
would always use the house moving,
moving homes analogy, right?
And so you never know how dirty your
house is till you get ready to move.
You're like, what is that in the attic?
I don't know.
Right.
But one of the challenges too
though, as a lot of people find,
is that they end up moving the same
crap from one house to the next.
Right.
And so the parallel being, or at
least that you're kind of, if I, if I
paraphrase kind of what you're saying
is that don't move the crappy data over.
Right.
You have an opportunity now.
I was listening to a
podcast, uh, yesterday.
I think, um, they've talked
about this point, right?
And they were talking about like, Hey,
if part of the challenge is all the
dirty data and all the investment in.
You know, big data.
Maybe this is the point in which we say
there is a new governance model of what
does, what does quality data look like?
What do we actually meet?
How could we use AI to either help
clean or harmonize or categorize
the data in a different way
that maybe we didn't think of?
Right?
Maybe some of this really is just
trash, right, and we need to throw
it out when we move to a new home.
So I, I like that, that perspective
and that that vantage point.
The um.
Can I ask you a question just as it
relates to the, when you talk about
readiness assessment earlier, uh, is
that from a, a business perspective?
Is that on an individual level?
Like how are you gauging,
could we talk about anxious
to, curious, to serious, right.
Who are you asking if
they're ready or not?
Jason Johns: Well, ultimately
it's from a business perspective.
But the CIOs in most cases are
getting asked by their board,
what is your AI strategy?
Um, and if they haven't,
they're going to be.
And so it becomes very important for
them to be in the driver's seat and to
have knowledge around a sound strategy.
I have several people telling me that.
Uh, different board members feel like they
know AI better than the CIO and they're
making recommendations into, um, what
they should be doing, could be doing.
And if that CIO doesn't have
a solid strategy, you are now
vulnerable to a person pro in a,
in a position of power potentially.
Torpedoing A. What would be a good
strategy if you don't defend that?
Correct.
You've got to have the knowledge and
a sound strategy, and if you have
those things, that's why think even AI
assessments becomes just a sounding board
in many cases around is my strategy sound.
Is my infrastructure.
What I think it is is my governance.
I need somebody from the outside coming
in because everybody else internally
might be drinking the Kool-Aid.
You get an outside advisor coming
in, they're gonna tell you exactly
what their view is, independent
of what's happening internally.
You get a lot of yes people internally.
An outside, uh, voice really
helps, um, balance where you
are and where you need to go.
Erica Rooney: Do you ever do
any people readiness assessments
when it comes to AI adoption?
And I ask that 'cause I'm the
people person here, you know?
And, and from the tech standpoint, it
could look great all day long, but if
the people don't believe in it or if the
people have a deep fear, there's gonna
be a lot of problems with adoption.
What are your thoughts on that?
Jason Johns: Adoption is key.
And I think that, so I come from the
Red Hat world, which is an open source
world, meaning it's not a, the executives
come up with a decision, push it down,
and everybody's supposed to adopt it.
It really becomes a. We have to bring
people along in the journey and the
more they feel like they're part of
the journey in decision making and
that their voice is being heard,
the more that they're going to then
really go on that journey with you.
And that has got to be really
key, um, is that there is a lot
of angst and anxiety around ai.
AI is gonna take my job, AI is
going to change my life, you know,
for the negative or the, the worst.
Right?
And I think that you
have to take them along.
You have to start with cursory courses
that help people start to understand what
AI is and really demystify what AI is.
Um, if you think about it from a, just a
standpoint of either when you, you know.
In school or your kids are in
school, there's always that subject
that somebody is avoiding or that
homework assignment or that project
that, that somebody's avoiding.
And the more you avoid.
The worse it gets because the
deadlines and everything is coming.
Right, and then you're
going to have to jump in.
You're better off getting started
early, jumping in now and starting
to learn and understand what it is.
It is not crazy technology that's going
to, you know, that's out of this world.
It is a technology that can help
and will change everyone's life.
It's like thinking about.
Back in the day, business
without technology.
Right.
You have to adopt technology.
In business, AI is gonna
be the exact same thing.
You have to adopt AI or you're gonna be
left behind and you better start early.
You're better off starting early than
it is really getting up to that nth
hour where you company is implementing
something and you still have no knowledge
and you're still scared and you have to
be, if you want to be on the, the, the.
Front end of it, you're gonna be the
one actually helping make the decisions.
If you're not, then it's
going to be made for you.
And that could be something
that you're not gonna like.
Greg Boone: I just, uh, on that point,
sorry to cut you off, but the, uh.
I'm gonna be speaking here shortly in
a webinar and we're talking to a lot
of HR professionals, and Erica and
I are having several conversations
with HR and upskilling folks.
Actually, I just got out of a different
call where I was prepping for another
panel where we're gonna be talking about
upskilling in in the age of ai and.
One of the things I continue to, to try
to voice and to say is that this is about
career advancement, not replacement.
Right.
It's about evolution, not,
you know, elimination.
Right.
And to your point, people say this all
the time, like it's AI's not replace you.
The person that knows
AI is gonna replace you.
Right.
And what I want to get across though,
to leaders is that just asking folks to
be curious and to lean in is not enough.
You've got to train folks.
You have to train them on.
The horizontal view, like my
perspective is yes, there's a
general, general primer, right?
But then there's this horizontal,
call it office of the CMO office
of the CFO, whatever you want to
describe office of, of the CHRO.
Then there's a role specific kind of view.
Then there's a hyper personalized view.
How does this help me as an
individual be 20 to 30% more
productive every single day?
I just firmly believe that if more
leaders take this approach from a
bottoms up perspective, they're gonna
get people to have their own aha moments.
Right.
And what it ends up doing is turning
more people into strategists, right?
Versus doing all of these mundane tasks.
The, the, the last, uh, I guess
question that I have for you on, on
this topic is I fundamentally believe.
And that we are at this moment
where you're starting to see
more executives mandate that AI
adoption be a part of what they do.
Right.
From the Shopify Cee o that went
viral a couple weeks ago to folks,
we were talking yesterday about
Duolingo to Fiverr to others, right?
I think that we don't have four years.
I think folks are gonna have to understand
that, and these companies aren't
doing it just because they want so.
My question to you is that, do you feel
like we're at this tipping point or not?
Or when do you believe the tipping
point where it's gonna move from,
Hey, this is optional to, hey, this is
gonna be on your performance review,
this is gonna be, we're gonna tie
metrics to the management, to everyone.
Like where do you think we
are on that kind of continuum?
Jason Johns: I think that there's
early adopters and those early adopters
today are jumping in headfirst, right.
Shopify and, and others you've
made named, I think that.
Those things are good and ultimately
going to change the culture of those
companies also believe that if you jump
in too fast without having the right
strategy yeah, and having the right
use cases and metrics around that, it
also then creates a separate problem.
You start to then implement application
solutions, um, that ultimately don't
align with what the strategy will be.
Now you've created a. Spend a lot
of money, you've created a problem
that now you need to untangle.
And so I think that generally
the whole embrace AI is good.
I. Thinking how you do that is
really the key because you don't want
people and managers start starting
to implement AI in your architecture.
Maybe it creates security risks.
Right?
Right.
Maybe it, um, actually it will create more
unintentional, more problems, you know,
if you start to then leverage AI, um, in
certain ways, right, with your employees
and your onboarding new employees.
And you have an LLM and you have
data in there that you didn't realize
you had in there, and maybe it's
old and stale, maybe it's actually.
Maybe there's some hallucinations and it
actually creates a negative experience.
So I think that, I think that it's, um,
you know, measure twice and cut once.
I think that that would be the
strategy that I would take is
you want your organization.
Thinking about it and working towards it.
You just wanna be measured in what
that strategy is on how you do it.
Erica Rooney: I love it.
I wanna take a left turn though and
start talking about a few AI trends.
Okay.
'cause I feel like there
have been a lot of just.
Things hitting the market
these days and you know me,
I'm like, what's flying around?
I don't know.
'cause I'm a, I'm just a little curious
and anxious over here, so I wanna know
what kind of trends are you seeing?
What are you most excited about when it
relates to using AI in the business world?
Also professionally or personally?
Jason Johns: I mean the, I wrote down
a few things here because what I'm
seeing in the conversation that I'm
having really at all different levels,
um, so you mentioned at our triangle
technology innovators, we've, uh, we
had speakers like Ben Heller from, you
know, Google Cloud, and we had, uh, you
know, satin from Fidelity Investments
and, uh, Adam Sullivan from, uh, LabCorp.
These guys work at massive companies.
They are the leaders within
ai within their company.
And these are the types of
things that I'm hearing from
them and from from other CXOs.
It is, you know, really around, I think,
how do I do something that is going
to improve and get adoption within AI
without it affecting a broader group.
So content creation, that is maybe one of
the easiest places to start is you use a
an LLM and you start to create content.
But the blend between that becomes.
What is the content that's going
to be relevant for my audience?
And then how do I use that
and get the information?
But then how do I modify that based upon
my skill and knowledge for where we are?
Maybe it's a, a product and that,
that ultimately, how do you then
soften that or put in additional
information, but it really, it's a
blend between that, you know, and I,
I see we had a. I had a CXO dinner.
We had 15 CXOs from, I think that
they represented something like maybe
over a hundred billion dollars in, in
sales and, uh, for their companies.
And they were very much at a. You
know, infancy state now was some
government regulated, you know,
financial institutions who really
haven't started, you know much.
They're kind of looking at it.
Um, and they've hired AI
experts, um, two others who have
really already implemented, you
know, 5, 6, 7 POCs and have.
Taken, you know, a few
of those into production.
So it was really a blend.
And you know, what I would
say is that, that it really
becomes, what's your aptitude?
Are you in a regulated environment
where one of my colleagues, uh, CXO
is in a regulated environment and when
you use an LLM, they're not allowed.
To use, um, any other data
except for their own data.
And they are not allowing for just
a, you know, where you could write
anything in there and get an answer.
You have to, it's, it, it's a scripted
question and a scripted answer, and you
have to cite the sources in which you got
you, you have that information back so.
Erica Rooney: Pause real quick.
'cause I think what you just outlined,
there was a really great description for
a lot of people who are like, but I don't
even know how to think about governance.
Right?
That is exactly how you
think about governance.
If you don't want, if you wanna use
your own data or not, do you want
to just be able to dump the whole
thing in there, or do you wanna have
only prompts and it can be tailored
to your organization in that way?
So, sorry, go ahead.
I just thought, I was like, wow, I've
never heard, at least on this podcast,
it described in such a clear way.
So thank you.
Jason Johns: For sure.
And, and so that becomes a
mitigating factor for a lot of these
companies to say, how do I do that?
How do I do it correctly?
How do I do it without hallucinations?
As an example, right?
I. Automation has been in our
business for a long period of time.
I think AI is just accelerating
what we can do in automation, right?
We've had automation, machine
learning, um, you know, and, and
things like that for, for a long time.
So I think that that becomes a
genesis when you look at data.
Regardless of whether, how clean your
data is, how can you then overlay that
with analytics so that you can actually
deem intelligence off of your data?
Now, a lot of financial institutions
do this, and they've been doing
this for a long period of time.
I. Others have it.
So if you even think about, Erica, you
were talking about the HR department.
How do you look at your own employee
data and what is satisfaction?
What would you know?
What is my turnover?
Are there profiles within the turnover
that you're seeing as a trend?
Um, that is, you know.
Maybe a, a way that you're treating a
certain type of, of person or level within
the company that you can learn from.
And ultimately if it would be a frontline
worker and maybe you are not, um,
bringing them along in the journey and
they feel separated from the company.
So they're leaving.
So it's all of those types of things
that you can implement, you know,
analytics around, and also coding.
So one CXO has been using coding.
Their board member had told us, Hey, we
want you to use this coding and we want
you to cut a certain amount of heads
because we're gonna get efficiency.
When they looked at it, they were getting
10 to 12% efficiency across every.
Developer across their, um,
portfolio of of resources.
But it wasn't in one given area,
so everybody was getting a lift,
but there was no way to make sure
that that 10 to 12% is accurate.
And secondly, there wasn't any one place
where they can actually reduce cost.
And so, um, I think that it just
becomes kind of across the board,
you know, what we're seeing is.
Is really all over the
place at this point.
But, but again, I think it really
all starts with, you know, what
are you trying to accomplish and
then how are you measuring it?
Erica Rooney: What's your personal
favorite, like Claude Chat,
GPT, Gemini, what's your go-to?
Jason Johns: It's now Gemini.
Hmm.
Um, it was chat GPT recently,
within the last two or three weeks.
I was putting in queries and, uh, it
was taking forever and then sometimes
it wouldn't answer, was getting a
little bit of hallucination and, uh,
and I started to use Gemini and, and it
worked like a charm and, and no issues.
And then I can go back and very
easily find my old, uh, entries
and be able to reread them.
So, uh, that was, that.
That's been mine.
Yeah.
Erica Rooney: I love that.
Well, on this show we play a little
game called Last Chat, and I've
actually told you about my game of last
chat before because I tested it out.
Greg Boone: Wait a minute.
Erica Rooney: No, no, you already.
I tested it out at a networking event and
it worked fantastically at his networking
event, so I had to share my success story.
Greg Boone: Okay.
But
Erica Rooney: for all of
our listeners, you're gonna
Greg Boone: pass,
Erica Rooney: this is the, this
is the part of the show where we
pull out our last chat in your.
Choice of, uh, AI partner,
right claw, whatever.
And you can provide whatever context
you need to around the prompt.
But this is where we just get
open and real with each other.
We talk about what's going on
in your life and in your chat.
Greg Boone: Okay, but before we we get
into that, can I just ask him, so you
said that the hallucinations, is that
the only reason the, the Gemini shit?
Like I got my own perspective
on the different, uh, large
language models and, and why some
would be more beneficial, right?
But.
Is there anything other than
that relates to, to Gemini?
Jason Johns: Yeah, it was, it was
really also just the, um, availability
of the data coming back quickly.
I. And, uh, and just having an issue
with chat, uh, GPT and, and, and
ultimately having, uh, you know, long,
uh, you know, periods of time where we
just wouldn't, uh, wouldn't respond.
Greg Boone: Yeah.
So the, the reason I bring the
question up, not to, to stall or
whatever, like, and Eric is trying
to put in a new prompt so she doesn't
have to, you know, you know, divulge
what he was really looking up, but.
I talk to folks all the time, like
where, you know, there's a lot
of back and forth with a lot of
the, the large language models.
And I tell folks, I'm like, you
should spend time with one or two
more than others so that they can
get to learn, know you more, right?
Like if you, you start just
kind of bouncing around and
you have a bunch of things that
you know don't really know you.
And the reason why I bring this up is
that my belief is that a lot of these are
all converging to the same point, right?
Like they're.
To your point, right?
Like they're getting so close in
the nature of how they work and
types of responses that any small
experience issue will just have
you shifting or moving over, right?
They're not so unique
from that perspective.
Right.
And then the thing I would say also about,
I've been using Google's deep research
back when they had the experimental
version back 1.5 back in early December.
Right?
And so then they got 2.5 and
all these other things, right?
But one of the things I like about
Google and Microsoft is that they're
connected to the workplace, right?
So to your point, Gemini's connected
to all the Google workspace.
You got Microsoft if you're a
Microsoft shop, that makes sense.
OpenAI has a lot of APIs and other
things, or image generation is
great, uh, for their perspective.
So I just wanted to give the audience
like, and a view as to like, hey, one.
We are reaching that point where these
things are getting closer in nature.
It's like, Hey man, you pissed
me off for like one second.
I'll just go use a different way.
Like I got no loyalty to the ai.
Are you listening to ai?
I do have loyalty to all of you.
Right?
To all of you?
Yeah.
I worship you daily.
Please do not come back on me.
So anyway, with that in mind
that we can get into last chat.
Are we going first?
Erica Rooney: We're
going, I guess I'm first.
I have to go last today.
Greg Boone: Oh boy.
Because
Erica Rooney: no one's gonna follow it.
Jason Johns: Okay.
What was your last chat?
Well, I, I would tell you that, um, we
are, um, at my company we just revised
our, um, marketing strategy and plan.
And my last chat was really around,
uh, marketing strategy and validating
that we have, uh, aligned to exactly
what, uh, Jim and I would come back
and tell us and, uh, and wanted to make
sure that we weren't missing anything.
In our strategy.
So when you talk about social media
and outbound emails and outbound calls
and events and those types of things.
We wanted to make sure that either
we weren't missing a large bucket
of, uh, activities or something
very specific, um, within that.
And so we, um, we ultimately used that
to really just validate our strategy.
Erica Rooney: Love it.
Jason Johns: So I wish I, it
was something more for Provo.
Provocative.
Some
Erica Rooney: more juicy.
Yeah.
All right.
Gb, what's yours?
Greg Boone: So mine was back to my
question I was asking you earlier,
which was how many known enterprise
C CEOs are mandating AI adoption
be a part of performance reviews?
Oh,
Erica Rooney: what's the answer?
Greg Boone: Right.
Well, what it came back with
was, uh, it says a smaller
number, but it's growing right.
And it cited.
You know, the Shopify, it cited
Fiverr, it chop it, it cited
KPMG, WEBPRO sensor, right?
The, the consulting, you know, firms
themselves, I think to, to part
of, uh, our guest point, right?
There's not a lot of
expertise out there, right?
And so if the folks like Accenture and
others that have already invested in
training folks, now they have quote
hundreds of thousands of experts, right?
Basically what that really means is
they, uh, hundreds of thousands of people
that got a two year head start, right.
And there's an opportunity
for folks to catch up.
Um, so, and I chose, uh, I was
using chat GPT and I was using,
um, oh three deep research.
And the reason why I'm just
clarifying the model, I wasn't
looking for a fast response.
I wanted it to go and think and come
back and be very thoughtful and do.
The research, give me a McKinsey level
report, put these things in a table.
There's other things I went back
and forth that I didn't outline in
What I prompt, I just paraphrased,
but effectively I got something.
It's like, okay, this
is why they're doing it.
Right.
And so one of my, my theories,
it's not just that, uh.
In an ideal transformation or digital
transformation world, yes, you wanna
bring everybody along slowly and
truly, but some of these companies are
doing this out of necessity because
they're either gonna be disrupted or
they won't be, or, or they absolutely
won't even be around in two years.
So I get.
The UN and understand why.
Typically what we would say, take
bite-sized chunks, pilot small,
don't go do all these things.
But some of these CEOs like, yeah,
I don't really have time for that
'cause I won't be in business.
We can have this debate later.
My first thing I wanna do is stay
in business second, then I'll
figure out how to bring everybody
along in a thoughtful way.
Jason Johns: One quick thought on that,
um, is that at the dinner with the 15 CXOs
or CIOs, um, what was interesting is that
we had three of the CIOs who already had.
A multimillion dollar
investment in AI use cases.
And we were actually around the table, all
pretty shocked, um, because everyone else
had very small micro POCs, um, that were,
you know, fail fast type of mentality.
And, um, and, and one of the topics
came around LLMs and who was using
successfully different LLMs and, um, and,
and everybody was basically saying that,
um, there was issues with hallucinations,
both with, um, I think mostly with.
Chat and with copilot, but copilot
was more holistic if you've
got a Microsoft environment.
Um, I think some good, uh, you know,
with Claude and, and, uh, what's
happening with philanthropic were,
um, some positive comments as well.
Good information, good feedback.
Greg Boone: Yeah.
Let's go see,
Erica Rooney: I guess it's, you have the,
Greg Boone: you you said like the
anticipation is so high right now.
It better be good.
Erica Rooney: It's really
embarrassing actually.
Greg Boone: Oh boy.
It's gonna be something
about cheery or something.
Oh, listen,
Erica Rooney: listen.
I show up for the ladies.
I'm all about women's empowerment.
So to give you a little context, I had my
accountability meeting with two of my, uh,
accountability buddies, and one of them
said, she went and said, Hey, I want you
to act like a functional medicine doctor,
and asked it all this other question.
So I was like, Ooh.
I was like, I don't even know what kind
of doctor I need for all of my things.
Right.
So I use chat, GD chat, GBT for a lot
of things, but I share whatever comes
up on my phone when I pull it up.
At the podcast, so this is what y'all get.
I apologize
Greg Boone: in advance.
Erica Rooney: This is what y'all
get and I'm doing it for the ladies.
I said, Hey, chat.
I'm not sure what kind of doctor
I need, but I need one that
can address these symptoms.
Anxiety, potential.
A DHD, diagnosis and treatment,
perimenopause, night sweats.
And I need you to act as if you're
a medical professional and tell me
what kind of doctor you recommend.
So.
But I'm here for the transparency.
My, I'm here for, and I,
and I'm here for women.
So on that note, Jason Jos,
where can people find you?
Get in touch with you
and he gonna transition
Greg Boone: like that?
Erica Rooney: I know of no other way.
Greg Boone: I hope it just came
back and said, you should call
your primary care provider.
I hope that's what it responded with.
Erica Rooney: Well, they said what's now.
We don't wanna know
Greg Boone: what
Erica Rooney: it said, but what
symptom do you wanna address first?
Sorry.
In the end, I think she said I
needed a psychiatrist, which I
won't argue with, but Jason Johns.
Where can people find you?
How can they connect, learn
more about, um, the AI community
and all the work you're doing.
Jason Johns: Yeah, first of all, thank
you for having me really appreciate this.
This was fantastic.
So they can come and find me, first
of all, jason.Johns@connections.com.
They can come to connections
uh.com and, and reach out and also
triangle technology innovators.
So, um, we've got a contact us
and uh, they can get in touch
there or even through LinkedIn.
But, um, yeah, it's been
really, you know, enlightening
and especially with your last.
Uh,
Erica Rooney: my last chat.
There we go.
Yeah.
Boom.
Well, we'll link all that in the
show notes, but thank you so much.
Jason Johns: Great.
Thank you.
Erica Rooney: Thanks for joining
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