July 2, 2025 • 47 mins
AI isn’t optional anymore — it’s mission-critical. But how do leaders move beyond the buzzwords and actually adopt AI in a way that drives real business outcomes?
In this episode of AI: Voice or Victim, hosts Erica Rooney and Greg Boone (aka AISerious™) sit down with Jason Johns, CEO of ConnXions, an AI-powered sourcing and procurement platform. Jason has spent decades in emerging tech, from open-source Linux to the AI frontier, and he's now helping companies navigate AI transformation without losing their people — or their minds.
This episode covers:
- How AI readiness assessments work (and why your data probably isn’t ready)
- What smart leaders are doing right now to prepare their teams
- The importance of AI adoption vs. AI implementation
Whether you're a curious executive, a nervous HR leader, or someone just wondering what AI means for your future — this is the episode for you.
📣 Call to Action:
👉 If your company hasn’t talked AI strategy, forward this episode to your CIO or CEO.
Then ask: “Are we leading the change… or reacting to it?”
👉 Don’t forget to subscribe, leave a review, and share this episode with someone navigating the AI revolution.
Subscribe to AI: Voice or Victim for more conversations that move you from AI anxious to AI curious. Hosted by Erica Rooney and Greg Boone aka AISerious™, we're helping people and organizations embrace AI ethically, strategically, and with humanity at the center.
Follow us and join the movement to shape the future — before it shapes us.
🔗 Follow us and dive deeper:
Greg Boone on LinkedIn: https://www.linkedin.com/in/gregboone
Erica Rooney on LinkedIn: https://www.linkedin.com/in/ericarooney/
© 2025 Walk West Production
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Jason Johns (00:06):
Adoption is key.
And I think that, so I come from theRed Hat world, which is an open source
world, meaning it's not a, the executivescome up with a decision, push it down,
and everybody's supposed to adopt it.
It really becomes a, we have to bringpeople along in the journey and the
(00:26):
more they feel like they're part ofthe 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 (00:45):
AI isn't the future.
It's now, and whether you're in hr,sales, operations, or leadership, the
choices you make today will determinewhether you thrive or get left behind.
Erica Rooney (00:55):
Today we have a very special guest joining us and that is my
friend Jason Johns, who is the founderand Cee O of Connections, an AI sourcing
platform, and B2B IT services marketplace.
Now he's got over a decade ofexperiencing enhancing global partnerships
and streamlining IT solutions.
But he has taken a left turn intothe world of AI and he's really
(01:20):
become this leader in the community.
And that's why I'm so excited tohave him here because he's not just
focused on how it will change hisbusiness 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 lineto board an airplane of all places.
So.
(01:40):
We're bringing him here to the podcast,but if you are unsure about AI these
days, and you're wondering if it meansthe end of the world is near, or maybe
you're just trying to get your companyon board, this conversation is for you.
We are gonna talk about Jason's tipsfor business leaders, the latest AI
trends that he's excited about, andhow we can embrace AI as a. Competitive
(02:02):
advantage because on his LinkedIn hesaid, y'all AI is no longer optional.
So get comfortable and get ready to beinspired with some real world insights.
Jason, I am so excited to have you here.
How are you?
Jason Johns (02:16):
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 (02:23):
Let's go.
And I'm gonna drop you right inbecause you've been in tech for.
Forever, really.
Right.
Long time.
Since the beginning of time and nowyou are leading an AI platform and
building community, can you take meback to that moment in time where
you really realized that this iswhat you wanted to do going forward?
I.
Jason Johns (02:43):
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, backin the days of Red Hat, when it
was really looking at Linux andhow do you scale and how do you.
Fight the giant of Microsoft.
(03:03):
You know, it was really rolling up thesleeves, building a business there.
And uh, and ultimately then, uh, I'vebeen 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 procurementplatform, it really came into play
(03:25):
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 intowhat AI was going to mean.
Not only for our product, but alsohow we're gonna run our business.
And that was really theturning point of understanding,
(03:48):
learning and then implementing.
Erica Rooney (03:51):
Okay.
What made you go all in onbuilding community around this?
And just for everybody listening, Iwent to this incredible networking
event that he put together.
You brought leaders in ai, fromGoogle, from LabCorp, everyone
together to talk about it.
And in a very.
Ai.
Serious way.
Very technical way.
Right?
(04:11):
And this is why I love theshow, 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 communityand bring all these people together.
Jason Johns (04:27):
It was really out of necessity, the community.
So I've got a big network in theRaleigh area, and, uh, ultimately
these CXOs CIOs, CFOs were asking me.
What I thought, and itwas, how do I do this?
How would this make a differencein my, you know, business and
(04:50):
how I really develop my strategy?
Where do I go?
What technologies are rightfor certain use cases?
How do I even make adetermination around these things?
So I decided to createtriangle technology innovators.
And we have then been, uh,really bringing together leaders
(05:13):
within the area, um, to learn.
Uh, basically the motto of thegroup is network, learn and grow.
Um, there's no selling in this group.
It is really about networking withother key executives, understanding,
you know, who's doing what, uh, but alsothen bringing in experts and leaders
so that they can provide guidance.
(05:35):
It's really hard right now because people,there is really very few experts, but
there are people that have already doneit somewhat successfully or at least gone
through the initial phase successfully.
And they can share that informationwith others and that's what we're doing.
Greg Boone (05:54):
Yeah.
So there's effectively, there's folksthat have, uh, kind of led the charge,
but to your point, there's not a lotof 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 newtechnology, or at least one that is
exposed to a lot more folks, you know,going back to November of 22 and open ai,
(06:17):
you know, kind of launches to the worldchat, GPT always tell folks that that
gave a bunch of executives air cover toreally start talking about ai, right?
So it's awesome that you'rebringing 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 partlyoutta necessity as well, right?
(06:38):
Kind of how you laid it out.
Now, this necessity was more aboutmaking 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 outthere, right, and you're having this
community of experts, or at leastmore experienced folks in the field
(06:59):
with these business leaders, whattypes of challenges are you hearing
these days around actually adopting?
Right?
Because everyone keepstalking about use cases.
But very few people are actuallyadopting the technology to be
able to make the use cases.
Jason Johns (07:12):
Yeah, great question.
What I'm seeing is.
Uh, I'm gonna go back a level.
What I'm seeing is that that trueleaders, whether they are a C-level
or a board, they're looking at thisin just really a few different ways.
Number one, if you think of what isthe key differentiator that my company
(07:36):
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 createoptimization or efficiency within my
company and doing that using technology.
(07:58):
So if these leaders are looking atit as key differentiation and how
do I essentially be more efficient,then that becomes such a key.
And core differentiator fortheir company moving forward.
Um, if leaders are not doing that,they are going to be the ones who
(08:20):
won't have that differentiation.
They're gonna still have their currentcosts and they may be losing out.
That may be.
Six months down the road, it might be10 years down the road, but if they're
not figuring that out now, then theyare going to be really on the bottom
(08:41):
and in a very vulnerable position.
So, so we look at that as, asthere's lots of things going on.
But if you're in leadership, when I talkto leaders, they are talking a lot about
use cases, what they are struggling with.
What should my strategy be?
(09:02):
How do I develop my strategy?
Um, what is my infrastructure thatcan support certain use cases?
How do I make sure that I don'thave information or data that's
going outside of my firewalls?
How do I make sure that I amprotecting my information?
(09:26):
Right?
Um, think that you start to lookat all of these different things.
When I talk to my customers,am I comfortable using AI
and gen AI information?
If something goes wrong, isthat going to jeopardize my
relationship with that customer?
So what, what I'm seeingright now are a couple things.
(09:51):
First generation strategy, whichwill change and evolve over time.
It is not a set it andforget it type of scenario.
It is.
I need to keep going back to it.
Number two, what does my infrastructurelook like and how can I. Have an
infrastructure that is ready foruse cases in in AI and Gen ai.
(10:16):
And three, ultimately, what arethose use cases that I can start with
internally that are gonna potentiallyhave an effect of my internal teams,
but I'm not going to put that out to my.
Anything that is customer facingat this point until I can really
kick the tires and also measure it.
(10:38):
And then I think the last is, doI have the right operations and
the right skills within my team?
They're now calling it AIops to able to even manage.
These different use cases.
And so we're seeing all of that across theboard and I'm having conversations with
(10:58):
CXOs and board, uh, board members almoston a daily basis around these topics.
And it's very challenging atthe, at this present moment.
Erica Rooney (11:10):
I mean, both of us were like, we got a question.
My first question is like, around.
Readiness assessments, right?
Because that's when likeboom, everyone needs that.
They need to just know what that is.
But the problem is we don'tquite know what we need to be
fully ready for these things.
So how do we even think abouta readiness assessment or
what are you seeing out there?
I.
Jason Johns (11:29):
So my company has put together, um, a, a really
a, a group of experts that helpswith a readiness assessment.
It's an AI read, uh, readinessassessment, and it comes down to looking
at what is their data and applicationsand what is that flow of data.
(11:52):
It comes down to looking at governance.
What kind of governance do you have onthe 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 whatyou're looking for and what your
(12:14):
threshold is as a company around risk.
And you combine all of that and you thenstart 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 (12:41):
I got one comment, then I'll let you talk about it.
Oh yeah.
I don't know that, here's my thoughtas I'm hearing this, and I've
worked in a lot of companies whereall of the data is rough, right?
And that's just immediately I could feelmy anxiety start to come up in my throat.
And I imagine that's where a lotof people live because they're just
like, I don't even know how to getthat data as clean as possible.
(13:04):
And I love how you said like, what isthat one thing that we can do today?
What is the one step wecan take really just.
Acknowledging that it is just that it'sa first step because I know the answer to
my own question, but I share it just toacknowledge all, all of my other anxiety
ridden friends out there and their dirtydata is the time will pass anyways.
(13:25):
Right.
My mother has always said that to me.
When you have a big goal or likeif you're going after a PhD or a
four year degree, like don't tellme how it's gonna take four years.
I mean, the time's gonna pass anyways.
Right?
And, and we know two things to betrue here is that AI is here and
it's only going to be coming evenmore and the time will pass anyways.
(13:46):
So get after it.
Jason Johns (13:48):
But go ahead Greg, what were
Erica Rooney (13:48):
you
Jason Johns (13:48):
gonna say?
And, and one comment thoughto that it is not a sprint.
It's a marathon.
And what do you do whenit's a marathon train?
Accordingly, you aregonna 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.
(14:09):
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 sameapproach 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'rethen going to be the superstar athlete
(14:33):
who has that endurance, who then hasthat regiment and you can make it
the 26.2 miles and, and that you areready for it, but you don't try and
take it all, all in one big chunk.
You really break it upinto small milestones.
And that's part of really how doyou build not only a strategy,
but then tactics that help you getthere really one day at a time.
Erica Rooney (14:57):
Hmm.
I love a good fitness analogy.
Greg Boone (15:00):
Yeah.
We had a guest recently, uh, DanaPees that used this, the very
similar, she was both trying totrain 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 thatthat also, you know, took, uh, kind of
this kind of bite-sized, uh, approach.
One of the things, I was gonna comeback to one, one, she said that
she's worked at other companies.
(15:20):
She's worked for me twice.
Right.
And so when she said that I worked atother 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 wasat, at least with me, but I digress.
The, uh, a couple points that Ijust wanted to just follow up just
to reiterate to our audience is.
One of the things you describedwas basically what, um, the, I, I
recently got a certification fromMIT on AI leadership transformation,
(15:43):
and they talked about the concept oftheir forgiveness continuum, right.
From internal to external.
And they said, you're gonnatry these things first.
Try them internally.
Don't just go directly to yourcustomer because the forgiveness
there is not as high as internally.
Right.
And so, I guess a little bit ofmy, not necessarily pushback,
but how I would flip the equationbased on those four kind of, uh.
(16:05):
Uh, levers, if you will,that you talked about.
And I think the last one you talkedabout something related to the
people, which is my firm belief.
And part of the reason, again, whywe have this podcast is that business
leaders need to focus on training everyemployee in their organization on the
benefits and the productivity gains ofai, not just on the business application.
(16:26):
'cause what people are missing isthat 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 makehere is just that this is the first
technology in my generation, right, thataffects every single employee, right?
(16:50):
And so typically when I'm doingdigital transformation, I'm focusing
on IT or marketing or some group.
I only gotta worry about oneor two potential saboteurs that
don't want to go with this.
Right now you have 90% of the organizationthat say, Hey, I don't even know
what you're talking about right now.
Right?
And so what ends up happening is that thebusiness, myself included, I've fallen
(17:10):
into the trap in times, at times, right?
And say, Hey, we've gotthese 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, youknow, senior, the, the CXOs out
there, it's like, look, spend sometime focusing on the productivity
because like yourself, right?
(17:32):
Always say it's a binary choice.
We're either gonna help you grow revenue.
Or we're gonna create operationalexcellence, which generally means
you're gonna be able to cut costs.
And this is one of the first technologiesyou can actually do both at the same time.
You can actually cut costs whilegrowing your business, creating
those key differentiators.
So I loved how you laid that out.
So my only real comment here isthat I, I really wish and hope.
(17:55):
That CXOs start spending a lot of timetraining everyone, not just the technical
folks or the folks that are gonna bein charge of the, uh, the TIGER team
for the new use case down the road.
Jason Johns (18:06):
That's a great point.
I think once you have your strategy andyou start to implement the technologies.
It is really a requirement to bring inthose teams that are non-technical, um,
and train them up and to provide reallya kind of easy to use, easy button for
(18:28):
how you use this in your daily life.
And then to reinforce that, um, in yourkey metrics, in your one-on-ones, and
you have to implement that technologyon a day in and day out basis, or it's
going to fail when you think about data.
Data has been a mess everywhere.
(18:50):
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 ofour core data out of our ERP and Oracle
and move that over and cleanse and apen that data, because we were actually
working on the very first renewals model.
(19:13):
Really probably anywhere.
And then we had to load that intoSalesforce so that the sales reps had
visibility into those renewals so thatthey can then engage those customers.
So I've been working in data, dirtydata, um, for a long period of time.
And the reality is, is thatyou really need to make an
(19:33):
assessment around, you know.
First, how good is your data?
And are you gonna append that orare you just gonna use it as is?
Or are you going to then look at thegovernance if you don't have clean data?
So very few have clean data.
How do you actually create governancethat then modifies the input of that data?
(19:57):
So then you have a.
Better set of data and you mighthave to actually create a new data
store or database that is with thecleaner data that you are then gonna
start fresh and essentially buildyour AI solutions and LLM models on,
Greg Boone (20:16):
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 yourhouse 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 toothough, as a lot of people find,
is that they end up moving the samecrap from one house to the next.
Right.
And so the parallel being, or atleast that you're kind of, if I, if I
(20:37):
paraphrase kind of what you're sayingis that don't move the crappy data over.
Right.
You have an opportunity now.
I was listening to apodcast, uh, yesterday.
I think, um, they've talkedabout 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 saythere is a new governance model of what
does, what does quality data look like?
(20:59):
What do we actually meet?
How could we use AI to either helpclean or harmonize or categorize
the data in a different waythat maybe we didn't think of?
Right?
Maybe some of this really is justtrash, right, and we need to throw
it out when we move to a new home.
So I, I like that, that perspectiveand that that vantage point.
The um.
Can I ask you a question just as itrelates to the, when you talk about
(21:22):
readiness assessment earlier, uh, isthat 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 ifthey're ready or not?
Jason Johns (21:36):
Well, ultimately it's from a business perspective.
But the CIOs in most cases aregetting 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 forthem to be in the driver's seat and to
(21:58):
have knowledge around a sound strategy.
I have several people telling me that.
Uh, different board members feel like theyknow AI better than the CIO and they're
making recommendations into, um, whatthey should be doing, could be doing.
And if that CIO doesn't havea solid strategy, you are now
(22:24):
vulnerable to a person pro in a,in a position of power potentially.
Torpedoing A. What would be a goodstrategy if you don't defend that?
Correct.
You've got to have the knowledge anda sound strategy, and if you have
those things, that's why think even AIassessments becomes just a sounding board
(22:49):
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 comingin because everybody else internally
might be drinking the Kool-Aid.
You get an outside advisor comingin, they're gonna tell you exactly
(23:12):
what their view is, independentof what's happening internally.
You get a lot of yes people internally.
An outside, uh, voice reallyhelps, um, balance where you
are and where you need to go.
Erica Rooney (23:27):
Do you ever do any people readiness assessments
when it comes to AI adoption?
And I ask that 'cause I'm thepeople person here, you know?
And, and from the tech standpoint, itcould look great all day long, but if
the people don't believe in it or if thepeople have a deep fear, there's gonna
be a lot of problems with adoption.
What are your thoughts on that?
Jason Johns (23:47):
Adoption is key.
And I think that, so I come from theRed Hat world, which is an open source
world, meaning it's not a, the executivescome up with a decision, push it down,
and everybody's supposed to adopt it.
It really becomes a. We have to bringpeople along in the journey and the
(24:08):
more they feel like they're part ofthe 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 reallykey, um, is that there is a lot
of angst and anxiety around ai.
(24:30):
AI is gonna take my job, AI isgoing to change my life, you know,
for the negative or the, the worst.
Right?
And I think that youhave to take them along.
You have to start with cursory coursesthat help people start to understand what
AI is and really demystify what AI is.
(24:51):
Um, if you think about it from a, just astandpoint of either when you, you know.
In school or your kids are inschool, there's always that subject
that somebody is avoiding or thathomework assignment or that project
that, that somebody's avoiding.
And the more you avoid.
The worse it gets because thedeadlines and everything is coming.
(25:15):
Right, and then you'regoing to have to jump in.
You're better off getting startedearly, jumping in now and starting
to learn and understand what it is.
It is not crazy technology that's goingto, you know, that's out of this world.
It is a technology that can helpand will change everyone's life.
(25:39):
It's like thinking about.
Back in the day, businesswithout technology.
Right.
You have to adopt technology.
In business, AI is gonnabe the exact same thing.
You have to adopt AI or you're gonna beleft behind and you better start early.
You're better off starting early thanit is really getting up to that nth
(26:02):
hour where you company is implementingsomething and you still have no knowledge
and you're still scared and you have tobe, if you want to be on the, the, the.
Front end of it, you're gonna be theone actually helping make the decisions.
If you're not, then it'sgoing to be made for you.
And that could be somethingthat you're not gonna like.
Greg Boone (26:24):
I just, uh, on that point, sorry to cut you off, but the, uh.
I'm gonna be speaking here shortly ina webinar and we're talking to a lot
of HR professionals, and Erica andI are having several conversations
with HR and upskilling folks.
Actually, I just got out of a differentcall where I was prepping for another
panel where we're gonna be talking aboutupskilling in in the age of ai and.
(26:44):
One of the things I continue to, to tryto 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 allthe time, like it's AI's not replace you.
The person that knowsAI is gonna replace you.
Right.
And what I want to get across though,to leaders is that just asking folks to
(27:04):
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 myperspective 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 todescribe office of, of the CHRO.
Then there's a role specific kind of view.
(27:25):
Then there's a hyper personalized view.
How does this help me as anindividual be 20 to 30% more
productive every single day?
I just firmly believe that if moreleaders take this approach from a
bottoms up perspective, they're gonnaget people to have their own aha moments.
Right.
And what it ends up doing is turningmore people into strategists, right?
(27:45):
Versus doing all of these mundane tasks.
The, the, the last, uh, I guessquestion that I have for you on, on
this topic is I fundamentally believe.
And that we are at this momentwhere you're starting to see
more executives mandate that AIadoption be a part of what they do.
Right.
From the Shopify Cee o that wentviral a couple weeks ago to folks,
(28:07):
we were talking yesterday aboutDuolingo to Fiverr to others, right?
I think that we don't have four years.
I think folks are gonna have to understandthat, and these companies aren't
doing it just because they want so.
My question to you is that, do you feellike we're at this tipping point or not?
Or when do you believe the tippingpoint where it's gonna move from,
Hey, this is optional to, hey, this isgonna be on your performance review,
(28:29):
this is gonna be, we're gonna tiemetrics to the management, to everyone.
Like where do you think weare on that kind of continuum?
Jason Johns (28:35):
I think that there's early adopters and those early adopters
today are jumping in headfirst, right.
Shopify and, and others you'vemade named, I think that.
Those things are good and ultimatelygoing to change the culture of those
companies also believe that if you jumpin too fast without having the right
(28:59):
strategy yeah, and having the rightuse cases and metrics around that, it
also then creates a separate problem.
You start to then implement applicationsolutions, um, that ultimately don't
align with what the strategy will be.
Now you've created a. Spend a lotof money, you've created a problem
(29:21):
that now you need to untangle.
And so I think that generallythe whole embrace AI is good.
I. Thinking how you do that isreally the key because you don't want
people and managers start startingto implement AI in your architecture.
(29:41):
Maybe it creates security risks.
Right?
Right.
Maybe it, um, actually it will create moreunintentional, more problems, you know,
if you start to then leverage AI, um, incertain ways, right, with your employees
and your onboarding new employees.
And you have an LLM and you havedata in there that you didn't realize
(30:05):
you had in there, and maybe it'sold and stale, maybe it's actually.
Maybe there's some hallucinations and itactually 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 thestrategy that I would take is
you want your organization.
(30:26):
Thinking about it and working towards it.
You just wanna be measured in whatthat strategy is on how you do it.
Erica Rooney (30:34):
I love it.
I wanna take a left turn though andstart talking about a few AI trends.
Okay.
'cause I feel like therehave been a lot of just.
Things hitting the marketthese 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 curiousand anxious over here, so I wanna know
what kind of trends are you seeing?
What are you most excited about when itrelates to using AI in the business world?
(30:58):
Also professionally or personally?
Jason Johns (31:00):
I mean the, I wrote down a few things here because what I'm
seeing in the conversation that I'mhaving really at all different levels,
um, so you mentioned at our triangletechnology innovators, we've, uh, we
had speakers like Ben Heller from, youknow, Google Cloud, and we had, uh, you
know, satin from Fidelity Investmentsand, uh, Adam Sullivan from, uh, LabCorp.
(31:22):
These guys work at massive companies.
They are the leaders withinai within their company.
And these are the types ofthings 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 AIwithout it affecting a broader group.
(31:50):
So content creation, that is maybe one ofthe 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 goingto be relevant for my audience?
(32:11):
And then how do I use thatand get the information?
But then how do I modify that based uponmy 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 additionalinformation, but it really, it's a
blend between that, you know, and I,I see we had a. I had a CXO dinner.
(32:35):
We had 15 CXOs from, I think thatthey represented something like maybe
over a hundred billion dollars in, insales and, uh, for their companies.
And they were very much at a. Youknow, infancy state now was some
government regulated, you know,financial institutions who really
(32:55):
haven't started, you know much.
They're kind of looking at it.
Um, and they've hired AIexperts, um, two others who have
really already implemented, youknow, 5, 6, 7 POCs and have.
Taken, you know, a fewof those into production.
So it was really a blend.
(33:15):
And you know, what I wouldsay is that, that it really
becomes, what's your aptitude?
Are you in a regulated environmentwhere one of my colleagues, uh, CXO
is in a regulated environment and whenyou use an LLM, they're not allowed.
To use, um, any other dataexcept for their own data.
(33:36):
And they are not allowing for justa, you know, where you could write
anything in there and get an answer.
You have to, it's, it, it's a scriptedquestion and a scripted answer, and you
have to cite the sources in which you gotyou, you have that information back so.
Erica Rooney (33:54):
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'teven know how to think about governance.
Right?
That is exactly how youthink about governance.
If you don't want, if you wanna useyour own data or not, do you want
to just be able to dump the wholething in there, or do you wanna have
only prompts and it can be tailoredto your organization in that way?
(34:16):
So, sorry, go ahead.
I just thought, I was like, wow, I'venever heard, at least on this podcast,
it described in such a clear way.
So thank you.
Jason Johns (34:23):
For sure.
And, and so that becomes amitigating 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 ourbusiness for a long period of time.
I think AI is just acceleratingwhat we can do in automation, right?
(34:47):
We've had automation, machinelearning, um, you know, and, and
things like that for, for a long time.
So I think that that becomes agenesis when you look at data.
Regardless of whether, how clean yourdata is, how can you then overlay that
with analytics so that you can actuallydeem intelligence off of your data?
(35:10):
Now, a lot of financial institutionsdo this, and they've been doing
this for a long period of time.
I. Others have it.
So if you even think about, Erica, youwere talking about the HR department.
How do you look at your own employeedata and what is satisfaction?
What would you know?
What is my turnover?
(35:30):
Are there profiles within the turnoverthat you're seeing as a trend?
Um, that is, you know.
Maybe a, a way that you're treating acertain type of, of person or level within
the company that you can learn from.
And ultimately if it would be a frontlineworker and maybe you are not, um,
(35:53):
bringing them along in the journey andthey feel separated from the company.
So they're leaving.
So it's all of those types of thingsthat you can implement, you know,
analytics around, and also coding.
So one CXO has been using coding.
Their board member had told us, Hey, wewant you to use this coding and we want
(36:16):
you to cut a certain amount of headsbecause we're gonna get efficiency.
When they looked at it, they were getting10 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,
(36:36):
but there was no way to make surethat that 10 to 12% is accurate.
And secondly, there wasn't any one placewhere they can actually reduce cost.
And so, um, I think that it justbecomes kind of across the board,
you know, what we're seeing is.
Is really all over theplace at this point.
But, but again, I think it reallyall starts with, you know, what
(36:59):
are you trying to accomplish andthen how are you measuring it?
Erica Rooney (37:02):
What's your personal favorite, like Claude Chat,
GPT, Gemini, what's your go-to?
Jason Johns (37:08):
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, itwas taking forever and then sometimes
it wouldn't answer, was getting alittle bit of hallucination and, uh,
and I started to use Gemini and, and itworked like a charm and, and no issues.
(37:30):
And then I can go back and veryeasily find my old, uh, entries
and be able to reread them.
So, uh, that was, that.
That's been mine.
Yeah.
Erica Rooney (37:40):
I love that.
Well, on this show we play a littlegame called Last Chat, and I've
actually told you about my game of lastchat before because I tested it out.
Greg Boone (37:47):
Wait a minute.
Erica Rooney (37:48):
No, no, you already.
I tested it out at a networking event andit worked fantastically at his networking
event, so I had to share my success story.
Greg Boone (37:57):
Okay.
But
Erica Rooney (37:58):
for all of our listeners, you're gonna
Greg Boone (37:59):
pass,
Erica Rooney (37:59):
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 contextyou need to around the prompt.
But this is where we just getopen and real with each other.
We talk about what's going onin your life and in your chat.
Greg Boone (38:18):
Okay, but before we we get into that, can I just ask him, so you
said that the hallucinations, is thatthe only reason the, the Gemini shit?
Like I got my own perspectiveon the different, uh, large
language models and, and why somewould be more beneficial, right?
But.
Is there anything other thanthat relates to, to Gemini?
Jason Johns (38:35):
Yeah, it was, it was really also just the, um, availability
of the data coming back quickly.
I. And, uh, and just having an issuewith 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 (38:53):
Yeah.
So the, the reason I bring thequestion up, not to, to stall or
whatever, like, and Eric is tryingto put in a new prompt so she doesn't
have to, you know, you know, divulgewhat he was really looking up, but.
I talk to folks all the time, likewhere, you know, there's a lot
of back and forth with a lot ofthe, the large language models.
And I tell folks, I'm like, youshould spend time with one or two
more than others so that they canget to learn, know you more, right?
(39:16):
Like if you, you start justkind of bouncing around and
you have a bunch of things thatyou know don't really know you.
And the reason why I bring this up isthat 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 inthe nature of how they work and
types of responses that any smallexperience issue will just have
(39:37):
you shifting or moving over, right?
They're not so uniquefrom 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 experimentalversion back 1.5 back in early December.
Right?
And so then they got 2.5 andall these other things, right?
But one of the things I like aboutGoogle and Microsoft is that they're
(39:57):
connected to the workplace, right?
So to your point, Gemini's connectedto all the Google workspace.
You got Microsoft if you're aMicrosoft shop, that makes sense.
OpenAI has a lot of APIs and otherthings, or image generation is
great, uh, for their perspective.
So I just wanted to give the audiencelike, and a view as to like, hey, one.
We are reaching that point where thesethings are getting closer in nature.
It's like, Hey man, you pissedme off for like one second.
(40:19):
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 mindthat we can get into last chat.
Are we going first?
Erica Rooney (40:35):
We're going, I guess I'm first.
I have to go last today.
Greg Boone (40:38):
Oh boy.
Because
Erica Rooney (40:39):
no one's gonna follow it.
Jason Johns (40:41):
Okay.
What was your last chat?
Well, I, I would tell you that, um, weare, 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 exactlywhat, uh, Jim and I would come back
(41:05):
and tell us and, uh, and wanted to makesure that we weren't missing anything.
In our strategy.
So when you talk about social mediaand outbound emails and outbound calls
and events and those types of things.
We wanted to make sure that eitherwe weren't missing a large bucket
of, uh, activities or somethingvery specific, um, within that.
(41:27):
And so we, um, we ultimately used thatto really just validate our strategy.
Erica Rooney (41:32):
Love it.
Jason Johns (41:33):
So I wish I, it was something more for Provo.
Provocative.
Some
Erica Rooney (41:36):
more juicy.
Yeah.
All right.
Gb, what's yours?
Greg Boone (41:38):
So mine was back to my question I was asking you earlier,
which was how many known enterpriseC CEOs are mandating AI adoption
be a part of performance reviews?
Oh,
Erica Rooney (41:50):
what's the answer?
Greg Boone (41:51):
Right.
Well, what it came back withwas, uh, it says a smaller
number, but it's growing right.
And it cited.
You know, the Shopify, it citedFiverr, it chop it, it cited
KPMG, WEBPRO sensor, right?
The, the consulting, you know, firmsthemselves, I think to, to part
of, uh, our guest point, right?
(42:12):
There's not a lot ofexpertise out there, right?
And so if the folks like Accenture andothers that have already invested in
training folks, now they have quotehundreds of thousands of experts, right?
Basically what that really means isthey, uh, hundreds of thousands of people
that got a two year head start, right.
And there's an opportunityfor folks to catch up.
Um, so, and I chose, uh, I wasusing chat GPT and I was using,
(42:34):
um, oh three deep research.
And the reason why I'm justclarifying the model, I wasn't
looking for a fast response.
I wanted it to go and think and comeback and be very thoughtful and do.
The research, give me a McKinsey levelreport, put these things in a table.
There's other things I went backand forth that I didn't outline in
What I prompt, I just paraphrased,but effectively I got something.
(42:55):
It's like, okay, thisis why they're doing it.
Right.
And so one of my, my theories,it's not just that, uh.
In an ideal transformation or digitaltransformation world, yes, you wanna
bring everybody along slowly andtruly, but some of these companies are
doing this out of necessity becausethey're either gonna be disrupted or
they won't be, or, or they absolutelywon't even be around in two years.
(43:16):
So I get.
The UN and understand why.
Typically what we would say, takebite-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 stayin business second, then I'll
figure out how to bring everybodyalong in a thoughtful way.
Jason Johns (43:35):
One quick thought on that, um, is that at the dinner with the 15 CXOs
or CIOs, um, what was interesting is thatwe had three of the CIOs who already had.
A multimillion dollarinvestment in AI use cases.
And we were actually around the table, allpretty shocked, um, because everyone else
(43:57):
had very small micro POCs, um, that were,you know, fail fast type of mentality.
And, um, and, and one of the topicscame 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.
(44:22):
Chat and with copilot, but copilotwas 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 (44:37):
Yeah.
Let's go see,
Erica Rooney (44:38):
I guess it's, you have the,
Greg Boone (44:40):
you you said like the anticipation is so high right now.
It better be good.
Erica Rooney (44:44):
It's really embarrassing actually.
Greg Boone (44:46):
Oh boy.
It's gonna be somethingabout cheery or something.
Oh, listen,
Erica Rooney (44:50):
listen.
I show up for the ladies.
I'm all about women's empowerment.
So to give you a little context, I had myaccountability meeting with two of my, uh,
accountability buddies, and one of themsaid, 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 kindof doctor I need for all of my things.
(45:10):
Right.
So I use chat, GD chat, GBT for a lotof 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 (45:21):
in advance.
Erica Rooney (45:22):
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 doctorI 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'rea medical professional and tell me
what kind of doctor you recommend.
So.
(45:44):
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 youand he gonna transition
Greg Boone (45:56):
like that?
Erica Rooney (45:58):
I know of no other way.
Greg Boone (46:00):
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 (46:05):
Well, they said what's now.
We don't wanna know
Greg Boone (46:07):
what
Erica Rooney (46:07):
it said, but what symptom do you wanna address first?
Sorry.
In the end, I think she said Ineeded a psychiatrist, which I
won't argue with, but Jason Johns.
Where can people find you?
How can they connect, learnmore about, um, the AI community
and all the work you're doing.
Jason Johns (46:27):
Yeah, first of all, thank you for having me really appreciate this.
This was fantastic.
So they can come and find me, firstof all, jason.Johns@connections.com.
They can come to connectionsuh.com and, and reach out and also
triangle technology innovators.
So, um, we've got a contact usand uh, they can get in touch
(46:48):
there or even through LinkedIn.
But, um, yeah, it's beenreally, you know, enlightening
and especially with your last.
Uh,
Erica Rooney (46:57):
my last chat.
There we go.
Yeah.
Boom.
Well, we'll link all that in theshow notes, but thank you so much.
Jason Johns (47:01):
Great.
Thank you.
Erica Rooney (47:07):
Thanks for joining us on ai, voice or victim if you
want to stay competitive in the.
AI age.
Start now.
Take one insight from today's episode andput it into practice in the next 24 hours.
Make sure to follow us, shareyour thoughts, and subscribe
for more actionable AI insights.
See you next time.
Adoption is key.
And I think that, so I come from theRed Hat world, which is an open source
world, meaning it's not a, the executivescome up with a decision, push it down,
and everybody's supposed to adopt it.
It really becomes a, we have to bringpeople along in the journey and the
(00:26):
more they feel like they're part ofthe 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 (00:45):
AI isn't the future.
It's now, and whether you're in hr,sales, operations, or leadership, the
choices you make today will determinewhether you thrive or get left behind.
Erica Rooney (00:55):
Today we have a very special guest joining us and that is my
friend Jason Johns, who is the founderand Cee O of Connections, an AI sourcing
platform, and B2B IT services marketplace.
Now he's got over a decade ofexperiencing enhancing global partnerships
and streamlining IT solutions.
But he has taken a left turn intothe world of AI and he's really
(01:20):
become this leader in the community.
And that's why I'm so excited tohave him here because he's not just
focused on how it will change hisbusiness 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 lineto board an airplane of all places.
So.
(01:40):
We're bringing him here to the podcast,but if you are unsure about AI these
days, and you're wondering if it meansthe end of the world is near, or maybe
you're just trying to get your companyon board, this conversation is for you.
We are gonna talk about Jason's tipsfor business leaders, the latest AI
trends that he's excited about, andhow we can embrace AI as a. Competitive
(02:02):
advantage because on his LinkedIn hesaid, y'all AI is no longer optional.
So get comfortable and get ready to beinspired with some real world insights.
Jason, I am so excited to have you here.
How are you?
Jason Johns (02:16):
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 (02:23):
Let's go.
And I'm gonna drop you right inbecause you've been in tech for.
Forever, really.
Right.
Long time.
Since the beginning of time and nowyou are leading an AI platform and
building community, can you take meback to that moment in time where
you really realized that this iswhat you wanted to do going forward?
I.
Jason Johns (02:43):
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, backin the days of Red Hat, when it
was really looking at Linux andhow do you scale and how do you.
Fight the giant of Microsoft.
(03:03):
You know, it was really rolling up thesleeves, building a business there.
And uh, and ultimately then, uh, I'vebeen 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 procurementplatform, it really came into play
(03:25):
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 intowhat AI was going to mean.
Not only for our product, but alsohow we're gonna run our business.
And that was really theturning point of understanding,
(03:48):
learning and then implementing.
Erica Rooney (03:51):
Okay.
What made you go all in onbuilding community around this?
And just for everybody listening, Iwent to this incredible networking
event that he put together.
You brought leaders in ai, fromGoogle, from LabCorp, everyone
together to talk about it.
And in a very.
Ai.
Serious way.
Very technical way.
Right?
(04:11):
And this is why I love theshow, 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 communityand bring all these people together.
Jason Johns (04:27):
It was really out of necessity, the community.
So I've got a big network in theRaleigh area, and, uh, ultimately
these CXOs CIOs, CFOs were asking me.
What I thought, and itwas, how do I do this?
How would this make a differencein my, you know, business and
(04:50):
how I really develop my strategy?
Where do I go?
What technologies are rightfor certain use cases?
How do I even make adetermination around these things?
So I decided to createtriangle technology innovators.
And we have then been, uh,really bringing together leaders
(05:13):
within the area, um, to learn.
Uh, basically the motto of thegroup is network, learn and grow.
Um, there's no selling in this group.
It is really about networking withother key executives, understanding,
you know, who's doing what, uh, but alsothen bringing in experts and leaders
so that they can provide guidance.
(05:35):
It's really hard right now because people,there is really very few experts, but
there are people that have already doneit somewhat successfully or at least gone
through the initial phase successfully.
And they can share that informationwith others and that's what we're doing.
Greg Boone (05:54):
Yeah.
So there's effectively, there's folksthat have, uh, kind of led the charge,
but to your point, there's not a lotof 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 newtechnology, or at least one that is
exposed to a lot more folks, you know,going back to November of 22 and open ai,
(06:17):
you know, kind of launches to the worldchat, GPT always tell folks that that
gave a bunch of executives air cover toreally start talking about ai, right?
So it's awesome that you'rebringing 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 partlyoutta necessity as well, right?
(06:38):
Kind of how you laid it out.
Now, this necessity was more aboutmaking 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 outthere, right, and you're having this
community of experts, or at leastmore experienced folks in the field
(06:59):
with these business leaders, whattypes of challenges are you hearing
these days around actually adopting?
Right?
Because everyone keepstalking about use cases.
But very few people are actuallyadopting the technology to be
able to make the use cases.
Jason Johns (07:12):
Yeah, great question.
What I'm seeing is.
Uh, I'm gonna go back a level.
What I'm seeing is that that trueleaders, whether they are a C-level
or a board, they're looking at thisin just really a few different ways.
Number one, if you think of what isthe key differentiator that my company
(07:36):
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 createoptimization or efficiency within my
company and doing that using technology.
(07:58):
So if these leaders are looking atit as key differentiation and how
do I essentially be more efficient,then that becomes such a key.
And core differentiator fortheir company moving forward.
Um, if leaders are not doing that,they are going to be the ones who
(08:20):
won't have that differentiation.
They're gonna still have their currentcosts and they may be losing out.
That may be.
Six months down the road, it might be10 years down the road, but if they're
not figuring that out now, then theyare going to be really on the bottom
(08:41):
and in a very vulnerable position.
So, so we look at that as, asthere's lots of things going on.
But if you're in leadership, when I talkto leaders, they are talking a lot about
use cases, what they are struggling with.
What should my strategy be?
(09:02):
How do I develop my strategy?
Um, what is my infrastructure thatcan support certain use cases?
How do I make sure that I don'thave information or data that's
going outside of my firewalls?
How do I make sure that I amprotecting my information?
(09:26):
Right?
Um, think that you start to lookat all of these different things.
When I talk to my customers,am I comfortable using AI
and gen AI information?
If something goes wrong, isthat going to jeopardize my
relationship with that customer?
So what, what I'm seeingright now are a couple things.
(09:51):
First generation strategy, whichwill change and evolve over time.
It is not a set it andforget it type of scenario.
It is.
I need to keep going back to it.
Number two, what does my infrastructurelook like and how can I. Have an
infrastructure that is ready foruse cases in in AI and Gen ai.
(10:16):
And three, ultimately, what arethose use cases that I can start with
internally that are gonna potentiallyhave an effect of my internal teams,
but I'm not going to put that out to my.
Anything that is customer facingat this point until I can really
kick the tires and also measure it.
(10:38):
And then I think the last is, doI have the right operations and
the right skills within my team?
They're now calling it AIops to able to even manage.
These different use cases.
And so we're seeing all of that across theboard and I'm having conversations with
(10:58):
CXOs and board, uh, board members almoston a daily basis around these topics.
And it's very challenging atthe, at this present moment.
Erica Rooney (11:10):
I mean, both of us were like, we got a question.
My first question is like, around.
Readiness assessments, right?
Because that's when likeboom, everyone needs that.
They need to just know what that is.
But the problem is we don'tquite know what we need to be
fully ready for these things.
So how do we even think abouta readiness assessment or
what are you seeing out there?
I.
Jason Johns (11:29):
So my company has put together, um, a, a really
a, a group of experts that helpswith a readiness assessment.
It's an AI read, uh, readinessassessment, and it comes down to looking
at what is their data and applicationsand what is that flow of data.
(11:52):
It comes down to looking at governance.
What kind of governance do you have onthe 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 whatyou're looking for and what your
(12:14):
threshold is as a company around risk.
And you combine all of that and you thenstart 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 (12:41):
I got one comment, then I'll let you talk about it.
Oh yeah.
I don't know that, here's my thoughtas I'm hearing this, and I've
worked in a lot of companies whereall of the data is rough, right?
And that's just immediately I could feelmy anxiety start to come up in my throat.
And I imagine that's where a lotof people live because they're just
like, I don't even know how to getthat data as clean as possible.
(13:04):
And I love how you said like, what isthat one thing that we can do today?
What is the one step wecan take really just.
Acknowledging that it is just that it'sa first step because I know the answer to
my own question, but I share it just toacknowledge all, all of my other anxiety
ridden friends out there and their dirtydata is the time will pass anyways.
(13:25):
Right.
My mother has always said that to me.
When you have a big goal or likeif you're going after a PhD or a
four year degree, like don't tellme how it's gonna take four years.
I mean, the time's gonna pass anyways.
Right?
And, and we know two things to betrue here is that AI is here and
it's only going to be coming evenmore and the time will pass anyways.
(13:46):
So get after it.
Jason Johns (13:48):
But go ahead Greg, what were
Erica Rooney (13:48):
you
Jason Johns (13:48):
gonna say?
And, and one comment thoughto that it is not a sprint.
It's a marathon.
And what do you do whenit's a marathon train?
Accordingly, you aregonna 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.
(14:09):
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 sameapproach 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'rethen going to be the superstar athlete
(14:33):
who has that endurance, who then hasthat regiment and you can make it
the 26.2 miles and, and that you areready for it, but you don't try and
take it all, all in one big chunk.
You really break it upinto small milestones.
And that's part of really how doyou build not only a strategy,
but then tactics that help you getthere really one day at a time.
Erica Rooney (14:57):
Hmm.
I love a good fitness analogy.
Greg Boone (15:00):
Yeah.
We had a guest recently, uh, DanaPees that used this, the very
similar, she was both trying totrain 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 thatthat also, you know, took, uh, kind of
this kind of bite-sized, uh, approach.
One of the things, I was gonna comeback to one, one, she said that
she's worked at other companies.
(15:20):
She's worked for me twice.
Right.
And so when she said that I worked atother 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 wasat, at least with me, but I digress.
The, uh, a couple points that Ijust wanted to just follow up just
to reiterate to our audience is.
One of the things you describedwas basically what, um, the, I, I
recently got a certification fromMIT on AI leadership transformation,
(15:43):
and they talked about the concept oftheir forgiveness continuum, right.
From internal to external.
And they said, you're gonnatry these things first.
Try them internally.
Don't just go directly to yourcustomer because the forgiveness
there is not as high as internally.
Right.
And so, I guess a little bit ofmy, not necessarily pushback,
but how I would flip the equationbased on those four kind of, uh.
(16:05):
Uh, levers, if you will,that you talked about.
And I think the last one you talkedabout something related to the
people, which is my firm belief.
And part of the reason, again, whywe have this podcast is that business
leaders need to focus on training everyemployee in their organization on the
benefits and the productivity gains ofai, not just on the business application.
(16:26):
'cause what people are missing isthat 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 makehere is just that this is the first
technology in my generation, right, thataffects every single employee, right?
(16:50):
And so typically when I'm doingdigital transformation, I'm focusing
on IT or marketing or some group.
I only gotta worry about oneor two potential saboteurs that
don't want to go with this.
Right now you have 90% of the organizationthat say, Hey, I don't even know
what you're talking about right now.
Right?
And so what ends up happening is that thebusiness, myself included, I've fallen
(17:10):
into the trap in times, at times, right?
And say, Hey, we've gotthese 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, youknow, senior, the, the CXOs out
there, it's like, look, spend sometime focusing on the productivity
because like yourself, right?
(17:32):
Always say it's a binary choice.
We're either gonna help you grow revenue.
Or we're gonna create operationalexcellence, which generally means
you're gonna be able to cut costs.
And this is one of the first technologiesyou can actually do both at the same time.
You can actually cut costs whilegrowing your business, creating
those key differentiators.
So I loved how you laid that out.
So my only real comment here isthat I, I really wish and hope.
(17:55):
That CXOs start spending a lot of timetraining everyone, not just the technical
folks or the folks that are gonna bein charge of the, uh, the TIGER team
for the new use case down the road.
Jason Johns (18:06):
That's a great point.
I think once you have your strategy andyou start to implement the technologies.
It is really a requirement to bring inthose teams that are non-technical, um,
and train them up and to provide reallya kind of easy to use, easy button for
(18:28):
how you use this in your daily life.
And then to reinforce that, um, in yourkey metrics, in your one-on-ones, and
you have to implement that technologyon a day in and day out basis, or it's
going to fail when you think about data.
Data has been a mess everywhere.
(18:50):
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 ofour core data out of our ERP and Oracle
and move that over and cleanse and apen that data, because we were actually
working on the very first renewals model.
(19:13):
Really probably anywhere.
And then we had to load that intoSalesforce so that the sales reps had
visibility into those renewals so thatthey can then engage those customers.
So I've been working in data, dirtydata, um, for a long period of time.
And the reality is, is thatyou really need to make an
(19:33):
assessment around, you know.
First, how good is your data?
And are you gonna append that orare you just gonna use it as is?
Or are you going to then look at thegovernance if you don't have clean data?
So very few have clean data.
How do you actually create governancethat then modifies the input of that data?
(19:57):
So then you have a.
Better set of data and you mighthave to actually create a new data
store or database that is with thecleaner data that you are then gonna
start fresh and essentially buildyour AI solutions and LLM models on,
Greg Boone (20:16):
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 yourhouse 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 toothough, as a lot of people find,
is that they end up moving the samecrap from one house to the next.
Right.
And so the parallel being, or atleast that you're kind of, if I, if I
(20:37):
paraphrase kind of what you're sayingis that don't move the crappy data over.
Right.
You have an opportunity now.
I was listening to apodcast, uh, yesterday.
I think, um, they've talkedabout 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 saythere is a new governance model of what
does, what does quality data look like?
(20:59):
What do we actually meet?
How could we use AI to either helpclean or harmonize or categorize
the data in a different waythat maybe we didn't think of?
Right?
Maybe some of this really is justtrash, right, and we need to throw
it out when we move to a new home.
So I, I like that, that perspectiveand that that vantage point.
The um.
Can I ask you a question just as itrelates to the, when you talk about
(21:22):
readiness assessment earlier, uh, isthat 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 ifthey're ready or not?
Jason Johns (21:36):
Well, ultimately it's from a business perspective.
But the CIOs in most cases aregetting 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 forthem to be in the driver's seat and to
(21:58):
have knowledge around a sound strategy.
I have several people telling me that.
Uh, different board members feel like theyknow AI better than the CIO and they're
making recommendations into, um, whatthey should be doing, could be doing.
And if that CIO doesn't havea solid strategy, you are now
(22:24):
vulnerable to a person pro in a,in a position of power potentially.
Torpedoing A. What would be a goodstrategy if you don't defend that?
Correct.
You've got to have the knowledge anda sound strategy, and if you have
those things, that's why think even AIassessments becomes just a sounding board
(22:49):
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 comingin because everybody else internally
might be drinking the Kool-Aid.
You get an outside advisor comingin, they're gonna tell you exactly
(23:12):
what their view is, independentof what's happening internally.
You get a lot of yes people internally.
An outside, uh, voice reallyhelps, um, balance where you
are and where you need to go.
Erica Rooney (23:27):
Do you ever do any people readiness assessments
when it comes to AI adoption?
And I ask that 'cause I'm thepeople person here, you know?
And, and from the tech standpoint, itcould look great all day long, but if
the people don't believe in it or if thepeople have a deep fear, there's gonna
be a lot of problems with adoption.
What are your thoughts on that?
Jason Johns (23:47):
Adoption is key.
And I think that, so I come from theRed Hat world, which is an open source
world, meaning it's not a, the executivescome up with a decision, push it down,
and everybody's supposed to adopt it.
It really becomes a. We have to bringpeople along in the journey and the
(24:08):
more they feel like they're part ofthe 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 reallykey, um, is that there is a lot
of angst and anxiety around ai.
(24:30):
AI is gonna take my job, AI isgoing to change my life, you know,
for the negative or the, the worst.
Right?
And I think that youhave to take them along.
You have to start with cursory coursesthat help people start to understand what
AI is and really demystify what AI is.
(24:51):
Um, if you think about it from a, just astandpoint of either when you, you know.
In school or your kids are inschool, there's always that subject
that somebody is avoiding or thathomework assignment or that project
that, that somebody's avoiding.
And the more you avoid.
The worse it gets because thedeadlines and everything is coming.
(25:15):
Right, and then you'regoing to have to jump in.
You're better off getting startedearly, jumping in now and starting
to learn and understand what it is.
It is not crazy technology that's goingto, you know, that's out of this world.
It is a technology that can helpand will change everyone's life.
(25:39):
It's like thinking about.
Back in the day, businesswithout technology.
Right.
You have to adopt technology.
In business, AI is gonnabe the exact same thing.
You have to adopt AI or you're gonna beleft behind and you better start early.
You're better off starting early thanit is really getting up to that nth
(26:02):
hour where you company is implementingsomething and you still have no knowledge
and you're still scared and you have tobe, if you want to be on the, the, the.
Front end of it, you're gonna be theone actually helping make the decisions.
If you're not, then it'sgoing to be made for you.
And that could be somethingthat you're not gonna like.
Greg Boone (26:24):
I just, uh, on that point, sorry to cut you off, but the, uh.
I'm gonna be speaking here shortly ina webinar and we're talking to a lot
of HR professionals, and Erica andI are having several conversations
with HR and upskilling folks.
Actually, I just got out of a differentcall where I was prepping for another
panel where we're gonna be talking aboutupskilling in in the age of ai and.
(26:44):
One of the things I continue to, to tryto 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 allthe time, like it's AI's not replace you.
The person that knowsAI is gonna replace you.
Right.
And what I want to get across though,to leaders is that just asking folks to
(27:04):
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 myperspective 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 todescribe office of, of the CHRO.
Then there's a role specific kind of view.
(27:25):
Then there's a hyper personalized view.
How does this help me as anindividual be 20 to 30% more
productive every single day?
I just firmly believe that if moreleaders take this approach from a
bottoms up perspective, they're gonnaget people to have their own aha moments.
Right.
And what it ends up doing is turningmore people into strategists, right?
(27:45):
Versus doing all of these mundane tasks.
The, the, the last, uh, I guessquestion that I have for you on, on
this topic is I fundamentally believe.
And that we are at this momentwhere you're starting to see
more executives mandate that AIadoption be a part of what they do.
Right.
From the Shopify Cee o that wentviral a couple weeks ago to folks,
(28:07):
we were talking yesterday aboutDuolingo to Fiverr to others, right?
I think that we don't have four years.
I think folks are gonna have to understandthat, and these companies aren't
doing it just because they want so.
My question to you is that, do you feellike we're at this tipping point or not?
Or when do you believe the tippingpoint where it's gonna move from,
Hey, this is optional to, hey, this isgonna be on your performance review,
(28:29):
this is gonna be, we're gonna tiemetrics to the management, to everyone.
Like where do you think weare on that kind of continuum?
Jason Johns (28:35):
I think that there's early adopters and those early adopters
today are jumping in headfirst, right.
Shopify and, and others you'vemade named, I think that.
Those things are good and ultimatelygoing to change the culture of those
companies also believe that if you jumpin too fast without having the right
(28:59):
strategy yeah, and having the rightuse cases and metrics around that, it
also then creates a separate problem.
You start to then implement applicationsolutions, um, that ultimately don't
align with what the strategy will be.
Now you've created a. Spend a lotof money, you've created a problem
(29:21):
that now you need to untangle.
And so I think that generallythe whole embrace AI is good.
I. Thinking how you do that isreally the key because you don't want
people and managers start startingto implement AI in your architecture.
(29:41):
Maybe it creates security risks.
Right?
Right.
Maybe it, um, actually it will create moreunintentional, more problems, you know,
if you start to then leverage AI, um, incertain ways, right, with your employees
and your onboarding new employees.
And you have an LLM and you havedata in there that you didn't realize
(30:05):
you had in there, and maybe it'sold and stale, maybe it's actually.
Maybe there's some hallucinations and itactually 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 thestrategy that I would take is
you want your organization.
(30:26):
Thinking about it and working towards it.
You just wanna be measured in whatthat strategy is on how you do it.
Erica Rooney (30:34):
I love it.
I wanna take a left turn though andstart talking about a few AI trends.
Okay.
'cause I feel like therehave been a lot of just.
Things hitting the marketthese 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 curiousand anxious over here, so I wanna know
what kind of trends are you seeing?
What are you most excited about when itrelates to using AI in the business world?
(30:58):
Also professionally or personally?
Jason Johns (31:00):
I mean the, I wrote down a few things here because what I'm
seeing in the conversation that I'mhaving really at all different levels,
um, so you mentioned at our triangletechnology innovators, we've, uh, we
had speakers like Ben Heller from, youknow, Google Cloud, and we had, uh, you
know, satin from Fidelity Investmentsand, uh, Adam Sullivan from, uh, LabCorp.
(31:22):
These guys work at massive companies.
They are the leaders withinai within their company.
And these are the types ofthings 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 AIwithout it affecting a broader group.
(31:50):
So content creation, that is maybe one ofthe 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 goingto be relevant for my audience?
(32:11):
And then how do I use thatand get the information?
But then how do I modify that based uponmy 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 additionalinformation, but it really, it's a
blend between that, you know, and I,I see we had a. I had a CXO dinner.
(32:35):
We had 15 CXOs from, I think thatthey represented something like maybe
over a hundred billion dollars in, insales and, uh, for their companies.
And they were very much at a. Youknow, infancy state now was some
government regulated, you know,financial institutions who really
(32:55):
haven't started, you know much.
They're kind of looking at it.
Um, and they've hired AIexperts, um, two others who have
really already implemented, youknow, 5, 6, 7 POCs and have.
Taken, you know, a fewof those into production.
So it was really a blend.
(33:15):
And you know, what I wouldsay is that, that it really
becomes, what's your aptitude?
Are you in a regulated environmentwhere one of my colleagues, uh, CXO
is in a regulated environment and whenyou use an LLM, they're not allowed.
To use, um, any other dataexcept for their own data.
(33:36):
And they are not allowing for justa, you know, where you could write
anything in there and get an answer.
You have to, it's, it, it's a scriptedquestion and a scripted answer, and you
have to cite the sources in which you gotyou, you have that information back so.
Erica Rooney (33:54):
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'teven know how to think about governance.
Right?
That is exactly how youthink about governance.
If you don't want, if you wanna useyour own data or not, do you want
to just be able to dump the wholething in there, or do you wanna have
only prompts and it can be tailoredto your organization in that way?
(34:16):
So, sorry, go ahead.
I just thought, I was like, wow, I'venever heard, at least on this podcast,
it described in such a clear way.
So thank you.
Jason Johns (34:23):
For sure.
And, and so that becomes amitigating 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 ourbusiness for a long period of time.
I think AI is just acceleratingwhat we can do in automation, right?
(34:47):
We've had automation, machinelearning, um, you know, and, and
things like that for, for a long time.
So I think that that becomes agenesis when you look at data.
Regardless of whether, how clean yourdata is, how can you then overlay that
with analytics so that you can actuallydeem intelligence off of your data?
(35:10):
Now, a lot of financial institutionsdo this, and they've been doing
this for a long period of time.
I. Others have it.
So if you even think about, Erica, youwere talking about the HR department.
How do you look at your own employeedata and what is satisfaction?
What would you know?
What is my turnover?
(35:30):
Are there profiles within the turnoverthat you're seeing as a trend?
Um, that is, you know.
Maybe a, a way that you're treating acertain type of, of person or level within
the company that you can learn from.
And ultimately if it would be a frontlineworker and maybe you are not, um,
(35:53):
bringing them along in the journey andthey feel separated from the company.
So they're leaving.
So it's all of those types of thingsthat you can implement, you know,
analytics around, and also coding.
So one CXO has been using coding.
Their board member had told us, Hey, wewant you to use this coding and we want
(36:16):
you to cut a certain amount of headsbecause we're gonna get efficiency.
When they looked at it, they were getting10 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,
(36:36):
but there was no way to make surethat that 10 to 12% is accurate.
And secondly, there wasn't any one placewhere they can actually reduce cost.
And so, um, I think that it justbecomes kind of across the board,
you know, what we're seeing is.
Is really all over theplace at this point.
But, but again, I think it reallyall starts with, you know, what
(36:59):
are you trying to accomplish andthen how are you measuring it?
Erica Rooney (37:02):
What's your personal favorite, like Claude Chat,
GPT, Gemini, what's your go-to?
Jason Johns (37:08):
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, itwas taking forever and then sometimes
it wouldn't answer, was getting alittle bit of hallucination and, uh,
and I started to use Gemini and, and itworked like a charm and, and no issues.
(37:30):
And then I can go back and veryeasily find my old, uh, entries
and be able to reread them.
So, uh, that was, that.
That's been mine.
Yeah.
Erica Rooney (37:40):
I love that.
Well, on this show we play a littlegame called Last Chat, and I've
actually told you about my game of lastchat before because I tested it out.
Greg Boone (37:47):
Wait a minute.
Erica Rooney (37:48):
No, no, you already.
I tested it out at a networking event andit worked fantastically at his networking
event, so I had to share my success story.
Greg Boone (37:57):
Okay.
But
Erica Rooney (37:58):
for all of our listeners, you're gonna
Greg Boone (37:59):
pass,
Erica Rooney (37:59):
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 contextyou need to around the prompt.
But this is where we just getopen and real with each other.
We talk about what's going onin your life and in your chat.
Greg Boone (38:18):
Okay, but before we we get into that, can I just ask him, so you
said that the hallucinations, is thatthe only reason the, the Gemini shit?
Like I got my own perspectiveon the different, uh, large
language models and, and why somewould be more beneficial, right?
But.
Is there anything other thanthat relates to, to Gemini?
Jason Johns (38:35):
Yeah, it was, it was really also just the, um, availability
of the data coming back quickly.
I. And, uh, and just having an issuewith 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 (38:53):
Yeah.
So the, the reason I bring thequestion up, not to, to stall or
whatever, like, and Eric is tryingto put in a new prompt so she doesn't
have to, you know, you know, divulgewhat he was really looking up, but.
I talk to folks all the time, likewhere, you know, there's a lot
of back and forth with a lot ofthe, the large language models.
And I tell folks, I'm like, youshould spend time with one or two
more than others so that they canget to learn, know you more, right?
(39:16):
Like if you, you start justkind of bouncing around and
you have a bunch of things thatyou know don't really know you.
And the reason why I bring this up isthat 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 inthe nature of how they work and
types of responses that any smallexperience issue will just have
(39:37):
you shifting or moving over, right?
They're not so uniquefrom 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 experimentalversion back 1.5 back in early December.
Right?
And so then they got 2.5 andall these other things, right?
But one of the things I like aboutGoogle and Microsoft is that they're
(39:57):
connected to the workplace, right?
So to your point, Gemini's connectedto all the Google workspace.
You got Microsoft if you're aMicrosoft shop, that makes sense.
OpenAI has a lot of APIs and otherthings, or image generation is
great, uh, for their perspective.
So I just wanted to give the audiencelike, and a view as to like, hey, one.
We are reaching that point where thesethings are getting closer in nature.
It's like, Hey man, you pissedme off for like one second.
(40:19):
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 mindthat we can get into last chat.
Are we going first?
Erica Rooney (40:35):
We're going, I guess I'm first.
I have to go last today.
Greg Boone (40:38):
Oh boy.
Because
Erica Rooney (40:39):
no one's gonna follow it.
Jason Johns (40:41):
Okay.
What was your last chat?
Well, I, I would tell you that, um, weare, 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 exactlywhat, uh, Jim and I would come back
(41:05):
and tell us and, uh, and wanted to makesure that we weren't missing anything.
In our strategy.
So when you talk about social mediaand outbound emails and outbound calls
and events and those types of things.
We wanted to make sure that eitherwe weren't missing a large bucket
of, uh, activities or somethingvery specific, um, within that.
(41:27):
And so we, um, we ultimately used thatto really just validate our strategy.
Erica Rooney (41:32):
Love it.
Jason Johns (41:33):
So I wish I, it was something more for Provo.
Provocative.
Some
Erica Rooney (41:36):
more juicy.
Yeah.
All right.
Gb, what's yours?
Greg Boone (41:38):
So mine was back to my question I was asking you earlier,
which was how many known enterpriseC CEOs are mandating AI adoption
be a part of performance reviews?
Oh,
Erica Rooney (41:50):
what's the answer?
Greg Boone (41:51):
Right.
Well, what it came back withwas, uh, it says a smaller
number, but it's growing right.
And it cited.
You know, the Shopify, it citedFiverr, it chop it, it cited
KPMG, WEBPRO sensor, right?
The, the consulting, you know, firmsthemselves, I think to, to part
of, uh, our guest point, right?
(42:12):
There's not a lot ofexpertise out there, right?
And so if the folks like Accenture andothers that have already invested in
training folks, now they have quotehundreds of thousands of experts, right?
Basically what that really means isthey, uh, hundreds of thousands of people
that got a two year head start, right.
And there's an opportunityfor folks to catch up.
Um, so, and I chose, uh, I wasusing chat GPT and I was using,
(42:34):
um, oh three deep research.
And the reason why I'm justclarifying the model, I wasn't
looking for a fast response.
I wanted it to go and think and comeback and be very thoughtful and do.
The research, give me a McKinsey levelreport, put these things in a table.
There's other things I went backand forth that I didn't outline in
What I prompt, I just paraphrased,but effectively I got something.
(42:55):
It's like, okay, thisis why they're doing it.
Right.
And so one of my, my theories,it's not just that, uh.
In an ideal transformation or digitaltransformation world, yes, you wanna
bring everybody along slowly andtruly, but some of these companies are
doing this out of necessity becausethey're either gonna be disrupted or
they won't be, or, or they absolutelywon't even be around in two years.
(43:16):
So I get.
The UN and understand why.
Typically what we would say, takebite-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 stayin business second, then I'll
figure out how to bring everybodyalong in a thoughtful way.
Jason Johns (43:35):
One quick thought on that, um, is that at the dinner with the 15 CXOs
or CIOs, um, what was interesting is thatwe had three of the CIOs who already had.
A multimillion dollarinvestment in AI use cases.
And we were actually around the table, allpretty shocked, um, because everyone else
(43:57):
had very small micro POCs, um, that were,you know, fail fast type of mentality.
And, um, and, and one of the topicscame 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.
(44:22):
Chat and with copilot, but copilotwas 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 (44:37):
Yeah.
Let's go see,
Erica Rooney (44:38):
I guess it's, you have the,
Greg Boone (44:40):
you you said like the anticipation is so high right now.
It better be good.
Erica Rooney (44:44):
It's really embarrassing actually.
Greg Boone (44:46):
Oh boy.
It's gonna be somethingabout cheery or something.
Oh, listen,
Erica Rooney (44:50):
listen.
I show up for the ladies.
I'm all about women's empowerment.
So to give you a little context, I had myaccountability meeting with two of my, uh,
accountability buddies, and one of themsaid, 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 kindof doctor I need for all of my things.
(45:10):
Right.
So I use chat, GD chat, GBT for a lotof 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 (45:21):
in advance.
Erica Rooney (45:22):
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 doctorI 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'rea medical professional and tell me
what kind of doctor you recommend.
So.
(45:44):
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 youand he gonna transition
Greg Boone (45:56):
like that?
Erica Rooney (45:58):
I know of no other way.
Greg Boone (46:00):
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 (46:05):
Well, they said what's now.
We don't wanna know
Greg Boone (46:07):
what
Erica Rooney (46:07):
it said, but what symptom do you wanna address first?
Sorry.
In the end, I think she said Ineeded a psychiatrist, which I
won't argue with, but Jason Johns.
Where can people find you?
How can they connect, learnmore about, um, the AI community
and all the work you're doing.
Jason Johns (46:27):
Yeah, first of all, thank you for having me really appreciate this.
This was fantastic.
So they can come and find me, firstof all, jason.Johns@connections.com.
They can come to connectionsuh.com and, and reach out and also
triangle technology innovators.
So, um, we've got a contact usand uh, they can get in touch
(46:48):
there or even through LinkedIn.
But, um, yeah, it's beenreally, you know, enlightening
and especially with your last.
Uh,
Erica Rooney (46:57):
my last chat.
There we go.
Yeah.
Boom.
Well, we'll link all that in theshow notes, but thank you so much.
Jason Johns (47:01):
Great.
Thank you.
Erica Rooney (47:07):
Thanks for joining us on ai, voice or victim if you
want to stay competitive in the.
AI age.
Start now.
Take one insight from today's episode andput it into practice in the next 24 hours.
Make sure to follow us, shareyour thoughts, and subscribe
for more actionable AI insights.
See you next time.
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