Generative AI is not just another tech wave. According to Collin Graves, founder of North Labs and former U.S. Air Force air crew member, it is a more profound shift than the internet itself. In this episode of Executive Connect, Collin explains why the winners in the AI era will be the companies that anchor technology strategy in human connection, disciplined leadership, and data clarity.

From flying NATO Special Operations missions to building one of the top fractional data and cloud advisory firms, Collin shares why mid market companies cannot afford to “spray and pray” with AI investments. Instead, they must combine a strong data foundation, precise execution, and a culture of accountability to stay ahead. If you want a playbook for using cloud, AI, and leadership to scale through data chaos, this conversation is your tactical edge.

What You Will Learn

• Why generative AI represents a bigger business shift than the internet 
• How to balance technology with human connection in decision-making 
• Why mid market companies need fractional data teams instead of siloed hires 
• The foundation every AI strategy must build before scaling 
• How military lessons like in briefings and out briefings create accountability and trust in corporate teams 
• A roadmap for consolidating tools, tightening feedback loops, and avoiding wasted investment 

Chapters:
00:00 Why human connection matters in the AI era
00:41 Military precision meets cloud data strategy
02:00 From Air Force missions to early cloud obsession
04:28 First startup and the Dropbox before Dropbox
06:05 Launching North Labs and advisory first model
08:44 Fractional data teams explained
09:36 Avoiding “spray and pray” AI investments
11:41 AI engineers vs data engineers different disciplines
14:31 Building a data culture that drives decisions
16:29 From rear view reports to windshield visibility
21:47 Consolidating tools and using AI for automation
25:58 Mid markets acting like startups with AI agility
28:03 Leadership lessons from high stakes missions
30:02 In brief and out brief building accountability
34:16 Delegation and empowerment as growth drivers
39:39 Why cross training builds resilience and profit
41:35 Removing fear of training your replacement
44:34 Where to start with AI and data modernization
46:43 How to connect with Collin

Melissa

(0:01) What do elite military strategy and cloud data analytics have in common? (0:08) Precision. (0:09) And today’s guest on the Executive Connect podcast, Colin Graves, knows a thing or two about both of them.

(0:16) As a former U.S. Air Force air crew member supporting NATO Special Operation Forces, Colin now leads North Labs, leading over a thousand companies turning data chaos into strategic clarity. (0:31) In this episode, he breaks down how cloud strategy isn’t just tech, it’s leadership. (0:38) If you’re scaling your business or just trying to make sense of the data mess, buckle up.

(0:43) This is your Tactical Edge. (0:45) Welcome, Colin.

Collin

(0:47) Hey, Melissa. (0:48) So glad to be here. (0:49) Thanks for having me.

Melissa

(0:51) I love your journey. (0:53) It’s nothing short of extraordinary, from flying missions in supporting of NATO Special Operations to founding one of the top fractional cloud analytic firms. (1:07) Talk to me a little bit about that transition because that’s a big one.

(1:11) And then maybe secondly, how has working in the military shaped your thoughts on leadership as you work at North Labs?

Collin

(1:21) Yeah, great questions. (1:22) I got my start in cloud computing really early. (1:27) I’m one of the old greyhounds at the track.

(1:30) I first learned about the concept of cloud computing. (1:34) I remember like it was yesterday. (1:36) I was walking to an aircraft in Ellsworth, South Dakota, Burr.

(1:40) I was supporting the coolest aircraft to ever live, the B-1 bomber. (1:45) I was just turning wrenches at the time as a hydraulics troop, which seems really cool until you get to the B-1, which is like, it’s the most active aircraft since 9-11. (1:58) It is also one of the least mission capable planes in the history of the Air Force, which all that means is just it’s broken all the time.

(2:07) And everything that breaks on the B-1 is hydraulics. (2:10) So I had my work cut out for me as a spunky 18-year-old. (2:15) And my dad sent me a press release that Amazon was going to start leasing data center space to companies on a per second basis.

(2:26) And he had just told me maybe six months prior, he was telling me a story about his time working for Medtronic, saying how much they were spending on a data center. (2:38) And it was just this exorbitant sum, a bigger number than I could even fathom. (2:45) And I thought, you know what, that seems like a really solid concept.

(2:49) I should learn about this. (2:51) And I became obsessed. (2:53) So during my time in the military, first at Ellsworth, which was basically just deployments to the Middle East, and then eventually transitioning to NATO in Germany, I learned everything I could about the cloud.

(3:08) I was one of the first 10 fully certified folks in the world for AWS. (3:12) I think eight of the first 10 worked at Amazon. (3:15) So it was like me and one other guy or gal out there who thought, let’s do this.

(3:21) And that’s been the story ever since. (3:24) So I started my first company while I was still actively serving, flying 300 days a year to the Middle East, former Soviet Union and Africa with the baddest people on the planet. (3:39) I always tell people I was the least impressive person on an aircraft at all times.

(3:45) And I’m totally okay with that because I got to fly with SEALs and Green Beret and MARSOC and Delta Forces and all of these guys who are just the elite of the elite and learn about their mindset, learn about how they executed together as a tight knit unit. (4:06) And I always say, I didn’t know it at the time, but the lessons I was learning from a leadership and execution perspective, they lead me today. (4:17) They lead the companies I advise, the companies I invest in, and obviously the company I own.

(4:23) So yeah, I started my first group while flying around. (4:28) My first company, effectively, we built Dropbox. (4:32) I think most of your listeners are familiar with that concept, but this was well before the concept of software as a service came to fruition.

(4:42) So we were building a tool for construction companies to share large files in the field over 2G cellular. (4:51) So you’ll imagine you’re a superintendent on a job, you get to a job site an hour away, you go, oh my gosh, I forgot those plans at the office. (5:00) You’d have to hop in your truck, turn around, drive back to HQ, grab it, drive back.

(5:06) We built a simple solution that said, great, it’s going to live in the cloud. (5:09) If you need it and realize you don’t have it, you can access it from your computer with your little 2G module and it’ll pull down. (5:17) It might take a while because 2G is nothing like the 5G ultra wideband we have now, but you’d get the job done.

(5:25) You could pull it down in maybe 10 minutes instead of a two hour round trip. (5:29) So I sold that in 14, about six months after I got out of the military, signed my life away with a non-compete for two years as a mid-twenties, not knowing anything about business. (5:43) I signed a non-compete that said, not only will you not start a tech company for two years, you won’t start any company for two years.

(5:49) You can’t mow lawns for a living if you wanted to. (5:53) And so I ended up (5:55) consulting in the Bay area for a couple of years with some large tech companies that are still (5:59) around today, helping them transition into the cloud and started North Labs in 2016, really with (6:05) an eye on, okay, how do I take my technical chops and distill that into useful advisory (6:15) engagements for growth stage and mid-market companies who want to make sound investments (6:22) in technology, but who maybe don’t have the in-person staff to help navigate those waters. (6:28) So they sort of came to rely on me for that. (6:33) And obviously nowadays we have an amazing team in the background of engineers, cloud engineers, data engineers.

(6:40) We’ve got PhD AI engineers on staff. (6:44) So really across the entire data and AI gamut is where we become a trusted advisor to our customers and help them make sound business decisions in their investments to help propel their businesses forward.

Melissa

(7:00) I love it. (7:01) I need to unpack one thing you said. (7:03) And what I hear is your through line is you’ve been hanging out with smarter people than you, your entire career, which I love and is such a smart thing to do, Colin, because we know that we’re the average of the people, the vibe people we hang around the most.

(7:21) And it sounds like the people you were hanging out with really sharpened your pencil and got you where you are today. (7:28) So I love my next kind of thing I want to talk about with you. (7:32) I love, love the fractional model these days.

(7:37) I think it’s really neat for companies to be able to use fractional data teams. (7:43) And I know North Labs operates in fractional partnerships with large companies. (7:49) So can you talk a little bit about that concept, about what exactly is fractional data teams for our listeners?

(7:57) And then kind of second level of that, talk about why use them in the world today?

Collin

(8:06) Yeah, it’s a great question. (8:07) So North Labs is what I call an advisory first execution capable group, right? (8:16) So what I’ve learned, let’s just, let’s take the elephant in the room with generative AI today, right?

(8:23) I’ve never seen a shift in business thinking like I have with generative AI. (8:31) I would argue that it’s a more profound shift than cloud computing, than SaaS, perhaps a more profound shift than the internet. (8:41) Because I say that for one reason, back when the internet came around, people were extremely hesitant, executives were extremely hesitant.

(8:49) When the cloud came around, executives were extremely hesitant. (8:54) With generative AI, it almost feels like the hesitation is only in not knowing how to proceed, as opposed to, hey, we’re not going to do this for a few years, we’re going to see how it plays out. (9:08) Now, what the conversations that I have with executives is, if we don’t start to execute, and we don’t have a solid plan in place over the next five years, we’re going to miss the boat.

(9:20) And we’re going to be completely leapfrogged by our competition. (9:25) And we’re only going to do it to ourselves. (9:27) So the hesitation is almost in, where do I begin and ensure I don’t just spray and pray?

(9:36) How do I do this in a very logical manner to drive business operations forward? (9:43) And that’s where I think a fractional model works super well. (9:49) Most organizations, whether you’re growth stage, so you’re just shy of that 10 million in revenue, and you think you’re going to get over it.

(9:57) Really, where we specialize is that mid market tranche. (10:01) So defined as 10 million through a billion, we tend to hang out in that 10 million to $500 million range with most of our customers. (10:09) Although we’ve worked with some of the Fortune 100.

(10:13) And it’s a great way to minimize investment and maximize productivity or maximize output. (10:21) Because you don’t need to go out and hire a full time executive on staff in order to start collecting those quick wins, get a solid foundation in place, and really help your organization understand where the next three years are going to take you. (10:38) And then when it comes to execution, you have really two options.

(10:43) You can go and hire a large internal team, but most people don’t understand who they need to execute on that vision because you hear the term AI and you go, all right, I’m going to go hire an AI engineer, not knowing that AI needs to run on a really solid data foundation. (11:02) And those are very different disciplines and very different practices that haven’t yet converged in the market. (11:10) So an AI engineer is really good at the application side, the workflow side, but their work is predicated on a stable foundation of the home, so to speak, from a data perspective.

(11:23) And so with a fractional model, what it allows us to do is say, all right, you might spend with us the equivalent of bringing on one engineer, two engineers, but you’re actually going to have access to six or seven different disciplines for that cost. (11:41) And we can get really precise with who’s needed when. (11:46) So you’re caught, your investment goes further and you’re able to start measuring that return on investment faster.

(11:56) So that’s why Northlabs is designed the way it is. (11:59) I feel like my industry is very, I consider my industry what’s called a barbell industry. (12:05) And that is, there’s a spectrum, but the vast majority of firms that look like mine are on either side of the barbell.

(12:15) You have the deep hardcore technology implementers who love a small amount of technologies, love to sell a specific piece of technology. (12:25) Then on the other side, you have these ultra expensive, not going to name names, ultra expensive consulting firms who you’ll pay an arm and a leg to to get a plan in place who maybe don’t have the best reputation from an execution standpoint. (12:41) So when I started Northlabs, the idea was like, look, the mid market needs this more than anyone.

(12:46) How do we bring both of those capabilities into one conversation and actually help those investments bear fruit and measure the results within the organization? (12:58) So that’s where we started. (13:02) And yeah, like you said, we’ve delivered over a thousand projects since I started the company in 2016.

(13:08) We just hit that number a few months ago. (13:12) And yeah, we just, I feel so strongly that with the way technology is heading, that human connection and that personal relationship aspect of business is only going to be more needed over the next five years because technology is now very capable of creating so much noise in the ecosystem. (13:36) And that’s only going to increase, not decrease.

(13:39) And so we always start on that personal basis, sit at the table with executives and help them plan their execution strategy in a manner that we’ve seen work over and over again.

Melissa

(13:52) Yeah. (13:53) So many things to unpack with that. (13:55) But I was thinking when you said it, I laugh because you’re spot on.

(13:59) I think about the day talking to clients about taking their data off their desktop computer and putting it into the cloud. (14:07) And it was like the hardest thing to talk to them about doing, but now you’re spot on AI, no big deal, no big deal. (14:17) And so I thought I’m sitting here laughing.

(14:19) I’m trying not to laugh loud because I have so many of these conversations going. (14:24) But, and it’s funny. (14:25) I also love like those back to those firms that develop plans.

(14:32) You know, I think about plans. (14:35) They’re about as good to have is the data that you have. (14:38) Like you need to have all the data to develop the plan.

(14:42) And if we don’t have data, we’re building a plan on somebody else’s plan and somebody else’s data. (14:47) We’re just removing the names and tweaking a few things. (14:50) So let’s talk about building a culture around data.

(14:57) Let’s talk a little bit about that and how you help organizations, not just talk the talk, but actually build a culture where part of the decision-making is built on the data.

Collin

(15:11) Yeah. (15:12) That’s a, it’s a complex equation. (15:15) It seems, it seems very simple, you know, like, Hey guys, why wouldn’t you want to use data to help steer the ship instead of gut intuition or, you know, quarterly financial reports.

(15:26) Right. (15:27) But a big part of what we do to your point is behavior change and the change management around that and, and letting executives who have successfully run these businesses for sometimes decades and decades that it’s okay to, to keep using your Spidey senses, but allow the data to help you orient where those Spidey senses get triggered or not. (15:55) Right.

(15:57) And to, and to your point, if there’s one thing we know it’s that there has been a, a, an explosion of software systems in an average business over the last 10 years, just a statistic for you. (16:10) I think 10 year, I think in 2015, 2016, I work a lot on our, within our manufacturing and industrial division. (16:18) We serve as customers across basically every vertical, but I help head up our manufacturing industrial 10 years ago, there was an, for a mid-market manufacturer, there was an average of 38 data producing systems in a business.

(16:35) So that’s it systems, that’s operational systems, financial systems, whatever the case may be. (16:41) Now that number has eclipsed 100 in an average mid-market organization. (16:48) It’s it’s bananas.

(16:50) And so where I often like to start with customers is saying, look, let’s, let’s not beat around the bush. (16:59) Not all of your systems are created equally. (17:01) If, if I, if I forced you to eliminate all, but 10% of your systems, I know which ones that you would choose.

(17:10) You would choose a core operational software, a core sales software, a core financial software, and then perhaps a core marketing software. (17:20) So let’s start there. (17:21) Those systems carry what I call the center of gravity of the organization.

(17:27) So if we can get that data really sort of solid, right, we’re inputting data correctly. (17:35) We’re not botching stuff. (17:37) We trust that data generally.

(17:40) Now we’re in a position to start allowing the cream to rise to the top from an insights perspective. (17:48) So we can start tracking how the data is changing within the business. (17:54) So we can make smarter decisions.

(17:56) And I equate this to like, when we, when you drive a car, if you’re driving to the grocery store or to target, hopefully you’re looking out your windshield, right? (18:07) You’re, you’re looking forward, but businesses historically have operated looking through their rear view mirror. (18:15) You have monthly and quarterly financial reviews.

(18:18) You have month end sales reviews, personnel performance reviews, whatever the case may be. (18:24) So now the shift in technology and what that’s bringing to organizations is allowing that shift from the rear view mirror, trailing historical data, which requires greater steering inputs to the car as you’re driving to target or the grocery store to now being able to look out the windshield. (18:47) And we know that subconsciously we’re always making small corrections in the steering wheel as we drive.

(18:53) But this, that same philosophical shift is now being unlocked in, in business. (19:00) And that, that really is the most exciting part to me is being able to see an obstacle ahead of you before you reach it. (19:08) Cause if there’s a tree down in the road, like there was on my drive in this morning, you’re not going to see it.

(19:14) If you’re looking in your rear view mirror, you’re, you’re going to feel it. (19:18) And then you’re going to look out the rear view and see the tree and go, geez, I hope the car is okay. (19:24) Right.

(19:24) And that’s really how businesses have operated for a long time.

Melissa

(19:29) It’s so true. (19:30) I love that you brought that out because I find that we do things because we’ve always done it that way. (19:37) And nobody wants to, you know, shake the tree, upset the apple cart, however you want to call it.

(19:43) But I think the one thing we know, and we know for certain is the world and technology is changing so, so fast. (19:52) And if you layer in the AI, like you were talking about, it’s, it’s, it’s bananas, how fast it’s changing. (19:58) And so to use the same processes, tools, plans that we, you know, from the back from like the eighties and nineties, when there wasn’t even a lot of this happening is just throw that plan out the window, right?

(20:12) We use the data that got us here to build the new plan around technology and, you know, our values and what we’re driving towards that, you know, you’re spot on. (20:24) I see it over and over again, where, you know, people aren’t questioning, like, why are we doing it this way? (20:31) Like, why are we manually doing this?

(20:34) Like, why are we not just using the tool to collect the data and then analyzing the data? (20:40) And like you said, we have the cream that rose to the top. (20:43) Now, what do we get?

(20:43) What’s the business plan based on what we find? (20:46) We’re making decisions because somebody else said we should, or somebody else liked a tool versus getting the right information. (20:55) So I want to talk about the, something you alluded to the cloud landscape, which you know, in my world, in cybersecurity and technology, there are so, so, so many cloud tools, acronyms, shiny things, shiny platforms.

(21:12) It’s a maze right now to, with IT leaders that I work with, you know, you said hundreds of tools, the IT divisions have, you know, 50 tools just for cybersecurity and not alone everything else they’ve got going on. (21:27) So what’s your guidance for leaders that are trying to make sense of all this or trying to make smart decisions, scale their cloud investment without, you know, getting behind, getting overwhelmed and spending too much of the corporate dollars?

Collin

(21:47) That’s a great question. (21:48) I think the biggest fundamental shift we’re going to see throughout the next five, seven years is there’s going to be a consolidation of tooling that occurs within the average organization. (22:02) Because think about how we’ve been operating for the last decade and why that software explosion occurred.

(22:08) And I know I’m preaching to the choir here. (22:11) You use a tool and someone on your team says, Hey, this tool has a gap either in user administration, or it doesn’t quite capture this data that we want. (22:23) What has the answer been?

(22:25) Historically, we’re going to buy another tool and sit it alongside the existing tool. (22:30) We’re going to pay for all of this tool, even though we only need five, maybe 10% of its total capabilities, right? (22:39) And we do what’s called stacking of those tools.

(22:43) And that process happens over and over and over again, where you’re using a hodgepodge of tools and really only leveraging a small percentage of its overall capability. (22:56) Because the rest of what you bought with that new tool is already covered by your old tool, right? (23:02) But you’ve got a five-year deal with this group and the team generally likes it.

(23:06) Oh no, we’re not getting rid of it. (23:08) We’re just going to add to it. (23:10) So what I’m really excited about with AI is that not only is it an AI conversation, but it’s allowing for a conversation around even something like data automation, which has been around for an extremely long time for the record, but wasn’t historically like the hottest thing for teams to talk about.

(23:37) But this concept now of, okay, yeah, we’d like to see this little extra addition in this tool we currently have. (23:45) Now, instead of spending tens (23:47) or hundreds of thousands of dollars on a second tool, why can’t we just build a little AI workflow (23:55) that goes and grab some data from that software that’s already exist, already running, (24:01) judge it up a little bit, maybe grab some supporting data from elsewhere in the organization (24:07) and deliver those capabilities the team’s looking for in 30 days, in 60 days, as opposed to a 12 (24:15) month onboarding period after contracts have been signed, which could take a year on its own. (24:21) And now you’re really effectively delaying this value recognition for your team by two years, two and a half years. (24:29) That’s been normal for a super long time for mid-market organizations.

(24:34) Two years has been like, yeah, it’s basically next week. (24:37) So what AI is going to do is it’s going to tighten up those feedback loops so much that now these mid-markets are going to continue to be able to operate like a startup. (24:49) They’re going to be able to be very agile, very dynamic, very flexible in their decision-making prowess, which we haven’t really ever seen.

(25:00) And that to me is so exciting to get these mid-market organizations who are so near and dear to me, especially in manufacturing industrial. (25:09) I grew up running around shop floors, factory floors. (25:15) My father was the first person in our entire extended family to swap the blue collar for the white collar.

(25:22) So I grew up pushing a broom down the hallways of a valve manufacturing plant. (25:28) You know what I mean? (25:28) So this idea that you can that you can take really solidified, huge employers and make them operate faster, not without humans, but allowing the humans to be even more capable and even more dangerous and spend their time doing more of what they love to do versus how businesses operated historically, which is like maybe that’s 10 or 20% of your day.

(25:58) And the other 80% is just like what I call undifferentiated heavy lifting, which is just like, I’ve always had to do this as part of the job. (26:06) I hate it, but I got to do it. (26:09) Imagine if every employee is now spending a hundred percent of their time doing what they love to do and thinking through things.

(26:18) It’s an explosion of productivity gain and operational excellence that we’re going to see take hold here in the next five, seven, 10 years.

Melissa

(26:31) Yeah. (26:31) I love it. (26:32) So many things in that.

(26:33) And I have to say, I’m trying not to laugh, but you’re so spot on like one year for onboarding. (26:39) I’ve seen a client wait three years to onboard a tool and it’s like, we’re almost there. (26:45) I mean, we have a million dollars in change orders and God knows what else, but it’s true.

(26:51) It’s so, so true. (26:52) And everybody’s been there and I love what you’re also saying about technology and AI. (27:01) I often wonder our work weeks, all of us are trying to get every piece of juice out of the 24 hours in our day.

(27:12) And what I love about the place we’re in Colin is it’s going, the productivity of all of our lives are going to go up and therefore we are going to have, in my own opinion, I don’t, this is my opinion. (27:26) I think the work week is going to be condensed in a way that it’s never been condensed before because so much is going to get done quicker versus trying to figure out how to figure out how to do things. (27:39) We’re just going to do things.

(27:40) I don’t know how many times you’ve read, re-read or read again, a contract or a PowerPoint or a whatever. (27:47) We don’t need to do any of that anymore. (27:50) It’s easy to read things, easy to resolve things.

(27:54) Here’s how you punctuate. (27:56) It’s done for us. (27:57) And so I want to pivot gears a little bit and talk about one of the things I love about you.

(28:03) And I think it says so much about your character, A, that you’ve served in the military, because I think one of the coolest things I’ve learned from my own father, who has also served, is how he’s able to deal with high stakes teams, high pressure environments. (28:22) I want to get your philosophy on that. (28:24) What is your leadership philosophy for building and sustaining top tier teams?

(28:32) I know you mentioned you had some PhDs in AI. (28:35) Talk to me about the leadership piece from your perspective.

Collin

(28:39) Yeah, that’s a great question. (28:42) I think there are two main takeaways that I learned, and this is actually, I just visited him out in California. (28:50) One of my closest friends in the world is a SEAL, and he’s finally getting ready to retire after 20 years of just amazing service to the country.

(29:01) He’s just one of the coolest humans in the entire world. (29:06) His name’s Rob. (29:07) And he has done some wicked cool stuff in his time.

(29:13) And I remember flying around with him. (29:16) We were in Eastern Africa and helping support the children of a civil war zone. (29:25) And he was our team leader for a particular mission.

(29:32) And in our in-briefing, in the military, you have what are called in-briefings and out-briefings, which are a briefing ahead of time. (29:40) Everyone’s locked and loaded, ready to go. (29:42) And then you always, always, always hold an out-brief after the fact, even if it executes perfectly.

(29:50) The out-briefing is an opportunity. (29:53) This is big in the Thunderbirds and the Blue Angels, for example. (29:58) Super high stakes, super narrow margins.

(30:02) It’s where everyone has an opportunity to be the first one to say where they didn’t execute perfectly as part of the plan, even if the plan went off without a hitch. (30:13) It’s a place where you get the opportunity to say, hey guys, I was a little late in my drop. (30:21) I was, you know, I was late on my breach for that door.

(30:25) Sorry, I was off by three seconds. (30:27) I’m going to fix it and I’m going to be better next time. (30:32) But if you don’t call yourself out in the out-briefings, it’s an opportunity for anyone of any rank to call you out without any punishment, right?

(30:46) And we don’t see that a lot in business. (30:48) There’s this aspect of the hierarchy and my boss, and I don’t want to make my boss mad because that person runs my performance reviews, yada, yada, yada. (30:58) So at North Labs, we’ve really ingrained a culture of in-briefings and out-briefings, even for stuff that our team can do in our sleep.

(31:08) And in-briefings may take 10 minutes, right? (31:11) But we’re always going to make sure that our gear is ready to go, that we’re hooked up to the static line before we jump out of the plane. (31:19) Simple routine stuff, right?

(31:22) And after execution, we’re coming together and we’re having that out-briefing and we’re saying, here’s where I’m going to improve for next time. (31:29) Here’s where I’m going to be 1% better the next time we do this routine work for you all. (31:33) Okay?

(31:34) So that’s one piece. (31:36) And I would encourage any business leader to start thinking about that because it’s so different from normal business practice that it completely helps build this culture of trust and camaraderie that is going to be harder and harder to maintain over the years as part of this AI explosion. (31:58) I’m telling you now, I’m reading the tea leaves.

(32:00) We’re going to have to spend a lot of time on cohesion and camaraderie for the folks on our team.

Melissa

(32:07) Second is- It’s the accountability piece. (32:12) In tech, it’s a lot of like, we can’t do this until they finish that. (32:17) Or we can’t, when I worked in engineering, we can’t start this until the concrete guys finish that.

(32:24) What I hear you saying, it’s rigorous accountability and calling yourself. (32:29) And the other thing that’s cool about what you just said is we’re all getting better doing that. (32:34) Because sometimes as humans, we don’t notice areas that maybe we messed up on, not because we’re blind.

(32:42) We just don’t see it. (32:43) So we’re getting independent views into things like, hey, Melissa, did you notice that? (32:49) And I’m like, oh, wow.

(32:50) I didn’t even notice that. (32:51) Thank you so much. (32:53) I appreciate next time I’m going to do this.

(32:55) It’s accountability. (32:56) And like you were saying, relationship building, where you’re feeling safe to share your weaknesses as a person versus the armor we put on when we come to work in most companies.

Collin

(33:10) Yeah. (33:10) Most of the time in a corporate environment, and I operate within a lot of corporate environments, people come to work with their armor on, in more of a defensive posture. (33:22) I’m going to do my job really well in defense of my position, in defense of my salary and my benefits and things like that.

(33:33) But if you can develop, if you can sort of turn that to say, look, you’re one of the best and brightest, and this is your thing to own. (33:44) And we want you to own it and tell us what you need in order to own it, but then take accountability for when you don’t own it perfectly. (33:53) That’s actually a really cool thing.

(33:55) I mean, that’s deeply ingrained in us as people is progress and improvement and not just for like a promotion review once a year or once every couple of years. (34:06) It’s like daily, hey, this is where I didn’t quite get it and I’m going to improve and I’m going to be better next time. (34:14) But that goes hand in hand with the second point.

(34:16) And that is the military is gigantic. (34:23) Gigantic, right? (34:24) It’s almost a trillion dollar budget.

(34:27) We have all of the stuff that we do as a military. (34:31) What most people don’t know is that we are the primary military provider for literally a hundred nations around the world who might have their own military, but they’re really taking direction from the United States. (34:47) And the only reason why the military can move as quickly as it does on things is because there’s a deeply rooted tradition in deep delegation.

(35:01) So the military is very good at delegating responsibility, pushing it as far down the totem pole as humanly possible. (35:12) Obviously still huge decisions being made at the top, but when it comes to actually executing on a mission, you got to remember, most SEALs are mid-rank enlisted guys, right? (35:25) You’ll have some officers in there, but most of them are mid-rank enlisted and they’re able to design, plan, execute, and improve on their missions on their own.

(35:39) Brass doesn’t fly in from the Pentagon and go, all right guys, let’s talk about how that mission went. (35:43) They’re doing it themselves. (35:45) And that to me is really, really key because you still have some organizational structures where it’s sort of like, hey, the real decisions are being made at that mid-level management area of the totem pole.

(36:03) And all of you underneath are just the worker bees. (36:06) You’re just the ones typing on the keyboards. (36:10) And I believe that people are generally far more capable of execution than corporate America or traditional business structures have given them credit for.

(36:24) And so we have a big culture at North Labs where it’s like, look, we hired you because we think you’re one of the best. (36:32) And this is on you, even though you are an associate engineer, you’re not senior or principal or partner, but we think that you can do it better than anyone. (36:44) And so we’re here in the upper chains of management to help clear the road in front of you and give you enough gas to get to your destination.

(36:57) But we’re going to stay the heck out of the way until you tell us that you need support. (37:04) And then again, pair that with the in briefings, out briefings, it creates for a really, really cool dynamic and people feel empowered. (37:13) And when people feel empowered, they stick around, they don’t kick their feet up at their desk in the middle of the workday because they really feel like they’re contributing to a much larger vision than just their specific title in the organization.

Melissa

(37:34) Yeah. (37:34) And that’s one of my things I most admire about the military. (37:39) When I think of the military, I think just ninja, world-class, best leadership model there ever is, ever was, probably ever will be.

(37:49) Because I think about in the hierarchy of organizations, you have a CEO all the way down the chain. (37:58) If somebody fell down or was out, the person below them 99% of the time would not be able to step in. (38:06) And that’s the one thing I love about the military.

(38:09) If somebody’s down, somebody can step in and knows what to do. (38:14) They’re trained, they’re cross-trained, they’re trained up, trained down, trained sideways. (38:18) And I think that’s the one thing I wish I saw more in the corporate world today is I often feel a lot of times we don’t train up or train sideways out of fear that someone might take our job or fear that we don’t want them to have those kinds of skills or we’re giving them too much transparency or whatever the stories we tell ourselves.

(38:41) I think if you have so many people that can help and support each other, like you were saying, ins and outs of whatever you guys are working on, you’re able to create a place where people can solve problems that’s maybe not even their job or fixing some of the most amazing things I’ve seen in my career. (39:05) And the biggest problems I’ve seen solved were not always from the engineers or the people that should have been solving the problem. (39:12) They were from the people below that just saw a better way to do things.

(39:17) And if those people are empowered to speak up and share and bring that idea forward, I think about how more profitable and happy people would be in the workforce.

Collin

(39:30) Yeah. (39:31) Like you said, that’s the armor we were talking about when you said people are worried about training their replacement. (39:38) What happens if I take six hours to teach Melissa how I do something?

(39:44) Is she going to eventually Heisman me out of the way? (39:48) Because she’s already a leader and can step into that position. (39:53) We have to figure out how to remove that hesitation from within our ranks because all it is, is it’s self-defeating, right?

(40:03) It’s inefficient, it’s unproductive, counterproductive in some cases. (40:09) And all that’s doing is dropping the boat anchor in the water while you’re trying to go full steam ahead. (40:15) It’s just slowing things down.

(40:17) I think technology doesn’t solve that. (40:21) That’s a commitment to the people side of the business because everything I think about is people, process, and technology. (40:29) And even though I’m a tech advisor to these groups, sometimes I use zero technology to help them solve a problem, a constraint.

(40:38) I’m trained in the theory of constraints, right? (40:42) Sometimes it’s a matter of, hey, you two just need to connect once a week for 10 minutes. (40:48) And here’s a scorecard with five things you can plug in.

(40:52) And our constraint’s gone. (40:54) It’s not going to require tech onboarding. (40:57) There’s no building effort here.

(41:00) Hell, use generative AI to create the spreadsheet for you if you want. (41:04) But if we do this, now all of a sudden we’ve reduced friction in that process and the engine runs more healthily.

Melissa

(41:15) And it goes back to what you were saying at the beginning. (41:18) You were hanging around with the best and the brightest, the smartest doers there were on the face of the earth. (41:23) And if you’re hanging around and you’re fearful to teach Melissa for six hours how to do something, you’re not going to be around the best and the brightest.

(41:33) By teaching people, we grow as people. (41:37) And let’s say that person does take your job. (41:41) That’s great because now you got to go out and tool up again and figure out some more.

(41:47) I mean, I know of course it’s scary and that’s non-empathetic, but the point is, is you’re learning and growing and you’re operating out of a place of abundance versus I can’t help this person and I can’t support this person. (42:00) And so there’s so many lessons to learn from the military. (42:04) And that’s literally one of my number one favorites is just helping someone up and teaching someone how to step in.

(42:13) There’s so much to be said about that, but thank you so much for being here. (42:19) So fun. (42:21) I want to get kind of in closing, any final thoughts or anything we didn’t touch on that you want to leave with our listeners?

Collin

(42:32) I think business is changing. (42:36) And I’m not just saying that as a guy who has represented changing technology landscapes for the last 15, 16, almost 18 years of his career. (42:47) I think now is the time for organizations to start reflecting on where their current state is and where like people know better than anyone, where process gets mucky, where things quite aren’t as fluid as they could be.

(43:07) And that’s where I think that’s where the first application of this new world of data and AI modernization should be focused within any business. (43:20) That’s the first thing I talk about with clients when they give me a call is just where does it hurt the most? (43:27) Where are you stuck the most often?

(43:30) And how might we think about freeing you up there? (43:33) And what do you think would happen to the organization as a result? (43:37) So it’s a great thought exercise.

(43:40) It starts every one of my conversations. (43:43) And I would challenge everyone listening to think about that for their organizations, because that’s where this isn’t going away. (43:58) It’s the idea of AI and advanced data capabilities is only going to grow over the years.

(44:06) So the organizations that are starting to think about it now represent the cohort of business who are going to outperform their peers over the next decade and beyond. (44:19) And I truly believe that.

Melissa

(44:22) 100%. (44:23) 100%. (44:24) So get ahead of it, everyone.

(44:26) Thank you so much again for being here. (44:29) You’re a superstar, rock star, all the things. (44:32) Colin, where can our listeners connect with you and learn more about what you guys are doing?

Collin

(44:38) Yeah. (44:38) Our website is northlabs.com. (44:41) My favorite place to connect with people is on LinkedIn.

(44:43) I’m backslash in slash Colin with two Ls, graves like dead people. (44:50) I love to connect. (44:51) I love to offer advice just as someone who’s been around this for so, so long.

(45:00) And I just love to meet new people and hear about what they’re doing. (45:03) So I’d encourage everyone to reach out and connect and shoot me a hello. (45:10) And I’m happy to be a resource to anyone as they need it.

Melissa

(45:15) That’s great. (45:17) And that is the Executive Connect podcast.

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Generative AI Business Strategy, AI Shift Bigger Than the Internet, Data and Leadership, Fractional Data Teams, Collin Graves, North Labs, Military Leadership in Business, Cloud Analytics, Building a Data Culture, AI Adoption Playbook, Mid Market Digital Transformation, Human Connection in Tech