AI and Automation's Evolution

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This is Beyond Confidence with your host w park. Do
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This is beyond confidence and now here's your host div Park.
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Good morning listeners. So it's Tuesday and a fun day
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to be with you. So recently, it was very interesting.
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I was talking to one of my clients and she
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was very very upset at her boss, and so as
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we were kind of going through our coaching session, and
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I invited her to think of her boss as a
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five year old child, because she was not in a
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position to leave just immediately. And I shared with her that, like,
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you don't think about it, think of your boss as
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a five year old, and when you see him as
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a five year old, and then let's say, you know
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he's throwing ten and how do you feel about that?
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And she said that, surprisingly, I can be kind. And
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so here's the thing. Kindness is not just something that
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you do it in action. It's also how you see
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other people and how you think about them, so not
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knowing what may be going on in their lives, because
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think about it, when you are giving away the space
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in your head, you're actually giving away that primary real
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estate which is so precious. So it's not about going
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and allowing people to be mean to you, it's just
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about how you are reacting to it. So this way
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you can be kind to yourself, you can kind to
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be others. And if you're ready to step up your game,
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do get our books. And here's what I can share
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with you that it's trueways. Not only you step up
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your game, not only you help us reread the message,
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but we also help Kiva entrepreneurs by donating partial proceeds
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from our book sales. So let's bring in on our
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guests to welcome Craig.
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Well, thank you, Diva, it's great to be here. Thank
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you for having me.
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Absolutely so great. Share with us. If you're a call
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a moment or a person who left a positive mark
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on you in your childhood, are you well?
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In my in my youth, it was my probably my
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uncle Ted. He was my father passed away when I
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was quite young, and Ted kind of became my mentor
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in life, and he was just always doing projects and
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he was very optimistic. He emphasized both hands on ability
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to do things but also mental abilities to do things.
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And you know, he really was a very important influence
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in my life.
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Yeah, no, definitely, it's so important to have those mentors
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in life, because usually for kids, they emulate adults and
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tell me like, did you have any specific interest in
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your youth?
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Uh? So I I liked basketball quite a bit. I
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played all through high school, and I had some scholarships
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to Division three. Uh, you know basketball, so you know,
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colleges and so forth. I ended up going to Georgetown,
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and unfortunately when I got there, they had upgraded the
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program to Division one with Patrick Ewing and really fabulous players.
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So I never actually got to play in college. But
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I've always loved sports.
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Uh.
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And uh, you know, I've always liked I've always liked writing.
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That's something I've always I've always done. You know, one
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thing that Uncle Ted did is he discouraged me from
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going into journalism. So that's not a good field, and
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so I kind of listened to them, and you know,
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I came back to it. After working in the software
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industry for quite a bit of time, I found I was,
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you know, I wasn't really that happy, you know, being
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kind of the entrepreneur and the business guy. So I
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became a what's called an industry analyst, which is sort
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of a journalist, but it's a journalist that only covers
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the technology industry, and I've been doing that for eighteen
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years and really the second part of my career has
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been the happiest because I finally found you know, the
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area that you know, really you know, gave me passion
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in my work life.
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Yeah, so tell us what led you to the software.
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Well, I was originally an economist, and I was working
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on government contracts in Washington, d C. Where I stayed
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after college and graduate school, and I just you know,
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I was working for a big company and all of
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a sudden, there were no economic projects, and so they
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put me on a technology project to uh try to
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forecast the next generation of data communications. And I was
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looking at it and yeah, it's kind of an interesting story.
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I was like very junior. I knew nothing about technology,
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but I was with in an old key punch room
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with all these sort of revolving door consultants that had
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filtered in from other from their from their federal work,
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and uh, no one knew what to do, and I
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started just writing. All of a sudden, I was kind
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of leading the project and I was as I was
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looking at the data communications world. And that was early
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we're talking, you know, ancient history now, uh, you know,
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it just struck me that boy, there's something here, this
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might be a good place to learn about. And then
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I just became more and more technical as uh, you know,
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as I progressed. Yeah.
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Absolutely, So did you just kind of stay in the
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federal area or did you also work in the private industry? No.
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I I grew weary of the federal contracting area because
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I just felt you were never really making any any
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really strong progress. You know. So, as many things happened,
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I met a girl at a bar, I went up
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to Boston, where I live now, and I started working
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in a private sector at technology companies, which I did
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for a number of years. Yeah.
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Yeah, So you mentioned that you know about federal contract
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that it didn't go anywhere. So what would you say
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was the primary difference between the two Because in a
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lot of people are our listeners are thinking, you know,
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what area to go for, like especially the youngsters, they're
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deciding and they may have different options. So if many
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were to choose, what would you say at the pros
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and cons of both industry?
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Well, so, you know, you see what's happening right now, right,
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you know. So when I was working out for this
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company called Miner Corporation, which is a nonprofit think tank,
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and it did but basically it kind of tried to
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keep the contractors honest and and sort of protecting the
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federal government, uh in a lot of ways. But it
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was a fascinating place. But I wanted to work on
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public policy, you know, and you had to shift in administrations, uh,
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with with Reagan coming in all of a sudden, it
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was all defense. So you have these shifts that go
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with the political wins. That makes it adds a variability
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to your you know, to your job prospects. Uh. And
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it's still more dramaticly evident than what's been happening the
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last few weeks, right uh with with you know, the
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organization coming in and you know, a new leadership coming
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in and saying, you know what, we don't need all
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and we don't need all these federal jobs. Uh. And
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you know, this is a lot of great work that's
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been done for years by by a lot of people.
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So we'll see how that that shakes out. So that's
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kind of the downside, you know, on the on the
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private sector side, it's not you know, a picnic there either,
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because you have much less loyalty from companies to employees,
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and I write about that in my book Random Acts
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that you know you no longer can you count on.
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And there's so many mergers and acquisitions, you know, there's
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so much globalization. Still, you have technology and automation coming in,
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which is a a big piece of what I write
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about that are you know, going to affect and shift
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jobs around pretty dramatically. So there's really no you know,
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perfect place. But I would say that, you know, the
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private sector does give you ultimately more flexibility in terms
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of job repositioning than the federal government.
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Mm hmm. And definitely that makes sense. So as you
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continued in your career, you mentioned that, you know, you
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became a broadcaster, you became a technical person analyzing trends
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and predicting the wins of tomorrow. So if you can
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give are so many times what happens is that people think, like, oh,
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open AI is here, like l l MS, which is
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the large is it like large uge models?
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Yes, and now they're talking about small language models.
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Yeah, learnning language models are here, and then there's you know,
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all the processing and all of that. And yet if
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we kind of take a look at it, that automation
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and AI has been part of our lives for a
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long time. So if you can just kind of share
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with us the transition, that'll be great for those who
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don't know.
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Yeah, I mean we're not we're not following it narrow a,
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which is what you're referring to. You know, we've had
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machine learning that's been doing predictive analytics for a long time,
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you know, twenty years probably in various applications, but this
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generative AI large language models is very much more powerful.
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So you know, basically by training these models on the
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entire Internet and all that activity, you're able to get
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very very close to what I call in the book
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human capable automation or HCA. So you're starting to see
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a AI able to make the same kind of connections
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that a human makes. You know, think of it this way.
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You know you probably don't read your insurance policies very much. Yeah,
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I certainly don't. You know, usually there's like two pages
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of things they cover in one hundred pages of things
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they don't right, written by lawyers for other lawyers to read.
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So these models can actually allow you to have a
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conversation with a set of data like that, So you
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can actually have an AI interface to that and and
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you know ask so, you know, how long have I
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been covered? How much am I paying for this policy?
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Well?
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My errors actually get you know, it's a life insurance.
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So there's this whole new yeah, you know power that
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that comes from this ability to uh to make connections
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in a way that that human that that that humans do. Uh.
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And you know we're at the cusp of that now,
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in the early part of that. And these models are
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getting better and stronger and more capable all the time.
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So at some point, you know you're going to have
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what was called a g I artificial general intelligence, which
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is you know, uh, that's that's probably some people say
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at Forrester By the way, I work at Forrester Research,
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which is a big research company. You know, we're predicting
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that's going to know two three years away, we'll have
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a g I in the workplace. So this is a
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fun fundamentally different from that narrow AI that we've been
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using for quite a bit of time.
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Right And as we are talking about a g I,
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I know, I'm jumping into something more complex and will
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kind of come back to the original one. Now, there
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are so many talks about having the retrieval augmented generation,
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and Intel is already pretty big into implementing and a
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lot of other companies are. So can you tell for
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our listeners, you know, just the basic difference in machine learning,
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data sciences AGI and the ritual augmented generation from their
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perspective as to how is this going to impact them?
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Yeah, and that's exactly what you know. This this book
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is attempting to do. So I explain, I explain the
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relevant technology, but I do it in the context of
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four different distinct worker categories. So what's relevant for your
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audience is is not in general what the technology does,
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but how would affect the job role that they're in.
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You know, So if they're a human touch worker, you know,
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which might be you know, helping in a healthcare environment
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or even working in a warehouse, it's a very different
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set of technology that that's you know, real robotics that's
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going to affect those jobs. And I think very positively
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you're going to have you know, android robotics, You're going
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to have janitorial robotic robotics. You're going to have a
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lot of uh, physical automation that's going to make their
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jobs a lot safer and a lot less dirty and
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a lot better and there's enough you know, a need
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for human agility that uh, you'll see those jobs actually
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get paid better and be and be safer. So that's
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very very positive. If you're in the middle, you know,
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there there you have more problems because the type of
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lower level cognitive work that's done by middle jobs is
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perfect for software robots, you know. And and one of
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the my book before this was called Invisible Robots. In
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the Quiet of the Night, I was just writing about
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these RPA bots that were operating in the cloud that
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were able to come in and take over a desktop
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and do what Harry was doing for eight minutes, you know.
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And if you had one hundred Harries doing the same
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eight minute task, you know, maybe a thousand times a month,
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you could build a bot that would actually take those
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hours out. And now these are visual robots when you
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add gen ai to them, are able to even do more.
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So the middle is going to get, you know, hollowed
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out much more. And of course it's suffered from globalization
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over time, and it's already suffered from automation. So the
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path for the middle is different. And that's why I've
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separated into these four categories. The path for the middle
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is to become more of an AI operator and how
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do you use AI actually start doing jobs for those
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that have higher levels of credentials, more professional credentials, because
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AI really augments what you can do and allow you
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to be the parallee who can now do much more
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of what a lawyer does. We've already seen that with
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CNAs and nurse practitioners that are using that will use
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automation to make more planning and basically be more capable,
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so they'll get more and more value going. So there's
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a positive path for the middle as well, but it's
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the numbers don't work out as well as they do
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for the human touchworkers and of course the digital elite,
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which are the top category. That's probably everybody on your
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on your ball here. You know, actually you have a
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very good, uh diverse demographic for your shows, which is
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I applaud the digital league are going to suffer a
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lot from gen AI doing more of those human connections
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that that that's part of their their their job. So
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data science you mentioned, you know that applies to all
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of this. You know, it's all about the data that
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you expose to the models, and you mentioned rag you know,
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RAG is the sort of retrieval method to expose the
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right data. So think of this way. This you know,
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this amazing foundational model that's trained on the entire Internet, right,
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but you to make it work for companies, it's got
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to go and get all the enterprise data that the
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company has that's within their firewall or on their private cloud.
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And that's what RAG is doing. You know, RAG is
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going in saying, you know what, I'm going to retrieve
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this PDFs that's in this repository that are your standard
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operating procedures. Yeah, and I'm going to retrieve that and
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augment it somehow a lot of fancy terms like vector
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databases and so forth, but basically, I'm going to prepare
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it for the MOOB. So now it knows your specific
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enterprise context and it can create the work patterns that
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are needed to get something done. You know, and that's
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where the you know, the genius of all this is that,
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you know, all the automation we have today. You know,
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someone some you know, big integrator or software developer had
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to write very specific, prescriptive, step by step automation to
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get something done. And now you just feed this information
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into a model, and the model figures out the steps
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that are needed. So it's going to be very disruptive
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to the whole software industry because a lot of the
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business logic that's in those rule based systems will be displaced.
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Buy these what are called AI agents or AI models
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that are going to take that away. Is that too
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much detail for your own answer? No?
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No, no, It's like I have been playing in this
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area for quite some time. So it's important for people
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to understand that we have had these systems and how
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as you mentioned that it is getting ahead of it.
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It is about developing those metacognitive skills to training AI
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not to think for you, but to think with you.
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So this way what happened because it cannot read your mind,
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and EI is as good as the context and the
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role and the input that you're providing.
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So exactly there is.
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A whole and the key is like you know, dispursing
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the fear that people are feeling. So people are you know,
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there are a lot of professionals, like even software engineers who
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say like, oh, you know, there is a no code
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like you know, especially Claude Anthropic. It is really good
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for code generation and you can kind of drop it
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in there and it will generate the whole code for you.
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So what would be your message to them, especially with
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the mindset, because what happens is human beings get in
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their own way.
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Mm. Yeah, So that's an interesting one because personally, I
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think that the you know, uh, you know, it used
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to be, you know, five seven, eight years ago the
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advice to you know, people coming out of high school
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or even college was learned to code, you know, and
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now and now clearly that's not the right path. But
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I think that the idea that what's not as well understood,
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I think is that the actual coding part of a
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so for development is only about thirty percent of it.
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So most of the time that a software developer spends
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is trying to understand what the heck the business wants
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or the requirements, you know, understanding the testing patterns, you know,
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trying to break down a process, you know, decompose it
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and figure out what should be automated. Those are all
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things that are done in that what we call the SDLC,
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the software development life cycle. So you are going to
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have a strong augmentation for that thirty percent, but it's
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not going to completely disrupt or eliminate any of those jobs.
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You know, my my advice to the you know, you know,
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we we we use this term human in the loop, uh,
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you know quite a bit. And you know, the developer
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will have a model in the loop you know, to
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help them, and yeah, they'll spend a lot less time
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figuring out you know what, what little specific things that
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I do that make a problem. You know, they'll be
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able to have, you know, a a wireframe of the
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software you built by the model and then they'll take
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it from there. So it's going to reduce you know,
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a lot of their hours. But I think what they
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have to do is is become stronger on the process
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understanding side, on that decomposition side, on how to curate
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the data properly. What data is important? To your point,
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it's it's about the data on the way in there's
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going to be there's so much automation that's being planned.
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That's really why I wrote the book. I mean, I'm
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in the techno. Uh you know, I'm talking to business
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leaders that are just frantic to apply AI to everything.
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I'm talking to the tech vendors you know, who just
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can't wait, you know, to you know, there are four
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hundred startups in just in building AI agents, which are
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essentially digital workers.
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You know, you're absolutely right, And if you have anybody
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has had a chance to play with cha GPPRO, it
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will go like and they've just recently released the operators
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and they will go inside your computer. They'll take over
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your computer, book appointments for your book, your travels for you.
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So it's already here and the key is, like, you know,
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I want to come back to what you said is
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knowing the process, and the key to know is that
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human intervention is going to be there. It's going to
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be doing your grunt work. So it's the whole idea
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is even for the companies, is to take care of
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that repetitive work, that grunt and the grind for the
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people and allowing them to think, opening up the room
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for more curiosity and critical thinking. And then that happens
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when we create that intersection where humans are doing the thinking. Yes,
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I know, CHGBT pro and now deep seek can do
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connect the dots and all that. And yet that human
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experience that you're bringing is going to be more and
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more valuable based on what you're saying. So am I
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hearing it right?
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Yeah? No, you're you're hearing it right. I'd probably make
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a few a few edits to that, but I think
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it's right. I think there's going to be much greater
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premium on the human element or the accent. I think
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for a lot of companies, I like to say, the
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you know, the the margin is and the memories. You know,
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if you can create a human experience as part of
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your customer experience, that's going to be more more memorable.
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Uh.
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And you know, right now I have a chapter on
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on called, uh, you know, self service Hell, because we've
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we've really gone almost too far and forcing people to
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become you know, unpaid employees on the first day of
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the job and doing self service for you know, for
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various retail operations and others, and you know, we're we're
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about reaching that tipping point where clickage and where you know,
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people are just getting really frustrated. Spain has a uh
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now finds companies if they don't get a human on
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the phone for a certain period of time. So I
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think there's going to be a smart companies will use
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humans in in in more innovative ways. The one thing
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I would headed to what you said, though, is, you know,
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one of the terms that has seeped into the tech
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industry and in the you know, in general faster than
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any word I've ever seen is agentic agentic AI. Uh
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And and this is basically, you know, the idea that
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you have the model and full control of the process
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and and and implementing and making various agents that are
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built to do various things coordinate in kind of an
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any flow kind of way. Uh And right now there's
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no truly agentic applications that that we can find. I
425
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have a report coming out called, you know, the gentic
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agents are aware siting, but we're we're close to that.
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The frameworks, some of them that you've mentioned that are
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coming into play, the AGENTA AAI frameworks are going to
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allow us to build very very complex systems of that
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app that are self optimizing, self adapting, that are a
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complete control of a process. And for the business to
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make that investment, they're going to need to replace a human. Yeah.
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In other words, a lot of these copilots that you
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hear about. Agent Force was on you know from Salesforce
435
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was a Super Bowl commercial, right, you know, they're not
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making any money for any of those companies. They're not
437
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being monetized because putting a model in front of you,
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Diva and making you a little smarter is you know,
439
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you're you're probably not going to pay three hundred dollars
440
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a month for that, right, But when you add when
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when when you add a strong action component to it,
442
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where you have this sort of multi flow or any
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flow coordination of agents, you're starting to companies will pay
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three thousand dollars a month if they can replace Harry
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And that's what they're that's what they're doing. So while
446
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a lot of the narrative of the tech industry, I
447
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talked to tech beners all the time, I say, oh, well,
448
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there's going to be more there's going to be more
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interesting people to do. They're going to be more strategic
450
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and more planning. The reality is the numbers just don't work.
451
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They just are not enough of those kind of jobs
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to really all set. The numbers will be taken away
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by AI doing a lot of these things.
454
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So we've touched upon a lot of different points, and
455
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so what would be the best path forward for our audience.
456
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Well, you know, I think if you're on your daughter
457
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is in the basement playing video games for six months
458
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or so, I think you go out and buy my
459
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book MAXI of Automation, Say go get a job. I
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really do think that, you know, there has to be
461
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a better understanding of the automation trends that are occurring
462
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and how they affect the job that you're in. And
463
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you know, I interviewed you know, one hundred uh and
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I you know, probably have written out forty different workers
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and I listened to their stories, you know, I have,
466
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you know, and so the machinist who was a top
467
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performer for twenty years, and you know, they upgraded his machine,
468
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you know, so that now instead of doing the machine
469
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operation which he was top performer, he had to enter
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you know, G codes you know, into the machine. Basically
471
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the setup had gotten much more complicated. And he went
472
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was boss and said, you know, I just can't do
473
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this anymore, you know, you know, can I just run
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the machine and somebody else can? You know, and some
475
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you know, a young guy tried to help him train him.
476
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You know, you talk to you know, I talked to
477
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this woman named Julie in a big call center and
478
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she's having to train her software robots really to do
479
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all of her updates and all her all her work.
480
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You can kind of see, you know, to be genius
481
00:28:15.359 --> 00:28:17.920
to see where this is going. So for each of
482
00:28:17.960 --> 00:28:21.519
those kind of roles, you know, there are steps you
483
00:28:21.559 --> 00:28:24.720
can take and that are very positive and a lot
484
00:28:24.799 --> 00:28:26.519
of it is kind of the things you start to
485
00:28:26.519 --> 00:28:28.640
talk about in the beginning of the show. It's about
486
00:28:29.480 --> 00:28:34.000
constructive ambition and correct and you know, there are a
487
00:28:34.000 --> 00:28:37.400
lot of specifics. Some of the things that you look
488
00:28:37.440 --> 00:28:40.200
at when you look at upgrading your skills are just overwhelming.
489
00:28:40.720 --> 00:28:45.119
But take an incremental approach, set your goals modestly so
490
00:28:45.200 --> 00:28:49.240
you can have strong, you know, achievement. An area that
491
00:28:49.279 --> 00:28:52.440
I'm I'm fascinated with is critical thinking, you know, which
492
00:28:52.480 --> 00:28:54.640
is you know, the reason we're all here today is
493
00:28:54.680 --> 00:28:57.599
because there was a you know, a Russian who you know,
494
00:28:57.720 --> 00:29:01.279
had gotten the nuclear codes that the US was attacking
495
00:29:01.400 --> 00:29:05.279
with nuclear with five nuclear missiles, and he didn't pull
496
00:29:05.359 --> 00:29:09.160
the trigger because he noticed that there were some anomalies.
497
00:29:09.200 --> 00:29:14.759
He applied critical thinking before he basically counter the the UH.
498
00:29:14.960 --> 00:29:17.640
You know, there's probably a very little known guy you
499
00:29:17.680 --> 00:29:21.359
know that saved us uh and he is all critical thinking.
500
00:29:21.920 --> 00:29:24.119
So one of the things about critical thinking is that
501
00:29:24.200 --> 00:29:27.839
it it's not just for the digital elite, which is
502
00:29:27.880 --> 00:29:31.079
a lot how it started. It's not just for you know,
503
00:29:31.160 --> 00:29:33.440
you know people with the more advanced credentials. It can
504
00:29:33.480 --> 00:29:36.480
be applied to anyone working in a warehouse or so forth.
505
00:29:36.880 --> 00:29:39.759
And now there's a lot of information to make it
506
00:29:39.799 --> 00:29:43.359
more adaptable to and more understandable and less academic and
507
00:29:43.359 --> 00:29:46.960
its approach. I really like that aspect of it. I
508
00:29:46.960 --> 00:29:49.599
think this idea of being an AI operator, you know,
509
00:29:49.680 --> 00:29:53.079
for for those that are working in the in the
510
00:29:53.519 --> 00:29:56.680
human touch area. A lot of kids today have great
511
00:29:56.720 --> 00:30:00.119
device skills. They want to work on their phones, they
512
00:30:00.160 --> 00:30:02.079
live on their phones, and there are a lot of
513
00:30:02.160 --> 00:30:06.680
jobs that want those skills. So you know, why work
514
00:30:06.680 --> 00:30:09.759
in McDonald's. You know, when when you can get a
515
00:30:09.839 --> 00:30:12.279
job that can leverage skills that you have, have a
516
00:30:12.359 --> 00:30:17.640
much better job, you know, the the human touch workers.
517
00:30:18.000 --> 00:30:20.559
There are certain industries that are applying automation in ways
518
00:30:20.559 --> 00:30:24.400
that benefit them, you know, such as security with with
519
00:30:24.519 --> 00:30:27.559
the android, you know, robotic officers. That's a great area
520
00:30:27.599 --> 00:30:32.400
to go into. It's much better than food service, you know.
521
00:30:32.480 --> 00:30:35.519
So that's the kind of advice that I that I get.
522
00:30:35.839 --> 00:30:38.960
You have to look at the way companies are trending
523
00:30:39.000 --> 00:30:42.359
and investing in automation and how that can make your
524
00:30:42.400 --> 00:30:45.279
job either better or worse. And and there's some cases
525
00:30:45.279 --> 00:30:48.119
that can make your job a lot better. I love
526
00:30:48.160 --> 00:30:52.880
the path that the middle has by leveraging AI to
527
00:30:53.160 --> 00:30:57.839
do the work more educated and credential you know, professional workers,
528
00:30:58.319 --> 00:31:00.680
you're definitely going to see that erosion. You know, so
529
00:31:00.720 --> 00:31:03.480
the digital leader are going to see erosion from the bottom,
530
00:31:03.680 --> 00:31:06.240
and they're going to see erosion directly, you know, by
531
00:31:06.960 --> 00:31:10.079
by jen Ai and and Ai doing more of what
532
00:31:10.119 --> 00:31:13.759
they do. But and then there's the work life pioneers,
533
00:31:13.759 --> 00:31:16.720
which is the fourth category worker. I think there's a
534
00:31:17.480 --> 00:31:19.359
you know, it's been trending for a while, right if
535
00:31:19.359 --> 00:31:21.839
you look at the number of so called gig workers
536
00:31:21.960 --> 00:31:24.720
or contract workers, it's thirty percent. It's been growing, you know,
537
00:31:24.720 --> 00:31:28.119
it's been ticking up, and it really accelerated after the pandemic.
538
00:31:29.440 --> 00:31:32.680
And the pandemic like the sequoia trees that they don't have,
539
00:31:32.759 --> 00:31:36.720
the the you know, the cones that only only surface
540
00:31:36.759 --> 00:31:40.079
after a big devastating fire, and the pandemic really opened
541
00:31:40.079 --> 00:31:43.160
the door, you know, opened the door to a lot
542
00:31:43.160 --> 00:31:45.240
of people, you know, just questioning what they're doing in
543
00:31:45.279 --> 00:31:48.519
their lives and I have some really good stories of
544
00:31:49.480 --> 00:31:52.599
you know, like a David White, who you know, adopted
545
00:31:52.599 --> 00:31:56.119
a child and just changed his life, and he became
546
00:31:56.200 --> 00:32:00.119
a foster care advocate, you know, a think tankler foster care.
547
00:32:00.000 --> 00:32:03.279
They noticed that all the foster care kids, they moved
548
00:32:03.279 --> 00:32:05.319
three or four times in there in their early years,
549
00:32:05.359 --> 00:32:08.000
and they always put their belongings in a garbage band.
550
00:32:08.960 --> 00:32:11.279
So he got his first act was to get all
551
00:32:11.319 --> 00:32:15.880
the luggage companies to donate luggage to the foster care community,
552
00:32:17.559 --> 00:32:19.640
you know. And there's a lot of companies that are
553
00:32:19.640 --> 00:32:23.960
just focusing on certification and education. That is very practical.
554
00:32:24.759 --> 00:32:28.559
You know, the government's not going to solve the skills problem.
555
00:32:28.599 --> 00:32:31.000
The skills problem is going to be solved more by
556
00:32:31.039 --> 00:32:35.119
the private sector. And even a Walmart, who is you know,
557
00:32:35.039 --> 00:32:38.000
you beat up a lot for all kinds of things.
558
00:32:38.359 --> 00:32:42.119
They have, like a hundred Walmart universities, which are interestingly
559
00:32:42.160 --> 00:32:45.400
in old warehouses that you know, are because of analytics
560
00:32:45.400 --> 00:32:47.359
and supply chain are now vacant. So they set them
561
00:32:47.440 --> 00:32:50.480
up as universities. And even in one woman, you know,
562
00:32:50.519 --> 00:32:54.960
and she'd never graduated from anything in life, so they
563
00:32:54.960 --> 00:32:59.119
have a formal graduation ceremony with blue gowns and caps,
564
00:32:59.200 --> 00:33:01.559
and she said, you know, or my kids to see
565
00:33:01.599 --> 00:33:04.640
me graduating. You know, let's just say, you know. It
566
00:33:04.680 --> 00:33:07.519
was a heart wrenching moment. So there are companies that
567
00:33:07.559 --> 00:33:14.319
are providing certifications and skills advancements that I think everyone
568
00:33:14.319 --> 00:33:15.119
can take advantage of.
569
00:33:16.359 --> 00:33:21.359
Yeah, No, that is fantastic, and it's just stepping into it,
570
00:33:21.519 --> 00:33:24.920
leaning into it because this can have an impact on
571
00:33:25.000 --> 00:33:27.839
mental health. And then of course getting your book. So
572
00:33:27.920 --> 00:33:31.519
tell us a little bit about your book and where
573
00:33:31.559 --> 00:33:34.119
can people find you and find your book.
574
00:33:34.880 --> 00:33:37.319
Well, I'd love to thank you so much again, thanks
575
00:33:37.359 --> 00:33:40.519
for having me on. Well, Lindie mentioned mental health. I have.
576
00:33:41.079 --> 00:33:44.319
The two main things about the book are specific skills
577
00:33:44.319 --> 00:33:47.960
to help workers in these four categories, starting with the
578
00:33:48.000 --> 00:33:50.240
trends affecting them from automation, and then what they can
579
00:33:50.319 --> 00:33:52.960
do about it. But it's not written for academics. It's
580
00:33:53.000 --> 00:33:55.039
not written for the business to make more money with
581
00:33:55.119 --> 00:33:58.200
technology for workers. So that's the first main thing. The
582
00:33:58.240 --> 00:34:00.880
middle section, I have a whole section with three chapters
583
00:34:00.920 --> 00:34:04.759
on mental health and I'm not in any way trained
584
00:34:06.799 --> 00:34:10.559
in that regard. I speak only to the mental health
585
00:34:10.599 --> 00:34:14.400
effects of automation. Yeah, and I have a chapter called
586
00:34:15.000 --> 00:34:18.039
invasion in surveys that we did, and I have a
587
00:34:18.079 --> 00:34:21.280
lot of data from Forester Research here to support some
588
00:34:21.320 --> 00:34:23.480
of the ideas. I'm not overwhelming the reader with a
589
00:34:23.480 --> 00:34:26.199
lot of data. But you know, when you look at
590
00:34:26.199 --> 00:34:31.400
the Random Acts of Automation, which is actually a site,
591
00:34:31.440 --> 00:34:33.760
public site that people can if they have a bad
592
00:34:33.800 --> 00:34:36.360
experience with automation, they can throw it into this site
593
00:34:36.400 --> 00:34:38.960
and then they track them and they're thousands and thousands
594
00:34:39.000 --> 00:34:43.159
of thousands. So I took the database and sorted it,
595
00:34:43.239 --> 00:34:47.039
and it turned out that of the three trust issues,
596
00:34:48.239 --> 00:34:52.199
invasion issues, and what I called isolation issues, that invasion
597
00:34:52.639 --> 00:34:56.440
was number one by about forty percent. Of all negative
598
00:34:56.719 --> 00:35:01.400
reported effects due to automation were invasive monitoring in base
599
00:35:01.480 --> 00:35:05.880
of use of biometrics, you know, you know, I have
600
00:35:06.360 --> 00:35:07.159
I have stories.
601
00:35:07.400 --> 00:35:10.840
There's a you know, the the company that owns you know,
602
00:35:10.880 --> 00:35:14.480
Madison Square Garden, they have a biometric for coming into
603
00:35:14.480 --> 00:35:19.800
their events, and you know, they basically if you were
604
00:35:19.880 --> 00:35:24.239
involved in any lawsuit against this entity, which gets sued
605
00:35:24.280 --> 00:35:26.719
all the time, you were a block.
606
00:35:26.960 --> 00:35:29.280
You know. So there was some woman with her daughter
607
00:35:29.360 --> 00:35:34.199
going to see the Roquettes one, you know, December and
608
00:35:34.719 --> 00:35:36.960
she never even heard of the company, but worked for
609
00:35:37.000 --> 00:35:39.480
a law firm that actually had some litigation with them.
610
00:35:39.559 --> 00:35:42.039
She was the bight access. I mean this is you know,
611
00:35:42.079 --> 00:35:46.119
I talked to McDonald's employee that they do you know,
612
00:35:46.559 --> 00:35:51.840
analytics on every single store mcconels real time, and if
613
00:35:51.840 --> 00:35:55.920
they're forecasting that there'll be a slowness between say ten
614
00:35:56.119 --> 00:36:00.840
and twelve o'clock at a particular store, they'll send a
615
00:36:00.880 --> 00:36:03.639
text to an employee who might be on a bus
616
00:36:03.679 --> 00:36:05.159
to come to work and tell them not to come,
617
00:36:05.360 --> 00:36:07.880
not to show up. Now, New York just passed a
618
00:36:07.960 --> 00:36:14.519
law to prevent that all of these you know, this invasive,
619
00:36:14.639 --> 00:36:17.119
all of this automation. There's a woman in Australia that
620
00:36:17.599 --> 00:36:20.119
one of the biggest insurance company in Australia that they
621
00:36:20.119 --> 00:36:25.760
put listening software on our desktop and they tracked every
622
00:36:25.880 --> 00:36:30.239
mouse movement, every time spent, you know, everything, And that
623
00:36:30.360 --> 00:36:33.239
is something that's out loud in Germany, but that's something
624
00:36:33.280 --> 00:36:35.280
that's creeping in. It's a whole area called you know,
625
00:36:35.360 --> 00:36:41.440
task mining. But you know, they fired her and they
626
00:36:41.480 --> 00:36:43.960
had this report of how many days she left a
627
00:36:43.960 --> 00:36:45.880
little bit early, how many times she did this, how
628
00:36:45.880 --> 00:36:48.840
many of that which she refuted. She said, that's not accurate,
629
00:36:50.039 --> 00:36:51.960
but she had no knowledge that they were doing this
630
00:36:52.079 --> 00:36:54.639
kind of monitoring, you know, on her. So anyway, I
631
00:36:54.679 --> 00:36:57.239
could go on and on, but this is why I
632
00:36:57.239 --> 00:37:00.679
wanted to. This type of monitoring really affect the mental
633
00:37:00.719 --> 00:37:05.719
stability and health of employees. And of course trust issues
634
00:37:05.760 --> 00:37:08.599
are are just huge. Now you can't trust almost anything
635
00:37:08.639 --> 00:37:11.199
you see on the internet except for this program, right,
636
00:37:11.599 --> 00:37:14.519
But you know, so so you.
637
00:37:14.480 --> 00:37:18.360
Know, you're bringing you know, live people, so right, a.
638
00:37:18.360 --> 00:37:21.599
Lot of people, so basically you know, and and the
639
00:37:21.639 --> 00:37:25.760
isolation aspect. I think one of the aspects of a
640
00:37:25.760 --> 00:37:28.079
lot of their work is that the digital leader not
641
00:37:28.159 --> 00:37:32.480
interacting with with mintal workers or human touch workers as much.
642
00:37:33.559 --> 00:37:35.440
You know, and we tend to be, you know, so
643
00:37:36.559 --> 00:37:39.239
digitally focused because now we're not just on our phones
644
00:37:39.559 --> 00:37:41.840
at work. I mean we're we're staring at screens. A
645
00:37:41.840 --> 00:37:43.960
lot of us are staring at screens during work all
646
00:37:44.000 --> 00:37:48.000
the time. So there's a lot of this. The whole
647
00:37:48.039 --> 00:37:51.280
aspect of random acts is that we have this juggernaut
648
00:37:51.280 --> 00:37:55.760
tech industry that's that's bursting with enthusiasm, innovation and money
649
00:37:56.079 --> 00:37:58.760
to build the next thing. But they don't know what
650
00:37:58.800 --> 00:38:02.559
this next thing will do. It's unpredictable because inherently all
651
00:38:02.599 --> 00:38:07.360
of this is based on probability. It's it's not different
652
00:38:07.400 --> 00:38:09.840
than going to Las Vegas and playing the craft tables.
653
00:38:10.360 --> 00:38:13.519
You know, you have you have statistics that are taking
654
00:38:13.559 --> 00:38:18.239
a best guess based on all kinds of factors. There's there's,
655
00:38:18.639 --> 00:38:23.000
there's there, there's potential error and unpredictable aspects. Uh, you know,
656
00:38:23.079 --> 00:38:25.480
riddle through all of this that that that are going
657
00:38:25.519 --> 00:38:27.840
on at a greater rate than ever. So that's the
658
00:38:27.840 --> 00:38:29.920
whole purpose of the book Random Acts, which is available
659
00:38:29.960 --> 00:38:34.119
on Amazon. Uh, it's uh, you know, the publisher is
660
00:38:34.119 --> 00:38:36.360
called post Hill, which is a great publisher out of
661
00:38:37.280 --> 00:38:41.599
out of National, Tennessee. And uh, happy to finally get
662
00:38:41.599 --> 00:38:41.920
it done.
663
00:38:43.280 --> 00:38:46.320
Well, congratulations and sounds like it's going to help a
664
00:38:46.400 --> 00:38:49.639
lot of people. So I'm sure that you know, our
665
00:38:49.679 --> 00:38:52.159
audience is going to definitely go and get a copy
666
00:38:52.199 --> 00:38:53.920
of it. And if they want to connect with you,
667
00:38:54.039 --> 00:38:55.679
what's the best way to get in.
668
00:38:55.639 --> 00:39:00.599
Touch with Well, I'm all over LinkedIn, but you know,
669
00:39:00.719 --> 00:39:03.280
all throw out my email if you want to contact me,
670
00:39:03.760 --> 00:39:08.960
just see Leclair That's l. E. C. L A R
671
00:39:09.000 --> 00:39:10.239
at Forester dot com.
672
00:39:11.440 --> 00:39:11.960
Fantastic.
673
00:39:12.760 --> 00:39:14.360
Yeah.
674
00:39:14.840 --> 00:39:18.000
Well, thank you for joining us, Craig and sharing your
675
00:39:18.000 --> 00:39:22.960
insights and giving us different perspectives that how we can
676
00:39:23.119 --> 00:39:25.400
go forward, how we can approach it, and how we
677
00:39:25.440 --> 00:39:29.679
can kind of continue working with it and growing with it.
678
00:39:30.519 --> 00:39:32.159
So thank you for joining.
679
00:39:32.039 --> 00:39:34.880
Us and thank you for having me.
680
00:39:35.599 --> 00:39:39.199
Absolutely thank you to your audience for joining us, because
681
00:39:39.239 --> 00:39:42.719
without you, the show would not be possible. You are
682
00:39:42.840 --> 00:39:45.239
the life and soul of our show. Reach out to us,
683
00:39:45.320 --> 00:39:48.400
let us know how we can support you and help
684
00:39:48.480 --> 00:39:52.800
you live the life you deserve and achieve your goals.
685
00:39:53.119 --> 00:39:55.639
And thank you one for making the show technically possible.
686
00:39:56.880 --> 00:39:59.280
Thank you for being part of Beyond Confidence. With your
687
00:39:59.280 --> 00:40:01.599
host dev part Are, we hope you have learned more
688
00:40:01.639 --> 00:40:04.320
about how to start living the life you want. Each
689
00:40:04.360 --> 00:40:07.280
week on Beyond Confidence, you hear stories of real people
690
00:40:07.360 --> 00:40:11.840
who've experienced growth by overcoming their fears and building meaningful relationships.
691
00:40:12.199 --> 00:40:15.599
During Beyond Confidence, Dvpark shares what happened to her when
692
00:40:15.639 --> 00:40:18.039
she stepped out of her comfort zone to work directly
693
00:40:18.079 --> 00:40:21.159
with people across the globe. She not only coaches people
694
00:40:21.159 --> 00:40:24.679
how to form hard connections, but also transform relationships to
695
00:40:24.760 --> 00:40:28.159
mutually beneficial partnerships. As they strive to live the life
696
00:40:28.159 --> 00:40:30.480
they want. If you are ready to live the life
697
00:40:30.559 --> 00:40:34.280
you want and leverage your strengths, learn more at www
698
00:40:34.360 --> 00:40:38.239
dot dvpark dot com and you can connect with dvat
699
00:40:38.280 --> 00:40:41.800
contact at dvpark dot com. We look forward to you
700
00:40:41.960 --> 00:40:43.039
joining us next week