How to Get Started with AI in Your Business (Practical Tips & Real Technologies) - Part 1

What does it really take to get started with artificial intelligence in a small or mid-sized company right here in the U.S.?

We're breaking it down in this two-part series.

In part 1, Matt Kirchner shares lessons from his recent trip overseas and what he learned visiting 26 advanced tech companies in six days. From open-source innovation and mandatory AI education to the work ethic driving global competition, Matt explains why the time to act on AI is now, and how American business leaders can take practical steps to stay ahead.

He connects global insights to the realities of U.S. manufacturing and education, explores what it means to see before others see in the age of AI, and outlines the first practical technologies every organization should understand, from AI agents and MCP servers to embedded smart technology and digital twins.

In this episode:

• What China’s open-source approach to AI is teaching the world about speed and innovation
• Why small and mid-sized U.S. businesses can’t afford to wait on AI adoption
• The two traits every leader needs to thrive in the AI era and how to apply them today
• How manufacturers are already using AI for predictive maintenance, analytics, and smart equipment
• The real-world technologies, like MCP servers, AI agents, and digital twins, that can start transforming your operations now

Including...the first 5 technologies from A Manufacturer’s Guide To AI Tech.

FULL SHOW NOTES (plus links & resources): https://techedpodcast.com/appliedai

Want to see all the videos and data? Watch this episode on YouTube.

We want to hear from you! Send us a text.

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Episode Transcript

TechEd Podcast Introductio (00:09):
This is the TechEd podcast, where we
feature leaders who are shaping,innovating and disrupting
technical education and theworkforce. These are the stories
of organizations leading thecharge to change education, to
rethink the workforce and toembrace emerging technology.
You'll find us here everyTuesday on our mission to secure
the American Dream for the nextgeneration of STEM and workforce

(00:32):
talent.

Melissa Martin (00:34):
Hey, TechEd podcast fans, it's your
producer, Melissa Martin. I amso excited for the episode we
have in store for you today.
It's all about artificialintelligence and the data and
the stories and information wehave to share with you is stuff
you probably haven't heardbefore, from real examples, from
how AI is being used on theother side of the world to

(00:55):
practical strategies you can usein your business today, Matt has
so much in store for you on thisweek's episode. In fact, there's
so much to share that we turnthis into a two part series. So
you're catching part one today,and next week we'll air part
two. The other reason I'mhopping on here with you is that
this episode is also availableas a video on our YouTube

(01:19):
channel. So if you're a visuallearner, if you want to see the
charts, the graphs, the videosand the pictures behind all of
these stories that Matt is goingto share head on over to our
YouTube channel and watch thisepisode there. In fact, you'll
be seeing more of us in thecoming months. As you know, our
podcast streams on AppleSpotify, 43 other podcast

(01:40):
platforms, basically anywhereyou can get a podcast, but we're
also on YouTube, and in thecoming months, you're going to
see more videos from us onYouTube. So if that's how you
like to consume your content,and if you'd like to watch more
of our podcasts, you can do thattoday. Head on over to
youtube.com/at. TechEd, podcast.

(02:03):
That's youtube.com/the at signTechEd podcast. And now here's
Matt,

Matt Kirchner (02:12):
welcome into the TechEd podcast. It's Matt
Kirkner, and it is just me. Justme this week, talking about
artificial intelligence and morespecifically, talking about AI
in manufacturing, and let's beeven more specific than that,
talking about AI in small to midsize manufacturing companies.
You know, I still spent so muchtime around manufacturing. I'm

(02:33):
on the board of severalmanufacturing companies,
investor in severalmanufacturing companies. Still
love manufacturing, where Ispent the majority of my career.
And one of the things I hear sooften from our small to mid size
manufacturers is I know I needto be doing something in AI. I
know it can help me improve mybusiness. I know that there are
problems in my organization thatcan be addressed using

(02:54):
artificial intelligence, butI'll be honest with you, Matt, I
just don't have anywhere. I justdon't have any idea where to
start today we're going toanswer that question. Today.
We're going to talk about, ifI'm in a small to mid size
manufacturing company, or, forthat matter, a small to mid size
company in general, or maybe asmall to mid size educational
institution. Doesn't matter.
We're going to talk about somereally practical ways to apply

(03:15):
artificial intelligence in ourorganization. I want to start,
and if you're you're checkingout the video, you will see that
we have a picture of Shanghai,China. And the reason I'm
starting with that picture isthat I just, in the month of
August of 2025 spent a week inShanghai, and actually a week in

(03:35):
China in general, Shenzhen,Guangzhou, Wanzhou, with an H
and then Shenzhen, or Shanghai,should say, to finish off the
week. And I learned a ton. Andit really was an eye opening
trip for me, and I want to sharea little bit about what I
learned on that trip. The firstthing is that China is way more
entrepreneurial than I expectedit to be, way more profit
driven. The free enterprisesystem, in many ways, is alive

(03:57):
and well in China. I mean, wesaw companies that were private
equity backed, we saw companiesthat were venture capital
backed, and not just venturecapital from the Chinese
government, but in many cases,Chinese venture capital from
private investors. And also,believe it or not, venture
capital from the United Statesthat was coming from private
investors here in the US super,super entrepreneurial and profit

(04:18):
driven. Number two, and this isreally important, the code for
AI in China is all open source.
And so what we mean by that isthat here in the US, if I'm
open, AI, if I am meta, AI, ifI'm if I'm a US based AI
innovator and AI company, I'mkeeping all of my my code, all

(04:40):
of my programming, all of myalgorithms, all of my know how
really close to the vest. I'mnot sharing that outside of my
company. I'm not sharing thatoutside of my organization. I've
got that all locked down. That'smy secret sauce. That is my IP
in China, it is exactly theopposite. And in China, all of
the code is open source. Onceyou innovate something from a
coding. Side, from a softwareside, you know, that's available

(05:02):
on GitHub to the whole world ina month or two afterwards, and
so, so it's all open source.
It's a much different model. Andwhat, what that's doing is it's
driving the innovation in China,not just the coding side, not
just the programming side ofartificial intelligence, but
what we call physical AI, or thephysical manifestation of
artificial intelligence. Inother words, how are we
utilizing artificialintelligence on physical assets,

(05:23):
like humanoid robots, likemanufacturing equipment, like
the Alexa speaker that somefolks have in their in their
kitchen. So how are we taking AIand then manifesting itself in
its physical form? And that isreally where the innovation is
happening in China, and it ishappening fast. We were at a
humanoid robot, robot companythat a number of my colleagues,

(05:45):
a number of the folks I wastraveling with, had been at
literally, like three monthsbefore. And they said the rate
of innovation in that company inthose three months that it
ensued since their last visit,blew them away. The management
teams, the leadership teams inChina all really young. It
feels, felt like every one ofthem was like late 20s, early
30s that were running these techcompanies. And by the way, I
visited 26 tech companies in sixdays when I was in China. So we

(06:09):
saw a lot of differenttechnology and was there with
some folks that kind of had aback door to a lot of these
organizations. And we got to seesome things that, you know, if I
was just over there on a generalbusiness trip, I probably would
have had a hard time seeingreally, really insightful. But
one of the insightful things washow young the management teams
and the leadership teams were inthese in these companies, a lot

(06:30):
of them, in fact, I would sayalmost all of them, it seemed,
were educated here in the UnitedStates, and either because they
chose to, or because we toldthem they had to, they went back
to China, and they're innovatingthere, but super young
leadership teams, incrediblework ethic. You know, we were at
a humanoid robot company inShenzhen, eight o'clock at
night, so we had some long days,but 8pm at night, and here we

(06:52):
are in this facility, and it'sfull of engineers. It's full of
people who are working so 8pmmechanical engineers, controls
engineers, software engineers. Imean, that company was alive
with people working at 8pm spentsome time at what we call the
chatgpt of China company calledMiramax, and that was in

(07:13):
Shanghai. As a matter of fact,cots were lining the walls in
that company. People weresleeping at work. People were
working until they got tired,and then they then they would
take a nap. You know, we werethere at 230 in the afternoon,
and the and the facility wasstill two thirds full at 230 in
the afternoon on a Saturday, bythe way. So that's the point of
that. Of course, during the weekyou would expect that, not

(07:33):
necessarily on a Saturday,really, really great work ethic
to their credit. In China. Ithought I knew how Amazon
worked. I thought I, you know,there's like, some guy in a
facility in LA who was, youknow, putting some product up
online, and then I would find itwhen I was searching for
something, and it would land inmy in my feed, on Amazon, and
then I would look at the ratingsand look at the price, and I

(07:56):
liked it and I bought it. Notexactly, we were at a complex a
cluster of E commerce companies.
It was three buildings. Everyone of the buildings was, you
know, by my recollection, atleast 70 stories tall. I think
at least one of them was 90stories tall. Within those three
buildings, 1000 e commercecompanies were working like

(08:16):
crazy to reach the Americanmarket. And what they do is they
have these e commercemerchandise managers, people
that are managing maybe a seriesof 15 products, and they are
battling with each other to makethose products show up as high
in whatever the marketplace is,whether it's Amazon, Wayfair,
temu, Home Depot, all of thesedifferent companies that have

(08:37):
marketplaces, and so they arecompeting with each other to
show up as high as possible onthose marketplaces. They were
changing out backgrounds ontheir products, changing out the
layouts, changing prices in realtime. We even saw one of them
that showed us a program,basically an AI agent that they
had built that goes, get this totheir competitor's website,

(08:57):
scrapes the data from theircompetitor's website. And while
it's scraping the data, pricing,data, product descriptions and
so on, it is simultaneouslyburning their competitors ad
spend and lowering theircompetitors conversion rate,
meaning lowering the number ofcompanies that were buying the
product from them, from theircompetitor. And obviously, when

(09:18):
that conversion rate goes down,so too does the frequency with
which that product will show upon the marketplace is
absolutely, absolutelyfascinating. We visited a
company called info mind. Whatthey were doing was just blew me
away. They basically, I'm notgoing to explain this perfectly,
but they've kind of hacked intothe algorithm of a platform like
Tiktok, and they claim that theyunderstand how to maximize the

(09:42):
appearance of a company'sproducts on a platform like
Tiktok. You can take, I thinkthey said it was like, take 10
videos, and they could turn thatinto like, 1000 different
videos. So if they have 10videos from from somebody like
us, so you know, somebody that'strying to advertise on Tiktok.
Um, they can chop that up insuch a way that tick tock won't
recognize that it's seeing thesame video over and over and

(10:05):
over again. And so it keepscoming up in your feed. If you,
if you see a product, and youspend a little bit of extra time
on your on your tick tock feed,on that particular product,
it's, it's going to keep comingup. We know that. That's how
tick tock works. That's howYouTube shorts works. There's a
number of other social mediaplatforms that work exactly the
same way, and they have reallytapped into that in a way that

(10:26):
they can put tremendous amountsof content out on us social
media and get us to convert intopurchasing products from them by
going to, for instance, Tiktokshop. Another really interesting
insight that I looked at, andthat I noticed when I was in
China is that every singlecompany had an education
strategy. So a lot of companieswould have their products for

(10:46):
the commercial marketplace, andthen they would have their
products for education. They allhad a separate product line or a
separate focus on education. Andhere in the US, I'll just be
honest, a lot of times we have ahard time getting employers to
recognize the importance thatthey need to have a separate
strategy for education, thatthey need to be engaging in
education. But in China, it's,it's, it's endemic. In every

(11:08):
single company, they'rerecognizing that they need to
bring up the next level and thenext layer of talent. The other
thing that was interesting is wewere told that artificial
intelligence education, AIeducation, is mandatory in K 12
in China. And here again, I youknow, there's a lot of great
things going on here in the US,and K 12 starting to see more

(11:28):
and more things on on the AIfront but, but we're like
evangelists trying to convinceschools that they need to be
doing this. And in China,they've already figured out how
important it is, and that it'sliterally mandatory for K 12
students in China to have aIeducation. That's one of the
reasons, by the way, that I'msuch a huge advocate for the
Discover AI platform that isgetting tremendous traction here

(11:49):
in the United States, wherestudents are going through a 16
hour e learning course, and inlearning about artificial
intelligence and applied AI andwhat we call the edge to cloud
continuum. And then they gothrough a series of experiences,
and they're, you know, each ofthese experiences is call it,
you know, 45 hours long, give ortake. And it's experiences like
drones and unmanned groundvehicles, or ground drones or

(12:12):
autonomous vehicles, like like,you know, like a self driving
car that you might see in a citylike Phoenix, or they're
learning about 3d design andfabrication, or industry 4.0 and
and smart sensors and smartdevices and manufacturing and
smart energy. I mean, there'sjust so many different ways that
we can teach students how AImanifests itself in the edge to
cloud continuum. That's what'shappening with Discover AI. You

(12:35):
know, a very quick note thatthat I do have a financial
interest in that, in thatproject, so I want to make sure
that we're clear about that, infull transparency. But it's
really, really gaining traction.
We need to continue to do that.
China's already made that kindof education mandatory. We're
just figuring that out here inthe United States of America.
Final thing, going back to thatfinal observation on China, I
should say, going back to thatexample of spending time in

(12:59):
Shenzhen at that facility, thatwas the E commerce facility,
with 1000 companies, and by theway, 10s of 1000s. They said
100,000 employees. That was hardfor me to believe, but easily,
10s upon 10s of 1000s of peopleworking in that E commerce
facility in that cluster, Iasked one of the one of the one
of the CEOs of one of those ecommerce companies. I said, How
do you train all thesemerchandise managers? He said,

(13:21):
Oh, that we do that at thecluster. We all do that
together. We have a commontraining program. Even some of
these companies that werecompetitive, right? I mean, we
saw companies that were sellingvery similar products into
exactly the same market,competing with each other in the
marketplace, but collaboratingin the classroom and
understanding that they werethey all benefited from a rising
tide, lifting all ships andinvesting in education

(13:42):
collectively, really, reallyinteresting, and something that,
quite honestly, we don't seequite as often as we probably
should here in the United Statesof America. So the reason I
bring all that up as we start aconversation about AI and small
companies is this is what we'reup against, guys. You know, I'm
a lifelong American. I am a hugefan of the free enterprise
system. I am a huge fan offreedom. Mindy would call me a

(14:04):
patriot. I certainly would referto myself that way. I'm not
terrified, but I'm they've gotmy attention. I mean, the things
that we saw when we were when wewere in China, we've got our
work cut up for us here in theUnited States. You know, all is
not lost. There's still we'restill leading in a lot of
technological areas. There'sstill a lot of reason for
optimism, but it's not going tohappen on autopilot. On
autopilot. And you know, oureconomic success here in this

(14:25):
country is not a birthright, andit is something that you know,
that we earn every day, thatthat the previous generations
earned for my generation, that Icertainly hope to and strive to
earn for my generation andgenerations to come. But that
doesn't happen on autopilot. Ithappens when you pay attention
to reality and you innovate, andit's time for us here in the
United States to innovate. Allright, so let's get into this AI

(14:48):
concept a little bit. I had agreat experience last August. I
had an opportunity to meet anincredible author. His name is
John C Maxwell. If you're notfamiliar with with John Max.
Well, he has written over 70books on the topic of
leadership, several of them NewYork Times, number one, best
selling books on the topic ofleadership, servant leadership,

(15:10):
ethical leadership, just, just aphenomenal author. And I had the
opportunity to sit in the sameroom with John and hear him
speak small room in westernWisconsin, and then, following
dinner, almost by happenstance,sat down next to him just to
introduce myself, and we talkedfor a half an hour. So here I am
sitting next to this iconicleadership author, and we just,
we just chatted and had awonderful conversation. He

(15:32):
wanted to know about how to howto apply artificial intelligence
in his organization, which wasreally, really cool. And I just
kind of took in his leadership,his sage leadership advice.
Here's one of the things he saidthat evening that has stuck with
me ever since. He said, youknow, in the past, all leaders,
you know, see more than otherssee. In other words, you know,
if you're a leader of yourorganization, if you've risen in

(15:54):
the ranks in an organization,it's because you could see more.
Either you saw more opportunity,or you saw a bigger part of the
business, or you saw how piecesof the business fit together,
but you recognize things in thatorganization that others did
not, and over the course of timethat provided an opportunity for
you to step into a leadershipposition and be on a leadership
journey. He said, Look, that wasgreat in the past, but he said

(16:15):
in the year 2025 and goingforward, he said, you know, from
now on, it's no longer enough tosee more than others see going
forward. We have to be able tosee, and here's what he said, we
have to be able to see beforeothers see. We need to be able
to look into the future. We needto see what's coming at us and
get there before anybody elsedoes. I think, you know, I'll
just be honest. That's one ofthe things that I've really

(16:36):
worked hard to do. And I thinkhad some success in working with
with other leaders andorganizations, had some success
in being able to do exactlythat, but, but we have to be
able to, in our leadershippositions, see now, ahead of the
curve, and see what's coming atus. What we're talking about for
the rest of this episode isreally seeing and looking at
what's coming at us in the fieldof artificial intelligence. So I

(16:59):
want to introduce the theaudience, at least in concept,
to a great friend of mine. He'ssomebody that's a previous guest
on the on the podcast. We'lllink his episode up in the show
notes. His name is Leo Reddy andand Leo is just a terrific,
terrific friend. He ran the NATOdesk at the State Department for
several presidents here in theUnited States of America. And

(17:19):
many people will credit the workthat Leo did, along with several
colleagues and under hisleadership, with creating a
platform to end the Cold Warback in the late 80s, early 90s,
and the work that he did wascalled the Helsinki Process. He
was the architect of theHelsinki Accords, which many
people believe led to detente,which was kind of a sharing of

(17:41):
information and knowledge acrosspolitical divides that laid the
groundwork many believe for theend of the Cold War. Leo is
still with us, and he's still agreat friend, and he's in his
early 90s. Now, I get to see Leoseveral times a year, and I love
telling the story. You know, Ivisited Leo and his wonderful
wife, Penny. They over thecourse of the summer, they spent

(18:03):
time in a beautiful homeoverlooking Green Bay, not the
city, but the water, the bayitself in what we call Door
County, which is kind of thelet's call it the Cape Cod of
the Midwest, it's absolutelybeautiful, great vacation place.
And I get up there several timesa year. So I was paying Leo a
visit, and we were sitting outon his beautiful deck
overlooking Green Bay and justgetting caught up. And I and I

(18:25):
said to Leo, I said, Leo, what'swhat's keeping you busy? And he
said, You know what, Matt? Hesaid, I am so fascinated by
artificial intelligence I can'tget enough of it. I'm reading
everything I can about it. He'sstill, he's still working in his
business. And he said, I'mtrying to understand how, how
it's going to affect ourbusiness, and how it's going to
change our business. And I justtell people, you know, if you
look at what's going on inChina, where k 12 education is

(18:48):
mandating AI as part of thecurriculum, and then you look at
somebody like Leo, and if Chinatells us that it's never too
early to learn about artificialintelligence, you know, Leo, and
my good friend Leo already showsus that it's never too late. You
know, here at 92 years old, he'sas fascinated as ever by AI.
And, you know, I just lovepeople. And as I get a little

(19:08):
bit deeper into my career, andyou look at some folks that are
still innovating later in theircareers, you know, there's a
great article in the Wall StreetJournal last February about an
investor who's betting on peoplein their 50s and 60s. You know
they're saying basically theconcept being they're older,
they're more experienced,they're in a better position to
be able to gain our confidenceas investors, because they're

(19:30):
just maybe a little bit moredependable, or they know what's
coming at them. And then therewas another article that ran in
early October of 2025 that saidthe new age of entrepreneurship.
70 to 79 it says septenarians,those in their 70s, of course,
starting new businesses,leveraging technology experience
and decades worth of contacts. Agreat article, by the way, by

(19:51):
author Claire Hansberry. Really,really enjoyed that article, the
one on the 50s and 60s, by BenCohen. I read both of them quite
frequently in the Wall StreetJournal. So. Here we are in an
age, and I've got so manyfriends now, as I get, you know,
deeper into my career, and I'vegot some friends that have
retired, as you know, I'm nevergoing to retire, but, but
there's people my age that arelooking to All right, you know,
in the next 510, 15 years, thattime is my life is going to come

(20:12):
along. I can't tell you how manypeople have said, you know, I'm
so glad I'm late in my careerwith all this technology and all
this change, I just don't knowhow I'd manage it, and thank
goodness. I'm not in my early20s. And I'm like, Are you
kidding me? Like, this is thebest time in the history of
humanity to be in business withall this change, with all this
technology, with all thisopportunity, I'm having so much
fun, and I just, I just can'tget over the fact that we live

(20:34):
in this incredible time. And whynot make absolutely everything
of it point being, I don't careif you're 20 years old. I don't
care if you're 80 or 90, or likemy friend Leo, 92 years old. AI
is going to change your life.
It's going to change business.
Let's get on top of this. Let'sunderstand it, and let's enjoy
the ride, because it is going tobe a great ride. We really only
need two things, by the way, twothings to be successful in the

(20:55):
age of artificial intelligence.
Now this isn't original to MattKirkner, I'm stealing it from my
friend Barbara humpton, who isthe CEO for the time being,
although has recently announceda career transition, the CEO of
Siemens, 46,000 employees, $20billion company, join me on
this. On this podcast is verypodcast for a great episode in
2024 we'll link that one up aswell. Check them out. Check it

(21:17):
out in the show notes. Andhere's what Barbara said, you
know? She said, all you need inthis age is two things,
curiosity and initiative. Andshe said, if you have curiosity
and initiative, the world isyours. That's true. If you're a
14 year old kid, that is true ifyou're an 8292 year old man or
woman, the world is yours. Withcuriosity and initiative, hang
on to it. I'm curious about AI.

(21:39):
I'm taking initiative in AI inour businesses. We'll talk about
that in a little bit, but thoseare the only two things we need.
And by the way, Barbara humptonalso told me that this is the
greatest time in the world. Sheshares my opinion, greatest time
in the world to be in Americanmanufacturing with all the
changes in technology. She said,this is the opportunity for our
young people to get in on theground floor, get in on the
ground floor of a manufacturingRenaissance. And you know, you

(22:02):
just look at some data, datathat I was looking at just last
year, sorry, just last month, atthe construction spending here
in the United States for newmanufacturing facilities is, as
it's at an all time high. Imean, it is orders of magnitude
higher than it has ever been inthe past. And so you look at the
opportunities that are going tobe presented as a result of this

(22:22):
huge growth in manufacturing,infrastructure spending,
greatest time in the world to bein manufacturing. Okay, take a
deep breath. I'm going to askthe audience a question, and
that question is this, if ittakes five machines five minutes
to make five widgets, okay,think about that. If it takes
five machines five minutes tomake five widgets. How many
minutes will it take 100machines to make 100 widgets?

(22:46):
The obvious answer, of course,is, let's go through that one
more time. If it takes fivemachines five minutes to make
five widgets, how many minuteswill it take 100 machines to
make 100 widgets? And the answeris not actually 100 although it
seems like it would be. Theanswer is five. And if we go
back to our Eli Goldratt, if wego back to the theory of

(23:06):
constraints, if we understandmanufacturing throughput, the
truth is, if it takes five fivemachines, five minutes to make
five widgets, it will take 100machines five minutes to make
100 widgets. The reason I bringthat up, the reason so many
people get that wrong, is thatwe go through life seeing the
same problems, the samechallenges, the same production
lines, the same people everysingle day, and thinking that we

(23:29):
need to solve them exactly thesame way. Artificial
Intelligence, by the way, is allabout solving problems in
businesses, right? If we're justimplementing AI for the sake of
implementing AI, we are we havea solution in search of a
problem, right? What we shouldbe doing is identifying those
problems in our organizations,in our manufacturing businesses,
where AI might be able to helpsolve them. How do we improve

(23:50):
throughput? How do we improveyield? How do we improve
employee experience or customercommunication or lead time? So
you fill in the blank. How do wedo that? And can AI be a tool
that is useful in doing that,and that's really what we're
chatting about here when wethink about artificial
intelligence in business. And solet's go through and let's talk
about a number of differenttechnologies. As we slow our

(24:12):
minds down a little bit, as werecognize that we can't just
jump to conclusions answer aquestion, like five machines in
five minutes, just off the topof our head, and we have to
think through exactly what we'relooking at and how we solve
problems. And we're going totalk about several technologies
that I think are totallytransforming manufacturing, and
we'll continue to do so. Let'scall it a manufacturer's guide

(24:33):
to AI tech really quickly thetop the topics in the content.
And if you're in manufacturingand you're like, where do I
start with my AI journey. Thatquestion is going to be answered
for you by the time we're donehere. We're going to talk about
some complicated things, like AIagents and MCP servers. And then
we're going to get to the easystuff, embedded smart
technology, digital twins,vector databases, generative pre

(24:55):
trained transformers, commonlycalled gpts, intelligent data
prediction. Autonomous mobilerobots, smart drones and
manufacturing, AI poweredindustrial robots, next gen, AI
metrology and smart materials.
Are you ready? You ready for usto spend a little time together
talking about AI inmanufacturing? Number one, AI
agents and MCP servers, buildingyour own manufacturing? AI

(25:19):
engine. So let's chat about whatthat concept looks like. I love
the game of baseball. As Irecord this, we are in the
middle of the MLB playoffs herein 2025 huge fan of baseball. I
want us to imagine for themoment that you are a
professional baseball playerabout to negotiate your first
contract, and think for a minuteabout all the things that would

(25:42):
be important to you innegotiating that contract.
You're going to sign your firstcontract, it could be for just a
ton of money, more money thanyou've ever imagined. What are
those things that you want tomake sure you're considering
when you're signing thatcontract? And if you make a
list, as I did, it's going to bethings like the length of the
contract, is it guaranteed? Areyou getting endorsements through
companies, whether they're localor whether they are national?

(26:05):
How desirable is the city inwhich you're planning to live?
When are you going to start?
What does the coaching stafflook like? What are their
training facilities look like?
Do they have a past record? Isthere hope for the future in
terms of winning and losing?
Those are all the kinds ofthings that we're going to be
thinking of when we think aboutoptimizing a Major League

(26:25):
Baseball contract, right in theworld of AI. What we would call
that combination of factors thatwe're going to consider as we
step into our contract, what wewould call those is our
knowledge graph, right? So it'san interconnected series of
different topics and differentthings, some of them more
important to us than others. Allof those things collectively,
though, are what are going todefine whether or not we're

(26:46):
going to like our contract andlike for whatever period of time
we're signing that contract for,like the world that we're living
in, at least as far as MajorLeague Baseball. So now we have
to prioritize all that stuff.
Let's say we're inmanufacturing. What do we use
when we have a whole bunch ofdata, and we need to put a list
together, and we need tomanipulate that data, and we
need to prioritize that data,and we need to analyze that

(27:07):
data. Where do we put the data?
And if you've spent any time atall in manufacturing, as I did
for 25 years, running companiesand another 10 now, serving them
and investing in them, you knowthat all that data goes into a
spreadsheet. Right where wouldwe be in manufacturing without
Microsoft Excel? And then oncewe have that data in Excel, we

(27:29):
need to give that to somebodywho is then going to negotiate
our contract on our behalf andmake sure that all those things,
contract length, endorsement,training facilities, win, loss,
record, all those things thatare important to us, obviously,
money being pretty close,probably for many, to the top of
the list. What's the actualcompensation we need somebody
that's going to go negotiatethat for us? Who do we hire to

(27:49):
do that in Major LeagueBaseball? Right? If you just pay
any attention at all theprofessional sports, you know
that that individual is anagent. So now we've got an
agent. We've got somebody that'sgoing to negotiate the deal on
our behalf. All right, that'show we negotiate a Major League
Baseball contract. Now let'sflip that into I want to improve
productivity in a manufacturingfacility. So how am I going to

(28:13):
go about optimizing productionin manufacturing in the age of
artificial intelligence, if Ineed to kind of use the same
logic that we just used inoptimizing our major league
baseball contract. Well, we'dstart by making another list.
What are all those things thatare important in optimizing
production? So in a baseballcontract, salary guarantee,

(28:34):
training facility, coachingstaff, production, what are we
looking at? Quality, cost, lot,control, supply chain,
workforce, production schedules,gross profit, inventory, all of
these different maybe someenvironmental factors, all of
these different criteria and allthese different factors that are
going to go into makingproduction in our manufacturing

(28:56):
operation run at optimumproductivity. So we're talking
about things like efficiency.
We're talking about things likeyield. We're talking about
things like maximizing uptime onmachines, all of those things.
How do we go about doing thatnow? Well, when we were a major
league baseball player, we had aknowledge graph, right? And we
created that knowledge graph andwe put all of our data together.
Well, we're going to do the samething in a manufacturing

(29:17):
facility. We're going to gatherand locate and identify all the
data that we think we need toanalyze in order to optimize
production. All right, that listof data is going to be really,
really complicated and really,really big. It's going to come
from all kinds of disparatespreadsheets we're using across
the business. It's going to comefrom our ERP system or our MRP
system. It's going to come fromour financial accounting system.

(29:39):
It might come from our customerrelationship management system.
We're going to have all of thisdata that is coming at us. Might
even come from software that'sbeing run on different pieces of
equipment, different machines.
That is going to be a ton ofdata. That is going to be way
more data, by the way, than wecan analyze in a simple
spreadsheet. What do we need todo that? Then what we use is

(30:01):
what's called an MCP server,right? So we're gonna model
context protocol server. And nowthe what that does is kind of
outside the scope of thispodcast. We could spend a whole
hour, a couple hours, talkingabout an MCP server. Look it up.
You know, there's all kinds ofgreat training online to kind of
familiarize you with thatconcept. Write those three
letters down, m, c, p serverthat is going to be the

(30:24):
aggregator for all of our data.
That is where we are going topull all of this district,
disparate data and create ourlarge language model, this whole
combination of all thisconnected, interconnected and
disconnected data that we'reusing in manufacturing to
optimize our manufacturingoperations. We're going to
aggregate that at our MCPserver. Now we need in our
analogy about the baseballcontract, where we were

(30:45):
analyzing all the factors thatwould make a successful
contract, we were putting it ina spreadsheet and we hired,
remember who I we hired to doour our analysis and negotiate
our contract? Was our agent?
Well, guess what? We're going todo exactly the same thing in
manufacturing. We are going tohire an agent to analyze all

(31:06):
this data on our behalf and helpus optimize production, although
in this case, it's not going tobe a baseball agent with a with
a suit on and maybe a bunch ofjerseys in his office. This is
going to be what we call an AIagent, or a digital employee.
When you hear people talk aboutagentic AI or you hear people
talk about using AI agents, andsometimes people use words like
that because they want toconfuse us, or because it makes

(31:27):
them feel important. It's reallysimple. It's just a digital
employee. You're creating adigital agent that is going to
go and do a process for you, inthis case, go through all of our
data so that we can figure outhow we can optimize production
by reviewing all the data fromour Knowledge Graph, things like
cost, inventory, production,scheduling, customer, revenue,
all these other things that havebeen aggregated by our MCP

(31:51):
server. Now, if that soundscomplicated, the reason I start
with that is because it actuallygets easier from here. And a lot
of times when we think aboutman, I got to start my AI
journey. And I don't knowanything about MCP servers or
knowledge graphs or AI agents. Iget it. I can tell you it's
important for you to familiarizeyourself with that sooner than
later in several of ourcompanies, and I spend most of

(32:13):
my time now in the distributionspace. We're using all of those
things. And by the way, thesearen't gigantic companies,
right? They're small, mid sizedcompanies. We have certain
companies of 25 people, wherewe've got two or three people
just working on the data side,believe it or not, so that's
kind of the idea is that wedon't need to be a billion
dollar company to do some ofthis stuff, even at a really
high level. But at the sametime, if you're not in a

(32:35):
position to hire a couple ofdata scientists, if you don't
have people with that kind oftalent on in your business, if
you don't want to pay aqualified consultant, and
there's a lot of unqualifiedones, so choose carefully. But
you don't want to pick aqualified consultant to do this
work for you, that's okay.
There's a lot of other ways thatwe can deploy AI in small to mid
size manufacturing operations.
Let's keep going, because fornow, it doesn't need to be all

(32:58):
that complicated. So let's go tonumber two, embedded smart
technology, as folks know, ifyou pay any attention to this
podcast, we're huge fans ofFANUC robotics, largest robotics
company in the world, largestCNC company in the world, as
well. Mike Chico, President andCEO, four time guest here on the
TechEd podcast and and justwonderful and wonderful people

(33:19):
at FANUC. And I'm a hugeadvocate, and I've been to their
facility in Rochester Hills,Michigan, more times than I can
count. Same thing for theirfacility in Hoffman estates,
Illinois, and I've had the honorof traveling to Japan and
visiting their facility outsideTokyo several times as well. But
if you buy a Fanuc robot today,collaborative robot, a six axis

(33:39):
industrial robot, believe it ornot, that's got enough smart
technology on it when it arrivesat your facility, smart sensors,
smart devices, measuring thingslike force, disturbances,
temperature, moisture, sendingall that information up through
the edge to cloud continuum thatour kids learn about in discover
AI, by the way, through the edgeto cloud continuum. And those
robots right now will useartificial intelligence, and

(34:01):
probably more accurately, somedata analysis and algorithms to
predict their own future failureand order their own replacement
parts before that failure everhappened. So many of the
manufacturing machines and somuch of the manufacturing
equipment that you're buyingright now today comes with smart
technology embedded on it. Wesee that in education too, and

(34:21):
I'll give you a couple examplesfrom education today. One of the
things that I think is reallyimportant as we think about our
AI journeys as a quick aside, isthat our educators, in many
cases, are innovating fasterthan our employers are. And one
of the things that I've been abig advocate for, and I've told
lots of educationalinstitutions, and many of them
have taken us up on the advice,fortunately, and they may be

(34:43):
getting the advice from othersand not taking all the credit,
but I'll take some and gettingahead of some of these
technologies and recognizingthat, look, we've got to we've
got to put ourselves in aposition as educators to advance
research and advance practicalapplications of artificial
intelligence. We wait until our.
Employers are begging for it,it's going to be way too late,
and we're not going to have aworkforce available to them that

(35:03):
they're going to need. Theyalready need, if they don't know
it. We need to create thatworkforce today. And so lots of
great, innovative organizationsthat are taking the first steps,
and even second and third stepsinto artificial intelligence.
One of those great institutionsis the Waukesha County Technical
College. That's Waukesha CountyTechnical College, their CEO,
President, rich Barnhouse, Dr.

(35:24):
Rich Barnhouse, great friend ofthe podcast. Great friend of
mine, personally. And we'll linkhis episode too. He's been on
the podcast as well talkingabout innovation and education.
Reason I bring him up is thatwhen we talk about embedded
smart intelligence on industrialassets, that organization is
leading in terms of training itsindustrial employers on the next
step in manufacturingtechnology, advanced

(35:46):
manufacturing and AI technology,proud to serve on the advisory
board, by the way, for theWaukesha County Technical
College applied AI Lab inWaukesha, Wisconsin. Great
things happening there. And youcan see this kind of technology
there. And really at overthrow.
You think it's 1600 fan ex certschools that are you know, that
have the same type of technologyembedded on their robots that'll

(36:06):
predict future failure. It's notjust robots. By the way, just
about any new manufacturingequipment that you're buying, if
it's got any technology embeddedin it whatsoever, is going to
come embedded with smarttechnology. So our machining
centers, conveyors, PLC, drivenmanufacturing equipment and so
on. So much of this has smartsensors and smart devices, as we

(36:28):
all know. That means, when wetalk about a device or a sensor
being smart, means it hasembedded intelligence, it can
think on its own, and it cancommunicate, and it can
communicate on its own withother sensors and devices, so
much smart technology. I mean,you think about a machining
center that is measuring thingslike bearing failure, spindle
degradation, wearing of ballscrews. It's measuring the

(36:50):
coolant system in measuringspindles and hydraulics and
drives. I mean, at toolchangers, there's so many
different things that we canmeasure both predictively and in
terms of monitoring machinehealth that allows us to predict
future failure and then avoidthat failure before it ever
happens. And that's usingartificial intelligence. When
you're sourcing new equipment inthe year 2025 and beyond, those

(37:12):
are key questions you need to beasking your suppliers. Tell me
about the smart technology onthis equipment. Tell me about
the software that I can use tomonitor this equipment tell me
about the predictive analyticscapabilities that are embedded
on this equipment or through thecomputer network or a Cloud
connection that I can actuallyleverage the data that is being
produced by that machine. And ifthere aren't good answers for

(37:34):
those questions, keep shopping.
Because the truth is that herein the year 2025 that is
absolutely table stakes formanufacturing equipment. So
then, once we have that data,like we said, we can put that up
on the cloud, right? All kindsof ways to take data from a
machine tool, put it up on thetop cloud. Another great
example, my friend David A Gearyat Gateway Technical College,

(37:55):
another Wisconsin example. I'm aMilwaukee guy, so So my my
examples this month or thisweek, I should say, are coming
from the state of Wisconsin.
Gateway Technical College.
They're using MT link i, whichis another FANUC product, and
they are pulling data from allkinds of manufacturing
equipment, programmable logiccontrollers, machine tools,

(38:15):
meaning machining centers,robots. You know, all of this
data that is coming off of theirmanufacturing equipment up into
the cloud, conveyors,autonomous mobile robots,
automated guided vehicles, so wecan pull all that data up to the
cloud, create a data set, andthen use and which is really
similar to what we were talkingabout when we talked about our
MCP server, use our AI agentsthat we've created, or Somebody

(38:39):
else has created, or maybethere's a turnkey solution that
a supplier has already createdto go through and monitor and
measure what is taking place onthe floor based upon the data
we're pulling from ourmanufacturing assets. So
incredible things that we'reable to do now in the world of
artificial intelligence, as itrelates to equipment on the edge
in manufacturing. All right,let's, let's talk about digital

(39:01):
twins. And I want to give us anexample that has nothing to do,
or very little to do, at leastdirectly with manufacturing. But
one of the most interestingstudies that I've seen in really
anywhere, and I'll tell you whyin a minute, is what we call the
Harvard piano experiment. It wasa long, long time ago, or I
shouldn't say, a long, long timeago, but over 10 years ago,
maybe over 15 years ago. Buthere's, here's what they did in

(39:23):
the Harvard piano experiment.
They had a short song, right? Soa series of keys that somebody
would play with one hand on thepiano. And so they came up with
this little song that peoplecould play. And they took a few
groups of people to study howthey learned that song. So the
first, the first group ofpeople, was what they called

(39:46):
their physical practice group.
These are the people that satdown with one hand, played the
song five days in a row, twohours a day. So two hours every
day for five days, they playedthe same song as part of this
study, and that was their theirphysical practice group. They
also had a control group. And ofcourse, if we know anything
about the world of research, inour control group, you know
that's the group. In this case,they did nothing that control

(40:07):
group did, didn't spend any timeplaying that little song on the
piano. So those are their twogroups. But oh, by the way, they
added a third group. And this iswhere the story gets really
interesting. They added a groupcalled their mental rehearsal
group. So whereas the physicalpractice group played the song
every day two hours a day forfive days on the piano, the
mental rehearsal group playedthe song mentally every day for

(40:30):
five days on no piano. They justdid it in their heads. So they
did it. They did this wholeexperiment mentally, and then
they looked at the results. Theysaid they were actually looking
at what happened in the brainsof the people that had gone
through this exercise. So thefirst thing that probably isn't
surprising to any of us is forthat control group that didn't
practice physically, that didn'tpractice mentally, they saw no

(40:53):
significant changes at all inwhat they call the motor cortex
maps of the brain that controlthat element of learning. So no
change in the control group.
Doesn't surprise us a bit, butit's important for us to
recognize that as we compare itto the physical practice group,
which actually saw significantexpansion in the motor cortex
area controlling the practicefingers. So in the part of the

(41:15):
brain that controls those fivefingers, they saw significant
expansion in the motor cortexarea for those individuals in
the physical practice group.
Then they looked at the mentalrehearsal group, and you know,
if you if you're probablyexpecting is it's like, it's one
thing to practice something byhand, right? Another thing in
total, to practice something ifyou're just practicing that
particular thing in your brain.

(41:38):
But what they found, and this isThe fascinating part, is that
there was a comparable expansionin the motor cortex area of the
mental rehearsal group that wassimilar to that of the physical
practice group. So think aboutthat for a minute. This group
that was practicing mentallytheir brains and their their
motor cortex area thatcontrolled the five fingers,

(41:59):
even though they weren't movingthose fingers, even though they
weren't physically practicing,they were just practicing, they
were just thinking about this,two hours a day, five days a
week, showed a comparableexpansion in motor cortex area
to the group that actuallyphysically practiced. And this
has all kinds of implicationsfor by the way, psychology and
that you know, how we train ourbrains to think, and the power
of our brains and our mentalthoughts can really create a

(42:22):
situation, in some cases, thatis as real to us as the physical
experience of that we could do,podcast upon podcast and that
concept. But here's the reason Ibring that up in kind, in the
context of manufacturing, andthat is this we can use the same
context, not in terms of playingpiano, but in terms of

(42:42):
optimizing production andmanufacturing, and what I'm what
I mean by that is, I always talkabout the scariest day in the
life of a manufacturing engineeror an industrial engineer. What
is the scariest day in the lifeof a manufacturing or an
industrial engineer? And thatis, if you've never worked in
manufacturing, the day that youmake a change to the
manufacturing process, becauseif you're the person that makes

(43:05):
that change, and all of asudden, you improve throughput,
or you improve uptime, or you'reable to deliver more product to
your customers more quickly, oryou're getting more quality
product and less rework in themanufacturing plant, if you're
the person responsible for that,you are a rock star, right? I
mean, the all the way up to theCEO of the company and the
board, they're going to hearabout how this industrial

(43:25):
engineer had this beautifulinsight and improved
productivity and made a hugedifference for the company, for
its employees and for itscustomers. If it doesn't work,
exactly the opposite happens. Ifthat doesn't work, and you shut
the line down, you shut acustomer's line down, you create
a quality problem, you havepeople working Saturday on
overtime to fix something thatyou created during the week. If

(43:46):
you're the person that doesthat, you are the biggest dog in
that company until someone elsemakes a bigger mistake than you
did. That's the scariest day inthe life of an industrial
engineer. Well, guess what? Weno longer have to just go out to
the shop floor and change what'sgoing on in the shop floor in
the same sense that the Harvardpiano experiment said, Look, we
don't necessarily have to engagewith something physically in

(44:08):
order to improve it or in orderto learn it. The same is true in
manufacturing, in the life ofdigital twins. So what we do
when we have a digital twin iswe created an exact digital
replica of a physical asset. Weconnect the two of them
together, or at least inform thedigital twin of what's going on
in the manufacturing facility.
Now, when we want to innovate,we innovate not on the physical
asset itself. We innovate on thedigital twin, and not until that

(44:30):
process or product is absolutelyperfect, do we take the digital
manifestation of thatimprovement and put it out on
the shop floor. And then once wedo, we've got a really, really
high degree of confidence thatwhatever we change is going to
be effective, because we testedit over and over and digitally.
The same is true for a newproduction line. We can do that
digitally as well. Create a newproduction line. Perfect it in

(44:51):
the cloud. Perfect, it in thedigital universe, and then when
we get it perfectly runningexactly the way we want it to
the. Then we build it on theshop floor. Digital twins are
leveraging artificialintelligence in all kinds of
ways, because we can use AI tooptimize that process, then
digitally before we put thatphysical manifestation of that
process improvement on the shopfloor. So digital twins

(45:13):
absolutely huge. We're throughthree of them. We're on to
number four. Number four isvector databases. Vector
databases. So I'm going to askyou a true or false question.
True or False The day is comingwhere we can put all of these
things, our work instructions,our standard operating
procedures, our product manuals,our troubleshooting guides,

(45:34):
product schematics, pastprocedures, default settings,
operating variables, all ofthese important data points in
manufacturing true or false, theday is coming where we can put
all of these in one spot, askany question and get the answer
on demand. All right, so in yourhead, how many say true for all
those things that we'll be ableto put them in one spot, ask a

(45:55):
question and get the answer assoon as we want it, and how many
say false and the truth is, I'llaccept either answer on this
episode of The TechEd podcast,because if you said true that
certainly we are getting really,really close to the day where we
can do that, where we can putall of these things, product
manuals, past procedures,default settings, SOPs on the

(46:15):
cloud. We can pull all thesethings together, and we can just
ask a question. The reason I'llaccept false as an answer is
because I would tell you thatthat day is already here. It's
not even coming. We are alreadyat the point where we can do
that. I've got a good friend whoruns a company out on the East
Coast, and his company didexactly this, troubleshooting
guides, product manuals,supplier data, put it into one

(46:37):
what we call a vector database,which is basically a database
that's pulling data frommultiple different sources,
multiple different areas, andmade a queryable database using
artificial intelligence. In hisparticular case, he transformed
his ability to perform technicalservice for his customers and in
his plant using a platformcalled pine cone. And pine cone

(46:57):
is basically a vector databasethat takes data indexes, it
allows you to query that data,find similarities or commonality
in the data, and then producesresults. Is it perfect? No,
nothing in the world is but itis really, really close to at
least providing insights totechnicians and to technical
people in terms of going,knowing where to go to find a

(47:19):
solution. Now pine cone AI isthat is that product. We'll link
it up in the show notes. No, noeconomic interest on our case,
in our case, in any, in any waywhatsoever, with pine cone AI,
other than it's kind of a coolproduct. There's other ones out
there that do the same thing.
Milvis, we the eight PG vectorChroma quadrant, which is Q, D,

(47:40):
R, A N T, by the way, there's alot of different vector database
platforms that are availablewidely. Pick one that's right
and load up that data, becauseit's incredible how quickly we
can take troubleshooting manualsin the manufacturing plant,
standard operating procedures,past procedures, all of our
equipment, documentation, andput it into a database that

(48:01):
allows us to query it and askquestions. And how much faster
will your maintenance team, forexample, or your
electromechanical folks, or yourautomation folks, troubleshoot a
solution or find a solution,troubleshoot a problem, find a
solution, if they just have toquery a database on their phone,
as opposed to going into somedisorganized in many cases,
Library of supplier data andother documents to try and find

(48:23):
the root cause of a problem. Sovector databases write those
down. Super, super important,all right. Number five,
generative, pre trainedtransformers. By now, almost
everybody's using some versionof these, right? This is your
chat. GPT, I like perplexity. Ilike Claude. Some people use
meta. AI, a lot of folks in someof our businesses leverage

(48:44):
leverage copilot by Microsoft,but that's what a GPT generative
pre trained transformers has theability to generate content.
It's been pre trained on a largelanguage model, and it can
transform that data in the largelanguage model into usable data
for us in this day and age, youcan go on to chat GPT or
perplexity, and ask it anyquestion, get an answer. And

(49:04):
that answer, by the way, isusually pretty darn close to the
right one, if not perfect, andgetting better every day. So so
we, a lot of us, are using gpts.
Let's talk about where some ofthis technology is going. If
you're a fan of Netflix, and youwill take this into the the
entertainment realm. Here alittle bit. Some of you may have
seen, I've seen all thesepainkiller with Matthew
Broderick. Really, really good.

(49:26):
If Matthew Broderick's namedoesn't ring a bell, if you're a
youngster here, but you'vewatched Ferris Bueller's Day
Off, you recognize that name, orat least you recognize his
character as Ferris Bueller,same guy in painkiller great
series called Narcos, which is,which is about the drug
ecosystem, really, kind of showsthe underbelly, in some ways, of

(49:47):
illicit drugs, how they get tothe United States. Another one
called Griselda. Same kind of atopic, great, great series on
Netflix. Well, here's the reasonI bring all three of those up.
There's an executive producer ofall. Three of them. So same,
same individual was produced allthree of those series which were
just, just huge hits on Netflix.
His name is Eric Newman andEric, and He is the executive

(50:09):
producer of all three of thoseGriselda Narcos and painkiller
so why do I bring that up? Hewas on the podcast. We will also
link up that episode in the shownotes. But here's what he said,
and it's just really, reallystuck with me ever since he said
it in the exact quote is, youoften hear people say, well, a
computer is not going to writethe great American screenplay.
And he said that on the TechEdpodcast, and he followed that up

(50:33):
with this. He said, Yes, itwill. At some point, it will.
You know, of course it will.
Here we have the one of thegreatest, most iconic executive
producers in all of Hollywoodsaying and predicting that at
some point, a computer, at somepoint, artificial intelligence,
is going to write the next greatAmerican screenplay. And if any

(50:55):
of us are using regular gpts,like the ones I mentioned
before, you know that that'strue. You know, they're super
powerful. I will also tell youthat in as much as a lot of
folks are just using their gptsto ask it a question, where
should I go for dinner? How do Imake chicken? Marsala, what? You
know, whatever question we haveover the course of the day, the
things that we can use gpts forin business are often overlooked

(51:17):
by a lot of folks in and aroundmanufacturing. I mean, you can
use it for brainstormingcontent. If you need to write an
article or a blog, you can dothat. We've used it in several
of our businesses to perform afirst review on contracts. We
use it to write the podcast shownotes. We'll talk about that a
little bit later. Organizingcontent and ideas. Got to draft
a sensitive email, puttingtogether an onboarding plan,

(51:40):
drafting abstracts, draftingsponsorship models, verbiage for
negotiations, putting together alearning plan for a new
employee. And even by the way,putting together this list,
which our team as they put ittogether for me, used a GPT to
brainstorm what should be on thelist. And all these things are
things that we are doing inmultiple businesses of ours. So

(52:01):
easy, really easy. Lift, light,lift, easy starting point if
you're using chat, GPT,perplexity, Claude, co pilot,
whatever. There's probably a lotof applications in your business
that maybe you haven't thoughtof, and you can expand into
that.

Melissa Martin (52:16):
Well, that is going to do it for us this week.
For part one of this episode. Iknow there was a ton of great
content in there, and believeme, there's more. I know you
were probably waiting for thenext item on the list, and we
promise you'll get all of thatnext week, Tuesday. So make sure
you're subscribed. Subscribe toour YouTube channel. Subscribe
to us on Apple Spotify. You'llfind us there. In the meantime,

(52:40):
you can catch show notes foreverything we talked about on
this week's episode at TechEdpodcast.com/applied, AI. We'll
see you next Tuesday. You.