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Logistics Career Evolution in the Age of AI · Speaking of Supply Chain

• 0:06
  Speaker 1: You're listening to Speaking of Supply Chain, a meboch podcast.
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  Speaker 1: This is a show for logistics professionals looking to learn
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  Speaker 1: more about the latest innovations in supply chain. Each episode
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  Speaker 1: will feature a conversation on topics such as mitigating supply
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  Speaker 1: chain disruption and reducing risk, current automation trends, sustainability initiatives,
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  Speaker 1: and more. Let's dive right in.
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  Speaker 2: Hello, and welcome to Speaking of Supply Chain, where we
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  Speaker 2: explore trends, current events, and innovations impacting the logistics and
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  Speaker 2: supply chain industries. I'm your host, Ellen Wood. Artificial intelligence
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  Speaker 2: is reshaping the way we work, from automating mundane tasks
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  Speaker 2: to revolutionizing entire industries. AI is changing the landscape of
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  Speaker 2: the workplace. Join us as we chat with Michael Walker
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  Speaker 2: from Smithos, who is going to shed some light on
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  Speaker 2: the opportunities and challenges that AI presents for future work forces.
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  Speaker 2: Welcome to Speaking of Supply Chain.
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  Speaker 3: Michael, Thank you, Ollen, it's a pleasure to be here.
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  Speaker 3: I appreciate you taking the time.
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  Speaker 2: Now did I pronounce that right? It is smith Os.
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  Speaker 3: It's Smith actually so, but instead of an I is
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  Speaker 3: with a Y yes.
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  Speaker 2: Yes, okay, So Smith os. So I'm looking forward to
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  Speaker 2: our topic today and all of the insight that you're
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  Speaker 2: going to bring to us about AI in various industries,
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  Speaker 2: but specifically in the supply chain. And I've talked about
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  Speaker 2: AI on the podcast with a couple different guests. We've
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  Speaker 2: talked with with Pete Grett from Blackrock about AI, We've
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  Speaker 2: talked with Michael Greenberg from Third Brain Digital, and we're
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  Speaker 2: talking about it again because this topic is so pervasive,
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  Speaker 2: It is such a huge component to anything these days,
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  Speaker 2: much less supply chain. So what is going on right
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  Speaker 2: now in AI in the workplace? Because it is absolutely exploding.
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  Speaker 2: We can barely keep up with it. What's the current thing?
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  Speaker 3: Yeah, I think if you ask me that question tomorrow
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  Speaker 3: and then again Thursday and Friday of next week, there
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  Speaker 3: will be three different answers, because it seems that's how
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  Speaker 3: fast that it moves at the moment. Absolutely, But I
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  Speaker 3: think what we're seeing, more than anything in a tech
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  Speaker 3: space or any sort of tool related is like almost
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  Speaker 3: a new renaissance happening around AI, Like it can free
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  Speaker 3: up a lot of your autonomous and repetitive tasks so
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  Speaker 3: that we as individuals can get back to doing what
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  Speaker 3: we do really well, which is creativity and ingenuity and
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  Speaker 3: more on the humanities and the arts side of things,
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  Speaker 3: and really let the AI do the data analysis and
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  Speaker 3: looking at repetitions and diving into the data and that
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  Speaker 3: sort of thing. So I would say where we are
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  Speaker 3: today is still at a place of early understanding, and
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  Speaker 3: you'd be surprised that at smith OS we work with
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  Speaker 3: some of the largest corporations out there, and even those
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  Speaker 3: companies are still at the very early stages of understand
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  Speaker 3: and how do we implement and how do we bring
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  Speaker 3: this into our technology and ensure security and all of
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  Speaker 3: that sort of stuff. So today I think everybody's probably
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  Speaker 3: in the same spot, which hopefully is comforting to most people,
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  Speaker 3: which is I don't know, right We're all getting to
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  Speaker 3: learn every day more and more what is AI? How
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  Speaker 3: does it work? How can I utilize it? And then
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  Speaker 3: I think we'll get into this today some of its applications,
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  Speaker 3: but we're still all just in a learning phase. I
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  Speaker 3: think that's how i'd summarize it.
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  Speaker 2: There are a lot of misconceptions out there right now.
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  Speaker 2: There's a lot of confusion and a lot of gray
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  Speaker 2: area when it comes to the jobs that AI is
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  Speaker 2: taking over and you mentioned you know, it's doing the
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  Speaker 2: data analysis, it's doing all of the repetitive and easily
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  Speaker 2: programmable tasks, but a lot of that I know I've
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  Speaker 2: talked with with some of our previous guests about you know,
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  Speaker 2: this is taking away a lot of those learning experiences
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  Speaker 2: from that newer generation of workers where when we came
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  Speaker 2: into these careers, we had to do a lot of
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  Speaker 2: the scut work, a lot of the research and the
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  Speaker 2: understanding of the data and manipulating and making sure we
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  Speaker 2: understood it, and that was a learning process for us. Now,
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  Speaker 2: if we're teaching AI to do it, then we've got
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  Speaker 2: to teach not only these young workers and our future
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  Speaker 2: workforce how to do these things using the AI obviously,
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  Speaker 2: but then to make the decisions based on it. Are
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  Speaker 2: they going to be prepared to make the decisions without
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  Speaker 2: having that base knowledge of understanding how the data was
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  Speaker 2: brought together and manipulated and understood and read or are
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  Speaker 2: we just going to have to kind of gloss over
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  Speaker 2: that and say, okay, this is it's like a calculator.
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  Speaker 2: It's doing something that you learned how to do a
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  Speaker 2: long time ago, but it's not necessary to do that
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  Speaker 2: in your head anymore or do that manually.
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  Speaker 3: Yeah, it's really dependent on the industry, the company, and
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  Speaker 3: then the job. I think what I would say from
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  Speaker 3: a summary standpoint is number one, there's not a whole
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  Speaker 3: lot of reason to fear, right. I know that there
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  Speaker 3: are there are some loud voices in the space, and
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  Speaker 3: maybe I won't name names, but I think that most
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  Speaker 3: people would associate with AI, and I think they make
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  Speaker 3: it seem like this really scary figure in the room, right, Like,
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  Speaker 3: am I able to understand it? Is it overly complicated?
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  Speaker 3: Is it coming from my job? Like? Am I going
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  Speaker 3: to have to relearn a skill? And halfway through my
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  Speaker 3: career that maybe is I'm not able to do very well.
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  Speaker 3: I don't think that's the case at all. Right. It's
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  Speaker 3: like we used to have town criers, right, who would
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  Speaker 3: go out and they would literally stand in the town
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  Speaker 3: square and tell everybody the news from the last week
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  Speaker 3: or the last month, or what's going on and what's happening.
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  Speaker 3: And then the printing press came along and then people
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  Speaker 3: had newspapers. But that created new jobs, right where you
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  Speaker 3: had reporters, right, and you had people who would write,
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  Speaker 3: and then people would distribute the papers, and so I
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  Speaker 3: think this is a similar sort of evolution, and I
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  Speaker 3: think you don't have to understand every single element of
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  Speaker 3: the process, right, Like, if you just know what you
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  Speaker 3: do really well, and you understand how to utilize AI
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  Speaker 3: or particular AI type tools to help augment your job
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  Speaker 3: rather than replace what you're already doing, then you're already
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  Speaker 3: head of the curve and there's not much more to mandage,
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  Speaker 3: you know. It's very similar to I'd like to say,
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  Speaker 3: when internet marketing became a thing, right, that used to
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  Speaker 3: be a term, right, internet marketing, and it just meant
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  Speaker 3: I could use this device and go online and I
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  Speaker 3: could market my business. Now there's not really such a thing, right,
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  Speaker 3: there's paid advertising and SEO and SEM and retargeting and
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  Speaker 3: remarketing and influencer and social media. It's very similar today.
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  Speaker 3: I think the term AI is what people know and understand,
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  Speaker 3: and so from that comes a lot of kind of confusion,
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  Speaker 3: and like you're saying, do I have to understand data analysis?
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  Speaker 3: Do I have to be afraid I'm going to lose
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  Speaker 3: my ability to do these things? And the short answer
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  Speaker 3: I think is no. I think if you understand the technology,
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  Speaker 3: you embrace it earn the tools as they come in
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  Speaker 3: and figure out how they work best for you as
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  Speaker 3: an individual and as an organization, then you can only
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  Speaker 3: benefit from it in the long term.
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  Speaker 2: So what key skills do you think are going to
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  Speaker 2: be valuable using that AI augmented workplace. I mean, we've
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  Speaker 2: got we've got the technical skills. Obviously, depending on which
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  Speaker 2: AI platform you're using to support your business, whether it's internal,
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  Speaker 2: whether it's an open source, you're going to need to
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  Speaker 2: understand how to work with that tool. But what are
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  Speaker 2: some of the even some soft skills that are going
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  Speaker 2: to be more important when AI is doing a lot
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  Speaker 2: of that analysis work.
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  Speaker 3: Yeah, believe it or not, I don't think it will
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  Speaker 3: change a lot from the soft skills you need today.
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  Speaker 3: Things like critical thinking and problem solving, things like emotional intelligence.
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  Speaker 3: All the things that can make you successful as an
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  Speaker 3: employee from a soft skill standpoint, I think are the same.
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  Speaker 3: When it comes to the technology, like you mentioned, there
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  Speaker 3: are like it's smith OS. We have a whole certification
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  Speaker 3: program so you can come in, you can get certified
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  Speaker 3: on the platform as an AI agent engineer. It doesn't
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  Speaker 3: require any development or coding experience because it's all drag
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  Speaker 3: and drop, and so now your operations manager can build
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  Speaker 3: AI agents that will help them do their job better.
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  Speaker 3: Your HR manager can build a series of agents that
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  Speaker 3: can help them do their job better. So from a
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  Speaker 3: soft skill standpoint, I think what we'll really see kind
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  Speaker 3: of rise to the top are the creative elements, So
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  Speaker 3: the emotional intelligence, the creativity, the empathy. Those are the
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  Speaker 3: sort of things that I think as artificial intelligence and
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  Speaker 3: technology gets more adopted at a mass scale, those are
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  Speaker 3: the things that are going to stand out as an
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  Speaker 3: employee to an employer, and as an organization or a
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  Speaker 3: brand to your customer base. So those are things that
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  Speaker 3: have always been important I just think there'll be more
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  Speaker 3: so over the coming two to five years.
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  Speaker 2: So really building up that personal and the human element
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  Speaker 2: in those jobs. If we've got a machine taking over
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  Speaker 2: the machine stuff that the machine can do, then building
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  Speaker 2: up those interpersonal skills are going to be just as
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  Speaker 2: important or even more important, because we don't have that
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  Speaker 2: need for the mechanical skills, so we don't have to
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  Speaker 2: The technical skills are going to be there, they're always
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  Speaker 2: going to be there, but that person to person is
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  Speaker 2: where the value is going to lie.
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  Speaker 3: Yeah, absolutely, And if you think about it, it's like,
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  Speaker 3: if I want to go to my boss and suggest
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  Speaker 3: a new technology or tool, or ask for more budget,
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  Speaker 3: whatever it may be, I need to have all those
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  Speaker 3: soft skills. I have to be able to communicate well
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  Speaker 3: understand how what I'm asking for can translate into real
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  Speaker 3: business results. Maybe take your creative approach or critical thinking
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  Speaker 3: approach to problem solving. What problems are these going to solve?
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  Speaker 3: We'll just spend more time doing those sorts of things
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  Speaker 3: because I think we'll spend less time on the technological aspect,
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  Speaker 3: and we'll spend less time in sort of the data
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  Speaker 3: analysis aspect that technology can handle, and it'll free us
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  Speaker 3: human beings up to really just be more creative, think
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  Speaker 3: outside of the box a little bit more, and really
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  Speaker 3: get down to solving problems and not having to worry
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  Speaker 3: about so much the execution of the solutions to those problems.
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  Speaker 2: So you mentioned that you do a lot of training
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  Speaker 2: and getting people onboarded onto your platform. What are some
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  Speaker 2: of the common challenges that individuals face when they're learning
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  Speaker 2: this new skill, this new tool.
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  Speaker 3: Yeah, Number one is anxiety for sure. I think most
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  Speaker 3: people it's a new technology, it's scary, we hear so much,
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  Speaker 3: like you wouldn't believe even the number of students who
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  Speaker 3: in some of them are high school students, some are
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  Speaker 3: college students who are write in on a contact form
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  Speaker 3: and say, hey, I'm studying this or I want to
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  Speaker 3: major in this. Should I still do it? Like will
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  Speaker 3: this still be a job? Or is AI going to
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  Speaker 3: replace this? Like so many people have so much anxiety
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  Speaker 3: around artificial intelligence that that mental block is probably the
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  Speaker 3: biggest challenge to start. Then, of course there's some skill gaps,
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  Speaker 3: And when I say skill gaps, I mean like technologically speaking,
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  Speaker 3: in platform wise, but it's no different than CRMs. Right
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  Speaker 3: before CRMs, we were all managing our data and customer
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  Speaker 3: relationship data and spreadsheets or Excel or in Alledger or
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  Speaker 3: whatever it may be. And then at some point you
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  Speaker 3: had to make the jump into CRMs. But once you
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  Speaker 3: learned one of them, it's pretty easy to transition to
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  Speaker 3: other ones, right understanding like the hierarchy of data, how
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  Speaker 3: do I input data and keep the records there and
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  Speaker 3: that sort of thing, And I think this is the
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  Speaker 3: same thing. So the biggest challenge is definitely the mental
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  Speaker 3: block of kind of having some anxiety around is this
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  Speaker 3: for me, Can I do it? Should I be a
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  Speaker 3: little bit afraid of it? And then number two is
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  Speaker 3: a skills gap because it's brand new technology. But what
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  Speaker 3: I tell most people is it is just as new
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  Speaker 3: to me and to the people I work with as
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  Speaker 3: it is to you. So you're not too late. There's
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  Speaker 3: nothing to be anxious about. We're all learning together and
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  Speaker 3: we're all in the same boat.
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  Speaker 2: Well, your point when you get those students emailing in
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  Speaker 2: on your contact form saying should I even bother majoring
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  Speaker 2: in this in college? I mean earlier you said tomorrow,
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  Speaker 2: the answer is going to be different. Next Thursday, the
• 12:10
  Speaker 2: answer is going to be different. So we can tell
• 12:13
  Speaker 2: these kids maybe it's going to resemble it. You're going
• 12:17
  Speaker 2: to have some use for that education. It's not wasted.
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  Speaker 2: But at the same time, what you think you're going
• 12:22
  Speaker 2: to be doing today is probably not what you're going
• 12:25
  Speaker 2: to be doing in five years when you graduate. So
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  Speaker 2: how do we alleviate that level of anxiety while being
• 12:32
  Speaker 2: honest about the fact that this is changing and we
• 12:35
  Speaker 2: have no idea what it's going to look like in
• 12:37
  Speaker 2: five years, what this landscape is going to be.
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  Speaker 3: Yeah, I think it's and maybe I'm overly optimistic. In
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  Speaker 3: some sense, that could very well be the case, but
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  Speaker 3: I think businesses have always faced the same challenges, whether
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  Speaker 3: it was one hundred years ago, a decade ago, or
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  Speaker 3: a month ago, And that is how do I stand out?
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  Speaker 3: How do I make sure that the people who are
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  Speaker 3: experiencing the problems know that I exist? And then how
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  Speaker 3: do I make sure that my customers or my client base,
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  Speaker 3: whoever it is, is happy and want to continue to
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  Speaker 3: do business with me. I think if you keep that
• 13:12
  Speaker 3: in mind from a professional standpoint, and you just study
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  Speaker 3: something that you're passionate about and that you don't mind
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  Speaker 3: spending your time doing, the rest of it falls into place.
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  Speaker 3: The tools of the trade will always change, right Like
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  Speaker 3: it's I don't think I could do mental math today
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  Speaker 3: if you force me to, right because I haven't had
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  Speaker 3: to do it since high school. But the fact is,
• 13:32
  Speaker 3: I always have a calculator in my pocket with a phone.
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  Speaker 3: So if I just understand what numbers do I need
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  Speaker 3: to add together or multiply together to get the answer
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  Speaker 3: I'm looking for, then I'll have the tools to do it.
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  Speaker 3: And I think this is a similar thing. So if
• 13:45
  Speaker 3: you're passionate about working in technology, going into supply chain,
• 13:50
  Speaker 3: whatever it may be, as long as you study what
• 13:53
  Speaker 3: you're passionate about and you understand just the basics of
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  Speaker 3: solving problems and how to help a business achieve it,
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  Speaker 3: our goals that they have, and you know, the tools
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  Speaker 3: of the trade are always going to change. The only
• 14:04
  Speaker 3: constant there is that that will remain the same, and
• 14:07
  Speaker 3: you'll be able to overcome and learn those I think
• 14:10
  Speaker 3: personally that's the easy part.
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  Speaker 2: I agree, it's it's just going to be a tool.
• 14:15
  Speaker 2: But this is a tool that we're training to think.
• 14:19
  Speaker 2: We're training to look for for certain aspects, certain key
• 14:24
  Speaker 2: data points, certain whatever, and to make decisions or to
• 14:28
  Speaker 2: come to conclusions based on that data. So as it
• 14:31
  Speaker 2: becomes more integrated into our work, what are some ethical
• 14:34
  Speaker 2: considerations that we should be mindful of?
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  Speaker 3: Yeah, absolutely so. From an organizational standpoint, I think the
• 14:41
  Speaker 3: obvious starting point is data security. It really doesn't matter
• 14:44
  Speaker 3: what kind of company you are. I mentioned CRMs before.
• 14:47
  Speaker 3: We store like every one of us as individuals has
• 14:51
  Speaker 3: so much of our personal data out there on the Internet,
• 14:54
  Speaker 3: whether we like it or not, and companies house that data.
• 14:57
  Speaker 3: So when you're using AI, which when we say AI
• 15:01
  Speaker 3: right now, we really are talking about a series of
• 15:04
  Speaker 3: large language models. We want to make sure that any
• 15:07
  Speaker 3: data we give to that, or whatever tools we're using
• 15:10
  Speaker 3: to analyze our data, has security and compliance build into it, right,
• 15:15
  Speaker 3: so that our data is not being shared, our customers
• 15:18
  Speaker 3: data is not being shared, it's not being leaked. That's
• 15:20
  Speaker 3: the first one. Then, of course, I think there's a
• 15:23
  Speaker 3: lot of kind of ethical governance and oversight that I
• 15:26
  Speaker 3: think industries and possibly even governments are going to have
• 15:30
  Speaker 3: to start to consider, right, is, if we start to
• 15:33
  Speaker 3: allow artificial technology to make decisions for us, what's the
• 15:37
  Speaker 3: moral compass for some of those decisions, Like when you
• 15:39
  Speaker 3: start thinking of supply chain if we're talking about from
• 15:43
  Speaker 3: a ENGO standpoint, right and we're talking about let's say
• 15:47
  Speaker 3: food distribution for international NGOs, for example, if you leave
• 15:51
  Speaker 3: that to a series of large language models to make
• 15:55
  Speaker 3: decisions on, well, how are you sure that there's the
• 15:58
  Speaker 3: right moral foundation for how those decisions are being made?
• 16:02
  Speaker 3: And that's a very clear kind of downfall in one
• 16:05
  Speaker 3: sense of artificial intelligence is it doesn't have that empathetic
• 16:09
  Speaker 3: and moral foundation that us as human beings do. So
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  Speaker 3: those are the two things that I think in the
• 16:15
  Speaker 3: near term, we're going to have to really kind of
• 16:17
  Speaker 3: talk about as an industry and just make sure that
• 16:19
  Speaker 3: we have the right solutions in place and the right
• 16:22
  Speaker 3: infrastructures and guide rails to making sure that everything we
• 16:26
  Speaker 3: do is secure and we're not releasing people's information. And
• 16:30
  Speaker 3: then number two, that we have some sort of ethical
• 16:33
  Speaker 3: and moral foundations so that we can guide these models
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  Speaker 3: and let them know here's what is a clear ethical boundary,
• 16:39
  Speaker 3: or here's a clear moral boundary, and so anything that
• 16:42
  Speaker 3: we do or decisions we make have to be within
• 16:44
  Speaker 3: those within those guidelines.
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  Speaker 2: So that brings the human back into the equation of
• 16:50
  Speaker 2: using the AI tool and then having to make a
• 16:53
  Speaker 2: decision using that moral compass, using that ethical consideration. Is
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  Speaker 2: it possible that we will get to point that we'll
• 17:01
  Speaker 2: be able to program that into the AI models.
• 17:04
  Speaker 3: Yeah, well, I think right now the failsafe for that
• 17:07
  Speaker 3: or the kill switch, if you will, or people like
• 17:09
  Speaker 3: you and me, I think we're probably still a ways
• 17:12
  Speaker 3: away before any organization leaves sole decision making power to
• 17:17
  Speaker 3: an AI model, and I think that's probably a good thing. Today,
• 17:20
  Speaker 3: I think that most people are using these tools like
• 17:24
  Speaker 3: a smith OS to help them augment what they're already
• 17:29
  Speaker 3: doing rather than replace individuals or functions within their organization.
• 17:34
  Speaker 3: So in that scenario, it' would be people like you
• 17:38
  Speaker 3: and myself were sitting down and looking at the data
• 17:40
  Speaker 3: analysis that a particular model has done, for example, and saying, okay,
• 17:45
  Speaker 3: now with that analysis done, here's how I'm going to
• 17:48
  Speaker 3: interpret it, and then here are the decisions that would
• 17:50
  Speaker 3: be made from it. So I think as long as
• 17:52
  Speaker 3: we in the mid to year term, have that human
• 17:56
  Speaker 3: being in place that are making the final decisions, we
• 17:59
  Speaker 3: don't have to work too much about is this model
• 18:02
  Speaker 3: going to run off the rails? Is it going to
• 18:04
  Speaker 3: make a decision and send that decision out to four
• 18:07
  Speaker 3: thousand people before anybody looks at it? You know, it's
• 18:09
  Speaker 3: still it's the onus is still on us to kind
• 18:12
  Speaker 3: of make those decisions.
• 18:14
  Speaker 2: Sure, well, And I think getting back into the data
• 18:19
  Speaker 2: protection and the security that you were talking about as well,
• 18:22
  Speaker 2: when we're talking about these different AI models, you can't
• 18:26
  Speaker 2: just take your customer data and go out to a
• 18:28
  Speaker 2: chat GPT because that's open source. They're using that data
• 18:32
  Speaker 2: not only to generate what it is that you're trying
• 18:35
  Speaker 2: to achieve, but also to teach itself and then they
• 18:39
  Speaker 2: have your data. And so companies like yours that offer
• 18:43
  Speaker 2: these platforms and these models that can do it more.
• 18:47
  Speaker 2: I don't want to say privately or internally, but that
• 18:50
  Speaker 2: really is the case where it's a closed source and
• 18:54
  Speaker 2: you're able to input your data and teach the algorithm
• 18:57
  Speaker 2: what you're trying to achieve with this data in order
• 19:00
  Speaker 2: to have that level of security. But then how are
• 19:04
  Speaker 2: all these models learning from each other? I think that's
• 19:07
  Speaker 2: the question of is one model going to take over
• 19:11
  Speaker 2: the industry where other models are going to lag behind?
• 19:14
  Speaker 2: And what sort of ethical questions does that pose? Is
• 19:17
  Speaker 2: is the information of what is capable still going to
• 19:22
  Speaker 2: be governing all of the possibilities out there?
• 19:26
  Speaker 3: What are your thoughts? Yeah, it's a great question. I'm
• 19:30
  Speaker 3: trying to think if there's any question we could asked more.
• 19:32
  Speaker 3: If this isn't the top question, it's definitely number two
• 19:35
  Speaker 3: is exactly that. So any large corporation, any corporation or
• 19:41
  Speaker 3: industry that has ties to the government, even certain there
• 19:46
  Speaker 3: are so many industries that have regulation around it where
• 19:48
  Speaker 3: they can't deploy into the cloud right, So open source
• 19:52
  Speaker 3: first and foremost is kind of a non starter for
• 19:54
  Speaker 3: a lot of these companies for the reason you mentioned,
• 19:56
  Speaker 3: but also cloud deployment, because in a cloud scenario, you
• 19:59
  Speaker 3: have to take all of your data give it to
• 20:02
  Speaker 3: the cloud that can be accessed maliciously right through means
• 20:06
  Speaker 3: of hacking by accident. Anyone who has access to the
• 20:09
  Speaker 3: cloud can download it locally, So any cloud deployment is
• 20:13
  Speaker 3: an issue. What we've done to try to address that
• 20:16
  Speaker 3: is we've built what's called a local runtime environment. So
• 20:20
  Speaker 3: if you are a large transportation company, you have contracts
• 20:24
  Speaker 3: with the government or multi corporations that have strict security
• 20:28
  Speaker 3: and compliance standards. We have our agent builder, which is
• 20:33
  Speaker 3: where you can bring multiple models together so you're not
• 20:35
  Speaker 3: reliant on anyone. You can connect any of your internal applications.
• 20:40
  Speaker 3: You can build models on open AI which is CHAGBT,
• 20:44
  Speaker 3: on anthropic which is CLAUDE. You can bring them all
• 20:46
  Speaker 3: together for the AI orchestration as it's called. But then
• 20:50
  Speaker 3: you can essentially download those agents and you can run
• 20:54
  Speaker 3: them locally. You can run them on your own servers,
• 20:56
  Speaker 3: on your own AWS servers, on prem as we call it,
• 21:00
  Speaker 3: and in that scenario, you don't have to release any
• 21:02
  Speaker 3: of your private data to the cloud. So you can
• 21:04
  Speaker 3: ensure that it's secure, so you can still use technologies
• 21:08
  Speaker 3: like smith os to build these agents that can be
• 21:11
  Speaker 3: used to optimize your business in many different ways without
• 21:15
  Speaker 3: having to risk the security of your data by deploying
• 21:19
  Speaker 3: it into the cloud by using something like a local
• 21:22
  Speaker 3: runtime environment. So that's one of the ways that we've
• 21:25
  Speaker 3: tried to address it, and I think for now, at
• 21:27
  Speaker 3: least as it stands in a very cloud native world,
• 21:30
  Speaker 3: that's probably one of the first places to start if
• 21:33
  Speaker 3: you're an organization that's concerned about that.
• 21:36
  Speaker 2: Okay, well, and coming back to the question that we
• 21:40
  Speaker 2: talked about a little bit earlier, and that was, you know,
• 21:43
  Speaker 2: these young kids that are questioning whether or not they're
• 21:47
  Speaker 2: going to have a career that they thought they would.
• 21:49
  Speaker 2: What are some new exciting career opportunities that are emerging
• 21:53
  Speaker 2: due to these advancements. Because obviously it's going to create
• 21:56
  Speaker 2: new jobs, it's going to create additional things that humans
• 22:00
  Speaker 2: need to do. What are some of those things that
• 22:03
  Speaker 2: are that are on the horizon for our young talent
• 22:06
  Speaker 2: workforce that's up and coming.
• 22:08
  Speaker 3: Yeah, I think there's two that come to mind. Number
• 22:11
  Speaker 3: one are AI agent engineers. There have been so many
• 22:14
  Speaker 3: studies done by Goldman, Sachs Gartner, pretty much every foundation,
• 22:20
  Speaker 3: business and marketing foundation out there on the number of
• 22:23
  Speaker 3: agents compared to employees over the next ten years, and
• 22:27
  Speaker 3: you'll find anywhere from one to five for every employee,
• 22:31
  Speaker 3: there will be one to five agents who are deployed
• 22:34
  Speaker 3: to help those employees do their jobs. And so obviously
• 22:37
  Speaker 3: somebody needs to be able to build those agents, right,
• 22:40
  Speaker 3: So AI agent engineers will be number one, And again
• 22:44
  Speaker 3: I think it's not as scary as it sounds. Tools
• 22:47
  Speaker 3: like Smith can help you do that without being a developer,
• 22:49
  Speaker 3: without having to learn to code. But number two, and
• 22:53
  Speaker 3: this is I think the really the long term opportunity
• 22:56
  Speaker 3: is what we call MACE and geers or multi agent
• 23:01
  Speaker 3: systems engineers. So as AI sort of gets more into
• 23:06
  Speaker 3: a more mature state, you'll have a lot of different
• 23:11
  Speaker 3: custom agents and applications. You'll have obviously businesses who build
• 23:17
  Speaker 3: the next version of TA GPT where instead of a
• 23:19
  Speaker 3: single model, it's multimodel and these sorts of things. You
• 23:23
  Speaker 3: need to bring those all together, right, So when you
• 23:26
  Speaker 3: have these multiple connection points of different AI tools and
• 23:31
  Speaker 3: different AI systems, that's what we call a multi agent
• 23:34
  Speaker 3: system and artificial intelligence, and that's where systems can speak
• 23:38
  Speaker 3: to one another, learn from one another, they can call
• 23:41
  Speaker 3: upon one another to complete these large and complex tasks,
• 23:44
  Speaker 3: and you need engineers who can put those things together.
• 23:48
  Speaker 3: So those multi agent systems engineers, I think are going
• 23:51
  Speaker 3: to be incredible demand over the next probably two to
• 23:54
  Speaker 3: five years and beyond. And I think those are just
• 23:57
  Speaker 3: two those are just two of the starting points. I know.
• 24:00
  Speaker 2: I think of just let's say, two years ago, a
• 24:04
  Speaker 2: system's analyst or a data analysis A data analyst was
• 24:08
  Speaker 2: such a small thing and then it got this huge boom,
• 24:10
  Speaker 2: and now we're replacing it with an AI that can
• 24:14
  Speaker 2: do the same thing, that can analyze that data. So
• 24:17
  Speaker 2: I believe that there's going to be a number of
• 24:19
  Speaker 2: different iterations of professions or job titles or whatever you
• 24:23
  Speaker 2: want to call it, that skills that people need to
• 24:26
  Speaker 2: learn and jobs that need to be filled. That as
• 24:29
  Speaker 2: we grow and as we learn how to use these
• 24:32
  Speaker 2: tools most effectively in our society, these jobs are going
• 24:36
  Speaker 2: to evolve very very quickly. And so I think the
• 24:40
  Speaker 2: thing to tell our students right now, and the thing
• 24:42
  Speaker 2: to tell that up and coming workforce is be flexible,
• 24:46
  Speaker 2: be willing to learn, be willing to challenge yourself, and
• 24:50
  Speaker 2: be willing to try and work with these tools because
• 24:53
  Speaker 2: it's going to be exciting. You're getting in on the
• 24:54
  Speaker 2: ground floor of a new technology. It's kind of like
• 24:57
  Speaker 2: the tech boom back in the nineties, and this is
• 25:00
  Speaker 2: your chance to do it. This is your generation's opportunity.
• 25:04
  Speaker 3: So what are some.
• 25:06
  Speaker 2: Maybe success stories or epic fails, either are excellent stories.
• 25:11
  Speaker 2: But what's something that you can tell us about a
• 25:14
  Speaker 2: client that you've worked with or a deployment that you've
• 25:18
  Speaker 2: done that was able to make a huge difference.
• 25:21
  Speaker 3: Yeah, absolutely, there's On a personal level, I have probably
• 25:25
  Speaker 3: a thousand fail stories that I could share while I
• 25:28
  Speaker 3: was trying to learn how to how to really become
• 25:31
  Speaker 3: more of an AIG engineer myself. But on the success side,
• 25:36
  Speaker 3: So we work with a client called Halo. This is
• 25:39
  Speaker 3: Halo Dog Callers, a company started by Caesar Malon who's
• 25:44
  Speaker 3: the dog whisperer, and a Stanford grad named Michael Ahrman,
• 25:49
  Speaker 3: and they have utilized artificial intelligence to connect a lot
• 25:55
  Speaker 3: of their kind of inventory management with their product and
• 25:59
  Speaker 3: ordering and then their customer service. And the way we've
• 26:02
  Speaker 3: deployed that early on is through a chat agent, so
• 26:06
  Speaker 3: you can come to the website, you can ask questions
• 26:08
  Speaker 3: about your order, questions about delivery time, updates, connect with
• 26:13
  Speaker 3: customer service, sort of do all of that in real
• 26:16
  Speaker 3: time with AI without having to wait on the phone
• 26:20
  Speaker 3: for customer service representative or wait while they're searching for
• 26:23
  Speaker 3: more information. And one of the reasons that's so important
• 26:26
  Speaker 3: is in the customer service world, you want to try
• 26:29
  Speaker 3: to de escalate those types of concerns as much as possible,
• 26:33
  Speaker 3: right and the last thing you want when you're kind
• 26:35
  Speaker 3: of frustrated with an order or trying to speak to
• 26:38
  Speaker 3: customer service is to be transferred from person to person
• 26:41
  Speaker 3: or department to department, or not get the answers you're
• 26:43
  Speaker 3: looking for. This is an area where AI is just
• 26:47
  Speaker 3: such a great place to start today because the technology
• 26:51
  Speaker 3: is already there, the use case has been proven, and
• 26:54
  Speaker 3: it works really well. So I'd say that's probably one
• 26:57
  Speaker 3: of our earlier success stories. But we've you know, we've
• 27:01
  Speaker 3: seen it across so many different industries that it's pretty
• 27:04
  Speaker 3: agnostic to industry.
• 27:05
  Speaker 2: And I will ask this question just for a personal
• 27:08
  Speaker 2: because I use the open source GPTs. We're testing some
• 27:12
  Speaker 2: different things internally with some internal platforms at work, but
• 27:17
  Speaker 2: even just for fun to use AI or chat GPT
• 27:21
  Speaker 2: or Gemini or Claude or whichever one. What have you
• 27:24
  Speaker 2: done for fun with AI, just to see what it
• 27:27
  Speaker 2: can do?
• 27:28
  Speaker 3: Yeah, I'll uh, my wife will probably see this social
• 27:32
  Speaker 3: she'll know my secret here soon. But date night ideas
• 27:36
  Speaker 3: is a great one, right, Okay, Like just in a
• 27:39
  Speaker 3: personal note, So when you are looking for a new
• 27:43
  Speaker 3: restaurant in town, for example, but with a little bit
• 27:45
  Speaker 3: different kind of experience, or looking to do something different
• 27:49
  Speaker 3: in the city you've lived in for a while that
• 27:51
  Speaker 3: you maybe haven't had a chance to try before, that's
• 27:54
  Speaker 3: a really great way to use something like chat GPT.
• 27:57
  Speaker 3: In a more boring sense, I use it for organizing
• 28:01
  Speaker 3: almost everything that I do. I have found that these
• 28:05
  Speaker 3: large language models are really great at going from zero
• 28:09
  Speaker 3: to one in a creative sense. So if I'm staring
• 28:12
  Speaker 3: at a blank screen and I'm trying to write, let's say,
• 28:15
  Speaker 3: some website copy or some emails or something like that,
• 28:19
  Speaker 3: it can be so hard to go from a blank
• 28:22
  Speaker 3: page to even the first five or ten words. But
• 28:25
  Speaker 3: if I go into a chat GP or my favorite
• 28:28
  Speaker 3: which is anthropics, claud I can go from zero to
• 28:31
  Speaker 3: one very quickly, and it creates the spark, It gets
• 28:35
  Speaker 3: things moving. It allows you to kind of get your
• 28:38
  Speaker 3: creative process underway, and to me, I have a ton
• 28:41
  Speaker 3: of fun like that. So both on the personal and
• 28:43
  Speaker 3: professional sides, those are two ways that I use it
• 28:46
  Speaker 3: that maybe could be could be helpful with other people
• 28:48
  Speaker 3: out there. I know.
• 28:50
  Speaker 2: I use it for the same reason to generate some idea,
• 28:53
  Speaker 2: to get those creative juices flowing. I mean, in marketing,
• 28:57
  Speaker 2: we have to generate a lot of content and some
• 29:00
  Speaker 2: times it feels like you're writing the same thing over
• 29:02
  Speaker 2: and over and over and over, and you're like, Okay,
• 29:05
  Speaker 2: try and do this in a new tone. Read this
• 29:07
  Speaker 2: back to me, or give me this prompt in someone
• 29:11
  Speaker 2: else's voice, pretend like your Sylvester Stallone, or pretend like
• 29:15
  Speaker 2: you're Tom Cruise, or pretend like you're whomever, and give
• 29:19
  Speaker 2: me this back in their tone of voice from this
• 29:22
  Speaker 2: character that they played, or one thing that I've done
• 29:25
  Speaker 2: that is not in AI. But I've used it to
• 29:30
  Speaker 2: kind of prompt myself to try something different and look
• 29:33
  Speaker 2: at something from a different angle. I'm a huge Harry
• 29:35
  Speaker 2: Potter fan, I've read all the books, watched all the movies.
• 29:38
  Speaker 2: I love Harry Potter, but in thinking about it one time,
• 29:41
  Speaker 2: I realized, you know that these books took place in
• 29:44
  Speaker 2: the nineties, and these kids grew up at the same
• 29:47
  Speaker 2: time that I grew up and was their age. Another
• 29:49
  Speaker 2: show popular in the nineties Saved by the Bell. So
• 29:53
  Speaker 2: one time, when reading through the series again, I pictured
• 29:56
  Speaker 2: all of the cast of Harry Potter as the cast
• 29:58
  Speaker 2: of Saved by the Bell in that tone of voice,
• 30:02
  Speaker 2: that idea, that feeling of that high school situation as
• 30:06
  Speaker 2: opposed to the high school at Hogwarts. So I mean
• 30:09
  Speaker 2: those types of things where you just reimagine it in
• 30:12
  Speaker 2: a different perspective, a slightly different twist to it, and
• 30:16
  Speaker 2: that's where you get those creative juices flowing. And no,
• 30:19
  Speaker 2: that is not going to help me in any way
• 30:22
  Speaker 2: sense or form professionally, except that that's going to get
• 30:26
  Speaker 2: my own creative juices flowing. That's going to get me
• 30:30
  Speaker 2: thinking and looking at a problem from a different angle.
• 30:32
  Speaker 2: And I think that's one of the great things that
• 30:35
  Speaker 2: some of these content generators, even the open source ones,
• 30:38
  Speaker 2: are able to do without with very little risk. I mean,
• 30:41
  Speaker 2: who's going to care. If I decide to have this
• 30:44
  Speaker 2: recipe read back to me in the tone of some
• 30:48
  Speaker 2: other celebrity or influencer, it's going to give me a laugh,
• 30:52
  Speaker 2: it's going to give me an idea.
• 30:54
  Speaker 3: Yeah. Absolutely. I do something very similar with the great
• 30:58
  Speaker 3: Michael Scott, the World's best boss from the office. So
• 31:01
  Speaker 3: if I'm having a little bit of a routine or
• 31:03
  Speaker 3: mundane day, you know, I'll put something in there with
• 31:06
  Speaker 3: the little character I've created in chat GPT for Michael
• 31:09
  Speaker 3: Scott and have it tell me a joke, right, a
• 31:11
  Speaker 3: little pick me up for the day. But I think
• 31:14
  Speaker 3: that's the humanizing and really the beautiful side of artificial intelligence.
• 31:20
  Speaker 3: And then I hope that more and more people kind
• 31:22
  Speaker 3: of look at it that way and see it that way,
• 31:24
  Speaker 3: which is it's probably the most or one of the
• 31:27
  Speaker 3: most useful things or advancements we've seen in a really
• 31:31
  Speaker 3: really long time my lifetime, almost certainly. But it can
• 31:34
  Speaker 3: also be a beautiful thing to help you discover and explore,
• 31:38
  Speaker 3: help you cheer you up if you're in a dowed mood,
• 31:41
  Speaker 3: which can be a tremendous benefit both personally and professionally.
• 31:45
  Speaker 3: So I think us just getting more familiar with these
• 31:48
  Speaker 3: tools and being more comfortable using them can do a lot.
• 31:51
  Speaker 3: It can help produce the anxieties, it can improve life
• 31:54
  Speaker 3: and even the smallest amounts and so many different ways.
• 31:58
  Speaker 3: And the more we all electively kind of raise our
• 32:02
  Speaker 3: knowledge and understanding and close the knowledge gap between where
• 32:06
  Speaker 3: we are now and where AI is of let's say
• 32:09
  Speaker 3: next month, the better we all will be for it right,
• 32:12
  Speaker 3: and we can adopt it quicker, We can have better
• 32:15
  Speaker 3: conversations around them, We can discuss the ethical and moral
• 32:19
  Speaker 3: dilemmas around them, because people really have a better understanding
• 32:23
  Speaker 3: and intellect around what AI really is. And it's no
• 32:26
  Speaker 3: longer I fear it because I don't understand it or
• 32:30
  Speaker 3: don't know it sort of thing. So yeah, I love
• 32:33
  Speaker 3: that example.
• 32:35
  Speaker 2: Well, and I think you bring up an excellent point
• 32:38
  Speaker 2: again with the ethics and the morality is when we're
• 32:41
  Speaker 2: doing these things and being creative and stepping outside the
• 32:44
  Speaker 2: box and getting it to pretend to be someone it's not,
• 32:48
  Speaker 2: then there is that human element, back to the beginning
• 32:52
  Speaker 2: of our conversation, where there has to be a human
• 32:54
  Speaker 2: at the end of this reviewing whatever this output is,
• 32:58
  Speaker 2: understanding what it is that it's trying to tell you,
• 33:00
  Speaker 2: because you can't just go in and create content in
• 33:03
  Speaker 2: the voice of someone else and pretend to be that
• 33:05
  Speaker 2: other person and plagiarize in that way or impersonate someone else,
• 33:10
  Speaker 2: because there's first of all, that's wrong. It's morally wrong.
• 33:14
  Speaker 2: We understand that, but it also has the downside of
• 33:19
  Speaker 2: not actually being that intelligent. It has limitations, and it
• 33:24
  Speaker 2: can do things that it knows based on what's out there,
• 33:27
  Speaker 2: what's open source information, but it's not going to have
• 33:30
  Speaker 2: that decision making capability. It's not necessarily going to have
• 33:34
  Speaker 2: the same even vocabulary as some of those individuals. I
• 33:38
  Speaker 2: remember seeing a clip recently of Neil degrast Tyson and
• 33:41
  Speaker 2: he was saying this AI had generated something that he
• 33:45
  Speaker 2: might have said, but it used different vocabulary. So he
• 33:47
  Speaker 2: realized very quickly that it wasn't actually a quote that
• 33:50
  Speaker 2: he said, because it's not the way he would have
• 33:52
  Speaker 2: said it. And I think that brings us back to
• 33:55
  Speaker 2: there always has to be a human sitting behind the
• 33:58
  Speaker 2: desk making the fine decision based on whatever the AI does,
• 34:02
  Speaker 2: because we can't let it just go out and do things.
• 34:05
  Speaker 2: It can do a lot of the thinking, it can
• 34:07
  Speaker 2: do a lot of the manipulation of the information, but
• 34:11
  Speaker 2: it can't be the final say, it can't be the
• 34:14
  Speaker 2: final decision.
• 34:15
  Speaker 3: Yeah, And there's real world examples of where this is
• 34:19
  Speaker 3: incredibly important, like Tesla famously had and every self driving
• 34:24
  Speaker 3: car autonomous vehicles had to sit down and consider this right,
• 34:28
  Speaker 3: which is like if your cars driving straight and it's
• 34:31
  Speaker 3: about to hit two pedestrians and the only option is
• 34:34
  Speaker 3: to turn right and hit one pedestrian, and those are
• 34:36
  Speaker 3: the only options, Like what should the car be programmed
• 34:38
  Speaker 3: to do? And consider right? And the world's best minds
• 34:42
  Speaker 3: and ethics and morality and they all came together and
• 34:45
  Speaker 3: had to discuss these things because ultimately we're the ones,
• 34:49
  Speaker 3: at least for now, behind the keys when it comes
• 34:51
  Speaker 3: to this stuff, right, And we have to tell these models,
• 34:56
  Speaker 3: and we have to tell them here's what to consider,
• 34:58
  Speaker 3: Here's how much weight to give to consider A versus B.
• 35:01
  Speaker 3: So you're absolutely right, at least for the very foreseeable future,
• 35:05
  Speaker 3: we're the ones making the decisions on these things. But again,
• 35:09
  Speaker 3: I think it comes back to the why it's just
• 35:12
  Speaker 3: so important for everyone to dive in and start getting familiar,
• 35:17
  Speaker 3: even if you only use it in your personal life
• 35:19
  Speaker 3: or you only use it in your professional life, because eventually,
• 35:22
  Speaker 3: these are things that are going to affect every single
• 35:24
  Speaker 3: one of us, Right is how AI is used, whether
• 35:27
  Speaker 3: it's for surveillance and route planning, and you know, all
• 35:31
  Speaker 3: sorts of different things. And the more knowledgeable we all
• 35:35
  Speaker 3: are about it, then the better conversations that we can have,
• 35:38
  Speaker 3: and then hopefully, ultimately, the better decisions that we can
• 35:41
  Speaker 3: make as a group and as a society.
• 35:44
  Speaker 2: I think you brought up an excellent use case of
• 35:46
  Speaker 2: the chatbot on a website when you go and you
• 35:49
  Speaker 2: have a question for a company or something that you've done.
• 35:53
  Speaker 2: How many of us don't realize that those chatbots have
• 35:55
  Speaker 2: been run by AI for so long behind the scenes,
• 36:00
  Speaker 2: some person sitting there typing back and forth with you.
• 36:03
  Speaker 2: In most cases, now there are some that do, but
• 36:06
  Speaker 2: in most cases those chatbots where you're just asking a question,
• 36:10
  Speaker 2: are delivering information based on a database of information that
• 36:13
  Speaker 2: they have about their company. And so we've been interacting
• 36:16
  Speaker 2: with these this whole time without even really thinking about
• 36:19
  Speaker 2: it or realizing it. And the more we pay attention
• 36:23
  Speaker 2: to it, the more we'll understand its capabilities and its limitations.
• 36:28
  Speaker 3: That's exactly right, and even before that you call your
• 36:32
  Speaker 3: bank and all of a sudden you have automated menus, right,
• 36:34
  Speaker 3: press one for this and two for this and say
• 36:37
  Speaker 3: a word. And so this is technology. I think a
• 36:39
  Speaker 3: lot of people are at least in some way, shape
• 36:44
  Speaker 3: or form familiar with. But when it comes down to
• 36:47
  Speaker 3: even the organizational level, I mean, if you think of
• 36:50
  Speaker 3: you know, my grandfather was a long haul truck driver, right,
• 36:53
  Speaker 3: so I could only imagine if he had the ability
• 36:56
  Speaker 3: for enhanced route optimization. Right. Even another use case that
• 37:02
  Speaker 3: we've looked at with I'll just say a government entity
• 37:05
  Speaker 3: is predictive maintenance, right to make sure that you ensure
• 37:08
  Speaker 3: the least amount of downtimes for delivery vehicles and things
• 37:12
  Speaker 3: like that. Like, there's so many real world applications that
• 37:15
  Speaker 3: are there today and that can be used today that
• 37:18
  Speaker 3: it's really just the literacy thing. It's an AI literacy.
• 37:21
  Speaker 3: If we can make sure that our organizational leaders have
• 37:26
  Speaker 3: as much information and knowledge on this as possible, and
• 37:29
  Speaker 3: then the individuals for using them have the same thing,
• 37:32
  Speaker 3: then we can all move forward in the right direction.
• 37:35
  Speaker 2: All right, Well, I think that brings us to the
• 37:38
  Speaker 2: end of this episode. Thank you so much, Michael for
• 37:41
  Speaker 2: joining us today and talking a little bit more about
• 37:44
  Speaker 2: AI and where it's going to take our talent workforce
• 37:47
  Speaker 2: in the future. For our guests out there or for
• 37:50
  Speaker 2: our listeners out there, if you have a suggestion for
• 37:53
  Speaker 2: a topic or would like to be a guest on
• 37:54
  Speaker 2: the show, we'd love to hear from you. You can
• 37:57
  Speaker 2: contact me at podcasts at mebock at any time. As always,
• 38:01
  Speaker 2: thank you for listening to Speaking of Supply Chain. If
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