November 7, 2024 • 39 mins
Logistics professionals asking “Will AI take my job?” can rest easy, as the human touch remains an invaluable part of the supply chain—and will for some time. But what we do know is that AI *will* change your job, and the sooner logistics professionals can begin developing the hard and soft skills necessary to thrive in the AI-assisted workplace, the more successful they’ll be.
This week, host Ellen Wood speaks with Michael Walker, Chief Marketing Officer at SmythOS about overcoming issues like AI anxiety and skill gaps in order to better meet the current inflection point. Drawing parallels to historical shifts like the move from town criers to newspapers, Michael reassures us that AI is not to be feared but embraced and highlights the exciting new job and career opportunities it brings.
Join us as we discuss:
This week, host Ellen Wood speaks with Michael Walker, Chief Marketing Officer at SmythOS about overcoming issues like AI anxiety and skill gaps in order to better meet the current inflection point. Drawing parallels to historical shifts like the move from town criers to newspapers, Michael reassures us that AI is not to be feared but embraced and highlights the exciting new job and career opportunities it brings.
Join us as we discuss:
- The current state and collective understanding of AI
- The role of human oversight in AI-generated content
- AI orchestration for enhanced data security and compliance.
- Whether AI will deprive future workers of valuable learning experiences
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:06):
You're listening to Speaking of Supply Chain, a meboch podcast.
This is a show for logistics professionals looking to learn
more about the latest innovations in supply chain. Each episode
will feature a conversation on topics such as mitigating supply
chain disruption and reducing risk, current automation trends, sustainability initiatives,
and more. Let's dive right in.
Speaker 2 (00:32):
Hello, and welcome to Speaking of Supply Chain, where we
explore trends, current events, and innovations impacting the logistics and
supply chain industries. I'm your host, Ellen Wood. Artificial intelligence
is reshaping the way we work, from automating mundane tasks
to revolutionizing entire industries. AI is changing the landscape of
the workplace. Join us as we chat with Michael Walker
(00:54):
from Smithos, who is going to shed some light on
the opportunities and challenges that AI presents for future work forces.
Welcome to Speaking of Supply Chain.
Speaker 3 (01:02):
Michael, Thank you, Ollen, it's a pleasure to be here.
I appreciate you taking the time.
Speaker 2 (01:06):
Now did I pronounce that right? It is smith Os.
Speaker 3 (01:09):
It's Smith actually so, but instead of an I is
with a Y yes.
Speaker 2 (01:13):
Yes, okay, So Smith os. So I'm looking forward to
our topic today and all of the insight that you're
going to bring to us about AI in various industries,
but specifically in the supply chain. And I've talked about
AI on the podcast with a couple different guests. We've
talked with with Pete Grett from Blackrock about AI, We've
talked with Michael Greenberg from Third Brain Digital, and we're
(01:36):
talking about it again because this topic is so pervasive,
It is such a huge component to anything these days,
much less supply chain. So what is going on right
now in AI in the workplace? Because it is absolutely exploding.
We can barely keep up with it. What's the current thing?
Speaker 3 (01:58):
Yeah, I think if you ask me that question tomorrow
and then again Thursday and Friday of next week, there
will be three different answers, because it seems that's how
fast that it moves at the moment. Absolutely, But I
think what we're seeing, more than anything in a tech
space or any sort of tool related is like almost
(02:19):
a new renaissance happening around AI, Like it can free
up a lot of your autonomous and repetitive tasks so
that we as individuals can get back to doing what
we do really well, which is creativity and ingenuity and
more on the humanities and the arts side of things,
and really let the AI do the data analysis and
(02:41):
looking at repetitions and diving into the data and that
sort of thing. So I would say where we are
today is still at a place of early understanding, and
you'd be surprised that at smith OS we work with
some of the largest corporations out there, and even those
companies are still at the very early stages of understand
and how do we implement and how do we bring
(03:02):
this into our technology and ensure security and all of
that sort of stuff. So today I think everybody's probably
in the same spot, which hopefully is comforting to most people,
which is I don't know, right We're all getting to
learn every day more and more what is AI? How
does it work? How can I utilize it? And then
I think we'll get into this today some of its applications,
(03:24):
but we're still all just in a learning phase. I
think that's how i'd summarize it.
Speaker 2 (03:28):
There are a lot of misconceptions out there right now.
There's a lot of confusion and a lot of gray
area when it comes to the jobs that AI is
taking over and you mentioned you know, it's doing the
data analysis, it's doing all of the repetitive and easily
programmable tasks, but a lot of that I know I've
talked with with some of our previous guests about you know,
(03:50):
this is taking away a lot of those learning experiences
from that newer generation of workers where when we came
into these careers, we had to do a lot of
the scut work, a lot of the research and the
understanding of the data and manipulating and making sure we
understood it, and that was a learning process for us. Now,
(04:10):
if we're teaching AI to do it, then we've got
to teach not only these young workers and our future
workforce how to do these things using the AI obviously,
but then to make the decisions based on it. Are
they going to be prepared to make the decisions without
having that base knowledge of understanding how the data was
(04:31):
brought together and manipulated and understood and read or are
we just going to have to kind of gloss over
that and say, okay, this is it's like a calculator.
It's doing something that you learned how to do a
long time ago, but it's not necessary to do that
in your head anymore or do that manually.
Speaker 3 (04:48):
Yeah, it's really dependent on the industry, the company, and
then the job. I think what I would say from
a summary standpoint is number one, there's not a whole
lot of reason to fear, right. I know that there
are there are some loud voices in the space, and
maybe I won't name names, but I think that most
people would associate with AI, and I think they make
(05:10):
it seem like this really scary figure in the room, right, Like,
am I able to understand it? Is it overly complicated?
Is it coming from my job? Like? Am I going
to have to relearn a skill? And halfway through my
career that maybe is I'm not able to do very well.
I don't think that's the case at all. Right. It's
like we used to have town criers, right, who would
(05:33):
go out and they would literally stand in the town
square and tell everybody the news from the last week
or the last month, or what's going on and what's happening.
And then the printing press came along and then people
had newspapers. But that created new jobs, right where you
had reporters, right, and you had people who would write,
and then people would distribute the papers, and so I
(05:54):
think this is a similar sort of evolution, and I
think you don't have to understand every single element of
the process, right, Like, if you just know what you
do really well, and you understand how to utilize AI
or particular AI type tools to help augment your job
rather than replace what you're already doing, then you're already
(06:14):
head of the curve and there's not much more to mandage,
you know. It's very similar to I'd like to say,
when internet marketing became a thing, right, that used to
be a term, right, internet marketing, and it just meant
I could use this device and go online and I
could market my business. Now there's not really such a thing, right,
there's paid advertising and SEO and SEM and retargeting and
(06:35):
remarketing and influencer and social media. It's very similar today.
I think the term AI is what people know and understand,
and so from that comes a lot of kind of confusion,
and like you're saying, do I have to understand data analysis?
Do I have to be afraid I'm going to lose
my ability to do these things? And the short answer
I think is no. I think if you understand the technology,
(06:58):
you embrace it earn the tools as they come in
and figure out how they work best for you as
an individual and as an organization, then you can only
benefit from it in the long term.
Speaker 2 (07:08):
So what key skills do you think are going to
be valuable using that AI augmented workplace. I mean, we've
got we've got the technical skills. Obviously, depending on which
AI platform you're using to support your business, whether it's internal,
whether it's an open source, you're going to need to
understand how to work with that tool. But what are
some of the even some soft skills that are going
(07:30):
to be more important when AI is doing a lot
of that analysis work.
Speaker 3 (07:36):
Yeah, believe it or not, I don't think it will
change a lot from the soft skills you need today.
Things like critical thinking and problem solving, things like emotional intelligence.
All the things that can make you successful as an
employee from a soft skill standpoint, I think are the same.
When it comes to the technology, like you mentioned, there
are like it's smith OS. We have a whole certification
(07:59):
program so you can come in, you can get certified
on the platform as an AI agent engineer. It doesn't
require any development or coding experience because it's all drag
and drop, and so now your operations manager can build
AI agents that will help them do their job better.
Your HR manager can build a series of agents that
can help them do their job better. So from a
(08:21):
soft skill standpoint, I think what we'll really see kind
of rise to the top are the creative elements, So
the emotional intelligence, the creativity, the empathy. Those are the
sort of things that I think as artificial intelligence and
technology gets more adopted at a mass scale, those are
the things that are going to stand out as an
(08:42):
employee to an employer, and as an organization or a
brand to your customer base. So those are things that
have always been important I just think there'll be more
so over the coming two to five years.
Speaker 2 (08:53):
So really building up that personal and the human element
in those jobs. If we've got a machine taking over
the machine stuff that the machine can do, then building
up those interpersonal skills are going to be just as
important or even more important, because we don't have that
need for the mechanical skills, so we don't have to
(09:16):
The technical skills are going to be there, they're always
going to be there, but that person to person is
where the value is going to lie.
Speaker 3 (09:23):
Yeah, absolutely, And if you think about it, it's like,
if I want to go to my boss and suggest
a new technology or tool, or ask for more budget,
whatever it may be, I need to have all those
soft skills. I have to be able to communicate well
understand how what I'm asking for can translate into real
business results. Maybe take your creative approach or critical thinking
(09:45):
approach to problem solving. What problems are these going to solve?
We'll just spend more time doing those sorts of things
because I think we'll spend less time on the technological aspect,
and we'll spend less time in sort of the data
analysis aspect that technology can handle, and it'll free us
human beings up to really just be more creative, think
outside of the box a little bit more, and really
(10:06):
get down to solving problems and not having to worry
about so much the execution of the solutions to those problems.
Speaker 2 (10:13):
So you mentioned that you do a lot of training
and getting people onboarded onto your platform. What are some
of the common challenges that individuals face when they're learning
this new skill, this new tool.
Speaker 3 (10:24):
Yeah, Number one is anxiety for sure. I think most
people it's a new technology, it's scary, we hear so much,
like you wouldn't believe even the number of students who
in some of them are high school students, some are
college students who are write in on a contact form
and say, hey, I'm studying this or I want to
major in this. Should I still do it? Like will
(10:46):
this still be a job? Or is AI going to
replace this? Like so many people have so much anxiety
around artificial intelligence that that mental block is probably the
biggest challenge to start. Then, of course there's some skill gaps,
And when I say skill gaps, I mean like technologically speaking,
in platform wise, but it's no different than CRMs. Right
(11:07):
before CRMs, we were all managing our data and customer
relationship data and spreadsheets or Excel or in Alledger or
whatever it may be. And then at some point you
had to make the jump into CRMs. But once you
learned one of them, it's pretty easy to transition to
other ones, right understanding like the hierarchy of data, how
(11:27):
do I input data and keep the records there and
that sort of thing, And I think this is the
same thing. So the biggest challenge is definitely the mental
block of kind of having some anxiety around is this
for me, Can I do it? Should I be a
little bit afraid of it? And then number two is
a skills gap because it's brand new technology. But what
I tell most people is it is just as new
(11:48):
to me and to the people I work with as
it is to you. So you're not too late. There's
nothing to be anxious about. We're all learning together and
we're all in the same boat.
Speaker 2 (11:59):
Well, your point when you get those students emailing in
on your contact form saying should I even bother majoring
in this in college? I mean earlier you said tomorrow,
the answer is going to be different. Next Thursday, the
answer is going to be different. So we can tell
these kids maybe it's going to resemble it. You're going
to have some use for that education. It's not wasted.
(12:20):
But at the same time, what you think you're going
to be doing today is probably not what you're going
to be doing in five years when you graduate. So
how do we alleviate that level of anxiety while being
honest about the fact that this is changing and we
have no idea what it's going to look like in
five years, what this landscape is going to be.
Speaker 3 (12:40):
Yeah, I think it's and maybe I'm overly optimistic. In
some sense, that could very well be the case, but
I think businesses have always faced the same challenges, whether
it was one hundred years ago, a decade ago, or
a month ago, And that is how do I stand out?
How do I make sure that the people who are
experiencing the problems know that I exist? And then how
(13:02):
do I make sure that my customers or my client base,
whoever it is, is happy and want to continue to
do business with me. I think if you keep that
in mind from a professional standpoint, and you just study
something that you're passionate about and that you don't mind
spending your time doing, the rest of it falls into place.
The tools of the trade will always change, right Like
(13:23):
it's I don't think I could do mental math today
if you force me to, right because I haven't had
to do it since high school. But the fact is,
I always have a calculator in my pocket with a phone.
So if I just understand what numbers do I need
to add together or multiply together to get the answer
I'm looking for, then I'll have the tools to do it.
And I think this is a similar thing. So if
(13:45):
you're passionate about working in technology, going into supply chain,
whatever it may be, as long as you study what
you're passionate about and you understand just the basics of
solving problems and how to help a business achieve it,
our goals that they have, and you know, the tools
of the trade are always going to change. The only
constant there is that that will remain the same, and
(14:07):
you'll be able to overcome and learn those I think
personally that's the easy part.
Speaker 2 (14:12):
I agree, it's it's just going to be a tool.
But this is a tool that we're training to think.
We're training to look for for certain aspects, certain key
data points, certain whatever, and to make decisions or to
come to conclusions based on that data. So as it
becomes more integrated into our work, what are some ethical
(14:34):
considerations that we should be mindful of?
Speaker 3 (14:37):
Yeah, absolutely so. From an organizational standpoint, I think the
obvious starting point is data security. It really doesn't matter
what kind of company you are. I mentioned CRMs before.
We store like every one of us as individuals has
so much of our personal data out there on the Internet,
whether we like it or not, and companies house that data.
(14:57):
So when you're using AI, which when we say AI
right now, we really are talking about a series of
large language models. We want to make sure that any
data we give to that, or whatever tools we're using
to analyze our data, has security and compliance build into it, right,
so that our data is not being shared, our customers
(15:18):
data is not being shared, it's not being leaked. That's
the first one. Then, of course, I think there's a
lot of kind of ethical governance and oversight that I
think industries and possibly even governments are going to have
to start to consider, right, is, if we start to
allow artificial technology to make decisions for us, what's the
moral compass for some of those decisions, Like when you
(15:39):
start thinking of supply chain if we're talking about from
a ENGO standpoint, right and we're talking about let's say
food distribution for international NGOs, for example, if you leave
that to a series of large language models to make
decisions on, well, how are you sure that there's the
right moral foundation for how those decisions are being made?
(16:02):
And that's a very clear kind of downfall in one
sense of artificial intelligence is it doesn't have that empathetic
and moral foundation that us as human beings do. So
those are the two things that I think in the
near term, we're going to have to really kind of
talk about as an industry and just make sure that
we have the right solutions in place and the right
(16:22):
infrastructures and guide rails to making sure that everything we
do is secure and we're not releasing people's information. And
then number two, that we have some sort of ethical
and moral foundations so that we can guide these models
and let them know here's what is a clear ethical boundary,
or here's a clear moral boundary, and so anything that
we do or decisions we make have to be within
(16:44):
those within those guidelines.
Speaker 2 (16:47):
So that brings the human back into the equation of
using the AI tool and then having to make a
decision using that moral compass, using that ethical consideration. Is
it possible that we will get to point that we'll
be able to program that into the AI models.
Speaker 3 (17:04):
Yeah, well, I think right now the failsafe for that
or the kill switch, if you will, or people like
you and me, I think we're probably still a ways
away before any organization leaves sole decision making power to
an AI model, and I think that's probably a good thing. Today,
I think that most people are using these tools like
a smith OS to help them augment what they're already
(17:29):
doing rather than replace individuals or functions within their organization.
So in that scenario, it' would be people like you
and myself were sitting down and looking at the data
analysis that a particular model has done, for example, and saying, okay,
now with that analysis done, here's how I'm going to
interpret it, and then here are the decisions that would
(17:50):
be made from it. So I think as long as
we in the mid to year term, have that human
being in place that are making the final decisions, we
don't have to work too much about is this model
going to run off the rails? Is it going to
make a decision and send that decision out to four
thousand people before anybody looks at it? You know, it's
still it's the onus is still on us to kind
(18:12):
of make those decisions.
Speaker 2 (18:14):
Sure, well, And I think getting back into the data
protection and the security that you were talking about as well,
when we're talking about these different AI models, you can't
just take your customer data and go out to a
chat GPT because that's open source. They're using that data
not only to generate what it is that you're trying
(18:35):
to achieve, but also to teach itself and then they
have your data. And so companies like yours that offer
these platforms and these models that can do it more.
I don't want to say privately or internally, but that
really is the case where it's a closed source and
you're able to input your data and teach the algorithm
(18:57):
what you're trying to achieve with this data in order
to have that level of security. But then how are
all these models learning from each other? I think that's
the question of is one model going to take over
the industry where other models are going to lag behind?
And what sort of ethical questions does that pose? Is
(19:17):
is the information of what is capable still going to
be governing all of the possibilities out there?
Speaker 3 (19:26):
What are your thoughts? Yeah, it's a great question. I'm
trying to think if there's any question we could asked more.
If this isn't the top question, it's definitely number two
is exactly that. So any large corporation, any corporation or
industry that has ties to the government, even certain there
are so many industries that have regulation around it where
(19:48):
they can't deploy into the cloud right, So open source
first and foremost is kind of a non starter for
a lot of these companies for the reason you mentioned,
but also cloud deployment, because in a cloud scenario, you
have to take all of your data give it to
the cloud that can be accessed maliciously right through means
of hacking by accident. Anyone who has access to the
(20:09):
cloud can download it locally, So any cloud deployment is
an issue. What we've done to try to address that
is we've built what's called a local runtime environment. So
if you are a large transportation company, you have contracts
with the government or multi corporations that have strict security
and compliance standards. We have our agent builder, which is
(20:33):
where you can bring multiple models together so you're not
reliant on anyone. You can connect any of your internal applications.
You can build models on open AI which is CHAGBT,
on anthropic which is CLAUDE. You can bring them all
together for the AI orchestration as it's called. But then
you can essentially download those agents and you can run
(20:54):
them locally. You can run them on your own servers,
on your own AWS servers, on prem as we call it,
and in that scenario, you don't have to release any
of your private data to the cloud. So you can
ensure that it's secure, so you can still use technologies
like smith os to build these agents that can be
used to optimize your business in many different ways without
(21:15):
having to risk the security of your data by deploying
it into the cloud by using something like a local
runtime environment. So that's one of the ways that we've
tried to address it, and I think for now, at
least as it stands in a very cloud native world,
that's probably one of the first places to start if
you're an organization that's concerned about that.
Speaker 2 (21:36):
Okay, well, and coming back to the question that we
talked about a little bit earlier, and that was, you know,
these young kids that are questioning whether or not they're
going to have a career that they thought they would.
What are some new exciting career opportunities that are emerging
due to these advancements. Because obviously it's going to create
(21:56):
new jobs, it's going to create additional things that humans
need to do. What are some of those things that
are that are on the horizon for our young talent
workforce that's up and coming.
Speaker 3 (22:08):
Yeah, I think there's two that come to mind. Number
one are AI agent engineers. There have been so many
studies done by Goldman, Sachs Gartner, pretty much every foundation,
business and marketing foundation out there on the number of
agents compared to employees over the next ten years, and
you'll find anywhere from one to five for every employee,
(22:31):
there will be one to five agents who are deployed
to help those employees do their jobs. And so obviously
somebody needs to be able to build those agents, right,
So AI agent engineers will be number one, And again
I think it's not as scary as it sounds. Tools
like Smith can help you do that without being a developer,
without having to learn to code. But number two, and
(22:53):
this is I think the really the long term opportunity
is what we call MACE and geers or multi agent
systems engineers. So as AI sort of gets more into
a more mature state, you'll have a lot of different
custom agents and applications. You'll have obviously businesses who build
(23:17):
the next version of TA GPT where instead of a
single model, it's multimodel and these sorts of things. You
need to bring those all together, right, So when you
have these multiple connection points of different AI tools and
different AI systems, that's what we call a multi agent
system and artificial intelligence, and that's where systems can speak
(23:38):
to one another, learn from one another, they can call
upon one another to complete these large and complex tasks,
and you need engineers who can put those things together.
So those multi agent systems engineers, I think are going
to be incredible demand over the next probably two to
five years and beyond. And I think those are just
two those are just two of the starting points. I know.
Speaker 2 (24:00):
I think of just let's say, two years ago, a
system's analyst or a data analysis A data analyst was
such a small thing and then it got this huge boom,
and now we're replacing it with an AI that can
do the same thing, that can analyze that data. So
I believe that there's going to be a number of
different iterations of professions or job titles or whatever you
(24:23):
want to call it, that skills that people need to
learn and jobs that need to be filled. That as
we grow and as we learn how to use these
tools most effectively in our society, these jobs are going
to evolve very very quickly. And so I think the
thing to tell our students right now, and the thing
to tell that up and coming workforce is be flexible,
(24:46):
be willing to learn, be willing to challenge yourself, and
be willing to try and work with these tools because
it's going to be exciting. You're getting in on the
ground floor of a new technology. It's kind of like
the tech boom back in the nineties, and this is
your chance to do it. This is your generation's opportunity.
Speaker 3 (25:04):
So what are some.
Speaker 2 (25:06):
Maybe success stories or epic fails, either are excellent stories.
But what's something that you can tell us about a
client that you've worked with or a deployment that you've
done that was able to make a huge difference.
Speaker 3 (25:21):
Yeah, absolutely, there's On a personal level, I have probably
a thousand fail stories that I could share while I
was trying to learn how to how to really become
more of an AIG engineer myself. But on the success side,
So we work with a client called Halo. This is
Halo Dog Callers, a company started by Caesar Malon who's
(25:44):
the dog whisperer, and a Stanford grad named Michael Ahrman,
and they have utilized artificial intelligence to connect a lot
of their kind of inventory management with their product and
ordering and then their customer service. And the way we've
deployed that early on is through a chat agent, so
(26:06):
you can come to the website, you can ask questions
about your order, questions about delivery time, updates, connect with
customer service, sort of do all of that in real
time with AI without having to wait on the phone
for customer service representative or wait while they're searching for
more information. And one of the reasons that's so important
(26:26):
is in the customer service world, you want to try
to de escalate those types of concerns as much as possible,
right and the last thing you want when you're kind
of frustrated with an order or trying to speak to
customer service is to be transferred from person to person
or department to department, or not get the answers you're
looking for. This is an area where AI is just
(26:47):
such a great place to start today because the technology
is already there, the use case has been proven, and
it works really well. So I'd say that's probably one
of our earlier success stories. But we've you know, we've
seen it across so many different industries that it's pretty
agnostic to industry.
Speaker 2 (27:05):
And I will ask this question just for a personal
because I use the open source GPTs. We're testing some
different things internally with some internal platforms at work, but
even just for fun to use AI or chat GPT
or Gemini or Claude or whichever one. What have you
done for fun with AI, just to see what it
(27:27):
can do?
Speaker 3 (27:28):
Yeah, I'll uh, my wife will probably see this social
she'll know my secret here soon. But date night ideas
is a great one, right, Okay, Like just in a
personal note, So when you are looking for a new
restaurant in town, for example, but with a little bit
different kind of experience, or looking to do something different
(27:49):
in the city you've lived in for a while that
you maybe haven't had a chance to try before, that's
a really great way to use something like chat GPT.
In a more boring sense, I use it for organizing
almost everything that I do. I have found that these
large language models are really great at going from zero
to one in a creative sense. So if I'm staring
(28:12):
at a blank screen and I'm trying to write, let's say,
some website copy or some emails or something like that,
it can be so hard to go from a blank
page to even the first five or ten words. But
if I go into a chat GP or my favorite
which is anthropics, claud I can go from zero to
one very quickly, and it creates the spark, It gets
(28:35):
things moving. It allows you to kind of get your
creative process underway, and to me, I have a ton
of fun like that. So both on the personal and
professional sides, those are two ways that I use it
that maybe could be could be helpful with other people
out there. I know.
Speaker 2 (28:50):
I use it for the same reason to generate some idea,
to get those creative juices flowing. I mean, in marketing,
we have to generate a lot of content and some
times it feels like you're writing the same thing over
and over and over and over, and you're like, Okay,
try and do this in a new tone. Read this
back to me, or give me this prompt in someone
(29:11):
else's voice, pretend like your Sylvester Stallone, or pretend like
you're Tom Cruise, or pretend like you're whomever, and give
me this back in their tone of voice from this
character that they played, or one thing that I've done
that is not in AI. But I've used it to
kind of prompt myself to try something different and look
(29:33):
at something from a different angle. I'm a huge Harry
Potter fan, I've read all the books, watched all the movies.
I love Harry Potter, but in thinking about it one time,
I realized, you know that these books took place in
the nineties, and these kids grew up at the same
time that I grew up and was their age. Another
show popular in the nineties Saved by the Bell. So
one time, when reading through the series again, I pictured
(29:56):
all of the cast of Harry Potter as the cast
of Saved by the Bell in that tone of voice,
that idea, that feeling of that high school situation as
opposed to the high school at Hogwarts. So I mean
those types of things where you just reimagine it in
a different perspective, a slightly different twist to it, and
that's where you get those creative juices flowing. And no,
(30:19):
that is not going to help me in any way
sense or form professionally, except that that's going to get
my own creative juices flowing. That's going to get me
thinking and looking at a problem from a different angle.
And I think that's one of the great things that
some of these content generators, even the open source ones,
are able to do without with very little risk. I mean,
(30:41):
who's going to care. If I decide to have this
recipe read back to me in the tone of some
other celebrity or influencer, it's going to give me a laugh,
it's going to give me an idea.
Speaker 3 (30:54):
Yeah. Absolutely. I do something very similar with the great
Michael Scott, the World's best boss from the office. So
if I'm having a little bit of a routine or
mundane day, you know, I'll put something in there with
the little character I've created in chat GPT for Michael
Scott and have it tell me a joke, right, a
little pick me up for the day. But I think
(31:14):
that's the humanizing and really the beautiful side of artificial intelligence.
And then I hope that more and more people kind
of look at it that way and see it that way,
which is it's probably the most or one of the
most useful things or advancements we've seen in a really
really long time my lifetime, almost certainly. But it can
(31:34):
also be a beautiful thing to help you discover and explore,
help you cheer you up if you're in a dowed mood,
which can be a tremendous benefit both personally and professionally.
So I think us just getting more familiar with these
tools and being more comfortable using them can do a lot.
It can help produce the anxieties, it can improve life
and even the smallest amounts and so many different ways.
(31:58):
And the more we all electively kind of raise our
knowledge and understanding and close the knowledge gap between where
we are now and where AI is of let's say
next month, the better we all will be for it right,
and we can adopt it quicker, We can have better
conversations around them, We can discuss the ethical and moral
(32:19):
dilemmas around them, because people really have a better understanding
and intellect around what AI really is. And it's no
longer I fear it because I don't understand it or
don't know it sort of thing. So yeah, I love
that example.
Speaker 2 (32:35):
Well, and I think you bring up an excellent point
again with the ethics and the morality is when we're
doing these things and being creative and stepping outside the
box and getting it to pretend to be someone it's not,
then there is that human element, back to the beginning
of our conversation, where there has to be a human
at the end of this reviewing whatever this output is,
(32:58):
understanding what it is that it's trying to tell you,
because you can't just go in and create content in
the voice of someone else and pretend to be that
other person and plagiarize in that way or impersonate someone else,
because there's first of all, that's wrong. It's morally wrong.
We understand that, but it also has the downside of
(33:19):
not actually being that intelligent. It has limitations, and it
can do things that it knows based on what's out there,
what's open source information, but it's not going to have
that decision making capability. It's not necessarily going to have
the same even vocabulary as some of those individuals. I
remember seeing a clip recently of Neil degrast Tyson and
(33:41):
he was saying this AI had generated something that he
might have said, but it used different vocabulary. So he
realized very quickly that it wasn't actually a quote that
he said, because it's not the way he would have
said it. And I think that brings us back to
there always has to be a human sitting behind the
desk making the fine decision based on whatever the AI does,
(34:02):
because we can't let it just go out and do things.
It can do a lot of the thinking, it can
do a lot of the manipulation of the information, but
it can't be the final say, it can't be the
final decision.
Speaker 3 (34:15):
Yeah, And there's real world examples of where this is
incredibly important, like Tesla famously had and every self driving
car autonomous vehicles had to sit down and consider this right,
which is like if your cars driving straight and it's
about to hit two pedestrians and the only option is
to turn right and hit one pedestrian, and those are
(34:36):
the only options, Like what should the car be programmed
to do? And consider right? And the world's best minds
and ethics and morality and they all came together and
had to discuss these things because ultimately we're the ones,
at least for now, behind the keys when it comes
to this stuff, right, And we have to tell these models,
and we have to tell them here's what to consider,
(34:58):
Here's how much weight to give to consider A versus B.
So you're absolutely right, at least for the very foreseeable future,
we're the ones making the decisions on these things. But again,
I think it comes back to the why it's just
so important for everyone to dive in and start getting familiar,
even if you only use it in your personal life
(35:19):
or you only use it in your professional life, because eventually,
these are things that are going to affect every single
one of us, Right is how AI is used, whether
it's for surveillance and route planning, and you know, all
sorts of different things. And the more knowledgeable we all
are about it, then the better conversations that we can have,
and then hopefully, ultimately, the better decisions that we can
(35:41):
make as a group and as a society.
Speaker 2 (35:44):
I think you brought up an excellent use case of
the chatbot on a website when you go and you
have a question for a company or something that you've done.
How many of us don't realize that those chatbots have
been run by AI for so long behind the scenes,
some person sitting there typing back and forth with you.
In most cases, now there are some that do, but
(36:06):
in most cases those chatbots where you're just asking a question,
are delivering information based on a database of information that
they have about their company. And so we've been interacting
with these this whole time without even really thinking about
it or realizing it. And the more we pay attention
to it, the more we'll understand its capabilities and its limitations.
Speaker 3 (36:28):
That's exactly right, and even before that you call your
bank and all of a sudden you have automated menus, right,
press one for this and two for this and say
a word. And so this is technology. I think a
lot of people are at least in some way, shape
or form familiar with. But when it comes down to
even the organizational level, I mean, if you think of
(36:50):
you know, my grandfather was a long haul truck driver, right,
so I could only imagine if he had the ability
for enhanced route optimization. Right. Even another use case that
we've looked at with I'll just say a government entity
is predictive maintenance, right to make sure that you ensure
the least amount of downtimes for delivery vehicles and things
(37:12):
like that. Like, there's so many real world applications that
are there today and that can be used today that
it's really just the literacy thing. It's an AI literacy.
If we can make sure that our organizational leaders have
as much information and knowledge on this as possible, and
then the individuals for using them have the same thing,
(37:32):
then we can all move forward in the right direction.
Speaker 2 (37:35):
All right, Well, I think that brings us to the
end of this episode. Thank you so much, Michael for
joining us today and talking a little bit more about
AI and where it's going to take our talent workforce
in the future. For our guests out there or for
our listeners out there, if you have a suggestion for
a topic or would like to be a guest on
the show, we'd love to hear from you. You can
(37:57):
contact me at podcasts at mebock at any time. As always,
thank you for listening to Speaking of Supply Chain. If
you enjoyed this episode, please rate and review us on
whatever podcast platform you prefer, Apple Podcasts, Google, Spotify, and
iHeartRadio and be sure to tune in next time.
Speaker 1 (38:18):
Meeboch Consulting is one of the largest and most globally
recognized supply chain consulting, engineering and advisory firms. For nearly
fifty years, we've helped clients achieve supply chain excellence and
sustainable competitive advantage across the entire spectrum of the supply
chain by delivering improvements and innovation strategically, tactically, and digitally.
(38:39):
To learn more, visit meeboch dot com. You've been listening
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in supply chain information. Thanks for listening, until next time.
You're listening to Speaking of Supply Chain, a meboch podcast.
This is a show for logistics professionals looking to learn
more about the latest innovations in supply chain. Each episode
will feature a conversation on topics such as mitigating supply
chain disruption and reducing risk, current automation trends, sustainability initiatives,
and more. Let's dive right in.
Speaker 2 (00:32):
Hello, and welcome to Speaking of Supply Chain, where we
explore trends, current events, and innovations impacting the logistics and
supply chain industries. I'm your host, Ellen Wood. Artificial intelligence
is reshaping the way we work, from automating mundane tasks
to revolutionizing entire industries. AI is changing the landscape of
the workplace. Join us as we chat with Michael Walker
(00:54):
from Smithos, who is going to shed some light on
the opportunities and challenges that AI presents for future work forces.
Welcome to Speaking of Supply Chain.
Speaker 3 (01:02):
Michael, Thank you, Ollen, it's a pleasure to be here.
I appreciate you taking the time.
Speaker 2 (01:06):
Now did I pronounce that right? It is smith Os.
Speaker 3 (01:09):
It's Smith actually so, but instead of an I is
with a Y yes.
Speaker 2 (01:13):
Yes, okay, So Smith os. So I'm looking forward to
our topic today and all of the insight that you're
going to bring to us about AI in various industries,
but specifically in the supply chain. And I've talked about
AI on the podcast with a couple different guests. We've
talked with with Pete Grett from Blackrock about AI, We've
talked with Michael Greenberg from Third Brain Digital, and we're
(01:36):
talking about it again because this topic is so pervasive,
It is such a huge component to anything these days,
much less supply chain. So what is going on right
now in AI in the workplace? Because it is absolutely exploding.
We can barely keep up with it. What's the current thing?
Speaker 3 (01:58):
Yeah, I think if you ask me that question tomorrow
and then again Thursday and Friday of next week, there
will be three different answers, because it seems that's how
fast that it moves at the moment. Absolutely, But I
think what we're seeing, more than anything in a tech
space or any sort of tool related is like almost
(02:19):
a new renaissance happening around AI, Like it can free
up a lot of your autonomous and repetitive tasks so
that we as individuals can get back to doing what
we do really well, which is creativity and ingenuity and
more on the humanities and the arts side of things,
and really let the AI do the data analysis and
(02:41):
looking at repetitions and diving into the data and that
sort of thing. So I would say where we are
today is still at a place of early understanding, and
you'd be surprised that at smith OS we work with
some of the largest corporations out there, and even those
companies are still at the very early stages of understand
and how do we implement and how do we bring
(03:02):
this into our technology and ensure security and all of
that sort of stuff. So today I think everybody's probably
in the same spot, which hopefully is comforting to most people,
which is I don't know, right We're all getting to
learn every day more and more what is AI? How
does it work? How can I utilize it? And then
I think we'll get into this today some of its applications,
(03:24):
but we're still all just in a learning phase. I
think that's how i'd summarize it.
Speaker 2 (03:28):
There are a lot of misconceptions out there right now.
There's a lot of confusion and a lot of gray
area when it comes to the jobs that AI is
taking over and you mentioned you know, it's doing the
data analysis, it's doing all of the repetitive and easily
programmable tasks, but a lot of that I know I've
talked with with some of our previous guests about you know,
(03:50):
this is taking away a lot of those learning experiences
from that newer generation of workers where when we came
into these careers, we had to do a lot of
the scut work, a lot of the research and the
understanding of the data and manipulating and making sure we
understood it, and that was a learning process for us. Now,
(04:10):
if we're teaching AI to do it, then we've got
to teach not only these young workers and our future
workforce how to do these things using the AI obviously,
but then to make the decisions based on it. Are
they going to be prepared to make the decisions without
having that base knowledge of understanding how the data was
(04:31):
brought together and manipulated and understood and read or are
we just going to have to kind of gloss over
that and say, okay, this is it's like a calculator.
It's doing something that you learned how to do a
long time ago, but it's not necessary to do that
in your head anymore or do that manually.
Speaker 3 (04:48):
Yeah, it's really dependent on the industry, the company, and
then the job. I think what I would say from
a summary standpoint is number one, there's not a whole
lot of reason to fear, right. I know that there
are there are some loud voices in the space, and
maybe I won't name names, but I think that most
people would associate with AI, and I think they make
(05:10):
it seem like this really scary figure in the room, right, Like,
am I able to understand it? Is it overly complicated?
Is it coming from my job? Like? Am I going
to have to relearn a skill? And halfway through my
career that maybe is I'm not able to do very well.
I don't think that's the case at all. Right. It's
like we used to have town criers, right, who would
(05:33):
go out and they would literally stand in the town
square and tell everybody the news from the last week
or the last month, or what's going on and what's happening.
And then the printing press came along and then people
had newspapers. But that created new jobs, right where you
had reporters, right, and you had people who would write,
and then people would distribute the papers, and so I
(05:54):
think this is a similar sort of evolution, and I
think you don't have to understand every single element of
the process, right, Like, if you just know what you
do really well, and you understand how to utilize AI
or particular AI type tools to help augment your job
rather than replace what you're already doing, then you're already
(06:14):
head of the curve and there's not much more to mandage,
you know. It's very similar to I'd like to say,
when internet marketing became a thing, right, that used to
be a term, right, internet marketing, and it just meant
I could use this device and go online and I
could market my business. Now there's not really such a thing, right,
there's paid advertising and SEO and SEM and retargeting and
(06:35):
remarketing and influencer and social media. It's very similar today.
I think the term AI is what people know and understand,
and so from that comes a lot of kind of confusion,
and like you're saying, do I have to understand data analysis?
Do I have to be afraid I'm going to lose
my ability to do these things? And the short answer
I think is no. I think if you understand the technology,
(06:58):
you embrace it earn the tools as they come in
and figure out how they work best for you as
an individual and as an organization, then you can only
benefit from it in the long term.
Speaker 2 (07:08):
So what key skills do you think are going to
be valuable using that AI augmented workplace. I mean, we've
got we've got the technical skills. Obviously, depending on which
AI platform you're using to support your business, whether it's internal,
whether it's an open source, you're going to need to
understand how to work with that tool. But what are
some of the even some soft skills that are going
(07:30):
to be more important when AI is doing a lot
of that analysis work.
Speaker 3 (07:36):
Yeah, believe it or not, I don't think it will
change a lot from the soft skills you need today.
Things like critical thinking and problem solving, things like emotional intelligence.
All the things that can make you successful as an
employee from a soft skill standpoint, I think are the same.
When it comes to the technology, like you mentioned, there
are like it's smith OS. We have a whole certification
(07:59):
program so you can come in, you can get certified
on the platform as an AI agent engineer. It doesn't
require any development or coding experience because it's all drag
and drop, and so now your operations manager can build
AI agents that will help them do their job better.
Your HR manager can build a series of agents that
can help them do their job better. So from a
(08:21):
soft skill standpoint, I think what we'll really see kind
of rise to the top are the creative elements, So
the emotional intelligence, the creativity, the empathy. Those are the
sort of things that I think as artificial intelligence and
technology gets more adopted at a mass scale, those are
the things that are going to stand out as an
(08:42):
employee to an employer, and as an organization or a
brand to your customer base. So those are things that
have always been important I just think there'll be more
so over the coming two to five years.
Speaker 2 (08:53):
So really building up that personal and the human element
in those jobs. If we've got a machine taking over
the machine stuff that the machine can do, then building
up those interpersonal skills are going to be just as
important or even more important, because we don't have that
need for the mechanical skills, so we don't have to
(09:16):
The technical skills are going to be there, they're always
going to be there, but that person to person is
where the value is going to lie.
Speaker 3 (09:23):
Yeah, absolutely, And if you think about it, it's like,
if I want to go to my boss and suggest
a new technology or tool, or ask for more budget,
whatever it may be, I need to have all those
soft skills. I have to be able to communicate well
understand how what I'm asking for can translate into real
business results. Maybe take your creative approach or critical thinking
(09:45):
approach to problem solving. What problems are these going to solve?
We'll just spend more time doing those sorts of things
because I think we'll spend less time on the technological aspect,
and we'll spend less time in sort of the data
analysis aspect that technology can handle, and it'll free us
human beings up to really just be more creative, think
outside of the box a little bit more, and really
(10:06):
get down to solving problems and not having to worry
about so much the execution of the solutions to those problems.
Speaker 2 (10:13):
So you mentioned that you do a lot of training
and getting people onboarded onto your platform. What are some
of the common challenges that individuals face when they're learning
this new skill, this new tool.
Speaker 3 (10:24):
Yeah, Number one is anxiety for sure. I think most
people it's a new technology, it's scary, we hear so much,
like you wouldn't believe even the number of students who
in some of them are high school students, some are
college students who are write in on a contact form
and say, hey, I'm studying this or I want to
major in this. Should I still do it? Like will
(10:46):
this still be a job? Or is AI going to
replace this? Like so many people have so much anxiety
around artificial intelligence that that mental block is probably the
biggest challenge to start. Then, of course there's some skill gaps,
And when I say skill gaps, I mean like technologically speaking,
in platform wise, but it's no different than CRMs. Right
(11:07):
before CRMs, we were all managing our data and customer
relationship data and spreadsheets or Excel or in Alledger or
whatever it may be. And then at some point you
had to make the jump into CRMs. But once you
learned one of them, it's pretty easy to transition to
other ones, right understanding like the hierarchy of data, how
(11:27):
do I input data and keep the records there and
that sort of thing, And I think this is the
same thing. So the biggest challenge is definitely the mental
block of kind of having some anxiety around is this
for me, Can I do it? Should I be a
little bit afraid of it? And then number two is
a skills gap because it's brand new technology. But what
I tell most people is it is just as new
(11:48):
to me and to the people I work with as
it is to you. So you're not too late. There's
nothing to be anxious about. We're all learning together and
we're all in the same boat.
Speaker 2 (11:59):
Well, your point when you get those students emailing in
on your contact form saying should I even bother majoring
in this in college? I mean earlier you said tomorrow,
the answer is going to be different. Next Thursday, the
answer is going to be different. So we can tell
these kids maybe it's going to resemble it. You're going
to have some use for that education. It's not wasted.
(12:20):
But at the same time, what you think you're going
to be doing today is probably not what you're going
to be doing in five years when you graduate. So
how do we alleviate that level of anxiety while being
honest about the fact that this is changing and we
have no idea what it's going to look like in
five years, what this landscape is going to be.
Speaker 3 (12:40):
Yeah, I think it's and maybe I'm overly optimistic. In
some sense, that could very well be the case, but
I think businesses have always faced the same challenges, whether
it was one hundred years ago, a decade ago, or
a month ago, And that is how do I stand out?
How do I make sure that the people who are
experiencing the problems know that I exist? And then how
(13:02):
do I make sure that my customers or my client base,
whoever it is, is happy and want to continue to
do business with me. I think if you keep that
in mind from a professional standpoint, and you just study
something that you're passionate about and that you don't mind
spending your time doing, the rest of it falls into place.
The tools of the trade will always change, right Like
(13:23):
it's I don't think I could do mental math today
if you force me to, right because I haven't had
to do it since high school. But the fact is,
I always have a calculator in my pocket with a phone.
So if I just understand what numbers do I need
to add together or multiply together to get the answer
I'm looking for, then I'll have the tools to do it.
And I think this is a similar thing. So if
(13:45):
you're passionate about working in technology, going into supply chain,
whatever it may be, as long as you study what
you're passionate about and you understand just the basics of
solving problems and how to help a business achieve it,
our goals that they have, and you know, the tools
of the trade are always going to change. The only
constant there is that that will remain the same, and
(14:07):
you'll be able to overcome and learn those I think
personally that's the easy part.
Speaker 2 (14:12):
I agree, it's it's just going to be a tool.
But this is a tool that we're training to think.
We're training to look for for certain aspects, certain key
data points, certain whatever, and to make decisions or to
come to conclusions based on that data. So as it
becomes more integrated into our work, what are some ethical
(14:34):
considerations that we should be mindful of?
Speaker 3 (14:37):
Yeah, absolutely so. From an organizational standpoint, I think the
obvious starting point is data security. It really doesn't matter
what kind of company you are. I mentioned CRMs before.
We store like every one of us as individuals has
so much of our personal data out there on the Internet,
whether we like it or not, and companies house that data.
(14:57):
So when you're using AI, which when we say AI
right now, we really are talking about a series of
large language models. We want to make sure that any
data we give to that, or whatever tools we're using
to analyze our data, has security and compliance build into it, right,
so that our data is not being shared, our customers
(15:18):
data is not being shared, it's not being leaked. That's
the first one. Then, of course, I think there's a
lot of kind of ethical governance and oversight that I
think industries and possibly even governments are going to have
to start to consider, right, is, if we start to
allow artificial technology to make decisions for us, what's the
moral compass for some of those decisions, Like when you
(15:39):
start thinking of supply chain if we're talking about from
a ENGO standpoint, right and we're talking about let's say
food distribution for international NGOs, for example, if you leave
that to a series of large language models to make
decisions on, well, how are you sure that there's the
right moral foundation for how those decisions are being made?
(16:02):
And that's a very clear kind of downfall in one
sense of artificial intelligence is it doesn't have that empathetic
and moral foundation that us as human beings do. So
those are the two things that I think in the
near term, we're going to have to really kind of
talk about as an industry and just make sure that
we have the right solutions in place and the right
(16:22):
infrastructures and guide rails to making sure that everything we
do is secure and we're not releasing people's information. And
then number two, that we have some sort of ethical
and moral foundations so that we can guide these models
and let them know here's what is a clear ethical boundary,
or here's a clear moral boundary, and so anything that
we do or decisions we make have to be within
(16:44):
those within those guidelines.
Speaker 2 (16:47):
So that brings the human back into the equation of
using the AI tool and then having to make a
decision using that moral compass, using that ethical consideration. Is
it possible that we will get to point that we'll
be able to program that into the AI models.
Speaker 3 (17:04):
Yeah, well, I think right now the failsafe for that
or the kill switch, if you will, or people like
you and me, I think we're probably still a ways
away before any organization leaves sole decision making power to
an AI model, and I think that's probably a good thing. Today,
I think that most people are using these tools like
a smith OS to help them augment what they're already
(17:29):
doing rather than replace individuals or functions within their organization.
So in that scenario, it' would be people like you
and myself were sitting down and looking at the data
analysis that a particular model has done, for example, and saying, okay,
now with that analysis done, here's how I'm going to
interpret it, and then here are the decisions that would
(17:50):
be made from it. So I think as long as
we in the mid to year term, have that human
being in place that are making the final decisions, we
don't have to work too much about is this model
going to run off the rails? Is it going to
make a decision and send that decision out to four
thousand people before anybody looks at it? You know, it's
still it's the onus is still on us to kind
(18:12):
of make those decisions.
Speaker 2 (18:14):
Sure, well, And I think getting back into the data
protection and the security that you were talking about as well,
when we're talking about these different AI models, you can't
just take your customer data and go out to a
chat GPT because that's open source. They're using that data
not only to generate what it is that you're trying
(18:35):
to achieve, but also to teach itself and then they
have your data. And so companies like yours that offer
these platforms and these models that can do it more.
I don't want to say privately or internally, but that
really is the case where it's a closed source and
you're able to input your data and teach the algorithm
(18:57):
what you're trying to achieve with this data in order
to have that level of security. But then how are
all these models learning from each other? I think that's
the question of is one model going to take over
the industry where other models are going to lag behind?
And what sort of ethical questions does that pose? Is
(19:17):
is the information of what is capable still going to
be governing all of the possibilities out there?
Speaker 3 (19:26):
What are your thoughts? Yeah, it's a great question. I'm
trying to think if there's any question we could asked more.
If this isn't the top question, it's definitely number two
is exactly that. So any large corporation, any corporation or
industry that has ties to the government, even certain there
are so many industries that have regulation around it where
(19:48):
they can't deploy into the cloud right, So open source
first and foremost is kind of a non starter for
a lot of these companies for the reason you mentioned,
but also cloud deployment, because in a cloud scenario, you
have to take all of your data give it to
the cloud that can be accessed maliciously right through means
of hacking by accident. Anyone who has access to the
(20:09):
cloud can download it locally, So any cloud deployment is
an issue. What we've done to try to address that
is we've built what's called a local runtime environment. So
if you are a large transportation company, you have contracts
with the government or multi corporations that have strict security
and compliance standards. We have our agent builder, which is
(20:33):
where you can bring multiple models together so you're not
reliant on anyone. You can connect any of your internal applications.
You can build models on open AI which is CHAGBT,
on anthropic which is CLAUDE. You can bring them all
together for the AI orchestration as it's called. But then
you can essentially download those agents and you can run
(20:54):
them locally. You can run them on your own servers,
on your own AWS servers, on prem as we call it,
and in that scenario, you don't have to release any
of your private data to the cloud. So you can
ensure that it's secure, so you can still use technologies
like smith os to build these agents that can be
used to optimize your business in many different ways without
(21:15):
having to risk the security of your data by deploying
it into the cloud by using something like a local
runtime environment. So that's one of the ways that we've
tried to address it, and I think for now, at
least as it stands in a very cloud native world,
that's probably one of the first places to start if
you're an organization that's concerned about that.
Speaker 2 (21:36):
Okay, well, and coming back to the question that we
talked about a little bit earlier, and that was, you know,
these young kids that are questioning whether or not they're
going to have a career that they thought they would.
What are some new exciting career opportunities that are emerging
due to these advancements. Because obviously it's going to create
(21:56):
new jobs, it's going to create additional things that humans
need to do. What are some of those things that
are that are on the horizon for our young talent
workforce that's up and coming.
Speaker 3 (22:08):
Yeah, I think there's two that come to mind. Number
one are AI agent engineers. There have been so many
studies done by Goldman, Sachs Gartner, pretty much every foundation,
business and marketing foundation out there on the number of
agents compared to employees over the next ten years, and
you'll find anywhere from one to five for every employee,
(22:31):
there will be one to five agents who are deployed
to help those employees do their jobs. And so obviously
somebody needs to be able to build those agents, right,
So AI agent engineers will be number one, And again
I think it's not as scary as it sounds. Tools
like Smith can help you do that without being a developer,
without having to learn to code. But number two, and
(22:53):
this is I think the really the long term opportunity
is what we call MACE and geers or multi agent
systems engineers. So as AI sort of gets more into
a more mature state, you'll have a lot of different
custom agents and applications. You'll have obviously businesses who build
(23:17):
the next version of TA GPT where instead of a
single model, it's multimodel and these sorts of things. You
need to bring those all together, right, So when you
have these multiple connection points of different AI tools and
different AI systems, that's what we call a multi agent
system and artificial intelligence, and that's where systems can speak
(23:38):
to one another, learn from one another, they can call
upon one another to complete these large and complex tasks,
and you need engineers who can put those things together.
So those multi agent systems engineers, I think are going
to be incredible demand over the next probably two to
five years and beyond. And I think those are just
two those are just two of the starting points. I know.
Speaker 2 (24:00):
I think of just let's say, two years ago, a
system's analyst or a data analysis A data analyst was
such a small thing and then it got this huge boom,
and now we're replacing it with an AI that can
do the same thing, that can analyze that data. So
I believe that there's going to be a number of
different iterations of professions or job titles or whatever you
(24:23):
want to call it, that skills that people need to
learn and jobs that need to be filled. That as
we grow and as we learn how to use these
tools most effectively in our society, these jobs are going
to evolve very very quickly. And so I think the
thing to tell our students right now, and the thing
to tell that up and coming workforce is be flexible,
(24:46):
be willing to learn, be willing to challenge yourself, and
be willing to try and work with these tools because
it's going to be exciting. You're getting in on the
ground floor of a new technology. It's kind of like
the tech boom back in the nineties, and this is
your chance to do it. This is your generation's opportunity.
Speaker 3 (25:04):
So what are some.
Speaker 2 (25:06):
Maybe success stories or epic fails, either are excellent stories.
But what's something that you can tell us about a
client that you've worked with or a deployment that you've
done that was able to make a huge difference.
Speaker 3 (25:21):
Yeah, absolutely, there's On a personal level, I have probably
a thousand fail stories that I could share while I
was trying to learn how to how to really become
more of an AIG engineer myself. But on the success side,
So we work with a client called Halo. This is
Halo Dog Callers, a company started by Caesar Malon who's
(25:44):
the dog whisperer, and a Stanford grad named Michael Ahrman,
and they have utilized artificial intelligence to connect a lot
of their kind of inventory management with their product and
ordering and then their customer service. And the way we've
deployed that early on is through a chat agent, so
(26:06):
you can come to the website, you can ask questions
about your order, questions about delivery time, updates, connect with
customer service, sort of do all of that in real
time with AI without having to wait on the phone
for customer service representative or wait while they're searching for
more information. And one of the reasons that's so important
(26:26):
is in the customer service world, you want to try
to de escalate those types of concerns as much as possible,
right and the last thing you want when you're kind
of frustrated with an order or trying to speak to
customer service is to be transferred from person to person
or department to department, or not get the answers you're
looking for. This is an area where AI is just
(26:47):
such a great place to start today because the technology
is already there, the use case has been proven, and
it works really well. So I'd say that's probably one
of our earlier success stories. But we've you know, we've
seen it across so many different industries that it's pretty
agnostic to industry.
Speaker 2 (27:05):
And I will ask this question just for a personal
because I use the open source GPTs. We're testing some
different things internally with some internal platforms at work, but
even just for fun to use AI or chat GPT
or Gemini or Claude or whichever one. What have you
done for fun with AI, just to see what it
(27:27):
can do?
Speaker 3 (27:28):
Yeah, I'll uh, my wife will probably see this social
she'll know my secret here soon. But date night ideas
is a great one, right, Okay, Like just in a
personal note, So when you are looking for a new
restaurant in town, for example, but with a little bit
different kind of experience, or looking to do something different
(27:49):
in the city you've lived in for a while that
you maybe haven't had a chance to try before, that's
a really great way to use something like chat GPT.
In a more boring sense, I use it for organizing
almost everything that I do. I have found that these
large language models are really great at going from zero
to one in a creative sense. So if I'm staring
(28:12):
at a blank screen and I'm trying to write, let's say,
some website copy or some emails or something like that,
it can be so hard to go from a blank
page to even the first five or ten words. But
if I go into a chat GP or my favorite
which is anthropics, claud I can go from zero to
one very quickly, and it creates the spark, It gets
(28:35):
things moving. It allows you to kind of get your
creative process underway, and to me, I have a ton
of fun like that. So both on the personal and
professional sides, those are two ways that I use it
that maybe could be could be helpful with other people
out there. I know.
Speaker 2 (28:50):
I use it for the same reason to generate some idea,
to get those creative juices flowing. I mean, in marketing,
we have to generate a lot of content and some
times it feels like you're writing the same thing over
and over and over and over, and you're like, Okay,
try and do this in a new tone. Read this
back to me, or give me this prompt in someone
(29:11):
else's voice, pretend like your Sylvester Stallone, or pretend like
you're Tom Cruise, or pretend like you're whomever, and give
me this back in their tone of voice from this
character that they played, or one thing that I've done
that is not in AI. But I've used it to
kind of prompt myself to try something different and look
(29:33):
at something from a different angle. I'm a huge Harry
Potter fan, I've read all the books, watched all the movies.
I love Harry Potter, but in thinking about it one time,
I realized, you know that these books took place in
the nineties, and these kids grew up at the same
time that I grew up and was their age. Another
show popular in the nineties Saved by the Bell. So
one time, when reading through the series again, I pictured
(29:56):
all of the cast of Harry Potter as the cast
of Saved by the Bell in that tone of voice,
that idea, that feeling of that high school situation as
opposed to the high school at Hogwarts. So I mean
those types of things where you just reimagine it in
a different perspective, a slightly different twist to it, and
that's where you get those creative juices flowing. And no,
(30:19):
that is not going to help me in any way
sense or form professionally, except that that's going to get
my own creative juices flowing. That's going to get me
thinking and looking at a problem from a different angle.
And I think that's one of the great things that
some of these content generators, even the open source ones,
are able to do without with very little risk. I mean,
(30:41):
who's going to care. If I decide to have this
recipe read back to me in the tone of some
other celebrity or influencer, it's going to give me a laugh,
it's going to give me an idea.
Speaker 3 (30:54):
Yeah. Absolutely. I do something very similar with the great
Michael Scott, the World's best boss from the office. So
if I'm having a little bit of a routine or
mundane day, you know, I'll put something in there with
the little character I've created in chat GPT for Michael
Scott and have it tell me a joke, right, a
little pick me up for the day. But I think
(31:14):
that's the humanizing and really the beautiful side of artificial intelligence.
And then I hope that more and more people kind
of look at it that way and see it that way,
which is it's probably the most or one of the
most useful things or advancements we've seen in a really
really long time my lifetime, almost certainly. But it can
(31:34):
also be a beautiful thing to help you discover and explore,
help you cheer you up if you're in a dowed mood,
which can be a tremendous benefit both personally and professionally.
So I think us just getting more familiar with these
tools and being more comfortable using them can do a lot.
It can help produce the anxieties, it can improve life
and even the smallest amounts and so many different ways.
(31:58):
And the more we all electively kind of raise our
knowledge and understanding and close the knowledge gap between where
we are now and where AI is of let's say
next month, the better we all will be for it right,
and we can adopt it quicker, We can have better
conversations around them, We can discuss the ethical and moral
(32:19):
dilemmas around them, because people really have a better understanding
and intellect around what AI really is. And it's no
longer I fear it because I don't understand it or
don't know it sort of thing. So yeah, I love
that example.
Speaker 2 (32:35):
Well, and I think you bring up an excellent point
again with the ethics and the morality is when we're
doing these things and being creative and stepping outside the
box and getting it to pretend to be someone it's not,
then there is that human element, back to the beginning
of our conversation, where there has to be a human
at the end of this reviewing whatever this output is,
(32:58):
understanding what it is that it's trying to tell you,
because you can't just go in and create content in
the voice of someone else and pretend to be that
other person and plagiarize in that way or impersonate someone else,
because there's first of all, that's wrong. It's morally wrong.
We understand that, but it also has the downside of
(33:19):
not actually being that intelligent. It has limitations, and it
can do things that it knows based on what's out there,
what's open source information, but it's not going to have
that decision making capability. It's not necessarily going to have
the same even vocabulary as some of those individuals. I
remember seeing a clip recently of Neil degrast Tyson and
(33:41):
he was saying this AI had generated something that he
might have said, but it used different vocabulary. So he
realized very quickly that it wasn't actually a quote that
he said, because it's not the way he would have
said it. And I think that brings us back to
there always has to be a human sitting behind the
desk making the fine decision based on whatever the AI does,
(34:02):
because we can't let it just go out and do things.
It can do a lot of the thinking, it can
do a lot of the manipulation of the information, but
it can't be the final say, it can't be the
final decision.
Speaker 3 (34:15):
Yeah, And there's real world examples of where this is
incredibly important, like Tesla famously had and every self driving
car autonomous vehicles had to sit down and consider this right,
which is like if your cars driving straight and it's
about to hit two pedestrians and the only option is
to turn right and hit one pedestrian, and those are
(34:36):
the only options, Like what should the car be programmed
to do? And consider right? And the world's best minds
and ethics and morality and they all came together and
had to discuss these things because ultimately we're the ones,
at least for now, behind the keys when it comes
to this stuff, right, And we have to tell these models,
and we have to tell them here's what to consider,
(34:58):
Here's how much weight to give to consider A versus B.
So you're absolutely right, at least for the very foreseeable future,
we're the ones making the decisions on these things. But again,
I think it comes back to the why it's just
so important for everyone to dive in and start getting familiar,
even if you only use it in your personal life
(35:19):
or you only use it in your professional life, because eventually,
these are things that are going to affect every single
one of us, Right is how AI is used, whether
it's for surveillance and route planning, and you know, all
sorts of different things. And the more knowledgeable we all
are about it, then the better conversations that we can have,
and then hopefully, ultimately, the better decisions that we can
(35:41):
make as a group and as a society.
Speaker 2 (35:44):
I think you brought up an excellent use case of
the chatbot on a website when you go and you
have a question for a company or something that you've done.
How many of us don't realize that those chatbots have
been run by AI for so long behind the scenes,
some person sitting there typing back and forth with you.
In most cases, now there are some that do, but
(36:06):
in most cases those chatbots where you're just asking a question,
are delivering information based on a database of information that
they have about their company. And so we've been interacting
with these this whole time without even really thinking about
it or realizing it. And the more we pay attention
to it, the more we'll understand its capabilities and its limitations.
Speaker 3 (36:28):
That's exactly right, and even before that you call your
bank and all of a sudden you have automated menus, right,
press one for this and two for this and say
a word. And so this is technology. I think a
lot of people are at least in some way, shape
or form familiar with. But when it comes down to
even the organizational level, I mean, if you think of
(36:50):
you know, my grandfather was a long haul truck driver, right,
so I could only imagine if he had the ability
for enhanced route optimization. Right. Even another use case that
we've looked at with I'll just say a government entity
is predictive maintenance, right to make sure that you ensure
the least amount of downtimes for delivery vehicles and things
(37:12):
like that. Like, there's so many real world applications that
are there today and that can be used today that
it's really just the literacy thing. It's an AI literacy.
If we can make sure that our organizational leaders have
as much information and knowledge on this as possible, and
then the individuals for using them have the same thing,
(37:32):
then we can all move forward in the right direction.
Speaker 2 (37:35):
All right, Well, I think that brings us to the
end of this episode. Thank you so much, Michael for
joining us today and talking a little bit more about
AI and where it's going to take our talent workforce
in the future. For our guests out there or for
our listeners out there, if you have a suggestion for
a topic or would like to be a guest on
the show, we'd love to hear from you. You can
(37:57):
contact me at podcasts at mebock at any time. As always,
thank you for listening to Speaking of Supply Chain. If
you enjoyed this episode, please rate and review us on
whatever podcast platform you prefer, Apple Podcasts, Google, Spotify, and
iHeartRadio and be sure to tune in next time.
Speaker 1 (38:18):
Meeboch Consulting is one of the largest and most globally
recognized supply chain consulting, engineering and advisory firms. For nearly
fifty years, we've helped clients achieve supply chain excellence and
sustainable competitive advantage across the entire spectrum of the supply
chain by delivering improvements and innovation strategically, tactically, and digitally.
(38:39):
To learn more, visit meeboch dot com. You've been listening
to Speaking of Supply Chain a meboch podcast, keep connected
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