The Digital Slate: Rethinking the Enterprise in an AI-Native World

Hello, and welcome to the latest edition of Digital CXO Leadership Insight series. I'm your host, Mike Bazar. Today we're with Israel Forest, who's the newly appointed CEO for Valis, and we're talking about the state of digital transformation, which I think we've been at now for at least a better part of a decade.

Israel, welcome to show. Yes, thank you, Mike. Thank you for having me.

It's a pleasure to represent, uh, VALIS here. So, uh, you've been with the company for a while and you're kind of the newly appointed CEO, but what are you seeing out there? What's kind of the goals for the company and where are the gaps that you guys are gonna plug?

Yeah, no, it's a great question. So, you know, valance has been around for about two decades, and we've spent the last two decades really helping companies, uh, and organizations modernize how teams collaborate, how they deliver work. We've done that by implementing and scaling, you know, leading platforms to support how work gets done.

Um, and but over the last year, I would say we've seen, we've seen a clear shift in our customers priorities, CTOs and heads of engineerings. They're not just asking about platform modernization, they're not trying to get to the cloud or get in the latest tool. Uh, in fact, I would say the reverse, uh, the opposite is happening.

They're being inundated by just a plethora of new tools, um, new capabilities, new vendors out there, uh, and a lot of 'em are getting stuck and trying to figure out how to navigate this rapid evolution of the developer experience. Uh, the market's crowded. There's a lot of competing narratives, there's a lot of hype, but I, I think we'd all agree, there are very few companies that have actually achieved tangible enterprise grade results, uh, in this sort of, uh, in this new world.

So our focus is shifting from being a platform implementation company to really focusing on execution performance in AI accelerated world. Like how do we help organizations redesign how workflows through the very functions, uh, of the organization in this new and exciting world. Well, to your point about that, how much of these challenges are really technology related, or are they just as much cultural and kind of maybe even old fashioned inertia?

Yeah, no, I, I think, uh, you know, it's funny, I was talking to a colleague of mine and we were, you know, it's often, I, I I think a lot of us are students of history look back and, you know, they say history doesn't repeat itself, but it certainly rhymes. So you can look back at a lot of these, um, big waves of change or renovation that have come through and ask yourself like what prevented adoption and value and then what ultimately unlocked adoption and value. And, you know, we can go back in this context.

You can go back to, uh, as the org as, uh, organization shifted from Agile to, from waterfall to Agile, and you know, how that started and what, what did it take to finally hit this tipping point where we started getting real acceleration from it. Um, you can go back to the, uh, industrial, you know, engineering and how they've modernized factories. Um, in fact, I was listening to a podcast the other day with, um, uh, Jim Farley, the CEO of Ford, and he was talking about what he needed to do to really transform forward.

So there's a lot of, I mean, this idea that there's innovation and rapid changes that are, uh, capable, and yet it doesn't seem to change the world in the way that we want it to until something happens. And you ask yourself what is that something that happens? And, uh, I think if you look at a lot of those examples, and you can probably apply the same thing to AI, is there's a lot of optimization in context.

You know, you have a team that has a set of processes and you say there's a new tool that's wildly better. And so they look within the way they've orchestrated things. You, you know, you can apply it to the, the world of agile.

If you simply took one team and said, be more agile in a waterfall world, it, it, it, it would be, you know, 1% better, 2% better. If you went to a factor and you try to automate one step or one person's process, you would get incremental improvements. But it's not until we're ready to rethink the whole process from end to end and start with a clean slate that I think you get the real benefit.

And, you know, I think this is why you're seeing, uh, uh, a, a divergence, uh, between some of the, what we call now call AI native companies versus the companies that have been around for a while because the AI native companies, they have no baggage. They can just start with all the capabilities that exist today. Whereas the companies that have, you know, years and years of legacy capabilities, legacy ways of working, they have a much harder time, uh, changing how they fundamentally get work done.

And therefore, um, they're, they experiment, but they experiment in compartmentalized ways. And I think that to me, that's fundamentally, uh, why we're seeing this, uh, limited value in an enterprise scale and what ultimately has to happen for us to help customers get value at enterprise scale. Everybody and his brother is talking about AI these days and they're running into these issues.

And some folks say, you know, it's the greatest thing since sliced bread. And others are basically saying, I'm running into the same bottle next twice as fast. Alright, from your perspective, is AI at the very least gonna force us to revisit these digital transformation conversations that we seem to be, have had, maybe off and on, but not consistently enough?

I think, um, I think it, so if you were just to compare AI to the its predecessors in terms of digital transformation, I think the, the, the multiplier effect that it could have is greater than others. And I think that is certainly true. So if done right, it has the potential of dramatically compressing cycle times of, of, I don't wanna say infinitely, but dramatically increasing capacity.

And a lot of the constraints that you previously had, they're no longer constraints. So I do think it's in some ways is different than some of the prior, um, uh, impetus is for digital transformation. But I also think inherent in that is it is going to be harder for us to achieve those, uh, benefits because I think it is gonna require that companies rethink completely how they do things in order to get that value.

So I, in, you know, in some ways I think it's, it's bigger than its predecessors, but it's because of that it's gonna be much harder. And I think the companies that do it well are what, at least what we're seeing and what we're advising customers is they are figuring out how to, um, carve out end-to-end parts of the business. It's a particular product or a particular segment and, and it's something that is, uh, high, high potential, but maybe low risk, but something that they could get an end-to-end benefit from and say, go rethink this in the world of ai, but do it in a, call it a pilot, but do it in a standalone, NewCo Spinco, whatever you wanna call it, so that they can see what would it look like without the baggage that we have.

I, I mentioned Jim Farley before, but you know, when he was trying to get four electrified, that's what he did. He said, let's take this part of the business. Let's, let's free it from the baggage of Ford and see what you can do if you were just your own company.

And then how do you then impart some of that experience and learning back into the larger company? And I think that's what it's gonna take, um, for executives to, uh, to drive AI benefit across the, uh, across the entire organization. And again, just to pull on that thread, it, it wasn't, you know, it wasn't some, uh, VP of manufacturing that made that commitment was the CEO of Ford that personally went and did that.

And so I do think that's one of the other hallmarks of companies that are gonna be successful are those where you have leaders at the very, very top who are not just mandating the change, but actually leading the pack. And I don't say hands-on 'cause Jim wasn't actually manufacturing, but really driving or creating this space, if you will, for that change to happen within the organization. I think the same is gonna be true for, um, AI in the engineering and product development context.

I think one of the challenges that I hear from people is a lot of the folks who suffered from the initial wave of fear of missing out investing heavily, and then they woke up one morning and discovered that some application that they normally use in the app built in all the capabilities that they just spent the last nine months experimenting with. And, you know, suddenly a million dollars went out the door, but then it turned out to be, it's a feature of something else. So do we have to get smart about what projects we're gonna actually invest in?

Yeah, I do think, and that's, you know, that's what we spend a lot of our time is really connecting strategy to work and helping customers do that. And I know we talk a lot about, you know, observability as an example in, in the AI world, and we think about a lot in the context of the technology. Like how do you observe what the agents is doing and how do you have certainty in a non-deterministic world?

But I think it actually, it, you can look, I mean certainly there's this observability in technical context, but there's also observability in the work context because you can scale bad decisions just as quickly now. And so this idea that you have, like, you make a plan, you roll it out, you see what happens, you come back in a few months and observe it in this world, uh, you actually have to be observing that almost on a daily basis. So this ability to connect strategy to work, um, really has to happen much better and much faster because you will cycle off, you know, off strategy if you will very quickly with the scale and capabilities of these new tools.

So I think that's gonna be another interesting challenge for organizations is how do you, um, uh, move from a leadership perspective and how do you, uh, drive the, uh, company direction all the way down to the person or agent that's developing the code in doing that in a way where you're, um, you're actually keeping everyone flying in the right direction. And I do think though, um, the, the counterpoint to that is it does lower the cost of getting it wrong because, you know, you can, you can, because, you know, capacity is not as limited as, well, I don't wanna say it's free, but it's not as limited as it was. It's not that hard for you to, as an organization to try five or six different things and get far enough to be validated.

Whereas, you know, in the past that was probably a, a 3, 4, 5 month endeavor before you realize that we kinda made a mistake here. So it is a double-edged sword, I think as it always is with, uh, with transformation it could, it, it hurts, but if you use it right, it can be in, uh, incredibly valuable. How do we navigate the, who moved my cheese conversation?

'cause a lot of the stuff around AI or even digital transformation that matter, you know, always had this sense of, well, there's gonna be change in how does it affect my job and, and the age of ai now it's become, you know, oh, well, we're gonna reduce our headcount by X, Y, z and you know, a lot of people kind of look at that with, uh, shall we say, less than enthusiastic response. So how do we kinda, you know, get people to kinda wrap their heads around how to use AI in a way that maybe benefits everyone? Yeah.

Well, I, I think, you know, I'll go on a little bit of a soapbox, but I don't think some of the, the narratives that some of the public company CEOs are taking out there, which is I reduce my headcount X because of ai. I think that's, I think that's posturing a lot of their parts. Um, I don't think it's actually true.

I think they feel compelled to demonstrate benefit and value because of either investments they've made or because of pressure they're under. So they feel pressure to demonstrate that they're achieving value. But I don't actually think that's true or helpful.

Um, I I, and I think if you look, again, if you just look back at history, um, every other innovation, you know, step function innovation has not replaced, necessarily replaced people, but change the, the, the, the layer at which people are being impactful and therefore allowed people to be far more scalable. And so I think, uh, you know, you can look, I don't know if you remember watching the movie, uh, hidden Figures, um, about the nasa, um, you know, the Apollo missions, and I forget the name of the, the woman who was the, um, she was the head of the computer department, which were the people that did computing. And um, there's a scene where, uh, she comes in and she tells her team, they just put an IBM mainframe in here that can do 27,000 calculations in a second.

And the, the her team said, oh my God, we're gonna be out of jobs. And she's like, no, we're gonna become operators of the machine. And I think that mm-hmm.

You know, again, it was, you know, 50 years ago now. But I think the same is true. The people that are going to learn how to leverage and harness those tool and become more harness engineers as opposed to developers are gonna be able to produce, I don't say infinitely more, but substantially more in the same amount of time and the people that are gonna struggle to make that leap.

I, you know, unfortunately, I think there are gonna be some people left behind that always is gonna be the case. Um, but I don't, I don't think fundamentally it means that we're, uh, uh, eliminating positions. I think it's just a, a matter of changing where people spend their time and learning to become multipliers, uh, for the technology.

So what do you see digital CXOs doing time and again, that just makes you shake your head a little bit and go, folks, we might wanna be a little bit smarter than that. Well, you know, I think the, um, uh, a couple things. First of all, uh, trying to make vendor decisions right now I don't think is smart.

I think that world is gonna, I mean, you know, you, you think about, imagine it wasn't that long ago when we thought open AI open AI had this insurmountable headstart on everybody, and a year later the landscape changed completely, Gemini sprinted ahead and now anthropic sprinting ahead. So I think trying to, to pick the winners on the model space is probably not productive. It's probably not where you spend your time.

Um, so I think that's one thing not to do is worry too much about that. I also think, um, while we want experimentation, I don't think experimentation is really gonna help us figure out how to get value from ai. I really do think it is, um, uh, finding ways to rethink your business and the bigger your business is, the harder that it's gonna be to do, that's gonna be the, the challenge of these larger co corporations is how do I reinvent myself while still running my business?

And I think that's, to me, the idea of more experiments, let's get everyone to consume ai, let's, let's measure adoption without actually defining what we're gonna change and how we're gonna change and why it's gonna be better, I think is creating a lot of confusion. I mean, you get a little bit of value, but you don't get a tremendous amount of value. I think the smart C-suite individuals, uh, are leading from the, from the top.

They're finding ways to carve out parts of the business and asking the, the, uh, the business leaders of those teams to rethink everything in this new world. And I think those leaders will make more progress than the ones that frankly don't have the courage to do it. And I think that's, it's, it's, uh, it's gonna take a lot of courage for, uh, for leaders, particularly if leaders are large companies to, uh, reinvent themselves in this new world, uh, while managing the, you know, the legacy that they have.

Right. So given all that, what's your best advice to those leaders? Um, you know, should they walk the floor and get more and deeper into the organization?

Or is there something that they should be doing that they're not doing? Yeah, I mean, I do think it starts at the top. I think if you look at, uh, I don't wanna say every, but certainly there are a lot of examples of real corporate transformations where real change was driven.

And almost always it was because there's a, there's a leader at the top hands-on driving that change. You know, I spent a number of years at Salesforce and I, I, uh, remember the story of Salesforce moving from Waterfall to Agile, which was a, a massive change for that company back in oh 5, 0 6 or seven, I forget what year it was. And it happened because Parker Harris, the, the, you know, founder and CTO led that change and, and not only did lead the change, they came to him and said, let's pilot it with one team.

And, and he said, no, we are rolling this out through the whole company. We're doing it. It's gonna be hard.

It's gonna take months and months and we're probably gonna make mistakes, but in the end, it's the right thing to do. Now, that was a huge bet. It obviously worked well for Salesforce and for Parker.

Um, but I think you think about the courage and the conviction that that took on his part, uh, I think modeling that behavior, maybe not quite so bold, but that type of behavior, I think is the, that's what you wanna model. You wanna model, uh, leaders who have a vision, they're convicted about it, they're willing to take on risk, they're willing to make mistakes. Um, they find a way to create this space for their teams and give their teams permission to make some mistakes, experiment, challenge the status quo, free themselves of the baggage and the constraints that exist in the other parts of the organization, and then take those learnings and then start playing them back into the larger organizations.

I think those, those are the, those will be the winners. I think the smaller the organization is, the easier that is to do, the larger the organization is, the harder that is to do. Hey folks, you're in it here.

It's one thing to create the plan, but you know, it's another thing to do the work and there's no substitute for it. Israel, thanks for being on the show. Yeah, thank you, Mike.

It's pleasure talking to you. Bye-bye. And thank you all for watching the latest episode of the digital CXO Leadership Inside series.

You can find this episode and others on our website. We invite you to check them all out. Until then, we'll see you next time.