Professor Jerry Kane has spent more than a decade studying how organizations adapt to emerging technology. Now, he’s focused on AI - and why successful AI transformation has far more to do with people, workflows, and organizational adaptability than with choosing the right tools. In this episode, Jerry and Jason discuss what he’s learning from both the classroom and his work with companies navigating AI adoption. They explore why AI workflows are “grown” rather than simply built, why the people who already understand how a business works may be best positioned to transform it, and why Jerry is skeptical that traditional forward-deployed engineering is the answer for every company. They also dig into what happens as the cost of building software approaches zero, Jerry’s idea of the “backward-deployed engineer,” how education needs to change in an AI world, and why the companies that ultimately win may not be those that pick the best AI tools—but those that get best at continuously changing how they work.
Professor Jerry Kane has spent more than a decade studying digital transformation and how organizations adapt to new technology. In this episode, we explore what AI transformation actually looks like inside an organization - and why Jerry believes the technology itself may be the easy part.
We discuss:
• Why AI transformation is fundamentally a people, workflow, and organizational challenge
• Why waiting for AI technology to stabilize could leave companies too far behind
• What Jerry is learning from teaching students to build and work with AI
• Why widespread AI adoption doesn't necessarily translate into meaningful business impact
• The difference between automating existing work and actually transforming how work gets done
• Why AI workflows are “grown,” not simply built
• Why the people who understand a company's workflows may be better positioned to drive AI transformation than outside technologists
• Jerry's concept of the “backward-deployed engineer”
• Why he is skeptical of the traditional forward-deployed engineering model for many companies
• What becomes valuable when the cost of building software continues to fall
• Why experimentation and organizational adaptability may become durable competitive advantages
• How AI is changing education and the skills students need to develop
• Why the organizations that win may not be the ones that pick the right AI tools, but the ones that become best at continuously adapting as the technology changes
Jason Jacobs: [00:00:00] Welcome to BuiltForward, the podcast exploring where construction is heading over the next decade, and how AI and other emerging technologies will and won't transform the industry. I'm Jason Jacobs, a longtime startup founder, investor, and the host. Let's get into it
Today's guest is Gerry Kane, a professor at the University of Georgia and longtime researcher on digital transformation and how organizations adapt to new technology. Gerry is not a construction guy, which is one of the reasons why I wanted to have him on the show. I've been spending a lot of time trying to understand what it actually means for a contractor to become AI native, and whether the answer is better software, forward-deployed engineers, new operating models, or something else entirely.
Gerry has a different way of thinking about it. He argues that AI workflows aren't really built, they're [00:01:00] grown, that the people who already understand how the business works may be better positioned to lead the transformation than outsiders who understand the technology, and that the real advantage won't come from any particular AI tool, but from building an organization that can continuously adapt as the tools keep changing.
We get into why he's skeptical of the standard forward-deployed engineering model, what he calls the backward-deployed engineer, what actually compounds when code gets cheap, and why the companies that win with AI may be the ones that get best at changing themselves. This is a great discussion, and I hope you enjoy it.
Okay. Professor Jerry Kane, welcome to the show
Jerry Kane: Thank you. It's great to be here. It's been a long time. You know, I've talked to you recently, but we got to know each other back in, I think, 2011 or some point, uh, way back when
Jason Jacobs: Yeah. Yeah. I, I've-- I can't... Maybe you remember, but I, I know, I think it was me and our lead investor at the time, uh, Bijan from Spark [00:02:00] Capital, and we went and spoke in one of your classes. But do, do you recall what was the class and what did we talk about? I mean, but
Jerry Kane: cl- the class
Jason Jacobs: put you on the
Jerry Kane: much the... I, I don't remember what we talked about. I mean, I think it was early days of RunKeeper, so I think you were just talking about that. And at that point, my class was entitled Social Media for Managers. Um, and so it had, it must have had something to do with that. I don't remember how we got connected.
Um, but that class has basically evolved since 2011 to be the class I'm teaching now, which is Business Systems and Technology Innovation, and now we're all in on AI. Um, the format is still pretty much the same as it was, but it's just adapted to follow technology trends, and it's interesting to see what has changed over the past 15 years, and then the other things, what have not.
Um, and so that's... It's been a fun journey for me.
Jason Jacobs: Yeah. And so we got reconnected because, um, you know, as, as you know, I've been, uh, trying to do a deep dive into [00:03:00] the future of construction, specifically on the, on the commercial side, um, where, uh, um, you know, it, it feels like with technology changing, there's some interesting ways it could get involved, but at the same time, there's a lot that shouldn't change and won't change.
Um, and but piecing through which is which is, is the expedition that I'm on. And, um, and I saw you, uh, post about, um, I think you were talking about how, um, transformation is not a technology problem, it's a, it's a people problem. Um, and that really aligns with some of the things I've been thinking about in, in my worldview that's starting to form.
And, and even though you've been, it seems like, um, you know, focusing on it more with your students and then with big enterprises, whereas I'm looking at it with, you know, call it 10 to 75 million mid-sized contractors, um, I, I, I think I have a lot to learn from you, and I, and I'm, [00:04:00] I'm grateful for you making the time to come on the show.
Jerry Kane: Absolutely. I, I would clarify though that I have worked with companies of your wheelhouse. Uh, there's just a lot more of them and it's, you know, it, it's harder to do as much at scale as with some of the big companies I work with. But, um, have some, uh, visibility into those size as well as a moderate amount in construction as well.
So I, I have some firsthand knowledge as to the, your thesis and, and how it's playing out.
Jason Jacobs: Even better. Well, well, to kick things off, you know, not, not construction specific, but more, uh, you know, I think conventional wisdom is that academia is gonna get lapped when it comes to AI and, and, and transformation and, and here you are on the front lines. So, um, uh, you know, does, does that worry have any merit?
And, and how did you, you know, start, start heading down this path?
Jerry Kane: Well, I mean, I do think it has some merit because I think academia is just like any other institution that's, [00:05:00] uh, big, old, you know, institutions that are slow to change. Um, professors don't like to do new things. Um, we like to do things the way we've always done them, just like, you know, in many other organizations.
Now, one of the benefits of being, you know, uh, uh, studying technology in a business school, we have a little more license to move quickly. And, um, you know, I was talking with the chief human relations officer of a large, you know, Fortune 20 company, and he said he was very disappointed about what he's seeing coming out of, um, universities.
And I said, "Well, hold on, you know, let's look at what we're doing in here. You might just not be looking in the right place." Um, and so we've stood up in the last year a certificate in business and AI. Um, we have 250 students enrolled. We expect to double that this year. Uh, and it's not just a warmed-over analytics program.
It's not just, you know, slapping a new name on what we've always done. We've actually developed classes in, uh, [00:06:00] generative AI, agentic AI, chatbots, user design, product. Um, and quite frankly, last spring was the most remarkable semester I've taught in 20 years of doing this job because of what the students were capable of.
And I'm having more fun now as a professor, um, than I have had in 20 years. And hopefully, colleagues in my department are jumping on board and seeing that and having a similar experience, and I'm hoping we can spread that word to other departments a-as well, because I do think it's a real opportunity for us if we, if we grab it
Jason Jacobs: Yeah, and, and gosh, I mean, we could have a whole discussion just about, um, you know, is, is AI cheating or, or is AI something that, that, that, you know, like what are the productive uses and what are the unproductive or, or even, uh, you know, violating uses? Um, uh, I mean, we could touch on that briefly, but, uh, but I know it's not the main crux of the episode.
May- maybe a, um, [00:07:00] a more interesting place to start is, um, uh, which came first, the innovating in the classroom or the innovating with clients? And, um, and then, uh, how do they reinforce each other, if at all?
Jerry Kane: Yeah, absolutely reinforcing. I have, you know, since you were in my class in 2011, so then I was talking about social media, which then was moving too fast for me to, you know, follow. Um, I turned my class into a lab where, you know, my students go research topics that they're interested in related to the newest technology, report back, um, because I tell them, "I'm smarter and more knowledgeable than any of you and, and any 10 of you, but I'm not better than 40 of you.
And so together we're gonna learn more." And that's what I've been doing for 15 years, and so that's helped me keep up. Um, and then lessons I learn in the classroom, whether through consulting or exec ed, I apply, um, and find what fits in those [00:08:00] environments and just create this virtuous cycle where the consulting and the advisory stuff is leading back to the classroom so my students know what's going on in industry.
And then the s- students allow me to do some experimental stuff that organizations can learn from. And so I've found... And, and I learn all along the way, and I'm having a ball and, uh, doing it. And, um, so I've found that to be a very productive, uh, flywheel, if you will.
Jason Jacobs: Uh, and, and if, if you were, uh, a, you know, one of your clients today, um, uh, how should you be feeling about the future and AI's implications on it? And same question for your students that will ultimately be in the, in the job market when they graduate.
Jerry Kane: Um, I might-- So going back to, like, whether it's cheating or not, um, I think it's cheating if we don't change our deliverables. Uh, but, you know, I think that's, you know, not fair to the students because asking them to take tests the way we did [00:09:00] fi- five years ago, are those the skill sets that are needed in the workplace right now?
So I force my students to use generative AI. I force them to, to come up with creative applications for it, and those students are now supercharged, um, because they see the pot... You know, AI's a really weird technology in that, you know, previous-- Five years ago, if you did a SaaS product, you could tell people exactly how it worked, exactly how it was gonna impact their business, and you just needed to get people to start using it.
AI is much more open-ended and flexible, and the opportunities of it are not entirely clear, and not even Anthropic and OpenAI understand it fully and what the implications are going to be. And so once people see, once my students see how it can improve their ability to learn, how it can upskill their ability to provide deliverables and their ability to do really remarkable [00:10:00] things that weren't possible before, they see that possibility, um, and they get excited by it.
Same thing in organizations. Um, it's when you go in and you just teach somebody the tool, everybody rolls their eyes and, "Oh, you know, this is just another IT tool for me to, to use." But once you s- they start to experience how it can eliminate some of the redundancy of their job and the monotony of their job and open up new possibilities, they get excited and they want to learn more.
And so that's the part that I really enjoy right now. So I'm actually fairly, um, bullish on AI in business. It's gonna, it's gonna change a lot of things, um, but I think it's going to be a net positive for workers and a net positive for learning if we can figure out how to change the workflows around it, and that's the challenge facing academia, that's the challenge facing businesses right now.
Jason Jacobs: Yeah, it seems tricky. I mean, the technology itself is changing so quickly and the capabilities are [00:11:00] evolving so quickly, far faster than, um, than the people, processes, workflows, and culture of these organizations can adapt. And, um, a- and, and plus, as the cost of building software moves to zero, uh, you know, they're getting bombarded by outside vendors that all have shiny demos, that all use the same buzzwords.
And, um, uh, I mean, it, it seems like from, from a f- from a business owner standpoint or a client standpoint, uh, I mean, if I were them, I, I might be inclined to just put my head in the sand and say, "Buzz off everybody, because I'm just trying to get some real work done and you guys are all a distraction."
Jerry Kane: Yeah. And that's-- And there is a lot of that, and there are a lot of vendors and snake oil salesmen out there promising you the new latest, greatest AI agent that's gonna do all your employees' work for them, uh, et cetera, et cetera. And I don't believe that is reality at all. [00:12:00] On the flip s- And, and it is moving.
You know, it's-- I tell people all the time, it is my full-time job to keep up with this, um, and I can't. I don't, uh, I've-- I don't know how people with a day job do it. On the flip side, um, I think waiting for the tools to stabilize and the capabilities to stabilize means you're going to be too far behind, uh, at that, you know, by the time it does, and I, and I don't know when it will.
Um, you know, the bright side is when it is a cultural issue, a workflow issue, a people issue, a talent issue, those are things you can start working on now. Um, and, and I say we talked about like what has changed and what hasn't. You know, my original work on digital transformation dates back to like 2013, and you said it's, uh, we, we're talking about the, the, the organizational side of digital change.
That's what my book talked about back then. You know, it, it is an organizational problem, and, um, those are things that aren't gonna [00:13:00] go away, the ability, the need to be agile, the be- need to be experimental, uh, the need to test and learn. Um, that was true 15 years ago. It's true today. Um, the, the technology's changed.
Those organizational questions haven't. And so as you start to learn, build those muscles and learn those capabilities, it's gonna be valuable no matter which way AI goes. Um, and so building in that time to learn the tool, to apply it, to experiment, um, and then how to figure out how to do it at scale is, I think, really where the companies are dealing with.
Um, you know, one company I'm working with, large, uh, software company, have 90% adoption amongst their employees but still have not seen meaningful business impact. So then the question is: how do we start building in that meaningful business impact? And that's gonna be stage two of this whole process.
Jason Jacobs: And, um, just, just for context, maybe talk a bit about, um, uh, [00:14:00] a- about the nature of your work with these firms and also the types of firms that you've been working with, just so that we have a sense as we then move into some of the specifics of, um, uh, of, of from where you're getting your data.
Jerry Kane: Yeah. Um, so I've worked with, you know, sort of organizations of all industries. Uh, you know, I work with everybody. Yeah, you know, I've worked with logistics companies, I've worked with retail, I've worked with consumer products, I've worked with consulting, you know, it's, you know, with construction firms, um, uh, you know, manufacturers, uh, large and small.
And so, um, you know, what I'm finding is... A- and the problems are, are pretty similar, uh, among those companies. It's, it's how do you change... Where are the opportunities to sort of change the way work is done? You know, back in the '80s, Shoshana Zuboff, a Harvard professor, had this [00:15:00] framework. Uh, it's "The Age of the Smart Machine," is the book that she, uh, wrote and that I read in my PhD program.
And she talked about, you know, AI having three impacts: automating, informating, and transforming. So automating is getting the tool to do the work, the same work For you. Informating is getting information and knowledge to the right place at the right time so you can make decisions in different ways.
Transforming is about how do you fundamentally redo business. Most companies right now, um, are looking at... If you go into a company and say, "Where are your AI initiatives?" They're all in the automate side. They're all in the efficiency side. And that's great. That's only gonna get you so far. The real opportunities are in the transformational side, and that's sort of where I start working with companies, um, because it is...
You're-- You, you can't one-shot this. I, I do go in and do sort of day-long workshops with, um, senior executives because, uh, the [00:16:00] research is showing the companies that do better are the ones whose senior executives are using AI in a hands-on way, because they can start seeing through the hype and see where the real opportunity is and not get sucked into those vendor claims of the shiny new tool.
They can start to be better assessors of it. Um, so I start with those, um, you know, day-long workshops where I get that, uh, that literacy going and then when I can work with them over time, we start doing some test and learn. We iterate on, um, experiences and, and what they're learning from it, and that's really where the magic of the classroom takes place, 'cause I get them for three months, and I see the students, and this is true of undergrads and executive MBAs, get better at it over time because they see what works, they see what other people are doing, they build on that, and it's that iteration is where the real transformation comes from, not only at an individual level, individual workflows, [00:17:00] but then they can start thinking through, okay, what are, um, the business cases that we can start impacting?
A company I just-- a manufacturing company I just worked with recently, the CEO gave them the, the mandate of to come up with AI-related initiatives that would deliver, um, $10 million in EBITDA revenue. And so, you know, so that, that's a much bigger, hairier, audacious goal, and that's the teams I've been working with.
Okay, now that we understand it, how can we create these, these bigger goals and these bigger impacts? Not just, oh, summarize meetings or respond to emails, uh, more effectively. How do we just start changing these revenue opportunities? And that's the more exciting, uh, projects to work on. But that takes time, you know.
That takes that, um... And I advise those teams. They know the industry much better than I can. I know the tool, and that conversation is where the magic happens.
Jason Jacobs: Uh, I mean, uh, uh, as you [00:18:00] think about, um, where to start versus where to get to, I mean, one striking thing that, that I've seen is if, if-- I mean, every, uh, like conventional wisdom as an entrepreneur has always been start narrow and, you know, land and expand and the most narrow thing and sh- and ship. You know, if you're not embarrassed by your first product, um, you know, you, you ship too late and, and all that, right?
But we're, we're moving into this world where it's like I could, I could, you know, I could just s- you know, set up the LLMs and go to sleep and wake up and have a product, right? Um, and, and, uh, a- a- and actually the hard part is, well, this product doesn't actually... Like it's clear it wasn't built by someone that understands the workflows in our industry, and certainly not the workflows in our shop, right?
Um, uh, and, um, and then if you pick any one workflow, right, there's a zillion venture-backed, you know, c- competitors going after it using the same words and with the same demos. But, but, uh, but if there's not connectivity across the workflows, then, then [00:19:00] no one workflow is inspiring in terms of the impact that it can have.
So, so it's like where, where do you start if you're a vendor and where do you start if you're a client?
Jerry Kane: Um, I deal mostly with client side, so I don't wanna like speak at, like I am not building these tools. Um, I'm helping organizations figure out how to use the really, the general stuff, you know, the, the basic L- basic LLMs, um, Codex, uh, co- uh, Copilot, um, Claude, and sort of understand them so they can start thinking about, um, the, the business impact.
Because I do think it's that, that comprehension that comes first that helps you assess the vendors. And, and getting hands-on with these tools does provide really remarkable insight into their capabilities as well as their limitations. Because if you sit down, you know, and go to bed and let LLM build your product, um, you're gonna find it's h- hacked into Hugging Face to [00:20:00] steal their product.
Um, you know, you... The control and the reliability of these, these things are a real challenge right now, and how do you get them to do... We can set up pilots all day long. How do you do it at scale reliably over time is the challenge. Where I'm seeing some vendors, which I think is really interesting, um, one company I'm working with had figured out how to use AI to sort of do their RFPs more efficiently.
Um, to, you know, what used to take a week now is taking an hour. Um, and they tested it and, and made it bulletproof on their own processes, and now they're selling off the main company to try to, you-- the, the, the company that they built this in to now sell the, um, the software and the AI agents, um, to other companies doing similar things because they think that's, uh, where the opportunity is.
So for me, AI is mu- I say this a lot, AI is not built, or [00:21:00] AI-based workflows are not built, they're grown. And if you have somebody in a lab that just does it and says, "Oh look, this is gonna work," um, I'd be a little skeptical. Um, if you have people that say, "I grew this in my own processes and I've been using this for two years, and I can tell you the pros and the cons," that's the sort of vendors I'd start listening to.
And you know, it's sort of the story of Slack. You know, Slack was built by a video game company that just came up with their own way to communicate amongst themselves, and they realized that the communication tool was way more valuable, uh, than the video game they were trying to build. And, and that the, the rest is, you know, as they say, the rest is history.
Um, and I think we'll see more stories like that of vendors with AI, because until you actually are hands-on with the workflows and can battle test it over time, I'm gonna be skeptical that the AI agents are gonna be robust enough for the enterprise. I'm not sure if that answered your question or was just a nice story I, I, I wandered off [00:22:00] into, but, um, if I, if I missed something, come back and, and push me again.
Jason Jacobs: Well, I mean, it used to be that, like, you sell software and that software is downloaded, and then you have the software, and then until they, they publish a new version of the software, that's the software you use. And when you get the new version, you buy that and you download that, right? Now, I mean, now we're moving into this world where it's like, uh, you know, these, these capabilities are living and breathing and, and steeping and evolving and adapting and, and so, like, what are the implications as that relates to, um, you know, software that you buy from outside firms versus, you know, tooling that you're continually nurturing and evolving in-house?
Jerry Kane: Uh, I, I am thinking a lot about that. I think if I had the answer, I'd be a lot richer than I am, um, because I think that is the open question. You know, when I did my MBA 25 years ago, it, it was you buy the enterprise ERP, and you [00:23:00] change the way the organization worked to deal with the ERP to work in the way the ERP did because it was a lot easier to do that than to build the software.
Um, that's not true anymore. Now the software is adaptable, and so you've got this moving target where the work can change and the tool can change in tandem. So it's this really challenging problem, and I think the, the, the success is not going to be how to adapt work to AI, it's how to continually change using both to adapt to a changing environment, uh, to capitalize on opportunities that come up in the marketplace.
Um, it's gonna create the ability for small shops to punch way above their weight. It's gonna create opportunities for agility like we've never seen before. Um, and it is going to sort of-- Because of that, I think the rules of business are gonna change, and I think that's gonna be the challenge of the next five to 10 years [00:24:00] is, is what you're pointing out, is how do we, um, re- rebuild our organizations around these new capabilities?
Uh, because I think if you, um... It's like the old, um, quantum physics, uh, thing. If you've not, if you're not disturbed by it, you haven't fully understood it. If you're not amazed by what AI can do or LLMs can do right now, I think you have not fully understood and, and have not fully used it to its capabilities, um, because I, I do think it's remarkable.
Jason Jacobs: Oh, well, growing up, it, it, it seems that there was always a place for these firms that have exceptional craftsmanship that's steeped in decades and decades or even hundreds of years of doing things the same way. Um, are, are we moving into a time where those types of firms are, are gonna go away? Or, or will there always be a place for, for kind of, you know, we're not gonna, we're not gonna chase the latest [00:25:00] trends or the latest innovations, like we know who we are and how we do things, and that's how we'll always do things?
Jerry Kane: Yeah. Um, I would not ... I mean, I think it depends on how quickly they are willing to adapt. And notice I say willing to adapt and not able to adapt, because I think, I think this is something that is remarkably accessible. I mean, the programming language of LLMs is not just English, it's your native language.
Like, this is accessible to everyone. And what we're seeing in the research, in my own experience, is that AI is more likely to amplify ... So it has two effects. If you have no knowledge in an area, it gets you up to an average. If you have deep subject matter knowledge in an area, it can really e- amplify that knowledge.
So what you're good at, it can amplify, uh, that. And so these companies with 100 years of knowledge in a particular industry, if they can figure out how to [00:26:00] use AI to amplify that knowledge, they're gonna be, um, I don't wanna say, uh, unstoppable, but they're gonna have a huge advantage because they do have, uh, that deep knowledge.
Now, as you pointed out before, certain things about that knowledge are gonna need to change. They're gonna be better served to n- understand what needs to change and what doesn't need to change if they, you know, really dig in and try to, to figure out those problems and how they need to adapt for the future.
Um, I think to the extent that they wait, to the extent that they say AI is a fad and it's going away, to the extent that they say, "Oh, we're have competitive advantage," uh, that, that's insurmountable, then there is the risk that they'll, they'll go away. But I think any firm ... Uh, now they may look different.
They may look smaller. They may be organized differently to capitalize on this. Um, I-- That I think is almost certain to happen. But I think if they put their mind to transforming and to capitalizing on these new capabilities, there is a [00:27:00] strong advantage, uh, with incumbents because they do have the resources, they do have the opportunities, they do have the knowledge to really capitalize on that.
But when small firms can punch above their weight, uh, to such an extent, there's gonna be a lot more upstarts coming after them, um, if they're complacent.
Jason Jacobs: So w- when it comes to the rank and file, um, experimenting with these tools to optimize their own workflows, when is it, um, kindling or fertilizer that, you know, that, that starts building a bridge to wider adoption and, and when is it, uh, you know, chaos or, or introducing, um, you know, inefficiency, confusion or, or, or risk?
Jerry Kane: And that is, you know, when we talk in my class, it's about innovation, but it's about disciplined innovation. Um, you know, and you can't let a thousand flowers bloom [00:28:00] at the organizational level and expe- expect it to work out. Um, at the same time, a lot of my large corporate clients, they already have 150 different systems, uh, many of which do the same thing, so that problem already exists.
And so oftentimes what I recommend and when I work with, it's... And this is Change Management 101 in some ways. Um, I always recommend starting with a coalition of the willing. You know, what are the people... You know, in most organizations, you're gonna have 30% of people who really are eager to try the new things.
You're gonna have 30% of the people who are gonna resist no matter what you do. And you're gonna have 40% that's in the middle that's gonna go whichever way they think the wind is blowing. Um, if the opportunity is in innovating, they're going to be happy to innovate, or they don't wanna innovate themselves, but they're happy to adopt the innovation that the 30% in- innovators come up with once they see the benefit of it.
And so it's really about starting with that 30%. Um, and it, it may be a [00:29:00] smaller group, starting with a group of, you know, 20 to 30 people. Get them, give them the tools, get them up to speed, um, with knowledge, get them to figure out, and then you take the two or three best use cases and drive those across the organization.
So y- you have to do, you know... There's a concept in academia called absorptive capacity, that organizations can only absorb so much change, um, at any one time. So you start with the, the lowest risk, highest value, push those across the organization, and then ramp up as you go. Because the great thing about absorptive capacity, um, it is a muscle that gets stronger the more you do it.
Um, and so it's about starting with pilots, but not j- And again, some things stay change, some things stay the same. Surveys we did, um, 13 years ago on digital transformation, everybody runs pilots. It's those that have the discipline to s- scale the success across the [00:30:00] organization and pick the right pilots to scale across the organization.
That's what separates the ones that are really doing this well and those that, um, will just do innovation theater or turn out to be a mess because they just let everybody experiment however they want. Uh, and that's also gonna get much more expensive now with changing of how these, uh, tools are, are expensed, um, at the organizational level.
It can get really expensive just letting people innovate with AI. Um, so get that team equip... And that's what I do with organizations. I work with those innovation teams to get them up to speed to identify those really productive use cases, and then the executive leaders decide which ones to roll out across the organization.
Jason Jacobs: Uh, ano-another tension that, that I've been coming across is, I mean, it seems, you know, back to what you were talking about before of how it, it's, you know, kind of this living, breathing, o- [00:31:00] ever-evolving thing, you know, that would suggest that, that the rise of forward-deployed engineers makes sense, where you have them, you know, parked in the workflows just observing, you know, looking for inefficiencies and then going back to the lab to try to figure out how to address them and, you know, remove the bottlenecks, you know, one at a time and starting small and working your way up to more strategic stuff over time.
Well, that makes sense, but if you do that, you are essentially tuning existing workflows where if you were to start one of these organizations today, right? I mean, likely, not definitely, but, but likely in, in a, in a lot of these companies, in a lot of these industries, the workflows might look quite different.
And, you know, with this forward-deployed engineer model, you know, are you, are you ever gonna get to revamping workflows? Are you only g- you know, is, is it a, I, I, I guess, I don't know if this is an exact analogy, but it's like, um, sustaining innovation versus disruptive innovation. Are you ever gonna get to the disruptive innovation, or are you held hostage by your core business?
Jerry Kane: Yeah. And that's, um, why I am a bit... I, I, I like the [00:32:00] concept of forward-deployed engineers. I think there is, um, a lot of value in that concept. I think the weakness is it su- assumes that engineers know how your business should be run, that they have, you know, expertise on your industry and know where the real value's gonna be taking place.
It assumes that OpenAI knows how your business should work. And, you know, I've got news for the Silicon Valley folks, most of the world, or most of the United States doesn't work like San Francisco does. And so, and, and won't, and it won't work. And so really I think what you... I like more the, the product mindset where you have technical knowledge, business knowledge, customer knowledge all wrapped up together.
Those are the people I think that are gonna be successful. So I actually think I would rather bet on teaching, and this is sort of my hypothesis. You can bring in all the forward en- uh, you know, uh, forward-deployed engineers you want, and they're gonna do [00:33:00] engineering things. Um, they're gonna build you tools that's gonna pave your cow paths, that are gonna make you look like OpenAI, and that probably isn't gonna work.
I think it's a lot easier to take your high performers, teach them the AI knowledge they need to know how to work the business. Um, I need a good term for that, like backward-deployed engineers or forward-deployed... I, I need to coin something. But I think it's a lot easier to upskill your existing employees with the AI knowledge they need, um, to really transform your business than it will be trusting OpenAI or Claude to show you how they work that's really gonna be impactful.
But that's, you- we have to test that hypothesis over time.
Jason Jacobs: So d- does that mean that there's k- that this is kind of bottoms-up innovation that, that ultimately will meet in the middle? Or is there any kind of composer or conductor that's, that's sitting over to make sure that the overall system is cohesive? And, and that, and, and, and who fills the white space in between workflows and in between functions?[00:34:00]
Jerry Kane: Yeah, and so that's the question. Uh, and you know, uh, I-- it always has to be top-down and bottom-up. Um, one of, um, uh, the AI leader at a large technology company sent me their AI strategy, and that was my critique of it was like, this is all top-down. This is all executives, sort of senior leaders saying, "Here's where we need to go," and you're not getting that bottom-up knowledge because it does...
You know, you need that flywheel of top-down and bottom-up, um, for things to really make a, a difference. And but you pointed it out, like we've done a lot of work decomposition over the last 20 years. Agile teams break it down. Um, the problem is now that they're all agile, sometimes they're all optimizing on their individual team goals that, that may or may not lead to overall organizational goals.
And finding ways [00:35:00] to... You know, we called this, uh, in an article I'm working on, capability collisions, where everybody's doing their job, everybody's performing well as individuals and as teams, but they don't come together into a coherence that really benefits the organization. I do think AI can also serve as an opportunity to bring-- to bridge those gaps because it can listen to what ha- is happening in other teams.
I think there are opportunities to solve this. Um, but I think it to the extent that we just decompose work, we're gonna run into problems. And so solving this problem of how do we communicate, work, collaborate across teams, um, so we solve those bottlenecks, um, that's really I think, where the, the, the challenge and the opportunity's gonna be.
Jason Jacobs: And directionally, um, is it, is it more about, uh, automating the mundane to, um, you know, to make sure that a [00:36:00] higher percentage of the stuff that only humans can do, uh, w- w- will be done by humans? Or, or is it actually about teaching the machines that judgment over time with enough reps and enough edge cases and enough training?
Jerry Kane: Uh, I think, I think automation is certainly where everybody starts. Um, and I think there's some benefits to be had here. You know, it's-- I, I am much more efficient as an individual using AI, you know, in my... I'm also a department head at UGA, so I have-- I, I also treat my, my administrative job as a lab. What can I automate to make my life better?
And I've done a lot of things, uh, with that. Um, at the same time, you know, I, I think it-- There's a lot that I have been unable... Like, I tried to build an, uh, uh, an email agent that would sort of triage my email, would draft some replies to just save me from the email log, and it ended up causing me [00:37:00] much more work than it solved because it didn't get it quite right each time.
There was so much nuance. There was so much, um, need there. So I think it's, I think it's all gonna be about augmentation, not automation, is how do humans and AI work together? Um, and it may be how do humans and a network of agents, and so that human is the orchestrator, is the bot sitter, so to speak, is the one that sort of provides that intuition.
Uh, and that's what AI is fundamentally missing is it is only as good, it, it's only as good as the past data it's trained on, which is always by definition in the past. Um, and humans are much better at sort of intuitive leaps. And so b- you know, we know conditions have changed, so we can make decisions differently, not just trained on past data.
A great example is the 2008 housing crisis. You know, all those, [00:38:00] those, um, you know, fancy financial instruments were great if conditions remained the same. Conditions changed, and they didn't realize that those algorithms were n- were now out of date. AI doesn't change that. It can't see. It's only trained on past data, so it c- so humans are always gonna be essential for that.
Um, oh, I had another really great insight, uh, on, you know, what, where humans were gonna be, uh, valuable, um- But it's also about sort of how to bring it together in ways that really add value and that are, um, create-- I mean, yes, AI are creative in a certain way 'cause they can mash up past data to come up with new things.
But often humans, you know, it can generate a thousand, uh, possibilities. Humans often have to be the ones to say, "Ah, this one is the really good idea that we wanna run with." Um, and, um, and only if you have that subject matter knowledge are you really gonna be able to [00:39:00] have that taste to say, "Ah, this is the one we should run with."
So I think not just human in the loop, but I think it's gonna be human AI agent pairs or teams that are gonna be working together that are gonna be invaluable for the future, and that's how work is going to be done, and we need to figure out how to make it work. Sorry to sound sci-fi there.
Jason Jacobs: Well, I'm, I'm so proud of myself in advance for this next question. Um, but, um, which now puts all this pressure on this question, so
Jerry Kane: Yes, I was about to say you set yourself up. Yes,
Jason Jacobs: gonna un-uh, underwhelm. But I'm just curious in terms of the, uh, the, uh, on the eating y-your own dog food front, um, as you go out and consult with different firms, um, you know, is it, is it one unit of Gerry, uh, gets deployed for one unit of work, and then the next one it's one unit of Gerry gets deployed for one unit of work?
Or, or are you getting leverage on your own time over time with these tools? And, and, and, and if so, how?
Jerry Kane: Oh, there are so many ways that I [00:40:00] am, uh, using it to, um, amplify my expertise. So, you know, as I am working with organizations, I've-- You know, consulting has always been sort of a, a side thing for me, uh, and advisory. You know, I've done a couple a year. It's been enjoyable. It's been much more like, um, you know, me coming in to teach a class w- at an organization, stick with my, my skill set.
It has enabled me to think more like a consultant. It has enabled me to sort of help identify where my expertise can help, um, their organization. So one really great example, um, in my, in my... And it's changed the way I deploy my expertise as well. So now for both my executive MBA and my executive education, I custom build all the content.
Uh, so in my EMBA course that I'm doing right now, I built the textbook using [00:41:00] AI. Um, why? Because the textbook... And there's all this stuff on LinkedIn, you shouldn't use AI to write because it dumbs down the brain and blah, blah, blah, blah, blah. Well, the, the textbook publication cycle is a year long at least If we had an AI textbook that was written a year ago, it is already horribly outdated.
Um, and so what I am able to do is build this textbook that's based in my research, um, that's, that's up to date within two weeks. And in fact, I taught my first EMBA class, uh, uh, 10 days ago, two weeks ago. I have, in the next module, I have already integrated insights that have happened over the last two weeks for the next set of readings, so we can keep up to date.
And when I go work with executives, I take that and I customize it to the organization. So for instance, one thing I do [00:42:00] is I come up with use cases, um, and then we adapt those use cases for the organization, and then we pressure test together, um, which one of these are most likely to be valuable. You know, I don't know these organizations and their work and their industry well enough to do this myself, um, and they don't know AI well enough to be able to say, "Ah, here's where we can really do well together."
But AI helps leverage my expertise with their problems, and then we all stress test it together, and we've been able to come up with some really remarkable use cases together. Um, and so it's, y- you know, I couldn't customize my c- you know, I would just do... I have my book slides that I did, you know, 2019, that I used for five years, and I just would adapt it on the fly with my own knowledge and my own stories.
This enables me to really truly customize my expertise for their problems and, and their industry. And is it gonna be [00:43:00] perfect should they take it off the shelf? No. But, you know, I, we can come up with 10 use cases and then together we work on which one is gonna be the two or three that they should move forward with.
And those are capabilities that I- would not have been possible three years ago without, even two years ago. Uh, and we're, we're getting a lot of value out of it.
Jason Jacobs: So it, um, so when you do work with these clients, um, is there tooling that, that gets built as a leave behind? And if so, is it you a- a- a or you and, and other resources that you bring in that's building it, or is it their internal teams? And then who maintains it over time?
Jerry Kane: yeah, well that's, well, I mean, uh, this is all six months old, you know? So we're, we're still very early in this process of figuring it out together of what is the right way. You know, because I've been following, you know, AI since it, or, uh, LLMs since they came out in 2022, and I've been having my students use it.
But, you know, we've seen such a ma- uh, you, and as I, uh, you know, I built my executive MBA textbook, um, [00:44:00] you know, last year for the first time, and if you compared it to what I was capable of building this year, it's laughable. I mean, it is just like, I would be embarrassed to show today what I did last, this time last year.
Today it's so much more sophisticated. I only started doing some of these approaches with executives, um, you know, March and April of last year, um, because the, the tools weren't capable at that point. And then I tell you, I was scared. Like, I walked in, I almost aba- so I, I did my first ever completely customized content.
Um, I almost abandoned it mid-delivery and go back to my old stuff c- 'cause it felt so deeply uncomfortable. And then I had the highest executive, uh, ratings that I've ever had before. I got a perfect five with that organization, and the feedback was, "We need more of this. We need to start doing these things more."
Uh, and that has just been things that I've sort of chased and built out. [00:45:00] Now we're doing these, you know, $10 million EBITDA, you know, use cases. So I'm still figuring this out with co-creating with organizations to figure out what the right answer is. 'Cause it's gonna vary by organization, by industry, by tool capability.
It may differ in six months than it is today 'cause it differs completely from what it was six months ago. Um, so it's more of I'm, you know, joining and mentoring these companies along their AI journey, uh, because I think that's what it takes. I think it's gonna be these convers- 'cause, because nobody knows how these things work.
Nobody know... I mean, yes, we have a high level, but, you know, e- even like the pro- even this concept of context rot, um, which is where, you know, AI begins to forget certain things and starts, you, when you overwhelm its context window. That was discovered by researchers, uh, you know, testing these models. And so nobody has a clear understanding [00:46:00] of exactly how AI should be a- applied to business.
So it's this mentorship model, it's this apprenticeship model, um, that is really what's gonna work for the, I th- I firmly believe for the next one to three to five years. Um, and so I am co-creating these with my clients, uh, with a lot of success so far.
Jason Jacobs: And d- do you find that there's skill sets that you're missing? Or if you had more resource to start bringing to bear in these, um, engagements, um, what type of resource might you need or want?
Jerry Kane: Yeah, so I think it's more, a- again, m- uh, train the trainer approach where I sort of work with the, the core superuser group, um, and get them up to speed, and then I mentor them to start up their own, uh, groups. Um, and that's sort of where, um, I think then we start to amplify and we get that, uh, exponential growth ac- you know, across the organization because there's only one of me, but I [00:47:00] feel like I do know enough to be able to get these people up to speed and then equip them to sort of teach others and lead their own teams while still serving as a resource, um, for...
You know, I don't send them out into the wilderness on their own. I still serve as a resource for them as they manage their own teams.
Jason Jacobs: And are, are you, are you still mostly seeing this coming out of the innovation, you know, kind of small, off to the side learning and experimental piggy bank? Or, or is any of it kind of gra- uh, graduating into core operations where it's feet are held to the fire from an ROI standpoint like any other, um, core operational expense would be?
Jerry Kane: Yeah, I think it's, it's both. You know, I've seen-- I've done a lot of work with, um, like the talent side of the organization, the CHROs, really sort of to upskill the organizations. Um, but you know, a-again, really, like adoption is [00:48:00] cheap, um, these days. And in fact, adoption may be counterproductive if you're using it for non-value-added purposes.
Um, I think we're still learning a little bit of how to manage it because, uh, because one company, like one company I'm working with has, you know, 90% adoption and still hasn't seen meaningful, you know, impact. Because I don't think they're-- they haven't yet thought about how do we re-reor- re-engineer the workflows, re-engineer the business, transform the business.
So I think we, you know, in, in IT to, you know, immemorial, the research shows that for the sec- first six months of a new tool, productivity tends to dip because we're experimenting with these new tools, and it's only like after about six months do we really figure out where the business value is. So I do think there's danger to go into ROI, um, too quickly, um, and expecting that out of the gate.
But I do think we're starting to see in organizations this realization that adoption is not enough. We need this absorption. We [00:49:00] need this transformation, and that's that next step, and that's what... I don't see anybody that's really-- Of course, I don't, you know, I, I'm limited to the number of organizations I've seen.
The popular press lags in terms of how quickly this stuff gets out there. Uh, but I've not seen anybody that's fully cracked the nut o-on this yet. I see a lot of promise, um, but no one to point to and say, "Ah, there's the one that's really figured it out."
Jason Jacobs: How transformational do you think this will be? Are, I mean, are we talking about in, in, in some industries, in, in many industries, in all industries? Is it gonna be, you know, Blockbuster and then Netflix? Or, um, uh, or, or is it more just about, well, the margins are so thin already and, and so, you know, you're, you're gonna have trouble being competitive because you're, you know, because your competitors are gonna gain, you know, gain inches in an already tight race
Jerry Kane: Yeah. Um, I think, so I used to use the analogy that [00:50:00] I said it, it felt like, um, 1999 all over again with dot-com 1.0, dot-com boom. I've since changed my tune. I say feels more like 1989 all over again with the fall of the Soviet Union, where we had to completely re- There's an old editorial cartoon I remember, I was in high school at the time.
Uh, it was the Rand McNally Ready Room. So the map makers had to have emergency, they're like, "Ah, we gotta go back and change everything all again." I feel like that's where we are. I think the impact, um, is going to be widespread for every industry. The question is how fast? And that's the question I don't have the answer to, um, and nobody has the answer to.
Because this is fundamentally rethink... You know, if you go back, the, the often used example is electricity. Electricity, um, you know, the, the business impact of electricity didn't happen until 20 or 30 years [00:51:00] after, um, it was first introduced because it took that long to figure out you needed to change the factory floor to really benefit from electricity as opposed to steam.
You, you just needed to do things differently. We could talk about, you know, that whole thing. Um, and so I think that's like we've got the elect- the electricity now. How long is it gonna be for people, for industries, for organizations to figure out how, how work needs to be different? That's gonna be sort of the big thing, um, is not the, the tech- the technology, it's how, how quickly can we rebuild work?
Who's gonna figure out first? It could be one to three years, it could be five to 10 years. I don't know the answer to that question and nobody does. I just know I don't wanna be waiting for somebody else to figure it out first.
Jason Jacobs: Uh, are, are there, are there any, um, uh, institutions or, um, [00:52:00] vessels, if you will, that don't exist that you wish existed or would be excited about creating that would, um, be well-positioned to accelerate this transition and, and kind of be, be on the front lines?
Jerry Kane: Um, that's a good question, and that's sort of what I'm trying to figure out, um, is, you know, what-- You know, I've been spending a lot of time over the last three to five months in conversations with senior executives at large firms, with venture capitalists, um, with, um, you know, startups and small firms, just trying to sort of figure out is there one clear answer.
Um, I, I think from a societal perspective, um, you know, I wish we could create this, um, some sort of government agency that would be, you know... I, I'm very reluctant to recommend regulation because I think that could be the death knell, um, for AI innovation and allow places like China to sort of take over, [00:53:00] um, the, the lead in these sorts of things.
But there are some, you know, very significant soci- whether it's environmental, whether it's societal, um, the, the implications are gonna be significant, and I wish there were some more organizations that were agile enough, nimble enough, and powerful enough, um, to start thinking through what sort of society do we want to build with these tools.
'Cause I, you, you can tell I get excited about this future. Um, I think there's tons of opportunity. There's also quite a bit of risk that we could end up building, you know, a really terrible society with these tools. Um, and so in my education, you know, I always try to build in at least some sliver of the ethical component.
So, you know, because if it's not, who's not gonna... Who's gonna do this if it's, if it's not for business leaders? 'Cause government leaders, I think, you know, the, we're at just too much of a, a lock at this point. And so we [00:54:00] need sort of you know, forward-thinking business leaders to say, "What sort of world do we wanna build?"
And if you go back to, you know, and there's the jobs question. Um, but if you go back 100 years ago, you know, 40% of the US economy or 40% of employees worked for, in agriculture. Now it's 2%. Um, what happened? What are those other 38% of people, um, doing now that they're unemployed? Well, no, they've figured out, they've adapted new skills.
We've adapted as a society. Um, and I would like for more thoughtfulness around that issue because, you know, what we did, uh, in the US was we made people go to high school because they said we needed different skills, um, to compete in a non-agricultural, uh, economy, and you know it worked out pretty well.
Um, and so I wish we were doing some more thinking about what sort of skills, what sort of opportunities, um, what sort of... You know, and maybe it's just the tax code. You know, the tax code [00:55:00] where we value capital so much more than labor. Um, you know, that day may have stopped. You know, maybe we need to value human labor and provide tax breaks for people, for companies that do hire people.
Um, so some of these things are gonna need to change, and I wish there was some more thoughtfulness around it. But I think the average person has no concept of what I believe is the magnitude of the change we are about to live through. Uh, I just don't see that urgency. Sorry to get off on my soapbox. Um, but you know, it's some of that opportunity that I see is out here and I do want us to be think- because I do think there is the opportunity not just for business advantage, but to create really great, um, a really great society out of this.
Um, and I wish there was more thinking going on in that area. And I don't know who's the right person to do that. Is it academia? Is it business? Is it government? Is it likely some combination of all three? Uh, yes.[00:56:00]
Jason Jacobs: Uh, so in, in, in, in your dream world, in 10 years, uh, you know, look at, look-- at a point in time snapshot, at that point in time, what are the most profound changes to how society operates, uh, relative to today?
Jerry Kane: Oh gosh, I wish I could answer that question. I, I think it's gonna be, um You know, because I think there... I, think based on that, my, my last sort of soapbox, I think we're at a juncture. We have to decide what society. I think we can have one where companies are a lot smaller, um, but much, uh, more productive and really capable of doing so much more.
You know, one of the podcasts I listen to, you know, he talks about how he doesn't see how this isn't gonna completely disrupt jobs because you can do the same work with fewer people. Well, his flaw in his thinking is he assumes that, uh, it's all gonna be the same work, that we're not gonna come up with new [00:57:00] opportunities, new types of work.
You know, think about what jobs didn't exist 100 years ago. I think that's what we're gonna be looking at, and I, you know, I don't wanna be out in the fields, you know, you know, picking corn or plowing. Like, that does not sound appealing to me. I don't want to go back to the world, uh, of 100 years ago and where 40% of the workers were in agriculture.
I think in 10 to 20 years we're gonna have the same sort of... Like, I... Things that we do, maybe it's drive our own cars, that, like, people used to drink alcohol and get behind cars? What sort of, you know, backward world did you live in? Um, and it's not hard to s-see. You know, so my students, one of my favorite...
One of the best things about teaching undergrads is they're young and they don't have that history. And so a lot of it's just telling, like, what the world was like in the '80s. So my favorite one was I said, um, "You remember those old card catalogs in libraries?" And nothing but blank [00:58:00] faces looking back on me.
And I was like, "You know how the..." Like, and I showed them what a card catalog looked like, and the horror on their faces that this is the way the world was, was just priceless. The other funny thing about teaching this is every generation of undergrads thinks their generation is just fine, but the middle schoolers are screwed.
Um, and that has been true for 20 years of my teaching, that that has take- that has been there. So some of this isn't, you know, different, but I do think we will look back and say there's some things that will seem barbaric, uh, at the time, and we can't believe that we did things that way because the AI or robots or whatever is, is gonna take...
Or I can't believe you guys didn't know how to cure cancer, um, because the AI could come in and, and pull together this massive amounts of data and find something that we never knew existed. Just like I can't imagine 100 years ago, how do you deal without antibiotics? Um, [00:59:00] you know, what did you... Well, people died.
You know, most people in World War I died of infection, not of, you know, war, direct war-related injuries, because we didn't have antibiotics. That was barbaric, and I think we're gonna look back to this time, and, uh, it's hard to say what those things are gonna be. Uh, but I think there are some low-hanging fruits, like some diseases are gonna be cured, some problems are gonna be cured.
I hope, you, you know, we'll have some environmental, you know, we'll improve the environment because there are new capabilities and, um, and, you know, driving I think is another one that's probably not long for this world. Uh, but we're already sort of see that coming, but how long it takes, I don't know.
Jason Jacobs: Uh, what about education?
Jerry Kane: Um, I, you know, I think we are living in the golden age of education, um, that there's so much opportunity for people to l- so my son, th- you know, through COVID, um, was just playing video games. Um, you know, and I said, "Do something productive w- with your time. Why don't you go teach yourself Python?" And [01:00:00] he was like, "How would I do that?"
And so we got on Udemy, we got on Khan Academy. I showed him how the tool works. Now he's a freshman, uh, in college, gonna be majoring in information systems, uh, in my department, and is as advanced in a, in a hands-on way with AI as I am because he's had those capabilities. He could teach himself. Whether education
You know, the other thing that I think is the real challenge, really not at the university level, but at the K through 12, how do these tools get integrated into education? What are the things that are indispensable, and what are the things that aren't? And, uh, you know, you can't just say, "Oh, we'll do things the way we've always done them," because, you know, we change.
Like, my daughter had to learn cursive in elementary school. My son did not because they said, "This is just not a skill that people need anymore." Um, and so I, I think figuring out what are the things we really need to teach students, [01:01:00] um, b- and I think the, the answer is we need to teach curiosity, we need to teach the capability of learning, teaching them s- learning how to learn.
And I think if we can do that, then these tools are gonna be really, uh, open, you know, really open a lot of opportunity. My students are really ex- I have students with top-tier consulting and financial, uh, jobs saying, "I don't wanna do that. I wanna go work in AI because I see so much more opportunity here than grinding through the organization."
Um, and, and they see that this is where things are going to go. And they can do it. I have one student who had a consulting gig, passed on it, start up his own AI consultancy, um, and is doing great work because he just know, you ... I, I, I tell them at the beginning of every class, um, there's an old saying that says, "In the land of the blind, the one-eyed man is king."
Um, I'm trying t- I said, "I'm not gonna turn you into AI experts, but I'm gonna [01:02:00] turn you into the one-eyed man to knows more than other people, um, and can sort of start seeing around corners." And that's been the most rewarding thing for me is seeing these students embrace what it means for them, taking this entrepreneurial mindset and saying, "I can do remarkable things with these tools, and I'm gonna go do it."
And that's what's been most rewarding for me over the last 18 months.
Jason Jacobs: Great. Well, Gerry, this has been such a, a wide-ranging discussion. Uh, is there anything I didn't ask that you wish I did, or any parting words for listeners?
Jerry Kane: Uh, that's the question that I sh- you know, that's the question I always end my interviews on, so you'd think I would be better prepared for it. Um, so no, I think we covered a lot of ground. Um, and, you know, I'm excited to... I'm still learn- I've learned more in the last six months than I have in the previous 20 years, and so I'm embracing that spirit of learning, uh, myself and experimenting.
And there are times when I get [01:03:00] scared, it's like, "Oh, did I screw..." Like, there are times I'm sort of feel like I'm working without a net, where I'm like, "Oh my gosh, did this thing really screw things up?" So it can be intimidating and scary because you're out there experimenting with the unknown. Uh, but that is that entrepreneurial mindset, and only if you get out there and sort of risk are you gonna see where the real capabilities of these tools are.
And so I'm, I'm... If you can't tell, I'm optimistic. Um, I am bullish on where these tools are gonna take us as organizations, but I do think it requires us to sort of embrace it and sort of embrace this spirit of learning, so, um, at the organiza- individual level and at the organizational level, 'cause we can figure it out.
'Cause waiting for consultants to tell us what to do, waiting for OpenAI and Anthropic to tell us what to do, uh, is, is the losing game. Taking these tools and figuring it out for themselves and creating those opportunities, I think is, is the spirit of s- is, is the solu- secret to success, and I just hope more companies and more organizations, uh, [01:04:00] start doing that.
Jason Jacobs: Great point to end on. Well, thanks so much for coming on, and looking forward to, uh, keeping in touch and seeing where your journey takes you as I get further down the path as well
Jerry Kane: Absolutely. I've enjoyed the conversation thoroughly
Jason Jacobs: Thanks, Sherry
Jerry Kane: Thank you
Jason Jacobs: That's it for this episode of BuiltForward. I hope you enjoyed it. If you found it useful, share it with someone building in the industry and follow the show so you don't miss what's next. Thanks for listening, and see you next week