Why Your Tech Stack is Breaking Your AI Strategy

Yeah, and, and before we hit this, this AI wave that we're currently in, I think the, the last greatest, best and greatest, uh, thing that was going on in the world of, of technology was, um, software as a service.

All of these different, um, SaaS tools and apps and all this other great stuff, um, that, that were invading enterprises.

Um, but now do you think that we're actually finding that these hundreds and maybe thousands of tools are actually now a disadvantage when it comes to, um, you know, AI? Especially s- if AI has to access data from all of these different systems, that that could be one pa- part where this tech stack is breaking.

Or give me some other examples of, of, of what you might be seeing out there when it comes to SaaS.

Certainly a proliferation, excess proliferation of applications can be a disadvantage.

Um, like what a-

Mm-hmm

... at the same time, too much, um, consolidation can be a disadvantage.

So it's really a matter of finding the balance.

So there's examples where you have kind of, I'm gonna call it like '90s versions of SaaS software, where it was on-prem, became SaaS, and it's this big heavyweight piece of software that's difficult to maintain, and people go, "Man, I can't change it.

We're just stuck on that." Well, that's not a very good place to be.

And then you also have, um, where, uh, situations where I have 47 different point solutions, and can I take that 47 different point solutions and get down to a dozen solutions that I can manage more effectively? There's two ends of that spectrum, I think, in SaaS or pre-SaaS, whatever software.

Um-

Right

... and, and now that's exacerbated with AI 'cause AI gives you, hey, I, I, like, uh, my team comes up with new point solutions with AI every single day, multiple of them, and if we just let them proliferate, I'm, I literally have no idea how to manage that.

So is this creating, uh, operational chaos for a lot of these companies that are discovering this, or is it, is there something else going on?

Uh, I think it is.

I think it's doing two things.

It is creating some operational chaos.

It's also creating operational wins.

So I think the opportunity to fo- like the glass half full view is let's stop, let's figure out a way to govern this such that we can identify the wins and then affect those wins, scale them, deploy them across the organization, and, um, contain experimentation to things with sort of definitive outcomes.

It's almost like, um-[lip smack] You have a strategy and go, most of our resources are oriented towards that strategy.

We're gonna allow for 5% drift so that we can experiment and find out what we don't know, identify things that we, we just haven't even thought about.

But most of it has to be oriented towards something that's purposeful.

I think that's what a lot-- I think the most effective, I'm gonna call it incumbents, AKA non-e-, um, AI native companies, the ones that are effective are doing that.

Yeah, and, and before we hit this, this AI wave that we're currently in, I think the, the last greatest, best and greatest, uh, thing that was going on in the world of, of technology was, um, software as a service.

All of these different, um, SaaS tools and apps and all this other great stuff, um, that, that were invading enterprises.

Um, but now do you think that we're actually finding that these hundreds and maybe thousands of tools are actually now a disadvantage when it comes to, um, you know, AI? Especially s- if AI has to access data from all of these different systems, that that could be one pa- part where this tech stack is breaking.

Or give me some other examples of, of, of what you might be seeing out there when it comes to SaaS.

Certainly a proliferation, excess proliferation of applications can be a disadvantage.

Um, like what a-

Mm-hmm

... at the same time, too much, um, consolidation can be a disadvantage.

So it's really a matter of finding the balance.

So there's examples where you have kind of, I'm gonna call it like '90s versions of SaaS software, where it was on-prem, became SaaS, and it's this big heavyweight piece of software that's difficult to maintain, and people go, "Man, I can't change it.

We're just stuck on that." Well, that's not a very good place to be.

And then you also have, um, where, uh, situations where I have 47 different point solutions, and can I take that 47 different point solutions and get down to a dozen solutions that I can manage more effectively? There's two ends of that spectrum, I think, in SaaS or pre-SaaS, whatever software.

Um-

Right

... and, and now that's exacerbated with AI 'cause AI gives you, hey, I, I, like, uh, my team comes up with new point solutions with AI every single day, multiple of them, and if we just let them proliferate, I'm, I literally have no idea how to manage that.

So is this creating, uh, operational chaos for a lot of these companies that are discovering this, or is it, is there something else going on?

Uh, I think it is.

I think it's doing two things.

It is creating some operational chaos.

It's also creating operational wins.

So I think the opportunity to fo- like the glass half full view is let's stop, let's figure out a way to govern this such that we can identify the wins and then affect those wins, scale them, deploy them across the organization, and, um, contain experimentation to things with sort of definitive outcomes.

It's almost like, um-[lip smack] You have a strategy and go, most of our resources are oriented towards that strategy.

We're gonna allow for 5% drift so that we can experiment and find out what we don't know, identify things that we, we just haven't even thought about.

But most of it has to be oriented towards something that's purposeful.

I think that's what a lot-- I think the most effective, I'm gonna call it incumbents, AKA non-e-, um, AI native companies, the ones that are effective are doing that.