Infusing Intelligence AI as a Partner GenAI and the Modern Mainframe – BMC Software

Hi everybody. Thank you for joining us on this series about AI in the mainframe environment. My name is Mitch Ashley, and I lead the software lifecycle engineering practice with the Futurum Group.

Now, this is part of a three part series, segment number two, where we're gonna be discussing infusing intelligence using AI as a partner in in mainframe environment with mainframe teams. Today I'm joined by Anthony Ro. Anthony is senior director of, uh, architecture for AI at BMC software.

Great to be talking with you, Anthony. Thanks. Pitch.

Thanks for having me. You know, when organizations feel they're ready, kind of take that next step. How does generative AI start to make an impact in the mainframe environment?

Yeah, so, you know, we start looking at AI as a partner. Yeah. Know, I was a developer for 30 plus years.

Boy, I would've loved to have AI at some point in my career. Why? Because from a developer's perspective, generative AI would've taken a lot of the toil out of my daily day activities.

I could have used AI in a way where just the grunt work I had to do day in and day out during my developer developer journey, AI could have been a tremendous help. I I, in that regard, take the friction outta outta my day. When it comes to the AI ops space, as an example, I spent a lot of years building, uh, data visualization solutions, dashboards, et cetera.

AI as a partner in that journey is how many times are we looking at operational dashboards and we're seeing blinking lights, or maybe we're not seeing certain things that may be in graphs or a data grid that's being shown. And, you know, we're, we're, we're looking at it. It would be nice if we had AI sitting there as your partner observing the same thing you are and then pointing things out to you, uh, that you would otherwise miss.

So I really liked that portion of generative AI making an impact day in and day out. This is kind of tied to what we talked about previous, previously. If I'm a next generation mainframer and I'm new to the mainframe space, having AI as a partner in my daily journey, whether that's infusion in products that I'm using, or that's knowledge-based access as we discussed, all of that constitutes that generative AI bubble to help me start my journey on that mainframe space.

So that's really where I see it. Helping folks on the mainframe accelerate their day by taking things off their plate that they shouldn't be worried about, and focus on the more important high value items and innovation that they should be doing day in and day out. You know, going from AI as being ready as an advisor now into AI as a partner, it seems like we're using ai, we're working alongside us as we're performing work, and it's providing information and, you know, giving us insights.

Maybe as you mentioned that we didn't have, I'm curious if you have some kind of real world examples. Regenerative AI is helping us. We do.

I, I I I, I, I mentioned I talk to a lot of customers and one of the things that comes up all the time is call ball, uh, code explain. They're looking at, you know, look at that next generation looking at code bases, that a 25, 30 year plus, and there's a lot of complexity. There's code comments in there that are outta date.

There are module comments that are there in there that are outta date. Real world example, we've delivered through VMC. Any assistant that capability to do that code explain to do that.

Document generation customers are using that. They've given us really good feedback on the usage with that. That's a high value return.

Another area that we focused on was our AI ox space, where BMC Amy assistant was able to go in there and, and, and when there was a, a problem in the environment, what's the root cause of this problem? We have a lot of very sophisticated machine learning models and information that's presented to the user in that product experience. When generative AI came onto the scene, it was a great opportunity for us is we were able now to take something that was very complex to explain and describe in a user experience and have B-M-C-M-E assistant come in and then just explain what the problem is, what the root cause was in plain terms.

Not only that D-M-C-M-E assistant that was also able to give next step recommendations on how to resolve or prevent that problem from happening again. So these are real world problems that we've gotten feedback on, on how the AI truly helped elevate the business value that we're delivering out of our solutions. Now, you've talked about AI as a partner, giving us insights, helping us along, maybe triaging or diagnosing what issues might be.

There's so much knowledge has gained in that process, but we're losing that knowledge with a lot of our workforce as they retire, move on, et cetera. Talk about that knowledge loss and how AI can help us with that. Yeah, so the knowledge loss is real as, as we all know.

And, you know, an AI system is only as good as its knowledge. And what I mean by that is not the LLM knowledge, it's your institutional knowledge. It's the knowledge that your staff carries day in and day out.

How do we capture that? How do we infuse that into an AI system such that the AI system has more context and relevance and becomes smarter for the users using that? That is critical.

So how do we capture that institutional knowledge? We, we, we augment or we have a facility within B-M-C-A-M-E assistant to capture that information and, and infuse it once it's infused. That's where the real power is.

And it's not just a chat conversation where you're gonna go to B-M-C-A-M-E assistant and you're gonna ask questions and you get responses back based on that captured knowledge, right? We talked about that in the previous video that was around the advisor model. But, but imagine capturing knowledge and infusing in a, in a product experience, maybe it's in the AI ops space, capturing that knowledge so that when events happen and things happen within a dashboard or a report gets generated and there's alerts and exceptions and alarms that were generated, instead of just giving these generic type of, uh, insights out of the product, we can customize those responses and put them in terms that are relevant for that organization.

And the way we could do that is by capturing that tribal knowledge. I have to repeat that. I can't say tribal knowledge is capturing that enterprise knowledge that your senior staff has, and based on, uh, on processes and workflows they have when certain situations arise, we can infuse that in.

So the AI can respond with in a way that, that, that is relevant to your organization, for your organization to take the right steps that's relevant for you. That's what capturing institutional knowledge within an AI experience to give you back responses in that regard. So as we move along the journey that you've talked about, you know, adapting, adapting and using ai, going from the stage of giving advice as we're working to actually guiding work, um, I don't know that we want to turn it all over to the AI all at once, right?

There's a step along the way, maybe a hybrid kind of ai talk about what that approach might look like. Yeah, that, that's, that's a really good question. So at this point, we've been talking about language models from the AI perspective and augmenting that with institutional knowledge or real time data as an example.

But generative AI itself is not enough, right? And when, when you, when you're looking at, um, infusing AI in, in, in your solutions and delivering that value to our customers, and a lot of times it's a, it, it's an, it's a, a, a collection of different AI techniques that we need to pull together to formulate the, the answer or the response that we want to give. And it's this combining of different AI techniques together to formulate that response.

That's what we call hybrid ai. So as an example, I mentioned the BMC, uh, a EOPS insight product. There's a lot of sophisticated machine learning models driving that product, that experience, but we augmented that with generative AI for the explainability and the next step recommendations.

So the, it's, it's the combination use of machine learning models with generative AI language models working them together. That's a great example of a hybrid AI solution. So you can combine things like, you know, classic rules-based AI with language models or rules-based AI with, with machine learning models.

What, what, whatever the application may be. It's this combination together. That's the way we talk about it, is in the terms of hybrid af.

And that's a key element on our next video on how our AI agents think along their journey. Very good. That'll be a nice incentive to watch that third part, I'm sure.

Yes. Well, before we get there, any, any final advice you have for organizations as there're beginning to integrate generative ai? So when it comes to generative ai, um, again, you, you gotta be really practical on your use cases that you want to apply with, uh, generative ai.

Start with the explainability no matter what your area is or, uh, your domain is that you wanna, uh, uh, apply ai, start with generative AI with explainability. Once you get that sorted out, move on to that next step. That next step is when you explain the situation, what's happening here?

How do we do the next step? What's my next step actions, whether that's to resolve a problem or the next step action could be very, could be simply as notifying someone that a certain situation is going on. But take those journey, that type of journey with your ai, do the, do the explain part, and then move to recommendations and next step with, with generative ai.

And how you're gonna do that is, especially with the recommend part, that's where you capture that institutional knowledge that you have with your senior staff that you infused into your AI system. It could provide all those insights to the AI I AI system on guiding it towards tho that output on those next steps. Well, thank you very much, Anthony.

It's great for you to share, uh, your experiences of your journey that you can help others along the way in that process. So you, you've joined us for our second segment in this three part series talking about infusing intelligence, AI as a partner, generative ai, and the main mainframe environment. We're sure happy that you've joined us and we hope that you'll stick around and, uh, check out the third segment.

We'll kind of give you a little hint there of some of the things that we're gonna be talking about. It's about acting with confidence when we have AI as an agent of change that's making things happen for us and with us in this environment. Thanks again for joining us with you on the next segment.