Future of Mainframes with AI-Powered Productivity and Flexibility with BMC Software’s Priya Doty

Hey everyone. We're back here on Tech Drunk tv. Welcome.

I'm, this is a part two. If you didn't catch our part one, I encourage you to go back and check it out. But I'm talking with Priya Doty.

And Priya is, uh, with B-M-C-A-M-I. And if you wanna understand Priya's background and B-M-C-A-M, I go do check out, uh, part one of this interview. I don't wanna rehash it 'cause we've got a lot of good stuff to cover here.

So, Priya, welcome back and thanks for joining me again. Hey, thanks Alan. So, Priya, we left off, we were talking about the, uh, knowledge expert that's now being built into the BMC software and, you know, we were analogizing it to kind of the, uh, uh, AI we're seeing built into developer IDs.

Yeah. Yeah. Right.

And, and, and I will tell you, I that's a great use of it, by the way. But we're also seeing the same sort of thing, the AI built into software testing, right? Testers that's right now have this little bug in their ear mm-hmm.

Telling them this is the test run or that's not the test run, or there's a mistake here and what, what have you. I think we're gonna see the same thing coming to the ops people, right? Percent.

Yeah. So how, how is that manifesting itself in, in the BMC suite? Yeah, absolutely.

Well, so on the BMC side, I mean, AI ops is a, a known quantity for us, but, but the thing that's really changing right now is we just announced the knowledge expert in October. And what that lets you do with B-M-C-A-E assistant is actually start to interact with a chat interface, ask questions as you're doing your daily workflow as a, an operations person, right? And that's, that's a big step forward because just like you said, like on the IDE side, we've started that journey about a year ago.

Um, on the ops side, we're now continuing to add functionality. And this is like very pervasive, right? Anybody who's using the ops ui, uh, can now leverage this, this kind of chat within it, within it.

So great for any team where they have, you know, different levels of resources, different levels of skills, uh, to be able to kind of harmonize across. Um, so we're pretty excited about it. We have, uh, lots of customers testing it out, uh, giving us feedback.

Uh, and it's been, it's been pretty cool to watch. You know, what I love about it, this kind of thing. Priya is on a team, team wide level.

It is fantastic, right? Because now everyone on the team has, has that same level of access to the knowledge base to the expertise. Yes.

But it's not just access in that, you know, and this was going back to the, I told you about the article I wrote about the difference between people using AI versus Google search. Yes, Yes. Yeah.

To some extent we always had that access, right? There were people using Google internally who put a knowledge base together that you could search. But when we have it as an AI sort of chat bot or generative AI like this, it's so much, it, it's, it's just a lot closer.

It seems, it's a lot easier to put your hand and finger on. It's always, it's like having an assistant with you, a coworker. So I'm call a digital worker right there with you all the time who is suggesting these things and can make these changes with you.

And, you know what I mean? It, it's, it's, it's heads and tails above just having a knowledge base with a search bar. Oh, indeed.

And I mean, I think you look at BMC, we have the entire software stack. So what we're doing is we're saying we're entering with chat and we're letting you start to type things in and ask questions as an operations person. But what we're quickly gonna start to do is let you ask questions, you know, uh, how much am I spending?

How can I pull back on this? Where might my problematic issues be? And starting to cross domains.

So you men mentioned a team, it's almost like you would recreate a, your environment in that, you know, digital sort of GPT context and ask questions that cross boundaries into the data space, into the ops space or the storage or, uh, you know, wherever it might be. And that's, I think that's what's the really exciting thing is this is the, you know, this is like one of the first steps towards that. And of course that absolutely, that's always gonna lead us towards the, the discussion on ag genix, right?

Because that's the, the other Big thing. But before we got to ENTs, there's one more point I want to make on this one. Clear.

You know, we mentioned before a lot of people are afraid about AI taking their job, costing them their job. Yes. This sort of knowledge base here, knowledge expert, uh, chat and so forth.

This is not meant to replace people as valuable as it is on a team wide basis. This gives every single person on the team the ability to be a superstar because they have the entire knowledge base of everything they want easier at literally at their fingertips, and in some cases even automated to pop in there. And this is why, you know, when they say you're not gonna lose your job to ai, you're gonna lose your job to someone who works AI better than you.

Absolutely. Absolutely. If you're an individual, learn to use this.

And, and I, I think it's the great paradox, right? You know, the, the theory is we're gonna be simpler because of gen ai. The reality is it's actually gonna be more complex.

But so what's gonna happen is if you're using those AI tools, you'll be able to master complexity faster and get to that higher order challenge faster. And so we're all gonna have to push ourselves harder. Maybe that's why our brains are rotting, I don't know.

Or, or snap videos. But, um, Could be that too. That too.

But yeah, I think there is a very valid point. I mean, we're not designing anything to replace anyone's job. We're, you know, we're designing things that help people with the workflows they already have today.

You know, that's, I think that's the other interesting thing about AI is so far we haven't developed any net new workflows. It's leveraging the existing processes and workflows and then trying to improve upon them. But you, the beauty of, you know what someone, so all the venture backed companies I ever helped co-found were funded by my friend Brad Feld.

It's pretty well known in in the VC world. Yeah. Brad always used to tell me, 99% of tech is evolutionary not revolutionary.

And AI is a little revolutionary. I won't deny it, but the way we use these knowledge, uh, bases and the way we interact it is, it's an evolution of us getting better, of us being able to use them, more of, of them being more useful to us. And it represents the future.

It's not some far out star trekky or Star Wars kind of future. This is a future you could put your hands on right now. BMC is rolling this out as you're watching this in essence, right?

Indeed. Indeed. And so I, I, I think people need to remember that.

I'll tell you something else. I, I was at, I've been at a bunch of conferences the last couple of weeks, Priya, and I was talking to someone a little younger than me who, who said, we were talking about this kind of stuff around generative chat bots and so forth, and they said, oh, chat generative AI was last year. This is the year of AG agentic ai.

Oh, it sure is. Yeah. But I feel like generative ai, I hardly got to know you, but, you know, boom.

That's how quick we're going. So, yep. Hey, we had our two, three years now, now we're, now we're at AG ai, so now we're at ag, ag Agentic, ai and Priya.

What is, you know, what does that mean? What does that mean for BMC in the mainframe world? Yeah, I mean, it, look, it's, it's, again, it's an early, everyone's talking ag gentech, it's early days and there's emerging standards.

We're focused on the MCP standard. I know there's others that are out there. I'm sure there will be more in the future, but the way we're thinking about it is a kind of a step process.

You know, we started, we're starting with embedding the functionality of Gen AI into our products. Like we've just been talking about in the ops domain and the devex domain. We're then looking at how do we turn them into agents, meaning autonomous, you know, either purely autonomous, but more likely to be, you know, sort of guided in a way, um, agents that can actually execute tasks.

And then the, you know, the ultimate goal is, uh, a workflow chain where you can have, uh, you know, an agentic workflow. And we already have some prototypes built of agentic workflows in the dev space, uh, you know, and things like that where it's chaining together different tools. I think one of the, um, biggest value propositions right now for, especially for the MCP, is just connecting all the different dev tools you might have, or connecting all the different ops tools you might have in the average day.

So I think that's probably gonna be use case number one. Um, and then use case number two will be sort of starting to automate some of those more repetitive tasks. Going back to the spectrum of trust, where people feel they can turn it over.

Um, as excited as customers are about ag agentic ai, and the, the term I hear the most commonly is they want self-healing systems, but by the same token, they're still very concerned about how much they're turning over to that self-healing system. And then how much, like if, if there's a challenge with their SLA, who's responsible for it, right? Is it, is it the IT operator?

Is it the software provider? You know, so there's a lot of questions like that. You know, we, this was a similar experience when cloud first came on the scene.

Yeah, right. Anytime, you know, you know, the old saying, you know, how you get stuff out of a cis ops hands, you gotta un un unopen his cold, stiff, dead hand to get it out of there, right? That's right.

It's the same, you know, there, there is going to be, I think this period where people are a little nervous about giving up control, about turning it over, automating it, letting a a, an agent kind of run full speed. But if you're not gonna let the agent run full speed, you know what I mean, that's kinda defeating the purpose too. So I do think we're gonna have a, a period of, of trust building and then, you know, learning, learning to just let it go and, and see where this, you know, how far and fast it can go.

Yeah, for sure. And, and I think it's, you know, keeping the human in the loop, but it's also being realistic. Like if there is an outage, if there is a problem, human's gonna have to be involved, right?

Yeah. Well, but you know, you, you look at, let me analogize it to another really like the, our electric grid. Yeah.

Right? Like, so I live in south Florida, right? The, we get hurricanes.

The electric grid really needs to be resilient. It needs to be automated, it needs to be able to, you know, uh, a a transformer goes out, a substation goes out, oh yeah. Reroute all of this.

That that's a world that's made for this kind stuff, right? And, and mainframes in a similar vein, right, in a similar vein, I, I really think, I really think mainframes are, are made for this, right? And, and, and ai, and this is where it's like a little chocolate and peanut butter.

I think the agent AI running in the mainframe environment has, has a lot of potential and a lot of, uh, you know, space to do to get stuff done. Yeah. Stuff done with disaster recovery, automated failover, those kinds of things.

Yeah. Especially for that, all Of these things. Yeah.

As long, absolutely. As long as you manage the ransomware element a bit, because, you know, there's always that fear of, um, that's something that BMC does a little bit with too, is, you know, around you don't want, um, you, you do need an immutable copy of your data somewhere, right? Yeah.

That has to be physically and logically separated from everything else. So that's the one, one place. But yeah, I agree with you.

There's a lot of automation built Sure. Is to these systems to, you know, drive the resiliency that they have. Yeah, no.

And so you're gonna take what's probably the most resilient system in existence right now and make it even more, uh, resilient with, with these a a AI agents, excuse me. Um, I gotta ask you the, the, the $64 billion question. Maybe it's the $3 trillion question.

That's how many data centers we're building? Oh my gosh. When does this get real Priya?

Oh, that's a very good question. Um, To a certain extent, it's real now, right? I mean, especially the generative stuff.

It's real now. It's real. Now I think every customer is, at least, lemme put it this way, many customers are in the evaluation phase.

In some, some state in the evaluation phase. There are a few that are further out, uh, that are further adopted. But we put in front of our customers in a, a maturity model, kind of a four step maturity model.

Most of them kind of fell between one and two in the, in the levels. And they're, they're evaluating, they're testing, they're piloting, they're looking at different solutions. That's kind of where they're at.

Um, I think the next phase is gonna be proof of value, proof of constant. Yes. And in my opinion, I think we're about probably one to two, maybe two to three years away from full production level activities.

But it's a journey. It doesn't happen overnight. Nobody's gonna put this stuff into market overnight.

So they're, you know, they've gotta take, take the steps to do that. You know, if it takes 12 to 18 months to put a typical change into market in production on the mainframe environment for, you know, for big changes, not like code, which is dropped frequently, um, then just think about AI and how much testing, how much piloting, how much, you know, you've gotta do around accuracy, governance and all those things. So yeah, I mean, it's 2025 now.

I think by this time in two years, we're gonna start to see some real significant implementations in production happening. Um, but I'll say like even now, I mean, we have customers using our solutions to do that code, explain, you know, throughout their organizations. And so the, the earlier adopters have started.

Absolutely. You know, my experience with these kinds of things, and I don't know if I've ever seen anything, this AI is the world unto itself, but my experience with technological innovation like this there, you know, there's no flag raised on the day it happens. There's not a button you pressed or, yeah.

You know, I I, I was talking to a quantum expert about this a couple months ago. When is Q Day gonna come? Right?

He said, you probably won't know until six months or a year after and you'll realize it came. Yeah. And it, and it may be the same sort of thing here where, because this is gonna sneak up on us because it's not an all at once thing.

It's a gradual building up of these things. Yeah. And then one day you're gonna turn around and say, oh my goodness.

The whole, the whole thing is, you know, these, these agents are running, they're doing it. Yeah. Um, so I think, and, and also I think this is just accelerating at such a pace.

If I was a betting man and I'm not, I I think two, three years might be far out there. I wouldn't be surprised to see a sizable, you know how they do the crossing the chasm model, right? Yeah.

You have 15% early adopters. Yeah. 35% early mainframe.

I wouldn't, that's 50%. I bet you were at that 50% mark in 18 months. Alright, We'll have to come back.

And shim, Shimmy said it here. I'm on tape doing it. Come back and we'll, we'll, we'll come Do it.

We'll come back, we'll come back. Compare notes. Absolutely.

Absolutely. Hey, this has been a great discussion. Um, I, I just want to end it with sort of a, a generalized mainframe kind of observation, right?

Look, this has the, the potential to modernize the mainframe. I think in ways even beyond the, the, the most ardent mainframe modernization people were clamoring for. Um, do you think that it'll, is it gonna satisfy the mass, not the masses 'cause the mainframe is what it is, but do you think this is what the people, are they gonna be satisfied, I guess is my question?

Or what more do they want? What more do they want on the mainframe platform you mean? Yeah.

Um, No, I think this, this has the potential to really help the customer that says, these are the things that I need to keep on this platform. And Gartner says now, I mean, they're saying that this platform is going to be very significant around whatever, for the next 25 years, if not longer. So I think that, you know, the, the, um, the name of the game for the customer is strategic flexibility.

If I can have flexibility in my code base, if I can link to these other processes, if my people are using all the latest and greatest ai, then I'm just gonna keep increasing my productivity and I'm reducing the risk. And that's really what it comes down to for the customer. So I think there's a lot of promise and AI driven transformation for customers.

And I don't think you can assume that it's all about moving off. It's also about improving what you have in place. And that's increasingly what I hear from the customer base is, is, you know, how do I do that?

How do I, how do I make this platform the foundation for the innovation I have to do for my company and, and do it faster and more efficiently and, and with happier people? Fantastic. Priya, I've taken way more than of your time than I, I know you went way over, but did I apologize?

Yeah, but you know, it was, it was worthwhile. I think. I hope you out there enjoyed it.

Uh, we'll continue. We have a lot more, uh, information coming with BMC and some of their announcements coming out. So stay tuned for that.

And we have a lot of content here on Techstrong that we worked in partnership with our friends at BMC. We, including, I think we have a couple of webinars and articles and all kinds of great content. So stay tuned for that.

But for now, this is Alan Shimmel for Text Drunk tv. Thanks for watching everyone.