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B2B Agility with Greg Kihlström™: MarTech, E-Commerce, & Customer Success
#73: AI in the 'real economy' with Bassem Hamdy, Briq
00:24:16
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A
With all the talk about the business impact of AI, why aren't more people talking beyond the most straightforward examples and more about sectors like construction, energy and manufacturing, where there's real potential for impact that has yet to be tapped? Agility requires a willingness to challenge the core processes of how work gets done, even in industries where how it's always been done is the default operating system, the agile brand.
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Foreign.
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B
To the B2B Agility podcast, where we look at the factors that drive success in B2B marketing with a focus on the people, processes, data and platforms that make B2B brands stand out and thrive in a competitive marketplace. I'm your host, Greg Kilstrom, advising Fortune 1000 brands on martech, marketing operations and CX, best selling author and speaker. Now let's get on to the show.
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A
So today we're going to talk about the practical, powerful and often overlooked application of AI in somewhat what some people call the real economy, the foundational industries that build and make our world. We're going to explore how AI powered digital workers are moving beyond the hype to create tangible value and what it takes to drive this kind of technological adoption in sectors traditionally resistant to change. To help me discuss this topic, I'd like to welcome Bassem Hamdi, CEO and co founder at Bric Basim. Welcome to the show.
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C
Thank you, Greg. That was a great intro and really nailed the problem set. But yeah, thank you. I'm very happy to be here, Greg.
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A
Wonderful. Yeah, I love it and definitely interesting topic and you know, can't wait to hear your insights on it. Before we do that though, why don't you give a little background on yourself and your role at Greg.
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C
So, believe it or not, 26 years in tech. So I started, I'm a little older than some of your founders. I started right when, you know, PCs, client server was a big deal. And then I transitioned from a traditional big ERP for those that don't know what ERP accounting software. I was chief marketing officer, had a product and then I transitioned to a hot cloud product called Procore. I was an early employee there, ran, I was EVP of marketing and strategy. We blew that company up. That cloud had just happened. It was a cloud revolution, which in all honesty was not different than client server. It just moved from hey, we're using my computer to I'm using somebody else's computer. But then came AI. And that, that's where I've been for the last, I was AI before it was AI for the last seven years. So yeah, it's Great to be here.
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A
Yeah, yeah, looking forward to it. Let's. Let's dive in here, and we're going to talk about a few things. But I want to start with this, you know, this concept of the real economy, and I kind of teed that up in the intro and the new digital workforce. So the term digital worker sounds a lot. It sounds less like a tool and more like a team member. But, you know, can you break down what that actually means and maybe give an. Give an example?
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C
Yeah, absolutely. You know, for those that are listening, when we talk about physical industries, when you talk about, you know, as you put it, Greg, the real economy, we're talking about people that take raw materials and turn them into something physical, right? So if you think about everybody out there that takes, you know, construction parts and puts them together, gets a building, you take, you know, raw materials, assemble them, you get a product. So the idea here is that that's the type of economy that we're talking about. Companies that build stuff, make stuff that are physical and tangible. Digital companies are not anything that brick has ever sold to. Like, we don't sell to companies like brick. We sell to those folks that build, lay roads, build bridges, airports, power generation, oil and gas. Like, as you mentioned, a lot of folks call that the real underlying economy. It makes up such a huge portion of what we build in the gdp, and it generally is overlooked by most software companies or technology companies. You know, anthropic didn't focus on, like, tuning their models to the latest, you know, water regulation when they're building a, you know, trailer park in Albuquerque, New Mexico. But somebody had to, and that's what we decided to do. I think what's interesting about the physical economy is what they build is technology. Right. So when you think about it, like, go back to the pyramids. We were building the pyramids. This is a construction company, did that to some extent. You know, that's the idea is everything that's around you is built by these companies. Now, what's also interesting is I think their resistance to change isn't as severe as some folks think. So if I was to go to an assembly line in the 90s, Ford assembly line, I would see hundreds of people on the assembly line handcrafting cars. Yeah, fast forward, you don't see any of that. You see robotic arms and a half dozen people. The idea here is maybe these industries, and maybe we were onto something early, maybe these industries adopt technology faster than we think. They just adopt technology that is built for them. And I think that's kind of the big differentiator. So I don't know how much resistance there is as there is. You know, I used to say this line, I don't think construction and these industries are slow to pick up technology. I think technology companies are slow to understand these industries and they are so different.
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A
Yeah.
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C
What.
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A
And maybe what are some of the areas where there might be less friction? You know, because to your point, it's. It's not. It's not being averse to all technology. It's probably. This doesn't make sense for my business because of X, Y and Z. Like, so where are areas where there's more. Where there's less friction?
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C
Yeah, I, I think it was easy to start for Brick in. In the back office. Right. So when we started, you know, building up brick, we started building these, you know, we bought. Technology was all the rage in the, you know, the late teens, early 20s. And that's where we grew up. Right. That's our vintage. And we built bots and we're like, you know, Candy's bots do more than, like, post to Twitter and buy the latest Nike shoes first. Like, what if we trained it to do something in the accounting department? And that was kind of near and dear to my heart. There are 300,000 open positions in white collar swivel seats in these industries. Like, people do not wake up, Greg. They wake up and want to be a podcaster. They don't go, hey, you know, my dream is to be a payroll clerk at a construction company. It's just not what happens. So these, these seats are empty. And so that was the lowest friction if, if what we did was bring in. I called it human augmentation. Now we kind of call it, unfortunately, human replacement technology. That did the work that a traditional, you know, payroll or AP or AR person would do. Yeah, that had the lowest barrier to entry for us because, you know, there aren't enough accountants at a company anyway. And as you scale, like the economy, regardless of what you've seen over the last six months, the economy is. Has boomed since BRIC started. I mean, yes, there are ups and downs, but overall, these companies are all growing and they don't like to put more, you know, overhead people in office. So it was a simpler sell where you see it transitioning. Now you go from worker to expert. And I think where you're seeing it is there's a vast amount of expertise that is leaving the industry aging out of the industry, retiring and not being replaced. So now Brick, you see this movement of, yes, we need a digital workforce to do, you know, check printing. But now we need a digital workforce that knows how to read a set of drawings that Chuck was the only guy that knew how to read. And so you're seeing this kind of transition out of the back office and into the operational front office. That's where. That's where it was. But the friction was much lower on the accounting side, for sure.
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A
And I mean, I think you bring up a. There's certainly a lot of talk about. And you mentioned this too, but, like, there's a lot of talk about AI taking away jobs, but I think there's conversation about what you're talking about here, which is there are roles. I mean, there's other industries like truck driving and quick service restaurants, you know, others where they can't fill. They can't find people to fill the positions and so on and so forth. And so, you know, I don't want to completely discount that there's going to be some jobs replaced, and certainly there's been layoffs in other industries. But, like, to me, this is a. This is an opportunity to elevate maybe the people that are still there in those roles and, and give them maybe better and more valuable. Like, is that the right way to think about.
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C
Yeah, I think that is. I say if you're in a job where you're literally acting like a machine, or if your job can be replaced by a machine pretty easily, your job is not rewarding as a human. I'm sorry, it's an inhumane job. So, yes, we need inhumane jobs going in there and typing AP invoices or doing, you know, moving files around the office. But listen, there were switchboard operators at one point. There, you know, there, there are jobs that there are. You know, bank tellers aren't exactly a booming industry. There are jobs that kind of transition out of the industries. And I, I don't, you know, I think at first people didn't want to embrace this idea that what we do is autonomous AI. So we install like. Like the easiest way to describe it for folks that haven't heard of brick is we're like the Roomba of workers, right? So we're, we're not a vacuum cleaner. We're not like software. We don't want push to think. We want something that wakes up and goes, is something dirty. Let me go clean that. And so that's. We're in the world of autonomous AI. And what autonomous AI does really, really well is it triggers itself on when something. When a condition is met. So when we think about that, you're 2 o' clock in the morning, an invoice comes in.
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Jenny or Johnny at the AP clerk is not going to manage that invoice. But the robot doesn't care. And I think that's when you think about where the jobs need to transition to. People are creative beings, they are social beings. A robot will never, ever replace, you know, something that can make a sale right there, there is, there is that handshake. There's no, there is that physical thing that humans long for. And the industries, the physical industries have these amazingly creative people in there, but we bogged them down with processes and things they just don't want to do. And so I think the more you take off of people's desks that are quote, unquote, inhumane, the double data entry, the tracking of invoices, the receipts, the filing of drawings, all of these things, the better their job becomes. So I know a lot of people are like, you know, I was reading the Wall Street Journal the other day and they're like, the AI apocalypse is coming. There's no white collar jobs anymore. And I'm like, well, maybe those were shitty white collar jobs and we should find better ones. Like, that's the general idea.
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A
You know, I've, I've probably said this a couple times already on this show, but you know, my, just to date myself a little bit, you know, my first job out of college was a webmaster at an Internet startup, which were two things that didn't exist when I entered high school. So, you know, I look at this time as similar. Like I see a lot of echoes in that of like I wouldn't have known what to study to even do what I was going to end up doing. You know, I went to school for photography in a dark room and you know, and stuff like that. And so, you know, I do feel like there's, there's, there's some knowns, but there's also some unknowns. And I don't think all the unknowns are bad, if that.
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C
Yeah, and you just said it, Beth. Like, the idea here is that there, the economy, we don't even know what the economy is going to produce two, three, four years from now. Right. I think when we think about we're moving in a direction in AI that we have to be careful on. We don't want to bubble it up. We don't want to. You know, when people talk about AI, you know, at parties, oh, you're an AI company. How interesting. Tell me more. You know, I I get very boring for them. I'm like, I want to differentiate what AI is because I think, you know, calling an agent that sets up a job, you know, or, you know, an opportunity in Salesforce, AI, that's a stretch, really. Like, you know, glorified macros are a stretch as AI. Right. It's like I instruct something to do something, and it does it. That's not AI, that's like obvious generational software movement. So age is really driving me crazy. But the other side is like, I mean, writing a poem from like generative AI, that's really important thing, predictive AI. Let's. Let's find out what's going to happen next. Yeah. And then a time is AI, which is what we do. Is it. You know, it's hard to say to the people, like you said, like, it's going to replace your job. It's like it's an inevitability. Don't be the guy or girl sitting in a seat with the wrench, putting the Ford car together when that robotic arm is rolled in. Like, you don't want to be that person. So that's really, you know, where things are going to be. And Greg, this is moving faster than I've ever seen. Like, I would say 40 to 50% of our code is now generatively written. Think about that. You know, four or five years ago, I was wrestling with other startups to hire engineers, like, physically, you know, fighting for these folks. And I don't even know if coding is going to be a job in five years. Right. So it's. There, There a lot of changes coming.
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A
I mean, I'm, you know, I'm, I'm vibe coding. I. I'll, I'll admit I'm doing a little bit.
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C
I don't.
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A
And I never wrote or I. I've written HTML. That's about as far as I got. But. Yeah, but, you know, to talk a little bit more about, about Brick and, and some of your successes, you know, you've had some, Some really impressive results. You know, one of the figures here saved clients over 135 million in labor costs. You, you know, given some of the, what we just talked about, some of the sensitivities and all that, like, how do you frame that in, you know, from a brand and positioning and sales perspective? Like, how do you, how do you frame that value proposition to companies in a way that is, you know, that is attractive and kind of. Again. Well, I'll, I'll let you answer it.
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C
Yeah. No, I mean, first of all, It's a super sensitive topic, right? So it's ethical. You have to talk about giving people purpose, giving them that proper job. But when you talk to management, it's a little bit more like if you're talking to CEO of $100 million company that's trying to grow, can't hire fast enough, and is like drowning in paperwork, they're not as interested in the ethical story behind human replacement technology, Right? So there is a balance there. But the way we describe it is, you know, a very bad golf analogy. The drive for show and the pup for dough. And so, you know, the drive for show is everybody wants to walk up the tee box, hit the ball as hard as you can, get it as far as it can, because everybody's watching you. The drive for show in autonomous AI is how many heads do I not have to hire? Right? And that's really kind of cool. There's like, hey, I used to have 12 people in this department. Four of them retired. I doubled the size of my company. Now I still have the same number of people in that department. That's a flex right now. That is a flex. But the pet for dough is something a little different. It's what is the cost of a mistake? What is the cost of latency of that data? So I'll give you an example. Our technology has autonomous risk managers. So think of it as. As a risk manager that analyzes things all over. And, you know, our technology gets email addresses and phone numbers. It is really like a human workers. And one of the things they get is certificates of insurance. And so you get these certificates of insurance, and you have to analyze it and make sure it meets the requirements of your company. So this vendor can come to you, come on site, and these are dangerous industries. These are dirty and dangerous industries. They better have insurance. And, you know, as a company grows, you need more risk managers. And so one of our clients was able not to hire another two risk managers because of our autonomous technology, which is fantastic. The president pulled me aside and goes, yes, we saved two headcount, but last year we missed an insurance certificate. There was a loss on a project, and that cost us 500,000 in a deductible. So, yes, we love to talk about, hey, we're saving a couple of swivel chairs. But the real return is.
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You know, AI tends to do things both deterministically and problematically, sometimes better than a human. You know, AI can read an insurance certificate just like a human can, but can also look for fraud. You know, one of the number One ways to send in a fraudulent, you know, insurance certificate is by cutting and pasting information into the PDF. Robots can detect that faster. Autonomous workers can detect that better than a human. And those are the things that, you know, there are things that humans will always need to do, and there are things that robots can do better, and that's. That's where we're landing.
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A
So to kind of get started down that path, I mean, it sounds like there's. There's probably some companies that kind of get it and. And maybe others that may understand it, but. But haven't, I guess, what legwork do they need to do to really take advantage of, like, how. How do they. Is there prep that should be done? Or, like, is it mindset shift? Is it all of the above? Like, what. What's the. What are the kind of. The first steps before this even starts?
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C
I think one of the biggest. If you ever see a failure to launch at, you know, autonomous workforce company like Brick, it really boils down to the company doesn't really have the ability to describe their process in any distinct way. What's interesting, it's like coming to Christmas dinner at a divorced couple's house.
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This is like the first time operations and accounting are talking about a work stream. That's what it feels like. It's like, how do you buy something? And the accountant will say, I need a purchase order number. And the operation guys, I never use purchase orders. What are you talking. And they're, like, looking at each other as if they've never spoken. They work in the same company. They cannot just ride their process. So I'm not talking about becoming like McKinsey and being like a business process engineer, but at least be able to write down what you do for a living and that. I think that's the key first step.
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A
Well, and I mean, to me, that seems like those companies that are. I would call it disorganized, and so much so that they can't describe their own processes. Like.
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C
Right.
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They're having. They have other. They have other issues already. Yeah.
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C
So.
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But no, that's. That's good to know. I mean, where then what. I mean, what does it. What does it look like to get started that, you know, let's say. Let's say the company is, you know, has, for the most part, has their ducks in a row and, you know, understands things. Like, what does the first step look like to start working with a company like brick?
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C
Yeah, 100%. The brick has a front end that you train like you would train an employee. Yeah, you can upload standard operating procedures. Tell Brick Auto, which is our, what we call a large action model. Tell Auto, you know, what do you, what are you, what systems are you using? We even have the ability to consume videos of people working and having that be decoded into AI, into autonomous workers. So taking videos of the actual person doing the entry, understanding a flowchart, just as if you hired Jenny or Johnny or Greg. Greg. If you started working at Company X tomorrow, they'd have to train you, they'd have to enable you. That level of training and enablement is the first step to getting an autonomous worker running. You know, you wouldn't want a worker that you hired asking you 50 million questions a day. You'd probably fire that person.
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A
Right?
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C
So you want to give it. You want to equip the worker to make decisions, to understand how things work, to use the software you're given to get the job done. And once that's done, I usually say autonomous worker is about 60% efficient at that point. Like the initial training is usually just happy path. It's not everything that could ever happen. You know, it doesn't take into consideration ED cases. But what ends up happening is once you go live with an autonomous worker, the edge cases start to appear and the worker starts raising their hand like, this doesn't make sense. You didn't train me on this. What should I do? What am I doing here? And it's like you start giving that feedback loop, that continuous learning model where you're going in and saying you made a mistake or you should have done it this way, or the reason why you stopped is because of this. And you're giving those instructions, that feedback loop, so that the next time it could be that much better. One of the pieces of tech that we've added to auto gen 5 is the idea of instinct. And that's actually probably the most exciting thing we're working on, where we make hundreds of educated guesses every day, maybe thousands. And an educated guess is like, should I do this or that maybe I'll do this. So it's not if this, then that, it's if this, then maybe that in certain scenarios. And teaching AI right now, to be, to have instinct is really cutting edge. It's, it's a really fun endeavor, but instinct comes from experience, so that's how you know, it's, it's never put the cart before the hearse. Train them on the basics, get them to be better at their job and then let them, you know, learn to do the next step. Themselves. And that's really when we talk about autonomous instinct. I think that's the holy grail where we're going.
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A
Well, Basim, thanks so much for joining today. One last question before we wrap up here. What do you do to stay agile in your role and how do you find a way to do it consistently?
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C
Oh, that's a tough one. I am the king of agile. So I actually, I can change my mind six times in an hour, so that's a problem. So maybe I'm a little too agile. So I've actually had to go the other way when the other way is tagging myself to a time horizon where I can't change my mind. So I can, you know, I, I'm, I'm, I don't know if you subscribe to Myers Briggs, but I'm an entj. I love inputs. I love getting inputs and then making decisions and molding my decisions based on inputs. I've received environmental from people, you know, from the markets tell me, and then shifting. That's great as a human, not great as the organization. So.
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I've actually gone the other way. I've actually said I'm going to do one week sprints and I'm going to look at things on Monday, we're going to set a ground and we're going to reassess on Friday instead of trying to reassess in real time. So that's what I'm doing.
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A
Yeah. Love it. Love it. Well, again, I'd like to thank Basim Hamdi, CEO and co founder at Brick, for joining the show. You can learn more about Basim and Brick by following the links in the show notes.
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C
Thanks, Jake.
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A
Thank you.
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B
Thanks again for listening to the B2B Agility podcast. If you enjoyed the show, please take a minute to subscribe and leave us a rating so that others can find the show more easily. You can access more episodes of the show at www.b2b agility.com. that's b2b agility.com. while you're there, check out my series of bestselling agile brand guides covering a wide variety of marketing technology topics. Or you can search for Greg Kilstrom on Amazon. Until next time, stay focused and stay agile.