00:00:05.511 Am I on?
00:00:06.932 Oh, there's no three, two, one.
00:00:09.974 Can you remove, delete this?
00:00:11.455 You can edit this, right?
00:00:14.035 All right.
00:00:14.916 Okay.
00:00:15.455 We are live, everybody.
00:00:17.417 Welcome to the show.
00:00:18.658 My name is Yassen Horanszky.
00:00:20.097 I'm the founder and CEO of
00:00:21.699 Luniko and your host for today.
00:00:23.739 And I've helped digitally
00:00:24.780 transform businesses of all
00:00:26.420 shapes and sizes from publicly traded
00:00:28.942 multi-billion dollar
00:00:29.922 international organizations
00:00:31.943 to local nonprofits and
00:00:33.645 everything else in between.
00:00:35.106 And just like you,
00:00:36.445 I'm wondering where
00:00:37.726 artificial intelligence and
00:00:39.228 the disruptive technology
00:00:41.048 that we've seen dominate
00:00:42.490 headlines this last year fits in and how.
00:00:46.171 So that's why we've created the show,
00:00:47.853 to bring on guests of all
00:00:48.933 levels and all types of
00:00:50.173 businesses to talk about AI
00:00:52.595 and its business adoption,
00:00:53.936 but strictly from a non-technical lens.
00:00:57.518 We'll be touching on topics
00:00:58.598 such as strategy, process, people,
00:01:01.500 emotions,
00:01:02.600 and anything else that we find
00:01:03.902 relevant to the guests that
00:01:04.902 we're bringing on.
00:01:06.322 We're going to drop as much
00:01:07.584 of the corporate jargon as
00:01:08.623 possible and keep the
00:01:09.584 discussion as simple as we can.
00:01:11.986 We're going to be as casual
00:01:13.027 as possible and have fun.
00:01:14.908 Now,
00:01:15.888 The name that we wanted for
00:01:17.028 the show was taken.
00:01:18.649 So we still don't have a name, guys.
00:01:20.388 Please continue to give your
00:01:21.450 name suggestions.
00:01:22.230 We highly appreciate it.
00:01:23.590 We're not going to let a
00:01:24.611 nameless show stop us.
00:01:26.430 So I'm going to introduce
00:01:27.572 you to our first guest today.
00:01:29.671 He's coming from sunny San Diego,
00:01:31.433 California.
00:01:32.472 And we've got Daron Giles,
00:01:34.274 who's been fortunate enough
00:01:35.793 to have worked in some of
00:01:36.875 the most interesting
00:01:38.394 advanced engineering
00:01:39.415 projects of our lifetime.
00:01:41.575 Most of his experience has
00:01:42.736 been in aerospace manufacturing,
00:01:44.477 where he's contributed to
00:01:45.537 building parts that put people on space,
00:01:48.379 in airplanes or defense products,
00:01:50.900 highly strategic and life-changing assets,
00:01:54.382 or in his latest role where
00:01:55.841 he's improving the
00:01:56.563 efficiency in biotech
00:01:57.783 manufacturing so that more
00:01:59.524 medical solutions can
00:02:01.045 continue to save and
00:02:02.525 improve our daily lives.
00:02:04.426 Now,
00:02:05.085 Daron is not your typical IT director.
00:02:07.888 He started out as a
00:02:08.807 manufacturing engineer
00:02:10.068 before becoming an
00:02:11.049 operations manager and
00:02:12.610 eventually taking the leap
00:02:14.390 towards managing technology
00:02:16.431 for a business.
00:02:17.632 And like many engineers,
00:02:18.752 he's a beer brewer.
00:02:21.433 And him and I met when we
00:02:23.354 worked down in California
00:02:24.735 on a number of digital
00:02:25.816 transformation initiatives
00:02:27.396 for an aerospace manufacturing company.
00:02:29.957 So, Daron,
00:02:30.957 thank you for being on the show today.
00:02:34.177 Yes, thanks for having me on the show.
00:02:35.961 It's great to talk to you.
00:02:37.304 I always enjoy spending time
00:02:38.706 with you and the team from Luniko.
00:02:41.979 Yeah, no, that's awesome, Daron.
00:02:43.661 So let's get to the questions, right?
00:02:45.622 Just so we can kind of start on here,
00:02:47.084 right?
00:02:47.625 And for those that are
00:02:48.566 watching the first time,
00:02:49.466 we'll be asking some
00:02:50.287 questions and kind of chatting about it.
00:02:51.927 We might pull up ChatGPT as we need to,
00:02:55.411 right?
00:02:55.570 This is an AI-related show.
00:02:57.432 But let's start with just the basics first,
00:02:59.794 right?
00:02:59.995 And I am going to be asking
00:03:01.015 this to everybody, Daron.
00:03:02.197 So what does AI mean to you?
00:03:08.143 So, so AI is, is,
00:03:09.782 is really it's synthetic
00:03:11.044 human intelligence, right?
00:03:12.864 Human intelligence so far
00:03:13.984 has only existed in humans.
00:03:16.245 And, you know,
00:03:17.724 now that technology has
00:03:18.844 advanced to the point that it has,
00:03:20.425 we're able to,
00:03:21.866 we're able to create
00:03:22.665 artificial intelligence through, you know,
00:03:25.526 computing and, and,
00:03:26.887 and computing processes that that's,
00:03:29.567 you know,
00:03:29.766 today comes close to simulating
00:03:31.806 human intelligence.
00:03:32.668 And I think the expectation
00:03:33.728 is in the future.
00:03:35.240 it will exceed and then far
00:03:37.360 exceed the intellect and
00:03:40.262 capacity and capability of
00:03:42.122 the human mind.
00:03:42.782 All right.
00:03:45.604 That makes sense.
00:03:46.865 I mean,
00:03:47.104 I look at it a little bit similar to you,
00:03:49.006 Daron.
00:03:49.265 I think it's about giving
00:03:50.686 the computer the ability to think, learn,
00:03:52.667 and adapt, right?
00:03:53.987 If I can kind of simplify this,
00:03:56.769 a lot of the technology
00:03:57.649 that we're seeing.
00:03:58.169 Obviously, when people talk about AI,
00:04:00.229 I think everybody thinks about chat GPT.
00:04:02.931 But the truth is that AI
00:04:05.312 technology really has been
00:04:06.614 around for a lot longer than that.
00:04:08.155 And some of it is integrated
00:04:09.836 into our daily life and we
00:04:11.057 don't really know it.
00:04:12.359 But this last year we've seen, well,
00:04:15.980 last year, right?
00:04:16.742 We saw a lot of claims in
00:04:17.802 terms of what AI can do.
00:04:19.564 We saw a lot of marketing.
00:04:21.266 We saw just a lot of things
00:04:23.086 being pushed out.
00:04:24.548 Now let's break out what's
00:04:26.088 marketing versus what's real.
00:04:27.348 And it's really hard to tell
00:04:29.149 nowadays with content being
00:04:30.790 so curated and done so well.
00:04:32.911 But I'm just going to cut to
00:04:34.091 a chase there.
00:04:34.892 What do you think is
00:04:35.692 bullshit when you look at
00:04:37.574 AI and its application?
00:04:42.896 You know,
00:04:43.476 I think fear is the bullshit about AI,
00:04:46.997 right?
00:04:47.338 I think media, you mentioned, you know,
00:04:49.699 media...
00:04:51.627 coverage over the generative AIs,
00:04:54.189 ChatGPT and BARD,
00:04:55.771 and it seems like
00:04:57.012 everybody's trying to catch
00:04:57.971 up now or be the leader.
00:05:01.514 But the, you know,
00:05:03.274 I think there's a lot of
00:05:06.117 fear being probably pushed
00:05:08.177 a little bit by, you know, by the media,
00:05:09.858 right?
00:05:10.420 Everybody understands the
00:05:12.180 media or that fear sells
00:05:14.322 newspapers or ads now.
00:05:17.891 And I really think people
00:05:18.831 are worried about like, oh, you know,
00:05:20.252 AI is going to take my job
00:05:21.793 and AI is going to take over the world.
00:05:24.194 And, you know, that's really, it's fear.
00:05:26.615 It's by nature not rational.
00:05:29.656 I'm not so worried about that.
00:05:33.598 So what do you think about
00:05:34.699 the application?
00:05:35.379 Do you think the
00:05:36.139 applications that we're seeing that, hey,
00:05:38.661 AI can solve this for you,
00:05:40.401 AI can solve that for you,
00:05:41.862 do you think that they're feasible?
00:05:44.244 Absolutely, yeah.
00:05:45.204 I think we're starting to
00:05:45.903 see some really...
00:05:48.735 valuable use cases with the, you know,
00:05:51.456 right now it's generative AI with,
00:05:53.677 you know,
00:05:53.918 both language model AI and even
00:05:56.538 just generative images.
00:06:00.660 And, you know,
00:06:01.439 I think there's another bit
00:06:02.839 of the fear is generative videos,
00:06:06.661 you know, spoof videos that would make
00:06:09.983 someone like Joe Biden or
00:06:11.343 Donald Trump appear to be
00:06:13.865 saying things or doing
00:06:14.805 things that they did not
00:06:16.286 actually do yet in a way
00:06:18.346 that's indistinguishable to a human.
00:06:20.206 Is that really Joe Biden doing that?
00:06:22.267 Or is that an AI?
00:06:26.050 Yeah, I've seen some of those.
00:06:27.290 I've seen like some of the
00:06:28.031 Joe Rogan and things like that.
00:06:29.451 And you can tell right now
00:06:30.531 that they're not real.
00:06:31.992 But I feel like, you know,
00:06:33.773 how long is it going to be
00:06:34.872 before you actually can't
00:06:35.894 tell whether they're real or not, right?
00:06:40.288 So let's shift over to kind
00:06:41.889 of an area that you're very familiar with,
00:06:44.069 right?
00:06:44.230 So you've been obviously in
00:06:45.050 the shop floor from as a
00:06:46.831 manufacturing engineer to
00:06:48.391 supporting the shop floor
00:06:49.692 as an IT director in your role today.
00:06:53.072 How do you think that this
00:06:54.213 technology is going to make
00:06:56.374 its way to the shop floor?
00:06:59.514 You know,
00:07:00.574 I think it's going to in the long term,
00:07:02.516 right?
00:07:03.596 It's going to have a really
00:07:05.055 significant impact on the shop floor.
00:07:08.223 And the way I think we're
00:07:09.365 going to see that is
00:07:10.706 through enhancing and
00:07:13.767 improving the work that the
00:07:16.689 workers are doing on the
00:07:17.610 shop floor to make
00:07:20.151 processes and products more consistent,
00:07:22.752 more reliable, higher quality.
00:07:26.435 And those attributes are
00:07:28.697 going to drive the other two goals,
00:07:32.838 faster and lower cost.
00:07:35.204 So I really, I'd say,
00:07:37.045 I think artificial
00:07:39.987 intelligence is going to
00:07:41.166 enhance the operator,
00:07:43.487 giving them the ability to do more,
00:07:46.290 more reliably, more consistently.
00:07:48.389 And, and it's, you know, in this, you know,
00:07:51.391 first, you know,
00:07:52.331 first phases of digitalization and,
00:07:54.572 you know, in, in industry of, um,
00:07:57.913 I've always used technology to, uh,
00:08:00.095 you know, to, to, to hit the goals,
00:08:03.055 right.
00:08:03.216 Better, faster, and cheaper.
00:08:05.208 And I think it's going to continue.
00:08:06.249 We're going to see new use
00:08:07.668 cases that continue to
00:08:09.670 evolve manufacturing processes.
00:08:14.093 And I think an exciting thing about it now,
00:08:16.735 part of why I think it's coming of age,
00:08:20.817 is the younger generations,
00:08:24.238 the generations behind us
00:08:26.500 have grown up on computers.
00:08:29.302 And so they're entering the
00:08:30.802 workforce with a lot more tech savvy than
00:08:35.423 the previous generations.
00:08:37.625 Right.
00:08:38.505 I think, you know,
00:08:40.067 where in the recent past, you know,
00:08:42.707 finding,
00:08:43.508 finding manufacturing technicians and,
00:08:45.470 and, you know, operators,
00:08:47.149 machine operators that have
00:08:48.591 that understanding, you know,
00:08:49.652 have both the experience,
00:08:50.912 the hands-on manufacturing experience,
00:08:52.813 know how things work and
00:08:55.394 they're afraid of computers
00:08:56.635 because they're new and they're change.
00:08:59.756 But nowadays,
00:09:01.419 You know,
00:09:01.679 people entering the workforce
00:09:03.282 grew up on computers.
00:09:04.783 They can't imagine or relate
00:09:07.345 to a time when computers didn't, you know,
00:09:11.087 fulfill so many different
00:09:13.190 functions in a pervasive
00:09:15.792 way in our lives.
00:09:18.519 Right.
00:09:18.818 And right now you've got a
00:09:20.539 child going through the university,
00:09:22.201 right?
00:09:22.380 So do you think that the
00:09:23.980 universities right now are
00:09:25.761 preparing for that kind of
00:09:27.903 new wave of what the
00:09:29.244 generational worker is
00:09:30.344 going to look like and
00:09:31.865 pushing some of these
00:09:32.845 technologies so that
00:09:33.826 they're not just ready for
00:09:35.746 them when they kind of hit the shop floor,
00:09:37.827 but they're the ones who
00:09:38.768 are in a way pushing for them?
00:09:41.990 Yeah, absolutely.
00:09:42.750 You know, I think the, you know,
00:09:45.750 the younger generation, you know,
00:09:47.943 With the big lead from universities,
00:09:50.965 they're pushing the technology,
00:09:52.424 they're pushing the envelope.
00:09:53.405 I mean,
00:09:53.885 a lot of the funded research for AI
00:09:58.027 began in academia,
00:10:00.008 is still being pursued in academia.
00:10:02.668 And I really feel like
00:10:05.524 not just from university
00:10:06.804 because people become tech savvy,
00:10:09.527 you know, from, from an even earlier age,
00:10:11.248 right.
00:10:11.489 Without going to college, people have,
00:10:13.070 everyone has cell phones
00:10:14.270 and is learning a lot of
00:10:15.331 new technology that was
00:10:16.351 just unfathomable 20 years, you know,
00:10:19.313 30 years ago.
00:10:19.794 Uh, so I,
00:10:21.956 I do feel like universities are
00:10:23.216 preparing people, uh, in,
00:10:25.078 in a lot of different ways,
00:10:27.399 but universities aren't,
00:10:28.480 aren't solely responsible for that, uh,
00:10:30.982 that evolution that's happening, right.
00:10:32.624 There's, there's lots of factors, uh,
00:10:35.854 around the world today that
00:10:38.495 are driving the adoption of technology.
00:10:42.258 Right.
00:10:42.859 Yeah, no, you're right on that there.
00:10:44.460 So you've got experience in aerospace,
00:10:47.621 you've got experience in biotech.
00:10:49.823 Out of those two industries, right,
00:10:51.365 and maybe some of the other
00:10:52.485 manufacturing industries
00:10:54.166 that you can think of, right,
00:10:55.326 where do you think that AI
00:10:56.628 is going to pick up faster
00:10:58.068 and where do you think it's
00:10:58.850 going to be taking a little
00:11:00.150 bit more time?
00:11:02.610 You know,
00:11:02.910 I think what we're seeing right
00:11:05.212 now with generative AI is
00:11:07.894 going to see use cases in
00:11:09.815 communications a lot, right?
00:11:11.216 The smart chat agents are, I think,
00:11:15.239 going to be a simpler use case, right?
00:11:18.860 I think we're probably
00:11:20.042 already seeing that
00:11:21.543 implemented in large scales
00:11:23.563 at large organizations
00:11:24.845 where they're able to not
00:11:27.005 just replace human
00:11:28.927 intellect with machine intellect,
00:11:30.927 but enhance
00:11:32.525 the human work and let the
00:11:37.208 computer do the hard part,
00:11:39.171 I guess is the way I think of that.
00:11:41.011 So generative AI responding
00:11:46.336 to inquiries from customers
00:11:48.076 and clients is where I
00:11:49.957 think we'll start to see
00:11:51.359 initial use cases for business.
00:11:54.902 I think there's a lot of
00:11:55.741 other entertainment type
00:11:56.923 use cases that will drive like with the
00:12:00.269 the fake videos and fake imagery,
00:12:02.370 fake audio.
00:12:04.971 That has no purpose that I
00:12:06.831 can think of in business today.
00:12:10.413 But that doesn't mean that
00:12:11.113 it won't turn into a useful
00:12:15.014 tool in the future.
00:12:17.235 We just don't know what that use is yet.
00:12:18.715 Okay.
00:12:21.263 And then what are some of the, I mean,
00:12:23.644 generative AI and just other uses of AI?
00:12:27.046 It's not something that you just turn on,
00:12:28.986 right?
00:12:29.307 Like what are some of the
00:12:30.227 things that you think
00:12:31.107 businesses need to be doing?
00:12:32.389 Some of the key milestones
00:12:33.669 or objectives that they
00:12:35.211 need to meet before they're
00:12:36.431 seriously considering going
00:12:37.751 down this path?
00:12:39.932 Yeah,
00:12:41.053 so I think of AI as kind of the top
00:12:44.034 of the technology pyramid.
00:12:46.030 And it's, you know,
00:12:46.892 maybe even if you think of
00:12:47.712 it like a skyscraper, it's the, uh,
00:12:49.374 the top floors that are
00:12:50.394 still under construction as the,
00:12:52.076 as the skyscraper grows, uh, you know,
00:12:55.719 closer to the sun.
00:12:56.899 Um, it, uh, it,
00:13:00.721 I think a company has to
00:13:01.842 have a solid foundation and the,
00:13:03.965 and then the layers of the
00:13:05.166 technology infrastructure
00:13:06.846 and capability before, you know,
00:13:09.208 trying to go for the end, the end goal,
00:13:12.530 right.
00:13:12.792 The, um, uh,
00:13:15.051 Rome wasn't built in a day.
00:13:17.350 Technology platforms and
00:13:19.111 technology infrastructure
00:13:20.351 for organizations is a lot of work,
00:13:24.113 both to design and then build,
00:13:29.894 and I think often
00:13:30.833 overlooked is the
00:13:31.553 maintenance activity also.
00:13:32.774 A company has to have the
00:13:36.394 infrastructure to know what
00:13:38.895 the use cases are or should
00:13:40.755 be to really understand the
00:13:44.403 The, the need, the unmet need that,
00:13:46.583 that they, uh, you know, the, the, the,
00:13:48.943 the tool or the solution will solve, uh,
00:13:51.725 they need to have the, uh,
00:13:52.625 the basic infrastructure to manage that.
00:13:54.765 And, you know,
00:13:55.186 basic infrastructure used to be, you know,
00:13:56.947 you have a server rack and
00:13:58.126 batteries and network
00:13:58.947 switches and all that,
00:13:59.727 all the other stuff.
00:14:00.488 But, you know, uh, with the cloud,
00:14:03.249 that's not so necessary anymore.
00:14:07.389 Now we're dependent on the internet,
00:14:08.971 right?
00:14:09.390 Without a internet connection.
00:14:10.951 If you don't have everything on site,
00:14:12.432 then you can't do anything.
00:14:14.197 So, uh, you know,
00:14:14.937 so the basic IT infrastructure, uh,
00:14:18.080 the nuts and bolts has to
00:14:19.421 be in place and it has to
00:14:20.802 be supported and maintained.
00:14:23.003 Uh, and then after that, the, you know,
00:14:25.364 the enterprise applications, um,
00:14:28.125 have to be in place, uh,
00:14:30.287 in the ISA 95 pyramid, which is, uh,
00:14:33.109 you know, uh, an organization, uh,
00:14:35.870 as produced by, uh,
00:14:37.750 Mesa manufacturing
00:14:38.812 execution software association.
00:14:41.844 They talk about the
00:14:42.524 different layers of the
00:14:44.825 tech stack for
00:14:47.306 manufacturing and
00:14:48.706 manufacturing execution.
00:14:50.807 You have to have each layer
00:14:55.208 as a foundation for the next layer.
00:14:58.931 We can't just build the top
00:15:00.471 without building the
00:15:01.971 support structure
00:15:02.812 underneath it to develop
00:15:06.913 and maintain the
00:15:07.953 application as they're created.
00:15:11.556 yeah otherwise everything's
00:15:12.557 just going to come
00:15:13.200 crumbling down which makes
00:15:15.504 a lot of sense right so I
00:15:17.086 mean you've got businesses
00:15:18.110 that that 2023 was all the hype about ai
00:15:22.759 And if you weren't throwing AI around,
00:15:24.941 it seems like you were
00:15:25.600 getting left behind.
00:15:27.422 But should businesses really
00:15:30.722 take a pause and kind of
00:15:32.283 assess some of those
00:15:34.004 foundational areas that
00:15:35.205 you're kind of talking about?
00:15:36.846 Do they need to redesign
00:15:38.866 their business processes with AI in mind?
00:15:41.988 Or is this just something that's like,
00:15:43.488 okay,
00:15:43.729 we're going to chip away a little
00:15:44.849 bit about this AI piece and
00:15:46.710 see what we can kind of
00:15:47.610 play around with while we
00:15:50.350 work on some of these
00:15:51.371 foundational things?
00:15:52.711 What do you think?
00:15:54.753 You know, I mean, really,
00:15:58.956 I think it's both, right?
00:16:00.956 You have to have the foundation,
00:16:02.076 but you also have to have the vision.
00:16:05.979 And, you know, you talked about, you know,
00:16:10.022 should companies just start
00:16:10.961 working on AI?
00:16:11.962 I think, you know, definitely not, right?
00:16:15.424 Companies should always
00:16:16.184 think about use cases and
00:16:18.570 always have a good solid
00:16:20.270 understanding of their business,
00:16:21.572 their business objectives,
00:16:23.974 the performance,
00:16:24.695 the metrics against those objectives,
00:16:27.736 and understand where there
00:16:28.518 are deficiencies and opportunities.
00:16:29.859 And then instead of thinking
00:16:31.419 about a tool that needs to
00:16:32.520 be adopted because it's a
00:16:34.883 nifty thing that
00:16:35.524 everybody's talking about,
00:16:37.504 you have to start with a use case,
00:16:38.745 start with a why.
00:16:41.327 Why do we wanna do this?
00:16:42.229 What do we wanna get out of it?
00:16:44.650 And
00:16:46.530 asking and answering that question of why,
00:16:48.732 we'll get to the what and the how.
00:16:54.498 But it's really important to start with,
00:16:57.059 this is an unmet need in
00:16:58.080 the business that we've identified,
00:17:00.081 and this is how we're going
00:17:02.764 to plug that with a technology.
00:17:08.929 If the foundation's not there,
00:17:12.092 the use case won't be successful.
00:17:14.113 That's just my experience.
00:17:16.505 too many times where uh you
00:17:19.727 know a project an
00:17:21.508 implementation a big
00:17:22.887 expensive project will fail
00:17:25.349 and it's because the
00:17:26.990 infrastructure wasn't in
00:17:27.990 place the idea itself the
00:17:29.872 use case wasn't well
00:17:30.912 thought out the overall
00:17:32.553 business process wasn't
00:17:33.733 thought out it was a you
00:17:35.535 know it was a an initiative
00:17:37.476 of here's a technology or a
00:17:38.695 tool we want to use this
00:17:39.676 tool but not a uh
00:17:42.037 not in the context of the
00:17:43.356 business and the business process.
00:17:44.857 It's just,
00:17:45.198 we want this tool because it's the shiny,
00:17:47.219 the newest,
00:17:47.618 shiniest bubble that
00:17:48.459 everyone's talking about.
00:17:50.799 So you're basically saying
00:17:51.859 it's gotta be fit for purpose, right?
00:17:53.721 We start with the,
00:17:54.340 why we start with the purpose.
00:17:56.061 And then we see,
00:17:56.882 we kind of work backwards
00:17:58.261 within there to define how
00:17:59.863 and what AI applications
00:18:02.183 really fit in with that purpose.
00:18:05.084 Yeah.
00:18:05.904 And in a lot of cases,
00:18:06.785 I think you're going to, you know,
00:18:08.045 a lot of organizations when they,
00:18:09.286 when they really,
00:18:10.731 take the time to look deeply
00:18:13.132 and reflect deeply about
00:18:14.452 their current technology
00:18:16.393 stack that the next
00:18:17.752 solution that's needed
00:18:18.692 isn't always going to be an AI solution.
00:18:20.614 The next solution that might
00:18:22.173 be needed might be
00:18:23.634 something a lot simpler
00:18:24.554 like some level of factory
00:18:27.914 automation or an enterprise
00:18:30.914 resource planning platform
00:18:32.395 or a planning and
00:18:33.056 scheduling platform that
00:18:36.496 solves a more immediate
00:18:37.497 problem or challenge for the business.
00:18:40.805 Yeah, no, I agree there, man.
00:18:45.455 What do you think are going
00:18:46.777 to be some of the
00:18:49.057 challenges that kind of come with that,
00:18:51.778 right?
00:18:52.038 If you've got the executive that says, hey,
00:18:54.619 we need to do AI because
00:18:56.339 that's what the
00:18:56.740 shareholders want or that's
00:18:57.901 what the market is
00:18:58.580 demanding or that's what
00:18:59.580 everybody else is doing, right?
00:19:01.902 That's obviously gonna
00:19:02.623 create some challenges for
00:19:04.063 people like you who have to
00:19:05.844 grab that vision and try to
00:19:08.045 fit it in where maybe that
00:19:09.384 purpose isn't quite clear.
00:19:12.546 You know, I think it's...
00:19:15.018 I think the two biggest
00:19:15.719 challenges are fairly generic.
00:19:18.902 They're not just unique to
00:19:20.683 AI or technology.
00:19:22.144 It's time and money.
00:19:24.126 I think that when companies
00:19:29.071 are considering a
00:19:30.673 technology and planning a project,
00:19:34.057 it's got to fit in the budget,
00:19:35.778 if it's capital budget or
00:19:36.959 operational budget.
00:19:39.236 it's got to fit.
00:19:41.296 I've seen too many times
00:19:42.876 where a project will fail
00:19:44.136 because it's not adequately budgeted.
00:19:47.998 We think, oh,
00:19:48.478 we're just going to do this
00:19:49.317 project and there's going
00:19:50.178 to be one guy sitting in
00:19:50.698 the corner doing this thing
00:19:51.738 for six months and then
00:19:53.659 magically we have a system
00:19:54.798 implemented for an
00:19:55.679 enterprise or for a site
00:19:57.979 within an enterprise.
00:20:01.680 That's not a recipe for success.
00:20:04.880 And I think that companies
00:20:06.661 have to be really objective about
00:20:08.896 technology is not cheap.
00:20:12.238 Computer systems can,
00:20:14.159 if they're poorly designed
00:20:15.739 and implemented, can be money pits.
00:20:18.359 And the difference between a
00:20:21.320 solution or a system that's
00:20:22.621 a money pit versus
00:20:23.741 something that's really
00:20:24.622 truly transformative and
00:20:27.123 value added to a business
00:20:30.923 can start with the idea or the funding.
00:20:36.846 The best idea in the world
00:20:38.702 with insufficient funding can't, you know,
00:20:41.243 can't, can't happen.
00:20:43.203 And, and the same way that, you know, uh,
00:20:46.865 uh, you know, if,
00:20:48.125 if a project is set out
00:20:49.746 with a unrealistic timeline, uh,
00:20:53.426 that can also lead to a failure.
00:20:56.367 Yeah.
00:20:56.928 So who do you think should
00:20:58.208 be leading these AI initiatives?
00:21:00.989 That's a, that's a good one, right?
00:21:02.128 It's, it's kind of a hot potato that gets,
00:21:03.849 uh, that gets, uh,
00:21:05.830 batted around in different organizations.
00:21:08.457 Where I think I've seen it
00:21:09.517 be most successful is when
00:21:11.018 organizations have an
00:21:12.657 operations technology group
00:21:15.719 that is not necessarily the
00:21:17.999 IT organization and not
00:21:19.398 necessarily operations organization,
00:21:21.859 but the bridge between the two.
00:21:23.380 Like you mentioned in my background,
00:21:24.779 that's where I've managed
00:21:27.080 to find my niche in
00:21:30.261 companies is bridging the
00:21:32.261 gap between operations,
00:21:34.342 even engineering and operations.
00:21:35.663 What does the business need?
00:21:38.031 Versus what is the art of
00:21:40.212 the possible today?
00:21:41.953 What can we really do?
00:21:42.755 What can we really pull off?
00:21:44.455 And what can we accomplish
00:21:46.817 within the time and cost
00:21:49.499 constraints that are set for us?
00:21:52.594 Now,
00:21:52.773 that's who you think should be leading
00:21:55.275 that, right?
00:21:55.734 And, you know,
00:21:56.556 maybe a lot of organizations
00:21:57.935 don't have that
00:21:59.196 organization that you're referring to.
00:22:00.856 Who do you think is actually
00:22:01.857 going to be leading most of
00:22:03.857 the AI implementation projects?
00:22:10.221 You know,
00:22:11.300 it feels like AI is so new that
00:22:15.163 it's more of an IT
00:22:16.782 challenge than anything else, right?
00:22:21.909 you know,
00:22:22.128 so much of the effort is going to
00:22:23.450 be first understanding what
00:22:24.671 you need and being able to
00:22:27.551 define your requirements in a way that,
00:22:29.854 you know,
00:22:30.094 a system developer can actually
00:22:31.775 turn that into a solution.
00:22:34.355 And, you know,
00:22:34.996 all the way through actually building,
00:22:38.317 designing and building or working with,
00:22:39.959 you know,
00:22:40.660 a third party and outside organization to,
00:22:42.621 you know,
00:22:43.500 to work the design and build activity.
00:22:48.003 Every system,
00:22:48.564 every project is going to
00:22:49.464 have a champion.
00:22:50.528 Right.
00:22:50.769 And the champion,
00:22:51.490 it matters less what part
00:22:53.151 of the organization it is
00:22:54.431 that then that they're a
00:22:56.031 strong champion and that
00:22:57.432 they're well supported by the business.
00:23:00.575 Right.
00:23:00.795 You know,
00:23:01.855 projects where the champion or
00:23:05.617 the owner is, you know,
00:23:07.138 they're doing this in their spare time.
00:23:08.819 It's a, you know,
00:23:12.061 not a primary role or
00:23:13.323 responsibility tends to, you know,
00:23:15.763 it means essentially that
00:23:17.005 the project is
00:23:17.724 deprioritized at the leadership level.
00:23:21.183 And that's, you know,
00:23:21.984 that's another recipe for a failure.
00:23:26.287 Kind of coming back to that
00:23:27.228 time thing that you're referring to,
00:23:29.048 right?
00:23:29.189 The person has to be able to
00:23:30.210 kind of have the time.
00:23:31.150 They've got to be able to
00:23:31.851 facilitate the requirements
00:23:34.071 from different parts of the organization,
00:23:36.232 right?
00:23:36.432 Because we talk a lot about
00:23:37.513 operations and I know
00:23:38.434 that's where your focus is,
00:23:39.994 but a lot of the use cases
00:23:41.737 that we're seeing where AI
00:23:42.957 can add a lot of value is
00:23:44.917 outside of operations, right?
00:23:46.378 On some of the more business
00:23:47.859 supporting functions, right?
00:23:49.480 So you probably want
00:23:51.463 somebody that can
00:23:52.224 facilitate not just their needs,
00:23:54.667 but the needs of the entire business.
00:23:56.310 And I feel like that, you know,
00:23:57.792 is going to be a tough
00:23:59.134 thing to figure out,
00:24:00.455 especially when you're
00:24:01.356 trying to figure out
00:24:02.157 something that is
00:24:03.119 relatively new to most people.
00:24:04.541 Right.
00:24:05.542 Right.
00:24:06.724 Right.
00:24:08.410 So let's talk about some of
00:24:11.290 the skill sets then, right?
00:24:13.811 That some of the people here,
00:24:15.053 whether you're the one
00:24:15.952 leading the initiative or
00:24:17.473 whether you're the one kind
00:24:18.394 of playing a role in the initiative,
00:24:19.994 right?
00:24:20.214 Like,
00:24:20.795 and you talk about the universities
00:24:23.655 and the next generation and
00:24:24.676 kind of workforce.
00:24:25.876 So what are the things that
00:24:26.897 you would be looking for
00:24:29.358 from these next generational workers?
00:24:34.218 Just thinking about my own
00:24:35.438 background and what I think
00:24:37.378 has helped me progress and
00:24:41.460 become what I am today is a
00:24:44.661 really broad and general understanding.
00:24:48.781 I think universities do this fairly well.
00:24:54.343 You can become a specialist
00:24:57.304 in a narrow niche field.
00:24:59.712 or you can be generalized in
00:25:01.773 your education, learning,
00:25:03.394 and even really capacity in
00:25:05.915 the workforce.
00:25:07.996 I fit in the latter, right?
00:25:09.817 I started as an engineer and
00:25:12.117 had the opportunity to go
00:25:12.897 back to school and get an
00:25:13.759 MBA and learned about
00:25:15.019 operations and finance and
00:25:16.900 accounting and basically
00:25:20.080 all the different aspects
00:25:21.682 of running a business.
00:25:25.083 I think that there's no one
00:25:26.923 skill set that is
00:25:30.904 or one talent that's always
00:25:33.346 going to be required.
00:25:34.067 I think it's the tech savvy,
00:25:36.930 the background,
00:25:37.490 that foundation layer of
00:25:38.652 not being afraid of technology.
00:25:39.992 I think we can take that for
00:25:40.913 granted in the next 10 to
00:25:43.695 20 years because everyone
00:25:44.916 will have a baseline of
00:25:46.377 technology savvy that far
00:25:48.579 exceeds what most in my
00:25:51.541 generation and certainly
00:25:52.603 most in our parents'
00:25:54.183 generation had brought to the workforce.
00:25:58.430 But I think it's really
00:25:59.150 going to be an
00:26:00.250 understanding of what is the business?
00:26:03.373 What is the business model?
00:26:04.794 How do we know what is our product?
00:26:07.576 What is our service?
00:26:08.476 How do we make money?
00:26:09.636 And being able to see the big picture,
00:26:12.999 right?
00:26:13.398 Seeing the forest and the trees.
00:26:17.981 I think it's very important
00:26:19.942 to be able to work
00:26:22.179 you know,
00:26:22.358 we talk about a 30,000 foot level,
00:26:24.520 very high level overview of, you know,
00:26:26.843 an organization, what's going on,
00:26:28.124 but also in the weeds, right.
00:26:29.444 It's at sea level.
00:26:31.106 And I think, uh,
00:26:33.127 a really important attribute in, uh,
00:26:35.289 successful employees in the
00:26:36.510 future in technology is going to be that,
00:26:39.294 that diversity and, and, and, uh,
00:26:42.375 broadness of understanding.
00:26:45.117 Uh, you know,
00:26:45.578 one of the things that I heard, um,
00:26:47.500 it was my advisor,
00:26:48.880 my undergraduate advisor
00:26:50.641 talked about when he talked about PhDs,
00:26:53.181 he wasn't disparaging PhDs, right?
00:26:54.721 He was a professor.
00:26:55.342 He's obviously had one of his own,
00:26:56.501 but he said, you know, when you get a PhD,
00:26:59.201 you're learning more and
00:27:00.182 more about less and less
00:27:01.563 until you know everything
00:27:02.482 there is to know about nothing at all.
00:27:06.044 And so I think that's, you know,
00:27:09.364 like that has a place, right?
00:27:10.865 For that, you know,
00:27:11.565 for the person who's going to, you know,
00:27:13.593 develop the next ai
00:27:15.114 technology that's going to
00:27:16.595 change the world like you
00:27:17.615 got to be that focused but
00:27:20.655 to you know the the
00:27:22.557 underlying technology isn't
00:27:24.278 the solution right the
00:27:25.499 underlying technology is
00:27:26.858 the enabler and the
00:27:28.500 solution is putting
00:27:29.421 together the unmet unmet
00:27:31.642 need with the the
00:27:34.103 technology that solves it
00:27:36.423 in a profitable use case
00:27:39.912 That's a very good point, Daron.
00:27:41.492 I think we're going to have to clip that,
00:27:42.634 man.
00:27:42.834 I really like the way that
00:27:43.755 you phrased that.
00:27:45.576 So it's really having understanding,
00:27:47.738 being able to look at the
00:27:48.598 big picture and really
00:27:50.661 being able to connect
00:27:52.402 different elements to what
00:27:54.183 you're doing and everything
00:27:55.984 tying back to that purpose, right?
00:27:57.625 Making sure that what you're
00:27:58.666 delivering and what you're
00:27:59.548 working on is connected to that purpose.
00:28:03.460 You know,
00:28:03.921 as I'm sitting here thinking
00:28:04.701 about it and hearing you
00:28:05.701 kind of repeat it back to me,
00:28:07.262 I think that there's an
00:28:08.223 intellectual curiosity
00:28:09.904 that's also a really
00:28:10.925 important attribute in technology, right?
00:28:17.029 I think that in IT in general,
00:28:19.011 we say that if you're not
00:28:20.152 always learning something new,
00:28:21.353 then you're falling behind
00:28:23.154 because things evolve so
00:28:24.855 rapidly and something that's necessary is
00:28:28.325 for someone to be willing
00:28:29.884 and able to continue learning,
00:28:31.705 to be a lifelong learner is a curiosity,
00:28:36.006 right?
00:28:36.787 Uh, I'm thinking about, you know,
00:28:37.926 we're operating, you know,
00:28:39.767 30,000 foot level down to sea level.
00:28:41.386 And, uh, you know, I'm thinking, you know,
00:28:43.827 and, and, and the, the, the real tech, uh,
00:28:47.008 leader needs to have that curiosity where,
00:28:50.288 you know, he sees the forest,
00:28:51.229 he sees the trees and he's
00:28:52.328 willing to get down on his
00:28:53.170 hands and knees and, you know,
00:28:54.269 dig through the, the, uh,
00:28:56.242 the dirt and the undergrowth
00:28:57.564 to see what's going on underground,
00:28:59.025 right?
00:28:59.204 Because you never know, right,
00:29:00.665 where the next solution is
00:29:02.106 going to come from.
00:29:03.248 It's probably not in an area
00:29:05.369 that you're already
00:29:05.950 thinking about because
00:29:07.951 there's so much capability
00:29:10.292 and businesses have so many
00:29:11.994 unmet needs that the
00:29:14.977 challenge isn't finding a
00:29:16.637 problem that needs solving.
00:29:17.558 It's finding the best
00:29:19.079 problems or the biggest
00:29:20.401 problems that need solving
00:29:21.701 first and then taking the time
00:29:25.828 Uh, to, you know, understand that,
00:29:28.170 understand the problem,
00:29:29.289 even before you start
00:29:30.029 working on the solution,
00:29:30.869 really understand the problem.
00:29:33.131 And then, and then to, uh, you know,
00:29:35.332 to start thinking about, okay,
00:29:36.892 the curiosity again,
00:29:38.231 researching what's the art
00:29:39.553 of the possible and what's
00:29:41.653 the art of the possible, you know,
00:29:43.614 in the near future, given the, uh,
00:29:45.794 the pace and rate of change,
00:29:48.474 and then being able to take,
00:29:49.615 take all that and, and, uh, you know,
00:29:51.875 structure that into a, uh,
00:29:53.777 a project or a plan.
00:29:55.680 that really builds this new
00:29:57.799 enabling feature.
00:30:00.740 That's very interesting that
00:30:01.980 you kind of frame it that way, right?
00:30:03.221 Because that's one of the
00:30:03.942 things that obviously as we
00:30:05.701 look to integrate this
00:30:07.461 technology into our own
00:30:08.762 offerings and kind of what
00:30:09.863 we do is one of the things
00:30:11.482 that we've been struggling with, right?
00:30:13.163 You've got these paradigms
00:30:14.743 in terms of what you're
00:30:16.384 used to and how you're used
00:30:17.565 to solving things.
00:30:19.224 And now you've got this new
00:30:20.484 access to this technology
00:30:22.365 that can completely shift
00:30:25.146 But you're going to miss it
00:30:26.969 if you don't really
00:30:28.250 challenge yourself and even
00:30:29.853 just be curious about it.
00:30:32.355 And it's not even like a
00:30:33.217 malicious thing or you're
00:30:34.419 trying to consciously omit it.
00:30:35.980 But it's just you don't know
00:30:37.582 what you don't know until
00:30:38.743 you start playing around with it.
00:30:40.766 And, you know,
00:30:41.267 the joke I make with the
00:30:42.166 team all the time is like, you know,
00:30:44.409 you got to fuck around to learn.
00:30:46.650 Right.
00:30:46.970 And it's and it's true.
00:30:48.991 And it boils down to kind of
00:30:50.893 a lot of the stuff that you're saying.
00:30:52.653 You got to play around with it.
00:30:53.855 You got to see it's easiest.
00:30:54.935 You got to see where it's good,
00:30:56.596 where it's not good.
00:30:57.617 And I think then you can
00:30:59.318 really carve out a path forward.
00:31:02.540 Yeah.
00:31:03.422 And it really goes back to curiosity.
00:31:04.942 Yeah.
00:31:05.717 Right.
00:31:06.397 And you have to, you know,
00:31:07.798 not everyone has that curiosity,
00:31:09.381 that intellectual curiosity to say, well,
00:31:11.762 there's something I'm interested in.
00:31:12.924 It seems to have no relevance to my, my,
00:31:15.346 my role or my occupation today,
00:31:17.269 but I'm curious.
00:31:18.750 And that curiosity is going
00:31:20.613 to enable learning.
00:31:22.317 that may or may not in the
00:31:23.898 end enable a new solution
00:31:26.479 to be developed.
00:31:27.720 And I want to touch on that may or may not,
00:31:30.019 right?
00:31:30.381 Because that's where
00:31:31.621 organizations need to be
00:31:32.941 open to experimenting.
00:31:35.501 And we're talking about the
00:31:37.282 context of time and money
00:31:39.042 and all these kind of
00:31:39.803 factors that play a role in it.
00:31:41.943 Experimenting isn't really a
00:31:43.404 word that people like to
00:31:45.145 throw around when time and
00:31:46.267 money is concerned,
00:31:47.106 especially from a business perspective.
00:31:49.087 But the truth is to really
00:31:50.489 maximize and understand as well,
00:31:52.269 to align to a lot of the
00:31:53.170 messaging that you're saying,
00:31:54.431 you're going to need to experiment.
00:31:57.232 What do you think about that?
00:31:59.054 Yeah, you know,
00:32:00.294 when you were talking about that,
00:32:01.295 it really reminded me of, you know,
00:32:03.615 the difference between the
00:32:04.636 old waterfall approach to
00:32:06.337 projects versus the, you know,
00:32:08.179 the agile approach.
00:32:10.461 agile methodology, right?
00:32:11.682 Where in a waterfall approach,
00:32:13.483 you tend to start with a
00:32:15.726 lot of effort on requirements,
00:32:17.748 trying to capture all the
00:32:18.688 requirements and then spend a, you know,
00:32:20.910 large amount of time
00:32:21.770 building the one monolithic
00:32:23.092 solution that meets all the requirements,
00:32:25.473 which in a lot of cases, if, you know,
00:32:27.655 if it's just not thought
00:32:28.717 out quite well enough,
00:32:29.616 or if something happens or changed,
00:32:31.199 or your, you know,
00:32:31.838 your thought leader wasn't
00:32:32.619 quite curious enough and he
00:32:33.621 didn't know what was really
00:32:34.520 possible or what the real need was,
00:32:37.723 you know, you can end up with a,
00:32:40.401 a failure, right?
00:32:44.664 And to add to that too,
00:32:46.705 a lot of this technology
00:32:47.846 has very heavy dependency
00:32:51.730 on things like data quality.
00:32:55.092 And especially the higher up
00:32:57.252 the chain that you go,
00:32:58.134 the more disconnected that
00:32:59.835 some of the executive team
00:33:01.296 members are from the state
00:33:03.517 of quality of their data,
00:33:05.137 of their systems, of their processes,
00:33:07.480 and whatnot.
00:33:08.380 And that's just natural with
00:33:09.681 any hierarchy that you've
00:33:11.923 got in the business.
00:33:14.304 And so, you know, AI,
00:33:16.464 machine learning and all
00:33:17.664 these kinds of tools will
00:33:18.684 need to really build on
00:33:21.306 data and the quality of it.
00:33:24.066 So what kind of role do you
00:33:25.226 think that that's going to
00:33:26.027 play with experimenting
00:33:27.307 things and running into?
00:33:29.047 We thought we could do this
00:33:30.067 and on paper we can.
00:33:32.008 But when we see what we got to work with,
00:33:34.729 we have this one ingredient.
00:33:36.888 that we just won't be able
00:33:38.528 to make the dish with
00:33:39.470 because it's a rotten
00:33:40.450 ingredient or it's not sour
00:33:42.130 enough or it doesn't have
00:33:43.250 the right acidity that's
00:33:44.891 needed to really turn this
00:33:46.631 dish into a five-star meal.
00:33:50.752 Yeah,
00:33:51.353 I think specifically data quality is
00:33:54.452 a very important part of
00:33:58.054 the evolution of technology systems.
00:34:00.694 And I think you're absolutely right.
00:34:03.036 Data is inherently
00:34:04.917 the lowest level, you know,
00:34:06.239 the C-level detail and
00:34:08.519 executives and leadership
00:34:10.521 tends not to be as closely
00:34:12.461 connected to the data as
00:34:14.402 you need to be to be able
00:34:16.043 to objectively assess is
00:34:17.324 this high quality data?
00:34:18.804 Do I even have the data that
00:34:20.246 I need to enable a use case
00:34:22.666 that I'm thinking about?
00:34:26.969 So to go back to, sorry about that,
00:34:30.490 just had a distraction for a moment.
00:34:32.711 Going back to the idea of data quality,
00:34:34.172 right?
00:34:36.717 I always think of a pilot,
00:34:38.057 a pilot for a project, right?
00:34:40.480 An experiment to say, okay,
00:34:41.840 this is our objective.
00:34:42.782 We want to try to do something this way.
00:34:44.402 You know, we can,
00:34:45.844 we can design a waterfall
00:34:46.965 project and then, you know, million,
00:34:48.947 million dollars or more and, and, uh,
00:34:50.768 you know, try to do something.
00:34:53.630 Um, or we could do it as a quick and dirty,
00:34:55.793 like let's hire an intern and, and, uh,
00:34:58.534 you know, have them, you know,
00:34:59.715 give them six months or three months and,
00:35:01.597 and set them loose with a,
00:35:03.378 a really general, um,
00:35:05.413 objective and see what he comes up with.
00:35:08.235 Uh, I think that's going to,
00:35:09.976 it's going to create a couple outcomes,
00:35:11.878 right?
00:35:12.358 Uh,
00:35:12.498 first is you'll get some objective
00:35:15.380 results that tell us about
00:35:17.141 the quality of the, uh, the,
00:35:20.463 the underlying, you know,
00:35:21.925 the foundational systems
00:35:23.125 and the data that they contain.
00:35:24.786 And also tell us about the
00:35:26.027 quality of the processes
00:35:27.548 they're responsible for creating, uh,
00:35:30.431 and maintaining that, that, uh,
00:35:32.512 essential business information.
00:35:35.152 uh, you know, with, uh, uh,
00:35:39.655 I forgot who it was when they said, uh,
00:35:41.356 you know, data is the new oil, right?
00:35:43.536 Data is the, the, the,
00:35:44.737 the new most valuable thing
00:35:46.159 in business because it can
00:35:47.539 be monetized in a bunch of
00:35:48.780 different ways.
00:35:50.681 Uh, you know, there's different,
00:35:52.702 just like there's different
00:35:53.704 qualities of crude.
00:35:54.643 I think there's light, sweet, crude,
00:35:56.164 and there's the, you know,
00:35:57.186 the oil sand stuff that
00:35:58.726 we're digging up out of the
00:35:59.606 ground that is, uh,
00:36:00.972 maybe abundant, but not quite as easy to,
00:36:03.373 to dig up.
00:36:03.954 Right.
00:36:04.094 We, we have to really, you know,
00:36:05.094 it's not just, it's not just oil.
00:36:07.655 It's a, you know,
00:36:08.695 it's a high quality or low quality and,
00:36:10.916 and, you know,
00:36:12.275 oil companies know all about that, right.
00:36:14.077 Same way organizations,
00:36:15.858 enterprises need to understand their,
00:36:18.898 their core value, their,
00:36:21.639 their underlying value, the data in their,
00:36:23.940 in their systems and, and, and what it is,
00:36:25.840 how, how valuable it is and,
00:36:28.092 how to increase that value,
00:36:29.932 how to derive value from it.
00:36:32.974 Right.
00:36:33.416 And I think when that
00:36:34.637 context was thrown out
00:36:36.237 there about data being the oil,
00:36:38.039 a lot of it had to do with
00:36:39.340 the value is not in this raw form,
00:36:42.422 but more so in being
00:36:45.224 processed and being refined
00:36:47.465 and everything else there
00:36:49.065 so that it can turn into energy, right?
00:36:50.907 Oil to you is not valuable in any way.
00:36:53.268 But what comes from it is valuable to you,
00:36:57.934 right?
00:36:58.096 It powers your house,
00:36:59.416 it powers your vehicles,
00:37:00.579 it creates your clothes,
00:37:02.181 it does a number of things.
00:37:05.445 Let's talk about not just
00:37:07.967 the data element of it,
00:37:08.987 but more so shifting towards learnings.
00:37:12.150 Because when you look at AI
00:37:13.311 integration for a business,
00:37:17.152 it's a similar type project
00:37:18.753 to previous digital
00:37:20.034 transformations or previous
00:37:21.855 system implementations.
00:37:23.396 You've got a current state.
00:37:24.996 that is going to change into
00:37:26.597 a future state,
00:37:27.679 and technology is going to
00:37:29.059 be the driver behind that, or the enabler,
00:37:32.643 as you kind of say, right?
00:37:34.224 But it's still a people business.
00:37:36.126 It's still driven by the
00:37:37.786 people that work within
00:37:38.827 there and operate it.
00:37:40.510 It's still dependent on the
00:37:41.630 data and everything else there.
00:37:44.913 Let's talk about some of the
00:37:45.853 things that went really bad
00:37:47.313 or really good that you've
00:37:49.275 been a part of throughout
00:37:51.155 your career on similar
00:37:53.317 projects and anything that
00:37:54.456 you can share here with the audience.
00:37:57.257 Sure.
00:37:57.617 Yeah.
00:37:58.619 I've always said the best
00:37:59.478 example is the example of what not to do.
00:38:02.199 And my understanding,
00:38:03.960 I think it's reported that
00:38:05.501 something like two thirds
00:38:06.782 or three quarters of all
00:38:08.443 ERP system implementation projects
00:38:12.045 will fail in one way or one
00:38:13.565 measure or another, right?
00:38:14.547 A failure might mean just
00:38:15.768 not achieving all of the
00:38:17.228 objectives of the initial project, or,
00:38:20.512 you know, failure could also be late or,
00:38:24.635 or over budget.
00:38:26.416 But, you know,
00:38:29.338 understanding that an
00:38:30.378 example of what not to do
00:38:31.599 is some of the useful
00:38:33.402 things or the most useful
00:38:35.242 cases to learn from.
00:38:36.204 I think that, you know,
00:38:39.215 time and budget again.
00:38:40.315 And then I think also just
00:38:42.315 the idea of respecting reality.
00:38:44.775 An organization needs to be
00:38:46.476 very objective and
00:38:47.476 realistic about this is
00:38:49.277 where we are today.
00:38:50.458 And given what we have in
00:38:53.217 our wheelhouse today,
00:38:54.978 are we capable of designing, building,
00:38:59.380 implementing and
00:39:00.599 maintaining an application, a tool,
00:39:03.320 a use case to do whatever
00:39:04.561 it is the company wants to
00:39:05.661 do next and making sure that that's
00:39:09.081 objectively estimated and is
00:39:13.222 adequately resourced,
00:39:15.422 both with time and money.
00:39:17.923 So you got to have that
00:39:18.844 organizational honesty is a must,
00:39:21.983 is kind of what you're
00:39:22.764 saying when you're starting
00:39:24.644 one of these journeys.
00:39:26.164 Yeah.
00:39:27.085 Respect for reality.
00:39:28.065 Got to honor reality,
00:39:29.045 respect reality and objective reality.
00:39:33.565 Not the world according to Daron,
00:39:34.947 but the world according to
00:39:37.079 know Daron and all the
00:39:38.099 other objective observers
00:39:39.882 that are that are uh seeing
00:39:42.425 the same thing right so
00:39:45.568 what else would you say is
00:39:46.889 kind of a good learning to
00:39:48.592 make sure that the ai
00:39:50.213 project doesn't become a
00:39:51.295 money pit but it actually
00:39:52.856 becomes a transformative
00:39:54.619 value-added implementation
00:39:57.844 you know, the, the,
00:39:58.804 I think a key success
00:39:59.885 factor in any sort of a
00:40:01.266 technology or implementation is, is the,
00:40:03.646 you know, again,
00:40:04.106 going back to the leadership, having that,
00:40:06.208 that solid core ownership and having it,
00:40:09.668 having that, um, you know, uh,
00:40:13.230 having that celebrated and, and, uh, and,
00:40:16.110 you know,
00:40:17.211 bestow having that
00:40:18.012 responsibility bestowed on the, uh,
00:40:20.012 on the,
00:40:20.432 the leaders of the organization
00:40:22.152 that really have the capability and the,
00:40:26.514 um,
00:40:28.117 and the charter to go
00:40:29.958 accomplish the objective of the project.
00:40:32.239 You know, I'm thinking back of, you know,
00:40:34.579 in the intro,
00:40:35.360 you mentioned our past
00:40:36.681 experience working together
00:40:37.760 at an aerospace company in
00:40:38.882 the Los Angeles area.
00:40:40.902 We had three individuals.
00:40:42.623 If you remember,
00:40:44.063 you came up with the term MIT, right?
00:40:46.204 Because I think it was Michael, Ian,
00:40:48.505 and Tamara.
00:40:49.625 And MIT, right?
00:40:51.606 I mean, it sounds good.
00:40:53.485 You know, it rolls off the tongue.
00:40:56.724 And MIT were empowered.
00:40:58.784 They were taken out of their day jobs for,
00:41:01.766 I think it was six or nine
00:41:02.826 months or so to focus on the project.
00:41:05.746 You know,
00:41:06.007 being an ERP implementation project,
00:41:08.327 three full-time resources
00:41:10.088 from within the business
00:41:11.289 was the right amount of resource.
00:41:16.210 I think the organization was
00:41:17.210 about 300 people at that time.
00:41:18.771 So, you know,
00:41:20.532 it was 1% of the organization
00:41:22.052 was dedicated on the project.
00:41:24.449 And that project was really
00:41:25.590 a wild success in large
00:41:28.630 part attributable to the
00:41:30.911 focus of that really strong
00:41:33.913 leadership team.
00:41:35.952 And that was enabled because
00:41:39.253 the business leadership was
00:41:41.775 very objective about this is what needs
00:41:44.371 this is what we need to do
00:41:45.313 to be successful in this project.
00:41:47.253 Now you,
00:41:47.594 you probably remember better than I,
00:41:48.775 because I joined the company in the,
00:41:50.215 in the, in the middle of the project.
00:41:52.358 I think that, uh,
00:41:53.297 that reality was realized
00:41:55.420 after failing a couple of times, right?
00:41:57.061 There was an initial
00:41:57.802 implementation consulting
00:41:58.981 group that failed because I
00:42:01.244 think they load, you know,
00:42:02.005 they were probably the
00:42:02.505 lowest bidder and didn't provide the, uh,
00:42:04.365 the time and support to the project.
00:42:07.487 And then there was a, you know, a second,
00:42:09.170 uh,
00:42:09.369 consultant that took over the project
00:42:10.911 leadership and, and, uh,
00:42:13.460 Uh, I think, uh, you know, I was a one,
00:42:15.601 one woman show and, um, you know,
00:42:18.125 didn't have the consensus
00:42:20.126 and buy-in wasn't able to
00:42:21.327 form the consensus and buy-in with the,
00:42:23.849 you know,
00:42:24.030 the leadership at the site or the,
00:42:25.572 you know, the, the,
00:42:26.253 the more executive
00:42:26.952 leadership of the company
00:42:27.893 that really had to pay the bills.
00:42:29.815 And, and after the, you know,
00:42:31.757 the first two attempts failed.
00:42:34.550 And when Luniko was brought
00:42:35.751 into the project to, you know,
00:42:37.132 to really to lead and
00:42:39.996 finish out the project that
00:42:41.217 had been started and, you know,
00:42:42.579 sort of been floundering for,
00:42:43.739 I don't know,
00:42:45.081 I think it was close to a
00:42:45.942 year or so by the time you
00:42:47.364 got involved in the lead role,
00:42:51.768 the business had learned very objectively,
00:42:53.929 it's not going to work if
00:42:54.891 we don't adequately resource it.
00:42:57.371 Yeah,
00:42:57.592 and I do recall the initial
00:42:58.972 conversations.
00:43:00.815 They were involved early on in the project,
00:43:02.697 but they weren't committed, right?
00:43:04.657 And I think there's a big
00:43:05.498 difference between
00:43:07.161 involving people and committing people.
00:43:09.342 And I do recall the
00:43:10.163 conversation with the VP of
00:43:11.565 operations at the time.
00:43:13.545 um ryan and he was like
00:43:16.266 these are my best people
00:43:17.507 like you know you're asking
00:43:19.608 for the best people that we
00:43:21.849 have on here right on our
00:43:23.530 shop and they're
00:43:24.070 representing engineering
00:43:25.271 they're representing
00:43:25.951 operations they're
00:43:27.072 representing quality and
00:43:28.552 it's like we really need
00:43:29.853 them on there to deliver
00:43:31.293 operations but he saw
00:43:34.074 strategically how important
00:43:36.576 it was to commit those three people
00:43:39.717 And that it'd be better for
00:43:41.097 the longevity and the
00:43:43.099 success of the business to
00:43:45.141 commit them to the project
00:43:47.101 because he realized it was
00:43:48.382 transformational so that it
00:43:50.605 could lead to better results.
00:43:51.945 And yeah,
00:43:52.405 it was a six month commitment and
00:43:54.347 it was going to be hard
00:43:55.427 transition in them and all
00:43:56.949 of that kind of stuff.
00:43:58.550 But that in the end,
00:43:59.369 it would be worth it
00:44:00.130 because it would have a
00:44:00.831 much stronger foundation,
00:44:02.811 better business processes,
00:44:04.652 and it would be something
00:44:05.492 that's aligned to their
00:44:06.853 business and in an
00:44:08.255 objective and a fit for purpose way.
00:44:10.536 And it worked out.
00:44:11.735 It did work out in the end there, right?
00:44:13.657 And I think that that's probably the
00:44:15.777 the frame of mind that a lot
00:44:17.619 of executives need to put
00:44:19.501 themselves in it's not just
00:44:21.742 who can we hire that's easy
00:44:23.443 that like you said the
00:44:24.545 interns that can come in
00:44:25.585 and kind of execute the
00:44:27.206 work on paper because it
00:44:28.807 says that you must do
00:44:30.349 gather requirements and you
00:44:31.951 know this person can gather
00:44:33.172 requirements and more think
00:44:35.634 about who are the people
00:44:37.335 that need to be committed to the project
00:44:40.257 so that this can be
00:44:41.137 successful and so that we
00:44:42.900 can get the value that
00:44:44.141 we're looking for from this
00:44:46.523 disruptive technology.
00:44:48.005 Otherwise, like you said,
00:44:48.925 it's just going to become
00:44:49.786 another money pit project
00:44:51.427 that companies are going to
00:44:52.409 be doing over and over and over again.
00:44:56.371 And, you know, yes, listening to you,
00:44:58.554 I think there's another
00:44:59.355 benefit that came out of
00:45:00.155 the project that actually
00:45:00.896 really had almost nothing
00:45:01.998 to do with the project itself.
00:45:03.994 By taking the three MVPs of
00:45:06.155 the organization, the best quality person,
00:45:08.416 the best engineering person,
00:45:09.617 I think best planner,
00:45:11.978 and committing them to not their day job,
00:45:14.039 but to another project
00:45:15.159 that's going to transform the business,
00:45:16.661 it created an opportunity
00:45:18.902 for other people in the
00:45:19.822 organization who are, I think,
00:45:21.682 more than prepared to step
00:45:23.583 up to fill those open roles
00:45:26.885 during the course of the project.
00:45:29.146 It actually grew the talent
00:45:31.547 base in the company.
00:45:32.547 If you're a
00:45:33.809 I forget the planner's name who was,
00:45:36.510 he had to print all the trappers, right?
00:45:39.731 He stepped up in a huge way
00:45:41.672 and he was given a huge
00:45:42.492 opportunity that had, you know,
00:45:45.452 the VP of operations at Rye
00:45:46.753 had not taken the very
00:45:49.173 difficult decision to pull
00:45:50.414 his best resources out of
00:45:51.855 operations and put them on this project.
00:45:54.115 He actually improved the
00:45:55.456 operational team because he
00:45:57.556 gave some people some new
00:45:58.498 experience that they might
00:45:59.297 not have had an opportunity
00:46:00.597 to ever have in that business
00:46:04.050 And that,
00:46:04.431 and that grew the overall talent
00:46:05.992 pool in the business that helped evolve,
00:46:09.072 you know, not the systems, but the, the,
00:46:11.873 the employees.
00:46:13.693 And, and that,
00:46:14.693 I think that that's part of the, you know,
00:46:16.634 the, the forest and the trees, right?
00:46:18.313 I mean, we, we took people who were at, uh,
00:46:20.135 maybe operating at a 500
00:46:21.715 foot level and moved them
00:46:23.335 up to a thousand or 5,000 foot level.
00:46:26.255 So they had bigger, bigger picture,
00:46:28.516 bigger responsibility, bigger roles,
00:46:30.516 bigger shoes to fill.
00:46:31.356 And man, they,
00:46:32.398 they were just so successful.
00:46:35.409 Yeah, that's a good point there.
00:46:36.791 And I think midterm and long term,
00:46:38.210 it makes sense in almost
00:46:40.552 every single way that you
00:46:41.452 can kind of think about it.
00:46:42.594 It's that short term fear
00:46:44.655 that I think holds you back a little bit,
00:46:46.876 right?
00:46:47.076 Because you're like, man,
00:46:47.896 what am I going to do?
00:46:49.280 without Ian running the
00:46:51.543 travelers and being out
00:46:52.704 there in the shop floor and
00:46:53.784 coordinating things, right?
00:46:55.206 I won't be able to rely on him to do that.
00:46:57.829 Or if there's quality issues,
00:46:59.070 Tamara's not gonna be involved on that,
00:47:01.172 right?
00:47:01.331 Cause she's gotta be
00:47:02.072 involved in this project
00:47:03.353 and things like that scare people.
00:47:06.315 Um, I, I think in retrospect, right,
00:47:08.416 everybody can look at that
00:47:09.657 project and say that that
00:47:10.719 was the right choice.
00:47:11.639 And I think that Ryan looks at his,
00:47:13.699 his operation, uh,
00:47:15.360 and he was able to get a
00:47:16.422 lot of benefits from being
00:47:18.003 able to commit that.
00:47:18.802 But in the moment it's, it's, it's scary,
00:47:21.023 man.
00:47:22.344 Yeah.
00:47:23.164 It feels like a big risk, right?
00:47:24.646 You know, it's a, it's a big threat to the,
00:47:27.347 uh, the current operation, right.
00:47:28.847 To,
00:47:29.108 to take your best people out of their
00:47:30.989 day jobs.
00:47:32.454 But it's, it's, it's, it's, you know,
00:47:34.115 it's part of the strategy.
00:47:35.297 It's, you know,
00:47:36.597 that was enabled in large
00:47:38.298 part because they had the foundation,
00:47:40.159 they had, you know,
00:47:41.641 no single points of failure as a,
00:47:43.822 as one idea that always comes up, right.
00:47:45.463 As, uh, you know, if you, if you take your,
00:47:48.284 your engineering manager
00:47:49.806 and put them on a project
00:47:51.027 and you have to have
00:47:51.786 somebody else that's able to step up,
00:47:53.387 even in a temporary
00:47:54.289 capacity to manage an
00:47:56.329 engineering function or
00:47:57.309 engineering department.
00:47:59.268 In a, in a smaller,
00:48:00.588 less prepared organization,
00:48:02.170 you're not going to be able to do that.
00:48:04.449 So let me ask you for these
00:48:06.610 type of projects, right?
00:48:08.130 Um, do you think you need to do that?
00:48:10.030 Do you think you need to be
00:48:10.952 grabbing your best people,
00:48:12.632 taking them out, putting them into this,
00:48:14.891 or do they need to be fully
00:48:16.512 committed or is this
00:48:17.592 something that they can be
00:48:18.552 more involved in?
00:48:19.413 Um, and somewhat committed to.
00:48:21.713 Well, you know, I,
00:48:24.554 I don't think I wouldn't
00:48:25.235 say it has to be your best people.
00:48:26.275 It has to be the right people.
00:48:27.856 You know, in this case, right,
00:48:29.016 it happened to be the right
00:48:30.617 people for the task were
00:48:32.376 the best people in the organization.
00:48:34.936 But, you know,
00:48:35.398 when we talked about your
00:48:36.637 idea earlier about, you know,
00:48:37.938 do you start with a huge
00:48:39.177 waterfall project or do you
00:48:40.378 start with experimentation?
00:48:42.099 You know,
00:48:42.998 the best person to run a
00:48:44.099 waterfall project is not
00:48:45.519 going to be the best person
00:48:46.679 to experiment and piddle
00:48:48.219 around with a technology to
00:48:49.960 find what is the art of the
00:48:51.541 possible and how can we
00:48:52.501 apply this to solve our problem?
00:48:54.601 So I think it's, you know,
00:48:57.541 it's not the best person,
00:48:58.422 it's the right person.
00:49:00.302 And often you don't know
00:49:02.023 that until you're looking back on it.
00:49:05.465 But I think that's part of
00:49:06.786 the vision that a leader
00:49:08.648 has to have is objectively,
00:49:12.650 where am I in the organization?
00:49:14.110 Can I afford to take the
00:49:15.972 right people for this
00:49:16.893 project out of their day jobs?
00:49:20.675 And will that have
00:49:21.873 Will the negative short-term
00:49:23.237 impact on the operation
00:49:24.842 outweigh the long-term
00:49:26.226 benefit that we expect to
00:49:27.851 receive from the new solution?
00:49:30.396 Yeah, that makes sense.
00:49:32.117 So let's have one more question here,
00:49:34.858 Daron, before we kind of wrap things up.
00:49:37.398 So what advice would you
00:49:38.759 give to people in similar roles?
00:49:42.360 Or maybe it's somebody who's
00:49:44.221 going from operations,
00:49:45.460 trying to transition into technology.
00:49:48.061 What advice would you give
00:49:49.202 them that could enable and
00:49:50.742 empower them in the future?
00:49:54.083 Yeah, I guess I'd take that in two parts.
00:49:59.177 what advice would I give to a business?
00:50:00.778 And then what advice would I
00:50:01.659 give to an individual or to a, you know,
00:50:03.601 to a,
00:50:04.300 an employee who's thinking about
00:50:05.641 their career.
00:50:06.262 So for, for business,
00:50:07.302 I think it's really
00:50:10.025 important to be objective,
00:50:12.025 to objectively understand
00:50:14.307 the strength of strengths
00:50:15.869 and weaknesses in the
00:50:16.648 business and the
00:50:17.750 opportunities that come with it.
00:50:20.972 And so it's,
00:50:23.954 it's very important to have that, that,
00:50:27.215 that solid,
00:50:28.762 well-founded vision that's
00:50:30.744 based in reality and
00:50:32.164 respects and honors reality, you know,
00:50:34.565 the good and the bad parts of the reality,
00:50:36.427 uh, and to, and to be just, you know,
00:50:39.847 to take that and, and,
00:50:41.028 and to be always making the
00:50:42.829 best choices for the organization, um,
00:50:46.871 and, and to be realist, you know,
00:50:48.652 realistic choices for the organization.
00:50:51.213 Uh, and then for the individual, I,
00:50:53.315 I think, you know, it's, I, I,
00:50:55.936 I had this conversation with my kid, uh,
00:50:59.021 you know, last year when they were,
00:51:01.523 Luca graduated in June, right?
00:51:03.103 So, you know,
00:51:04.724 we were talking about career
00:51:05.965 and career choices.
00:51:07.246 And, you know, my advice to Luca was,
00:51:09.228 you know,
00:51:10.608 most important thing about what
00:51:12.648 you're doing is that you enjoy it, right?
00:51:15.371 So don't take a job because
00:51:17.092 you think you need a
00:51:18.092 certain income or because you think,
00:51:19.632 you know,
00:51:19.893 this title or this paycheck or
00:51:21.873 this company is, you know,
00:51:25.295 what you want to do for the
00:51:26.097 rest of your life.
00:51:28.150 Think about what you want to do now,
00:51:29.652 what your short-term goals
00:51:31.775 are and your long-term goals,
00:51:34.277 and know that if you start
00:51:36.980 doing one thing today,
00:51:38.021 it does not mean that
00:51:38.762 you're going to do that for
00:51:39.563 the rest of your life.
00:51:41.945 Be open, be curious,
00:51:45.148 commit yourself to a
00:51:46.990 lifetime of learning.
00:51:49.164 And I think that's a recipe that makes,
00:51:51.469 you know,
00:51:51.748 that will help any individual to,
00:51:53.371 you know, to grow,
00:51:54.512 to evolve and prosper both
00:51:57.157 personally and professionally.
00:51:59.237 Right.
00:51:59.777 That's good advice.
00:52:00.918 Very good advice there.
00:52:02.239 So thanks a lot for coming on the show,
00:52:03.900 man.
00:52:04.121 It's been great chatting with you.
00:52:05.782 For all the listeners, right,
00:52:07.342 if you're here at this point, thank you.
00:52:10.483 You know, we're still looking for a name,
00:52:12.704 right?
00:52:12.985 So if anybody has kind of
00:52:14.425 any feedback or in terms of
00:52:16.608 how the production went or whatnot,
00:52:18.469 we'll welcome it.
00:52:19.369 But we are looking for a name.
00:52:20.630 So please email me or reach
00:52:22.472 out to me on LinkedIn or
00:52:24.052 wherever you want to help
00:52:25.313 us title our show.
00:52:27.956 And if you know anybody that
00:52:29.838 you think would be a great
00:52:30.697 fit for the show and you
00:52:32.300 want to get them in touch with me, please,
00:52:33.840 please let us know.
00:52:35.081 Daron, awesome to have you as always,
00:52:38.585 man.
00:52:38.804 I hope to be back in
00:52:40.106 California with you at some point, right?
00:52:41.806 Drinking some beers over the beach.
00:52:43.869 Hopefully in the near future.
00:52:46.175 Yeah, heck yeah.
00:52:47.255 Thank you for having me on.
00:52:48.536 It's always a pleasure talking with you.
00:52:49.996 Really enjoy time with you
00:52:52.518 and time in preparation in
00:52:54.179 advance with Gar and Sophie
00:52:56.119 and some of the other
00:52:58.240 consultants that you have
00:52:59.161 at Luniko that I've known now for,
00:53:01.661 I guess, going on like five years or so.
00:53:04.402 So thank you for having me.
00:53:05.603 It's always great to talk.
00:53:07.585 Call me anytime.
00:53:10.085 All right, guys.
00:53:12.126 Thanks, everybody.
00:53:13.688 See you next time.
00:53:15.681 Cheers.
