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The Harder You Work, The Behinder You Get · The Lagging Team

0:00
Chris Sims: Well, who says we're behind, right? And and and what
0:04
are we behind? And what was the standard? And who set the
0:07
standard? And is that even a reasonable standard? This is the
0:11
Devon Morris: lagging team. Falling behind is a position,
0:14
not a verdict. Today's episode: The harder you work, the
0:20
behinder you get. Hey, hey, hey, hey! I have someone here that I
0:30
probably, I really, really admire. He has gone on a journey
0:35
that I have pretty much followed without knowing I was following
0:38
him on my journey. Whether it is went to U of I, become a
0:42
certified Scrum trainer. You know, I remember reading this
0:45
book back in the day. But you see his name on the screen, and
0:49
I'm going to introduce him. So let me just start with the
0:52
introduction, because every time I start talking, and the next
0:55
thing you know, we're just kind of going. So first things first,
0:59
Chris Sims is an agile coach, a certified Scrum trainer. Was a
1:05
certified Scrum trainer before me, and the founder of Agile
1:08
Learning Labs, which I patterned my company after. They're the
1:14
co-author of two best-selling books: The Elements of Scrum and
1:20
Scrum: A Breathtakingly Brief and Agile Introduction. And
1:25
before any of that, Chris made a living as a software engineer,
1:30
musician, an auto mechanic. And I asked Chris here because they
1:36
spend years teaching teams how this work gets done, and because
1:42
they're personally started over more times than most people
1:49
across their entire career. So here's what I keep coming back
1:54
to, Chris: a team can be doing everything right by the playbook
2:01
they've actually been handed and still be behind because the
2:05
playbook stopped matching the actual work. Let's just start by
2:11
you telling me a time you watched the team, the ground
2:15
really move under a team.
2:19
Chris Sims: Yeah, well, thanks for having me on on your show.
2:22
I'm I'm excited to be here, and you know for as much as you were
2:27
saying nice things about me and all that, and saying you admired
2:30
me, it's very much a mutual admiration society here because
2:34
I've you know crossed paths paths with you so many times at
2:37
conferences and all of that and seen what you've done and been
2:40
like wow look at that! So yeah, a time I saw the ground move
2:46
under a team. So so there's a pattern I've seen quite a number
2:51
of times. The teams involved are leadership teams, and the
2:56
pattern looks like this. And I'll tell a specific story, but
2:59
the pattern looks like this, you know. We're a startup,
3:05
relatively small company. We've got some really good people on
3:09
the team. We're executing well. We're doing what we need to do.
3:14
We have our first success. Big influx of money. We're hiring.
3:21
We're growing. We're trying to move faster, and everything's
3:26
like ramping up, ramping up, going better, going faster until
3:30
suddenly it's not. And the the ground shifting is actually the
3:39
success and the growth that comes with the success, and the
3:46
change that you know is needed at that point, the pivot that's
3:51
needed by that leadership team is essentially they have to make
3:56
some decisions, right? They've been both the people holding the
4:00
vision, the people creating the structure and operations and
4:04
procedures and all that of the company, and the people like
4:09
doing the work to build the product. Right, they've been
4:11
doing all of that, and with growth comes a point where you
4:19
you can't do all that anymore, and while the people on the
4:25
leadership team arguably are the smartest people in the room at
4:29
any given point in time, if the folks around them keep leaning
4:36
on them for all the important decisions, they become this
4:40
bottleneck that holds the whole company back, and you know there
4:46
there's early on you're adding people, things are going faster,
4:50
right? Oh, we got more capacity. It's amazing, right? A little
4:53
bit like companies are doing now with AI, right? Oh, we start
4:57
using AI tools. Oh my goodness, we can build stuff so much.
5:00
Faster! This is great until you hit a bottleneck. Until you hit
5:05
a limiting factor, right? And for these leadership teams, they
5:08
hit this point where we're still adding way more people.
5:12
Devon Morris: Yeah,
5:12
Chris Sims: we're not going any faster.
5:14
Devon Morris: Yeah,
5:15
Chris Sims: right. We're not going any faster. In fact, if
5:17
anything, we're slowing down because now there's all the
5:19
overhead of like there's all these people to look after and
5:22
all of that, and so one team that I had the privilege of
5:29
getting to work with where there was success was a team where
5:35
very dramatically the CTO right who had who had been the tech
5:41
visionary for the company from from day
5:44
Devon Morris: one
5:44
Chris Sims: had this insight in a in a training session with you
5:48
know the other leaders other tech leaders in the organization
5:51
where they realized that very viscerally that they were the
5:55
bottleneck and yeah maybe they had the best ideas, but you
6:03
can't propagate that out to the whole organization, and you
6:06
can't know what's going on in detail at the on the ground in
6:12
all of the localized situations that all those people are
6:15
working in, and and so they realized they needed to step
6:21
back from making decisions and focus on guiding the people who
6:29
are going to make the decisions. Right, learning how to empower
6:31
other people, but also learning how to like coach and mentor and
6:34
all that while setting a big picture vision, but stepping
6:40
back from the details, right? And it's hard, right? It's hard.
6:45
I I had a corresponding failure story where you know the the
6:51
tech genius finds was a machine learning startup, and this
6:56
person was brilliant, absolutely brilliant, but they couldn't let
7:02
go of the control of making all of the important decisions
7:07
because other people would make them wrong, and all the people
7:11
around them believed that because this person was so
7:14
smart. And there just came a point where progress kind of
7:18
ground to a halt, right? They just couldn't get things done.
7:21
Right, they they had clients who wanted their technology, but of
7:26
course, you know, the start of every new client needs something
7:28
a little bit different. We got to change the product, whatever,
7:30
whatever. And this one person couldn't scale, right? They
7:34
couldn't they couldn't handle all of that, and eventually, you
7:41
know that company sadly imploded, right? Just kind of
7:45
under under the weight of, you know, the the bottleneck effect
7:51
of of having all the key technology decisions go through
7:54
that person.
7:56
Devon Morris: So, so here's here's why I I want to take a
8:00
step back because one of the things about you, Chris, that I
8:04
want the audience to understand is that the location where you
8:11
spent a lot of time working was where in this good old United
8:16
States of America,
8:17
Chris Sims: Silicon Valley,
8:19
Devon Morris: a very very innovative place, a place where
8:23
a lot of great tech companies came out of, and you're right
8:28
there in the seat of that. That is one of the reasons why I I
8:35
love Chris because he's right there where all that innovation
8:39
is happening, where all those super smart, super techy folks
8:43
are are right there, and so that's the experience that he
8:46
had, and that's an experience that I do not have. I've worked
8:50
with corporate America for sure, but not like those tech giants
8:54
like you have spent some time working with. And so you threw a
8:58
word out there. I want to make sure that some people are really
9:01
clear about. Give me the distinction between machine
9:06
learning and generative AI, because most people know about
9:10
LLMs right now and they think that that's everything. But
9:14
there was a time before that. Can you tell us a little bit
9:16
about that, Chris?
9:17
Chris Sims: I, you know what? I'm not the guy for that. I'll
9:20
be honest. I yeah, I am I am nobody's AI or machine learning
9:26
or LLM expert. So I am I am not the guy to give a good answer to
9:30
that question.
9:31
Devon Morris: Yeah, because I heard you. I heard you say
9:33
machine learning. I was just like, whoa, wait a minute. Been
9:36
doing that too, Chris. I've been like, I'm about to I'm about to
9:39
really tip my hat to you. I mean, I haven't done a lot of
9:42
machine learning stuff because obviously, I when I became a
9:45
developer, it was like I went into COBOL and stuff like that,
9:48
right? So the mainframe languages, so I can replace the
9:51
old heads. Because my philosophy at that time, Chris, was let me
9:55
become a developer to replace developers that I think are
9:58
going to pass away. And so I just had to start in my head
10:02
that all these old COBOL mainframe developers, these
10:05
systems ain't going nowhere, so they're gonna be the ones that's
10:08
gonna actually pass away, and then I'm gonna replace them and
10:12
get paid a lot of money. Yeah, right. So that's did that. And
10:17
so
10:18
Chris Sims: I remember, I remember the mid to late '90s,
10:22
Devon Morris: there was
10:22
Chris Sims: this huge resurgence in demand for for COBOL in
10:26
particular for Fortran to to some extent and all that, right?
10:29
These languages that had been more popular in years past with
10:34
the whole Y2K thing coming up, right? It was all of a sudden
10:37
it's like we got all this old code we've got to we've got an
10:41
update,
10:42
Devon Morris: and I took I took an eight month course, Chris,
10:44
and it got me into it. That's what I did. Nice.
10:48
So then, here's here's a question for you. So going back
10:50
to the story that you was telling, okay, when it when it
10:55
came to the leaders, right? Who noticed the shift first when it
11:06
came to that situation?
11:11
Chris Sims: That's a great question.
11:13
Devon Morris: Yeah.
11:16
Chris Sims: So I would say that the leadership team, the members
11:21
of the leadership team, noticed the change. They noticed the
11:28
symptoms, right? And I often describe this this situation.
11:32
I've written about it a few times. I call it "everyone is
11:34
busy, but delivery is slow, right? And the situation is like
11:38
we look around this place, and everybody's working really hard.
11:41
They're you know staying late. You know all right. Everybody's
11:45
like super busy, and yet it's taking longer and longer and
11:50
longer to get anything done. Yeah, and so the leaders
11:54
definitely saw the symptoms, right? Because they remember
11:57
like, hey, it wasn't that long ago when we were doing all the
12:01
hands-on work, and we got stuff, we got stuff done really
12:04
quickly, right?
12:07
Devon Morris: So then, what what is that difference like? Because
12:10
when you got your hands on it, and those initial founders,
12:14
they're going through with speed, and they're they're
12:17
looking to replace themselves because they can't they can't be
12:19
at that level anymore. What what has to get through a founder's
12:26
head to make that kind of shift?
12:28
Chris Sims: So so the light bulb moment I think is when they
12:34
realize that it's the exact things that got them to where
12:41
they are, right? The very strengths that have made them
12:43
successful,
12:44
Devon Morris: yeah,
12:46
Chris Sims: are now the things that are holding them back, and
12:49
that's a really tough light bulb to turn on, right? Because when
12:54
you have like I've I've made a career out of being really good
12:57
at doing this, and I do this, and I succeed, right? And and
13:02
you know I think of innovation, innovative technical solutions,
13:05
and I implement them, and you know blah blah blah, and like to
13:12
have the light bulb go on where it's like wait, yeah that got me
13:17
here, but it's not going to get me to where we need to go now,
13:21
and that's hard, right? And that's scary because it means
13:27
they have to pivot from playing to their strengths, but also
13:32
their comfort zone, to like something new, and and by and
13:38
large, right? The folks who who get into these leadership
13:41
positions and and find themselves in this problem that
13:45
comes with success that I'm describing. By and large,
13:49
they're pretty smart, adaptable people. So once they wrap their
13:51
head around it, they can do
13:54
Devon Morris: it.
13:54
Chris Sims: But it's still scary, right? Because it's they
13:56
need to do different things now.
13:59
Devon Morris: So then the ground is shaking right now when it
14:01
comes to AI. So, how are some of them thinking about how how do
14:08
you see them dealing with those shifts now? Because it's
14:11
happening under their feet. It's new technology that's changing
14:17
how we think about a lot of things. I mean, I'm I know
14:21
someone who you know who is working with five developers,
14:24
and now it's just him, and he's AI, and then those five
14:28
developers he no longer work with, and and he's super super
14:33
productive in getting things out, just being a one man shop.
14:37
But that just changes everything, and so as you
14:42
started seeing AI come in, what kind of impacts did you start
14:46
seeing?
14:49
Chris Sims: So, there's a there's a model that I really
14:53
like-a change model. It's called the Satir change model, and it
14:57
basically describes. How individuals, groups, large
15:02
organizations adapt and deal with change, and maybe I get the
15:11
image to you. You can share it, you know, at at some point here
15:15
in the in the podcast, right? You cut it in and post or
15:18
whatever you cool kids do. But but basically, it's a curve
15:23
where, like, on the vertical axis
15:25
Devon Morris: is
15:26
Chris Sims: what you care about, what you're trying to optimize,
15:28
right? You know, speed to market, revenue, whatever it is,
15:33
whatever it is that you want to optimize. And then on the
15:35
horizontal axis is time.
15:39
Devon Morris: Yeah.
15:40
Chris Sims: And so basically, you're you know, you're
15:42
traveling along at some status quo, right? We do what we do, we
15:46
get what we get, right? And then you introduce a change, right?
15:50
And it may be a change that seems like amazing, like oh my
15:52
goodness, AI! We need to start using AI, and it's going to make
15:57
everything better. And as soon as you introduce that big
16:00
change, of course. Performance drops off precipitously, right?
16:05
And in fact, not only does it drop, but you enter this chaotic
16:08
period, right? It's the chaos period where kind of the old
16:11
rules don't seem like they apply anymore. We there's this new
16:16
thing, but we don't really understand it yet. We don't know
16:20
how to use it, right? And so I've seen you know a lot of
16:25
that. I'm seeing organizations now, right, starting to have you
16:32
know AI governance just because of like all of the ways that AI
16:36
was used poorly early on, yes, and and some of some of the you
16:40
know bad things that happen from that, so you enter this chaos
16:43
period, and performance goes down, and then at some point
16:48
there's this moment, this transformational moment, where
16:52
we start to see how this new thing, right these days it's AI,
16:57
really could work to enable us to do what we were trying to do
17:00
better, and at that point, actually, the chaos abates, but
17:04
performance doesn't immediately go up. It still continues to dip
17:06
a little bit. But then, as we get into this period of
17:10
integration and practice, we get better and better at using it
17:15
skillfully, and performance goes up and up and up, and then
17:18
eventually, right, kind of levels off. And so I think right
17:21
now most of the places that I'm working with are, you know,
17:26
definitely the the change has happened, and lots of places are
17:32
in the chaos. Some places are starting to get into something
17:38
that's like, you know, we've got our heads wrapped around it.
17:42
We're doing it well. I'm seeing that a little bit more in
17:49
smaller shops, right? Where they have a few like really gifted
17:56
people who like kind of figure the technology out, figure out
18:00
how to tune it. Figure out how to get agents working well, et
18:03
cetera, et cetera. Sounds like your your friend is one of
18:06
those.
18:06
Devon Morris: Yes,
18:09
Chris Sims: and but I'm seeing a lot of places where what's
18:13
happening is we imagined the bottleneck was our ability to
18:21
make code changes, right? Our ability to generate new stuff,
18:27
and wow, right? AI is you know Copilot and you know whatnot are
18:32
like oh really good at creating lots of code, right? Very
18:36
quickly, and so that's definitely no longer the
18:41
bottleneck. If it ever was,
18:43
Devon Morris: the
18:43
Chris Sims: bottleneck now is figuring out what behavior
18:48
changes do we want to make, and how can we ensure that what
18:54
we're building continues to do what we intended to do, and how
18:59
can we release it safely, and how can we validate our ideas,
19:03
and like all of that stuff around the the you know the code
19:09
creation, and and it's interesting. And one of the
19:13
things I'm definitely seeing as a pattern is, and this and this
19:20
pattern I'm about to describe has has always existed, but it's
19:23
getting really exaggerated now with AI,
19:25
Devon Morris: which
19:26
Chris Sims: is that the cost of ownership of code
19:29
Chris Sims: is
19:31
Chris Sims: way higher than the cost of creation. Meaning, okay,
19:35
right, we can create lots of code, but do we understand it?
19:39
Can we maintain it? Right, all of that, and the more we let the
19:44
tools do it for us, the more the answer is like there's nobody
19:49
around here who actually knows what it's doing, and when
19:52
something breaks, it's like hope the magic genie can figure it
19:56
out because you know, and and so. That's that's increasing,
20:03
right? The cost the cost of code ownership is increasing, and
20:07
then you know figuring out what we should do, yeah, right. It's
20:11
also is also generally a big bottleneck right now. But I've
20:14
seen someone shared with me a really interesting graph a while
20:19
back about You know, code commits right the pull requests
20:27
and whatnot in in and code change in in Git and the the
20:34
churn like in the last six months has gone up just
20:38
dramatically right the average average life of a piece of code
20:44
is is gone. You know, it's like it's created, and then a couple
20:49
days later it's changed. A couple days later it's changed.
20:51
A couple days later it's changed, and and yeah, the
20:55
places that I've seen doing it well on the tech side are places
20:59
where they're actually embracing, or have always
21:02
embraced, the classic XP practices, test-driven
21:05
development, and that sort of thing. Because then you've got
21:11
this intentional harness around it that basically says this is
21:15
what our system should do, and then it's a little easier to
21:19
lean back and trust the AI to like create code that does what
21:24
we have very carefully specified it should do.
21:27
Devon Morris: You you mentioned you mentioned this being a
21:30
shift, and would you say this feels different than previous
21:40
shifts that happen. Oh, is it the
21:45
Chris Sims: ah? It's interesting. Yes and no, right?
21:53
So I, I kind of really started my tech career right about the
22:00
time there was this thing just starting to happen called the
22:03
internet and the World Wide Web, and that was a huge change.
22:09
Devon Morris: Yes,
22:10
Chris Sims: right. It was a business model change. It was a
22:13
technology change. All of those things, and this is at least
22:20
that big.
22:21
Devon Morris: No, now a lot of that stuff started happening at
22:26
U of I, right? Was you there at U of I during that time?
22:31
Chris Sims: I I was at U of I, yeah. Like in the Mosaic had
22:35
already happened, just happened when I got there.
22:40
Devon Morris: You know, I was there, right? So that's
22:42
exciting. So, okay, so that was definitely a big change, and you
22:47
were definitely right there to see where a lot of that actual
22:51
change was actually being born and coming out of, right? So
22:55
that definitely was a shift. Okay, so that shift you
22:59
experienced. What other shifts have you experienced in your
23:04
career?
23:06
Chris Sims: Well, I mean, personally in my career, my
23:09
goodness, some big shifts. My, I don't know that it was a career.
23:16
It was my first job, right? I worked at Burger King, made a
23:20
lot of whoppers. I was a vegetarian at the time.
23:25
Devon Morris: You was a Burger King. So
23:27
Chris Sims: something something about like expressing my
23:30
personal values through the work that I chose to do. It just not
23:33
occurred to me, right? And so then, you know, frankly, young
23:38
Chris was doing
23:39
Devon Morris: that. That Chris, did you have a Burger King job
23:41
at U of
23:43
Chris Sims: I? No, no, no. Because I did, I did U of I
23:45
later in life. So, so I was I was in my I was 30 years old
23:51
when I got to U of I.
23:52
Devon Morris: Okay, okay.
23:54
Chris Sims: So, so Burger King thing. Then I became an auto
23:57
mechanic, and that was you know maybe a little bit better fit
24:00
because I was really into cars and all of that.
24:03
Devon Morris: Yeah.
24:06
Chris Sims: But I, I personally, I discovered that being into
24:11
cars, right, being enthusiast,
24:14
Devon Morris: yeah,
24:15
Chris Sims: didn't didn't actually mean that I was going
24:17
to enjoy working on them for a living, right, and and so yeah,
24:24
wasn't loving that so much. But on the weekends, I was playing,
24:28
you know, rock and roll, and I didn't like the shop I was
24:34
working at, so I started looking around, looking in the want ads,
24:37
because back in the day we had to look in the back of the
24:39
newspaper to find a job, and so I'm looking for other mechanic
24:44
jobs, and lo and behold, I saw an ad for a bass player wanted
24:50
for full time traveling band. I'm like, what? I could make a
24:54
living at this, and so I went and auditioned. Somehow got. Gig
25:00
because quite frankly I was way underqualified, but got the gig
25:06
went on the road, ended up doing music through most of my 20s,
25:11
Devon Morris: okay,
25:11
Chris Sims: and enjoyed it. But somewhere in my mid to late 20s,
25:17
I had this aha moment where I realized that there was
25:22
something missing in my life.
25:24
Devon Morris: Okay,
25:25
Chris Sims: regular income, and so if I kept doing music for a
25:31
living, right, I was going to starve to death, right? Because
25:34
I was never going to be a rock star, right? I didn't, I didn't
25:36
have that level of talent, and I was just good enough to
25:40
recognize the people that did have that level of talent,
25:44
right? I looked around me and I'm like, oh, oh, oh, right.
25:48
That's not me.
25:49
Devon Morris: Yes.
25:50
Chris Sims: So, and I realized I was on the wrong side of the
25:53
supply and demand curve.
25:54
Devon Morris: Okay.
25:55
Chris Sims: Right. There were way more people who were at
25:57
least as good as me, and and not very many places for us to like
26:02
work and make a living and all that.
26:04
Devon Morris: Okay.
26:04
Chris Sims: So thus went back to school. Eventually ended up at U
26:07
of I, computer science degree. Actually, computer science and
26:12
math. It was the only way I could get into the program. Was
26:15
to take that like kind of dual thing. And then I was my good
26:19
fortune to graduate from U of I with a computer science degree,
26:24
like as the dot-com boom was like starting to boom. Wow! And so
26:28
yeah, that's that's how I landed out in Silicon Valley. Had a
26:33
pretty typical software developer career, right?
26:35
Developer becomes manager, becomes director, kind of a
26:38
thing. Yeah. And then started my company, and and there there
26:46
were shifts, right? So initially, I thought what I was
26:49
going to do was consult to engineering leaders on you know
26:56
kind of a variety of like how do you become a really effective
26:59
engineering leader, engineering manager, that sort of thing, and
27:04
I assumed that everybody was doing this agile stuff, right?
27:09
Because I I had picked it up actually before we had the word
27:13
agile, I'd run run across the folks doing extreme programming
27:17
in about 1999, 2000, right in there, and it worked so much
27:22
better than what we were doing before. That you know, it kind
27:26
of really helped me succeed in my career as a leader. Right?
27:29
It's like get teams together, get them to be effective, build
27:33
you know products well that are successful. And so I just kind
27:37
of assumed everybody was doing that. So I thought that was just
27:39
part of what I would bring to the table was, you know, coach
27:44
on that. And then it turned out once I got out out there and
27:47
started trying to like get gigs, I found out no people were just
27:52
waking up to this agile and Scrum stuff, and that's really
27:57
what they wanted help with. And so I pivoted my practice from
28:02
technical management more broadly to, you know, agile
28:06
stuff, and so you know, yeah, so definitely, definitely pivots
28:12
and changes.
28:12
Devon Morris: So here's the deal: that when you when you
28:14
talk about you coming into the agile space, period, in that
28:19
way, where you made that pivot toward changing the company to
28:24
move in that direction, coaching in that direction. Again, there
28:28
wasn't a lot of people doing it. Y'all were different over in
28:30
Silicon Valley. Y'all just right because y'all was a bubble,
28:34
right? And that bubble had maybe some stuff happening, right? But
28:39
you was at the beginning parts of even that bubble, right? And
28:43
so you started to see that thing grow, and then it started
28:47
spreading to other places, right? Because I didn't come
28:51
around it literally till 2005, right? And so you were
28:56
definitely around it prior to me, and when I came around. It
29:02
just wasn't a lot of, especially in the Midwest. It was dead, no,
29:05
no way in the Midwest, right? It was on the coast and in Texas
29:09
and and over there by you. So that's that's interesting. So
29:12
then, in that, you seen organizations start to shift to
29:17
a better ways of doing work, and managers trying to find those
29:22
better ways of doing work and trying to find better ways of
29:24
being more innovative. Along with that, you've seen the dot-com
29:28
boom happen, the bus happen. You've seen the internet kind of
29:33
started to come up, and so now when you look at, and I know you
29:37
say yes or no, when you look at this fabulous career that you've
29:41
actually had, because that's a fabulous career. No, there's not
29:46
too many people on the planet. Chris can say, "Look, I went
29:49
from working to Burger King, working to Burger King, to being
29:51
a musician to being an agitist, one of the top people in the in
29:56
the world doing that. There's not a lot of people that can say
29:59
that. Right, so with seeing all these shifts happen, what makes
30:05
this AI shift so different? It's
30:13
Chris Sims: a great question. So let me start with ways that it's
30:22
not so different, but maybe it's maybe it's the magnitude of of
30:26
it that makes it different. So when I was learning to program,
30:34
I you know became familiar with C and C plus plus. You know I
30:38
like these languages, right? I learned About object-oriented
30:41
programming, but they were esoteric might be the right
30:48
word. I'm not sure, but like it took a while to crack the nut of
30:53
being able to understand how to code in these languages.
30:55
Devon Morris: Yeah,
30:55
Chris Sims: right. And we there used to be a thing that people
30:59
would say early on, like when there'd be a group like at U of
31:03
I say trying to learn C, is they'd say you know there's
31:07
going to be two types of people: people who can you know
31:10
understand pointers, and those who can't, right? And and it's
31:15
just weird, you know, geeky whatever programming stuff, but
31:22
it it limited the pool of people who could do it, right? You had
31:25
to be able to understand algorithms and and things like
31:31
that at a certain level. You had to really get the theory at a
31:35
certain level to even be able to use the tools.
31:37
Devon Morris: Yeah.
31:38
Chris Sims: So then along came things like Visual Basic, and
31:42
Visual Basic made it such that like people the the bar got way
31:48
lower for like who could get in there and build an application.
31:51
Devon Morris: Yeah,
31:52
Chris Sims: right. You could draw your user interface, and
31:55
you know you start typing, and there was auto complete. So it's
31:59
like, oh, what I want to do right now is print something. So
32:04
pr oh it's going to auto complete with print and then
32:06
show me like the things that go in, and so it it made it way
32:09
easier, and suddenly a lot more people were were creating
32:15
software, but the quality wasn't great, right? And in fact, the
32:23
tools I remember at the at the time, Visual Basic got a bad
32:25
rap. It was like, oh, it's a terrible language. You know,
32:28
people write terrible programs with it that are unmaintainable,
32:31
and blah blah blah. And and I actually love that tool. I use
32:37
that tool a lot. I don't think it was a bad tool. I think what
32:41
it did was it lowered the bar so much that people could get in
32:44
and build things that they didn't know how to maintain,
32:48
right? Which hey, this sounds familiar. Didn't I say something
32:50
like this just a little while ago? So at one level, AI is like
32:55
that. It's it's empowering people who before didn't have
33:00
the qualifications, credentials, the whatever, right? To to get
33:05
in and do what they want to do, right? I was I was out camping
33:08
recently, and there was this person who was you know very
33:13
much not a tech person,
33:15
Devon Morris: okay,
33:16
Chris Sims: right? They were like social media kind of
33:18
person, and they were talking to us about the app that they just
33:22
built and you know had released into the app store and stuff,
33:27
and because the AI enabled them to do that, right? They were
33:33
like, "I want an app that does this and this and this and this
33:35
and this, and so it's changing the playing field, right? And I
33:43
think there are things in the past that have done that. This
33:47
the scale, right? And what's going on right now is, I think,
33:51
unprecedented, right? Because the all of the information and
33:56
material that these these large language models have sucked up,
34:00
and you know, theoretically, have have immediate access to
34:04
right in a way that you know most human brains don't right.
34:07
We don't have the capacity to hold all of that and access all
34:10
of that. It's interesting. It's also interesting in that it's a
34:19
big shift in terms of creating technology, creating code, in
34:25
that code is deterministic; it does exactly what you tell it
34:32
to. Doesn't mean it's going to do what you want it
34:34
Devon Morris: to,
34:34
Chris Sims: but if it's not doing what you, you know, if
34:36
it's doing something you don't want it to do or not doing what
34:39
you want
34:40
Chris Sims: it
34:41
Chris Sims: to do, ultimately it's because you've told it the
34:43
wrong thing, right? Where the AI and the LLMs and the agents,
34:51
they're non-deterministic, right? It's not it's not
34:56
deterministic code anymore, and it. That's that makes things
35:02
different, right? It it is in some ways like working with the
35:07
human,
35:08
Devon Morris: yeah,
35:09
Chris Sims: right. Because you can ask it to do a thing, and
35:10
it's got great capacity to do the thing, but it's not always
35:13
going to do it
35:15
Devon Morris: right. Yeah,
35:16
Chris Sims: and so if you or somebody else doesn't know
35:21
enough to identify that. Wait, that's not right. You know, it's
35:26
just like, hey, I had a coworker, and they're smart.
35:29
They know how to do things, and I asked them to do a thing, and
35:32
they they did the thing. Did they do it right? Hopefully,
35:36
more often than not, they did, but not always.
35:40
Devon Morris: That is, I think the way that you phrase that up
35:43
is beautiful. It provides a connection for people that they
35:51
wouldn't quite get, and the magnitude being the thing that
35:56
is really different because we've had shift after shift
36:01
after shift, Chris. We've seen them, right? But you, you going
36:06
back to the pointers, right? And and that visual, see the visual
36:11
basic, and how it kind of lowered the bar, and pretty much
36:18
AI had and took the playbook that people had, and just made
36:23
it more available to other folks that didn't have the skills up
36:27
there. That is, I never thought about it that way. But when I
36:33
think about examples from me doing software myself, that's
36:37
exactly what it is, man. That's exactly what it is, and so then
36:44
the question becomes: as AI is chomping up everything, you
36:52
know, what does a person do now?
36:57
Chris Sims: Such a great question, right? And I think
37:03
that what the AI tools don't have, at least not right now, is
37:15
judgment, and what's the word I'm looking for? Something along
37:27
the lines of I don't know if it's ethics, morals, but like
37:30
like what's the right thing to do here, right? And so I think
37:36
judgment and kind of decision making and all of that is more
37:42
critical than ever, right? Because we can we can do a lot
37:46
of things a lot faster, right? The cost of doing these things
37:50
Devon Morris: is
37:51
Chris Sims: lower, but which things should we do? Which
37:54
problem should we solve, right? If if our if our product can
37:58
suddenly have almost infinite features, well, which one should
38:02
it really have, right? Because the the one a study that I like
38:08
to point to sometimes is the was it 2019, 2018, or 2019 Pendo
38:14
feature adoption study, where they found like 80% of the
38:19
features in the software products they looked at were
38:22
rarely or never used, and so we're already building a lot of
38:28
things that at the time we're building it, we think, "Oh my
38:30
God, we got to have this. This is so important. And then it
38:33
turns out, right? Once it's out there, it's like, "No, it's
38:36
actually not that important. And in fact, it's probably
38:38
cluttering our product up and making it harder to use and
38:42
harder to maintain, et cetera, and so I think having the
38:48
judgment about what to build, which features to add, right,
38:56
and then also the the long term vision because I I don't know
38:59
that the AI is so good with like long-term vision, right? What
39:04
are we trying to do here with the product? What are we trying
39:06
to do as a team? What are we trying to do as a company?
39:10
Goodness help us! What are we trying to do as a country and as
39:13
a civilization? So, you know, choosing choosing what problems
39:20
to solve, I think, is is is going to become so much more
39:25
important.
39:27
Devon Morris: So then, as a as a leader, right? When you when you
39:34
have a situation here that is unprecedented at a magnitude
39:40
that nobody's ever experienced. How the heck do you prepare your
39:48
teams?
39:51
Chris Sims: Yeah, I I think that this is a place where some of
39:59
the real fundamental. Of Scrum, in particular, become more
40:05
important than ever.
40:06
Devon Morris: Okay.
40:06
Chris Sims: Right. The idea of inspect and adapt. The idea of
40:11
experiment. Right. The idea of try it. A guy that I I had the
40:17
privilege of working with for years. Sadly, he's he's he's no
40:20
longer with us. He's he's passed on. Steve Bockman, I was a
40:25
member, you know, at Agile Learning Labs for a bunch of
40:29
years, and his his mantra was, "Let's try it. How can we try
40:36
it, right? And so I think fostering an environment where
40:43
people can try things, but doing it wisely, right? Because in the
40:50
chaos, right, when people are just like you know, it's the
40:54
cowboy days of AI. Lots of bad stuff happens, but so does lots
40:59
of good stuff. But it's it's hard to figure out like well
41:02
what things worked well what things didn't work well and all
41:05
of that and then take that learning and propagate it and so
41:09
to the extent that we can just a little bit more thoughtfully and
41:15
mindfully try things and do them with a you know it's almost like
41:20
the scientific method right oh this is going to be an
41:22
experiment. We're going to try this. We're hoping for this
41:25
outcome, but we don't know, and we're going to see what we get,
41:29
and then circle back, and then decide what to do next. I think
41:35
is more more important than ever, and even during the you
41:41
know this big uptake of of using AI and agents and you know all
41:48
of that, even things that we think we figured out, six months
41:54
later we may have to go back and refigure them out, right?
41:57
Because it's it's shifting so fast.
42:02
Devon Morris: Wow. So so then, inevitably, a team, an
42:09
organization, group of people, they're going to get stuck to
42:15
get behind. And when you start saying let's experiment, and
42:20
then you have something at the magnitude that AI brings, you
42:25
know. How does one really deal with that, Chris? Because you
42:32
know you have people that got to get paid, and people who got to
42:36
eat, and you know customers that want something, and a team that
42:41
is behind, and it's the wild, wild west right now. Ooh, man,
42:48
what do you do when when they're behind?
42:51
Chris Sims: Yeah. So, couple of thoughts come to mind.
42:57
Devon Morris: Yeah,
42:58
Chris Sims: and the first, maybe it even challenges your basic
43:04
paradigm and premise, which, which is my first response is,
43:09
well, who says we're behind, right? And and and what are we
43:15
behind, and what was the standard, and who set the
43:17
standard, and is that even a reasonable standard, right? So
43:20
one of the things I've I've found is when multiple times
43:26
when I've been brought into organizations and they're like,
43:28
"Oh my goodness, we have these teams and they're behind, I find
43:33
out that the problem was well, like somebody set them up to
43:35
fail, right? Somebody set them up with a completely unrealistic
43:39
you know goal or set of expectations. So sometimes it's
43:43
not that we're well. Sometimes it's more about like, well, why
43:48
do we think we're behind, right? And who who set that bar, and is
43:53
that bar even a realistic bar and whatnot? So that's let me.
43:56
I'll just put that out there and then I set it aside. Okay. Okay.
44:00
And then the other thing that I see all the time is when we feel
44:08
like we're behind. Most people's response-it's a natural
44:13
response-is work harder.
44:17
Devon Morris: Yeah,
44:17
Chris Sims: right. When they put your put your nose to the
44:19
grindstone-is that the saying? That seems like a bad idea.
44:22
Putting your nose to a ground. That doesn't seem like lend
44:26
well, right? I agree.
44:30
Devon Morris: I agree with that.
44:32
Chris Sims: So, but but that's their natural response, right?
44:34
Like, oh, I'll work later. I'll work harder, right? And I think
44:40
very often when we're in like there's this fundamental shift
44:43
happening, we're not going to work harder ourselves out of it,
44:49
right? We actually need to like believe it or not. Very often,
44:53
what we need to do is slow down, and we need to like kind of look
44:58
around and go, okay, what's going on here? And what
45:02
assumptions have we always been able to just, you know, work
45:06
with that maybe it's time to question because they're not
45:11
true anymore? And you know, the the for the leadership team I
45:16
was talking about earlier in my example, for them it was like
45:20
the assumption was that, oh well, if it's important, we
45:23
should do it because we're the best and we got us here, right?
45:28
We got we got this company to where it is, right? So we could
45:31
get it to where it needs to go. And actually, that assumption is
45:34
not true anymore, right? And so if those leaders just work even
45:40
more hours, like that's that's not. They're not gonna catch up.
45:45
They're not gonna. They're gonna. They're gonna get more
45:47
and more behind. Right. The. I remember a time I was in this
45:52
with Agile Learning Labs early on when we were growing, and and
45:55
Steve Steve Bockman was coaching me, and and he was trying to get
46:03
me to have this insight. I wasn't there quite yet,
46:06
Devon Morris: and
46:06
Chris Sims: I remember I said, "I don't know. The harder I
46:09
work, the behinder I get. And and for those leaders, it was
46:16
really true, right? The harder they worked at this, the more
46:22
they were holding the company back, and so they had to. And
46:25
this is hard, right? Because assumptions are things that we
46:28
don't think about, right? They're built in, right? Like we
46:31
assume there's air here, and we're going to get to breathe
46:33
it. So we we never think about it. It's like that, right? It's
46:36
like how do you start to see the thing that you've never seen
46:39
before, and and in order to do that, you gotta slow down. You
46:46
gotta start like looking around and questioning things and
46:50
trying new things, and and that's so hard when you're
46:54
behind.
46:56
Devon Morris: Yeah, I'm with you, Chris. I really am with
46:58
you. No, it's it's in the chest. It don't feel right. So then
47:03
here's the question, right? Because with the magnitude of
47:07
AI, and now you're telling me, Chris, to slow down. But there's
47:13
been some cost here for teams and people because there's an
47:18
identity crisis as well because my expertise, I got to go back
47:25
to being a beginner at something with the magnitude of what the
47:29
AI is doing. So now what you're telling me is I got an identity
47:33
crisis because I got to go back to being a beginner, and you
47:37
want me to slow down at the same time, and we got these
47:40
assumptions that we made to get things out of. How do I get out
47:44
of this conundrum, man? Seriously,
47:47
Chris Sims: it's so hard. It's so hard. We need Devon.
47:51
Devon Morris: No, no, they need they need.
47:54
Chris Sims: We need you, man.
47:57
Devon Morris: I think I think a lot of people are having that
47:59
crash right now, man, and teams in particular, because it's a
48:07
different way to work together when you have this magnitude of
48:12
stuff that's just kind of come and hit you for the past three
48:15
to four years. So, been a very interesting journey, but yeah,
48:20
that is that slow down thing is is probably what I would say,
48:26
but it would be kicking me in the teeth, Chris. Me saying
48:32
that, so I got to wear braces or mouth guard or something because
48:36
it just doesn't work right. And then just finding ways to deal
48:39
with that identity crisis is that other thing that's
48:42
Chris Sims: yeah
48:44
Devon Morris: permeated the space right now. So if you're a
48:47
leader dealing with all that stuff, I mean, how do you
48:50
survive?
48:54
Chris Sims: Yeah, I I don't know honestly, right? But I do think
49:05
that some of the ingredients are humility, curiosity, and a
49:19
willingness to go into that uncomfortable place, right?
49:24
Because exactly you put your your finger right on it, like,
49:29
hey, I've done this thing I've done for a long time. I'm really
49:33
good at it, right? I have a lot of identity wrapped up in it.
49:38
Devon Morris: Yeah.
49:40
Chris Sims: Now that thing is maybe not as valuable as it was
49:43
two years ago, six months ago, last week, and I need to do
49:52
something different. And it's like, oh, but I'm, I don't know
49:57
if I'm good at that, right? That's. Scary. That is so scary,
50:05
but it's it's real, and it it's also interesting to me. I'm
50:11
seeing certain skill sets. The how can I put this? The
50:21
perception of the value of certain skill sets shifting and
50:24
changing in big ways.
50:26
Devon Morris: Yeah.
50:26
Chris Sims: So, you know, back in my days of writing code and
50:31
whatnot, in the organization, you know, maybe we had people
50:34
who were primarily like the code writers, right? And then they
50:39
were oh, right, a certain level of status, and you know
50:42
whatever, and then you know maybe above them, architects,
50:45
right? Oh, right, and then you know unfairly, but nonetheless,
50:51
I saw this all too often. Oh, then you know QA people, right?
50:55
A little lower, and then maybe you know people like you know
50:58
analysts, right? Down, down somewhere in there, and all of a
51:03
sudden, writing the code is no longer the magic.
51:10
Devon Morris: Yeah,
51:11
Chris Sims: and suddenly, like the analysts and the QA people
51:16
and folks like that, like those are actually the higher valued
51:23
skills suddenly, right? It's like, oh my goodness, we have
51:25
to. We need to understand what problems we need the magic to go
51:32
solve, right? The magic used to be the people who could code,
51:35
right? Now the magic is the people who know how to get the
51:38
AI to code.
51:40
Devon Morris: Yeah, like it's it's a it's a it's a beautiful
51:45
thing with us having been in this agile scrum space, because
51:52
we philosophically think about transparency, inspection,
51:58
adaptation, building things in smaller bits and pieces, so and
52:03
then we can get feedback sooner and shorten those feedback
52:07
loops, which fits perfectly into what we're what's happening
52:11
today. And so it's like the distinction of adapt versus
52:17
digging in, right? Like a lot of leaders right now, they're just
52:23
kind of digging in, and when you say magnitudes happen, you know,
52:28
there's a value shift, identity cost that's happened. So value
52:32
has shift on a team in terms of status as well. So, man, what
52:39
kind of skills do you have to have to deal with teams today?
52:44
Chris Sims: I I think it's you know more than more than ever.
52:51
Leaders need vision plus real leadership skills, right? And
53:01
and and what I mean by that is, we used to be able to get by on,
53:06
you know, the business leaders got by other business acumen,
53:09
and the technical leaders could get by on their technical
53:12
acumen, and all that. And I think when things are shifting,
53:20
you your core skill area, right? Your subject matter expertise,
53:26
very often is not what's going to carry the day anymore, right?
53:30
It's a your ability to adapt your thinking and your
53:34
approaches, and your ability to influence people around you to
53:41
do the same, and your ability to sense patterns in the chaos,
53:49
right? Because during in that chaos period in transition, if
53:57
we pay attention, there's a lot of gold in there to be mined,
54:02
right? Because there's this time where the old rules don't really
54:06
apply anymore, the new rules haven't kind of gotten
54:10
solidified, a lot of things happening and being tried, and
54:17
some of those crazy things are actually really amazing and
54:20
super valuable, but like part of it is is developing the sense to
54:27
notice that, and and part of it is social engineering, right?
54:37
Cultivating a culture where. We're going to share these
54:44
things. We're going to notice these things, and in the chaos,
54:48
especially, an awful lot of what we're trying is is going to
54:52
fail. And if we create an environment where you know
54:56
failure is not an option, right? Then then information. Flow goes
55:02
way down, and we need exactly the opposite. We need to we need
55:08
to embrace the fact that yeah, the rules are changing, right?
55:14
And we're going to try things and fail, and it's going to be
55:17
messy. But if that's okay, and we can share our struggles and
55:26
our failures, we only have to do that learning once, right? As
55:31
opposed to like if we create an environment where no, no, no,
55:34
it's not okay to fail, then that same mistake is going to happen
55:38
over and over and over and over throughout the organization, and
55:42
so, so I think, I think those are the things that leaders
55:47
need, and and and you know to some extent there's cultivating
56:00
a certain groundedness that people can look to and say,
56:05
"Okay, yeah, you know, the the ground is shifting underneath
56:09
Devon Morris: us,
56:10
Chris Sims: but you know, our our our leader is remaining
56:15
grounded, not not not stuck and dug in, but but remaining like
56:21
kind of present and paying attention and adapting and kind
56:32
of creating an environment where it's like, hey everybody, this
56:34
is a wild ride. Yes, it is. Let's go on it together, right?
56:39
This is exciting. Let's figure it out.
56:42
Devon Morris: So then, when you think about that leader, those
56:46
leaders that you're describing there, how do I rebuild my own
56:52
confidence, man?
56:58
Chris Sims: I, I, I think this is where again leaning back on
57:04
on you know Scrum stuff right agile stuff iteration is so such
57:12
a powerful thing right I try something it fails so I try
57:17
something different fails try something different oh that
57:20
worked out a little better. Yeah. So I try a variation on
57:24
it, right? And over time, we can iterate towards things that are
57:30
working better and better and better. And the thing that we
57:34
can hold onto and feel confident in is, in fact our ability to
57:42
adapt and change, which is a pretty powerful thing to to
57:48
start to get comfortable with. It's hard, right? You know, most
57:53
of us don't fundamentally don't like change very much, right? I
57:57
I think I became a change agent,
57:59
Devon Morris: yeah,
57:59
Chris Sims: fundamentally to you know, as as my own recovery as a
58:02
control freak, right? It's like change, ooh, scary, right? So so
58:07
maybe I'll learn how to get others through it. You just
58:11
Devon Morris: this confused me. So a musician that is a control
58:15
freak, how does that happen?
58:18
Chris Sims: Are you kidding? All, all of us, all of us,
58:24
right? I just
58:24
Devon Morris: maybe I got some thoughts about musicians that's
58:26
different, man. But I just, you know, just seems so easy for you
58:30
as a musician to say these things, right? And everybody
58:34
else is not a musician that can go by that rhythm. You can,
58:38
y'all can, y'all can decide on a new song, and we think about
58:42
y'all adapting in a moment, right? You know, that's the kind
58:46
of thing that you're basically saying leaders need to really be
58:50
able to do is adapt when shifting around them, yeah, them
58:56
to understand that. And if they don't do that, how do they build
59:00
the confidence in themselves. How do they build the confidence
59:04
back in the people that are all
59:08
of this stuff, all at the same time? Because it almost feels
59:11
like it's a one big gigantic human experiment. Is what's
59:17
happening to us with AI.
59:20
Chris Sims: I think it
59:21
Devon Morris: is. It's like every aspect of life, right? I'm
59:24
over here cooking, man. I'm over here cooking dishes that I would
59:27
never be able to cook at. You know, because I wouldn't think
59:33
about it in advance enough for me to go buy a cookbook that
59:37
would allow me to be be able to do that in the moment, and so it
59:41
just has had such an impact on our lives, man. That it's been
59:45
it's been crazy on so many levels. So that being said, we
59:49
we've talked about everything here, and I think there's so
59:52
many nuggets that we have that we can actually allow people to
59:56
take away. But I'm gonna ask a. Closing question, Chris.
1:00:02
Chris Sims: All right,
1:00:04
Devon Morris: what would you tell a leader whose team is
1:00:07
behind right now, and they know it in this age of AI?
1:00:15
Chris Sims: I would, I would say, I would ask them, what
1:00:22
support does that team need in order to be able to better learn
1:00:27
and adapt? And very often it's right. They need some space to
1:00:32
learn and adapt. So the like just working harder that shrinks
1:00:35
the space, right? You you have less space to learn and adapt.
1:00:39
So you got to find ways to like give them some breathing room to
1:00:41
learn and adapt, but what other support do they need? Right,
1:00:45
there's like psychological, social support. Right, is this a
1:00:48
psychologically safe space? Can people experiment? Are we
1:00:53
working in ways that support experimentation and learning?
1:01:03
Just a little, one little example. Shorter sprints support
1:01:08
more experimentation and trial and error. For ages and ages, I
1:01:17
was advocating one week sprints, right? And all of a sudden,
1:01:23
right? We got AI, and suddenly people are doing one-week
1:01:25
sprints. And now I actually know places that are doing one-day
1:01:28
sprints, right? But, but you know, the faster the iteration,
1:01:35
the faster the learning can happen. But you have to make
1:01:39
space for the learning.
1:01:41
Devon Morris: All that, right?
1:01:41
Chris Sims: You have you have to build in the space for them to
1:01:44
learn, right? Because otherwise, people are just going to work
1:01:46
harder, and I've coached
1:01:52
Devon Morris: just doesn't get them there.
1:01:53
Chris Sims: Yeah, yeah, and I I've coached people where you
1:01:56
know they're like, oh, right, this team is behind, and we
1:02:00
don't have time for a retrospective. We don't have
1:02:04
time to experiment with a new piece of technology. We don't
1:02:08
have time to like play with the AI. Blah blah blah. And and my
1:02:14
response is often, oh, I understand. You've got so much
1:02:18
wood to cut that you don't have time to sharpen your saw, right?
1:02:24
But it's like it's that-that's when you need it the most.
1:02:27
Devon Morris: Yeah,
1:02:27
Chris Sims: right. But it-it is counterintuitive. It really is.
1:02:31
Devon Morris: I agree. And this-this has definitely been
1:02:35
different than what I expected, Chris. You have been absolutely
1:02:39
amazing. You know I'm going to have you around again, but
1:02:43
here's what I'm going to ask you: Where should people find
1:02:46
you, and tell people what's coming up for you?
1:02:49
Chris Sims: Yeah, so Agile Learning Labs is my company, so
1:02:54
AgileLearningLabs.com, and lots of appearances coming up. I do a
1:03:01
fair amount of conference speaking and presenting.
1:03:04
Goodness, I'm going to be in Vancouver and Boston, Madison,
1:03:07
Wisconsin, Washington D.C. All in the next couple of months.
1:03:11
I'm going to be in Prague pretty soon, and then you'll find all
1:03:17
of this at AgileLearningLabs.com. So that's
1:03:19
probably the best place to go, so yeah, I would I would say go
1:03:23
there.
1:03:24
Devon Morris: Well, I'm gonna say you have been more than
1:03:27
amazing. I you know I appreciate you, man. You know I like I told
1:03:33
like I said to everybody earlier, it is always nice to
1:03:38
have people that are in front of you, and you can just look and
1:03:47
copy a little bit. You ain't got to innovate too much for too
1:03:51
much away from. Just kind of copy them a little bit to try to
1:03:56
get to where you need to get to. And I would say to you,
1:04:00
fundamentally, you are one of the people that I copied.
1:04:04
Bearded Eagle would have existed if learning, if Agile Learning
1:04:07
Labs didn't exist, because you were one of the people that I
1:04:10
saw independently doing it that wasn't with the crowd, and
1:04:16
that's the key, wasn't with the crowd, right? And still having
1:04:21
success, and so I'm gonna say, man, I really appreciate you,
1:04:25
Chris. If you need anything whatsoever, reach out to me,
1:04:29
man.
1:04:31
Chris Sims: Likewise, and I'm I'm really honored to be on your
1:04:35
show, and I look forward to our next conversation.
1:04:39
Devon Morris: All right, Chris, you have a good one, and
1:04:40
everybody, thank you so much. We will see you next time. Thank
1:04:45
you for joining us. Where every Tuesday and Thursday we work
1:04:50
toward bridging the gap for teams that are behind. Any
1:04:55
questions, comments, or you just want to start? Start your own
1:05:00
conversation with us. Go to thelaggingteam.com/start.
1:05:09
Otherwise, find us at any place you listen to your podcast.