From Legacy to AI: The Playbook for Transformation

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by the very definition that a company got to grow big, the entrepreneurial spirit, if it's not founder-led, and I tend to be binary, tends to have gone out, right?
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And in the world of AI, the reason I made that statement is that in the world of AI, there is no substitute to coding.
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A team of five or ten people more often than not can do more than a hundred, two hundred, a thousand people.
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I think to me the, in a larger company, the number of layers from the CEO down to the builders, it should be really, two to three layers, right?
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Um, you have seven, ten layers.
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It's crazy.
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It's crazy.
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Every time you go up a layer, you lose so much context and so much innovation can be stifled, just collapse down.
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The, the thing about organization is that when there is friction in an org, the org will, will auto create more teams, more bureaucracy with the intent to remove the friction, but in fact it's actually adding even more friction
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Hello, everybody.
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I'm your host, Ben Parker, and welcome back to Data Analytics Chat.
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I took a short break, and now we're ready t-for some more exciting conversations across the world of data and AI.
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Today, we're looking at a challenge facing almost every large organization.
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How do you transform for the AI era when you're still operating in a legacy environment?
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In today's episode, we're going to build a practical playbook for moving from legacy to AI and ex-explore why adopting a more entrepreneurial mindset could be the key to making that transformation successful.
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Uh, joining me today is Che Cabill, and he's got a broad background in enterprise and startup world.
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Do you wanna give the listeners, Che, a quick int-introduction to who you are and the work you've been leading?
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Yeah.
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First of all thanks for inviting me, Ben.
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I'm really grateful for this opportunity and, uh, this is a very hot topic right now, all over tech.
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So just to give a quick background I've been a technologist developing software my entire life, for decades now.
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I had the opportunity to, um, and, uh, and the, the fortune to start early during the internet phases with eBay, which was a great run where we invented technology, and also it gave me the, the financial freedom to start, experimenting about creating companies.
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Some of them did well, others not so well.
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But at the end of the day, it's all about how you correct and you learn how to build businesses.
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I created two companies, one that was sold...
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co-founded, definitely.
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Uh, one that was sold to Yahoo! In the early days, I was an early competitor of YouTube and then created another SaaS company in a transactional payment space that was developing ticketing application for, you know, zoos, aquarium, gardens, and the likes for about a decade.
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That was a lot of fun.
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And since then, exited both of them in good terms, and I've been doing a lot of deep AI, helping larger orgs now recently.
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So I'm pretty familiar with the concept of transforming companies from the inside out on, on, on larger corporation.
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It's been quite a journey and le- let's have a chat about what that means, how to go get it done, what works, what doesn't work, what needs to change, and, um, let's go through, through your life too then.
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Yeah, no, I mean, we're definitely going for s- a, like, fascinating period we're going through now.
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So it's, um, yeah, let's dive straight in.
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So I guess, I mean, why do large organizations I guess, look, our large organizations have got lots of resources, but I guess why do they frequently find transformation much harder than startups?
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Good question.
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So look, b- by, by the very definition that a company got to grow big, the entrepreneurial spirit, if it's not founder-led, and I tend to be binary, tends to have gone out, right?
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And in the world of AI, the reason I made that statement is that in the world of AI, there is no substitute to coding.
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The technology is moving so fast that the leaders that need to implement change cannot just judge AI on the basis of reading the press You know, or changing from A to B, there is, you know, advancement every single day.
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It goes so fast nobody can keep up.
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You gotta bring the, the coding aspect in the technical organization with the ability to connect back to the customer.
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So that is why it is difficult for large companies.
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Most large companies have a pretty many layers of management that in general, not because of the way it's been done for decades, have disconnected from the, the code base, from the coding to some extent with the customer base as well.
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And therefore it's extremely important to bring back the hands-on aspect to tinker, to experiment, and to develop your own opinion about how good AI is for your own business, for your own customers, as opposed to kind of like looking at it from the standpoint of, you know, conferences and reading materials and so forth.
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Yeah.
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So I cannot emphasize how when there is a deep technology changes, we go through waves, and AI is one of, in my opinion, the biggest waves I've ever seen.
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You gotta stay extremely hands-on to understand where to go for the needs of your customers as it relates to changing the product definition that you currently have.
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And the only way to do it, you gotta be extremely close to the way how AI is being built.
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It's pretty getting there because you can code much faster nowadays.
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AI is doing a lot of coding for you, and you can figure it out sooner for your organizations.
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My view is that there's no substitute being hands-on, which is a little bit a paradox for larger companies.
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So the larger companies have to understand how to focus, emphasize on actual builders that are doing the work, and not so much on the management side of the business, which is basically being disrupted.
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If that answers your question, Ben.
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Yeah, no.
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See, I guess there's, there's a lot more layers, isn't there, to like, obviously enterprise level, the communication.
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Obviously, there's more people.
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You know, and I mean, everybody in business, like, they wanna get A to B, don't they?
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It's that squiggly bit in the middle, like the change management element is so, so much more challenging when you got...
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if you're dealing with a larger company, isn't it?
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Correct.
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Right.
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There's a lot, um, whatever I just said, there's obviously a lot to unpack to unpack.
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I just wanted to now highlight the, the, the conclusion, which is becoming hands-on.
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So yes.
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I mean, let's talk about, let's unpack this a little bit.
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So there's a couple of, couple of elements to it.
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One of them is how you're gonna change the product definition, right?
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There's the, the second is how you change the scale of the organization, the people, the management layers and so on and so forth.
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So As it relates to the product definition, you know, always lead with the product.
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Without a good product, there's nothing to talk about'cause the customers are gonna walk away.
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So as it relates to the product definition, the biggest shock for big company is that most of the product definitions that are in place today for the legacy incumbent...
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Now, software companies, they tend to be what I call form based.
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Classical, web forms been around for years.
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You go from...
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You have a wizard, you go from a link to another link, a form to a form to a form.
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So ninety-nine percent of the definition is there.
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The customers are used to those behaviors.
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So what happens is that in the world of AI, forms are kind of gone, right?
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The user interface is conversational.
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You ask something to a piece of software that people are calling AI, and the system goes back, and you engage into a multi-turn conversation.
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So for the most part, if you look at, Claude, ChatGPT, very simple, almost as when Google came out of the gate, super simple input field, which is you ask something and it responds, right?
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So that kind of user interface puts the old user interface out of the door, right?
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And that's the opportunity for disruption for startups.
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For incumbent, they're kind of like, wow, being caught flat-footed, which is, oof, I've got all of that definition of the product that I've been building for decades, but the user experience has to fundamentally change, right?
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So there is two aspect to it.
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You can be very timid, a little assistant, and you say you've done AI.
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In general, the customer base is not gonna care that much'cause we are not leveraging so much AI as it, as it needs to be done.
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You have to have real boldness to be able to replace the definition of the product, something that is much more conversational.
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It can be a brand-new upgrade to a new user experience.
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You could stand up a new product that competes with your own legacy product, let it grow.
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It's your own company, it's your own customer base, and find a way to convert from old product to new product.
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Many ways to get that done.
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But at the end of the day, on the product user experience, you gotta make radical changes, right?
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And that's very difficult to do for a large company because they're afraid that they're gonna alienate their own customer base, and they're gonna take a slow roll, right?
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With a slow roll, the problem, we invite other competitors To kind of fill the gap while the company, the bigger company is getting slow.
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So you absolutely, those big companies need to be a lot bolder.
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They have to be willing to take bigger risk, right?
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That's number one.
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Now, you start there.
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Once you start there on the product and you get to that realization, look, the user experience has to be much more native AI.
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You need to redo a lot of the user experience heavier for the technical star, technical stack underneath.
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Then it begets the second question, well, how are you gonna deal with the organization?
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Now, the organization is built around the past.
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Roles are now changing.
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Individual contributors can do multiple roles in one person and go extremely fast.
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So what happens to the designer?
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What happens to QA?
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What happens to the product manager?
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So the companies really need to think that the old world of, you know, slowness, planning, roadmap, and so forth no longer works that well.
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Things are moving very fast.
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And when things move fast on the tech side and the startups are out there just nipping at the side and peeling away part of your business, you have no choice but to simplify that organization, right?
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And it's easy to say, look, when you have thousands of people, it's complex.
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But in the face of complexity, you gotta go back to basics.
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And back to basics is tight, small, lean teams connected directly to the customers, and they can move very fast.
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What does it mean?
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It means a lot of the corporate layers that have been, you know, put into place, project management, this and this and that, you really need to find a way to streamline that, right?
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So I always tell these larger companies, and that's how, you know, I try to lead by example.
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Create a small team super small, extremely connected to the customer base on a daily interaction of the customer base to go get the feedback and move super fast.
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All of the matrix and the, like, planning, dependency, and all of that, you put it out the way.
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You need to move very quickly because there is so much unknown in the space.
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But you have to go back to what software has always been ever since the birth of software, small, tight team, no fragmentation of the ownership.
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Avoid the, you know, extensive yearly roadmap planning.
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Those things are basically not working anymore.
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Tight team, fully empowered end to end, closely connected to the customer, then you can deliver value because you're getting real feedback from the customer base, and you can iterate
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quickly.
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So do you see it's more...
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Is it out-- The biggest problem, is it the outdated technology, or do you think it's more, the old way of thinking or legacy thinking?
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'Cause obviously the processes have been one way, now moving to this new AI adoption way.
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Do you think it's just- I, I think it, it- do you think it's more the technology or do you think it's, like, the legacy thinking?
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It's mostly the legacy thinking.
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Like, the concept of statues, the concept of seniority, the concept of entitlement.
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You gotta get it out the door.
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Uh, gotta get out the door, right?
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The only thing that matter is that what can you do, right?
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In AI, you learn as by doing.
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You don't learn because you have a senior title.
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You probably don't know-- If you have a senior title and you read about AI, you know less than a kid coming out of college that is com- coding out of AI that can be 10 times better than you.
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Mm-hmm.
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So once you realize that, you're gonna be real that put aside the entitlement, put aside the seniority, just focus on what you can contribute, what you can deliver, right?
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It's going back to raw mindset of delivering.
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Two things matter: build the product and sell the product, right?
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You have to co- This is what I keep saying is that collapse the management layers, flatten up the organization as much as possible to get rid of the legacy thinking, right?
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Because legacy thinking means seniority, entitlement, posture, power, and all these things.
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None of that matters in this new world, right?
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You gotta run on the fly.
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You gotta cut that out.
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You gotta get out and get it out there.
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Just to make my point and to prove you, look at a company like, you know, Anthropic.
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Right.
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Anthropic annualized run rate of probably fifty, sixty billion dollar in less than four years.
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It's bigger than most of those legacy companies out there.
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Let's get real.
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Sixty billion dollar in less than four years.
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It's a big company.
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Guess what?
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It's a flat company, right?
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You have CTOs from larger companies that go join Anthropic.
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They become MTS, member of technical staff.
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A sixty billion dollar revenue runway company is fundamentally flat.
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They are winning.
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That should be what you want to copy-
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Yeah
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to get a chance to not be disrupted.
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So a-and that's very, very difficult for companies to do because, you know, a lot of them have been there for a very, you know, they cannot flat down they climb the corporate ladder, but the corporate ladder in the wor-world of AI, I hate to say it, you know, you're climbing your level of incompetence.
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You gotta go back hands-on.
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You gotta go back.
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Super close to the customer, close to the product.
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And in a world where coding is so much easier, get into the code, right?
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Uh, it's just...
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That's what startups do.
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I see CEOs of larger company like Shopify, 150 billion dollar valuation company.
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The, the CEO is still coding to this day.
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He codes AI just to understand what are we talking about, so that it doesn't come from a line of managers that probably haven't done any AI other than read.
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So he gets in the middle of it, and he's gonna understand AI at Shopify to move forward the right way, right?
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That is a transformation that will work for Shopify.
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For others, you gotta make these drastic changes in-inside your organization.
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It doesn't mean people go.
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It means become an individual contributor, whatever it may be.
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Keep the right people, but you gotta have that mindset.
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It's very simple.
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The alternative for me is that I see the...
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You can get disrupted.
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There's many companies that spend years and billions of development in AI preha- There's not much to show for, right?
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So it's real.
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You know, I think we've said only the pioneer will survive.
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You've gotta go back to operating a startup within a larger company, right?
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If you're not founder led.
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That's a very difficult thing to do.
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Yeah.
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At least- And let's, let's unpack that then.
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Yeah.
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So I mean, ob-look, obviously, look, startups, yeah, have got the, that mindset.
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So I guess how can we...
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I guess how can you embed that in a larger organization then, this sort of entrepreneurial mindset?
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Very simple.
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You bring entrepreneurial talent into the house because probably a lot of it is gone and not there.
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Unless you are founder led.
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Founder led large companies, you know, CEO, like Shopify, Facebook and so forth, they understand that very well because we started from nothing to building a large company, and we understand that inevitably as you scale, you need to bring big tech folks, but they mix them, they mix them up with, uh, either we hire or we hire small companies for which we don't want the product, but we want the talent, right?
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And then we give them positions of power.
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Not that these guys care about power, but so that they can make the right changes inside the company.
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So if you are a CEO, and you have an organization where you have a lot of, quote-unquote, caretaker by the nature of, you know, what legacy did, bring entrepreneurial talent and empower them, and let them be, right?
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Because they're gonna do things that are completely outside of the processes that you've, built over decades.
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That is the onl-, in my opinion- That is the only way that I can see work, right?
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And some companies, especially the founder-leds- Mm-hmm are really good at doing that.
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You need to replicate that into other organization where the founders are gone, to bring that entrepreneurial spirit back in as uncomfortable as it might be.
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Yeah.
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I mean, so, I mean, we see that a lot with, like, the FAANG companies, like, I mean, a lot, a lot of the talent now are going to the other enterprise companies and startups because they're looking- Yeah for that.
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They want ownership, they want more scope.
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Obviously making more decisions.
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They're getting in getting in deep with the business, and we're definitely seeing so many more people want that.
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Yeah
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obviously, obviously- Yeah salaries across AI has gone, yeah, crazy now, isn't it?
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Just'cause pe- businesses need this talent.
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Like you said, they need to embed it in the business'cause, like, these people have been there, done it already, haven't they?
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Like, it's sort of-
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Yeah, yeah
00:17:16.977 --> 00:17:17.037
they-
00:17:17.037 --> 00:17:17.926
And, and, and do...
00:17:18.227 --> 00:17:18.826
That's right, Ben.
00:17:18.826 --> 00:17:28.797
And these people in general, they don't care about titles, which is why I always advocate for very flat company and avoid the, you, you know, the side effects of seniority that, you know, can stifle.
00:17:29.096 --> 00:17:31.757
Forget about the title, just think about the outcome, right?
00:17:31.757 --> 00:17:38.067
You bring somebody entrepreneurial, that person will call, that person will talk to customers, that person will have a sense of ownership.
00:17:38.366 --> 00:17:41.747
They will grow the business, and you can measure how they grow the business.
00:17:41.747 --> 00:17:42.817
You gotta pay them handsomely.
00:17:43.116 --> 00:17:44.156
There's no doubt about it, right?
00:17:44.636 --> 00:17:48.477
And it's not about- The, the corporate mindset is about what is the outcome?
00:17:48.517 --> 00:18:09.027
If your outcome is that you put a product out there, you measure the adoption, you measure the engagement metrics, the repeat, and you as an individual contributor/leader who's very hands-on and moving the needle clearly on those metrics with a tiny team, you're probably doing more than other teams that have 10 times the resources by all means you gotta pay them handsomely.
00:18:09.457 --> 00:18:13.817
Because we are doing the work of, we are being 10 times, 20, 100 times more efficient.
00:18:14.277 --> 00:18:16.277
That- that's the world in which we live.
00:18:16.386 --> 00:18:23.446
And think about it, w- with AI, the AI coding tools are unbelievably good, and they're getting better and better.
00:18:23.866 --> 00:18:24.576
You can...
00:18:24.656 --> 00:18:33.386
That's why I don't subscribe to, you know, management that would say,"Oh, we have a legacy technology." That's-- That argument doesn't hold water, right?
00:18:33.487 --> 00:18:40.866
If you really want, nowadays you can rewrite the code base at a fraction of the cost of what it would have been two years ago.
00:18:41.297 --> 00:18:57.287
So you gotta be bold, you gotta say,"I'm gonna rewrite the code base, it won't take that long." And after that, the issues may be more on the migration of the data in enterprise and completes more reality on the ground, but you gotta be willing to rewrite large section of your technology stack quickly.
00:18:57.386 --> 00:19:02.277
And it's not a, For some people it is their baby, and it, it may be hard, but you gotta do it to survive.
00:19:02.576 --> 00:19:02.817
Yeah.
00:19:03.027 --> 00:19:12.987
So I guess how can leaders create greater speed, ownership, experimentation without losing the governance and controls that a larger organization needs?
00:19:13.307 --> 00:19:20.007
The, the, at, at the tech level, a lot of the controls can be automated, right?
00:19:20.017 --> 00:19:24.646
Like, the pipeline to deploy the code nowadays are pretty sophisticated.
00:19:24.646 --> 00:19:35.416
You can run a lot of tests, you can do a lot of automation on security, like all of the teams that do a ton of like manual review for security and the likes.
00:19:35.416 --> 00:19:39.557
The, the in truth, you can actually automate a ton of it, especially with the new AI tools.
00:19:39.596 --> 00:19:45.386
So anything that is technical is almost always a candidate for automation.
00:19:45.396 --> 00:19:54.787
You gotta be willing to go from somebody who's had a job who's very manual on security or testing and just automate the hell out of it, right?
00:19:54.957 --> 00:20:04.557
There could be some resistance, and we use those people to bring the, the knowledge, but you g- you, you gotta automate, and those jobs are gonna change more into helping do the automation and so forth.
00:20:04.557 --> 00:20:12.237
So on the technical side The PII, the security and all that definitely can be automated and, there's a lot of headway being done.
00:20:12.267 --> 00:20:18.076
On the legal side, it's a little bit more tricky, uh, it's a bit more manual, right?
00:20:18.396 --> 00:20:30.557
But I would argue if the legal team is really working closely with the kind of security risk people, there is often an opportunity to automate again and again, right?
00:20:30.567 --> 00:20:41.926
So it's getting that mindset to like,"Okay, my job was manual." Don't fear to lose your job, but make your job automated so that you can do something at a higher level, right?
00:20:42.307 --> 00:20:46.527
Tough, because there's gonna be some resistance, but that's-- that can be done.
00:20:46.767 --> 00:20:47.666
That can be done, right.
00:20:47.666 --> 00:20:48.017
Mm-hmm.
00:20:48.017 --> 00:20:51.547
The AI tools nowadays are extremely powerful, right?
00:20:51.596 --> 00:20:55.166
On the dev side, we all know that this is where most of the su-success is.
00:20:55.267 --> 00:21:04.547
But on the legal side, anything, anytime there is something that is fairly manual, I would just like Do double click on it big time and see where, how much of that can be automated.
00:21:04.547 --> 00:21:07.297
And you'll be surprised, a lot of it can be automated.
00:21:07.817 --> 00:21:07.946
Yeah.
00:21:08.477 --> 00:21:19.386
So do you think it's more businesses just having that resistance, they just need to let go and just start going more, investing more into AI, automating more of the work basically?
00:21:19.457 --> 00:21:27.866
'Cause obviously there's, obviously- Yeah there's so much resistance from firms like, es- especially like if you're in the regulatory space, obviously there's a lot more to deal with, isn't there?
00:21:27.876 --> 00:21:28.737
And things like that.
00:21:28.737 --> 00:21:30.366
Obviously, each industry will have a different impact.
00:21:30.586 --> 00:21:31.217
That's correct.
00:21:31.227 --> 00:21:34.797
You gotta take the day one mindset, let go.
00:21:35.356 --> 00:21:42.666
The job that you worked so hard for decades to get to corporate ladder or whatever, day one, rethink it over.
00:21:43.247 --> 00:21:43.836
And that's difficult.
00:21:43.896 --> 00:21:46.487
That's what I keep saying is just going back to that entrepreneur.
00:21:47.067 --> 00:21:52.267
Yeah, do it for you before somebody else is gonna make a decision for you, which is not as comfortable.
00:21:52.457 --> 00:21:53.237
Do it yourself.
00:21:53.537 --> 00:21:53.696
Yeah.
00:21:53.696 --> 00:21:55.376
You're not gonna put yourself out of a job.
00:21:55.386 --> 00:21:56.666
You're gonna find something even bigger and better to do, but replace yourself with automation.
00:21:56.666 --> 00:21:59.106
Yeah, no, I definitely think like more roles, obviously it will evolve, and it's, I guess, like I said, there's, you still need humans behind the roles'cause obviously- Yeah AI can do automate,
00:21:59.406 --> 00:22:03.116
it can do great work, but obviously you still need like the thinking behind this, don't you?
00:22:03.116 --> 00:22:05.946
You need the engine telling what to do and-
00:22:06.106 --> 00:22:06.326
Yeah.
00:22:06.396 --> 00:22:07.467
Yeah, yeah actually steering it, don't you?
00:22:07.467 --> 00:22:09.777
You need to, someone still needs to be driving the car sort of thing.
00:22:10.186 --> 00:22:10.817
Yeah, of course.
00:22:10.817 --> 00:22:20.717
Look, I think leaders need to tell folks in a sincere way right now, not in a hypocrite way, that, look, you're replacing yourself, but you're also extending yourself, expanding yourself.
00:22:20.727 --> 00:22:20.747
Yeah.
00:22:20.866 --> 00:22:29.817
Like there's always more work that can ever be done because if you are creative, there's always new things to create, new opportunities to go after.
00:22:29.836 --> 00:22:36.606
So it's important that the, the corporate environment be such that, replace yourself, I'm gonna fire you.
00:22:36.636 --> 00:22:45.906
No, it's, there needs to be a contract with employees in such a way that they are comfortable to kind of like change the nature of a job to be more automated.
00:22:45.906 --> 00:22:53.057
So look, employees at the end of the day are gonna, vote with their feet, and they will know if the leaders are, are, are honest, sincere or not.
00:22:53.096 --> 00:22:56.247
And that's how the world works of, for, you know, of, of all times.
00:22:56.247 --> 00:23:02.317
I mean, a good company needs to keep that contract, that it doesn't mean that people go.
00:23:02.356 --> 00:23:04.527
It means that you're gonna do bigger things.
00:23:04.826 --> 00:23:08.307
It's easy to say, but I think that's just where the world needs to go, hmm.
00:23:08.406 --> 00:23:09.426
Mm-hmm.
00:23:09.426 --> 00:23:18.696
'Cause you expand your c- you expand your capabilities, which means you should be able to grow the business more than what you have done because you have, the brains are being used for something more important.
00:23:18.886 --> 00:23:19.366
Yeah.
00:23:19.576 --> 00:23:23.007
More ideas, more products, more creativity, da, da, da.
00:23:23.307 --> 00:23:23.576
Okay.
00:23:23.576 --> 00:23:26.176
So w- I guess obviously there's, there's endless opportunity now, isn't there?
00:23:26.207 --> 00:23:39.307
Um, so I guess where-- when an organization has so many different AI opportunities and, like, every business unit is gonna be yelling out to have their business case, how should it decide where to start?
00:23:39.636 --> 00:23:40.567
Good question.
00:23:40.866 --> 00:23:48.176
I usually start, you know, when I don't have a good answer or any other business, start with the customer.
00:23:48.426 --> 00:23:49.067
Start with the customer.
00:23:49.247 --> 00:23:54.576
If it impacts, you know, that the product is changing, obviously you go you talk to customers, right?
00:23:54.757 --> 00:23:58.186
Customers don't always know what else needs to be built.
00:23:58.227 --> 00:23:59.807
They don't really see the future as much.
00:23:59.807 --> 00:24:04.267
But start with the customers, engage conversation, and just tell them,"This is what I think I can do.
00:24:04.277 --> 00:24:09.356
What do you think?" Get through a process of validation from zero to one to ten.
00:24:09.366 --> 00:24:15.576
That's one aspect, the second aspect is if it's outside of the product, we already, covered that topic.
00:24:15.576 --> 00:24:18.737
You should look at ways to automate the business processes, right?
00:24:18.737 --> 00:24:21.457
So it's either the product or the internal business processes.
00:24:21.836 --> 00:24:25.217
On the product, for me, I talk to customers all the time.
00:24:25.616 --> 00:24:27.426
I exchange with them on a daily basis.
00:24:27.727 --> 00:24:29.436
I have twenty million ideas.
00:24:29.737 --> 00:24:32.817
You know, the one I-- I will present a few ideas.
00:24:32.817 --> 00:24:37.767
A, a lot of these customers will sh-shoot down some of them, but they're gonna harden up.
00:24:38.067 --> 00:24:42.416
They're gonna, catch on on a few good ones, and then I start to kind of,"Okay, I got a bite on this idea.
00:24:42.777 --> 00:24:51.916
Let me continue the conversation." And as I unveil it, then I can ask engineers to work with me, and we just go deeper, right?
00:24:51.916 --> 00:24:55.426
But I personally always start with the customer, always.
00:24:55.727 --> 00:24:57.047
And I think there is no substitute.
00:24:57.217 --> 00:25:03.836
If you don't start with the customer base, you cannot create, inward-based organization that may do tech for tech.
00:25:03.896 --> 00:25:06.406
Tech for the sake of doing tech, which happens a lot, by the way.
00:25:06.767 --> 00:25:08.346
There's a ton of waste in that respect there.
00:25:08.646 --> 00:25:10.826
Don't hesitate to pick up the phone and talk to customers.
00:25:11.126 --> 00:25:14.836
Keep it simple, especially when you are in such a big, uh, transformation phase.
00:25:15.207 --> 00:25:16.946
They know the frustration of the product.
00:25:17.237 --> 00:25:20.987
They know, you know, what doesn't work, and then just work with them.
00:25:21.326 --> 00:25:33.757
So then should companies need to modernize their entire technology and data environment first, or can they begin delivering AI value while legacy systems are still in place?
00:25:34.096 --> 00:25:35.596
I would start on the latter, right?
00:25:35.636 --> 00:25:40.836
Because i- if you change the whole thing, you're probably not gonna hit, right?
00:25:40.856 --> 00:25:45.406
You could be off base and take a few core mission critical workflow.
00:25:45.406 --> 00:25:48.176
That's what I mean, mission critical, not small workflows.
00:25:48.237 --> 00:25:50.227
If you-- that are not used that much.
00:25:50.227 --> 00:25:55.287
If you take a small workflow, you're being too timid, time is gonna go by, you're gonna be behind the, behind the eight ball.
00:25:55.307 --> 00:25:58.666
So take a core mission critical workflow and get it right.
00:25:58.666 --> 00:25:58.896
Right.
00:25:59.196 --> 00:26:04.797
The thing about AI is that it's actually very easy to put a prototype and, uh, out there, and you think you're doing great.
00:26:04.846 --> 00:26:08.047
Once you put it in front of customers, they're probably gonna say,"It doesn't work.
00:26:08.346 --> 00:26:10.366
The quality is not great.
00:26:10.376 --> 00:26:17.267
The hallucination is horrible." You know, we've seen that from the chatbots ten years ago to things are getting better now.
00:26:17.277 --> 00:26:17.376
Mm-hmm.
00:26:17.436 --> 00:26:18.207
Very low adoption.
00:26:18.237 --> 00:26:20.707
So you can see you put something out there, it doesn't mean that...
00:26:20.967 --> 00:26:23.916
Put it out there, work with your customer base, get it right.
00:26:23.916 --> 00:26:25.507
It may take six months, nine months.
00:26:25.676 --> 00:26:29.247
Once you get it right, it will inform a lot of the tech stack that needs to be done.
00:26:29.767 --> 00:26:35.037
Like I said earlier, don't design a tech stack because you have a title or you are reading a paper.
00:26:35.086 --> 00:26:36.146
It doesn't work that way.
00:26:36.727 --> 00:26:47.457
Get it from customers, find the issues, iterate quickly, make it right as fast as you can, and take those learnings and then over to the rest of your workflows quickly.
00:26:47.997 --> 00:26:58.146
And start, like, expanding the transformation on the rest of the tech stack, and then you chop down the architecture, and you start to move the other architecture out, right?
00:26:58.237 --> 00:27:00.396
And that's how I would do things.
00:27:00.406 --> 00:27:07.416
As a matter of fact- a lot of the legacy architecture there that are in place, I mean, the amount of rewrite that needs to be done, not at just code base, right?
00:27:07.457 --> 00:27:10.277
At the architecture level is just very significant.
00:27:10.287 --> 00:27:18.386
To give you an example, once you have a workflow well done, then most companies, especially in enterprise, they're gonna realize what the legacy mindset was in relational databases.
00:27:18.686 --> 00:27:35.676
Yeah, you do need relational database for some core transactional workflow and so forth, but most of those new product definition at the user interface level are very unstructured, which means that the rise of unstructured databases is upon us.
00:27:35.977 --> 00:27:39.797
And that's a fundamental architectural change on the entire organization.
00:27:40.376 --> 00:27:48.106
So the mix and match of relational SQL to a structure is a big shift happening in the industry with AI.
00:27:48.406 --> 00:27:54.477
And that changes a lot of their test stack dramatically, not to mention a lot of the roles underneath.
00:27:54.477 --> 00:27:54.836
So...
00:27:55.146 --> 00:28:07.317
But like I said, just get a workflow right, learn it, make it good, and then quickly go after other mission critical workflows as quickly as you can- Mm where, while you're essentially doing, um, a tech swap.
00:28:07.336 --> 00:28:11.886
And the tech swap is faster today than before because you, you can write a ton of code with AI.
00:28:12.186 --> 00:28:16.676
The harder part, not to underestimate, is plan the migration of the data, right?
00:28:16.676 --> 00:28:18.656
Your data may be dirty.
00:28:18.707 --> 00:28:19.547
You got a problem.
00:28:19.557 --> 00:28:25.196
What do you need to do, you know, to clean up that data, which what needs to be done to be able to do a good migration?
00:28:25.207 --> 00:28:30.707
That's where it's gonna be a little bit harder to, uh, squeeze the time it takes to go and get it done.
00:28:31.047 --> 00:28:31.176
Right.
00:28:31.396 --> 00:28:38.896
But that's how I would approach a, a massive technical transformation within an org, processes and management layers aside.
00:28:38.946 --> 00:28:43.457
That, that technical side, you, you can go quick but be careful on the migration of the data.
00:28:43.967 --> 00:28:44.207
Yeah.
00:28:44.277 --> 00:28:48.696
I, I mean, I guess our stakeholders are obviously gonna want to see results as soon as possible.
00:28:48.707 --> 00:29:01.406
So how can organizations move away from lengthy transformation programs and start testing, I guess, their ideas quickly, learning from users and, I guess, providing value as early as possible?
00:29:01.777 --> 00:29:16.346
I said just all of the planning artifacts that have been built over decades that worked for a different era, like, like I said earlier, you kind of need to Move it out the door and say,"Look, the roadmap, the yearly roadmap planning, that stuff is over," right?
00:29:16.346 --> 00:29:24.106
The pace at which AI is changing technoly- technology-wise so fast that what's gonna be in front of us in one year, we don't really know.
00:29:24.126 --> 00:29:29.916
So really compress your roadmap planning to be like in months, not even quarters, right?
00:29:29.946 --> 00:29:31.636
In months, at the most quarters.
00:29:31.636 --> 00:29:34.616
So compress your roadmap planning to be smaller.
00:29:34.916 --> 00:29:39.717
The time it takes to get stuff done is already compressed based on AI coding.
00:29:39.717 --> 00:29:45.717
So just change that and go in front of those customers, like I said, as fast as possible.
00:29:46.136 --> 00:29:51.777
In a world where development cost is going down, the bottleneck is the process.
00:29:52.037 --> 00:29:57.957
And the process bottleneck would be lengthy roadmap planning, Lengthy coordination of between the teams.
00:29:57.997 --> 00:30:02.747
And the way you resolve, resolve that is that don't do roadmap for a long period of time.
00:30:02.787 --> 00:30:05.227
If you are talking to customers, you know exactly what needs to be done.
00:30:05.527 --> 00:30:08.247
Execute quickly to outcome within a quarter or less.
00:30:08.547 --> 00:30:18.916
And on the, on the organization matrix, as I said earlier, self-contained team where there is no fragmentation of the ownership, directly custom, uh, connected to the customer base and deliver.
00:30:19.217 --> 00:30:35.297
Even if you need to go all of those teams, if they belong to different orgs, put them together in a true, you know, mission team, if you will, with one ownership, even though they have different bosses all over the place, deliver that value to the customer as quickly as possible.
00:30:35.747 --> 00:30:36.207
Okay.
00:30:36.507 --> 00:30:43.057
And then I guess, obviously, transformations, they always challenge firms obviously budgets, n- new ways of thinking.
00:30:43.547 --> 00:30:47.957
So how can leaders bring employees and stakeholders with them?
00:30:48.106 --> 00:30:53.196
On the tech side Engineers, PMs will be happy because things are gonna go faster.
00:30:53.297 --> 00:31:00.557
As I said earlier, the, the problem is that when things change, m-m-- human nature is to have fear.
00:31:00.997 --> 00:31:07.586
Fear that, you know, whatever they worked, their entitlement and seniority and all that may just go out the door.
00:31:07.666 --> 00:31:13.997
So it's important to let them know that, look, I mean, at least your, your comp stays more or less the same, but your role may change.
00:31:14.027 --> 00:31:16.207
You may go from manager to individual contributor.
00:31:16.507 --> 00:31:19.257
You-- your job is gonna change quite a bit.
00:31:19.307 --> 00:31:20.946
What you did manually has to be automated.
00:31:20.957 --> 00:31:28.136
So it goes back to the point I was making earlier don't create an environment of fear where people think they're gonna be replaced and let go.
00:31:28.257 --> 00:31:28.497
Yeah.
00:31:28.656 --> 00:31:29.477
Engage them.
00:31:29.487 --> 00:31:35.067
Be-- maintain that contract of employer to, to, to employee.
00:31:35.457 --> 00:32:00.406
Create an environment of trust like,"Look, even if your job is different or you're becoming an IC because there is gonna be a collapse of the management layers as just what's happening and inevitably it will happen, it doesn't mean that, just retrain yourself, learn how, how to do, learn how to manage less, learn how to be an individual contributor, learn how to focus on outcomes," just put them into a position of, of not having to fear.
00:32:00.557 --> 00:32:05.567
And that, that's difficult, so you need to, you, you need to kind of like walk the talk, right?
00:32:05.596 --> 00:32:10.537
You cannot be saying something and doing something six months later because the trust is gonna be gone.
00:32:10.537 --> 00:32:21.477
So maintain and enhance and strengthen that, that contract with the employee where they are good, they are talented, but doesn't mean that, they need to go.
00:32:21.517 --> 00:32:22.227
Just keep them.
00:32:22.406 --> 00:32:23.237
They are talented.
00:32:23.467 --> 00:32:25.717
The, the definition of the job is changing.
00:32:25.767 --> 00:32:31.007
And frankly speaking, for the most part, there is gonna be a lot of, um, manager to individual contributor transition.
00:32:31.007 --> 00:32:31.017
Right.
00:32:31.317 --> 00:32:34.537
Now, for those that cannot make that transition, it's...
00:32:34.547 --> 00:32:35.396
I just don't know what to say.
00:32:35.416 --> 00:32:37.217
That's just the world, where the world is going,
00:32:37.277 --> 00:32:38.067
yeah, and I do agree.
00:32:38.067 --> 00:32:39.356
I think you touched on an important point.
00:32:39.356 --> 00:32:40.136
I think fear is the big thing, isn't it?
00:32:40.136 --> 00:32:40.936
I think there's so much talk.
00:32:40.936 --> 00:32:41.116
Yeah,
00:32:41.386 --> 00:32:41.856
yeah.
00:32:41.856 --> 00:32:50.436
And s- I think it's a lot of noise that's just put in the marketplace where, in fairness, a lot of people don't know what's going on with AI, do they?
00:32:50.606 --> 00:32:52.686
It's still an unknown journey, isn't it, for many?
00:32:52.757 --> 00:32:53.916
It's- Yeah,
00:32:53.967 --> 00:32:54.116
yeah,
00:32:54.416 --> 00:32:54.436
yeah.
00:32:54.436 --> 00:32:57.767
A- a- and look- you know, it's gonna improve, but it's, it's still unknown, like, really.
00:32:57.767 --> 00:33:02.707
Like, a lot of businesses obviously they know what, how they can do it, but who knows what's gonna be the future.
00:33:02.707 --> 00:33:04.977
Like, it's still that it's a new era.
00:33:05.326 --> 00:33:05.616
Yeah.
00:33:05.836 --> 00:33:09.787
I think the future is gonna be there is a Cambrian explosion of startups.
00:33:10.176 --> 00:33:13.527
It's so much cheaper nowadays to start a company.
00:33:13.757 --> 00:33:15.346
I mean, the product development can be done much faster.
00:33:15.346 --> 00:33:19.086
You line up your customers and then you go all in on on, on go-to-market.
00:33:19.086 --> 00:33:23.767
So if there is a Cambrian explosion out there, I think there's gonna be tons of jobs in tech.
00:33:23.807 --> 00:33:36.826
There's gonna be- Yeah a lot of smaller company popping up and, and just going, you know, a- after what the big companies have built, which is what I keep telling the big companies that, look, every software company out there- In a big market is up for grabs.
00:33:37.047 --> 00:33:40.257
You gotta really adapt quickly or you're just gonna be replaced.
00:33:40.826 --> 00:33:42.926
And you gotta take that entrepreneurial spirit.
00:33:42.926 --> 00:33:45.517
So embrace the fear, embrace the unknown.
00:33:45.997 --> 00:33:47.626
Don't focus on fear.
00:33:47.926 --> 00:33:49.467
Don't think the unknown is bad.
00:33:49.616 --> 00:33:51.946
Take it as an, take it as an opportunity.
00:33:52.116 --> 00:33:57.626
The what, what's unknown is good for discovery, is good to create, is good for opportunity.
00:33:57.626 --> 00:34:05.196
So you need to take a mindset, which is,"Wow, look at all of the things I can do," versus like, my job is not the same.
00:34:05.196 --> 00:34:07.487
What's gonna happen to me?" Fall into a mindset.
00:34:07.576 --> 00:34:09.166
Look forward, don't look back.
00:34:09.626 --> 00:34:11.947
Just look forward about what you can do, right?
00:34:12.547 --> 00:34:19.987
Even if your job changes completely, just take that mindset, which is like,"Wow, I can create something like I've never been able to do it.
00:34:20.257 --> 00:34:24.987
What is it that I'm good at that I can just go into that new world?" Day one mindset.
00:34:25.527 --> 00:34:25.726
Yep.
00:34:25.967 --> 00:34:26.967
Anything is possible.
00:34:26.967 --> 00:34:33.077
Uh, so I mean, you also, you m- you touched on another point about obviously like, uh, like anyone could do pilots nowadays.
00:34:33.106 --> 00:34:39.896
Um, so I guess what's preventing the, these pilots from being scaled to like business critical solutions?
00:34:40.257 --> 00:34:41.666
You mean what's preventing do startups
00:34:42.257 --> 00:34:42.407
to scale?
00:34:42.547 --> 00:34:53.947
In terms of what's preventing like o- like I guess enterprises uh, to scale, like,'cause obviously e- like you said, like a- anyone can build a POC now, but I guess- Yeah the big challenge is going to enterprise level.
00:34:53.987 --> 00:34:57.097
So what's, where's the, I guess, the bottleneck there?
00:34:57.467 --> 00:35:00.606
Well, the good news about the big companies that they have a ton of customers, right?
00:35:00.606 --> 00:35:01.467
So they can...
00:35:01.476 --> 00:35:08.516
I- it's just what's preventing is e- e- exactly what I've been saying for the last, you know, it's just like the, the, the legacy mindset.
00:35:08.516 --> 00:35:10.436
Just stop doing something new.
00:35:10.927 --> 00:35:13.637
Just connect the builder to the customer directly.
00:35:13.646 --> 00:35:19.427
Like all of those layers that companies create from engineering to customers, ton of layers.
00:35:19.706 --> 00:35:20.666
That's kind of all, right?
00:35:20.666 --> 00:35:24.097
Because you are literally inventing, you're creating a startup within a company.
00:35:24.347 --> 00:35:26.327
Let the builders talk to customers.
00:35:26.766 --> 00:35:28.157
Don't put everything in the middle, right?
00:35:28.157 --> 00:35:34.166
Because but every time you put something in between things at the human level, the coordination tax is high.
00:35:34.367 --> 00:35:38.896
Not only that, but the translation drops, right?
00:35:39.237 --> 00:35:45.536
So the messaging has to be from the builder direct to the customer as big companies are not used to that, right?
00:35:45.606 --> 00:35:56.637
Because you, they built layers and layers of customer success, marketing, product management, you name it, to not put an engineer in front of the client, but the builder directly in front of the client.
00:35:57.056 --> 00:36:00.407
You gotta, you build, when you build, you're building for a customer.
00:36:00.657 --> 00:36:03.387
You gotta be able to talk to them on a daily basis.
00:36:03.686 --> 00:36:09.507
But now, if you are an engineer in big tech to talk to a client, well, you need to have 20 different approval.
00:36:09.806 --> 00:36:13.507
Product managers, customer success, marketing, da, da, da.
00:36:13.806 --> 00:36:19.896
By the time you go through all those layers, you know, they're demotivated, the messaging is gone, the translation is not there.
00:36:20.416 --> 00:36:23.317
It to-- it takes 20 million different years, nothing happens.
00:36:23.617 --> 00:36:26.246
You gotta go direct to customers from the builder.
00:36:26.606 --> 00:36:32.027
And obviously, look, how do you ins-ingrain this, like, builder mindset, I guess, into companies then?
00:36:32.027 --> 00:36:33.197
'Cause they won't...
00:36:33.237 --> 00:36:40.347
Do you gonna need constant innovation, or do you think that's gonna naturally progress now because of AI, AI working?
00:36:40.347 --> 00:36:40.927
Do you think it's...
00:36:41.317 --> 00:36:45.277
Or do you think companies still need to operate better in this
00:36:45.577 --> 00:36:45.637
regard?
00:36:45.757 --> 00:36:45.836
It's definitely.
00:36:45.836 --> 00:36:47.467
Like I said, look, you, look, you gotta...
00:36:47.766 --> 00:36:49.516
It, it, it doesn't happen easily.
00:36:49.556 --> 00:36:54.806
You gotta bring entrepreneurial talent at the top that pushes down.
00:36:55.327 --> 00:36:59.237
If you wait for it to be bottom up, good luck, right?
00:36:59.266 --> 00:37:05.536
You have too many organic resistance, organic, you know, entitlement built into the system.
00:37:05.577 --> 00:37:06.567
It will die off, right?
00:37:06.586 --> 00:37:06.617
Okay.
00:37:06.617 --> 00:37:09.547
And, and these entrepreneurial builders, they will walk out the door.
00:37:10.016 --> 00:37:11.246
They become very frustrated.
00:37:11.657 --> 00:37:12.817
That's just reality.
00:37:12.817 --> 00:37:15.056
I mean, we cannot be i-idealistic about these things.
00:37:15.056 --> 00:37:16.387
You can be idealistic about product.
00:37:16.806 --> 00:37:18.706
You should not be idealistic about human nature.
00:37:19.197 --> 00:37:28.697
So when it comes to that, bring somebody at the top who's entrepreneurial and just cut through all of that mindset and bring that entrepreneurial style.
00:37:28.717 --> 00:37:32.067
You know, one of the reason I like Elon Musk, you know, I'm not gonna talk politics.
00:37:32.067 --> 00:37:35.117
I have differences on his point of view, is that that's what he does.
00:37:35.416 --> 00:37:40.177
If he goes to a company- Say Twitter or the likes, I don't want to bring the wrong analogy.
00:37:40.186 --> 00:37:44.317
He will see all of these things because he's a born entrepreneur.
00:37:44.327 --> 00:37:52.036
He knows all of the scar tissue, and he just will do whatever it takes to go into a builder mindset directly connected to the customer.
00:37:52.367 --> 00:38:06.797
So if you are a large company struggling, bring entrepreneurial talent towards the top as close as possible to the CEO, if the CEO is not a founder, and just bring that experience, that mindset into the larger corporation.
00:38:07.097 --> 00:38:15.817
The last thing you want is leaders that are more like, you know, No, I've never done, built a company, started a company," very caretaker, which is not gonna get it.
00:38:15.817 --> 00:38:21.637
This is not a bottom-up exercise, this is a top-down exercise from bringing entrepreneurial talent into big tech.
00:38:22.226 --> 00:38:23.286
That's just the reality, right?
00:38:23.286 --> 00:38:25.047
That's just the way I see it in many places.
00:38:25.047 --> 00:38:26.777
As much as I can be idealistic on the
00:38:27.077 --> 00:38:27.427
product- The platform
00:38:27.726 --> 00:38:29.266
human nature doesn't really change.
00:38:29.266 --> 00:38:32.286
You gotta bring that, that style into a larger tech to transform.
00:38:32.806 --> 00:38:33.067
Yeah.
00:38:33.166 --> 00:38:38.936
No, I mean, it's gonna be, it's gonna be interesting how businesses do, do the change because it's, there's a lot going on, isn't it?
00:38:39.536 --> 00:38:40.237
There's a lot to deal with- Yeah.
00:38:40.237 --> 00:38:42.166
Yeah, yeah especially for these enterprise ones.
00:38:42.766 --> 00:38:44.447
Like, there's a massive change.
00:38:44.836 --> 00:38:57.856
And then like I said earlier, I think the, the, the good news is that I think these bigger companies, they really need to do acquihires or big AI companies bringing in part-part the founders in as much in charge as possible and just, like, reset the whole thing.
00:38:58.416 --> 00:39:00.186
We are going through a massive transformation.
00:39:00.487 --> 00:39:01.996
Those who don't adapt quickly will be...
00:39:02.056 --> 00:39:04.327
I mean, the incumbents will be very gone quickly.
00:39:04.467 --> 00:39:08.947
Maybe around, it's just gonna be one-fifth of the size of which they are then.
00:39:09.137 --> 00:39:10.927
And that's why, this is a super, super...
00:39:10.936 --> 00:39:15.572
You know, VCs are VCs, but they really understand every software company is up for grab.
00:39:16.101 --> 00:39:21.132
They really understand a startup will rewrite and go after the customer base of the incumbent very quickly, right?
00:39:21.141 --> 00:39:26.152
That's why they are just funding them to the tune of massive amount of money just for the sake of say, accelerate it.
00:39:26.452 --> 00:39:39.342
So you're gonna see a change of the guard into many markets unless the incumbents, are smart enough to be more entrepreneurial, aka bring that entrepreneurial mindset from the outside in and just shove it down.
00:39:39.731 --> 00:39:39.942
Okay.
00:39:40.411 --> 00:39:51.751
So then just to bring the playbook together then, so what are the most important steps leaders should remember when moving from a legacy environment into the AI era?
00:39:52.101 --> 00:39:54.681
I'd say number one, focus on builders, right?
00:39:55.072 --> 00:39:56.911
Builders, people that actually do the work.
00:39:57.211 --> 00:40:05.461
Number two, flatten up the org as much as you can to reduce the white space between the builder to the customer, right?
00:40:05.762 --> 00:40:08.891
Don't let the management layers get in the middle of that or all of the other artifacts.
00:40:09.351 --> 00:40:12.972
Builders connected to customers, that will go a very long way, right?
00:40:13.561 --> 00:40:19.222
And after that, which is what I've said all along if you don't have that in the house, bring the entrepreneurial talent as quickly as you can do.
00:40:19.521 --> 00:40:19.702
Right.
00:40:19.842 --> 00:40:28.262
I may be oversimplify, but that's those components, and the fourth one is really forget the orgs where you have big armies.
00:40:28.512 --> 00:40:29.161
Doesn't work.
00:40:29.681 --> 00:40:36.981
Tight teams, very tight teams, and definition of tight teams mean builders connected to the customers.
00:40:37.282 --> 00:40:43.342
A team of five or ten people more often than not can do more than a hundred, two hundred, a thousand people.
00:40:43.641 --> 00:40:50.282
So if you are smart enough to have tight teams in many different places, then you're good because you can create a lot of good products.
00:40:50.291 --> 00:40:59.871
You should always have the fountain of ideas whereby in the past you were limited by capacity, now you're just limited by your own backlog of ideas that would make sense for the customer.
00:40:59.871 --> 00:41:03.871
So tight teams, and go down flatter organization.
00:41:04.271 --> 00:41:12.722
I think to me the, in a larger company, the number of layers from the CEO down to the builders, it should be really, you know, two to three layers, right?
00:41:12.722 --> 00:41:14.161
Um, you have seven, ten layers.
00:41:14.172 --> 00:41:14.672
It's crazy.
00:41:15.181 --> 00:41:15.552
It's crazy.
00:41:15.561 --> 00:41:21.532
Every time you go up a layer, you lose so much context and so much innovation can be stifled, just collapse down.
00:41:22.001 --> 00:41:24.632
Yeah, no, it's, it's fascinating because previously, wasn't it?
00:41:24.632 --> 00:41:27.032
You'd have so large, huge teams.
00:41:27.032 --> 00:41:38.211
Now it does seem like- Yeah a lot of the companies now are building these relatively small f- like, businesses that are close to the- Yeah um, sort of leader f- f- or founder really of startups.
00:41:38.211 --> 00:41:38.407
Exactly.
00:41:38.407 --> 00:41:38.605
Um,
00:41:38.605 --> 00:41:39.001
so yeah, no.
00:41:39.012 --> 00:41:40.661
Well, look, look, B- Ben, look at NVIDIA.
00:41:40.961 --> 00:41:41.911
Yeah, the CEO-
00:41:42.211 --> 00:41:42.431
Yeah,
00:41:42.431 --> 00:41:47.072
exactly at NVIDIA, he's got, he's got fifty heroes you don't re- you, you don't need to manage that much nowadays.
00:41:47.092 --> 00:41:48.492
50, 100 direct, great.
00:41:48.532 --> 00:41:50.402
Go have a, have at it, right?
00:41:50.402 --> 00:41:51.981
You don't need to do one-on-ones all the time.
00:41:52.282 --> 00:41:55.391
A lot of the management playbooks built over the years, just think differently.
00:41:55.641 --> 00:41:58.552
Just think about the outcome, think about what people can do, enable them.
00:41:58.851 --> 00:42:02.411
Your job should be really as a leader to remove the, um, the friction.
00:42:02.452 --> 00:42:06.081
It's easier said than done, but to remove the friction, you gotta simplify the org.
00:42:06.481 --> 00:42:21.311
The, the thing about organization is that when there is friction in an org, the org will, will auto create more teams, more bureaucracy with the intent to remove the friction, but in fact it's actually adding even more friction.
00:42:21.311 --> 00:42:25.612
So they create layers upon layers because there's friction and bureaucracy creeps in.
00:42:25.641 --> 00:42:26.722
You've got to shake it out.
00:42:27.211 --> 00:42:27.461
Keep it simple.
00:42:27.472 --> 00:42:29.882
Yeah, I mean, people want more ownership nowadays.
00:42:29.981 --> 00:42:32.592
Like it's just obviously we do-- I do hiring.
00:42:32.592 --> 00:42:36.922
So m- a key thing for a lot of people moving is m- they want more ownership, like more scope.
00:42:37.052 --> 00:42:37.952
They want to make more impact.
00:42:37.952 --> 00:42:38.092
Yeah.
00:42:38.132 --> 00:42:43.211
And if you- Yeah can obviously do-- we've got the technology now to do the heavy lifting for people's jobs.
00:42:43.382 --> 00:42:46.342
Like if you're a coder- That's right look, you can you've got someone do that.
00:42:46.362 --> 00:42:51.371
You have got the autonomy now to go and do bigger things, and that's obviously what people want.
00:42:51.371 --> 00:42:53.061
They want more control on their work, don't they?
00:42:53.362 --> 00:42:53.751
That's right.
00:42:53.942 --> 00:42:59.181
But what people want is impact, and impact in the front organization doesn't tie back to your title.
00:42:59.202 --> 00:43:06.501
If you are moving the needle on a product, and you're able to create a$10 million product line very quickly, then you should be paid handsomely regardless of your title.
00:43:06.762 --> 00:43:12.141
And that pa- speaks specific person who's a builder, couldn't care less about the corporate, stuff.
00:43:12.202 --> 00:43:20.902
So focus on builders that are entrepreneurial, hi- high agency, they want to get things done they create good products, reward them.
00:43:21.101 --> 00:43:21.581
Simple.
00:43:21.882 --> 00:43:31.771
So then if, I guess, if a leadership team wanted to begin this journey over the next ninety days then, what would you recommend is the first meaningful action to take?
00:43:31.822 --> 00:43:36.862
Take a mission-critical workflow and rewrite it completely in AI, right?
00:43:37.161 --> 00:43:42.391
Tight team, small, everything that you know about how you organize and all that, cut it out.
00:43:42.882 --> 00:43:56.331
Find builders that can deliver, connect them to the customers, put that tight team, get out of the way, let them do their job, and learn from what they are doing, and then from what you learn of what they are doing, then you try to translate that over to the rest of the business as you go.
00:43:56.342 --> 00:44:04.672
So it's almost like create a pilot team, give them some kind of free rein, don't try to have human nature to control and all of that, and tell them how to do things.
00:44:04.862 --> 00:44:06.132
Let them figure how to go and get it done.
00:44:06.431 --> 00:44:07.202
And you have to let go.
00:44:07.711 --> 00:44:19.152
Once they go and get it done, they go in front of the customers and they see the success, you're gonna learn along, a lot along the way how you can take that mode of operation and just translate and replicate.
00:44:19.601 --> 00:44:20.501
But it's not rocket science.
00:44:20.501 --> 00:44:21.452
You have to let it go.
00:44:21.702 --> 00:44:24.081
Somebody talented will figure how to go and get it done.
00:44:24.382 --> 00:44:25.481
And- Let go of the control.
00:44:25.532 --> 00:44:26.952
You gotta let go of the control.
00:44:27.251 --> 00:44:27.592
Brilliant.
00:44:27.762 --> 00:44:28.001
Cool.
00:44:28.311 --> 00:44:30.152
Well, thanks for your time today, Jay.
00:44:30.291 --> 00:44:37.052
What I can do is I'll tag your LinkedIn link in the post of the podcast so people can reach out to you.
00:44:37.112 --> 00:44:38.572
But really thanks for your time today.
00:44:38.572 --> 00:44:40.411
It's been a pleasure having you on the podcast.
00:44:40.802 --> 00:44:41.402
Thank you, Ben.
00:44:41.452 --> 00:44:42.731
Thanks for the opportunity.
00:44:42.782 --> 00:44:44.492
it was fun, and I'm looking forward to more.